20231108爬虫学习更新

Spider/chapter02_爬虫基本库/Practice.py

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+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/11/7 19:09
+@Usage : 
+@Desc : 一个基本的练习：爬取 https://ssr1.scrape.center 电影描述以及detail等
+'''
+
+import requests
+import logging
+import re
+from urllib.parse import urljoin
+from os import makedirs
+from os.path import exists
+from pyquery import PyQuery as pq
+import json
+
+# 输出的日志级别
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(levelname)s: %(message)s')
+BASE_URL = 'https://ssr1.scrape.center'
+TOTAL_PAGE = 10
+
+RESULTS_DIR = 'results'
+exists(RESULTS_DIR) or makedirs(RESULTS_DIR)
+
+
+def scrape_page(url):
+    """
+    scrape page by url and return its html
+    :param url: page url
+    :return: html of page
+    """
+    logging.info('scraping %s...', url)
+    try:
+        response = requests.get(url)
+        if response.status_code == 200:
+            return response.text
+        logging.error('get invalid status code %s while scraping %s', response.status_code, url)
+    except requests.RequestException:
+        logging.error('error occurred while scraping %s', url, exc_info=True)
+
+
+def scrape_index(page):
+    index_url = f'{BASE_URL}/page/{page}'
+    return scrape_page(index_url)
+
+
+# 从页面HTML中获取详情页坐标
+def parse_index(html):
+    pattern = '<a.*?href="(.*?)".*?class="name">'
+    items = re.findall(pattern, html, re.S)
+    if not items:
+        return []
+    for item in items:
+        detail_url = urljoin(BASE_URL, item)
+        logging.info('get detail url %s', detail_url)
+        yield detail_url
+
+
+def scrape_detail(url):
+    return scrape_page(url)
+
+
+def parse_detail(html):
+    cover_pattern = re.compile(
+        'class="item.*?<img.*?src="(.*?)".*?class="cover">', re.S)
+    name_pattern = re.compile('<h2.*?>(.*?)</h2>')
+    categories_pattern = re.compile(
+        '<button.*?category.*?<span>(.*?)</span>.*?</button>', re.S)
+    published_at_pattern = re.compile('(\d{4}-\d{2}-\d{2})\s?上映')
+    drama_pattern = re.compile('<div.*?drama.*?>.*?<p.*?>(.*?)</p>', re.S)
+    score_pattern = re.compile('<p.*?score.*?>(.*?)</p>', re.S)
+
+    cover = re.search(cover_pattern, html).group(
+        1).strip() if re.search(cover_pattern, html) else None
+    name = re.search(name_pattern, html).group(
+        1).strip() if re.search(name_pattern, html) else None
+    categories = re.findall(categories_pattern, html) if re.findall(
+        categories_pattern, html) else []
+    published_at = re.search(published_at_pattern, html).group(
+        1) if re.search(published_at_pattern, html) else None
+    drama = re.search(drama_pattern, html).group(
+        1).strip() if re.search(drama_pattern, html) else None
+    score = float(re.search(score_pattern, html).group(1).strip()
+                  ) if re.search(score_pattern, html) else None
+    return {
+        'cover': cover,
+        'name': name,
+        'categories': categories,
+        'published_at': published_at,
+        'drama': drama,
+        'score': score
+    }
+
+
+def parse_detailByPyQuery(html):
+    doc = pq(html)
+    cover = doc('img.cover').attr('src')
+    name = doc('a > h2').text()
+    categories = [item.text() for item in doc('.categories button span').items()]
+    published_at = doc('.info:contains(上映)').text()
+    published_at = re.search('(\d{4}-\d{2}-\d{2})', published_at).group(1) \
+        if published_at and re.search('\d{4}-\d{2}-\d{2}', published_at) else None
+    drama = doc('.drama p').text()
+    score = doc('p.score').text()
+    score = float(score) if score else None
+    return {
+        'cover': cover,
+        'name': name,
+        'categories': categories,
+        'published_at': published_at,
+        'drama': drama,
+        'score': score
+    }
+
+
+def save_data(data):
+    """
+    save to json file
+    :param data:
+    :return:
+    """
+    name = data.get('name')
+    data_path = f'{RESULTS_DIR}/{name}.json'
+    # ensure_ascii值为False,可以保证中文字符在文件内能以正常的正文文本呈现，而不是unicode
+    # indent为2,可以使json可以两行缩进
+    json.dump(data, open(data_path, 'w', encoding='utf-8'),
+              ensure_ascii=False, indent=2)
+
+
+def main():
+    for page in range(1, TOTAL_PAGE):
+        index_html = scrape_index(page)
+        detail_urls = parse_index(index_html)
+        for detail_url in detail_urls:
+            data = parse_detail(scrape_detail(detail_url))
+            logging.info("get detail data %s", data)
+            save_data(data)
+            logging.info("save data successfully")
+
+
+def mainByMulti(page):
+    index_html = scrape_index(page)
+    detail_urls = parse_index(index_html)
+    for detail_url in detail_urls:
+        data = parse_detail(scrape_detail(detail_url))
+        logging.info("get detail data %s", data)
+        save_data(data)
+        logging.info("save data successfully")
+
+
+if __name__ == '__main__':
+    # 单进程
+    # main()
+    # 多进程
+    import multiprocessing
+
+    pool = multiprocessing.Pool()
+    pages = range(1, TOTAL_PAGE)
+    pool.map(mainByMulti, pages)
+    pool.close()
+    pool.join()

Spider/chapter02_爬虫基本库/httpx库_http2协议/__init__.py

@@ -0,0 +1,8 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/11/7 18:59
+@Usage : 
+@Desc :
+'''

Spider/chapter02_爬虫基本库/httpx库_http2协议/httpxLearning.py

@@ -0,0 +1,8 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/11/7 19:00
+@Usage : httpx库学习；request不支持http2.0相关协议。需要使用httpx。
+@Desc : httpx用法与request类似,具体内容参考:https://github.com/Python3WebSpider/HttpxTest
+'''

Spider/chapter02_爬虫基本库/request库/__init__.py

@@ -0,0 +1,8 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/11/7 15:51
+@Usage : 
+@Desc :
+'''

Spider/chapter02_爬虫基本库/request库/requestLearning.py

@@ -0,0 +1,219 @@
+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/11/7 15:51
+@Usage : 
+@Desc : request库学习
+'''
+
+import requests
+import re
+from requests.packages import urllib3
+from requests.auth import HTTPBasicAuth
+from requests_oauthlib import OAuth1
+
+'''
+基本get使用
+'''
+
+
+def get():
+    data = {
+        'name': 'germey',
+        'age': 25
+    }
+    response = requests.get('https://httpbin.org/get', params=data)
+
+    print(response.text)
+    print(type(response.json()))  # dict
+    print(response.json())
+    pass
+
+
+'''
+抓取网页:使用模式匹配，抓取标题
+'''
+
+
+def getPattern():
+    response = requests.get('https://ssr1.scrape.center/')
+    pattern = '<h2.*?>(.*?)</h2>'
+    pattern = re.compile(pattern, re.S)
+    titles = re.findall(pattern, response.text)
+
+    print(titles)
+
+    pass
+
+
+'''
+抓取二进制数据:使用模式匹配，抓取标题
+'''
+
+
+def getBinary():
+    response = requests.get('https://scrape.center/favicon.ico')
+    print(response.text)
+    print(response.content)
+
+    with open('favicon.ico', 'wb') as f:
+        f.write(response.content)
+
+    pass
+
+
+'''
+基本response的相关参数
+'''
+
+
+def getResponse():
+    response = requests.get('https://ssr1.scrape.center/')
+
+    print(type(response.status_code), response.status_code)
+    print(type(response.headers), response.headers)
+    print(type(response.cookies), response.cookies)
+    print(type(response.history), response.history)
+
+    exit() if not response.status_code == requests.codes.ok else print('Request Success!')
+
+
+'''
+基本post使用
+'''
+
+
+def post():
+    data = {
+        'name': 'germey',
+        'age': 25
+    }
+    response = requests.post('https://httpbin.org/get', data=data)
+
+    print(response.text)
+
+    pass
+
+
+'''
+高级用法:上传文件
+'''
+
+
+def postFile():
+    file = {
+        'file': open('favicon.ico', 'rb')
+    }
+    response = requests.post('https://httpbin.org/post', files=file)
+
+    print(response.text)
+
+    pass
+
+
+'''
+高级用法:cookie
+cookie成功模拟了登录状态，这样就能爬取登录之后才能看到的页面了
+'''
+
+
+def postCookie():
+    response = requests.get('https://www.baidu.com')
+    print(response.cookies)
+
+    for key, value in response.cookies.items():
+        print(key, "=", value)
+
+    pass
+
+
+'''
+Session维持:
+如果第一次请求利用request库的post方法登录了某个网站，第二次想获取成功登录后自己的个人信息，
+于是又用requests库的get方法区请求个人信息页面，这实际上相当于打开了两个浏览器，是两个完全独立的操作，这时需要维持Session
+'''
+
+
+def session():
+    s = requests.Session()
+    s.get("https://www.httpbin.org/cookies/set/number/123456789")
+    r = s.get('https://www.httpbin.org/cookies')
+    print(r.text)  # {"cookies": {"number": "123456789"}}
+
+
+'''
+SSL证书验证:
+有些网站的HTTPS证书可能并不被CA机构认可，出现SSL证书错误
+'''
+
+
+def SSL():
+    # response = requests.get("https://ssr2.scrape.center/")
+    # print(response.status_code)  # requests.exceptions.SSLError: HTTPSConnectionPool(host='ssr2.scrape.center', port=443): Max retries exceeded with url
+
+    urllib3.disable_warnings()
+    response = requests.get("https://ssr2.scrape.center/", verify=False)
+    print(response.status_code)  # 200
+
+
+'''
+超时验证:
+防止服务器不能即时响应
+'''
+
+
+def timeout():
+    # 设置超时时间1秒
+    response = requests.get("https://www.httpbin.org/get", timeout=1)
+    # 如果不设置，则永久等待，如果设置为timeout=(5,30)则连接超时时间5秒，读取超时时间30秒
+    print(response.status_code)
+
+
+'''
+身份认证:
+在访问启用了基本身份认证的网站时，首先会弹出一个认证窗口
+'''
+
+
+def Auth():
+    # 用户名和密码都是admin
+    response = requests.get("https://ssr3.scrape.center/", auth=('admin', 'admin'))
+
+    print(response.status_code)
+
+
+'''
+request还提供了其他认证方式，如OAuth认证，不过此时需要安装requests_oauthlib包
+'''
+def OAuth():
+    # 用户名和密码都是admin
+    url = 'https://api.twitter.com/1.1/account/verify_credentials.json'
+    auth = OAuth('your_app_key', 'your_app_sercet', 'user_oauth_token', 'user_oauth_token_secret')
+    response = requests.get(url, auth=auth)
+
+    print(response.status_code)
+
+
+
+'''
+代理设置:
+某些网站请求几次可以正常获取内容。但一旦开始大规模爬取，可能弹出验证码或者挑战到登录认证页面等
+可以使用代理来解决这个问题
+'''
+def proxy():
+    # 用户名和密码都是admin
+    url = 'https://api.twitter.com/1.1/account/verify_credentials.json'
+
+    proxies ={
+        'http':'http://10.10.10.10:1080',
+        'https':'http://user:password@10.10.10.10:1080/'
+    }
+
+    response = requests.get(url, proxy=proxies)
+
+    print(response.status_code)
+
+
+if __name__ == '__main__':
+    Auth()

Spider/chapter02_爬虫基本库/request库/ssr1_scrape_center.html

@@ -0,0 +1,953 @@
+<html lang="en">
+<head>
+
+    <meta charset="utf-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width,initial-scale=1">
+    <link rel="icon" href="/static/img/favicon.ico">
+    <title>Scrape | Movie</title>
+
+
+    <link href="/static/css/app.css" type="text/css" rel="stylesheet">
+
+    <link href="/static/css/index.css" type="text/css" rel="stylesheet">
+
+</head>
+<body>
+<div id="app">
+    <div data-v-74e8b908="" class="el-row" id="header">
+        <div data-v-74e8b908="" class="container el-col el-col-18 el-col-offset-3">
+            <div data-v-74e8b908="" class="el-row">
+                <div data-v-74e8b908="" class="logo el-col el-col-4">
+                    <a data-v-74e8b908="" href="/" class="router-link-exact-active router-link-active">
+                        <img data-v-74e8b908="" src="/static/img/logo.png" class="logo-image">
+                        <span data-v-74e8b908="" class="logo-title">Scrape</span>
+                    </a>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div data-v-7f856186="" id="index">
+        <div data-v-7f856186="" class="el-row">
+            <div data-v-7f856186="" class="el-col el-col-18 el-col-offset-3">
+
+                <div data-v-7f856186="" class="el-card item m-t is-hover-shadow">
+                    <div class="el-card__body">
+                        <div data-v-7f856186="" class="el-row">
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-8 el-col-sm-6 el-col-md-4">
+                                <a data-v-7f856186=""
+                                   href="/detail/1"
+                                   class="">
+                                    <img
+                                            data-v-7f856186=""
+                                            src="https://p0.meituan.net/movie/ce4da3e03e655b5b88ed31b5cd7896cf62472.jpg@464w_644h_1e_1c"
+                                            class="cover">
+                                </a>
+                            </div>
+                            <div data-v-7f856186="" class="p-h el-col el-col-24 el-col-xs-9 el-col-sm-13 el-col-md-16">
+                                <a data-v-7f856186="" href="/detail/1" class="name">
+                                    <h2 data-v-7f856186="" class="m-b-sm">霸王别姬 - Farewell My Concubine</h2>
+                                </a>
+                                <div data-v-7f856186="" class="categories">
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>剧情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>爱情</span>
+                                    </button>
+
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+                                    <span data-v-7f856186="">中国内地、中国香港</span>
+                                    <span data-v-7f856186=""> / </span>
+                                    <span data-v-7f856186="">171 分钟</span>
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+
+                                    <span data-v-7f856186="">1993-07-26 上映</span>
+
+                                </div>
+                            </div>
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-5 el-col-sm-5 el-col-md-4">
+                                <p data-v-7f856186=""
+                                   class="score m-t-md m-b-n-sm">
+                                    9.5</p>
+                                <p data-v-7f856186="">
+                                <div data-v-7f856186="" role="slider" aria-valuenow="4.75" aria-valuetext=""
+                                     aria-valuemin="0"
+                                     aria-valuemax="5" tabindex="0" class="el-rate">
+
+                <span class="el-rate__item" style="cursor: auto;"><i
+                        class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span
+                                            class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on"
+                                            style="color: rgb(239, 242, 247);"><i
+                                            class="el-rate__decimal el-icon-star-on"
+                                            style="color: rgb(247, 186, 42); width: 75.0%;"></i></i>
+                </span>
+                                </div>
+                                </p></div>
+                        </div>
+                    </div>
+                </div>
+
+                <div data-v-7f856186="" class="el-card item m-t is-hover-shadow">
+                    <div class="el-card__body">
+                        <div data-v-7f856186="" class="el-row">
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-8 el-col-sm-6 el-col-md-4">
+                                <a data-v-7f856186=""
+                                   href="/detail/2"
+                                   class="">
+                                    <img
+                                            data-v-7f856186=""
+                                            src="https://p1.meituan.net/movie/6bea9af4524dfbd0b668eaa7e187c3df767253.jpg@464w_644h_1e_1c"
+                                            class="cover">
+                                </a>
+                            </div>
+                            <div data-v-7f856186="" class="p-h el-col el-col-24 el-col-xs-9 el-col-sm-13 el-col-md-16">
+                                <a data-v-7f856186="" href="/detail/2" class="name">
+                                    <h2 data-v-7f856186="" class="m-b-sm">这个杀手不太冷 - Léon</h2>
+                                </a>
+                                <div data-v-7f856186="" class="categories">
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>剧情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>动作</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>犯罪</span>
+                                    </button>
+
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+                                    <span data-v-7f856186="">法国</span>
+                                    <span data-v-7f856186=""> / </span>
+                                    <span data-v-7f856186="">110 分钟</span>
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+
+                                    <span data-v-7f856186="">1994-09-14 上映</span>
+
+                                </div>
+                            </div>
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-5 el-col-sm-5 el-col-md-4">
+                                <p data-v-7f856186=""
+                                   class="score m-t-md m-b-n-sm">
+                                    9.5</p>
+                                <p data-v-7f856186="">
+                                <div data-v-7f856186="" role="slider" aria-valuenow="4.75" aria-valuetext=""
+                                     aria-valuemin="0"
+                                     aria-valuemax="5" tabindex="0" class="el-rate">
+
+                <span class="el-rate__item" style="cursor: auto;"><i
+                        class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span
+                                            class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on"
+                                            style="color: rgb(239, 242, 247);"><i
+                                            class="el-rate__decimal el-icon-star-on"
+                                            style="color: rgb(247, 186, 42); width: 75.0%;"></i></i>
+                </span>
+                                </div>
+                                </p></div>
+                        </div>
+                    </div>
+                </div>
+
+                <div data-v-7f856186="" class="el-card item m-t is-hover-shadow">
+                    <div class="el-card__body">
+                        <div data-v-7f856186="" class="el-row">
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-8 el-col-sm-6 el-col-md-4">
+                                <a data-v-7f856186=""
+                                   href="/detail/3"
+                                   class="">
+                                    <img
+                                            data-v-7f856186=""
+                                            src="https://p0.meituan.net/movie/283292171619cdfd5b240c8fd093f1eb255670.jpg@464w_644h_1e_1c"
+                                            class="cover">
+                                </a>
+                            </div>
+                            <div data-v-7f856186="" class="p-h el-col el-col-24 el-col-xs-9 el-col-sm-13 el-col-md-16">
+                                <a data-v-7f856186="" href="/detail/3" class="name">
+                                    <h2 data-v-7f856186="" class="m-b-sm">肖申克的救赎 - The Shawshank Redemption</h2>
+                                </a>
+                                <div data-v-7f856186="" class="categories">
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>剧情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>犯罪</span>
+                                    </button>
+
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+                                    <span data-v-7f856186="">美国</span>
+                                    <span data-v-7f856186=""> / </span>
+                                    <span data-v-7f856186="">142 分钟</span>
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+
+                                    <span data-v-7f856186="">1994-09-10 上映</span>
+
+                                </div>
+                            </div>
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-5 el-col-sm-5 el-col-md-4">
+                                <p data-v-7f856186=""
+                                   class="score m-t-md m-b-n-sm">
+                                    9.5</p>
+                                <p data-v-7f856186="">
+                                <div data-v-7f856186="" role="slider" aria-valuenow="4.75" aria-valuetext=""
+                                     aria-valuemin="0"
+                                     aria-valuemax="5" tabindex="0" class="el-rate">
+
+                <span class="el-rate__item" style="cursor: auto;"><i
+                        class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span
+                                            class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on"
+                                            style="color: rgb(239, 242, 247);"><i
+                                            class="el-rate__decimal el-icon-star-on"
+                                            style="color: rgb(247, 186, 42); width: 75.0%;"></i></i>
+                </span>
+                                </div>
+                                </p></div>
+                        </div>
+                    </div>
+                </div>
+
+                <div data-v-7f856186="" class="el-card item m-t is-hover-shadow">
+                    <div class="el-card__body">
+                        <div data-v-7f856186="" class="el-row">
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-8 el-col-sm-6 el-col-md-4">
+                                <a data-v-7f856186=""
+                                   href="/detail/4"
+                                   class="">
+                                    <img
+                                            data-v-7f856186=""
+                                            src="https://p1.meituan.net/movie/b607fba7513e7f15eab170aac1e1400d878112.jpg@464w_644h_1e_1c"
+                                            class="cover">
+                                </a>
+                            </div>
+                            <div data-v-7f856186="" class="p-h el-col el-col-24 el-col-xs-9 el-col-sm-13 el-col-md-16">
+                                <a data-v-7f856186="" href="/detail/4" class="name">
+                                    <h2 data-v-7f856186="" class="m-b-sm">泰坦尼克号 - Titanic</h2>
+                                </a>
+                                <div data-v-7f856186="" class="categories">
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>剧情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>爱情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>灾难</span>
+                                    </button>
+
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+                                    <span data-v-7f856186="">美国</span>
+                                    <span data-v-7f856186=""> / </span>
+                                    <span data-v-7f856186="">194 分钟</span>
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+
+                                    <span data-v-7f856186="">1998-04-03 上映</span>
+
+                                </div>
+                            </div>
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-5 el-col-sm-5 el-col-md-4">
+                                <p data-v-7f856186=""
+                                   class="score m-t-md m-b-n-sm">
+                                    9.5</p>
+                                <p data-v-7f856186="">
+                                <div data-v-7f856186="" role="slider" aria-valuenow="4.75" aria-valuetext=""
+                                     aria-valuemin="0"
+                                     aria-valuemax="5" tabindex="0" class="el-rate">
+
+                <span class="el-rate__item" style="cursor: auto;"><i
+                        class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span
+                                            class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on"
+                                            style="color: rgb(239, 242, 247);"><i
+                                            class="el-rate__decimal el-icon-star-on"
+                                            style="color: rgb(247, 186, 42); width: 75.0%;"></i></i>
+                </span>
+                                </div>
+                                </p></div>
+                        </div>
+                    </div>
+                </div>
+
+                <div data-v-7f856186="" class="el-card item m-t is-hover-shadow">
+                    <div class="el-card__body">
+                        <div data-v-7f856186="" class="el-row">
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-8 el-col-sm-6 el-col-md-4">
+                                <a data-v-7f856186=""
+                                   href="/detail/5"
+                                   class="">
+                                    <img
+                                            data-v-7f856186=""
+                                            src="https://p0.meituan.net/movie/289f98ceaa8a0ae737d3dc01cd05ab052213631.jpg@464w_644h_1e_1c"
+                                            class="cover">
+                                </a>
+                            </div>
+                            <div data-v-7f856186="" class="p-h el-col el-col-24 el-col-xs-9 el-col-sm-13 el-col-md-16">
+                                <a data-v-7f856186="" href="/detail/5" class="name">
+                                    <h2 data-v-7f856186="" class="m-b-sm">罗马假日 - Roman Holiday</h2>
+                                </a>
+                                <div data-v-7f856186="" class="categories">
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>剧情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>喜剧</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>爱情</span>
+                                    </button>
+
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+                                    <span data-v-7f856186="">美国</span>
+                                    <span data-v-7f856186=""> / </span>
+                                    <span data-v-7f856186="">118 分钟</span>
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+
+                                    <span data-v-7f856186="">1953-08-20 上映</span>
+
+                                </div>
+                            </div>
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-5 el-col-sm-5 el-col-md-4">
+                                <p data-v-7f856186=""
+                                   class="score m-t-md m-b-n-sm">
+                                    9.5</p>
+                                <p data-v-7f856186="">
+                                <div data-v-7f856186="" role="slider" aria-valuenow="4.75" aria-valuetext=""
+                                     aria-valuemin="0"
+                                     aria-valuemax="5" tabindex="0" class="el-rate">
+
+                <span class="el-rate__item" style="cursor: auto;"><i
+                        class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span
+                                            class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on"
+                                            style="color: rgb(239, 242, 247);"><i
+                                            class="el-rate__decimal el-icon-star-on"
+                                            style="color: rgb(247, 186, 42); width: 75.0%;"></i></i>
+                </span>
+                                </div>
+                                </p></div>
+                        </div>
+                    </div>
+                </div>
+
+                <div data-v-7f856186="" class="el-card item m-t is-hover-shadow">
+                    <div class="el-card__body">
+                        <div data-v-7f856186="" class="el-row">
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-8 el-col-sm-6 el-col-md-4">
+                                <a data-v-7f856186=""
+                                   href="/detail/6"
+                                   class="">
+                                    <img
+                                            data-v-7f856186=""
+                                            src="https://p0.meituan.net/movie/da64660f82b98cdc1b8a3804e69609e041108.jpg@464w_644h_1e_1c"
+                                            class="cover">
+                                </a>
+                            </div>
+                            <div data-v-7f856186="" class="p-h el-col el-col-24 el-col-xs-9 el-col-sm-13 el-col-md-16">
+                                <a data-v-7f856186="" href="/detail/6" class="name">
+                                    <h2 data-v-7f856186="" class="m-b-sm">唐伯虎点秋香 - Flirting Scholar</h2>
+                                </a>
+                                <div data-v-7f856186="" class="categories">
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>喜剧</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>爱情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>古装</span>
+                                    </button>
+
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+                                    <span data-v-7f856186="">中国香港</span>
+                                    <span data-v-7f856186=""> / </span>
+                                    <span data-v-7f856186="">102 分钟</span>
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+
+                                    <span data-v-7f856186="">1993-07-01 上映</span>
+
+                                </div>
+                            </div>
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-5 el-col-sm-5 el-col-md-4">
+                                <p data-v-7f856186=""
+                                   class="score m-t-md m-b-n-sm">
+                                    9.5</p>
+                                <p data-v-7f856186="">
+                                <div data-v-7f856186="" role="slider" aria-valuenow="4.75" aria-valuetext=""
+                                     aria-valuemin="0"
+                                     aria-valuemax="5" tabindex="0" class="el-rate">
+
+                <span class="el-rate__item" style="cursor: auto;"><i
+                        class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span
+                                            class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on"
+                                            style="color: rgb(239, 242, 247);"><i
+                                            class="el-rate__decimal el-icon-star-on"
+                                            style="color: rgb(247, 186, 42); width: 75.0%;"></i></i>
+                </span>
+                                </div>
+                                </p></div>
+                        </div>
+                    </div>
+                </div>
+
+                <div data-v-7f856186="" class="el-card item m-t is-hover-shadow">
+                    <div class="el-card__body">
+                        <div data-v-7f856186="" class="el-row">
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-8 el-col-sm-6 el-col-md-4">
+                                <a data-v-7f856186=""
+                                   href="/detail/7"
+                                   class="">
+                                    <img
+                                            data-v-7f856186=""
+                                            src="https://p0.meituan.net/movie/223c3e186db3ab4ea3bb14508c709400427933.jpg@464w_644h_1e_1c"
+                                            class="cover">
+                                </a>
+                            </div>
+                            <div data-v-7f856186="" class="p-h el-col el-col-24 el-col-xs-9 el-col-sm-13 el-col-md-16">
+                                <a data-v-7f856186="" href="/detail/7" class="name">
+                                    <h2 data-v-7f856186="" class="m-b-sm">乱世佳人 - Gone with the Wind</h2>
+                                </a>
+                                <div data-v-7f856186="" class="categories">
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>剧情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>爱情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>历史</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>战争</span>
+                                    </button>
+
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+                                    <span data-v-7f856186="">美国</span>
+                                    <span data-v-7f856186=""> / </span>
+                                    <span data-v-7f856186="">238 分钟</span>
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+
+                                    <span data-v-7f856186="">1939-12-15 上映</span>
+
+                                </div>
+                            </div>
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-5 el-col-sm-5 el-col-md-4">
+                                <p data-v-7f856186=""
+                                   class="score m-t-md m-b-n-sm">
+                                    9.5</p>
+                                <p data-v-7f856186="">
+                                <div data-v-7f856186="" role="slider" aria-valuenow="4.75" aria-valuetext=""
+                                     aria-valuemin="0"
+                                     aria-valuemax="5" tabindex="0" class="el-rate">
+
+                <span class="el-rate__item" style="cursor: auto;"><i
+                        class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span
+                                            class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on"
+                                            style="color: rgb(239, 242, 247);"><i
+                                            class="el-rate__decimal el-icon-star-on"
+                                            style="color: rgb(247, 186, 42); width: 75.0%;"></i></i>
+                </span>
+                                </div>
+                                </p></div>
+                        </div>
+                    </div>
+                </div>
+
+                <div data-v-7f856186="" class="el-card item m-t is-hover-shadow">
+                    <div class="el-card__body">
+                        <div data-v-7f856186="" class="el-row">
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-8 el-col-sm-6 el-col-md-4">
+                                <a data-v-7f856186=""
+                                   href="/detail/8"
+                                   class="">
+                                    <img
+                                            data-v-7f856186=""
+                                            src="https://p0.meituan.net/movie/1f0d671f6a37f9d7b015e4682b8b113e174332.jpg@464w_644h_1e_1c"
+                                            class="cover">
+                                </a>
+                            </div>
+                            <div data-v-7f856186="" class="p-h el-col el-col-24 el-col-xs-9 el-col-sm-13 el-col-md-16">
+                                <a data-v-7f856186="" href="/detail/8" class="name">
+                                    <h2 data-v-7f856186="" class="m-b-sm">喜剧之王 - The King of Comedy</h2>
+                                </a>
+                                <div data-v-7f856186="" class="categories">
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>剧情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>喜剧</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>爱情</span>
+                                    </button>
+
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+                                    <span data-v-7f856186="">中国香港</span>
+                                    <span data-v-7f856186=""> / </span>
+                                    <span data-v-7f856186="">85 分钟</span>
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+
+                                    <span data-v-7f856186="">1999-02-13 上映</span>
+
+                                </div>
+                            </div>
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-5 el-col-sm-5 el-col-md-4">
+                                <p data-v-7f856186=""
+                                   class="score m-t-md m-b-n-sm">
+                                    9.5</p>
+                                <p data-v-7f856186="">
+                                <div data-v-7f856186="" role="slider" aria-valuenow="4.75" aria-valuetext=""
+                                     aria-valuemin="0"
+                                     aria-valuemax="5" tabindex="0" class="el-rate">
+
+                <span class="el-rate__item" style="cursor: auto;"><i
+                        class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span
+                                            class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on"
+                                            style="color: rgb(239, 242, 247);"><i
+                                            class="el-rate__decimal el-icon-star-on"
+                                            style="color: rgb(247, 186, 42); width: 75.0%;"></i></i>
+                </span>
+                                </div>
+                                </p></div>
+                        </div>
+                    </div>
+                </div>
+
+                <div data-v-7f856186="" class="el-card item m-t is-hover-shadow">
+                    <div class="el-card__body">
+                        <div data-v-7f856186="" class="el-row">
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-8 el-col-sm-6 el-col-md-4">
+                                <a data-v-7f856186=""
+                                   href="/detail/9"
+                                   class="">
+                                    <img
+                                            data-v-7f856186=""
+                                            src="https://p0.meituan.net/movie/8959888ee0c399b0fe53a714bc8a5a17460048.jpg@464w_644h_1e_1c"
+                                            class="cover">
+                                </a>
+                            </div>
+                            <div data-v-7f856186="" class="p-h el-col el-col-24 el-col-xs-9 el-col-sm-13 el-col-md-16">
+                                <a data-v-7f856186="" href="/detail/9" class="name">
+                                    <h2 data-v-7f856186="" class="m-b-sm">楚门的世界 - The Truman Show</h2>
+                                </a>
+                                <div data-v-7f856186="" class="categories">
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>剧情</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>科幻</span>
+                                    </button>
+
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+                                    <span data-v-7f856186="">美国</span>
+                                    <span data-v-7f856186=""> / </span>
+                                    <span data-v-7f856186="">103 分钟</span>
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+
+                                </div>
+                            </div>
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-5 el-col-sm-5 el-col-md-4">
+                                <p data-v-7f856186=""
+                                   class="score m-t-md m-b-n-sm">
+                                    9.0</p>
+                                <p data-v-7f856186="">
+                                <div data-v-7f856186="" role="slider" aria-valuenow="4.75" aria-valuetext=""
+                                     aria-valuemin="0"
+                                     aria-valuemax="5" tabindex="0" class="el-rate">
+
+                <span class="el-rate__item" style="cursor: auto;"><i
+                        class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span
+                                            class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on"
+                                            style="color: rgb(239, 242, 247);"><i
+                                            class="el-rate__decimal el-icon-star-on"
+                                            style="color: rgb(247, 186, 42); width: 50.0%;"></i></i>
+                </span>
+                                </div>
+                                </p></div>
+                        </div>
+                    </div>
+                </div>
+
+                <div data-v-7f856186="" class="el-card item m-t is-hover-shadow">
+                    <div class="el-card__body">
+                        <div data-v-7f856186="" class="el-row">
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-8 el-col-sm-6 el-col-md-4">
+                                <a data-v-7f856186=""
+                                   href="/detail/10"
+                                   class="">
+                                    <img
+                                            data-v-7f856186=""
+                                            src="https://p0.meituan.net/movie/27b76fe6cf3903f3d74963f70786001e1438406.jpg@464w_644h_1e_1c"
+                                            class="cover">
+                                </a>
+                            </div>
+                            <div data-v-7f856186="" class="p-h el-col el-col-24 el-col-xs-9 el-col-sm-13 el-col-md-16">
+                                <a data-v-7f856186="" href="/detail/10" class="name">
+                                    <h2 data-v-7f856186="" class="m-b-sm">狮子王 - The Lion King</h2>
+                                </a>
+                                <div data-v-7f856186="" class="categories">
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>动画</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>歌舞</span>
+                                    </button>
+
+                                    <button data-v-7f856186="" type="button"
+                                            class="el-button category el-button--primary el-button--mini">
+                                        <span>冒险</span>
+                                    </button>
+
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+                                    <span data-v-7f856186="">美国</span>
+                                    <span data-v-7f856186=""> / </span>
+                                    <span data-v-7f856186="">89 分钟</span>
+                                </div>
+                                <div data-v-7f856186="" class="m-v-sm info">
+
+                                    <span data-v-7f856186="">1995-07-15 上映</span>
+
+                                </div>
+                            </div>
+                            <div data-v-7f856186="" class="el-col el-col-24 el-col-xs-5 el-col-sm-5 el-col-md-4">
+                                <p data-v-7f856186=""
+                                   class="score m-t-md m-b-n-sm">
+                                    9.0</p>
+                                <p data-v-7f856186="">
+                                <div data-v-7f856186="" role="slider" aria-valuenow="4.75" aria-valuetext=""
+                                     aria-valuemin="0"
+                                     aria-valuemax="5" tabindex="0" class="el-rate">
+
+                <span class="el-rate__item" style="cursor: auto;"><i
+                        class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on" style="color: rgb(247, 186, 42);"></i>
+                </span>
+
+                                    <span
+                                            class="el-rate__item" style="cursor: auto;"><i
+                                            class="el-rate__icon el-icon-star-on"
+                                            style="color: rgb(239, 242, 247);"><i
+                                            class="el-rate__decimal el-icon-star-on"
+                                            style="color: rgb(247, 186, 42); width: 50.0%;"></i></i>
+                </span>
+                                </div>
+                                </p></div>
+                        </div>
+                    </div>
+                </div>
+
+            </div>
+            <div class="el-loading-mask" style="display: none;">
+                <div class="el-loading-spinner">
+                    <svg viewBox="25 25 50 50" class="circular">
+                        <circle cx="50" cy="50" r="20" fill="none" class="path"></circle>
+                    </svg>
+                </div>
+            </div>
+        </div>
+        <div data-v-7f856186="" class="el-row">
+            <div data-v-7f856186="" class="el-col el-col-10 el-col-offset-11">
+                <div data-v-7f856186="" class="pagination m-v-lg">
+                    <div data-v-7f856186="" class="el-pagination is-background">
+                        <span class="el-pagination__total">共 102 条</span>
+
+                        <button type="button" disabled="disabled" class="btn-prev">
+                            <i class="el-icon el-icon-arrow-left"></i>
+                        </button>
+
+                        <ul class="el-pager">
+
+
+                            <li class="number active">
+                                <a href="/page/1">1</a>
+                            </li>
+
+
+                            <li class="number">
+                                <a href="/page/2">2</a>
+                            </li>
+
+
+                            <li class="number">
+                                <a href="/page/3">3</a>
+                            </li>
+
+
+                            <li class="number">
+                                <a href="/page/4">4</a>
+                            </li>
+
+
+                            <li class="number">
+                                <a href="/page/5">5</a>
+                            </li>
+
+
+                            <li class="number">
+                                <a href="/page/6">6</a>
+                            </li>
+
+
+                            <li class="number">
+                                <a href="/page/7">7</a>
+                            </li>
+
+
+                            <li class="number">
+                                <a href="/page/8">8</a>
+                            </li>
+
+
+                            <li class="number">
+                                <a href="/page/9">9</a>
+                            </li>
+
+
+                            <li class="number">
+                                <a href="/page/10">10</a>
+                            </li>
+
+
+                            <li class="number">
+                                <a href="/page/11">11</a>
+                            </li>
+
+
+                        </ul>
+
+                        <a href="/page/2" class="next">
+                            <button type="button" class="btn-next"><i class="el-icon el-icon-arrow-right"></i></button>
+                        </a>
+
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+</div>
+</body>

Spider/chapter02_爬虫基本库/re库_正则匹配/__init__.py

@@ -0,0 +1,8 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/11/7 17:30
+@Usage : 
+@Desc :
+'''

Spider/chapter02_爬虫基本库/re库_正则匹配/reLearning.py

@@ -0,0 +1,179 @@
+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/11/7 17:31
+@Usage : 
+@Desc : 正则匹配re库基本使用
+'''
+
+import re
+
+'''
+基本使用
+'''
+
+
+def baseUse():
+    content = 'hello 123456789 World_This is a Reges Demo'
+    pattern = '^hello\s(\d+)\sWorld'
+    result = re.match(pattern, content)
+
+    print(result)  # <re.Match object; span=(0, 21), match='hello 123456789 World'>
+    print(result.group())  # hello 123456789 World  输出匹配到的内容
+    print(result.group(1))  # 123456789  输出第一个被()包围的匹配结果
+    print(result.span())  # (0, 21)  输出匹配的范围
+
+
+# 高级用法
+'''
+贪婪匹配与非贪婪匹配
+.*表示尽可能多匹配字符
+.*?表示尽可能少匹配字符
+'''
+
+'''
+re.I 表示匹配对大小写不明感
+re.L 实现本地化识别匹配
+re.M 表示多航匹配，影响^和$
+re.S 表示匹配内容包括换行符在内的所有字符
+re.U 表示根据Unicode解析字符，这个表示会影响\w,\W,\b和\B
+re.S 表示匹配内容包括换行符在内的所有字符
+'''
+def prior():
+    content = '''hello 123456789 World_This
+    is a Reges Demo
+    '''
+    result = re.match('^he.*?(\d+).*?Demo$', content)
+    print(result.group(1))  # 未匹配到，报错 AttributeError: 'NoneType' object has no attribute 'group'
+    result = re.match('^he.*?(\d+).*?Demo$', content, re.S)
+    print(result.group(1))  # 123456789
+
+
+'''
+search:模糊匹配
+'''
+def search():
+    content = 'Extra stings Hello 1234567 World_This is a Regex Demo Extra stings'
+    result = re.match('Hello.*?(\d+).*?Demo', content)  # 必须要以Hello开头才能匹配到
+    print(result)  # None
+    result = re.search('Hello.*?(\d+).*?Demo', content)
+    print(result)  # <re.Match object; span=(13, 53), match='Hello 1234567 World_This is a Regex Demo'>
+
+
+def searchHtml():
+    html = '''<div id="songs-list">
+    <h2 class="title">经典老歌</h2>
+    <p class="introduction">
+    经典老歌列表
+    </p>
+    <ul id="list" class="list-group">
+    <li data-view="2">一路上有你</li>
+    <li data-view="7">
+    <a href="/2.mp3" singer="任贤齐">沧海一声笑</a>
+    </li>
+    <li data-view="4" class="active">
+    <a href="/3.mp3" singer="齐秦">往事随风</a>
+    </li>
+    <li data-view="6"><a href="/4.mp3" singer="beyond">光辉岁月</a></li>
+    <li data-view="5"><a href="/5.mp3" singer="陈慧琳">记事本</a></li>
+    <li data-view="5">
+    <a href="/6.mp3" singer="邓丽君">但愿人长久</a>
+    </li>
+    </ul>
+    </div>'''
+    result = re.search('<li.*?active.*?singer="(.*?)">(.*?)</a>', html, re.S)
+    if result:
+        print(result.group(1), result.group(2))
+
+    result = re.search('<li.*?singer="(.*?)">(.*?)</a>', html, re.S)
+    if result:
+        print(result.group(1), result.group(2))
+
+    result = re.search('<li.*?singer="(.*?)">(.*?)</a>', html)
+    if result:
+        print(result.group(1), result.group(2))
+
+'''
+findAll:找到所有匹配的
+'''
+def findall():
+    html = '''<div id="songs-list">
+    <h2 class="title">经典老歌</h2>
+    <p class="introduction">
+    经典老歌列表
+    </p>
+    <ul id="list" class="list-group">
+    <li data-view="2">一路上有你</li>
+    <li data-view="7">
+    <a href="/2.mp3" singer="任贤齐">沧海一声笑</a>
+    </li>
+    <li data-view="4" class="active">
+    <a href="/3.mp3" singer="齐秦">往事随风</a>
+    </li>
+    <li data-view="6"><a href="/4.mp3" singer="beyond">光辉岁月</a></li>
+    <li data-view="5"><a href="/5.mp3" singer="陈慧琳">记事本</a></li>
+    <li data-view="5">
+    <a href="/6.mp3" singer="邓丽君">但愿人长久</a>
+    </li>
+    </ul>
+    </div>'''
+    results = re.findall('<li.*?href="(.*?)".*?singer="(.*?)">(.*?)</a>', html, re.S)
+    print(results)
+    print(type(results))
+    for result in results:
+        print(result)
+        print(result[0], result[1], result[2])
+
+    results = re.findall('<li.*?>\s*?(<a.*?>)?(\w+)(</a>)?\s*?</li>', html, re.S)
+    for result in results:
+        print(result[1])
+
+
+'''
+sub:正则匹配修改文本
+使用正则匹配，去除掉能匹配上的内容
+'''
+def sub():
+    html = '''<div id="songs-list">
+    <h2 class="title">经典老歌</h2>
+    <p class="introduction">
+    经典老歌列表
+    </p>
+    <ul id="list" class="list-group">
+    <li data-view="2">一路上有你</li>
+    <li data-view="7">
+    <a href="/2.mp3" singer="任贤齐">沧海一声笑</a>
+    </li>
+    <li data-view="4" class="active">
+    <a href="/3.mp3" singer="齐秦">往事随风</a>
+    </li>
+    <li data-view="6"><a href="/4.mp3" singer="beyond">光辉岁月</a></li>
+    <li data-view="5"><a href="/5.mp3" singer="陈慧琳">记事本</a></li>
+    <li data-view="5">
+    <a href="/6.mp3" singer="邓丽君">但愿人长久</a>
+    </li>
+    </ul>
+    </div>'''
+    html = re.sub('<a.*?>|</a>', '', html)
+    print(html)
+    results = re.findall('<li.*?>(.*?)</li>', html, re.S)
+    for result in results:
+        print(result.strip())
+
+'''
+compile:编译
+将相关模式编译成pattern对象，进行复用
+'''
+def complie():
+    content1 = '2019-12-15 12:00'
+    content2 = '2019-12-17 12:55'
+    content3 = '2019-12-22 13:21'
+    pattern = re.compile('\d{2}:\d{2}')
+    result1 = re.sub(pattern, '', content1)
+    result2 = re.sub(pattern, '', content2)
+    result3 = re.sub(pattern, '', content3)
+    print(result1, result2, result3)
+
+if __name__ == '__main__':
+    sub()

TensorFlow_eaxmple/Model_train_test/RUL/test/LSTMTest.py

@@ -27,7 +27,7 @@ batch_size = 32
 EPOCH = 1000
 unit = 512  # LSTM的维度
 predict_num = 50  # 预测个数
-model_name = "LSTM"
+model_name = "cnn_LSTM"
 save_name = r"self_{0}_hidden{1}_unit{2}_feature{3}_predict{4}.h5".format(model_name, hidden_num, unit, feature,
                                                                           predict_num)
 
@@ -125,7 +125,8 @@ def predict_model(filter_num, dims):
     # LSTM = tf.keras.layers.LSTM(units=256, return_sequences=False)(LSTM)
 
     #### 自己
-    LSTM = LSTMLayer(units=512, return_sequences=True)(input)
+    LSTM = tf.keras.layers.Conv1D(512, kernel_size=8, padding='same')(input)
+    LSTM = LSTMLayer(units=512, return_sequences=True)(LSTM)
     LSTM = LSTMLayer(units=256, return_sequences=False)(LSTM)
 
     x = tf.keras.layers.Dense(128, activation="relu")(LSTM)
@@ -199,8 +200,7 @@ if __name__ == '__main__':
     # model.summary()
     # early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=100, mode='min', verbose=1)
     #
-    # history = model.fit(train_data, train_label_single, epochs=EPOCH,
-    #                     batch_size=batch_size, validation_data=(val_data, val_label_single), shuffle=True, verbose=2,
+    # history = model.fit(train_data, train_label_single, epochs=EPOCH, validation_data=(val_data, val_label_single), shuffle=True, verbose=1,
     #                     callbacks=[checkpoint, lr_scheduler, early_stop])
 
     #### TODO  测试

TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/plot_loss.py

@@ -0,0 +1,132 @@
+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/11/6 13:12
+@Usage : 
+@Desc :  画loss
+'''
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import rcParams
+
+import lttb
+
+file_name = "E:\论文写作\ISA论文返修\loss.txt"
+
+
+def aux_loss(file_name="E:\论文写作\ISA论文返修\loss.txt"):
+    mse1_list = []
+    mse2_list = []
+    mse3_list = []
+    total_list = []
+
+    with open(file_name, 'r', encoding='utf-8') as f:
+        i = 0
+        for ann in f.readlines():
+            ann = ann.strip('\n')  # 去除文本中的换行符
+            if i % 4 == 0:
+                # print(ann)
+                mse1 = float(ann.split(" ")[1])
+                mse1_list.append(mse1)
+            elif i % 4 == 1:
+                mse2 = float(ann.split(" ")[1])
+                mse2_list.append(mse2)
+            elif i % 4 == 2:
+                mse3 = float(ann.split(" ")[1])
+                mse3_list.append(mse3)
+            elif i % 4 == 3:
+                total = float(ann.split(" ")[5])
+                total_list.append(total)
+            i += 1
+
+    mse1_list = lttb.downsample(np.array([range(len(mse1_list)), mse1_list]).T, n_out=50)
+    mse2_list = lttb.downsample(np.array([range(len(mse2_list)), mse2_list]).T, n_out=50)
+    mse3_list = lttb.downsample(np.array([range(len(mse3_list)), mse3_list]).T, n_out=50)
+    total_list = lttb.downsample(np.array([range(len(total_list)), total_list]).T, n_out=50)
+
+    plot_aux_loss(mse1_list, mse2_list, mse3_list)
+
+
+def plot_aux_loss(mse1_list, mse2_list, mse3_list):
+    config = {
+        "font.family": 'Times New Roman',  # 设置字体类型
+        "axes.unicode_minus": False,  # 解决负号无法显示的问题
+        "axes.labelsize": 13
+    }
+    rcParams.update(config)
+    pic1 = plt.figure(figsize=(8, 3), dpi=200)
+    # plt.ylim(0, 0.6)  # 设置y轴范围
+    plt.plot(mse1_list[:, 1], label='Predict Head 1 Loss')
+    plt.plot(mse2_list[:, 1], label='Predict Head 2 Loss')
+    plt.plot(mse3_list[:, 1], label='Predict Head 3 Loss')
+    plt.title('Training loss')
+    plt.xlabel('epoch')
+    plt.ylabel('loss')
+    plt.legend(loc='upper right')
+    plt.grid(alpha=0.8)
+
+    plt.show()
+
+
+def main_loss(file_name="E:\论文写作\ISA论文返修\main_loss.txt"):
+    mse1_list = []
+    cross_Entropy_list = []
+    total_list = []
+
+    with open(file_name, 'r', encoding='utf-8') as f:
+        i = 0
+        for ann in f.readlines():
+            ann = ann.strip('\n')  # 去除文本中的换行符
+            if i % 3 == 0:
+                # print(ann)
+                mse1 = float(ann.split(" ")[1])
+                mse1_list.append(mse1)
+            elif i % 3 == 1:
+                cross_Entropy = float(ann.split(" ")[1])*0.02
+                cross_Entropy_list.append(cross_Entropy)
+            elif i % 3 == 2:
+                total = float(ann.split(" ")[5])
+                total_list.append(total)
+            i += 1
+
+    mse1_list = lttb.downsample(np.array([range(len(mse1_list)), mse1_list]).T, n_out=40)
+    cross_Entropy_list = lttb.downsample(np.array([range(len(cross_Entropy_list)), cross_Entropy_list]).T, n_out=40)
+    total_list = lttb.downsample(np.array([range(len(total_list)), total_list]).T, n_out=40)
+
+    plot_main_loss(mse1_list, cross_Entropy_list,total_list)
+
+# 0.014995  0.016387
+# 0.014384  0.015866
+# 0.014985  0.014261
+# 0.013008  0.01349
+# 0.013285  0.013139
+# 0.012999  0.012714
+# 0.011451  0.012477
+# 0.010055  0.012471
+# 0.010303  0.014595
+def plot_main_loss(mse1_list, mse2_list,total_loss):
+    config = {
+        "font.family": 'Times New Roman',  # 设置字体类型
+        "axes.unicode_minus": False,  # 解决负号无法显示的问题
+        "axes.labelsize": 13
+    }
+    rcParams.update(config)
+    pic1 = plt.figure(figsize=(8, 3), dpi=200)
+    # plt.ylim(0, 0.6)  # 设置y轴范围
+    plt.plot(mse1_list[:, 1], label='${L_{diff}}$')
+    plt.plot(mse2_list[:, 1], label='${L_{correct}}$')
+    plt.plot(total_loss[:, 1], label='${L_{main}}$')
+    plt.title('Training loss')
+    plt.xlabel('epoch')
+    plt.ylabel('loss')
+    plt.legend(loc='upper right')
+    plt.grid(alpha=0.8)
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    main_loss()

TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py

@@ -56,12 +56,12 @@ def plot_result_banda(result_data):
     fig, ax = plt.subplots(1, 1)
     plt.rc('font', family='Times New Roman')  # 全局字体样式
     font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5}  # 设置坐标标签的字体大小，字体
-    font2 = {'family': 'Times New Roman', 'weight': 'normal','size':7}  # 设置坐标标签的字体大小，字体
+    font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 7}  # 设置坐标标签的字体大小，字体
     plt.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.01, label="predict")
     # 画出 y=1 这条水平线
     plt.axhline(0.5, c='red', label='Failure threshold', lw=1)
     # 箭头指向上面的水平线
-    plt.axvline(result_data.shape[0] * 2 / 3-50, c='blue', ls='-.', lw=0.5, label='real fault')
+    plt.axvline(result_data.shape[0] * 2 / 3 - 50, c='blue', ls='-.', lw=0.5, label='real fault')
     # plt.axvline(415548, c='blue', ls='-.', lw=0.5, label='real fault')
     # plt.xticks(range(6), ('06/09/17', '12/09/17', '18/09/17', '24/09/17', '29/09/17'))  # 设置x轴的标尺
     plt.text(result_data.shape[0] * 5 / 6, 0.4, "Fault", fontsize=5, color='black', verticalalignment='top',
@@ -85,8 +85,8 @@ def plot_result_banda(result_data):
     # pad调整label与坐标轴之间的距离
     plt.tick_params(bottom=True, top=False, left=True, right=False, direction='inout', length=2, width=0.5, pad=1)
     # plt.yticks([index  for index in indices1], classes1)
-    plt.ylabel('Confidence',fontdict=font2)
-    plt.xlabel('Time',fontdict=font2)
+    plt.ylabel('Confidence', fontdict=font2)
+    plt.xlabel('Time', fontdict=font2)
     plt.tight_layout()
     # plt.legend(loc='best', edgecolor='black', fontsize=4)
     plt.legend(loc='upper right', frameon=False, fontsize=4.5)
@@ -98,11 +98,11 @@ def plot_result_banda(result_data):
                        bbox_to_anchor=(0.1, 0.1, 1, 1),
                        bbox_transform=ax.transAxes)
     axins.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.005, label="predict")
-    axins.axvline(result_data.shape[0] * 2 / 3-50, c='blue', ls='-.', lw=0.5, label='real fault')
+    axins.axvline(result_data.shape[0] * 2 / 3 - 50, c='blue', ls='-.', lw=0.5, label='real fault')
     plt.axhline(0.5, c='red', label='Failure threshold', lw=0.5)
     # 设置放大区间
     # 设置放大区间
-    zone_left = int(result_data.shape[0] * 2 / 3 -160)
+    zone_left = int(result_data.shape[0] * 2 / 3 - 160)
     zone_right = int(result_data.shape[0] * 2 / 3) + 40
     # zone_left = int(result_data.shape[0] * 2 / 3 +250)
     # zone_right = int(result_data.shape[0] * 2 / 3) + 450
@@ -132,6 +132,7 @@ def plot_result_banda(result_data):
     plt.show()
     pass
 
+
 def plot_result(result_data):
     parameters = {
         'figure.dpi': 600,
@@ -153,8 +154,7 @@ def plot_result(result_data):
     # 画出 y=1 这条水平线
     plt.axhline(0.5, c='red', label='Failure threshold', lw=1)
 
-
-    plt.axvline(result_data.shape[0] * 2 / 3-15, c='blue', ls='-.', lw=0.5, label='real fault')
+    plt.axvline(result_data.shape[0] * 2 / 3 - 15, c='blue', ls='-.', lw=0.5, label='real fault')
     plt.text(result_data.shape[0] * 5 / 6, 0.4, "Fault", fontsize=5, color='black', verticalalignment='top',
              horizontalalignment='center',
              bbox={'facecolor': 'grey',
@@ -188,11 +188,11 @@ def plot_result(result_data):
                        bbox_to_anchor=(0.1, 0.1, 1, 1),
                        bbox_transform=ax.transAxes)
     axins.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.005, label="predict")
-    axins.axvline(result_data.shape[0] * 2 / 3-15, c='blue', ls='-.', lw=0.5, label='real fault')
+    axins.axvline(result_data.shape[0] * 2 / 3 - 15, c='blue', ls='-.', lw=0.5, label='real fault')
     plt.axhline(0.5, c='red', label='Failure threshold', lw=0.5)
     # 设置放大区间
     # 设置放大区间
-    zone_left = int(result_data.shape[0] * 2 / 3 -100)
+    zone_left = int(result_data.shape[0] * 2 / 3 - 100)
     zone_right = int(result_data.shape[0] * 2 / 3) + 100
     x = list(range(result_data.shape[0]))
 
@@ -221,6 +221,25 @@ def plot_result(result_data):
     pass
 
 
+K = 18
+namuda = 0.01
+
+
+def EWMA(data, K=K, namuda=namuda):
+    # t是啥暂时未知
+    length, = data.shape
+    t = 0
+    data = pd.DataFrame(data).ewm(alpha=namuda).mean()
+    mid = np.mean(data, axis=0)
+    standard = np.sqrt(np.var(data, axis=0))
+    UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    return data, np.broadcast_to(mid, shape=[length, ]), np.broadcast_to(UCL, shape=[length, ]), np.broadcast_to(LCL,
+                                                                                                                 shape=[
+                                                                                                                     length, ])
+    pass
+
+
 def plot_MSE(total_MSE, total_max):
     parameters = {
         'figure.dpi': 600,
@@ -251,12 +270,13 @@ def plot_MSE(total_MSE, total_max):
     classes = ['01/09/17', '08/09/17', '15/09/17', '22/09/17', '29/09/17']
 
     plt.xticks([index + 0.5 for index in indices], classes, rotation=25)  # 设置横坐标方向，rotation=45为45度倾斜
-    plt.ylabel('Mse', fontsize=5)
+    plt.ylabel('MSE', fontsize=5)
     plt.xlabel('Time', fontsize=5)
     plt.tight_layout()
 
-    plt.plot(total_max, "--", label="max", linewidth=0.5)
-    plt.plot(total_MSE, label="mse", linewidth=0.5, color='purple')
+    plt.plot(total_max, "--", label="Threshold", linewidth=0.5, color='red')
+    plt.plot(total_max, "--", label="Threshold", linewidth=0.5, color='red')
+    plt.plot(total_MSE, label="MSE", linewidth=0.5, color='purple')
     plt.legend(loc='best', frameon=False, fontsize=5)
 
     # plt.plot(total_mean)
@@ -265,6 +285,54 @@ def plot_MSE(total_MSE, total_max):
     pass
 
 
+def plot_EWMA(model_name, total_MSE, total_mid, total_max, total_min):
+    parameters = {
+        'figure.dpi': 600,
+        'figure.figsize': (2.7, 2),
+        'savefig.dpi': 600,
+        'xtick.direction': 'in',
+        'ytick.direction': 'in',
+        'xtick.labelsize': 5,
+        'ytick.labelsize': 5,
+        'legend.fontsize': 5,
+    }
+    plt.rcParams.update(parameters)
+    plt.figure()
+    plt.rc('font', family='Times New Roman')  # 全局字体样式#画混淆矩阵
+    font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5}  # 设置坐标标签的字体大小，字体
+
+    result_data = total_MSE
+    # 画出 y=1 这条水平线
+    # plt.axhline(0.5, c='red', label='Failure threshold',lw=1)
+    # 箭头指向上面的水平线
+    # plt.arrow(result_data.shape[0]*2/3, 0.55, 2000, 0.085, width=0.00001, ec='red',length_includes_head=True)
+    # plt.text(result_data.shape[0] * 2 / 3 + 1000, 0.7, "real fault", fontsize=5, color='red',
+    #          verticalalignment='top')
+
+    plt.axvline(result_data.shape[0] * 2 / 3, c='blue', ls='-.', lw=0.5, label="real fault")
+
+    indices = [result_data.shape[0] * i / 4 for i in range(5)]
+    classes = ['09/01', '09/08', '09/15', '09/22', '09/29/']
+
+    plt.xticks([index + 0.5 for index in indices], classes, rotation=25)  # 设置横坐标方向，rotation=45为45度倾斜
+    plt.ylabel('MSE', fontsize=5)
+    plt.xlabel('Time', fontsize=5)
+    plt.tight_layout()
+
+    plt.plot(total_min, "--", label="LCL", linewidth=0.5, color='red')
+    plt.plot(total_mid, "--", label="mean(X)", linewidth=0.5)
+    plt.plot(total_max, "--", label="UCL", linewidth=0.5, color='red')
+    plt.plot(total_MSE, label="MSE", linewidth=0.5, color='purple')
+    plt.legend(loc='best', frameon=False, fontsize=5)
+
+    # plt.plot(total_mean)
+    # plt.plot(min)
+
+    plt.savefig('E:\论文写作\ISA论文返修\图片\比较方法的EWMD/{0}.png'.format(model_name))
+    plt.show()
+    pass
+
+
 def plot_Corr(data, size: int = 1):
     parameters = {
         'figure.dpi': 600,
@@ -333,16 +401,21 @@ def plot_bar(y_data):
 
     plt.bar(x_width[1], y_data[1], lw=1, color=['#F5E3C4'], width=0.5, label="GRU", edgecolor='black')
     plt.bar(x_width[2], y_data[2], lw=1, color=['#EBC99D'], width=0.5, label="CNN-GRU", edgecolor='black')
-    plt.bar(x_width[3], y_data[3], lw=1, color=['#FFC79C'], width=0.5, label="DCConv", edgecolor='black')
-    plt.bar(x_width[4], y_data[4], lw=1, color=['#BEE9C7'], width=0.5, label="RepDCConv", edgecolor='black')
-    plt.bar(x_width[5], y_data[5], lw=1, color=['#B8E9D0'], width=0.5, label="RNet-MSE", edgecolor='black')
-    plt.bar(x_width[6], y_data[6], lw=1, color=['#B9E9E2'], width=0.5, label="RNet", edgecolor='black')
-    plt.bar(x_width[7], y_data[7], lw=1, color=['#D6E6F2'], width=0.5, label="RNet-SE", edgecolor='black')
-    plt.bar(x_width[8], y_data[8], lw=1, color=['#B4D1E9'], width=0.5, label="RNet-L", edgecolor='black')
-    plt.bar(x_width[9], y_data[9], lw=1, color=['#AEB5EE'], width=0.5, label="RNet-D", edgecolor='black')
-    plt.bar(x_width[10], y_data[10], lw=1, color=['#D2D3FC'], width=0.5, label="ResNet-18", edgecolor='black')
-    plt.bar(x_width[11], y_data[11], lw=1, color=['#D5A9FF'], width=0.5, label="ResNet-C", edgecolor='black')
-    plt.bar(x_width[12], y_data[12], lw=1, color=['#E000F5'], width=0.5, label="JMNet", edgecolor='black')
+
+    plt.bar(x_width[3], y_data[13], lw=1, color=['#FCD58B'], width=0.5, label="CG-QA", edgecolor='black')
+    plt.bar(x_width[4], y_data[14], lw=1, color=['#FFBA75'], width=0.5, label="CG-3{0}".format(chr(963)), edgecolor='black')
+
+    plt.bar(x_width[5], y_data[3], lw=1, color=['#FFC79C'], width=0.5, label="DCConv", edgecolor='black')
+    plt.bar(x_width[6], y_data[4], lw=1, color=['#BEE9C7'], width=0.5, label="RepDCConv", edgecolor='black')
+    plt.bar(x_width[7], y_data[5], lw=1, color=['#B8E9D0'], width=0.5, label="RNet-MSE", edgecolor='black')
+    plt.bar(x_width[8], y_data[6], lw=1, color=['#B9E9E2'], width=0.5, label="RNet", edgecolor='black')
+    plt.bar(x_width[9], y_data[7], lw=1, color=['#D6E6F2'], width=0.5, label="RNet-SE", edgecolor='black')
+    plt.bar(x_width[10], y_data[8], lw=1, color=['#B4D1E9'], width=0.5, label="RNet-L", edgecolor='black')
+    plt.bar(x_width[11], y_data[9], lw=1, color=['#AEB5EE'], width=0.5, label="RNet-D", edgecolor='black')
+    plt.bar(x_width[12], y_data[10], lw=1, color=['#D2D3FC'], width=0.5, label="ResNet-18", edgecolor='black')
+    plt.bar(x_width[13], y_data[11], lw=1, color=['#D5A9FF'], width=0.5, label="ResNet-C", edgecolor='black')
+    plt.bar(x_width[14], y_data[12], lw=1, color=['#E000F5'], width=0.5, label="JRFPN", edgecolor='black')
+
 
     # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1)
     plt.xticks([])
@@ -350,14 +423,14 @@ def plot_bar(y_data):
     plt.xlabel('Methods', fontsize=22)
     # plt.tight_layout()
 
-    num1, num2, num3, num4 = 0, 1, 3, 0
+    num1, num2, num3, num4 = 0.015, 1, 3, 0
     plt.legend(bbox_to_anchor=(num1, num2), loc=num3, borderaxespad=num4, ncol=5, frameon=False, handlelength=1,
                handletextpad=0.45, columnspacing=1)
     plt.ylim([-0.01, 5])
     plt.show()
 
 
-def acc(y_data=list):
+def acc(y_data: list):
     parameters = {
         'figure.dpi': 600,
         'figure.figsize': (10, 6),
@@ -373,22 +446,31 @@ def acc(y_data=list):
     plt.rc('font', family='Times New Roman')  # 全局字体样式#画混淆矩阵
     font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5}  # 设置坐标标签的字体大小，字体
 
-    x_width = [i / 2 for i in range(0, len(y_data))]
+    x_width = [i for i in range(0, len(y_data))]
     # x2_width = [i + 0.3 for i in x_width]
 
-    plt.bar(x_width[0], y_data[0], lw=1, color=['#FAF4E1'], width=0.25, label="CNN", edgecolor='black')
-    plt.bar(x_width[1], y_data[1], lw=1, color=['#F5E3C4'], width=0.25, label="GRU", edgecolor='black')
-    plt.bar(x_width[2], y_data[2], lw=1, color=['#EBC99D'], width=0.25, label="CNN-GRU", edgecolor='black')
-    plt.bar(x_width[3], y_data[3], lw=1, color=['#FFC79C'], width=0.25, label="DCConv", edgecolor='black')
-    plt.bar(x_width[4], y_data[4], lw=1, color=['#BEE9C7'], width=0.25, label="RepDCConv", edgecolor='black')
-    plt.bar(x_width[5], y_data[5], lw=1, color=['#B8E9D0'], width=0.25, label="RNet-MSE", edgecolor='black')
-    plt.bar(x_width[6], y_data[6], lw=1, color=['#B9E9E2'], width=0.25, label="RNet", edgecolor='black')
-    plt.bar(x_width[7], y_data[7], lw=1, color=['#D6E6F2'], width=0.25, label="RNet-SE", edgecolor='black')
-    plt.bar(x_width[8], y_data[8], lw=1, color=['#B4D1E9'], width=0.25, label="RNet-L", edgecolor='black')
-    plt.bar(x_width[9], y_data[9], lw=1, color=['#AEB5EE'], width=0.25, label="RNet-D", edgecolor='black')
-    plt.bar(x_width[10], y_data[10], lw=1, color=['#D2D3FC'], width=0.25, label="ResNet-18", edgecolor='black')
-    plt.bar(x_width[11], y_data[11], lw=1, color=['#D5A9FF'], width=0.25, label="ResNet-C", edgecolor='black')
-    plt.bar(x_width[12], y_data[12], lw=1, color=['#E000F5'], width=0.25, label="JMNet", edgecolor='black')
+    _width = [i for i in range(0, len(y_data))]
+    # x2_width = [i + 0.3 for i in x_width]
+
+    plt.bar(x_width[0], y_data[0], lw=1, color=['#FAF4E1'], width=0.5, label="CNN", edgecolor='black')
+
+    plt.bar(x_width[1], y_data[1], lw=1, color=['#F5E3C4'], width=0.5, label="GRU", edgecolor='black')
+    plt.bar(x_width[2], y_data[2], lw=1, color=['#EBC99D'], width=0.5, label="CNN-GRU", edgecolor='black')
+
+    plt.bar(x_width[3], y_data[13], lw=1, color=['#FCD58B'], width=0.5, label="CG-QA", edgecolor='black')
+    plt.bar(x_width[4], y_data[14], lw=1, color=['#FFBA75'], width=0.5, label="CG-3{0}".format(chr(963)),
+            edgecolor='black')
+
+    plt.bar(x_width[5], y_data[3], lw=1, color=['#FFC79C'], width=0.5, label="DCConv", edgecolor='black')
+    plt.bar(x_width[6], y_data[4], lw=1, color=['#BEE9C7'], width=0.5, label="RepDCConv", edgecolor='black')
+    plt.bar(x_width[7], y_data[5], lw=1, color=['#B8E9D0'], width=0.5, label="RNet-MSE", edgecolor='black')
+    plt.bar(x_width[8], y_data[6], lw=1, color=['#B9E9E2'], width=0.5, label="RNet", edgecolor='black')
+    plt.bar(x_width[9], y_data[7], lw=1, color=['#D6E6F2'], width=0.5, label="RNet-SE", edgecolor='black')
+    plt.bar(x_width[10], y_data[8], lw=1, color=['#B4D1E9'], width=0.5, label="RNet-L", edgecolor='black')
+    plt.bar(x_width[11], y_data[9], lw=1, color=['#AEB5EE'], width=0.5, label="RNet-D", edgecolor='black')
+    plt.bar(x_width[12], y_data[10], lw=1, color=['#D2D3FC'], width=0.5, label="ResNet-18", edgecolor='black')
+    plt.bar(x_width[13], y_data[11], lw=1, color=['#D5A9FF'], width=0.5, label="ResNet-C", edgecolor='black')
+    plt.bar(x_width[14], y_data[12], lw=1, color=['#E000F5'], width=0.5, label="JRFPN", edgecolor='black')
 
     # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1)
     plt.xticks([])
@@ -397,7 +479,8 @@ def acc(y_data=list):
     # plt.tight_layout()
 
     num1, num2, num3, num4 = 0, 1, 3, 0
-    plt.legend(bbox_to_anchor=(num1, num2), loc=num3, borderaxespad=num4, ncol=5, frameon=False,handlelength=1,handletextpad=0.45,columnspacing=1)
+    plt.legend(bbox_to_anchor=(num1, num2), loc=num3, borderaxespad=num4, ncol=5, frameon=False, handlelength=1,
+               handletextpad=0.45, columnspacing=1)
     plt.ylim([60, 105])
     plt.show()
 
@@ -424,13 +507,18 @@ def plot_FNR1(y_data):
     plt.bar(x_width[0], y_data[0], lw=1, color=['#FAF4E1'], width=0.5 * 5 / 6, label="CNN", edgecolor='black')
     plt.bar(x_width[1], y_data[1], lw=1, color=['#F5E3C4'], width=0.5 * 5 / 6, label="GRU", edgecolor='black')
     plt.bar(x_width[2], y_data[2], lw=1, color=['#EBC99D'], width=0.5 * 5 / 6, label="CNN-GRU", edgecolor='black')
-    plt.bar(x_width[3], y_data[3], lw=1, color=['#FFC79C'], width=0.5 * 5 / 6, label="DCConv", edgecolor='black')
-    plt.bar(x_width[4], y_data[4], lw=1, color=['#BEE9C7'], width=0.5 * 5 / 6, label="RepDCConv", edgecolor='black')
-    plt.bar(x_width[5], y_data[5], lw=1, color=['#B8E9D0'], width=0.5 * 5 / 6, label="RNet-MSE", edgecolor='black')
-    plt.bar(x_width[6], y_data[6], lw=1, color=['#B9E9E2'], width=0.5 * 5 / 6, label="RNet", edgecolor='black')
-    plt.bar(x_width[7], y_data[7], lw=1, color=['#D6E6F2'], width=0.5 * 5 / 6, label="RNet-SE", edgecolor='black')
-    plt.bar(x_width[8], y_data[8], lw=1, color=['#B4D1E9'], width=0.5 * 5 / 6, label="RNet-L", edgecolor='black')
-    plt.bar(x_width[9], y_data[9], lw=1, color=['#AEB5EE'], width=0.5 * 5 / 6, label="RNet-D", edgecolor='black')
+
+    plt.bar(x_width[3], y_data[10], lw=1, color=['#FCD58B'], width=0.5 * 5 / 6, label="CG-QA", edgecolor='black')
+    plt.bar(x_width[4], y_data[11], lw=1, color=['#EBC99D'], width=0.5 * 5 / 6, label="CG-3{0}".format(chr(963)), edgecolor='black')
+
+    plt.bar(x_width[5], y_data[3], lw=1, color=['#FFC79C'], width=0.5 * 5 / 6, label="DCConv", edgecolor='black')
+    plt.bar(x_width[6], y_data[4], lw=1, color=['#BEE9C7'], width=0.5 * 5 / 6, label="RepDCConv", edgecolor='black')
+    plt.bar(x_width[7], y_data[5], lw=1, color=['#B8E9D0'], width=0.5 * 5 / 6, label="RNet-MSE", edgecolor='black')
+    plt.bar(x_width[8], y_data[6], lw=1, color=['#B9E9E2'], width=0.5 * 5 / 6, label="RNet", edgecolor='black')
+    plt.bar(x_width[9], y_data[7], lw=1, color=['#D6E6F2'], width=0.5 * 5 / 6, label="RNet-SE", edgecolor='black')
+    plt.bar(x_width[10], y_data[8], lw=1, color=['#B4D1E9'], width=0.5 * 5 / 6, label="RNet-L", edgecolor='black')
+    plt.bar(x_width[11], y_data[9], lw=1, color=['#AEB5EE'], width=0.5 * 5 / 6, label="RNet-D", edgecolor='black')
+
 
     # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1)
     plt.xticks([])
@@ -440,7 +528,7 @@ def plot_FNR1(y_data):
     plt.xlabel('Methods', fontsize=22)
     # plt.tight_layout()
 
-    num1, num2, num3, num4 = 0.05, 1, 3, 0
+    num1, num2, num3, num4 = 0.025, 1, 3, 0
     plt.legend(bbox_to_anchor=(num1, num2), loc=num3, borderaxespad=num4, ncol=5, frameon=False, handlelength=1,
                handletextpad=0.45, columnspacing=1)
 
@@ -479,15 +567,25 @@ def plot_FNR2(y_data):
     # plt.bar(x_width[8], y_data[8], lw=1, color=['#E000F5'], width=0.5 * 2 / 3, label="JMNet", edgecolor='black')
 
     plt.bar(x_width[0], y_data[0], lw=1, color=['#AEB5EE'], width=0.5 * 2 / 3, label="ResNet-18", edgecolor='black')
-    plt.bar(x_width[1], y_data[1],  color=['#FFFFFF'], label="    ")
-    plt.bar(x_width[2], y_data[2],  color=['#FFFFFF'], label="    ")
-    plt.bar(x_width[3], y_data[3],  color=['#FFFFFF'],  label="    ")
-    plt.bar(x_width[4], y_data[4],lw=1,  color=['#D5A9FF'], width=0.5 * 2 / 3, label="RNet-C", edgecolor='black')
-    plt.bar(x_width[5], y_data[5],  color=['#FFFFFF'], label="     ")
-    plt.bar(x_width[6], y_data[6],  color=['#FFFFFF'], label="     ")
+    plt.bar(x_width[1], y_data[1], color=['#FFFFFF'], label="    ")
+    plt.bar(x_width[2], y_data[2], color=['#FFFFFF'], label="    ")
+    plt.bar(x_width[3], y_data[3], color=['#FFFFFF'], label="    ")
+    plt.bar(x_width[4], y_data[4], color=['#FFFFFF'], label="    ")
+    plt.bar(x_width[5], y_data[5], color=['#FFFFFF'], label="    ")
+
+
+    plt.bar(x_width[6], y_data[6], lw=1, color=['#D5A9FF'], width=0.5 * 2 / 3, label="RNet-C", edgecolor='black')
+    plt.bar(x_width[7], y_data[7], color=['#FFFFFF'], label="     ")
+    plt.bar(x_width[8], y_data[8], color=['#FFFFFF'], label="     ")
+    plt.bar(x_width[9], y_data[9], color=['#FFFFFF'], label="     ")
+    plt.bar(x_width[10], y_data[10], color=['#FFFFFF'], label="     ")
+    plt.bar(x_width[11], y_data[11], color=['#FFFFFF'], label="     ")
+
     # plt.bar(x_width[7] + 2.0, y_data[10], lw=0.5, color=['#8085e9'], width=1, label="ResNet-18", edgecolor='black')
-    plt.bar(x_width[7], y_data[7],  color=['#FFFFFF'],  label="     ")
-    plt.bar(x_width[8], y_data[8],lw=1,  color=['#E000F5'], width=0.5 * 2 / 3, label="JMNet", edgecolor='black')
+
+
+    plt.bar(x_width[12], y_data[12], lw=1, color=['#E000F5'], width=0.5 * 2 / 3, label="JRFPN", edgecolor='black')
+    plt.bar(x_width[13], y_data[13], color=['#FFFFFF'], label="     ")
 
     # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1)
     plt.xticks([])
@@ -554,14 +652,14 @@ def plot_hot_one(data):
     pass
 
 
-def plot_mse(file_name_mse="../others_idea/mse",data:str=''):
+def plot_mse(file_name_mse="../others_idea/mse", data: str = ''):
     mse = np.loadtxt(file_name_mse, delimiter=",")
-    raw_data=np.loadtxt(data,delimiter=",")
-    raw_data=raw_data[:,:mse.shape[1]]
-    print("mse:",mse.shape)
-    print("raw_data:",raw_data.shape)
-    res=raw_data-mse
-    mse.shape[0]*2/3
+    raw_data = np.loadtxt(data, delimiter=",")
+    raw_data = raw_data[:, :mse.shape[1]]
+    print("mse:", mse.shape)
+    print("raw_data:", raw_data.shape)
+    res = raw_data - mse
+    mse.shape[0] * 2 / 3
     # mse = mse[2000:2300]
     # mse = mse[1800:2150]
 
@@ -589,20 +687,19 @@ def plot_mse(file_name_mse="../others_idea/mse",data:str=''):
         plt.axvline(res.shape[1] * 2 / 3, c='purple', ls='-.', lw=0.5, label="real fault")
         plt.plot(res[i, :], lw=0.5)
 
-
-
     plt.show()
 
+
 def plot_mse_single(file_name_mse="../self_try/compare/mse/JM_banda/banda_joint_result_predict1.csv"):
     mse = np.loadtxt(file_name_mse, delimiter=",")
 
-    print("mse:",mse.shape)
+    print("mse:", mse.shape)
 
-    need_shape=int(mse.shape[0]*2/3)
+    need_shape = int(mse.shape[0] * 2 / 3)
     # mse = mse[2000:2300]
     # mse = mse[1800:2150]
     # mse = mse[ need_shape+100:need_shape+377]
-    mse = mse[ need_shape-300:need_shape-10]
+    mse = mse[need_shape - 300:need_shape - 10]
 
     parameters = {
         'figure.dpi': 600,
@@ -613,7 +710,7 @@ def plot_mse_single(file_name_mse="../self_try/compare/mse/JM_banda/banda_joint_
         'xtick.labelsize': 6,
         'ytick.labelsize': 6,
         'legend.fontsize': 5,
-        'font.family':'Times New Roman'
+        'font.family': 'Times New Roman'
     }
     plt.rcParams.update(parameters)
     font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 7}  # 设置坐标标签的字体大小，字体
@@ -622,9 +719,9 @@ def plot_mse_single(file_name_mse="../self_try/compare/mse/JM_banda/banda_joint_
     plt.rc('font', family='Times New Roman')  # 全局字体样式#画混淆矩阵
     indices = [mse.shape[0] * i / 4 for i in range(5)]
     # classes = ['07:21','08:21', '09:21', '10:21', '11:21']
-    classes = ['01:58','02:58', '03:58', '04:58', '05:58']
+    classes = ['01:58', '02:58', '03:58', '04:58', '05:58']
 
-    plt.xticks([index  for index in indices], classes, rotation=25)  # 设置横坐标方向，rotation=45为45度倾斜
+    plt.xticks([index for index in indices], classes, rotation=25)  # 设置横坐标方向，rotation=45为45度倾斜
     # pad调整label与坐标轴之间的距离
     plt.tick_params(bottom=True, top=False, left=True, right=False, direction='inout', length=2, width=0.5, pad=1)
     plt.ylabel('Residual', fontdict=font2)
@@ -632,8 +729,6 @@ def plot_mse_single(file_name_mse="../self_try/compare/mse/JM_banda/banda_joint_
     plt.tight_layout()
     plt.plot(mse[:], lw=0.5)
 
-
-
     plt.show()
 
 
@@ -641,37 +736,36 @@ def plot_3d():
     # 线
     fig = plt.figure()
     ax = fig.add_subplot(projection='3d')
-    data,label = read_data(file_name='G:\data\SCADA数据\jb4q_8_delete_total_zero.csv',isNew=False)
+    data, label = read_data(file_name='G:\data\SCADA数据\jb4q_8_delete_total_zero.csv', isNew=False)
     print(data)
     print(data.shape)
-    x=range(data.shape[1])
-    y=range(data.shape[0])
-    color=[[0.16,0.14,0.13],
-    [0.89,0.09,0.05],
-    [0.12,0.56,1.00],
-    [0.01,0.66,0.62],
-    [0.63,0.04,0.83],
-    [0.63,0.32,0.18],
-    [0.20,0.63,0.79],
-    [0.50,0.16,0.16],
-    [0.61,0.4,0.12],
-    [1.00,0.38,0.00],
-    [0.53,0.81,0.92],
-    [0.13,0.55,0.13],
-    [1.00,0.89,0.52],
-    [0.44,0.50,0.41],
-    [0.20,0.63,0.79],
-    [0.00,0.78,0.55],
-    [1.00,0.39,0.28],
-    [0.25,0.41,0.88]]
+    x = range(data.shape[1])
+    y = range(data.shape[0])
+    color = [[0.16, 0.14, 0.13],
+             [0.89, 0.09, 0.05],
+             [0.12, 0.56, 1.00],
+             [0.01, 0.66, 0.62],
+             [0.63, 0.04, 0.83],
+             [0.63, 0.32, 0.18],
+             [0.20, 0.63, 0.79],
+             [0.50, 0.16, 0.16],
+             [0.61, 0.4, 0.12],
+             [1.00, 0.38, 0.00],
+             [0.53, 0.81, 0.92],
+             [0.13, 0.55, 0.13],
+             [1.00, 0.89, 0.52],
+             [0.44, 0.50, 0.41],
+             [0.20, 0.63, 0.79],
+             [0.00, 0.78, 0.55],
+             [1.00, 0.39, 0.28],
+             [0.25, 0.41, 0.88]]
     # c颜色，marker：样式*雪花
 
-    ax.plot( xs=y,ys=x, zs=data, c=color, marker="*")
+    ax.plot(xs=y, ys=x, zs=data, c=color, marker="*")
 
     plt.show()
 
 
-
 def test_result(file_name: str = result_file_name):
     # result_data = np.recfromcsv(file_name)
     result_data = np.loadtxt(file_name, delimiter=",")
@@ -680,7 +774,7 @@ def test_result(file_name: str = result_file_name):
     theshold = len(result_data)
     print(theshold)
     print(theshold * 2 / 3)
-    theshold = theshold * 2 / 3-50
+    theshold = theshold * 2 / 3 - 50
     # 计算误报率和漏报率
     positive_rate = result_data[:int(theshold)][result_data[:int(theshold)] < 0.66].__len__() / (
             theshold * 2 / 3)
@@ -783,22 +877,166 @@ def test_model_visualization(model_name=file_name):
 
     plot_hot(needed_data)
 
+def plot_loss():
+    from matplotlib import rcParams
+
+    config = {
+        "font.family": 'Times New Roman',  # 设置字体类型
+        "axes.unicode_minus": False,  # 解决负号无法显示的问题
+        "axes.labelsize": 13
+    }
+    rcParams.update(config)
+
+    pic1 = plt.figure(figsize=(8, 6), dpi=200)
+    plt.subplot(211)
+    plt.plot(np.arange(1, epochs + 1), src_acc_list, 'b', label='TrainAcc')
+    plt.plot(np.arange(1, epochs + 1), val_acc_list, 'r', label='ValAcc')
+    plt.ylim(0.3, 1.0)  # 设置y轴范围
+    plt.title('Training & Validation accuracy')
+    plt.xlabel('epoch')
+    plt.ylabel('accuracy')
+    plt.legend(loc='lower right')
+    plt.grid(alpha=0.4)
+
+    plt.subplot(212)
+    plt.plot(np.arange(1, epochs + 1), train_loss_list, 'b', label='TrainLoss')
+    plt.plot(np.arange(1, epochs + 1), val_loss_list, 'r', label='ValLoss')
+    plt.ylim(0, 0.08)  # 设置y轴范围
+    plt.title('Training & Validation loss')
+    plt.xlabel('epoch')
+    plt.ylabel('loss')
+    plt.legend(loc='upper right')
+    plt.grid(alpha=0.4)
+
+    # 获取当前时间戳
+    timestamp = int(time.time())
+
+    # 将时间戳转换为字符串
+    timestamp_str = str(timestamp)
+    plt.savefig(timestamp_str, dpi=200)
+
 
 if __name__ == '__main__':
+    # model_list = ["DCConv", "GRU"]
+    #
+    # for model_name in model_list:
+    #     # model_name = "CNN_GRU"
+    #     mse = np.loadtxt(
+    #         'E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\{0}/banda\mse.csv'.format(
+    #             model_name),
+    #         delimiter=',')
+    #     max = np.loadtxt(
+    #         'E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\{0}/banda\max.csv'.format(
+    #             model_name),
+    #         delimiter=',')
+    #     data, mid, UCL, LCL = EWMA(mse)
+    #
+    #     plot_EWMA(
+    #         model_name=model_name+"_banda",
+    #         total_MSE=data,
+    #         total_mid=mid,
+    #         total_max=UCL,
+    #         total_min=LCL
+    #     )
+    #
+    # model_list = ["RNet_D", "RNet_L","RNet_S","RNet_MSE"]
+    # predict_list = ["predict1","predict2","predict3"]
+    #
+    # for model_name in model_list:
+    #     for predict_head in predict_list:
+    #         # model_name = "RNet_L"
+    #         # predict_head = "predict3"
+    #         mse = np.loadtxt(
+    #             'E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\{0}/banda\{0}_banda_mse_{1}.csv'.format(
+    #                 model_name, predict_head),
+    #             delimiter=',')
+    #         max = np.loadtxt(
+    #             'E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\{0}/banda\{0}_banda_max_{1}.csv'.format(
+    #                 model_name, predict_head),
+    #             delimiter=',')
+    #         data, mid, UCL, LCL = EWMA(mse)
+    #
+    #         plot_EWMA(
+    #             model_name=model_name + predict_head+"_banda",
+    #             total_MSE=data,
+    #             total_mid=mid,
+    #             total_max=UCL,
+    #             total_min=LCL
+    #         )
+
+    # model_name = "RNet_D"
+    # predict_head = "predict1"
+    # mse = np.loadtxt(
+    #     'E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\{0}\{0}_timestamp120_feature10_mse_{1}.csv'.format(
+    #         model_name, predict_head),
+    #     delimiter=',')
+    # max = np.loadtxt(
+    #     'E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\{0}\{0}_timestamp120_feature10_max_{1}.csv'.format(
+    #         model_name, predict_head),
+    #     delimiter=',')
+    # data, mid, UCL, LCL = EWMA(mse)
+    #
+    # plot_EWMA(
+    #     model_name=model_name + predict_head ,
+    #     total_MSE=data,
+    #     total_mid=mid,
+    #     total_max=UCL,
+    #     total_min=LCL
+    # )
+
+    # test_result("E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_345\RNet_345_timestamp120_feature10_result.csv")
     # test_mse(fi)
     # test_result(
     #     file_name='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\ResNet\ResNet_timestamp120_feature10_result.csv')
     # test_corr()
     # acc()
-    # list = [3.77, 2.64, 2.35, 2.05, 1.76, 1.09, 0.757, 0.82, 1.1, 0.58, 0, 0.03, 0.02]
-    # test_bar(list)
-
-    # list=[98.56,98.95,99.95,96.1,95,99.65,76.25,72.64,75.87,68.74]
-    # plot_FNR1(list)
-    # #
-    # list=[3.43,1.99,1.92,2.17,1.63,1.81,1.78,1.8,0.6]
-    list=[3.43,1.99,1.92,2.17,1.8,1.81,1.78,1.8,0.6]
-    plot_FNR2(list)
+    # list = [
+    #     3.77,
+    #     2.64,
+    #     2.35,
+    #
+    #     2.05,
+    #     1.76,
+    #     1.09,
+    #     0.76,
+    #     0.82,
+    #     1.10,
+    #     0.58,
+    #     0,
+    #     0.03,
+    #     0.02,
+    #     0.21,
+    #     2.18,
+    # ]
+    #
+    # # test_bar(list)
+    # list = [
+    #     64.63,
+    #     65.26,
+    #     65.11,
+    #
+    #     66.6,
+    #     67.15,
+    #     73.86,
+    #     66.28,
+    #     75.24,
+    #     73.98,
+    #     76.7,
+    #     98.86,
+    #     99.38,
+    #     99.79,
+    #     70.41,
+    #     66.71
+    # ]
+    # # acc(list)
+    #
+    list=[98.56,98.95,99.95,96.1,95,99.65,76.25,72.64,75.87,68.74,88.36,95.52]
+    plot_FNR1(list)
+    # # # #
+    # # list=[3.43,1.99,1.92,2.17,1.63,1.81,1.78,1.8,0.6]
+    # list=[3.43,0,0,0,0,0,1.8,0,0,0,0,0,0.6,0]
+    # # list = [98.56, 98.95, 99.95, 96.1, 95, 99.65, 76.25, 72.64, 75.87, 68.74, 88.36, 95.52]
+    # plot_FNR2(list)
 
     # 查看网络某一层的权重
     # test_model_visualization(model_name = "E:\跑模型\论文写作/SE.txt")
@@ -810,11 +1048,11 @@ if __name__ == '__main__':
     # 单独预测图
     # plot_mse('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/banda_joint_result_predict3.csv',data='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/raw_data.csv')
     # plot_mse_single('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_D/banda\RNet_D_banda_mse_predict1.csv')
-    #画3d图
+    # plot_mse_single('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_3\RNet_3_timestamp120_feature10_result.csv')
+    # 画3d图
     # plot_3d()
 
-
-    #原始数据图
+    # 原始数据图
     # file_names='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/raw_data.csv'
     # data= np.loadtxt(file_names,delimiter=',')
 

TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/Joint_Monitoring_banda.py

@@ -7,7 +7,6 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
-from model.Dynamic_channelAttention.Dynamic_channelAttention import DynamicChannelAttention
 from condition_monitoring.data_deal import loadData_daban as loadData
 from model.Joint_Monitoring.Joint_Monitoring_banda import Joint_Monitoring
 
@@ -40,7 +39,7 @@ save_name = "../hard_model/weight/{0}_epoch16_0.0009_0.0014/weight".format(model
                                                      feature_num,
                                                      batch_size,
                                                      EPOCH)
-save_step_two_name = "../hard_model/two_weight/{0}_epoch24_9875_9867/weight".format(model_name,
+save_step_two_name = "../hard_model/two_weight/temp{0}/weight".format(model_name,
                                                                   time_stamp,
                                                                   feature_num,
                                                                   batch_size,
@@ -385,7 +384,7 @@ def train_step_one(train_data, train_label1, train_label2):
             k = k + 1
         val_loss, val_accuracy = model.get_val_loss(val_data=val_data, val_label1=val_label1, val_label2=val_label2,
                                                     is_first_time=True)
-        SaveBestModel(model=model, save_name=save_name, history_loss=history_val_loss, loss_value=val_loss.numpy())
+        SaveBestModel(model=model, save_name=save_name, history_loss=history_val_loss, loss_value=val_loss.numpy(),is_all=True)
         # SaveBestH5Model(model=model, save_name=save_name, history_loss=history_val_loss, loss_value=val_loss.numpy())
         history_val_loss.append(val_loss)
         history_loss.append(loss_value.numpy())
@@ -586,28 +585,32 @@ if __name__ == '__main__':
     #### TODO 第一步训练
     # 单次测试
     # train_step_one(train_data=train_data_healthy[:128, :, :], train_label1=train_label1_healthy[:128, :],train_label2=train_label2_healthy[:128, ])
+    # 整体训练
     # train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
 
-    # 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
-    # step_one_model = Joint_Monitoring()
-    # step_one_model.load_weights(save_name)
-    #
-    # step_two_model = Joint_Monitoring()
-    # step_two_model.load_weights(save_name)
+
 
     #### TODO 第二步训练
     ### healthy_data.shape: (300333,120,10)
     ### unhealthy_data.shape: (16594,10)
+
+    #### 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
+    step_one_model = Joint_Monitoring()
+    step_one_model.load_weights(save_name)
+
+    step_two_model = Joint_Monitoring()
+    step_two_model.load_weights(save_name)
+
     healthy_size, _, _ = train_data_healthy.shape
     unhealthy_size, _, _ = train_data_unhealthy.shape
-    # train_data, train_label1, train_label2, test_data, test_label1, test_label2 = split_test_data(
-    #     healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :, :],
-    #     healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
-    #     healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy,
-    #     unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy)
-    # train_step_two(step_one_model=step_one_model, step_two_model=step_two_model,
-    #                train_data=train_data,
-    #                train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1))
+    train_data, train_label1, train_label2, test_data, test_label1, test_label2 = split_test_data(
+        healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :, :],
+        healthy_label1=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
+        healthy_label2=train_label2_healthy[healthy_size - 2 * unhealthy_size:, ], unhealthy_data=train_data_unhealthy,
+        unhealthy_label1=train_label1_unhealthy, unhealthy_label2=train_label2_unhealthy)
+    train_step_two(step_one_model=step_one_model, step_two_model=step_two_model,
+                   train_data=train_data,
+                   train_label1=train_label1, train_label2=np.expand_dims(train_label2, axis=-1))
 
     ### TODO 测试测试集
     # step_one_model = Joint_Monitoring()

TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-C.py

@@ -18,7 +18,7 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
-from model.Dynamic_channelAttention.Dynamic_channelAttention import DynamicChannelAttention
+from model.ChannelAttention.Dynamic_channelAttention import DynamicChannelAttention
 from condition_monitoring.data_deal import loadData_daban as loadData
 from model.Joint_Monitoring.compare.RNet_C import Joint_Monitoring
 

TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet.py

@@ -15,7 +15,8 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
-from model.Dynamic_channelAttention.Dynamic_channelAttention import DynamicChannelAttention
+import time
+
 from condition_monitoring.data_deal import loadData_daban as loadData
 from model.Joint_Monitoring.compare.RNet import Joint_Monitoring
 
@@ -591,10 +592,21 @@ if __name__ == '__main__':
     # 导入第一步已经训练好的模型,一个继续训练，一个只输出结果
     step_one_model = Joint_Monitoring()
     step_one_model.load_weights(save_name)
+
+    # step_one_model.build(input_shape=(30,120,10))
+    # step_one_model.summary()
     #
     # step_two_model = Joint_Monitoring()
     # step_two_model.load_weights(save_name)
 
+    start = time.time()
+    # 中间写上代码块
+
+    step_one_model.predict(train_data_healthy, batch_size=32)
+    end = time.time()
+    print("data_size:", train_data_healthy.shape)
+    print('Running time: %s Seconds' % (end - start))
+
     # #### TODO 计算MSE
     healthy_size, _, _ = train_data_healthy.shape
     unhealthy_size, _, _ = train_data_unhealthy.shape

TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/resnet_18.py

@@ -8,7 +8,7 @@ import seaborn as sns
 from sklearn.model_selection import train_test_split
 from condition_monitoring.data_deal import loadData_daban as loadData
 from keras.callbacks import EarlyStopping
-
+import time
 '''超参数设置'''
 time_stamp = 120
 feature_num = 10
@@ -68,22 +68,7 @@ unhealthy_patience = 5
 font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 10}  # 设置坐标标签的字体大小，字体
 
 
-# train_data = np.load("../../../data/train_data.npy")
-# train_label = np.load("../../../data/train_label.npy")
-# test_data = np.load("../../../data/test_data.npy")
-# test_label = np.load("../../../data/test_label.npy")
 
-
-# CIFAR_100_data = tf.keras.datasets.cifar100
-# (train_data, train_label), (test_data, test_label) = CIFAR_100_data.load_data()
-# train_data=np.array(train_data)
-# train_label=np.array(train_label)
-# print(train_data.shape)
-# print(train_label.shape)
-# print(train_data)
-# print(test_data)
-#
-#
 # 重叠采样
 def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
     rows, cols = data.shape
@@ -328,6 +313,14 @@ if __name__ == '__main__':
     # model.save("./model/ResNet.h5")
     model = tf.keras.models.load_model("model/ResNet_banda/ResNet_banda_epoch10_9884.h5")
 
+    start = time.time()
+    # 中间写上代码块
+
+    model.predict(train_data_healthy, batch_size=32)
+    end = time.time()
+    print("data_size:", train_data_healthy.shape)
+    print('Running time: %s Seconds' % (end - start))
+
     # 结果展示
 
     healthy_size, _, _ = train_data_healthy.shape

TensorFlow_eaxmple/Model_train_test/condition_monitoring/返修/Cnn-gru.py

@@ -0,0 +1,496 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+
+
+'''
+@Author : dingjiawen
+@Date : 2022/10/11 18:52
+@Usage : 对比实验，与JointNet相同深度,进行预测
+@Desc :
+'''
+
+
+import tensorflow as tf
+import tensorflow.keras
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from condition_monitoring.data_deal import loadData_daban as loadData
+from model.Joint_Monitoring.Joint_Monitoring3 import Joint_Monitoring
+
+from model.CommonFunction.CommonFunction import *
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.models import load_model, save_model
+from keras.callbacks import EarlyStopping
+import random
+import time
+'''超参数设置'''
+time_stamp = 120
+feature_num = 10
+batch_size = 32
+learning_rate = 0.001
+EPOCH = 101
+model_name = "DCConv"
+'''EWMA超参数'''
+K = 18
+namuda = 0.01
+'''保存名称'''
+
+save_name = "./trianed/{0}_{1}_{2}.h5".format(model_name,
+                                                            time_stamp,
+                                                            feature_num,
+                                                            batch_size,
+                                                            EPOCH)
+save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weight_epoch14/weight".format(model_name,
+                                                                                                         time_stamp,
+                                                                                                         feature_num,
+                                                                                                         batch_size,
+                                                                                                         EPOCH)
+save_mse_name = "./mse/DCConv/banda/mse.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                              batch_size,
+                                                                              EPOCH)
+save_max_name = "./mse/DCConv/banda/max.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                             batch_size,
+                                                                              EPOCH)
+
+# save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                    time_stamp,
+#                                                                    feature_num,
+#                                                                    batch_size,
+#                                                                    EPOCH)
+# save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                                 time_stamp,
+#                                                                                 feature_num,
+#                                                                                 batch_size,
+#                                                                                 EPOCH)
+'''文件名'''
+'''文件名'''
+file_name = "G:\data\SCADA数据\SCADA_已处理_粤水电达坂城2020.1月-5月\风机15.csv"
+
+'''
+文件说明：jb4q_8_delete_total_zero.csv是删除了只删除了全是0的列的文件
+文件从0:415548行均是正常值(2019/7.30 00:00:00 - 2019/9/18  11:14:00)
+从415549:432153行均是异常值(2019/9/18  11:21:01 - 2021/1/18  00:00:00)
+'''
+'''文件参数'''
+# 最后正常的时间点
+healthy_date = 96748
+# 最后异常的时间点
+unhealthy_date = 107116
+# 异常容忍程度
+unhealthy_patience = 5
+
+
+def remove(data, time_stamp=time_stamp):
+    rows, cols = data.shape
+    print("remove_data.shape:", data.shape)
+    num = int(rows / time_stamp)
+
+    return data[:num * time_stamp, :]
+    pass
+
+
+# 不重叠采样
+def get_training_data(data, time_stamp: int = time_stamp):
+    removed_data = remove(data=data)
+    rows, cols = removed_data.shape
+    print("removed_data.shape:", data.shape)
+    print("removed_data:", removed_data)
+    train_data = np.reshape(removed_data, [-1, time_stamp, cols])
+    print("train_data:", train_data)
+    batchs, time_stamp, cols = train_data.shape
+
+    for i in range(1, batchs):
+        each_label = np.expand_dims(train_data[i, 0, :], axis=0)
+        if i == 1:
+            train_label = each_label
+        else:
+            train_label = np.concatenate([train_label, each_label], axis=0)
+
+    print("train_data.shape:", train_data.shape)
+    print("train_label.shape", train_label.shape)
+    return train_data[:-1, :], train_label
+
+
+# 重叠采样
+def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
+    rows, cols = data.shape
+    train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
+    train_label = np.empty(shape=[rows - time_stamp - 1, cols])
+    for i in range(rows):
+        if i + time_stamp >= rows:
+            break
+        if i + time_stamp < rows - 1:
+            train_data[i] = data[i:i + time_stamp]
+            train_label[i] = data[i + time_stamp]
+
+    print("重叠采样以后：")
+    print("data:", train_data)  # (300334,120,10)
+    print("label:", train_label)  # (300334,10)
+
+    if is_Healthy:
+        train_label2 = np.ones(shape=[train_label.shape[0]])
+    else:
+        train_label2 = np.zeros(shape=[train_label.shape[0]])
+
+    print("label2:", train_label2)
+
+    return train_data, train_label, train_label2
+
+
+# 归一化
+def normalization(data):
+    rows, cols = data.shape
+    print("归一化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 归一化
+    max = np.max(data, axis=0)
+    max = np.broadcast_to(max, [rows, cols])
+    min = np.min(data, axis=0)
+    min = np.broadcast_to(min, [rows, cols])
+
+    data = (data - min) / (max - min)
+    print("归一化之后:", data)
+    print(data.shape)
+
+    return data
+
+
+# 正则化
+def Regularization(data):
+    rows, cols = data.shape
+    print("正则化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 正则化
+    mean = np.mean(data, axis=0)
+    mean = np.broadcast_to(mean, shape=[rows, cols])
+    dst = np.sqrt(np.var(data, axis=0))
+    dst = np.broadcast_to(dst, shape=[rows, cols])
+    data = (data - mean) / dst
+    print("正则化之后:", data)
+    print(data.shape)
+
+    return data
+    pass
+
+
+def EWMA(data, K=K, namuda=namuda):
+    # t是啥暂时未知
+    t = 0
+    mid = np.mean(data, axis=0)
+    standard = np.sqrt(np.var(data, axis=0))
+    UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    return mid, UCL, LCL
+    pass
+
+
+
+
+
+def condition_monitoring_model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
+    GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
+    d1 = tf.keras.layers.Dense(300)(GRU1)
+    output = tf.keras.layers.Dense(10)(d1)
+
+    model = tf.keras.Model(inputs=input, outputs=output)
+
+    return model
+
+
+# trian_data:(300455,120,10)
+# trian_label1:(300455,10)
+# trian_label2:(300455,)
+def shuffle(train_data, train_label1, train_label2, is_split: bool = False, split_size: float = 0.2):
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(train_data,
+                                                                                                     train_label1,
+                                                                                                     train_label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=True,
+                                                                                                     random_state=100)
+    if is_split:
+        return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+    train_data = np.concatenate([train_data, test_data], axis=0)
+    train_label1 = np.concatenate([train_label1, test_label1], axis=0)
+    train_label2 = np.concatenate([train_label2, test_label2], axis=0)
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2
+    pass
+
+
+def split_test_data(healthy_data, healthy_label1, healthy_label2, unhealthy_data, unhealthy_label1, unhealthy_label2,
+                    split_size: float = 0.2, shuffle: bool = True):
+    data = np.concatenate([healthy_data, unhealthy_data], axis=0)
+    label1 = np.concatenate([healthy_label1, unhealthy_label1], axis=0)
+    label2 = np.concatenate([healthy_label2, unhealthy_label2], axis=0)
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(data,
+                                                                                                     label1,
+                                                                                                     label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=shuffle,
+                                                                                                     random_state=100)
+
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+
+    pass
+
+
+def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
+    history_loss = []
+    history_val_loss = []
+
+    val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
+                                                         val_label2=test_label2,
+                                                         is_first_time=False, step_one_model=step_one_model)
+
+    history_val_loss.append(val_loss)
+    print("val_accuracy:", val_accuracy)
+    print("val_loss:", val_loss)
+
+
+def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False):
+    # 获取模型的所有参数的个数
+    # step_two_model.count_params()
+    total_result = []
+    size, length, dims = test_data.shape
+    for epoch in range(0, size - batch_size + 1, batch_size):
+        each_test_data = test_data[epoch:epoch + batch_size, :, :]
+        _, _, _, output4 = step_two_model.call(each_test_data, is_first_time=False)
+        total_result.append(output4)
+    total_result = np.reshape(total_result, [total_result.__len__(), -1])
+    total_result = np.reshape(total_result, [-1, ])
+    if isPlot:
+        plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
+        # 画出 y=1 这条水平线
+        plt.axhline(0.5, c='red', label='Failure threshold')
+        # 箭头指向上面的水平线
+        # plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
+        #           alpha=0.9, overhang=0.5)
+        # plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
+        plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
+        plt.xlabel("time")
+        plt.ylabel("confience")
+        plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.grid()
+        # plt.ylim(0, 1)
+        # plt.xlim(-50, 1300)
+        # plt.legend("", loc='upper left')
+        plt.show()
+    return total_result
+
+
+def DCConv_Model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    input = tf.cast(input, tf.float32)
+
+    LSTM = tf.keras.layers.Conv1D(10, 3)(input)
+    LSTM = tf.keras.layers.Conv1D(20, 3)(LSTM)
+    LSTM = tf.keras.layers.GRU(20, return_sequences=True)(LSTM)
+    LSTM = tf.keras.layers.GRU(40, return_sequences=True)(LSTM)
+    LSTM = tf.keras.layers.GRU(80, return_sequences=False)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=64)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=128)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=2)(LSTM)
+
+    # LSTM = LSTM[:, -1, :]
+    # bn = tf.keras.layers.BatchNormalization()(LSTM)
+
+    # d1 = tf.keras.layers.Dense(20)(LSTM)
+    # bn = tf.keras.layers.BatchNormalization()(d1)
+
+    output = tf.keras.layers.Dense(128, name='output1')(LSTM)
+    output = tf.keras.layers.Dense(10, name='output')(output)
+    model = tf.keras.Model(inputs=input, outputs=output)
+    return model
+    pass
+
+
+def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True, predictI: int = 1):
+    predicted_data = new_model.predict(data)
+
+    temp = np.abs(predicted_data - label)
+    temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
+    temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
+    temp3 = temp1 / temp2
+    mse = np.sum((temp1 / temp2) ** 2, axis=1)
+    print("z:", mse)
+    print(mse.shape)
+
+    # mse=np.mean((predicted_data-label)**2,axis=1)
+    print("mse", mse)
+    if isStandard:
+        dims, = mse.shape
+        mean = np.mean(mse)
+        std = np.sqrt(np.var(mse))
+        max = mean + 3 * std
+        print("max:", max)
+        # min = mean-3*std
+        max = np.broadcast_to(max, shape=[dims, ])
+        # min = np.broadcast_to(min,shape=[dims,])
+        mean = np.broadcast_to(mean, shape=[dims, ])
+        if isPlot:
+            plt.figure(random.randint(1,9))
+            plt.plot(max)
+            plt.plot(mse)
+            plt.plot(mean)
+            # plt.plot(min)
+            plt.show()
+    else:
+        if isPlot:
+            plt.figure(random.randint(1, 9))
+            plt.plot(mse)
+            # plt.plot(min)
+            plt.show()
+            return  mse
+
+    return mse, mean, max
+    # pass
+
+
+# healthy_data是健康数据,用于确定阈值，all_data是完整的数据,用于模型出结果
+def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data, unhealthy_label, isPlot: bool = False,
+              isSave: bool = True, predictI: int = 1):
+    # TODO 计算MSE确定阈值
+    # TODO 计算MSE确定阈值
+
+    mse, mean, max = get_MSE(healthy_data, healthy_label, model)
+
+    # 误报率的计算
+    total, = mse.shape
+    faultNum = 0
+    faultList = []
+    faultNum = mse[mse[:] > max[0]].__len__()
+    # for i in range(total):
+    #     if (mse[i] > max[i]):
+    #         faultNum += 1
+    #         faultList.append(mse[i])
+
+    fault_rate = faultNum / total
+    print("误报率:", fault_rate)
+
+    # 漏报率计算
+    missNum = 0
+    mse1 = get_MSE(unhealthy_data, unhealthy_label, model, isStandard=False)
+
+    total_mse = np.concatenate([mse, mse1], axis=0)
+    total_max = np.broadcast_to(max[0], shape=[total_mse.shape[0], ])
+    # min = np.broadcast_to(min,shape=[dims,])
+    total_mean = np.broadcast_to(mean[0], shape=[total_mse.shape[0], ])
+    if isSave:
+        save_mse_name1 = save_mse_name
+        save_max_name1 = save_max_name
+
+        np.savetxt(save_mse_name1, total_mse, delimiter=',')
+        np.savetxt(save_max_name1, total_max, delimiter=',')
+
+    all, = mse1.shape
+
+
+    missNum = mse1[mse1[:] < max[0]].__len__()
+
+
+    print("all:", all)
+    miss_rate = missNum / all
+    print("漏报率:", miss_rate)
+
+
+
+    plt.figure(random.randint(1, 100))
+    plt.plot(total_max)
+    plt.plot(total_mse)
+    plt.plot(total_mean)
+    # plt.plot(min)
+    plt.show()
+    pass
+
+
+if __name__ == '__main__':
+    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = normalization(data=total_data)
+    train_data_healthy, train_label1_healthy, train_label2_healthy = get_training_data_overlapping(
+        total_data[:healthy_date, :], is_Healthy=True)
+    train_data_unhealthy, train_label1_unhealthy, train_label2_unhealthy = get_training_data_overlapping(
+        total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
+        is_Healthy=False)
+    #### TODO 第一步训练
+    # 单次测试
+    model = DCConv_Model()
+
+    checkpoint = tf.keras.callbacks.ModelCheckpoint(
+        filepath=save_name,
+        monitor='val_loss',
+        verbose=2,
+        save_best_only=True,
+        mode='min')
+    lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.001)
+
+    model.compile(optimizer=tf.optimizers.Adam(), loss=tf.losses.mse)
+    model.build(input_shape=(batch_size, time_stamp, feature_num))
+    model.summary()
+    early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=3, mode='min', verbose=1)
+
+    # history = model.fit(train_data_healthy[:train_data_healthy.shape[0] // 7, :, :],
+    #                     train_label1_healthy[:train_label1_healthy.shape[0] // 7, ], epochs=EPOCH,
+    #                     batch_size=batch_size * 10, validation_split=0.2, shuffle=True, verbose=1,
+    #                     callbacks=[checkpoint, lr_scheduler, early_stop])
+
+    ## TODO testing
+    # # test_data, test_label = get_training_data(total_data[:healthy_date, :])
+    # model = tf.keras.models.load_model(save_name)
+    # # mse, mean, max = get_MSE(test_data, test_label, new_model=newModel)
+    #
+    # start = time.time()
+    # # 中间写上代码块
+    #
+    # model.predict(train_data_healthy, batch_size=32)
+    # end = time.time()
+    # print("data_size:", train_data_healthy.shape)
+    # print('Running time: %s Seconds' % (end - start))
+    #
+    healthy_size, _, _ = train_data_healthy.shape
+    unhealthy_size, _, _ = train_data_unhealthy.shape
+    all_data, _, _ = get_training_data_overlapping(
+        total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
+
+    newModel = tf.keras.models.load_model(save_name)
+    # 单次测试
+    # getResult(newModel,
+    #           healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200,
+    #                        :],
+    #           healthy_label=train_label1_healthy[
+    #                         healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200, :],
+    #           unhealthy_data=train_data_unhealthy[:200, :], unhealthy_label=train_label1_unhealthy[:200, :],isSave=True)
+    getResult(newModel, healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+              healthy_label=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
+              unhealthy_data=train_data_unhealthy, unhealthy_label=train_label1_unhealthy,isSave=False)
+    # mse, mean, max = get_MSE(train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+    #                          train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], new_model=newModel)
+    pass

TensorFlow_eaxmple/Model_train_test/condition_monitoring/返修/Conv.py

@@ -0,0 +1,497 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+
+
+'''
+@Author : dingjiawen
+@Date : 2022/10/11 18:52
+@Usage : 对比实验，与JointNet相同深度,进行预测
+@Desc :
+'''
+
+
+import tensorflow as tf
+import tensorflow.keras
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from condition_monitoring.data_deal import loadData_daban as loadData
+from model.Joint_Monitoring.Joint_Monitoring3 import Joint_Monitoring
+
+from model.CommonFunction.CommonFunction import *
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.models import load_model, save_model
+from keras.callbacks import EarlyStopping
+import random
+import time
+'''超参数设置'''
+time_stamp = 120
+feature_num = 10
+batch_size = 32
+learning_rate = 0.001
+EPOCH = 101
+model_name = "DCConv"
+'''EWMA超参数'''
+K = 18
+namuda = 0.01
+'''保存名称'''
+
+save_name = "./trianed/{0}_{1}_{2}.h5".format(model_name,
+                                                            time_stamp,
+                                                            feature_num,
+                                                            batch_size,
+                                                            EPOCH)
+save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weight_epoch14/weight".format(model_name,
+                                                                                                         time_stamp,
+                                                                                                         feature_num,
+                                                                                                         batch_size,
+                                                                                                         EPOCH)
+save_mse_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse/CNN/banda/mse.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                              batch_size,
+                                                                              EPOCH)
+save_max_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse/CNN/banda/max.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                             batch_size,
+                                                                              EPOCH)
+
+# save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                    time_stamp,
+#                                                                    feature_num,
+#                                                                    batch_size,
+#                                                                    EPOCH)
+# save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                                 time_stamp,
+#                                                                                 feature_num,
+#                                                                                 batch_size,
+#                                                                                 EPOCH)
+'''文件名'''
+'''文件名'''
+'''文件名'''
+file_name = "G:\data\SCADA数据\jb4q_8_delete_all_zero.csv"
+
+'''
+文件说明：jb4q_8_delete_all_zero.csv是删除了除异常以外的所有0值的文件
+文件从0:300454行均是正常值(2019/7.30 00:00:00 - 2019/9/18  11:21:00)
+从300455:317052行均是异常值(2019/9/18  11:21:01 - 2019/9/29  23:59:00)
+'''
+'''文件参数'''
+# 最后正常的时间点
+healthy_date = 300454
+# 最后异常的时间点
+unhealthy_date = 317052
+# 异常容忍程度
+unhealthy_patience = 5
+
+
+def remove(data, time_stamp=time_stamp):
+    rows, cols = data.shape
+    print("remove_data.shape:", data.shape)
+    num = int(rows / time_stamp)
+
+    return data[:num * time_stamp, :]
+    pass
+
+
+# 不重叠采样
+def get_training_data(data, time_stamp: int = time_stamp):
+    removed_data = remove(data=data)
+    rows, cols = removed_data.shape
+    print("removed_data.shape:", data.shape)
+    print("removed_data:", removed_data)
+    train_data = np.reshape(removed_data, [-1, time_stamp, cols])
+    print("train_data:", train_data)
+    batchs, time_stamp, cols = train_data.shape
+
+    for i in range(1, batchs):
+        each_label = np.expand_dims(train_data[i, 0, :], axis=0)
+        if i == 1:
+            train_label = each_label
+        else:
+            train_label = np.concatenate([train_label, each_label], axis=0)
+
+    print("train_data.shape:", train_data.shape)
+    print("train_label.shape", train_label.shape)
+    return train_data[:-1, :], train_label
+
+
+# 重叠采样
+def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
+    rows, cols = data.shape
+    train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
+    train_label = np.empty(shape=[rows - time_stamp - 1, cols])
+    for i in range(rows):
+        if i + time_stamp >= rows:
+            break
+        if i + time_stamp < rows - 1:
+            train_data[i] = data[i:i + time_stamp]
+            train_label[i] = data[i + time_stamp]
+
+    print("重叠采样以后：")
+    print("data:", train_data)  # (300334,120,10)
+    print("label:", train_label)  # (300334,10)
+
+    if is_Healthy:
+        train_label2 = np.ones(shape=[train_label.shape[0]])
+    else:
+        train_label2 = np.zeros(shape=[train_label.shape[0]])
+
+    print("label2:", train_label2)
+
+    return train_data, train_label, train_label2
+
+
+# 归一化
+def normalization(data):
+    rows, cols = data.shape
+    print("归一化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 归一化
+    max = np.max(data, axis=0)
+    max = np.broadcast_to(max, [rows, cols])
+    min = np.min(data, axis=0)
+    min = np.broadcast_to(min, [rows, cols])
+
+    data = (data - min) / (max - min)
+    print("归一化之后:", data)
+    print(data.shape)
+
+    return data
+
+
+# 正则化
+def Regularization(data):
+    rows, cols = data.shape
+    print("正则化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 正则化
+    mean = np.mean(data, axis=0)
+    mean = np.broadcast_to(mean, shape=[rows, cols])
+    dst = np.sqrt(np.var(data, axis=0))
+    dst = np.broadcast_to(dst, shape=[rows, cols])
+    data = (data - mean) / dst
+    print("正则化之后:", data)
+    print(data.shape)
+
+    return data
+    pass
+
+
+def EWMA(data, K=K, namuda=namuda):
+    # t是啥暂时未知
+    t = 0
+    mid = np.mean(data, axis=0)
+    standard = np.sqrt(np.var(data, axis=0))
+    UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    return mid, UCL, LCL
+    pass
+
+
+
+
+
+def condition_monitoring_model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
+    GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
+    d1 = tf.keras.layers.Dense(300)(GRU1)
+    output = tf.keras.layers.Dense(10)(d1)
+
+    model = tf.keras.Model(inputs=input, outputs=output)
+
+    return model
+
+
+# trian_data:(300455,120,10)
+# trian_label1:(300455,10)
+# trian_label2:(300455,)
+def shuffle(train_data, train_label1, train_label2, is_split: bool = False, split_size: float = 0.2):
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(train_data,
+                                                                                                     train_label1,
+                                                                                                     train_label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=True,
+                                                                                                     random_state=100)
+    if is_split:
+        return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+    train_data = np.concatenate([train_data, test_data], axis=0)
+    train_label1 = np.concatenate([train_label1, test_label1], axis=0)
+    train_label2 = np.concatenate([train_label2, test_label2], axis=0)
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2
+    pass
+
+
+def split_test_data(healthy_data, healthy_label1, healthy_label2, unhealthy_data, unhealthy_label1, unhealthy_label2,
+                    split_size: float = 0.2, shuffle: bool = True):
+    data = np.concatenate([healthy_data, unhealthy_data], axis=0)
+    label1 = np.concatenate([healthy_label1, unhealthy_label1], axis=0)
+    label2 = np.concatenate([healthy_label2, unhealthy_label2], axis=0)
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(data,
+                                                                                                     label1,
+                                                                                                     label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=shuffle,
+                                                                                                     random_state=100)
+
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+
+    pass
+
+
+def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
+    history_loss = []
+    history_val_loss = []
+
+    val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
+                                                         val_label2=test_label2,
+                                                         is_first_time=False, step_one_model=step_one_model)
+
+    history_val_loss.append(val_loss)
+    print("val_accuracy:", val_accuracy)
+    print("val_loss:", val_loss)
+
+
+def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False):
+    # 获取模型的所有参数的个数
+    # step_two_model.count_params()
+    total_result = []
+    size, length, dims = test_data.shape
+    for epoch in range(0, size - batch_size + 1, batch_size):
+        each_test_data = test_data[epoch:epoch + batch_size, :, :]
+        _, _, _, output4 = step_two_model.call(each_test_data, is_first_time=False)
+        total_result.append(output4)
+    total_result = np.reshape(total_result, [total_result.__len__(), -1])
+    total_result = np.reshape(total_result, [-1, ])
+    if isPlot:
+        plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
+        # 画出 y=1 这条水平线
+        plt.axhline(0.5, c='red', label='Failure threshold')
+        # 箭头指向上面的水平线
+        # plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
+        #           alpha=0.9, overhang=0.5)
+        # plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
+        plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
+        plt.xlabel("time")
+        plt.ylabel("confience")
+        plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.grid()
+        # plt.ylim(0, 1)
+        # plt.xlim(-50, 1300)
+        # plt.legend("", loc='upper left')
+        plt.show()
+    return total_result
+
+
+def DCConv_Model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    input = tf.cast(input, tf.float32)
+
+    LSTM = tf.keras.layers.Conv1D(10, 3)(input)
+    LSTM = tf.keras.layers.Conv1D(20, 3)(LSTM)
+    LSTM = tf.keras.layers.Conv1D(20, 3)(LSTM)
+    LSTM = tf.keras.layers.Conv1D(40, 3)(LSTM)
+    LSTM = tf.keras.layers.Conv1D(80, 3)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=64)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=128)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=2)(LSTM)
+
+    LSTM = LSTM[:, -1, :]
+    # bn = tf.keras.layers.BatchNormalization()(LSTM)
+
+    # d1 = tf.keras.layers.Dense(20)(LSTM)
+    # bn = tf.keras.layers.BatchNormalization()(d1)
+
+    output = tf.keras.layers.Dense(128, name='output1')(LSTM)
+    output = tf.keras.layers.Dense(10, name='output')(output)
+    model = tf.keras.Model(inputs=input, outputs=output)
+    return model
+    pass
+
+
+def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True, predictI: int = 1):
+    predicted_data = new_model.predict(data)
+
+    temp = np.abs(predicted_data - label)
+    temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
+    temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
+    temp3 = temp1 / temp2
+    mse = np.sum((temp1 / temp2) ** 2, axis=1)
+    print("z:", mse)
+    print(mse.shape)
+
+    # mse=np.mean((predicted_data-label)**2,axis=1)
+    print("mse", mse)
+    if isStandard:
+        dims, = mse.shape
+        mean = np.mean(mse)
+        std = np.sqrt(np.var(mse))
+        max = mean + 3 * std
+        print("max:", max)
+        # min = mean-3*std
+        max = np.broadcast_to(max, shape=[dims, ])
+        # min = np.broadcast_to(min,shape=[dims,])
+        mean = np.broadcast_to(mean, shape=[dims, ])
+        if isPlot:
+            plt.figure(random.randint(1,9))
+            plt.plot(max)
+            plt.plot(mse)
+            plt.plot(mean)
+            # plt.plot(min)
+            plt.show()
+    else:
+        if isPlot:
+            plt.figure(random.randint(1, 9))
+            plt.plot(mse)
+            # plt.plot(min)
+            plt.show()
+            return  mse
+
+    return mse, mean, max
+    # pass
+
+
+# healthy_data是健康数据,用于确定阈值，all_data是完整的数据,用于模型出结果
+def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data, unhealthy_label, isPlot: bool = False,
+              isSave: bool = True, predictI: int = 1):
+    # TODO 计算MSE确定阈值
+    # TODO 计算MSE确定阈值
+
+    mse, mean, max = get_MSE(healthy_data, healthy_label, model)
+
+    # 误报率的计算
+    total, = mse.shape
+    faultNum = 0
+    faultList = []
+    faultNum = mse[mse[:] > max[0]].__len__()
+    # for i in range(total):
+    #     if (mse[i] > max[i]):
+    #         faultNum += 1
+    #         faultList.append(mse[i])
+
+    fault_rate = faultNum / total
+    print("误报率:", fault_rate)
+
+    # 漏报率计算
+    missNum = 0
+    mse1 = get_MSE(unhealthy_data, unhealthy_label, model, isStandard=False)
+
+    total_mse = np.concatenate([mse, mse1], axis=0)
+    total_max = np.broadcast_to(max[0], shape=[total_mse.shape[0], ])
+    # min = np.broadcast_to(min,shape=[dims,])
+    total_mean = np.broadcast_to(mean[0], shape=[total_mse.shape[0], ])
+    if isSave:
+        save_mse_name1 = save_mse_name
+        save_max_name1 = save_max_name
+
+        np.savetxt(save_mse_name1, total_mse, delimiter=',')
+        np.savetxt(save_max_name1, total_max, delimiter=',')
+
+    all, = mse1.shape
+
+
+    missNum = mse1[mse1[:] < max[0]].__len__()
+
+
+    print("all:", all)
+    miss_rate = missNum / all
+    print("漏报率:", miss_rate)
+
+
+
+    plt.figure(random.randint(1, 100))
+    plt.plot(total_max)
+    plt.plot(total_mse)
+    plt.plot(total_mean)
+    # plt.plot(min)
+    plt.show()
+    pass
+
+
+if __name__ == '__main__':
+    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = normalization(data=total_data)
+    train_data_healthy, train_label1_healthy, train_label2_healthy = get_training_data_overlapping(
+        total_data[:healthy_date, :], is_Healthy=True)
+    train_data_unhealthy, train_label1_unhealthy, train_label2_unhealthy = get_training_data_overlapping(
+        total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
+        is_Healthy=False)
+    #### TODO 第一步训练
+    # 单次测试
+    model = DCConv_Model()
+
+    checkpoint = tf.keras.callbacks.ModelCheckpoint(
+        filepath=save_name,
+        monitor='val_loss',
+        verbose=2,
+        save_best_only=True,
+        mode='min')
+    lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.001)
+
+    model.compile(optimizer=tf.optimizers.Adam(), loss=tf.losses.mse)
+    model.build(input_shape=(batch_size, time_stamp, feature_num))
+    model.summary()
+    early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=3, mode='min', verbose=1)
+
+    history = model.fit(train_data_healthy[:train_data_healthy.shape[0] // 7, :, :],
+                        train_label1_healthy[:train_label1_healthy.shape[0] // 7, ], epochs=EPOCH,
+                        batch_size=batch_size * 10, validation_split=0.2, shuffle=True, verbose=1,
+                        callbacks=[checkpoint, lr_scheduler, early_stop])
+
+    ## TODO testing
+    # test_data, test_label = get_training_data(total_data[:healthy_date, :])
+    # newModel = tf.keras.models.load_model(save_name)
+    # mse, mean, max = get_MSE(test_data, test_label, new_model=newModel)
+
+    start = time.time()
+    # 中间写上代码块
+
+    model.predict(train_data_healthy, batch_size=32)
+    end = time.time()
+    print("data_size:", train_data_healthy.shape)
+    print('Running time: %s Seconds' % (end - start))
+
+    healthy_size, _, _ = train_data_healthy.shape
+    unhealthy_size, _, _ = train_data_unhealthy.shape
+    all_data, _, _ = get_training_data_overlapping(
+        total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
+
+    newModel = tf.keras.models.load_model(save_name)
+    # 单次测试
+    # getResult(newModel,
+    #           healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200,
+    #                        :],
+    #           healthy_label=train_label1_healthy[
+    #                         healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200, :],
+    #           unhealthy_data=train_data_unhealthy[:200, :], unhealthy_label=train_label1_unhealthy[:200, :],isSave=True)
+    getResult(newModel, healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+              healthy_label=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
+              unhealthy_data=train_data_unhealthy, unhealthy_label=train_label1_unhealthy,isSave=True)
+    # mse, mean, max = get_MSE(train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+    #                          train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], new_model=newModel)
+    pass

TensorFlow_eaxmple/Model_train_test/condition_monitoring/返修/DCConv.py

@@ -0,0 +1,496 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+
+
+'''
+@Author : dingjiawen
+@Date : 2022/10/11 18:52
+@Usage : 对比实验，与JointNet相同深度,进行预测
+@Desc :
+'''
+
+
+import tensorflow as tf
+import tensorflow.keras
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from condition_monitoring.data_deal import loadData_daban as loadData
+from model.Joint_Monitoring.Joint_Monitoring3 import Joint_Monitoring
+
+from model.CommonFunction.CommonFunction import *
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.models import load_model, save_model
+from keras.callbacks import EarlyStopping
+import random
+import time
+'''超参数设置'''
+time_stamp = 120
+feature_num = 10
+batch_size = 32
+learning_rate = 0.001
+EPOCH = 101
+model_name = "DCConv"
+'''EWMA超参数'''
+K = 18
+namuda = 0.01
+'''保存名称'''
+
+save_name = "./trianed/{0}_{1}_{2}.h5".format(model_name,
+                                                            time_stamp,
+                                                            feature_num,
+                                                            batch_size,
+                                                            EPOCH)
+save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weight_epoch14/weight".format(model_name,
+                                                                                                         time_stamp,
+                                                                                                         feature_num,
+                                                                                                         batch_size,
+                                                                                                         EPOCH)
+save_mse_name = "./mse/DCConv/banda/mse.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                              batch_size,
+                                                                              EPOCH)
+save_max_name = "./mse/DCConv/banda/max.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                             batch_size,
+                                                                              EPOCH)
+
+# save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                    time_stamp,
+#                                                                    feature_num,
+#                                                                    batch_size,
+#                                                                    EPOCH)
+# save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                                 time_stamp,
+#                                                                                 feature_num,
+#                                                                                 batch_size,
+#                                                                                 EPOCH)
+'''文件名'''
+'''文件名'''
+file_name = "G:\data\SCADA数据\SCADA_已处理_粤水电达坂城2020.1月-5月\风机15.csv"
+
+'''
+文件说明：jb4q_8_delete_total_zero.csv是删除了只删除了全是0的列的文件
+文件从0:415548行均是正常值(2019/7.30 00:00:00 - 2019/9/18  11:14:00)
+从415549:432153行均是异常值(2019/9/18  11:21:01 - 2021/1/18  00:00:00)
+'''
+'''文件参数'''
+# 最后正常的时间点
+healthy_date = 96748
+# 最后异常的时间点
+unhealthy_date = 107116
+# 异常容忍程度
+unhealthy_patience = 5
+
+
+def remove(data, time_stamp=time_stamp):
+    rows, cols = data.shape
+    print("remove_data.shape:", data.shape)
+    num = int(rows / time_stamp)
+
+    return data[:num * time_stamp, :]
+    pass
+
+
+# 不重叠采样
+def get_training_data(data, time_stamp: int = time_stamp):
+    removed_data = remove(data=data)
+    rows, cols = removed_data.shape
+    print("removed_data.shape:", data.shape)
+    print("removed_data:", removed_data)
+    train_data = np.reshape(removed_data, [-1, time_stamp, cols])
+    print("train_data:", train_data)
+    batchs, time_stamp, cols = train_data.shape
+
+    for i in range(1, batchs):
+        each_label = np.expand_dims(train_data[i, 0, :], axis=0)
+        if i == 1:
+            train_label = each_label
+        else:
+            train_label = np.concatenate([train_label, each_label], axis=0)
+
+    print("train_data.shape:", train_data.shape)
+    print("train_label.shape", train_label.shape)
+    return train_data[:-1, :], train_label
+
+
+# 重叠采样
+def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
+    rows, cols = data.shape
+    train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
+    train_label = np.empty(shape=[rows - time_stamp - 1, cols])
+    for i in range(rows):
+        if i + time_stamp >= rows:
+            break
+        if i + time_stamp < rows - 1:
+            train_data[i] = data[i:i + time_stamp]
+            train_label[i] = data[i + time_stamp]
+
+    print("重叠采样以后：")
+    print("data:", train_data)  # (300334,120,10)
+    print("label:", train_label)  # (300334,10)
+
+    if is_Healthy:
+        train_label2 = np.ones(shape=[train_label.shape[0]])
+    else:
+        train_label2 = np.zeros(shape=[train_label.shape[0]])
+
+    print("label2:", train_label2)
+
+    return train_data, train_label, train_label2
+
+
+# 归一化
+def normalization(data):
+    rows, cols = data.shape
+    print("归一化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 归一化
+    max = np.max(data, axis=0)
+    max = np.broadcast_to(max, [rows, cols])
+    min = np.min(data, axis=0)
+    min = np.broadcast_to(min, [rows, cols])
+
+    data = (data - min) / (max - min)
+    print("归一化之后:", data)
+    print(data.shape)
+
+    return data
+
+
+# 正则化
+def Regularization(data):
+    rows, cols = data.shape
+    print("正则化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 正则化
+    mean = np.mean(data, axis=0)
+    mean = np.broadcast_to(mean, shape=[rows, cols])
+    dst = np.sqrt(np.var(data, axis=0))
+    dst = np.broadcast_to(dst, shape=[rows, cols])
+    data = (data - mean) / dst
+    print("正则化之后:", data)
+    print(data.shape)
+
+    return data
+    pass
+
+
+def EWMA(data, K=K, namuda=namuda):
+    # t是啥暂时未知
+    t = 0
+    mid = np.mean(data, axis=0)
+    standard = np.sqrt(np.var(data, axis=0))
+    UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    return mid, UCL, LCL
+    pass
+
+
+
+
+
+def condition_monitoring_model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
+    GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
+    d1 = tf.keras.layers.Dense(300)(GRU1)
+    output = tf.keras.layers.Dense(10)(d1)
+
+    model = tf.keras.Model(inputs=input, outputs=output)
+
+    return model
+
+
+# trian_data:(300455,120,10)
+# trian_label1:(300455,10)
+# trian_label2:(300455,)
+def shuffle(train_data, train_label1, train_label2, is_split: bool = False, split_size: float = 0.2):
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(train_data,
+                                                                                                     train_label1,
+                                                                                                     train_label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=True,
+                                                                                                     random_state=100)
+    if is_split:
+        return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+    train_data = np.concatenate([train_data, test_data], axis=0)
+    train_label1 = np.concatenate([train_label1, test_label1], axis=0)
+    train_label2 = np.concatenate([train_label2, test_label2], axis=0)
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2
+    pass
+
+
+def split_test_data(healthy_data, healthy_label1, healthy_label2, unhealthy_data, unhealthy_label1, unhealthy_label2,
+                    split_size: float = 0.2, shuffle: bool = True):
+    data = np.concatenate([healthy_data, unhealthy_data], axis=0)
+    label1 = np.concatenate([healthy_label1, unhealthy_label1], axis=0)
+    label2 = np.concatenate([healthy_label2, unhealthy_label2], axis=0)
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(data,
+                                                                                                     label1,
+                                                                                                     label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=shuffle,
+                                                                                                     random_state=100)
+
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+
+    pass
+
+
+def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
+    history_loss = []
+    history_val_loss = []
+
+    val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
+                                                         val_label2=test_label2,
+                                                         is_first_time=False, step_one_model=step_one_model)
+
+    history_val_loss.append(val_loss)
+    print("val_accuracy:", val_accuracy)
+    print("val_loss:", val_loss)
+
+
+def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False):
+    # 获取模型的所有参数的个数
+    # step_two_model.count_params()
+    total_result = []
+    size, length, dims = test_data.shape
+    for epoch in range(0, size - batch_size + 1, batch_size):
+        each_test_data = test_data[epoch:epoch + batch_size, :, :]
+        _, _, _, output4 = step_two_model.call(each_test_data, is_first_time=False)
+        total_result.append(output4)
+    total_result = np.reshape(total_result, [total_result.__len__(), -1])
+    total_result = np.reshape(total_result, [-1, ])
+    if isPlot:
+        plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
+        # 画出 y=1 这条水平线
+        plt.axhline(0.5, c='red', label='Failure threshold')
+        # 箭头指向上面的水平线
+        # plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
+        #           alpha=0.9, overhang=0.5)
+        # plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
+        plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
+        plt.xlabel("time")
+        plt.ylabel("confience")
+        plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.grid()
+        # plt.ylim(0, 1)
+        # plt.xlim(-50, 1300)
+        # plt.legend("", loc='upper left')
+        plt.show()
+    return total_result
+
+
+def DCConv_Model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    input = tf.cast(input, tf.float32)
+
+    LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=2)(input)
+    LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=4)(LSTM)
+    LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=8)(LSTM)
+    LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=16)(LSTM)
+    LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=32)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=64)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=128)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=2)(LSTM)
+
+    LSTM = LSTM[:, -1, :]
+    # bn = tf.keras.layers.BatchNormalization()(LSTM)
+
+    # d1 = tf.keras.layers.Dense(20)(LSTM)
+    # bn = tf.keras.layers.BatchNormalization()(d1)
+
+    output = tf.keras.layers.Dense(128, name='output1')(LSTM)
+    output = tf.keras.layers.Dense(10, name='output')(output)
+    model = tf.keras.Model(inputs=input, outputs=output)
+    return model
+    pass
+
+
+def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True, predictI: int = 1):
+    predicted_data = new_model.predict(data)
+
+    temp = np.abs(predicted_data - label)
+    temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
+    temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
+    temp3 = temp1 / temp2
+    mse = np.sum((temp1 / temp2) ** 2, axis=1)
+    print("z:", mse)
+    print(mse.shape)
+
+    # mse=np.mean((predicted_data-label)**2,axis=1)
+    print("mse", mse)
+    if isStandard:
+        dims, = mse.shape
+        mean = np.mean(mse)
+        std = np.sqrt(np.var(mse))
+        max = mean + 3 * std
+        print("max:", max)
+        # min = mean-3*std
+        max = np.broadcast_to(max, shape=[dims, ])
+        # min = np.broadcast_to(min,shape=[dims,])
+        mean = np.broadcast_to(mean, shape=[dims, ])
+        if isPlot:
+            plt.figure(random.randint(1,9))
+            plt.plot(max)
+            plt.plot(mse)
+            plt.plot(mean)
+            # plt.plot(min)
+            plt.show()
+    else:
+        if isPlot:
+            plt.figure(random.randint(1, 9))
+            plt.plot(mse)
+            # plt.plot(min)
+            plt.show()
+            return  mse
+
+    return mse, mean, max
+    # pass
+
+
+# healthy_data是健康数据,用于确定阈值，all_data是完整的数据,用于模型出结果
+def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data, unhealthy_label, isPlot: bool = False,
+              isSave: bool = True, predictI: int = 1):
+    # TODO 计算MSE确定阈值
+    # TODO 计算MSE确定阈值
+
+    mse, mean, max = get_MSE(healthy_data, healthy_label, model)
+
+    # 误报率的计算
+    total, = mse.shape
+    faultNum = 0
+    faultList = []
+    faultNum = mse[mse[:] > max[0]].__len__()
+    # for i in range(total):
+    #     if (mse[i] > max[i]):
+    #         faultNum += 1
+    #         faultList.append(mse[i])
+
+    fault_rate = faultNum / total
+    print("误报率:", fault_rate)
+
+    # 漏报率计算
+    missNum = 0
+    mse1 = get_MSE(unhealthy_data, unhealthy_label, model, isStandard=False)
+
+    total_mse = np.concatenate([mse, mse1], axis=0)
+    total_max = np.broadcast_to(max[0], shape=[total_mse.shape[0], ])
+    # min = np.broadcast_to(min,shape=[dims,])
+    total_mean = np.broadcast_to(mean[0], shape=[total_mse.shape[0], ])
+    if isSave:
+        save_mse_name1 = save_mse_name
+        save_max_name1 = save_max_name
+
+        np.savetxt(save_mse_name1, total_mse, delimiter=',')
+        np.savetxt(save_max_name1, total_max, delimiter=',')
+
+    all, = mse1.shape
+
+
+    missNum = mse1[mse1[:] < max[0]].__len__()
+
+
+    print("all:", all)
+    miss_rate = missNum / all
+    print("漏报率:", miss_rate)
+
+
+
+    plt.figure(random.randint(1, 100))
+    plt.plot(total_max)
+    plt.plot(total_mse)
+    plt.plot(total_mean)
+    # plt.plot(min)
+    plt.show()
+    pass
+
+
+if __name__ == '__main__':
+    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = normalization(data=total_data)
+    train_data_healthy, train_label1_healthy, train_label2_healthy = get_training_data_overlapping(
+        total_data[:healthy_date, :], is_Healthy=True)
+    train_data_unhealthy, train_label1_unhealthy, train_label2_unhealthy = get_training_data_overlapping(
+        total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
+        is_Healthy=False)
+    #### TODO 第一步训练
+    # 单次测试
+    model = DCConv_Model()
+
+    checkpoint = tf.keras.callbacks.ModelCheckpoint(
+        filepath=save_name,
+        monitor='val_loss',
+        verbose=2,
+        save_best_only=True,
+        mode='min')
+    lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.001)
+
+    model.compile(optimizer=tf.optimizers.Adam(), loss=tf.losses.mse)
+    model.build(input_shape=(batch_size, time_stamp, feature_num))
+    model.summary()
+    early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=3, mode='min', verbose=1)
+
+    history = model.fit(train_data_healthy[:train_data_healthy.shape[0] // 7, :, :],
+                        train_label1_healthy[:train_label1_healthy.shape[0] // 7, ], epochs=EPOCH,
+                        batch_size=batch_size * 10, validation_split=0.2, shuffle=True, verbose=1,
+                        callbacks=[checkpoint, lr_scheduler, early_stop])
+
+    ## TODO testing
+    # test_data, test_label = get_training_data(total_data[:healthy_date, :])
+    # newModel = tf.keras.models.load_model(save_name)
+    # mse, mean, max = get_MSE(test_data, test_label, new_model=newModel)
+
+    start = time.time()
+    # 中间写上代码块
+
+    model.predict(train_data_healthy, batch_size=32)
+    end = time.time()
+    print("data_size:", train_data_healthy.shape)
+    print('Running time: %s Seconds' % (end - start))
+
+    healthy_size, _, _ = train_data_healthy.shape
+    unhealthy_size, _, _ = train_data_unhealthy.shape
+    all_data, _, _ = get_training_data_overlapping(
+        total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
+
+    newModel = tf.keras.models.load_model(save_name)
+    # 单次测试
+    # getResult(newModel,
+    #           healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200,
+    #                        :],
+    #           healthy_label=train_label1_healthy[
+    #                         healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200, :],
+    #           unhealthy_data=train_data_unhealthy[:200, :], unhealthy_label=train_label1_unhealthy[:200, :],isSave=True)
+    getResult(newModel, healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+              healthy_label=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
+              unhealthy_data=train_data_unhealthy, unhealthy_label=train_label1_unhealthy,isSave=True)
+    # mse, mean, max = get_MSE(train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+    #                          train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], new_model=newModel)
+    pass

TensorFlow_eaxmple/Model_train_test/condition_monitoring/返修/GRU.py

@@ -0,0 +1,493 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+
+
+'''
+@Author : dingjiawen
+@Date : 2022/10/11 18:52
+@Usage : 对比实验，与JointNet相同深度,进行预测
+@Desc :
+'''
+
+
+import tensorflow as tf
+import tensorflow.keras
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from condition_monitoring.data_deal import loadData_daban as loadData
+from model.Joint_Monitoring.Joint_Monitoring3 import Joint_Monitoring
+
+from model.CommonFunction.CommonFunction import *
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.models import load_model, save_model
+from keras.callbacks import EarlyStopping
+import random
+import time
+'''超参数设置'''
+time_stamp = 120
+feature_num = 10
+batch_size = 32
+learning_rate = 0.001
+EPOCH = 101
+model_name = "DCConv"
+'''EWMA超参数'''
+K = 18
+namuda = 0.01
+'''保存名称'''
+
+save_name = "./trianed/{0}_{1}_{2}.h5".format(model_name,
+                                                            time_stamp,
+                                                            feature_num,
+                                                            batch_size,
+                                                            EPOCH)
+save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weight_epoch14/weight".format(model_name,
+                                                                                                         time_stamp,
+                                                                                                         feature_num,
+                                                                                                         batch_size,
+                                                                                                         EPOCH)
+save_mse_name = "./mse/DCConv/banda/mse.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                              batch_size,
+                                                                              EPOCH)
+save_max_name = "./mse/DCConv/banda/max.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                             batch_size,
+                                                                              EPOCH)
+
+# save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                    time_stamp,
+#                                                                    feature_num,
+#                                                                    batch_size,
+#                                                                    EPOCH)
+# save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                                 time_stamp,
+#                                                                                 feature_num,
+#                                                                                 batch_size,
+#                                                                                 EPOCH)
+'''文件名'''
+'''文件名'''
+file_name = "G:\data\SCADA数据\SCADA_已处理_粤水电达坂城2020.1月-5月\风机15.csv"
+
+'''
+文件说明：jb4q_8_delete_total_zero.csv是删除了只删除了全是0的列的文件
+文件从0:415548行均是正常值(2019/7.30 00:00:00 - 2019/9/18  11:14:00)
+从415549:432153行均是异常值(2019/9/18  11:21:01 - 2021/1/18  00:00:00)
+'''
+'''文件参数'''
+# 最后正常的时间点
+healthy_date = 96748
+# 最后异常的时间点
+unhealthy_date = 107116
+# 异常容忍程度
+unhealthy_patience = 5
+
+
+def remove(data, time_stamp=time_stamp):
+    rows, cols = data.shape
+    print("remove_data.shape:", data.shape)
+    num = int(rows / time_stamp)
+
+    return data[:num * time_stamp, :]
+    pass
+
+
+# 不重叠采样
+def get_training_data(data, time_stamp: int = time_stamp):
+    removed_data = remove(data=data)
+    rows, cols = removed_data.shape
+    print("removed_data.shape:", data.shape)
+    print("removed_data:", removed_data)
+    train_data = np.reshape(removed_data, [-1, time_stamp, cols])
+    print("train_data:", train_data)
+    batchs, time_stamp, cols = train_data.shape
+
+    for i in range(1, batchs):
+        each_label = np.expand_dims(train_data[i, 0, :], axis=0)
+        if i == 1:
+            train_label = each_label
+        else:
+            train_label = np.concatenate([train_label, each_label], axis=0)
+
+    print("train_data.shape:", train_data.shape)
+    print("train_label.shape", train_label.shape)
+    return train_data[:-1, :], train_label
+
+
+# 重叠采样
+def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
+    rows, cols = data.shape
+    train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
+    train_label = np.empty(shape=[rows - time_stamp - 1, cols])
+    for i in range(rows):
+        if i + time_stamp >= rows:
+            break
+        if i + time_stamp < rows - 1:
+            train_data[i] = data[i:i + time_stamp]
+            train_label[i] = data[i + time_stamp]
+
+    print("重叠采样以后：")
+    print("data:", train_data)  # (300334,120,10)
+    print("label:", train_label)  # (300334,10)
+
+    if is_Healthy:
+        train_label2 = np.ones(shape=[train_label.shape[0]])
+    else:
+        train_label2 = np.zeros(shape=[train_label.shape[0]])
+
+    print("label2:", train_label2)
+
+    return train_data, train_label, train_label2
+
+
+# 归一化
+def normalization(data):
+    rows, cols = data.shape
+    print("归一化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 归一化
+    max = np.max(data, axis=0)
+    max = np.broadcast_to(max, [rows, cols])
+    min = np.min(data, axis=0)
+    min = np.broadcast_to(min, [rows, cols])
+
+    data = (data - min) / (max - min)
+    print("归一化之后:", data)
+    print(data.shape)
+
+    return data
+
+
+# 正则化
+def Regularization(data):
+    rows, cols = data.shape
+    print("正则化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 正则化
+    mean = np.mean(data, axis=0)
+    mean = np.broadcast_to(mean, shape=[rows, cols])
+    dst = np.sqrt(np.var(data, axis=0))
+    dst = np.broadcast_to(dst, shape=[rows, cols])
+    data = (data - mean) / dst
+    print("正则化之后:", data)
+    print(data.shape)
+
+    return data
+    pass
+
+
+def EWMA(data, K=K, namuda=namuda):
+    # t是啥暂时未知
+    t = 0
+    mid = np.mean(data, axis=0)
+    standard = np.sqrt(np.var(data, axis=0))
+    UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    return mid, UCL, LCL
+    pass
+
+
+
+
+
+def condition_monitoring_model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
+    GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
+    d1 = tf.keras.layers.Dense(300)(GRU1)
+    output = tf.keras.layers.Dense(10)(d1)
+
+    model = tf.keras.Model(inputs=input, outputs=output)
+
+    return model
+
+
+# trian_data:(300455,120,10)
+# trian_label1:(300455,10)
+# trian_label2:(300455,)
+def shuffle(train_data, train_label1, train_label2, is_split: bool = False, split_size: float = 0.2):
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(train_data,
+                                                                                                     train_label1,
+                                                                                                     train_label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=True,
+                                                                                                     random_state=100)
+    if is_split:
+        return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+    train_data = np.concatenate([train_data, test_data], axis=0)
+    train_label1 = np.concatenate([train_label1, test_label1], axis=0)
+    train_label2 = np.concatenate([train_label2, test_label2], axis=0)
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2
+    pass
+
+
+def split_test_data(healthy_data, healthy_label1, healthy_label2, unhealthy_data, unhealthy_label1, unhealthy_label2,
+                    split_size: float = 0.2, shuffle: bool = True):
+    data = np.concatenate([healthy_data, unhealthy_data], axis=0)
+    label1 = np.concatenate([healthy_label1, unhealthy_label1], axis=0)
+    label2 = np.concatenate([healthy_label2, unhealthy_label2], axis=0)
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(data,
+                                                                                                     label1,
+                                                                                                     label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=shuffle,
+                                                                                                     random_state=100)
+
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+
+    pass
+
+
+def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
+    history_loss = []
+    history_val_loss = []
+
+    val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
+                                                         val_label2=test_label2,
+                                                         is_first_time=False, step_one_model=step_one_model)
+
+    history_val_loss.append(val_loss)
+    print("val_accuracy:", val_accuracy)
+    print("val_loss:", val_loss)
+
+
+def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False):
+    # 获取模型的所有参数的个数
+    # step_two_model.count_params()
+    total_result = []
+    size, length, dims = test_data.shape
+    for epoch in range(0, size - batch_size + 1, batch_size):
+        each_test_data = test_data[epoch:epoch + batch_size, :, :]
+        _, _, _, output4 = step_two_model.call(each_test_data, is_first_time=False)
+        total_result.append(output4)
+    total_result = np.reshape(total_result, [total_result.__len__(), -1])
+    total_result = np.reshape(total_result, [-1, ])
+    if isPlot:
+        plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
+        # 画出 y=1 这条水平线
+        plt.axhline(0.5, c='red', label='Failure threshold')
+        # 箭头指向上面的水平线
+        # plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
+        #           alpha=0.9, overhang=0.5)
+        # plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
+        plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
+        plt.xlabel("time")
+        plt.ylabel("confience")
+        plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.grid()
+        # plt.ylim(0, 1)
+        # plt.xlim(-50, 1300)
+        # plt.legend("", loc='upper left')
+        plt.show()
+    return total_result
+
+
+def DCConv_Model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    input = tf.cast(input, tf.float32)
+
+    LSTM = tf.keras.layers.GRU(10, return_sequences=True)(input)
+    LSTM = tf.keras.layers.GRU(20, return_sequences=True)(LSTM)
+    LSTM = tf.keras.layers.GRU(20, return_sequences=True)(LSTM)
+    LSTM = tf.keras.layers.GRU(40, return_sequences=True)(LSTM)
+    LSTM = tf.keras.layers.GRU(80, return_sequences=False)(LSTM)
+
+    # LSTM = LSTM[:, -1, :]
+    # bn = tf.keras.layers.BatchNormalization()(LSTM)
+
+    # d1 = tf.keras.layers.Dense(20)(LSTM)
+    # bn = tf.keras.layers.BatchNormalization()(d1)
+
+    output = tf.keras.layers.Dense(128, name='output1')(LSTM)
+    output = tf.keras.layers.Dense(10, name='output')(output)
+    model = tf.keras.Model(inputs=input, outputs=output)
+    return model
+    pass
+
+
+def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True, predictI: int = 1):
+    predicted_data = new_model.predict(data)
+
+    temp = np.abs(predicted_data - label)
+    temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
+    temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
+    temp3 = temp1 / temp2
+    mse = np.sum((temp1 / temp2) ** 2, axis=1)
+    print("z:", mse)
+    print(mse.shape)
+
+    # mse=np.mean((predicted_data-label)**2,axis=1)
+    print("mse", mse)
+    if isStandard:
+        dims, = mse.shape
+        mean = np.mean(mse)
+        std = np.sqrt(np.var(mse))
+        max = mean + 3 * std
+        print("max:", max)
+        # min = mean-3*std
+        max = np.broadcast_to(max, shape=[dims, ])
+        # min = np.broadcast_to(min,shape=[dims,])
+        mean = np.broadcast_to(mean, shape=[dims, ])
+        if isPlot:
+            plt.figure(random.randint(1,9))
+            plt.plot(max)
+            plt.plot(mse)
+            plt.plot(mean)
+            # plt.plot(min)
+            plt.show()
+    else:
+        if isPlot:
+            plt.figure(random.randint(1, 9))
+            plt.plot(mse)
+            # plt.plot(min)
+            plt.show()
+            return  mse
+
+    return mse, mean, max
+    # pass
+
+
+# healthy_data是健康数据,用于确定阈值，all_data是完整的数据,用于模型出结果
+def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data, unhealthy_label, isPlot: bool = False,
+              isSave: bool = True, predictI: int = 1):
+    # TODO 计算MSE确定阈值
+    # TODO 计算MSE确定阈值
+
+    mse, mean, max = get_MSE(healthy_data, healthy_label, model)
+
+    # 误报率的计算
+    total, = mse.shape
+    faultNum = 0
+    faultList = []
+    faultNum = mse[mse[:] > max[0]].__len__()
+    # for i in range(total):
+    #     if (mse[i] > max[i]):
+    #         faultNum += 1
+    #         faultList.append(mse[i])
+
+    fault_rate = faultNum / total
+    print("误报率:", fault_rate)
+
+    # 漏报率计算
+    missNum = 0
+    mse1 = get_MSE(unhealthy_data, unhealthy_label, model, isStandard=False)
+
+    total_mse = np.concatenate([mse, mse1], axis=0)
+    total_max = np.broadcast_to(max[0], shape=[total_mse.shape[0], ])
+    # min = np.broadcast_to(min,shape=[dims,])
+    total_mean = np.broadcast_to(mean[0], shape=[total_mse.shape[0], ])
+    if isSave:
+        save_mse_name1 = save_mse_name
+        save_max_name1 = save_max_name
+
+        np.savetxt(save_mse_name1, total_mse, delimiter=',')
+        np.savetxt(save_max_name1, total_max, delimiter=',')
+
+    all, = mse1.shape
+
+
+    missNum = mse1[mse1[:] < max[0]].__len__()
+
+
+    print("all:", all)
+    miss_rate = missNum / all
+    print("漏报率:", miss_rate)
+
+
+
+    plt.figure(random.randint(1, 100))
+    plt.plot(total_max)
+    plt.plot(total_mse)
+    plt.plot(total_mean)
+    # plt.plot(min)
+    plt.show()
+    pass
+
+
+if __name__ == '__main__':
+    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = normalization(data=total_data)
+    train_data_healthy, train_label1_healthy, train_label2_healthy = get_training_data_overlapping(
+        total_data[:healthy_date, :], is_Healthy=True)
+    train_data_unhealthy, train_label1_unhealthy, train_label2_unhealthy = get_training_data_overlapping(
+        total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
+        is_Healthy=False)
+    #### TODO 第一步训练
+    # 单次测试
+    model = DCConv_Model()
+
+    checkpoint = tf.keras.callbacks.ModelCheckpoint(
+        filepath=save_name,
+        monitor='val_loss',
+        verbose=2,
+        save_best_only=True,
+        mode='min')
+    lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.001)
+
+    model.compile(optimizer=tf.optimizers.Adam(), loss=tf.losses.mse)
+    model.build(input_shape=(batch_size, time_stamp, feature_num))
+    model.summary()
+    early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=3, mode='min', verbose=1)
+
+    # history = model.fit(train_data_healthy[:train_data_healthy.shape[0] // 7, :, :],
+    #                     train_label1_healthy[:train_label1_healthy.shape[0] // 7, ], epochs=EPOCH,
+    #                     batch_size=batch_size * 10, validation_split=0.2, shuffle=True, verbose=1,
+    #                     callbacks=[checkpoint, lr_scheduler, early_stop])
+
+    ## TODO testing
+    # test_data, test_label = get_training_data(total_data[:healthy_date, :])
+    model = tf.keras.models.load_model(save_name)
+    # mse, mean, max = get_MSE(test_data, test_label, new_model=newModel)
+
+    start = time.time()
+    # 中间写上代码块
+
+    model.predict(train_data_healthy, batch_size=32)
+    end = time.time()
+    print("data_size:", train_data_healthy.shape)
+    print('Running time: %s Seconds' % (end - start))
+
+    healthy_size, _, _ = train_data_healthy.shape
+    unhealthy_size, _, _ = train_data_unhealthy.shape
+    all_data, _, _ = get_training_data_overlapping(
+        total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
+
+    newModel = tf.keras.models.load_model(save_name)
+    # 单次测试
+    # getResult(newModel,
+    #           healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200,
+    #                        :],
+    #           healthy_label=train_label1_healthy[
+    #                         healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200, :],
+    #           unhealthy_data=train_data_unhealthy[:200, :], unhealthy_label=train_label1_unhealthy[:200, :],isSave=True)
+    getResult(newModel, healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+              healthy_label=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
+              unhealthy_data=train_data_unhealthy, unhealthy_label=train_label1_unhealthy,isSave=True)
+    # mse, mean, max = get_MSE(train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+    #                          train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], new_model=newModel)
+    pass

TensorFlow_eaxmple/Model_train_test/condition_monitoring/返修/RepDCConv.py

@@ -0,0 +1,520 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+
+
+'''
+@Author : dingjiawen
+@Date : 2022/10/11 18:52
+@Usage : 对比实验，与JointNet相同深度,进行预测
+@Desc :
+'''
+
+import tensorflow as tf
+import tensorflow.keras
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from condition_monitoring.data_deal import loadData_daban as loadData
+from model.Joint_Monitoring.Joint_Monitoring3 import Joint_Monitoring
+
+from model.CommonFunction.CommonFunction import *
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.models import load_model, save_model
+from keras.callbacks import EarlyStopping
+import random
+import time
+
+'''超参数设置'''
+time_stamp = 120
+feature_num = 10
+batch_size = 32
+learning_rate = 0.001
+EPOCH = 101
+model_name = "DCConv"
+'''EWMA超参数'''
+K = 18
+namuda = 0.01
+'''保存名称'''
+
+save_name = "./trianed/{0}_{1}_{2}.h5".format(model_name,
+                                              time_stamp,
+                                              feature_num,
+                                              batch_size,
+                                              EPOCH)
+save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weight_epoch14/weight".format(model_name,
+                                                                                                         time_stamp,
+                                                                                                         feature_num,
+                                                                                                         batch_size,
+                                                                                                         EPOCH)
+save_mse_name = "./mse/DCConv/banda/mse.csv".format(model_name,
+                                                    time_stamp,
+                                                    feature_num,
+                                                    batch_size,
+                                                    EPOCH)
+save_max_name = "./mse/DCConv/banda/max.csv".format(model_name,
+                                                    time_stamp,
+                                                    feature_num,
+                                                    batch_size,
+                                                    EPOCH)
+
+# save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                    time_stamp,
+#                                                                    feature_num,
+#                                                                    batch_size,
+#                                                                    EPOCH)
+# save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                                 time_stamp,
+#                                                                                 feature_num,
+#                                                                                 batch_size,
+#                                                                                 EPOCH)
+'''文件名'''
+'''文件名'''
+file_name = "G:\data\SCADA数据\SCADA_已处理_粤水电达坂城2020.1月-5月\风机15.csv"
+
+'''
+文件说明：jb4q_8_delete_total_zero.csv是删除了只删除了全是0的列的文件
+文件从0:415548行均是正常值(2019/7.30 00:00:00 - 2019/9/18  11:14:00)
+从415549:432153行均是异常值(2019/9/18  11:21:01 - 2021/1/18  00:00:00)
+'''
+'''文件参数'''
+# 最后正常的时间点
+healthy_date = 96748
+# 最后异常的时间点
+unhealthy_date = 107116
+# 异常容忍程度
+unhealthy_patience = 5
+
+
+def remove(data, time_stamp=time_stamp):
+    rows, cols = data.shape
+    print("remove_data.shape:", data.shape)
+    num = int(rows / time_stamp)
+
+    return data[:num * time_stamp, :]
+    pass
+
+
+# 不重叠采样
+def get_training_data(data, time_stamp: int = time_stamp):
+    removed_data = remove(data=data)
+    rows, cols = removed_data.shape
+    print("removed_data.shape:", data.shape)
+    print("removed_data:", removed_data)
+    train_data = np.reshape(removed_data, [-1, time_stamp, cols])
+    print("train_data:", train_data)
+    batchs, time_stamp, cols = train_data.shape
+
+    for i in range(1, batchs):
+        each_label = np.expand_dims(train_data[i, 0, :], axis=0)
+        if i == 1:
+            train_label = each_label
+        else:
+            train_label = np.concatenate([train_label, each_label], axis=0)
+
+    print("train_data.shape:", train_data.shape)
+    print("train_label.shape", train_label.shape)
+    return train_data[:-1, :], train_label
+
+
+# 重叠采样
+def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
+    rows, cols = data.shape
+    train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
+    train_label = np.empty(shape=[rows - time_stamp - 1, cols])
+    for i in range(rows):
+        if i + time_stamp >= rows:
+            break
+        if i + time_stamp < rows - 1:
+            train_data[i] = data[i:i + time_stamp]
+            train_label[i] = data[i + time_stamp]
+
+    print("重叠采样以后：")
+    print("data:", train_data)  # (300334,120,10)
+    print("label:", train_label)  # (300334,10)
+
+    if is_Healthy:
+        train_label2 = np.ones(shape=[train_label.shape[0]])
+    else:
+        train_label2 = np.zeros(shape=[train_label.shape[0]])
+
+    print("label2:", train_label2)
+
+    return train_data, train_label, train_label2
+
+
+# 归一化
+def normalization(data):
+    rows, cols = data.shape
+    print("归一化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 归一化
+    max = np.max(data, axis=0)
+    max = np.broadcast_to(max, [rows, cols])
+    min = np.min(data, axis=0)
+    min = np.broadcast_to(min, [rows, cols])
+
+    data = (data - min) / (max - min)
+    print("归一化之后:", data)
+    print(data.shape)
+
+    return data
+
+
+# 正则化
+def Regularization(data):
+    rows, cols = data.shape
+    print("正则化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 正则化
+    mean = np.mean(data, axis=0)
+    mean = np.broadcast_to(mean, shape=[rows, cols])
+    dst = np.sqrt(np.var(data, axis=0))
+    dst = np.broadcast_to(dst, shape=[rows, cols])
+    data = (data - mean) / dst
+    print("正则化之后:", data)
+    print(data.shape)
+
+    return data
+    pass
+
+
+def EWMA(data, K=K, namuda=namuda):
+    # t是啥暂时未知
+    t = 0
+    mid = np.mean(data, axis=0)
+    standard = np.sqrt(np.var(data, axis=0))
+    UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    return mid, UCL, LCL
+    pass
+
+
+def condition_monitoring_model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
+    GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
+    d1 = tf.keras.layers.Dense(300)(GRU1)
+    output = tf.keras.layers.Dense(10)(d1)
+
+    model = tf.keras.Model(inputs=input, outputs=output)
+
+    return model
+
+
+# trian_data:(300455,120,10)
+# trian_label1:(300455,10)
+# trian_label2:(300455,)
+def shuffle(train_data, train_label1, train_label2, is_split: bool = False, split_size: float = 0.2):
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(train_data,
+                                                                                                     train_label1,
+                                                                                                     train_label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=True,
+                                                                                                     random_state=100)
+    if is_split:
+        return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+    train_data = np.concatenate([train_data, test_data], axis=0)
+    train_label1 = np.concatenate([train_label1, test_label1], axis=0)
+    train_label2 = np.concatenate([train_label2, test_label2], axis=0)
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2
+    pass
+
+
+def split_test_data(healthy_data, healthy_label1, healthy_label2, unhealthy_data, unhealthy_label1, unhealthy_label2,
+                    split_size: float = 0.2, shuffle: bool = True):
+    data = np.concatenate([healthy_data, unhealthy_data], axis=0)
+    label1 = np.concatenate([healthy_label1, unhealthy_label1], axis=0)
+    label2 = np.concatenate([healthy_label2, unhealthy_label2], axis=0)
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(data,
+                                                                                                     label1,
+                                                                                                     label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=shuffle,
+                                                                                                     random_state=100)
+
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+
+    pass
+
+
+def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
+    history_loss = []
+    history_val_loss = []
+
+    val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
+                                                         val_label2=test_label2,
+                                                         is_first_time=False, step_one_model=step_one_model)
+
+    history_val_loss.append(val_loss)
+    print("val_accuracy:", val_accuracy)
+    print("val_loss:", val_loss)
+
+
+def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False):
+    # 获取模型的所有参数的个数
+    # step_two_model.count_params()
+    total_result = []
+    size, length, dims = test_data.shape
+    for epoch in range(0, size - batch_size + 1, batch_size):
+        each_test_data = test_data[epoch:epoch + batch_size, :, :]
+        _, _, _, output4 = step_two_model.call(each_test_data, is_first_time=False)
+        total_result.append(output4)
+    total_result = np.reshape(total_result, [total_result.__len__(), -1])
+    total_result = np.reshape(total_result, [-1, ])
+    if isPlot:
+        plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
+        # 画出 y=1 这条水平线
+        plt.axhline(0.5, c='red', label='Failure threshold')
+        # 箭头指向上面的水平线
+        # plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
+        #           alpha=0.9, overhang=0.5)
+        # plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
+        plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
+        plt.xlabel("time")
+        plt.ylabel("confience")
+        plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.grid()
+        # plt.ylim(0, 1)
+        # plt.xlim(-50, 1300)
+        # plt.legend("", loc='upper left')
+        plt.show()
+    return total_result
+
+
+def DCConv_Model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    input = tf.cast(input, tf.float32)
+
+    LSTM1 = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=2)(input)
+    LSTM2 = tf.keras.layers.Conv1D(10, 2, padding="causal", dilation_rate=2)(input)
+    LSTM3 = tf.keras.layers.Conv1D(10, 1, padding="causal", dilation_rate=2)(input)
+
+    t1 = tf.add(tf.add(LSTM1, LSTM2), LSTM3)
+
+    LSTM1 = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=4)(t1)
+    LSTM2 = tf.keras.layers.Conv1D(10, 2, padding="causal", dilation_rate=4)(t1)
+    LSTM3 = tf.keras.layers.Conv1D(10, 1, padding="causal", dilation_rate=4)(t1)
+
+    t2 = tf.add(tf.add(LSTM1, LSTM2), LSTM3)
+
+
+
+    LSTM1 = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=8)(t2)
+    LSTM2 = tf.keras.layers.Conv1D(10, 2, padding="causal", dilation_rate=8)(t2)
+    LSTM3 = tf.keras.layers.Conv1D(10, 1, padding="causal", dilation_rate=8)(t2)
+
+    t3 = tf.add(tf.add(LSTM1, LSTM2), LSTM3)
+
+
+    LSTM1 = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=16)(t3)
+    LSTM2 = tf.keras.layers.Conv1D(10, 2, padding="causal", dilation_rate=16)(t3)
+    LSTM3 = tf.keras.layers.Conv1D(10, 1, padding="causal", dilation_rate=16)(t3)
+
+    t4 = tf.add(tf.add(LSTM1, LSTM2), LSTM3)
+
+
+
+    LSTM1 = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=32)(t4)
+    LSTM2 = tf.keras.layers.Conv1D(10, 2, padding="causal", dilation_rate=32)(t4)
+    LSTM3 = tf.keras.layers.Conv1D(10, 1, padding="causal", dilation_rate=32)(t4)
+
+    t5 = tf.add(tf.add(LSTM1, LSTM2), LSTM3)
+
+
+    # LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=64)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=128)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=2)(LSTM)
+
+    LSTM = t5[:, -1, :]
+    # bn = tf.keras.layers.BatchNormalization()(LSTM)
+
+    # d1 = tf.keras.layers.Dense(20)(LSTM)
+    # bn = tf.keras.layers.BatchNormalization()(d1)
+
+    output = tf.keras.layers.Dense(128, name='output1')(LSTM)
+    output = tf.keras.layers.Dense(10, name='output')(output)
+    model = tf.keras.Model(inputs=input, outputs=output)
+    return model
+    pass
+
+
+def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True, predictI: int = 1):
+    predicted_data = new_model.predict(data)
+
+    temp = np.abs(predicted_data - label)
+    temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
+    temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
+    temp3 = temp1 / temp2
+    mse = np.sum((temp1 / temp2) ** 2, axis=1)
+    print("z:", mse)
+    print(mse.shape)
+
+    # mse=np.mean((predicted_data-label)**2,axis=1)
+    print("mse", mse)
+    if isStandard:
+        dims, = mse.shape
+        mean = np.mean(mse)
+        std = np.sqrt(np.var(mse))
+        max = mean + 3 * std
+        print("max:", max)
+        # min = mean-3*std
+        max = np.broadcast_to(max, shape=[dims, ])
+        # min = np.broadcast_to(min,shape=[dims,])
+        mean = np.broadcast_to(mean, shape=[dims, ])
+        if isPlot:
+            plt.figure(random.randint(1, 9))
+            plt.plot(max)
+            plt.plot(mse)
+            plt.plot(mean)
+            # plt.plot(min)
+            plt.show()
+    else:
+        if isPlot:
+            plt.figure(random.randint(1, 9))
+            plt.plot(mse)
+            # plt.plot(min)
+            plt.show()
+            return mse
+
+    return mse, mean, max
+    # pass
+
+
+# healthy_data是健康数据,用于确定阈值，all_data是完整的数据,用于模型出结果
+def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data, unhealthy_label, isPlot: bool = False,
+              isSave: bool = True, predictI: int = 1):
+    # TODO 计算MSE确定阈值
+    # TODO 计算MSE确定阈值
+
+    mse, mean, max = get_MSE(healthy_data, healthy_label, model)
+
+    # 误报率的计算
+    total, = mse.shape
+    faultNum = 0
+    faultList = []
+    faultNum = mse[mse[:] > max[0]].__len__()
+    # for i in range(total):
+    #     if (mse[i] > max[i]):
+    #         faultNum += 1
+    #         faultList.append(mse[i])
+
+    fault_rate = faultNum / total
+    print("误报率:", fault_rate)
+
+    # 漏报率计算
+    missNum = 0
+    mse1 = get_MSE(unhealthy_data, unhealthy_label, model, isStandard=False)
+
+    total_mse = np.concatenate([mse, mse1], axis=0)
+    total_max = np.broadcast_to(max[0], shape=[total_mse.shape[0], ])
+    # min = np.broadcast_to(min,shape=[dims,])
+    total_mean = np.broadcast_to(mean[0], shape=[total_mse.shape[0], ])
+    if isSave:
+        save_mse_name1 = save_mse_name
+        save_max_name1 = save_max_name
+
+        np.savetxt(save_mse_name1, total_mse, delimiter=',')
+        np.savetxt(save_max_name1, total_max, delimiter=',')
+
+    all, = mse1.shape
+
+    missNum = mse1[mse1[:] < max[0]].__len__()
+
+    print("all:", all)
+    miss_rate = missNum / all
+    print("漏报率:", miss_rate)
+
+    plt.figure(random.randint(1, 100))
+    plt.plot(total_max)
+    plt.plot(total_mse)
+    plt.plot(total_mean)
+    # plt.plot(min)
+    plt.show()
+    pass
+
+
+if __name__ == '__main__':
+    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = normalization(data=total_data)
+    train_data_healthy, train_label1_healthy, train_label2_healthy = get_training_data_overlapping(
+        total_data[:healthy_date, :], is_Healthy=True)
+    train_data_unhealthy, train_label1_unhealthy, train_label2_unhealthy = get_training_data_overlapping(
+        total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
+        is_Healthy=False)
+    #### TODO 第一步训练
+    # 单次测试
+    model = DCConv_Model()
+
+    checkpoint = tf.keras.callbacks.ModelCheckpoint(
+        filepath=save_name,
+        monitor='val_loss',
+        verbose=2,
+        save_best_only=True,
+        mode='min')
+    lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.001)
+
+    model.compile(optimizer=tf.optimizers.Adam(), loss=tf.losses.mse)
+    model.build(input_shape=(batch_size, time_stamp, feature_num))
+    model.summary()
+    # early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=3, mode='min', verbose=1)
+    #
+    # history = model.fit(train_data_healthy[:train_data_healthy.shape[0] // 7, :, :],
+    #                     train_label1_healthy[:train_label1_healthy.shape[0] // 7, ], epochs=EPOCH,
+    #                     batch_size=batch_size * 10, validation_split=0.2, shuffle=True, verbose=1,
+    #                     callbacks=[checkpoint, lr_scheduler, early_stop])
+
+    ## TODO testing
+    # test_data, test_label = get_training_data(total_data[:healthy_date, :])
+    newModel = tf.keras.models.load_model(save_name)
+    # mse, mean, max = get_MSE(test_data, test_label, new_model=newModel)
+
+    start = time.time()
+    # 中间写上代码块
+
+    model.predict(train_data_healthy, batch_size=32)
+    end = time.time()
+    print("data_size:", train_data_healthy.shape)
+    print('Running time: %s Seconds' % (end - start))
+
+    healthy_size, _, _ = train_data_healthy.shape
+    unhealthy_size, _, _ = train_data_unhealthy.shape
+    all_data, _, _ = get_training_data_overlapping(
+        total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
+
+    newModel = tf.keras.models.load_model(save_name)
+    # 单次测试
+    # getResult(newModel,
+    #           healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200,
+    #                        :],
+    #           healthy_label=train_label1_healthy[
+    #                         healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200, :],
+    #           unhealthy_data=train_data_unhealthy[:200, :], unhealthy_label=train_label1_unhealthy[:200, :],isSave=True)
+    getResult(newModel, healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+              healthy_label=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
+              unhealthy_data=train_data_unhealthy, unhealthy_label=train_label1_unhealthy, isSave=True)
+    # mse, mean, max = get_MSE(train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+    #                          train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], new_model=newModel)
+    pass

TensorFlow_eaxmple/Model_train_test/condition_monitoring/返修/__init__.py

@@ -0,0 +1,8 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/23 10:43
+@Usage : 
+@Desc :
+'''

TensorFlow_eaxmple/Model_train_test/condition_monitoring/返修/complete/Transformer.py

@@ -0,0 +1,101 @@
+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/23 10:43
+@Usage : 
+@Desc :  计算transformer的参数和时间复杂度  6层self_attention
+'''
+import tensorflow  as tf
+from tensorflow.keras import Model, layers, initializers
+import numpy as np
+from model.SelfAttention.SelfAttention import Block
+
+
+class Transformer(Model):
+
+    # depth表示的是重复encoder block的次数，num_heads表示的是在multi-head self-attention中head的个数
+    # MLP block中有一个Pre_logist,这里指的是，当在较大的数据集上学习的时候Pre_logist就表示一个全连接层加上一个tanh激活函数
+    # 当在较小的数据集上学习的时候，Pre_logist是没有的，而这里的representation_size表示的就是Pre_logist中全连接层的节点个数
+    # num_classes表示分类的类数
+    def __init__(self, embed_dim=768,
+                 depth=12, num_heads=12, qkv_bias=True, qk_scale=None,
+                 drop_ratio=0., attn_drop_ratio=0., drop_path_ratio=0.,
+                 representation_size=None, num_classes=1000, name="ViT-B/16"):
+        super(Transformer, self).__init__(name=name)
+
+        self.embed_dim = embed_dim
+        self.depth = depth
+        self.num_heads = num_heads
+        self.qkv_bias = qkv_bias
+        self.qk_scale = qk_scale
+        self.drop_ratio = drop_ratio
+        self.attn_drop_ratio = attn_drop_ratio
+        self.drop_path_ratio = drop_path_ratio
+        self.representation_size = representation_size
+        self.num_classes = num_classes
+
+        dpr = np.linspace(0., drop_path_ratio, depth)  # stochastic depth decay rule
+        # 用一个for循环重复Block模块
+        # 在用droppath时的drop_path_ratio是由0慢慢递增到我们所指定的drop_path_ratio的
+        # 所以我们在构建Block时，这里的drop_path_ratio时变化的，所以用 np.linspace方法创建一个等差数列来初始化drop_path_ratio
+        self.blocks = [Block(dim=embed_dim, num_heads=num_heads, qkv_bias=qkv_bias,
+                             qk_scale=qk_scale, drop_ratio=drop_ratio, attn_drop_ratio=attn_drop_ratio,
+                             drop_path_ratio=dpr[i], name="encoderblock_{}".format(i))
+                       for i in range(depth)]
+
+        self.norm = layers.LayerNormalization(epsilon=1e-6, name="encoder_norm")
+
+        # 接下来，如果传入了representation_size，就构建一个全连接层，激活函数为tanh
+        # 如果没有传入的话，就不做任何操作
+        if representation_size:
+            self.has_logits = True
+            self.pre_logits = layers.Dense(representation_size, activation="tanh", name="pre_logits")
+        else:
+            self.has_logits = False
+            self.pre_logits = layers.Activation("linear")
+
+        # 定义最后一个全连接层，节点个数就是我们的分类个数num_classes
+        self.head = layers.Dense(num_classes, name="head", kernel_initializer=initializers.Zeros())
+
+    def get_config(self):
+        # 自定义层里面的属性
+        config = (
+            {
+                'embed_dim': self.embed_dim,
+                'depth': self.depth,
+                'num_heads': self.num_heads,
+                'qkv_bias': self.qkv_bias,
+                'qk_scale': self.qk_scale,
+                'drop_ratio': self.drop_ratio,
+                'attn_drop_ratio': self.attn_drop_ratio,
+                'drop_path_ratio': self.drop_path_ratio,
+                'representation_size': self.representation_size,
+                'num_classes': self.num_classes
+            }
+        )
+        base_config = super(Transformer, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    def call(self, inputs, training=None):
+        # [B, H, W, C] -> [B, num_patches, embed_dim]
+        x = inputs  # [B, 196, 768]
+
+        for block in self.blocks:
+            x = block(x, training=training)
+
+        x = self.norm(x)
+        # 这里是提取class_toke的输出，然后用切片的方式，而刚刚是将class_toke拼接在最前面的
+        # 所以这里用切片的方式，去取class_toke的输出，并将它传递给pre_logits
+        x = self.pre_logits(x[:, 0])
+        # 最后传递给head
+        x = self.head(x)
+        # 为什么只用class_toke对应的输出，而不用每一个patches对应的输出呢？
+        # 可以参考原文bird 网络
+
+        return x
+
+
+if __name__ == '__main__':
+    # 使用方式
+    Transformer(embed_dim=10, depth=8, num_heads=1, num_classes=10)

TensorFlow_eaxmple/Model_train_test/condition_monitoring/返修/complete/__init__.py

@@ -0,0 +1,8 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/23 10:43
+@Usage : 
+@Desc :
+'''

TensorFlow_eaxmple/Model_train_test/condition_monitoring/返修/poor-Cnn-gru.py

@@ -0,0 +1,569 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+
+
+'''
+@Author : dingjiawen
+@Date : 2022/10/11 18:52
+@Usage : 对比实验，使用四分位图技术
+@Desc :
+'''
+
+import tensorflow as tf
+import tensorflow.keras
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from condition_monitoring.data_deal import loadData_daban as loadData
+from model.Joint_Monitoring.Joint_Monitoring3 import Joint_Monitoring
+
+from model.CommonFunction.CommonFunction import *
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.models import load_model, save_model
+from keras.callbacks import EarlyStopping
+import random
+import time
+
+'''超参数设置'''
+time_stamp = 120
+feature_num = 10
+batch_size = 32
+learning_rate = 0.001
+EPOCH = 101
+model_name = "DCConv"
+'''EWMA超参数'''
+K = 18
+namuda = 0.01
+'''保存名称'''
+
+save_name = "./trianed/{0}_{1}_{2}.h5".format(model_name,
+                                              time_stamp,
+                                              feature_num,
+                                              batch_size,
+                                              EPOCH)
+save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weight_epoch14/weight".format(model_name,
+                                                                                                         time_stamp,
+                                                                                                         feature_num,
+                                                                                                         batch_size,
+                                                                                                         EPOCH)
+save_mse_name = "./mse/DCConv/banda/mse.csv".format(model_name,
+                                                    time_stamp,
+                                                    feature_num,
+                                                    batch_size,
+                                                    EPOCH)
+save_max_name = "./mse/DCConv/banda/max.csv".format(model_name,
+                                                    time_stamp,
+                                                    feature_num,
+                                                    batch_size,
+                                                    EPOCH)
+
+# save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                    time_stamp,
+#                                                                    feature_num,
+#                                                                    batch_size,
+#                                                                    EPOCH)
+# save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+#                                                                                 time_stamp,
+#                                                                                 feature_num,
+#                                                                                 batch_size,
+#                                                                                 EPOCH)
+'''文件名'''
+'''文件名'''
+save_mse_name=r"./compare/mse/JM_banda/{0}_result.csv".format(model_name)
+'''文件名'''
+file_name = "G:\data\SCADA数据\SCADA_已处理_粤水电达坂城2020.1月-5月\风机15.csv"
+
+'''
+文件说明：jb4q_8_delete_total_zero.csv是删除了只删除了全是0的列的文件
+文件从0:96748行均是正常值(2019/12.30 00:00:00 - 2020/3/11  05:58:00)
+从96748:107116行均是异常值(2020/3/11  05:58:01 - 2021/3/18  11:04:00)
+'''
+'''文件参数'''
+# 最后正常的时间点
+healthy_date = 96748
+# 最后异常的时间点
+unhealthy_date = 107116
+# 异常容忍程度
+unhealthy_patience = 5
+
+
+def remove(data, time_stamp=time_stamp):
+    rows, cols = data.shape
+    print("remove_data.shape:", data.shape)
+    num = int(rows / time_stamp)
+
+    return data[:num * time_stamp, :]
+    pass
+
+
+# 不重叠采样
+def get_training_data(data, time_stamp: int = time_stamp):
+    removed_data = remove(data=data)
+    rows, cols = removed_data.shape
+    print("removed_data.shape:", data.shape)
+    print("removed_data:", removed_data)
+    train_data = np.reshape(removed_data, [-1, time_stamp, cols])
+    print("train_data:", train_data)
+    batchs, time_stamp, cols = train_data.shape
+
+    for i in range(1, batchs):
+        each_label = np.expand_dims(train_data[i, 0, :], axis=0)
+        if i == 1:
+            train_label = each_label
+        else:
+            train_label = np.concatenate([train_label, each_label], axis=0)
+
+    print("train_data.shape:", train_data.shape)
+    print("train_label.shape", train_label.shape)
+    return train_data[:-1, :], train_label
+
+
+# 重叠采样
+def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
+    rows, cols = data.shape
+    train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
+    train_label = np.empty(shape=[rows - time_stamp - 1, cols])
+    for i in range(rows):
+        if i + time_stamp >= rows:
+            break
+        if i + time_stamp < rows - 1:
+            train_data[i] = data[i:i + time_stamp]
+            train_label[i] = data[i + time_stamp]
+
+    print("重叠采样以后：")
+    print("data:", train_data)  # (300334,120,10)
+    print("label:", train_label)  # (300334,10)
+
+    if is_Healthy:
+        train_label2 = np.ones(shape=[train_label.shape[0]])
+    else:
+        train_label2 = np.zeros(shape=[train_label.shape[0]])
+
+    print("label2:", train_label2)
+
+    return train_data, train_label, train_label2
+
+
+# 归一化
+def normalization(data):
+    rows, cols = data.shape
+    print("归一化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 归一化
+    max = np.max(data, axis=0)
+    max = np.broadcast_to(max, [rows, cols])
+    min = np.min(data, axis=0)
+    min = np.broadcast_to(min, [rows, cols])
+
+    data = (data - min) / (max - min)
+    print("归一化之后:", data)
+    print(data.shape)
+
+    return data
+
+
+# 正则化
+def Regularization(data):
+    rows, cols = data.shape
+    print("正则化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 正则化
+    mean = np.mean(data, axis=0)
+    mean = np.broadcast_to(mean, shape=[rows, cols])
+    dst = np.sqrt(np.var(data, axis=0))
+    dst = np.broadcast_to(dst, shape=[rows, cols])
+    data = (data - mean) / dst
+    print("正则化之后:", data)
+    print(data.shape)
+
+    return data
+    pass
+
+
+def EWMA(data, K=K, namuda=namuda):
+    # t是啥暂时未知
+    t = 0
+    mid = np.mean(data, axis=0)
+    standard = np.sqrt(np.var(data, axis=0))
+    UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    return mid, UCL, LCL
+    pass
+
+
+def condition_monitoring_model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
+    GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
+    d1 = tf.keras.layers.Dense(300)(GRU1)
+    output = tf.keras.layers.Dense(10)(d1)
+
+    model = tf.keras.Model(inputs=input, outputs=output)
+
+    return model
+
+
+# trian_data:(300455,120,10)
+# trian_label1:(300455,10)
+# trian_label2:(300455,)
+def shuffle(train_data, train_label1, train_label2, is_split: bool = False, split_size: float = 0.2):
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(train_data,
+                                                                                                     train_label1,
+                                                                                                     train_label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=True,
+                                                                                                     random_state=100)
+    if is_split:
+        return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+    train_data = np.concatenate([train_data, test_data], axis=0)
+    train_label1 = np.concatenate([train_label1, test_label1], axis=0)
+    train_label2 = np.concatenate([train_label2, test_label2], axis=0)
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2
+    pass
+
+
+def split_test_data(healthy_data, healthy_label1, healthy_label2, unhealthy_data, unhealthy_label1, unhealthy_label2,
+                    split_size: float = 0.2, shuffle: bool = True):
+    data = np.concatenate([healthy_data, unhealthy_data], axis=0)
+    label1 = np.concatenate([healthy_label1, unhealthy_label1], axis=0)
+    label2 = np.concatenate([healthy_label2, unhealthy_label2], axis=0)
+    (train_data, test_data, train_label1, test_label1, train_label2, test_label2) = train_test_split(data,
+                                                                                                     label1,
+                                                                                                     label2,
+                                                                                                     test_size=split_size,
+                                                                                                     shuffle=shuffle,
+                                                                                                     random_state=100)
+
+    # print(train_data.shape)
+    # print(train_label1.shape)
+    # print(train_label2.shape)
+    # print(train_data.shape)
+
+    return train_data, train_label1, train_label2, test_data, test_label1, test_label2
+
+    pass
+
+
+def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
+    history_loss = []
+    history_val_loss = []
+
+    val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
+                                                         val_label2=test_label2,
+                                                         is_first_time=False, step_one_model=step_one_model)
+
+    history_val_loss.append(val_loss)
+    print("val_accuracy:", val_accuracy)
+    print("val_loss:", val_loss)
+
+
+def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False):
+    # 获取模型的所有参数的个数
+    # step_two_model.count_params()
+    total_result = []
+    size, length, dims = test_data.shape
+    for epoch in range(0, size - batch_size + 1, batch_size):
+        each_test_data = test_data[epoch:epoch + batch_size, :, :]
+        _, _, _, output4 = step_two_model.call(each_test_data, is_first_time=False)
+        total_result.append(output4)
+    total_result = np.reshape(total_result, [total_result.__len__(), -1])
+    total_result = np.reshape(total_result, [-1, ])
+    if isPlot:
+        plt.scatter(list(range(total_result.shape[0])), total_result, c='black', s=10)
+        # 画出 y=1 这条水平线
+        plt.axhline(0.5, c='red', label='Failure threshold')
+        # 箭头指向上面的水平线
+        # plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
+        #           alpha=0.9, overhang=0.5)
+        # plt.text(35000, 0.9, "Truth Fault", fontsize=10, color='black', verticalalignment='top')
+        plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
+        plt.xlabel("time")
+        plt.ylabel("confience")
+        plt.text(total_result.shape[0] * 4 / 5, 0.6, "Fault", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.text(total_result.shape[0] * 1 / 3, 0.4, "Norm", fontsize=10, color='black', verticalalignment='top',
+                 horizontalalignment='center',
+                 bbox={'facecolor': 'grey',
+                       'pad': 10})
+        plt.grid()
+        # plt.ylim(0, 1)
+        # plt.xlim(-50, 1300)
+        # plt.legend("", loc='upper left')
+        plt.show()
+    return total_result
+
+
+def DCConv_Model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    input = tf.cast(input, tf.float32)
+
+    LSTM = tf.keras.layers.Conv1D(10, 3)(input)
+    LSTM = tf.keras.layers.Conv1D(20, 3)(LSTM)
+    LSTM = tf.keras.layers.GRU(20, return_sequences=True)(LSTM)
+    LSTM = tf.keras.layers.GRU(40, return_sequences=True)(LSTM)
+    LSTM = tf.keras.layers.GRU(80, return_sequences=False)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=64)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(10, 3, padding="causal", dilation_rate=128)(LSTM)
+    # LSTM = tf.keras.layers.Conv1D(40, 3, padding="causal",dilation_rate=2)(LSTM)
+
+    # LSTM = LSTM[:, -1, :]
+    # bn = tf.keras.layers.BatchNormalization()(LSTM)
+
+    # d1 = tf.keras.layers.Dense(20)(LSTM)
+    # bn = tf.keras.layers.BatchNormalization()(d1)
+
+    output = tf.keras.layers.Dense(128, name='output1')(LSTM)
+    output = tf.keras.layers.Dense(10, name='output')(output)
+    model = tf.keras.Model(inputs=input, outputs=output)
+    return model
+    pass
+
+
+def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True, predictI: int = 1):
+    predicted_data = new_model.predict(data)
+
+    temp = np.abs(predicted_data - label)
+    temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
+    temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
+    temp3 = temp1 / temp2
+    mse = np.sum((temp1 / temp2) ** 2, axis=1)
+    print("z:", mse)
+    print(mse.shape)
+
+    # mse=np.mean((predicted_data-label)**2,axis=1)
+    print("mse", mse)
+    if isStandard:
+        dims, = mse.shape
+        mean = np.mean(mse)
+        std = np.sqrt(np.var(mse))
+        max = mean + 3 * std
+        print("max:", max)
+        # min = mean-3*std
+        max = np.broadcast_to(max, shape=[dims, ])
+        # min = np.broadcast_to(min,shape=[dims,])
+        mean = np.broadcast_to(mean, shape=[dims, ])
+        if isPlot:
+            plt.figure(random.randint(1, 9))
+            plt.plot(max)
+            plt.plot(mse)
+            plt.plot(mean)
+            # plt.plot(min)
+            plt.show()
+    else:
+        if isPlot:
+            plt.figure(random.randint(1, 9))
+            plt.plot(mse)
+            # plt.plot(min)
+            plt.show()
+            return mse
+
+    return mse, mean, max
+    # pass
+
+
+# healthy_data是健康数据,用于确定阈值，all_data是完整的数据,用于模型出结果
+def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data, unhealthy_label, isPlot: bool = False,
+              isSave: bool = True, predictI: int = 1):
+    # TODO 计算MSE确定阈值
+    # TODO 计算MSE确定阈值
+
+    mse, mean, max = get_MSE(healthy_data, healthy_label, model)
+
+    # 误报率的计算
+    total, = mse.shape
+    faultNum = 0
+    faultList = []
+    faultNum = mse[mse[:] > max[0]].__len__()
+    # for i in range(total):
+    #     if (mse[i] > max[i]):
+    #         faultNum += 1
+    #         faultList.append(mse[i])
+
+    fault_rate = faultNum / total
+    print("误报率:", fault_rate)
+
+    # 漏报率计算
+    missNum = 0
+    mse1 = get_MSE(unhealthy_data, unhealthy_label, model, isStandard=False)
+
+    total_mse = np.concatenate([mse, mse1], axis=0)
+    total_max = np.broadcast_to(max[0], shape=[total_mse.shape[0], ])
+    # min = np.broadcast_to(min,shape=[dims,])
+    total_mean = np.broadcast_to(mean[0], shape=[total_mse.shape[0], ])
+    if isSave:
+        save_mse_name1 = save_mse_name
+        save_max_name1 = save_max_name
+
+        np.savetxt(save_mse_name1, total_mse, delimiter=',')
+        np.savetxt(save_max_name1, total_max, delimiter=',')
+
+    all, = mse1.shape
+
+    missNum = mse1[mse1[:] < max[0]].__len__()
+
+    print("all:", all)
+    miss_rate = missNum / all
+    print("漏报率:", miss_rate)
+
+    plt.figure(random.randint(1, 100))
+    plt.plot(total_max)
+    plt.plot(total_mse)
+    plt.plot(total_mean)
+    # plt.plot(min)
+    plt.show()
+    pass
+
+
+def iqr_outliers(df: pd.DataFrame):
+    q1 = df.quantile(0.25)
+    q3 = df.quantile(0.75)
+    iqr = q3 - q1
+    Lower_tail = q1 - 1.5 * iqr
+    Upper_tail = q3 + 1.5 * iqr
+    outlier = []
+    for i in df.iloc[:, 0]:
+        if i > float(Upper_tail) or i < float(Lower_tail):  # float限定
+            outlier.append(i)
+    print("Outliers:", outlier)
+
+
+def iqr_outliers_np_all(df):
+    length, feature = df.shape
+    toatl_Outliers = set()
+    for i in range(feature):
+        cur = df[:, i]
+        cur = np.array(cur)
+        q1 = np.percentile(cur, [25])
+        q3 = np.percentile(cur, [75])
+        iqr = q3 - q1
+        Lower_tail = q1 - 1.5 * iqr
+        Upper_tail = q3 + 1.5 * iqr
+        cur_Outliers = []
+        for i in range(len(cur)):
+            if cur[i] > float(Upper_tail) or cur[i] < float(Lower_tail):  # float限定
+                toatl_Outliers.add(i)
+                cur_Outliers.append(i)
+        print("cur_Outliers.shape:", len(cur_Outliers))
+        print("cur_Outliers:", cur_Outliers)
+    print("Outliers.shape:", len(toatl_Outliers))
+    print("Outliers:", toatl_Outliers)
+    unhealthy_outlier = []
+    for s in toatl_Outliers:
+        if s >= healthy_date:
+            unhealthy_outlier.append(s)
+    print("unhealthy_outlier.shape:", len(unhealthy_outlier))
+    print("unhealthy_outlier.shape:", len(unhealthy_outlier)/(unhealthy_date-healthy_date))
+    print("unhealthy_outlier:", unhealthy_outlier)
+    return sorted(unhealthy_outlier)
+
+
+def sigma_outliers_np_all(df):
+    length, feature = df.shape
+    toatl_Outliers = set()
+    for i in range(feature):
+        cur = df[:, i]
+        cur = np.array(cur)
+        mean = np.mean(cur[:200000,])
+        sigma = np.sqrt(np.var(cur[:200000,]))
+
+        Lower_tail = mean - 3 * sigma
+        Upper_tail = mean + 3 * sigma
+        cur_Outliers = []
+        for i in range(len(cur)):
+            if cur[i] > float(Upper_tail) or cur[i] < float(Lower_tail):  # float限定
+                toatl_Outliers.add(i)
+                cur_Outliers.append(i)
+        print("cur_Outliers.shape:", len(cur_Outliers))
+        print("cur_Outliers:", cur_Outliers)
+    print("Outliers.shape:", len(toatl_Outliers))
+    print("Outliers:", toatl_Outliers)
+    unhealthy_outlier = []
+    for s in toatl_Outliers:
+        if s >= healthy_date:
+            unhealthy_outlier.append(s)
+    print("unhealthy_outlier.shape:", len(unhealthy_outlier))
+    print("unhealthy_outlier.shape:", len(unhealthy_outlier)/(unhealthy_date-healthy_date))
+    print("unhealthy_outlier:", unhealthy_outlier)
+    return sorted(unhealthy_outlier)
+
+
+if __name__ == '__main__':
+    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = normalization(data=total_data)
+
+    train_data_healthy, train_label1_healthy, train_label2_healthy = get_training_data_overlapping(
+        total_data[:healthy_date, :], is_Healthy=True)
+    train_data_unhealthy, train_label1_unhealthy, train_label2_unhealthy = get_training_data_overlapping(
+        total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
+        is_Healthy=False)
+
+    total_train_data = total_data[:unhealthy_date, :]
+    # unhealthy_outlier = iqr_outliers_np_all(total_train_data)
+    unhealthy_outlier = sigma_outliers_np_all(total_train_data)
+    # #### TODO 第一步训练
+    # # 单次测试
+    # model = DCConv_Model()
+    #
+    # checkpoint = tf.keras.callbacks.ModelCheckpoint(
+    #     filepath=save_name,
+    #     monitor='val_loss',
+    #     verbose=2,
+    #     save_best_only=True,
+    #     mode='min')
+    # lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.001)
+    #
+    # model.compile(optimizer=tf.optimizers.Adam(), loss=tf.losses.mse)
+    # model.build(input_shape=(batch_size, time_stamp, feature_num))
+    # model.summary()
+    # early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=3, mode='min', verbose=1)
+    #
+    # # history = model.fit(train_data_healthy[:train_data_healthy.shape[0] // 7, :, :],
+    # #                     train_label1_healthy[:train_label1_healthy.shape[0] // 7, ], epochs=EPOCH,
+    # #                     batch_size=batch_size * 10, validation_split=0.2, shuffle=True, verbose=1,
+    # #                     callbacks=[checkpoint, lr_scheduler, early_stop])
+    #
+    # ## TODO testing
+    # # # test_data, test_label = get_training_data(total_data[:healthy_date, :])
+    # # model = tf.keras.models.load_model(save_name)
+    # # # mse, mean, max = get_MSE(test_data, test_label, new_model=newModel)
+    # #
+    # # start = time.time()
+    # # # 中间写上代码块
+    # #
+    # # model.predict(train_data_healthy, batch_size=32)
+    # # end = time.time()
+    # # print("data_size:", train_data_healthy.shape)
+    # # print('Running time: %s Seconds' % (end - start))
+    # #
+    # healthy_size, _, _ = train_data_healthy.shape
+    # unhealthy_size, _, _ = train_data_unhealthy.shape
+    # all_data, _, _ = get_training_data_overlapping(
+    #     total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
+    #
+    # newModel = tf.keras.models.load_model(save_name)
+    # # 单次测试
+    # # getResult(newModel,
+    # #           healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200,
+    # #                        :],
+    # #           healthy_label=train_label1_healthy[
+    # #                         healthy_size - 2 * unhealthy_size:healthy_size - 2 * unhealthy_size + 200, :],
+    # #           unhealthy_data=train_data_unhealthy[:200, :], unhealthy_label=train_label1_unhealthy[:200, :],isSave=True)
+    # getResult(newModel, healthy_data=train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+    #           healthy_label=train_label1_healthy[healthy_size - 2 * unhealthy_size:, :],
+    #           unhealthy_data=train_data_unhealthy, unhealthy_label=train_label1_unhealthy,isSave=False)
+    # # mse, mean, max = get_MSE(train_data_healthy[healthy_size - 2 * unhealthy_size:, :],
+    # #                          train_label1_healthy[healthy_size - 2 * unhealthy_size:, :], new_model=newModel)
+    pass

TensorFlow_eaxmple/Model_train_test/condition_monitoring/返修/transformer_complete.py

@@ -0,0 +1,285 @@
+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/23 14:23
+@Usage : 
+@Desc :
+'''
+
+import tensorflow as tf
+import tensorflow.keras
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from condition_monitoring.data_deal import loadData_daban as loadData
+# from model.Joint_Monitoring.Joint_Monitoring_banda import Joint_Monitoring
+
+# from model.CommonFunction.CommonFunction import *
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.models import load_model, save_model
+from condition_monitoring.返修.complete.Transformer import Transformer
+from model.SelfAttention.SelfAttention import Block
+from keras.callbacks import EarlyStopping
+import time
+
+'''超参数设置'''
+time_stamp = 120
+feature_num = 10
+batch_size = 32
+learning_rate = 0.001
+EPOCH = 101
+model_name = "transformer"
+'''EWMA超参数'''
+K = 18
+namuda = 0.01
+'''保存名称'''
+
+save_name = "../model/joint/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+                                                                   time_stamp,
+                                                                   feature_num,
+                                                                   batch_size,
+                                                                   EPOCH)
+save_step_two_name = "../model/joint_two/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+                                                                                time_stamp,
+                                                                                feature_num,
+                                                                                batch_size,
+                                                                                EPOCH)
+
+save_mse_name = r"./compare/mse/JM_banda/{0}_result.csv".format(model_name)
+'''文件名'''
+file_name = "G:\data\SCADA数据\SCADA_已处理_粤水电达坂城2020.1月-5月\风机15.csv"
+
+'''
+文件说明：jb4q_8_delete_total_zero.csv是删除了只删除了全是0的列的文件
+文件从0:96748行均是正常值(2019/12.30 00:00:00 - 2020/3/11  05:58:00)
+从96748:107116行均是异常值(2020/3/11  05:58:01 - 2021/3/18  11:04:00)
+'''
+'''文件参数'''
+# 最后正常的时间点
+healthy_date = 96748
+# 最后异常的时间点
+unhealthy_date = 107116
+# 异常容忍程度
+unhealthy_patience = 5
+
+
+def remove(data, time_stamp=time_stamp):
+    rows, cols = data.shape
+    print("remove_data.shape:", data.shape)
+    num = int(rows / time_stamp)
+
+    return data[:num * time_stamp, :]
+    pass
+
+
+# 不重叠采样
+def get_training_data(data, time_stamp: int = time_stamp):
+    removed_data = remove(data=data)
+    rows, cols = removed_data.shape
+    print("removed_data.shape:", data.shape)
+    print("removed_data:", removed_data)
+    train_data = np.reshape(removed_data, [-1, time_stamp, cols])
+    print("train_data:", train_data)
+    batchs, time_stamp, cols = train_data.shape
+
+    for i in range(1, batchs):
+        each_label = np.expand_dims(train_data[i, 0, :], axis=0)
+        if i == 1:
+            train_label = each_label
+        else:
+            train_label = np.concatenate([train_label, each_label], axis=0)
+
+    print("train_data.shape:", train_data.shape)
+    print("train_label.shape", train_label.shape)
+    return train_data[:-1, :], train_label
+
+
+# 重叠采样
+def get_training_data_overlapping(data, time_stamp: int = time_stamp, is_Healthy: bool = True):
+    rows, cols = data.shape
+    train_data = np.empty(shape=[rows - time_stamp - 1, time_stamp, cols])
+    train_label = np.empty(shape=[rows - time_stamp - 1, cols])
+    for i in range(rows):
+        if i + time_stamp >= rows:
+            break
+        if i + time_stamp < rows - 1:
+            train_data[i] = data[i:i + time_stamp]
+            train_label[i] = data[i + time_stamp]
+
+    print("重叠采样以后：")
+    print("data:", train_data)  # (300334,120,10)
+    print("label:", train_label)  # (300334,10)
+
+    if is_Healthy:
+        train_label2 = np.ones(shape=[train_label.shape[0]])
+    else:
+        train_label2 = np.zeros(shape=[train_label.shape[0]])
+
+    print("label2:", train_label2)
+
+    return train_data, train_label, train_label2
+
+
+# 归一化
+def normalization(data):
+    rows, cols = data.shape
+    print("归一化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 归一化
+    max = np.max(data, axis=0)
+    max = np.broadcast_to(max, [rows, cols])
+    min = np.min(data, axis=0)
+    min = np.broadcast_to(min, [rows, cols])
+
+    data = (data - min) / (max - min)
+    print("归一化之后:", data)
+    print(data.shape)
+
+    return data
+
+
+# 正则化
+def Regularization(data):
+    rows, cols = data.shape
+    print("正则化之前:", data)
+    print(data.shape)
+    print("======================")
+
+    # 正则化
+    mean = np.mean(data, axis=0)
+    mean = np.broadcast_to(mean, shape=[rows, cols])
+    dst = np.sqrt(np.var(data, axis=0))
+    dst = np.broadcast_to(dst, shape=[rows, cols])
+    data = (data - mean) / dst
+    print("正则化之后:", data)
+    print(data.shape)
+
+    return data
+    pass
+
+
+def EWMA(data, K=K, namuda=namuda):
+    # t是啥暂时未知
+    t = 0
+    mid = np.mean(data, axis=0)
+    standard = np.sqrt(np.var(data, axis=0))
+    UCL = mid + K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    LCL = mid - K * standard * np.sqrt(namuda / (2 - namuda) * (1 - (1 - namuda) ** 2 * t))
+    return mid, UCL, LCL
+    pass
+
+
+def get_MSE(data, label, new_model):
+    predicted_data = new_model.predict(data)
+
+    temp = np.abs(predicted_data - label)
+    temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
+    temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
+    temp3 = temp1 / temp2
+    mse = np.sum((temp1 / temp2) ** 2, axis=1)
+    print("z:", mse)
+    print(mse.shape)
+
+    # mse=np.mean((predicted_data-label)**2,axis=1)
+    print("mse", mse)
+
+    dims, = mse.shape
+
+    mean = np.mean(mse)
+    std = np.sqrt(np.var(mse))
+    max = mean + 3 * std
+    # min = mean-3*std
+    max = np.broadcast_to(max, shape=[dims, ])
+    # min = np.broadcast_to(min,shape=[dims,])
+    mean = np.broadcast_to(mean, shape=[dims, ])
+
+    # plt.plot(max)
+    # plt.plot(mse)
+    # plt.plot(mean)
+    # # plt.plot(min)
+    # plt.show()
+    #
+    #
+    return mse, mean, max
+    # pass
+
+
+def condition_monitoring_model():
+    input = tf.keras.Input(shape=[time_stamp, feature_num])
+    conv1 = tf.keras.layers.Conv1D(filters=256, kernel_size=1)(input)
+    GRU1 = tf.keras.layers.GRU(128, return_sequences=False)(conv1)
+    d1 = tf.keras.layers.Dense(300)(GRU1)
+    output = tf.keras.layers.Dense(10)(d1)
+
+    model = tf.keras.Model(inputs=input, outputs=output)
+
+    return model
+
+
+def test(step_one_model, step_two_model, test_data, test_label1, test_label2):
+    history_loss = []
+    history_val_loss = []
+
+    val_loss, val_accuracy = step_two_model.get_val_loss(val_data=test_data, val_label1=test_label1,
+                                                         val_label2=test_label2,
+                                                         is_first_time=False, step_one_model=step_one_model)
+
+    history_val_loss.append(val_loss)
+    print("val_accuracy:", val_accuracy)
+    print("val_loss:", val_loss)
+
+
+if __name__ == '__main__':
+    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = normalization(data=total_data)
+
+    train_data_healthy, train_label1_healthy, train_label2_healthy = get_training_data_overlapping(
+        total_data[:healthy_date, :], is_Healthy=True)
+    train_data_unhealthy, train_label1_unhealthy, train_label2_unhealthy = get_training_data_overlapping(
+        total_data[healthy_date - time_stamp + unhealthy_patience:unhealthy_date, :],
+        is_Healthy=False)
+    #### TODO 第一步训练
+
+    ####### TODO  训练
+    model = Transformer(embed_dim=10, depth=5, num_heads=1, num_classes=10,representation_size=128)
+    checkpoint = tf.keras.callbacks.ModelCheckpoint(
+        filepath=save_name,
+        monitor='val_loss',
+        verbose=2,
+        save_best_only=True,
+        mode='min')
+    lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.001)
+
+    model.compile(optimizer=tf.optimizers.Adam(), loss=tf.losses.mse)
+    model.build(input_shape=(batch_size, time_stamp, feature_num))
+    model.summary()
+    early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=3, mode='min', verbose=1)
+
+    history = model.fit(train_data_healthy[:train_data_healthy.shape[0] // 7, :, :],
+                        train_label1_healthy[:train_label1_healthy.shape[0] // 7, ], epochs=EPOCH,
+                        batch_size=batch_size * 10, validation_split=0.2, shuffle=True, verbose=1,
+                        callbacks=[checkpoint, lr_scheduler, early_stop])
+    #
+    #
+    #
+    # #### TODO  测试
+
+    #
+    start = time.time()
+    # 中间写上代码块
+
+    model.predict(train_data_healthy, batch_size=32)
+    end = time.time()
+    print("data_size:", train_data_healthy.shape)
+    print('Running time: %s Seconds' % (end - start))
+
+    trained_model = tf.keras.models.load_model(save_name, custom_objects={'Block': Block})
+    #
+    #
+    #
+    # # 使用已知的点进行预测
+    #
+    # pass

TensorFlow_eaxmple/Model_train_test/model/ChannelAttention/Dynamic_channelAttention.py

@@ -97,6 +97,16 @@ class DynamicPooling(layers.Layer):
         self.pool_size = pool_size
         pass
 
+    def get_config(self):
+        # 自定义层里面的属性
+        config = (
+            {
+                'pool_size': self.pool_size
+            }
+        )
+        base_config = super(DynamicPooling, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
     def build(self, input_shape):
         if len(input_shape) != 3:
             raise ValueError('Inputs to `DynamicChannelAttention` should have rank 3. '

TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/Joint_Monitoring3.py

@@ -15,7 +15,6 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
-from model.Dynamic_channelAttention.Dynamic_channelAttention import DynamicChannelAttention, DynamicPooling
 from condition_monitoring.data_deal import loadData
 from model.LossFunction.smooth_L1_Loss import SmoothL1Loss
 import math

TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/Joint_Monitoring_banda.py

@@ -13,13 +13,13 @@ import tensorflow.keras as keras
 from tensorflow.keras import *
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
-from model.Dynamic_channelAttention.Dynamic_channelAttention import DynamicChannelAttention, DynamicPooling
-from condition_monitoring.data_deal import loadData
+from model.ChannelAttention.Dynamic_channelAttention import DynamicChannelAttention, DynamicPooling
 from model.LossFunction.smooth_L1_Loss import SmoothL1Loss
 import math
 
+MSE_loss1_list=[]
+MSE_loss2_list=[]
+MSE_loss3_list=[]
 
 class Joint_Monitoring(keras.Model):
 
@@ -28,12 +28,13 @@ class Joint_Monitoring(keras.Model):
         super(Joint_Monitoring, self).__init__()
         # step one
         self.RepDCBlock1 = RevConvBlock(num=3, kernel_size=5)
-        self.conv1 = tf.keras.layers.Conv1D(filters=conv_filter, kernel_size=1, strides=2, padding='SAME',kernel_initializer=0.7,bias_initializer=1)
+        # self.conv1 = tf.keras.layers.Conv1D(filters=conv_filter, kernel_size=1, strides=2, padding='SAME',kernel_initializer=0.7,bias_initializer=1)
+        self.conv1 = tf.keras.layers.Conv1D(filters=conv_filter, kernel_size=1, strides=2, padding='SAME')
         self.upsample1 = tf.keras.layers.UpSampling1D(size=2)
 
         self.DACU2 = DynamicChannelAttention()
         self.RepDCBlock2 = RevConvBlock(num=3, kernel_size=3)
-        self.conv2 = tf.keras.layers.Conv1D(filters=2 * conv_filter, kernel_size=1, strides=2, padding='SAME',kernel_initializer=0.7,bias_initializer=1)
+        self.conv2 = tf.keras.layers.Conv1D(filters=2 * conv_filter, kernel_size=1, strides=2, padding='SAME')
         self.upsample2 = tf.keras.layers.UpSampling1D(size=2)
 
         self.DACU3 = DynamicChannelAttention()
@@ -255,6 +256,11 @@ class Joint_Monitoring(keras.Model):
             print("MSE_loss1:", MSE_loss1.numpy())
             print("MSE_loss2:", MSE_loss2.numpy())
             print("MSE_loss3:", MSE_loss3.numpy())
+
+            # MSE_loss1_list.append(MSE_loss1.numpy())
+            # MSE_loss2_list.append(MSE_loss2.numpy())
+            # MSE_loss3_list.append(MSE_loss3.numpy())
+
             loss = MSE_loss1 + MSE_loss2 + MSE_loss3
             Accuracy_num = 0
 

TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/compare/RNet.py

@@ -15,7 +15,7 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from model.DepthwiseCon1D.DepthwiseConv1D import DepthwiseConv1D
-from model.Dynamic_channelAttention.Dynamic_channelAttention import DynamicChannelAttention, DynamicPooling
+from model.ChannelAttention.Dynamic_channelAttention import DynamicChannelAttention, DynamicPooling
 from condition_monitoring.data_deal import loadData
 from model.LossFunction.smooth_L1_Loss import SmoothL1Loss
 

TensorFlow_eaxmple/Model_train_test/model/SelfAttention/SelfAttention.py

@@ -0,0 +1,417 @@
+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/23 10:53
+@Usage : 即插即用的self_attention模块
+@Desc :
+'''
+import tensorflow  as tf
+from tensorflow.keras import Model, layers, initializers
+import numpy as np
+
+
+class PatchEmbed(layers.Layer):
+    """
+    2D Image to Patch Embedding
+    """
+
+    def __init__(self, img_size=224, patch_size=16, embed_dim=768):
+        super(PatchEmbed, self).__init__()
+        self.embed_dim = embed_dim
+        self.patch_size=patch_size
+        self.img_size = (img_size, img_size)
+        # 将图片划分成 img_size // patch_size行img_size // patch_size列的网格
+        self.grid_size = (img_size // patch_size, img_size // patch_size)
+        # 行列相乘，可以得到patch的数目，即14*14=196，到时候flatten层的输入即是196维
+        self.num_patches = self.grid_size[0] * self.grid_size[1]
+        # con2d是16*16，然后步距也是16，即可将图片划分
+        self.proj = layers.Conv2D(filters=embed_dim, kernel_size=patch_size,
+                                  strides=patch_size, padding='SAME',
+                                  kernel_initializer=initializers.LecunNormal(),
+                                  bias_initializer=initializers.Zeros())
+
+    def get_config(self):
+        # 自定义层里面的属性
+        config = (
+            {
+                'img_size': self.img_size[0],
+                'patch_size': self.patch_size,
+                'embed_dim': self.embed_dim
+            }
+        )
+        base_config = super(PatchEmbed, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    # 正向传播过程
+    def call(self, inputs, **kwargs):
+        # B-Batch的维度，H-高度, W-宽度, C-channel的维度
+        B, H, W, C = inputs.shape
+        # 设置的高和宽要和上面的保持一致，如果不一致会报错
+        # 这里和CNN模型不一样，CNN模型会有一个全局池化层来对图片大小进行一个池化，所以可以不限制图片的具体大小
+        # 而在transformer中是需要加上一个position embedding的，而这个层就需要知道图片的具体维度来设置position embedding的大小
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        # 将input传入设置好的conv2d，对其进行输出
+        x = self.proj(inputs)
+        # [B, H, W, C] -> [B, H*W, C]
+        # reshape成可以进入flatten的形式
+        x = tf.reshape(x, [B, self.num_patches, self.embed_dim])
+        return x
+
+
+class ConcatClassTokenAddPosEmbed(layers.Layer):
+    def __init__(self, embed_dim=768, num_patches=196, name=None):
+        super(ConcatClassTokenAddPosEmbed, self).__init__(name=name)
+        self.embed_dim = embed_dim
+        self.num_patches = num_patches
+
+    def get_config(self):
+        # 自定义层里面的属性
+        config = (
+            {
+                'num_patches': self.num_patches,
+                'embed_dim': self.embed_dim
+            }
+        )
+        base_config = super(ConcatClassTokenAddPosEmbed, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    def build(self, input_shape):
+        # 创建两个可训练的参数weight,对应于cls_token，pos_embed的weight
+        # shape第一维是batch维度，后面分别是1*768和197*768，trainable表示是可训练的参数
+        self.cls_token = self.add_weight(name="cls",
+                                         shape=[1, 1, self.embed_dim],
+                                         initializer=initializers.Zeros(),
+                                         trainable=True,
+                                         dtype=tf.float32)
+        self.pos_embed = self.add_weight(name="pos_embed",
+                                         shape=[1, self.num_patches + 1, self.embed_dim],
+                                         initializer=initializers.RandomNormal(stddev=0.02),
+                                         trainable=True,
+                                         dtype=tf.float32)
+
+    def call(self, inputs, **kwargs):
+        # 需要获取一下batch的，因为输入图片的时候不是一张图片，而是将图片打包成一个个batch同时输入进去
+        batch_size, _, _ = inputs.shape
+
+        # [1, 1, 768] -> [B, 1, 768]
+        # 把cls_token复制B份进行拼接，broadcast_to方法，将cls_token进行一下广播，在batch维度就可以变成batch维度
+        cls_token = tf.broadcast_to(self.cls_token, shape=[batch_size, 1, self.embed_dim])
+        # 与input进行拼接
+        x = tf.concat([cls_token, inputs], axis=1)  # [B, 197, 768]
+        # 加上位置编码
+        x = x + self.pos_embed
+
+        return x
+
+
+class SelfAttention(layers.Layer):
+    # 定义两个权重初始化方法，方便后续调用
+    k_ini = initializers.GlorotUniform()
+    b_ini = initializers.Zeros()
+
+    # dim是前面的embed的dimension,num_heads多头注意力的头数
+    def __init__(self,
+                 dim,
+                 num_heads=8,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 attn_drop_ratio=0.,
+                 proj_drop_ratio=0.,
+                 name=None):
+        super(SelfAttention, self).__init__(name=name)
+
+        self.dim = dim
+        self.num_heads = num_heads
+        self.qkv_bias = qkv_bias
+        self.qk_scale = qk_scale
+        self.attn_drop_ratio = attn_drop_ratio
+        self.proj_drop_ratio = proj_drop_ratio
+
+
+
+
+        # 每一个head的dimension=输入的dimension/num_heads
+        head_dim = dim // num_heads
+        # 做softmax时会除一个sqrt(dk),这里的scale就是那个sqrt(dk)——缩放因子
+        # 如果传入了qk_scale就用传入的，如果没传入就用sqrt(head_dim)
+        self.scale = qk_scale or head_dim ** -0.5
+        # 在有的做法中，生成QKV三个矩阵时会用三个全连接层
+        # 这里用3*dim维的大全连接层，可以达到一样的效果
+        self.qkv = layers.Dense(dim * 3, use_bias=qkv_bias, name="qkv",
+                                kernel_initializer=self.k_ini, bias_initializer=self.b_ini)
+        self.attn_drop = layers.Dropout(attn_drop_ratio)
+        # 这里的全连接层是生成Wo矩阵，将得到的b进一步拼接
+        # 由于这里multi-head self-attention模块的输入输出维度是一样的，所以这里的节点个数是dim
+        self.proj = layers.Dense(dim, name="out",
+                                 kernel_initializer=self.k_ini, bias_initializer=self.b_ini)
+        self.proj_drop = layers.Dropout(proj_drop_ratio)
+
+    def get_config(self):
+        # 自定义层里面的属性
+        config = (
+            {
+                'dim': self.dim,
+                'num_heads': self.num_heads,
+                'qkv_bias': self.qkv_bias,
+                'qk_scale': self.qk_scale,
+                'attn_drop_ratio': self.attn_drop_ratio,
+                'proj_drop_ratio': self.proj_drop_ratio,
+            }
+        )
+        base_config = super(SelfAttention, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    def call(self, inputs, training=None):
+        # 由于进入self-attention的时候就已经将展平过了，所以这里的inputs.shape只有三个维度
+        # [batch_size, num_patches + 1, total_embed_dim]
+        B, N, C = inputs.shape
+        # B, N, C = tf.shape(inputs)[0],tf.shape(inputs)[1],tf.shape(inputs)[2]
+
+        # qkv(): -> [batch_size, num_patches + 1, 3 * total_embed_dim]
+        qkv = self.qkv(inputs)
+        # 分成三份，分别对应qkv,C // self.num_heads得到每一个head的维度
+        # reshape: -> [batch_size, num_patches + 1, 3, num_heads, embed_dim_per_head]
+        # qkv = tf.reshape(qkv, shape=[B, N, 3, self.num_heads, C // self.num_heads])
+        qkv = tf.reshape(qkv, shape=[-1, N, 3, self.num_heads, C // self.num_heads])
+        # qkv = tf.keras.layers.Reshape(target_shape=[B, N, 3, self.num_heads, C // self.num_heads])(qkv)
+        # 用transpose方法，来调整一下维度的顺序，[2, 0, 3, 1, 4]表示调换之后的顺序
+        # transpose: -> [3, batch_size, num_heads, num_patches + 1, embed_dim_per_head]
+        qkv = tf.transpose(qkv, [2, 0, 3, 1, 4])
+        # [batch_size, num_heads, num_patches + 1, embed_dim_per_head]
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        # transpose: -> [batch_size, num_heads, embed_dim_per_head, num_patches + 1]
+        # 这里的矩阵相乘实际上指的是矩阵的最后两个维度相乘，而b的转置(transpose_b)
+        # 实际上是[batch_size, num_heads, embed_dim_per_head, num_patches + 1]
+        # multiply -> [batch_size, num_heads, num_patches + 1, num_patches + 1]
+        attn = tf.matmul(a=q, b=k, transpose_b=True) * self.scale
+        attn = tf.nn.softmax(attn, axis=-1)
+        attn = self.attn_drop(attn, training=training)
+
+        # 与v相乘得到b
+        # multiply -> [batch_size, num_heads, num_patches + 1, embed_dim_per_head]
+        x = tf.matmul(attn, v)
+        # 再用transpose调换一下顺序
+        # transpose: -> [batch_size, num_patches + 1, num_heads, embed_dim_per_head]
+        x = tf.transpose(x, [0, 2, 1, 3])
+        # reshape: -> [batch_size, num_patches + 1, total_embed_dim]
+        # x = tf.reshape(x, [B, N, C])
+        x = tf.reshape(x, [-1, N, C])
+
+        # 与Wo相乘，进一步融合得到输出
+        x = self.proj(x)
+        x = self.proj_drop(x, training=training)
+        return x
+
+
+class MLP(layers.Layer):
+    """
+    MLP as used in Vision Transformer, MLP-Mixer and related networks
+    """
+    # 定义两个权重初始化方法
+    k_ini = initializers.GlorotUniform()
+    b_ini = initializers.RandomNormal(stddev=1e-6)
+
+    # in_deatures表示输入MLP模块对应的dimension，mlp_ratio=4.0表示翻四倍
+    def __init__(self, in_features, mlp_ratio=4.0, drop_rate=0., name=None):
+        super(MLP, self).__init__(name=name)
+
+        self.in_features = in_features
+        self.mlp_ratio = mlp_ratio
+        self.drop_rate = drop_rate
+
+        self.fc1 = layers.Dense(int(in_features * mlp_ratio), name="Dense_0",
+                                kernel_initializer=self.k_ini, bias_initializer=self.b_ini)
+        self.act = layers.Activation("relu")
+        self.fc2 = layers.Dense(in_features, name="Dense_1",
+                                kernel_initializer=self.k_ini, bias_initializer=self.b_ini)
+        self.drop = layers.Dropout(drop_rate)
+
+    def get_config(self):
+        # 自定义层里面的属性
+        config = (
+            {
+                'in_features': self.in_features,
+                'mlp_ratio': self.mlp_ratio,
+                'drop_rate': self.drop_rate,
+            }
+        )
+        base_config = super(MLP, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    def call(self, inputs, training=None):
+        x = self.fc1(inputs)
+        x = self.act(x)
+        x = self.drop(x, training=training)
+        x = self.fc2(x)
+        x = self.drop(x, training=training)
+        return x
+
+
+class Block(layers.Layer):
+    def __init__(self,
+                 dim,
+                 num_heads=8,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 drop_ratio=0.,
+                 attn_drop_ratio=0.,
+                 drop_path_ratio=0.,
+                 name=None):
+        super(Block, self).__init__(name=name)
+
+        self.dim = dim
+        self.num_heads = num_heads
+        self.qkv_bias = qkv_bias
+        self.qk_scale = qk_scale
+        self.drop_ratio = drop_ratio
+        self.attn_drop_ratio = attn_drop_ratio
+        self.drop_path_ratio = drop_path_ratio
+
+
+
+
+        # LayerNormalization来进行正则化
+        self.norm1 = layers.LayerNormalization(epsilon=1e-6, name="LayerNorm_0")
+        # 调用Attention类，来实现MultiHeadAttention
+        self.attn = SelfAttention(dim, num_heads=num_heads,
+                                  qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                  attn_drop_ratio=attn_drop_ratio, proj_drop_ratio=drop_ratio,
+                                  name="MultiHeadAttention")
+        # 这里所一个判断，如果drop_path_ratio > 0.就构建droppath方法
+        # 如果drop_path_ratio < 0,就用linear,即输入是什么输出就是什么，不作操作
+        # droppath方式的实现Dropout+noise_shape=(None, 1, 1)就可以实现droppath方法
+        # 第一个None表示的是batch维度，第一个1表示的是num_patches+1,最后一个1表示的是embed_dimension
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = layers.Dropout(rate=drop_path_ratio, noise_shape=(None, 1, 1)) if drop_path_ratio > 0. \
+            else layers.Activation("linear")
+        self.norm2 = layers.LayerNormalization(epsilon=1e-6, name="LayerNorm_1")
+        self.mlp = MLP(dim, drop_rate=drop_ratio, name="MlpBlock")
+
+    def get_config(self):
+        # 自定义层里面的属性
+        config = (
+            {
+                'dim': self.dim,
+                'num_heads': self.num_heads,
+                'qkv_bias': self.qkv_bias,
+                'qk_scale': self.qk_scale,
+                'drop_ratio': self.drop_ratio,
+                'attn_drop_ratio': self.attn_drop_ratio,
+                'drop_path_ratio': self.drop_path_ratio,
+            }
+        )
+        base_config = super(Block, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    def call(self, inputs, training=None):
+        # 对应于图中第一个加号以前的部分
+        x = inputs + self.drop_path(self.attn(self.norm1(inputs)), training=training)
+        # 对应于图中第一个加号以后的部分
+        x = x + self.drop_path(self.mlp(self.norm2(x)), training=training)
+        return x
+
+
+class VisionTransformer(Model):
+
+    # depth表示的是重复encoder block的次数，num_heads表示的是在multi-head self-attention中head的个数
+    # MLP block中有一个Pre_logist,这里指的是，当在较大的数据集上学习的时候Pre_logist就表示一个全连接层加上一个tanh激活函数
+    # 当在较小的数据集上学习的时候，Pre_logist是没有的，而这里的representation_size表示的就是Pre_logist中全连接层的节点个数
+    # num_classes表示分类的类数
+    def __init__(self, img_size=224, patch_size=16, embed_dim=768,
+                 depth=12, num_heads=12, qkv_bias=True, qk_scale=None,
+                 drop_ratio=0., attn_drop_ratio=0., drop_path_ratio=0.,
+                 representation_size=None, num_classes=1000, name="ViT-B/16"):
+        super(VisionTransformer, self).__init__(name=name)
+
+        self.img_size=img_size
+        self.patch_size=patch_size
+        self.num_classes = num_classes
+        self.num_heads = num_heads
+
+        self.embed_dim = embed_dim
+        self.depth = depth
+        self.qkv_bias = qkv_bias
+        self.qk_scale = qk_scale
+        self.drop_ratio = drop_ratio
+        self.attn_drop_ratio = attn_drop_ratio
+        self.drop_path_ratio = drop_path_ratio
+        self.representation_size = representation_size
+
+        # 这里实例化了PatchEmbed类
+        self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, embed_dim=embed_dim)
+        # 将PatchEmbed类中的num_patches取出来赋值给num_patches
+        num_patches = self.patch_embed.num_patches
+        # 这里实例化了ConcatClassTokenAddPosEmbed类
+        self.cls_token_pos_embed = ConcatClassTokenAddPosEmbed(embed_dim=embed_dim,
+                                                               num_patches=num_patches,
+                                                               name="cls_pos")
+
+        self.pos_drop = layers.Dropout(drop_ratio)
+
+        dpr = np.linspace(0., drop_path_ratio, depth)  # stochastic depth decay rule
+        # 用一个for循环重复Block模块
+        # 在用droppath时的drop_path_ratio是由0慢慢递增到我们所指定的drop_path_ratio的
+        # 所以我们在构建Block时，这里的drop_path_ratio时变化的，所以用 np.linspace方法创建一个等差数列来初始化drop_path_ratio
+        self.blocks = [Block(dim=embed_dim, num_heads=num_heads, qkv_bias=qkv_bias,
+                             qk_scale=qk_scale, drop_ratio=drop_ratio, attn_drop_ratio=attn_drop_ratio,
+                             drop_path_ratio=dpr[i], name="encoderblock_{}".format(i))
+                       for i in range(depth)]
+
+        self.norm = layers.LayerNormalization(epsilon=1e-6, name="encoder_norm")
+
+        # 接下来，如果传入了representation_size，就构建一个全连接层，激活函数为tanh
+        # 如果没有传入的话，就不做任何操作
+        if representation_size:
+            self.has_logits = True
+            self.pre_logits = layers.Dense(representation_size, activation="tanh", name="pre_logits")
+        else:
+            self.has_logits = False
+            self.pre_logits = layers.Activation("linear")
+
+        # 定义最后一个全连接层，节点个数就是我们的分类个数num_classes
+        self.head = layers.Dense(num_classes, name="head", kernel_initializer=initializers.Zeros())
+
+    def get_config(self):
+        # 自定义层里面的属性
+        config = (
+            {
+                'img_size': self.img_size,
+                'patch_size': self.patch_size,
+                'embed_dim': self.embed_dim,
+                'depth': self.depth,
+                'num_heads': self.num_heads,
+                'qkv_bias': self.qkv_bias,
+                'qk_scale': self.qk_scale,
+                'drop_ratio': self.drop_ratio,
+                'attn_drop_ratio': self.attn_drop_ratio,
+                'drop_path_ratio': self.drop_path_ratio,
+                'representation_size': self.representation_size,
+                'num_classes': self.num_classes
+            }
+        )
+        base_config = super(VisionTransformer, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+
+    def call(self, inputs, training=None):
+        # [B, H, W, C] -> [B, num_patches, embed_dim]
+        x = self.patch_embed(inputs)  # [B, 196, 768]
+        x = self.cls_token_pos_embed(x)  # [B, 197, 768]
+        x = self.pos_drop(x, training=training)
+
+        for block in self.blocks:
+            x = block(x, training=training)
+
+        x = self.norm(x)
+        # 这里是提取class_toke的输出，然后用切片的方式，而刚刚是将class_toke拼接在最前面的
+        # 所以这里用切片的方式，去取class_toke的输出，并将它传递给pre_logits
+        x = self.pre_logits(x[:, 0])
+        # 最后传递给head
+        x = self.head(x)
+        # 为什么只用class_toke对应的输出，而不用每一个patches对应的输出呢？
+        # 可以参考原文bird 网络
+
+        return x

TensorFlow_eaxmple/Model_train_test/model/SelfAttention/__init__.py

@@ -0,0 +1,8 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/23 10:52
+@Usage : 
+@Desc :
+'''