爬虫学习、一些笔试题更新

Spider/BaseInformation

@@ -0,0 +1,5 @@
+这个文件夹存放了
+1）爬虫学习的一些案例，实际案例操作 https://github.com/Python3WebSpider
+2）实际爬取的一些网站等
+等
+爬虫学习中心：https://setup.scrape.center/

Spider/爬虫基本库/__init__.py

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+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 21:39
+@Usage : 
+@Desc :
+'''

Spider/爬虫基本库/urllib库/__init__.py

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+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 21:39
+@Usage : 
+@Desc :
+'''

Spider/爬虫基本库/urllib库/urllibLearning.py

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+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 21:40
+@Usage : urllib库学习
+@Desc :
+'''
+
+import urllib.request
+
+# 基本使用，返回一个response对象
+# 无data即get请求；有data即post请求，data为bytes类型
+response = urllib.request.urlopen("https://www.python.org")
+
+print(response.status)
+print(response.getheaders())
+print(response.getheader('Server'))

Spider/线程相关/__init__.py

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+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 21:38
+@Usage : 
+@Desc :
+'''

Spider/线程相关/多线程/MyThread.py

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+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 20:44
+@Usage : 学习多线程库thread的使用
+@Desc :
+'''
+
+'''
+参考：
+[1] https://cuiqingcai.com/3325.html
+'''
+
+import queue as Queue
+import threading
+import time
+
+exitFlag = 0
+
+
+class myThread(threading.Thread):
+    def __init__(self, threadID, name, q):
+        threading.Thread.__init__(self)
+        self.threadID = threadID
+        self.name = name
+        self.q = q
+
+    def run(self):
+        print("Starting " + self.name)
+        process_data(self.name, self.q)
+        print("Exiting " + self.name)
+
+
+def process_data(threadName, q):
+    while not exitFlag:
+        # 获得锁，成功获得锁定后返回True
+        # 可选的timeout参数不填时将一直阻塞直到获得锁定
+        # 否则超时后将返回False
+        queueLock.acquire()
+        if not workQueue.empty():
+            data = q.get()
+            queueLock.release()
+            print("{0} processing {1}".format(threadName, data))
+        else:
+            queueLock.release()
+        time.sleep(1)
+
+
+threadList = ["Thread-1", "Thread-2", "Thread-3"]
+nameList = ["One", "Two", "Three", "Four", "Five"]
+queueLock = threading.Lock()
+workQueue = Queue.Queue(10)
+threads = []
+threadID = 1
+
+# 创建新线程
+for tName in threadList:
+    thread = myThread(threadID, tName, workQueue)
+    thread.start()
+    threads.append(thread)
+    threadID += 1
+
+# 填充队列
+queueLock.acquire()
+for word in nameList:
+    workQueue.put(word)
+# release一个有一个线程能跑
+queueLock.release()
+
+# 等待队列清空
+while not workQueue.empty():
+    pass
+
+# 通知线程是时候退出
+exitFlag = 1
+
+# 等待所有线程完成
+for t in threads:
+    t.join()
+print("Exiting Main Thread")

Spider/线程相关/多线程/__init__.py

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+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 20:43
+@Usage : 
+@Desc :
+'''

Spider/线程相关/多进程/Lock.py

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+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 21:08
+@Usage : Lock相关的类:访问临界资源(共享资源)时互斥
+@Desc :
+'''
+
+'''
+参考：
+[1] https://cuiqingcai.com/3335.html
+'''
+
+from multiprocessing import Process, Lock
+import time
+
+
+class MyProcess(Process):
+    def __init__(self, loop, lock):
+        Process.__init__(self)
+        self.loop = loop
+        self.lock = lock
+
+    def run(self):
+        for count in range(self.loop):
+            time.sleep(0.1)
+            self.lock.acquire()
+            print('Pid: ' + str(self.pid) + ' LoopCount: ' + str(count))
+            self.lock.release()
+
+if __name__ == '__main__':
+    lock = Lock()
+    for i in range(10, 15):
+        p = MyProcess(i, lock)
+        p.start()

Spider/线程相关/多进程/MyMutiProcess.py

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+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 20:59
+@Usage : 学习多进程库mutiProcessing的使用
+@Desc :
+'''
+
+'''
+参考：
+[1] https://cuiqingcai.com/3335.html
+[2] https://blog.csdn.net/qq_38120851/article/details/122504028
+'''
+
+from multiprocessing import Process
+import time
+
+
+class MyProcess(Process):
+    def __init__(self, loop):
+        Process.__init__(self)
+        self.loop = loop
+
+    def run(self):
+        for count in range(self.loop):
+            time.sleep(1)
+            print('Pid: ' + str(self.pid) + ' LoopCount: ' + str(count))
+
+
+if __name__ == '__main__':
+    # 在这里介绍一个属性，叫做 deamon。每个线程都可以单独设置它的属性，如果设置为 True，当父进程结束后，子进程会自动被终止。
+    # 如果这里不join，只会打印Main process Ended!
+    for i in range(2, 10):
+        p = MyProcess(i)
+        p.daemon = True
+        p.start()
+        p.join()
+
+
+    print("Main process Ended!")

Spider/线程相关/多进程/Semaphore.py

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+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 21:12
+@Usage : Semaphore相关类：可以控制临界资源的数量，保证各个进程之间的互斥和同步。
+@Desc : 尚且有问题，没法消费，程序卡住
+'''
+
+from multiprocessing import Process, Semaphore, Lock, Queue
+import time
+import random
+
+buffer = Queue(10)
+empty = Semaphore(2)
+full = Semaphore(1)
+lock = Lock()
+
+
+class Consumer(Process):
+
+    def run(self):
+        global buffer, empty, full, lock
+        while True:
+            print('Consumer线程开始运行')
+            full.acquire()
+            lock.acquire()
+            time.sleep(1)
+
+            print('Consumer pop an {0}'.format(buffer.get()))
+            time.sleep(1)
+            lock.release()
+            empty.release()
+            print('Consumer 释放了锁')
+
+
+class Producer(Process):
+    def run(self):
+        global buffer, empty, full, lock
+        while True:
+            empty.acquire()
+            lock.acquire()
+            print('Producer 添加了锁')
+            num = random.randint(0,1)
+
+            print('Producer append {0}'.format(num))
+            buffer.put(num)
+            time.sleep(1)
+            lock.release()
+            full.release()
+            print('Producer 释放了锁')
+
+
+if __name__ == '__main__':
+    p = Producer()
+    c = Consumer()
+    p.daemon = c.daemon = True
+
+    p.start()
+    c.start()
+
+    p.join()
+    c.join()
+    print('Ended!')

Spider/线程相关/多进程/__init__.py

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+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 20:58
+@Usage : 
+@Desc :
+'''

Spider/线程相关/多进程/pool.py

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+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/17 21:28
+@Usage : pool池相关使用
+@Desc :
+'''
+
+from multiprocessing import Lock, Pool
+import time
+import requests
+from requests.exceptions import ConnectionError
+
+
+def scrape(url):
+    try:
+        print(requests.get(url))
+    except ConnectionError:
+        print('Error Occured ', url)
+    finally:
+        print('URL ', url, ' Scraped')
+
+
+def function(index):
+    print('Start process: ', index)
+    time.sleep(3)
+    print('End process', index)
+
+
+if __name__ == '__main__':
+
+    # 异步线程池使用
+    pool = Pool(processes=3)
+    for i in range(4):
+        pool.apply_async(function, (i,))
+
+    print("Started processes")
+    pool.close()
+    pool.join()
+    print("Subprocess done.")
+
+    # 便捷的map法
+    pool = Pool(processes=3)
+    urls = [
+        'https://www.baidu.com',
+        'http://www.meituan.com/',
+        'http://blog.csdn.net/',
+        'http://xxxyxxx.net'
+    ]
+    pool.map(scrape, urls)

TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py

@@ -346,7 +346,7 @@ def plot_bar(y_data):
 
     # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1)
     plt.xticks([])
-    plt.ylabel('False Positive Rate(%)', fontsize=22)
+    plt.ylabel('False Negative Rate(%)', fontsize=22)
     plt.xlabel('Methods', fontsize=22)
     # plt.tight_layout()
 
@@ -434,7 +434,7 @@ def plot_FNR1(y_data):
 
     # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1)
     plt.xticks([])
-    plt.ylabel('False Negative Rate(%)', fontsize=22)
+    plt.ylabel('False Positive Rate(%)', fontsize=22)
     # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1)
 
     plt.xlabel('Methods', fontsize=22)
@@ -491,7 +491,7 @@ def plot_FNR2(y_data):
 
     # plt.tick_params(bottom=False, top=False, left=True, right=False, direction='in', pad=1)
     plt.xticks([])
-    plt.ylabel('False Negative Rate(%)', fontsize=22)
+    plt.ylabel('False Positive Rate(%)', fontsize=22)
     # plt.xlabel('Time', fontsize=5)
     # plt.tight_layout()
 
@@ -796,7 +796,7 @@ if __name__ == '__main__':
     # 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=[3.43,1.99,1.92,2.17,1.63,1.81,1.78,1.8,0.6]
     list=[3.43,1.99,1.92,2.17,1.8,1.81,1.78,1.8,0.6]
     plot_FNR2(list)
 

TensorFlow_eaxmple/Model_train_test/temp/GetThreshold.py

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+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/12 16:14
+@Usage : 
+@Desc :
+'''
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+def getThreshold(data, cigma_num=1.5):
+    dims, = data.shape
+
+    mean = np.mean(data)
+    std = np.sqrt(np.var(data))
+    max = mean + cigma_num * std
+    min = mean - cigma_num * 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(data)
+    # # plt.plot(mean)
+    # plt.plot(min)
+    # plt.show()
+    #
+    #
+    return max, min
+    # pass
+
+
+def EWMA(data):
+    data1 = pd.DataFrame(data).ewm(span=5).mean()
+
+    # plt.plot(data)
+    # plt.plot(data1, color='blue')
+    # getThreshold(data)
+    # plt.show()
+
+    return data1
+
+
+if __name__ == '__main__':
+    data = np.load("E:\self_example\TensorFlow_eaxmple\Model_train_test/2012轴承数据集预测挑战\data\HI_DATA\Bearing1_1.npy")
+    a, b = data.shape
+
+    minlist = np.array([])
+    maxlist = np.array([])
+
+    for d in data:
+        max, min = getThreshold(d)
+        minlist = np.concatenate([minlist, min], axis=0)
+        maxlist = np.concatenate([maxlist, max], axis=0)
+
+    data = data.reshape([a * b, 1])
+
+    origin_data = data
+    # data = np.array([0.5, 5, 0.8, 4.0, 10.0, -0.1, -0.3, 0, 0.5, 6.5])
+    data = EWMA(data)
+
+    data = np.squeeze(data.values)
+
+    count = 0
+    origin_count = 0
+
+    for a, b, c, d in zip(data, maxlist, minlist, origin_data):
+        if c > a or a > b:
+            count += 1
+        if c > d or d > b:
+            origin_count += 1
+
+    print("原始劣质率:", origin_count / len(data) * 100, "%")
+    print("修复后劣质率:", count / len(data) * 100, "%")
+    plt.plot(origin_data, color='blue', label='Original data')
+    plt.plot(data, color='green', label='After data repair')
+    plt.plot(maxlist, color='red', label='upper Threshold')
+    plt.plot(minlist, color='red', label='lower Threshold')
+
+    plt.show()
+
+    # getThreshold(data)

TensorFlow_eaxmple/Model_train_test/temp/__init__.py

@@ -0,0 +1,8 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/12 16:14
+@Usage : 
+@Desc :
+'''

TensorFlow_eaxmple/Model_train_test/temp/data_repair(3).py

@@ -0,0 +1,155 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/12 17:51
+@Usage : 
+@Desc :
+'''
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+import pandas as pd
+import random
+
+path = "./data"
+
+def data_read(path):
+    print("read data...")
+    dirPath = path
+    files = os.listdir(dirPath)
+    data = np.zeros(5)
+
+    for file in files:
+        path = os.path.join(dirPath, file)
+        x = np.loadtxt(path, delimiter=",", usecols=4)
+        x = np.transpose(x)
+        x = np.expand_dims(x, 0)
+        if (data == 0).all():
+            data = x
+        else:
+            data = np.concatenate((data, x))
+    return data
+
+
+def getThreshold(data, cigma_num=3):
+    dims, = data.shape
+
+    mean = np.mean(data)
+    std = np.sqrt(np.var(data))
+    max = mean + cigma_num * std
+    min = mean - cigma_num * 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(data)
+    # # plt.plot(mean)
+    # plt.plot(min)
+    # plt.show()
+    #
+    #
+    return max, min
+    # pass
+
+
+def EWMA(data):
+    print("data repair...")
+    data1 = pd.DataFrame(data).ewm(span=1000).mean()
+
+    return data1
+
+
+def data_repair(origin_data, maxlist, minlist):
+    data = np.array(origin_data)
+    need_repair_max_index = []
+    need_repair_min_index = []
+    for index, b, c, d in zip(range(len(data)), maxlist, minlist, data):
+        if c > d:
+            need_repair_min_index.append(index)
+        if d > b:
+            need_repair_max_index.append(index)
+
+    # 生成随机数
+    # print("超过最大值的数量", len(need_repair_max_index))
+    # print("低于最小值的数量", len(need_repair_min_index))
+
+    maxlen = len(need_repair_max_index)
+    minlen = len(need_repair_min_index)
+
+    # max_repair = random.randint(int(maxlen * 0.7), maxlen)
+    # min_repair = random.randint(int(minlen * 0.7), minlen)
+
+    need_repair_max_index = random.sample(need_repair_max_index, int(maxlen * 0.8))
+    need_repair_min_index = random.sample(need_repair_min_index, int(minlen * 0.8))
+
+    for index in need_repair_max_index:
+        data[index] = maxlist[index]
+
+    for index in need_repair_min_index:
+        data[index] = minlist[index]
+
+    return data
+
+
+def calculate(path):
+    basePath = "./data"
+    folders = os.listdir(basePath)
+
+    for folder in folders:
+        filePath = os.path.join(basePath, folder)
+        print("文件夹: ", filePath)
+
+        data = data_read(filePath)
+        a, b = data.shape
+
+        minlist = np.array([])
+        maxlist = np.array([])
+
+        for d in data:
+            max, min = getThreshold(d)
+            minlist = np.concatenate([minlist, min], axis=0)
+            maxlist = np.concatenate([maxlist, max], axis=0)
+
+        data = data.reshape([a * b, 1])
+
+        origin_data = data
+        # data = np.array([0.5, 5, 0.8, 4.0, 10.0, -0.1, -0.3, 0, 0.5, 6.5])
+        data = data_repair(data[:, 0], maxlist, minlist)
+
+
+        count = 0
+        origin_count = 0
+
+        for a, b, c, d in zip(data, maxlist, minlist, origin_data):
+            if c > a or a > b:
+                count += 1
+            if c > d or d > b:
+                origin_count += 1
+
+        print("原始劣质率:", origin_count / len(data) * 100, "%")
+        print("修复后劣质率:", count / len(data) * 100, "%")
+
+        # origin_data = np.load("./origin_data.npy")
+        # data = np.load("./data.npy")
+        # maxlist = np.load("maxlist.npy")
+        # minlist = np.load("minlist.npy")
+
+        le, _ = origin_data.shape
+
+        x = [i for i in range(int(le))]
+
+        plt.scatter(x, origin_data[:, 0], color='blue', s=0.1, label='Original data')
+        plt.scatter(x, data, color='green', s=0.1, label='After data repair')
+        plt.scatter(x, maxlist, color='red', s=0.05, label='Upper Threshold')
+        plt.scatter(x, minlist, color='red', s=0.05, label='Lower Threshold')
+
+        plt.legend(loc='upper left', frameon=True)
+
+        plt.show()
+
+
+
+if __name__ == '__main__':
+    calculate(path)

TensorFlow_eaxmple/Model_train_test/temp/data_repair.py

@@ -0,0 +1,152 @@
+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/10/12 17:51
+@Usage : 
+@Desc :
+'''
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+import random
+
+path = "E:\data\PHM 2012轴承挑战数据集\phm-ieee-2012-data-challenge-dataset-master\Learning_set\Bearing1_1"
+
+
+def data_read(path):
+    print("read data...")
+    dirPath = path
+    files = os.listdir(dirPath)
+    data = np.zeros(5)
+
+    for file in files:
+        path = os.path.join(dirPath, file)
+        x = np.loadtxt(path, delimiter=",", usecols=4)
+        x = np.transpose(x)
+        x = np.expand_dims(x, 0)
+        if (data == 0).all():
+            data = x
+        else:
+            data = np.concatenate((data, x))
+    return data
+
+
+def getThreshold(data, cigma_num=1.5):
+    dims, = data.shape
+
+    mean = np.mean(data)
+    std = np.sqrt(np.var(data))
+    max = mean + cigma_num * std
+    min = mean - cigma_num * 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(data)
+    # # plt.plot(mean)
+    # plt.plot(min)
+    # plt.show()
+    #
+    #
+    return max, min
+    # pass
+
+
+def EWMA(data):
+    print("data repair...")
+    data1 = pd.DataFrame(data).ewm(span=35).mean()
+
+    return data1
+
+
+def data_repair(origin_data, maxlist, minlist):
+    data = np.array(origin_data)
+    need_repair_max_index = []
+    need_repair_min_index = []
+    for index, b, c, d in zip(range(len(data)), maxlist, minlist, data):
+        if c > d:
+            need_repair_min_index.append(index)
+        if d > b:
+            need_repair_max_index.append(index)
+
+    # 生成随机数
+    # print("超过最大值的数量", len(need_repair_max_index))
+    # print("低于最小值的数量", len(need_repair_min_index))
+
+    maxlen = len(need_repair_max_index)
+    minlen = len(need_repair_min_index)
+
+    # max_repair = random.randint(int(maxlen * 0.7), maxlen)
+    # min_repair = random.randint(int(minlen * 0.7), minlen)
+
+    need_repair_max_index = random.sample(need_repair_max_index, int(maxlen * 0.8))
+    need_repair_min_index = random.sample(need_repair_min_index, int(minlen * 0.8))
+
+    for index in need_repair_max_index:
+        data[index] = maxlist[index]
+
+    for index in need_repair_min_index:
+        data[index] = minlist[index]
+
+    return data
+
+
+def calculate(path):
+    data = data_read(path)
+    a, b = data.shape
+
+    minlist = np.array([])
+    maxlist = np.array([])
+
+    for d in data:
+        max, min = getThreshold(d)
+        minlist = np.concatenate([minlist, min], axis=0)
+        maxlist = np.concatenate([maxlist, max], axis=0)
+
+    data = data.reshape([a * b, 1])
+
+    origin_data = data
+    # data = np.array([0.5, 5, 0.8, 4.0, 10.0, -0.1, -0.3, 0, 0.5, 6.5])
+    # data = EWMA(data)
+    data = data_repair(data[:, 0], maxlist, minlist)
+
+    # data = np.squeeze(data.values)
+
+    count = 0
+    origin_count = 0
+
+    for a, b, c, d in zip(data, maxlist, minlist, origin_data):
+        if c > a or a > b:
+            count += 1
+        if c > d or d > b:
+            origin_count += 1
+
+    print("原始劣质率:", origin_count / len(data) * 100, "%")
+    print("修复后劣质率:", count / len(data) * 100, "%")
+
+    # origin_data = np.load("./origin_data.npy")
+    # data = np.load("./data.npy")
+    # maxlist = np.load("maxlist.npy")
+    # minlist = np.load("minlist.npy")
+
+    le, _ = origin_data.shape
+
+    x = [i for i in range(int(le))]
+
+    plt.scatter(x, origin_data[:, 0], color='blue', s=0.1, label='Original data')
+    plt.scatter(x, data, color='green', s=0.1, label='After data repair')
+    plt.scatter(x, maxlist, color='red', s=0.05, label='Upper Threshold')
+    plt.scatter(x, minlist, color='red', s=0.05, label='Lower Threshold')
+
+    plt.legend(loc='upper left', frameon=True)
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    calculate(path)

TensorFlow_eaxmple/Model_train_test/temp/readCsv.py

@@ -0,0 +1,21 @@
+
+import numpy as np
+import os
+
+dirPath = "G:\深度学习/2022-2023\小论文相关\code\PHM 2012轴承挑战数据集\phm-ieee-2012-data-challenge-dataset-master\Learning_set\Bearing1_1"
+files = os.listdir(dirPath)
+data = np.zeros(5)
+
+for file in files:
+    path = os.path.join(dirPath, file)
+    x = np.loadtxt(path, delimiter=",", usecols=4)
+    x = np.transpose(x)
+    x = np.expand_dims(x, 0)
+    if (data == 0).all():
+        data = x
+    else:
+        data = np.concatenate((data, x))
+        # print(data.shape)  (2803,2560)
+print(data.shape)
+
+# np.save("./data/Bearing1_1.npy", data, allow_pickle=True)

interview/Cainiao/src/main/java/com/markilue/interview/Question2.java

@@ -0,0 +1,70 @@
+package com.markilue.interview;
+
+import org.junit.Test;
+
+import java.util.Scanner;
+
+/**
+ *@BelongsProject: Cainiao
+ *@BelongsPackage: com.markilue.interview
+ *@Author: markilue
+ *@CreateTime: 2023-10-10  19:39
+ *@Description: TODO
+ *@Version: 1.0
+ */
+public class Question2 {
+
+    public static void main(String[] args) {
+        Scanner sc = new Scanner(System.in);
+        int n = sc.nextInt();
+        int[] nums = new int[n];
+        for (int i = 0; i < n; i++) {
+            nums[i] = sc.nextInt();
+        }
+    }
+
+    @Test
+    public void test() {
+        int[] nums = {2, 1, 3};
+        sovle(nums);
+    }
+
+    @Test
+    public void test1() {
+        int[] nums = {2, 1, 3,5,4};
+        sovle(nums);
+    }
+
+    public static void sovle(int[] nums) {
+
+        int result = 0;
+        int count = 0;//计算当前连续一样的情况
+        boolean flag = false;
+        for (int i = 0; i < nums.length; i++) {
+            if (nums[i] == i + 1) {
+                flag = true;
+                count++;
+            } else {
+                if (flag) {
+                    if (count % 2 == 0) {
+                        result += count / 2;
+                    } else {
+                        result += count / 2 + 1;
+                    }
+                }
+                count = 0;
+                flag = false;
+            }
+        }
+
+        if (count % 2 == 0) {
+            result += count / 2;
+        } else {
+            result += count / 2 + 1;
+        }
+
+
+        System.out.println(result);
+
+    }
+}

interview/Cainiao/src/main/java/com/markilue/interview/Question3.java

@@ -0,0 +1,97 @@
+package com.markilue.interview;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Scanner;
+
+/**
+ *@BelongsProject: Cainiao
+ *@BelongsPackage: com.markilue.interview
+ *@Author: markilue
+ *@CreateTime: 2023-10-10  20:03
+ *@Description: TODO
+ *@Version: 1.0
+ */
+public class Question3 {
+
+    public static void main(String[] args) {
+
+        Scanner sc = new Scanner(System.in);
+
+        int n = sc.nextInt();
+        Node[] nodes = new Node[n];
+        String next = sc.next();
+        for (int i = 0; i < next.length(); i++) {
+            Node node = new Node();
+            node.index = i + 1;
+            if ('R'==next.charAt(i)) {
+                node.color = true;
+            } else {
+                node.color = false;
+            }
+            nodes[i] = node;
+        }
+        for (int i = 0; i < n - 1; i++) {
+            int index1 = sc.nextInt();
+            int index2 = sc.nextInt();
+            nodes[index1 - 1].children.add(nodes[index2 - 1]);
+            nodes[index2 - 1].children.add(nodes[index1 - 1]);
+        }
+
+        int result = 0;
+
+        for (Node node : nodes) {
+            cur = 0;
+            blue = 0;
+            red = 0;
+            sovle(node);
+            result += cur;
+        }
+
+
+        System.out.println(result);
+
+    }
+
+    static int cur = 0;
+    static int red = 0;
+    static int blue = 0;
+
+
+    public static void sovle(Node node) {
+        if (node == null) {
+            return;
+        }
+        node.busy = true;
+        if (node.color) {
+            red++;
+        } else {
+            blue++;
+        }
+        if (red == blue) {
+            cur++;
+        }
+        for (Node child : node.children) {
+            if (!child.busy) sovle(child);
+        }
+        node.busy = false;
+    }
+}
+
+class Node {
+
+    int index;
+    boolean color;//true为R
+    boolean busy = false;
+    List<Node> children = new ArrayList<>();
+
+    public Node() {
+    }
+
+    public Node(boolean color, int index) {
+        this.color = color;
+        this.index = index;
+    }
+
+
+}

interviewForqiuzhao/HuaWei/src/main/java/com/markilue/interview/Question1.java

@@ -0,0 +1,92 @@
+package com.markilue.interview;
+
+import org.junit.Test;
+
+import java.util.Scanner;
+
+/**
+ *@BelongsProject: HuaWei
+ *@BelongsPackage: com.markilue.interview
+ *@Author: markilue
+ *@CreateTime: 2023-10-11  20:12
+ *@Description: TODO
+ *@Version: 1.0
+ */
+public class Question1 {
+
+    public static void main(String[] args) {
+
+        Scanner sc = new Scanner(System.in);
+        int M = sc.nextInt();
+        int N = sc.nextInt();
+        int n = sc.nextInt();
+        int[] nums = new int[n];
+
+        for (int i = 0; i < n; i++) {
+            nums[i] = sc.nextInt();
+        }
+
+        sovle(nums, M, N);
+
+
+    }
+
+    @Test
+    public void test() {
+        int M = 4;
+        int N = 6;
+        int[] nums ={2,1,2,2,3,2};
+        sovle(nums,M,N);
+    }
+
+    @Test
+    public void test1() {
+        int M = 2;
+        int N = 10;
+        int[] nums ={1,9,1,9,8,2};
+        sovle(nums,M,N);
+    }
+
+
+    public static void sovle(int[] nums, int M, int N) {
+        int result = 0;
+        int sum = 0;
+        int left = 0;
+        int temp = 0;
+
+        for (int i = 0; i < nums.length; i++) {
+            sum += nums[i];
+
+            if (i - left >= M - 1) {
+                if (sum > N) {
+                    temp = sum - N;
+                    result += temp;
+                }
+
+                int index = i;
+                int flag = temp;
+                while (temp > 0) {
+                    if (temp > nums[index]) {
+                        temp -= nums[index];
+                        nums[index] = 0;
+                        index--;
+                    } else {
+                        nums[index] -= temp;
+                        temp = 0;
+                    }
+                }
+
+                if (flag > 0) {
+                    sum = N - nums[left];
+                } else {
+                    sum -= nums[left];
+                }
+                left++;
+
+            }
+        }
+
+        System.out.println(result);
+
+    }
+}

interviewForqiuzhao/HuaWei/src/main/java/com/markilue/interview/Question2.java

@@ -0,0 +1,67 @@
+package com.markilue.interview;
+
+import org.junit.Test;
+
+import java.util.Arrays;
+import java.util.Scanner;
+
+/**
+ *@BelongsProject: HuaWei
+ *@BelongsPackage: com.markilue.interview
+ *@Author: markilue
+ *@CreateTime: 2023-10-11  19:31
+ *@Description: TODO
+ *@Version: 1.0
+ */
+public class Question2 {
+
+    public static void main(String[] args) {
+        Scanner sc = new Scanner(System.in);
+        String[] nums1 = sc.next().split(",");
+        int[] nums = new int[nums1.length];
+        for (int i = 0; i < nums1.length; i++) {
+            nums[i] = Integer.parseInt(nums1[i]);
+        }
+
+        solve(nums);
+    }
+
+    public static void solve(int[] nums) {
+        Arrays.sort(nums);
+        int n = nums.length;
+
+        int sum = 0;
+        int index = nums.length - 1;
+        for (; index >= 0 && nums[index] + sum > 0; index--) {
+            sum += nums[index];
+        }
+//        if (index > 0) {
+//            //继续判断负数的要不要
+//            for (; nums[index] + sum > 0; index--) {
+//                sum=nums[index] + sum;
+//            }
+//        }
+        int count = 1;
+        int result = 0;
+        for (int i = index + 1; i < nums.length; i++, count++) {
+            result += count * nums[i];
+        }
+
+        System.out.println(result);
+    }
+
+
+    @Test
+    public void test() {
+//        String[] nums1 = "-1,-8,0,5,-9".split(",");
+//        String[] nums1 = "4,3,2".split(",");
+        String[] nums1 = "-1,-4,-5".split(",");
+        int[] nums = new int[nums1.length];
+        for (int i = 0; i < nums1.length; i++) {
+            nums[i] = Integer.parseInt(nums1[i]);
+        }
+        solve(nums);
+//        System.out.println(Arrays.toString(nums));
+    }
+
+}

interviewForqiuzhao/HuaWei/src/main/java/com/markilue/interview/Question3.java

@@ -0,0 +1,16 @@
+package com.markilue.interview;
+
+/**
+ *@BelongsProject: HuaWei
+ *@BelongsPackage: com.markilue.interview
+ *@Author: markilue
+ *@CreateTime: 2023-10-11  20:35
+ *@Description: TODO
+ *@Version: 1.0
+ */
+public class Question3 {
+
+    public static void main(String[] args) {
+
+    }
+}

interviewForqiuzhao/MeiTuan/src/main/java/com/markilue/interview/Question3.java

@@ -4,6 +4,7 @@ import org.junit.Test;
 
 import java.util.Arrays;
 import java.util.Comparator;
+import java.util.HashMap;
 import java.util.Scanner;
 
 /**

interviewForqiuzhao/MeiTuan/src/main/java/com/markilue/interview/Question5.java

@@ -14,7 +14,6 @@ import java.util.Scanner;
  */
 public class Question5 {
 
-
     static int[] father;
 
     public static void main(String[] args) {