diff --git a/Leecode/src/main/java/com/markilue/leecode/greedy/MaxSubArray.java b/Leecode/src/main/java/com/markilue/leecode/greedy/MaxSubArray.java
new file mode 100644
index 0000000..09c94f4
--- /dev/null
+++ b/Leecode/src/main/java/com/markilue/leecode/greedy/MaxSubArray.java
@@ -0,0 +1,235 @@
+package com.markilue.leecode.greedy;
+
+import org.junit.Test;
+
+/**
+ * @BelongsProject: Leecode
+ * @BelongsPackage: com.markilue.leecode.greedy
+ * @Author: markilue
+ * @CreateTime: 2022-10-25  09:54
+ * @Description: TODO  力扣53题  最大子数组和：
+ * 给你一个整数数组 nums ，请你找出一个具有最大和的连续子数组（子数组最少包含一个元素），返回其最大和。
+ * 子数组 是数组中的一个连续部分。
+ * @Version: 1.0
+ */
+public class MaxSubArray {
+
+    @Test
+    public void test(){
+        int[] nums= {-2, 1, -3, 4, -1, 2, 1, -5, 4};
+        System.out.println(maxSubArray3(nums));
+    }
+
+    @Test
+    public void test1(){
+        int[] nums= {1};
+        System.out.println(maxSubArray(nums));
+    }
+
+    @Test
+    public void test2(){
+        int[] nums= {5,4,-1,7,8};
+
+        System.out.println(maxSubArray2(nums));
+    }
+
+    @Test
+    public void test3(){
+        int[] nums= {-2,-1};
+        System.out.println(maxSubArray(nums));
+    }
+
+
+    /**
+     * 使用一个最大数组记录每个位置可能的最大值和累加值，遇到正数则可以直接加，遇到负数则可以
+     * [-2,1,-3,4,-1,2,1,-5,4]
+     * max:[-2,1,1,4,4,5,6,6,6]
+     * 如果总和是正数，则直接累加，如果是负数直接抛弃
+     * total:[-2,1,-2,4,3,5,6,1,5]
+     * 速度击败43.09%，内存击败94.95%：优化：max只用在total最大的时候记录一次
+     */
+    public int maxSubArray(int[] nums) {
+        if(nums.length==1){
+            return nums[0];
+        }
+
+        int max = nums[0]; //记录上一个位置的最大值
+        int total = nums[0]; //记录上一个位置的累加值
+
+        for (int i = 1; i < nums.length; i++) {
+
+            if (nums[i] >= 0) {
+                //当前这个数为正数：
+                //判断total是否是正数，如果是负数则直接从当前开始加
+                //判断上个total比max大还是小，决定max
+                if(total>=0){
+                    max = max > total + nums[i] ? max : total+ nums[i];
+                    total=total+nums[i];
+                }else {
+                    max = max >  nums[i] ? max : nums[i];
+                    total=nums[i];
+                }
+            }else {
+                //当前这个数为负数
+                max=max>nums[i]?max:nums[i];
+                if(total<nums[i]){
+                    total=nums[i];
+                }else {
+                    total+=nums[i];
+                }
+
+            }
+
+
+        }
+        return max;
+
+
+    }
+
+
+    /**
+     * 使用一个最大数组记录每个位置可能的最大值和累加值，遇到正数则可以直接加，遇到负数则可以
+     * [-2,1,-3,4,-1,2,1,-5,4]
+     * max:[-2,1,1,4,4,5,6,6,6]
+     * 如果总和是正数，则直接累加，如果是负数直接抛弃
+     * total:[-2,1,-2,4,3,5,6,1,5]
+     * 速度击败43.09%，内存击败94.95%：优化：max只用在total最大的时候记录一次
+     */
+    public int maxSubArray1(int[] nums) {
+        if(nums.length==1){
+            return nums[0];
+        }
+
+        int max = nums[0]; //记录上一个位置的最大值
+        int total = nums[0]; //记录上一个位置的累加值
+
+        for (int i = 1; i < nums.length; i++) {
+
+            if (nums[i] >= 0) {
+                //当前这个数为正数：
+                //判断total是否是正数，如果是负数则直接从当前开始加
+                //判断上个total比max大还是小，决定max
+                if(total>=0){
+                    total=total+nums[i];
+                }else {
+                    total=nums[i];
+                }
+            }else {
+                //当前这个数为负数
+                if(total<nums[i]){
+                    total=nums[i];
+                }else {
+                    total+=nums[i];
+                }
+            }
+            if(max<total){
+                max=total;
+            }
+
+
+        }
+        return max;
+
+
+    }
+
+
+    /**
+     * 代码随想录贪心算法：
+     * count<0立刻停止计算，从num[i+1]重新开始
+     * 速度击败100%，内存击败93.21%
+     */
+    public int maxSubArray2(int[] nums) {
+
+        int max = Integer.MIN_VALUE; //记录上一个位置的最大值
+        int total = 0; //记录上一个位置的累加值
+
+        for (int i = 0; i < nums.length; i++) {
+            total+=nums[i];
+
+            //取区间累计最大值作为max
+            if(max<total){
+                max=total;
+            }
+
+            if(total<=0){
+                total=0;
+            }
+        }
+        return max;
+
+
+    }
+
+
+    /**
+     * 官方动态规划算法：
+     * 实际就是求max{f(i)},而f(i)可以考虑使用max{f(i-1)+nums[i],nums[i]}
+     * 速度击败100%，内存击败93.21%
+     */
+    public int maxSubArray3(int[] nums) {
+
+        int pre = 0, maxAns = nums[0];
+        for (int x : nums) {
+            pre = Math.max(pre + x, x);
+            maxAns = Math.max(maxAns, pre);
+        }
+        return maxAns;
+    }
+
+
+    /**
+     * 官方分治算法：
+     * 我们定义一个操作 get(a, l, r) 表示查询 aa 序列 [l,r] 区间内的最大子段和，那么最终我们要求的答案就是 get(nums, 0, nums.size() - 1)。如何分治实现这个操作呢？对于一个区间 [l,r][l,r]，我们取 m =(l+r)/2
+     * 对区间 [l,m] 和 [m+1,r] 分治求解。当递归逐层深入直到区间长度缩小为 1 的时候，递归「开始回升」。
+     * 这个时候我们考虑如何通过 [l,m] 区间的信息和 [m+1,r] 区间的信息合并成区间 [l,r] 的信息。最关键的两个问题是：
+     * 我们要维护区间的哪些信息呢？我们如何合并这些信息呢？
+     * 对于一个区间 [l,r]，我们可以维护四个量：
+     *   lSum 表示 [l,r] 内以 l 为左端点的最大子段和
+     *   rSum 表示 [l,r] 内以 r 为右端点的最大子段和
+     *   mSum 表示 [l,r] 内的最大子段和
+     *   iSum 表示 [l,r] 的区间和
+     * 速度击败100%，内存击败93.21%
+     */
+    public int maxSubArray4(int[] nums) {
+
+        return getInfo(nums, 0, nums.length - 1).mSum;
+    }
+
+    public class Status {
+        public int lSum, rSum, mSum, iSum;
+
+        public Status(int lSum, int rSum, int mSum, int iSum) {
+            this.lSum = lSum;
+            this.rSum = rSum;
+            this.mSum = mSum;
+            this.iSum = iSum;
+        }
+    }
+
+    public Status getInfo(int[] a, int l, int r) {
+        if (l == r) {
+            return new Status(a[l], a[l], a[l], a[l]);
+        }
+        int m = (l + r) >> 1;
+        //分
+        Status lSub = getInfo(a, l, m);
+        Status rSub = getInfo(a, m + 1, r);
+        //和
+        return pushUp(lSub, rSub);
+    }
+
+    //和的算法
+    public Status pushUp(Status l, Status r) {
+        int iSum = l.iSum + r.iSum;
+        //从最左边开始的最大值，因此会在两个中产生：一种是l的左边最大值，另一种是左边的总和加上右边的从左开始的最大值
+        int lSum = Math.max(l.lSum, l.iSum + r.lSum);
+        //右边同理
+        int rSum = Math.max(r.rSum, r.iSum + l.rSum);
+        //总体的最大值会从两个部分产生：一种是左边或者右边的最大值，另一种是左边的右最大值加上右边的左最大值
+        int mSum = Math.max(Math.max(l.mSum, r.mSum), l.rSum + r.lSum);
+        return new Status(lSum, rSum, mSum, iSum);
+    }
+
+}
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/__init__.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/__init__.py
new file mode 100644
index 0000000..972d180
--- /dev/null
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/__init__.py
@@ -0,0 +1,10 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+
+'''
+@Author : dingjiawen
+@Date : 2022/10/24 16:40
+@Usage : 
+@Desc :
+'''
\ No newline at end of file
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py
new file mode 100644
index 0000000..cfca8d7
--- /dev/null
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py
@@ -0,0 +1,220 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+import matplotlib.pyplot as plt
+import numpy as np
+import random
+import pandas as pd
+import seaborn as sns
+from condition_monitoring.data_deal.loadData import read_data
+
+'''
+@Author : dingjiawen
+@Date : 2022/10/20 21:35
+@Usage : 测试相关画图设置
+@Desc :
+'''
+
+result_file_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_C\RNet_C_timestamp120_feature10_result.csv"
+
+mse_file_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_D\RNet_D_timestamp120_feature10_mse_predict1.csv"
+
+max_file_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_D\RNet_D_timestamp120_feature10_max_predict1.csv"
+
+source_path = "G:\data\SCADA数据\jb4q_8_delete_total_zero.csv"
+
+
+def plot_result(result_data):
+    parameters = {
+        'figure.dpi': 600,
+        'figure.figsize': (2.8, 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
+    plt.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.5, 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.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.text(0, 0.55, "Threshold", fontsize=5, color='red',
+    #          verticalalignment='top')
+
+    plt.axvline(result_data.shape[0] * 2 / 3, 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',
+             horizontalalignment='center',
+             bbox={'facecolor': 'grey',
+                   'pad': 1.5,
+                   'linewidth': 0.1}, fontdict=font1)
+    plt.text(result_data.shape[0] * 1 / 3, 0.6, "Norm", fontsize=5, color='black', verticalalignment='top',
+             horizontalalignment='center',
+             bbox={'facecolor': 'grey',
+                   'pad': 1.5, 'linewidth': 0.1}, fontdict=font1)
+
+    indices = [result_data.shape[0] * i / 4 for i in range(5)]
+    classes = ['01/09/17', '08/09/17', '15/09/17', '22/09/17', '29/09/17']
+
+    indices1 = [i / 4 for i in range(5)]
+    classes1 = ['0', '0.25', 'Threshold', '0.75', '1']
+
+    # 第一个是迭代对象，表示坐标的显示顺序，第二个参数是坐标轴显示列表
+    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
+    # 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', fontsize=5)
+    plt.xlabel('Time', fontsize=5)
+    plt.tight_layout()
+    # plt.legend(loc='best',edgecolor='black',fontsize=3)
+    plt.legend(loc='best', frameon=False, fontsize=3)
+    # plt.grid()
+    plt.show()
+    pass
+
+
+def plot_MSE(total_MSE, total_max):
+    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 = ['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.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.legend(loc='best', frameon=False, fontsize=5)
+
+    # plt.plot(total_mean)
+    # plt.plot(min)
+    plt.show()
+    pass
+
+
+def plot_Corr(data, label):
+    parameters = {
+        'figure.dpi': 600,
+        'figure.figsize': (2.8, 2),
+        'savefig.dpi': 600,
+        'xtick.direction': 'inout',
+        'ytick.direction': 'inout',
+        'xtick.labelsize': 3,
+        'ytick.labelsize': 3,
+        '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': 4}  # 设置坐标标签的字体大小，字体
+
+    print("计算皮尔逊相关系数")
+    pd_data = pd.DataFrame(data)
+    person = pd_data.corr()
+    print(person)
+    # 画热点图heatmap
+    cmap = sns.heatmap(person, annot=True, annot_kws={
+        'fontsize': 2.6
+    })
+    classes = ['Gs', 'Gio', 'Gip', 'Gp', 'Gwt', 'En', 'Gft', 'Grt', 'Gwt', 'Et', 'Rs', 'Ap', 'Ws', 'Dw', 'Ges', 'Gt', 'Vx','Vy']
+    indices = range(len(person))
+    plt.title("Heatmap of correlation coefficient matrix", size=6, fontdict=font1)
+    # pad调整label与坐标轴之间的距离
+    plt.tick_params(bottom=False, top=False, left=False, right=False, direction='inout', length=2, width=0.5,pad=1)
+    plt.xticks([index + 0.5 for index in indices], classes, rotation=0)  # 设置横坐标方向，rotation=45为45度倾斜
+    plt.yticks([index + 0.5 for index in indices], classes, rotation=0)
+
+    # 调整色带的标签：
+    cbar = cmap.collections[0].colorbar
+    cbar.ax.tick_params(labelsize=4, labelcolor="black", length=2, width=0.5,pad=1)
+    cbar.ax.set_ylabel(ylabel="color scale", color="black", loc="center", fontdict=font1)
+    # plt.axis('off')  # 去坐标轴
+
+    plt.savefig('./corr.png')
+
+    plt.show()
+    pass
+
+
+def test_result(file_name: str = result_file_name):
+    # result_data = np.recfromcsv(file_name)
+    result_data = np.loadtxt(file_name, delimiter=",")
+    result_data = np.array(result_data)
+    print(len(result_data))
+    theshold=len(result_data)-1
+    print(theshold)
+    print(theshold*2/3)
+    #计算误报率和漏报率
+    positive_rate=result_data[:int(theshold*2/3)][result_data[:int(theshold*2/3)] < 0.66].__len__()/(theshold*2/3)
+    negative_rate=result_data[int(theshold*2/3):][result_data[int(theshold*2/3):] > 0.66].__len__()/(theshold*1/3)
+    print("误报率:",positive_rate)
+    print("漏报率",negative_rate)
+
+    # 画图
+    data = np.zeros([208, ])
+    result_data = np.concatenate([result_data, data], axis=0)
+    print(result_data)
+    print(result_data.shape)
+    plot_result(result_data)
+
+
+def test_mse(mse_file_name: str = mse_file_name, max_file_name: str = max_file_name):
+    mse_data = np.loadtxt(mse_file_name, delimiter=",")
+    max_data = np.loadtxt(max_file_name, delimiter=',')
+    mse_data = np.array(mse_data)
+    max_data = np.array(max_data)
+    print(mse_data.shape)
+    print(max_data.shape)
+    plot_MSE(mse_data, max_data)
+
+
+def test_corr(file_name=source_path, N=10):
+    needed_data, label = read_data(file_name=file_name, isNew=False)
+    print(needed_data)
+    print(needed_data.shape)
+    # plot_original_data(needed_data)
+    person = plot_Corr(needed_data, label)
+    person = np.array(person)
+    pass
+
+
+if __name__ == '__main__':
+    # test_mse()
+    test_result(file_name='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_345\RNet_345_timestamp120_feature10_result.csv')
+    # test_corr()
+    pass
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/DCConv.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/DCConv.py
index c8d4ef8..604561c 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/DCConv.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/DCConv.py
@@ -61,6 +61,16 @@ save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weigh
                                                                                                          feature_num,
                                                                                                          batch_size,
                                                                                                          EPOCH)
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_mse.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                              batch_size,
+                                                                              EPOCH)
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_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,
@@ -375,7 +385,8 @@ def DCConv_Model():
 
 # 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 = 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)
@@ -384,30 +395,48 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
     total, = mse.shape
     faultNum = 0
     faultList = []
-    for i in range(total):
-        if (mse[i] > max[i]):
-            faultNum += 1
-            faultList.append(mse[i])
+    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
-    missList = []
     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
 
-    flag=True
-    for i in range(all):
-        if (mse1[i] < max[0] and flag)  :
-            missNum += 1
-            missList.append(mse1[i])
-        elif(mse1[i]>max[0]):
-            flag=False
-    print("all:",all)
+
+    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
 
 
@@ -451,9 +480,16 @@ if __name__ == '__main__':
         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)
+              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
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/GRU.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/GRU.py
index 6f9cb75..f969f7e 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/GRU.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/GRU.py
@@ -23,6 +23,7 @@ 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
 
 '''超参数设置'''
 time_stamp = 120
@@ -36,7 +37,7 @@ K = 18
 namuda = 0.01
 '''保存名称'''
 
-save_name = "./model/{0}_timestamp{1}_feature{2}.h5".format(model_name,
+save_name = "./model/{0}_timestamp{1}_feature{2}_epoch1_loss0.005.h5".format(model_name,
                                                                              time_stamp,
                                                                              feature_num,
                                                                              batch_size,
@@ -46,6 +47,17 @@ save_step_two_name = "../hard_model/two_weight/{0}_timestamp{1}_feature{2}_weigh
                                                                                                          feature_num,
                                                                                                          batch_size,
                                                                                                          EPOCH)
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_mse.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                              batch_size,
+                                                                              EPOCH)
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_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,
@@ -182,46 +194,6 @@ def EWMA(data, K=K, namuda=namuda):
     pass
 
 
-def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True):
-    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.plot(max)
-            plt.plot(mse)
-            plt.plot(mean)
-            # plt.plot(min)
-            plt.show()
-    else:
-        if isPlot:
-            plt.plot(mse)
-            # plt.plot(min)
-            plt.show()
-            return  mse
-
-    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)
@@ -354,37 +326,102 @@ def GRU_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 = 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
+    total, = mse.shape
     faultNum = 0
     faultList = []
-    for i in range(total):
-        if (mse[i] > max[i]):
-            faultNum += 1
-            faultList.append(mse[i])
+    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
-    missList = []
-    mse1 = get_MSE(unhealthy_data, unhealthy_label, model,isStandard=False)
-    all,= mse1.shape
-    for i in range(all):
-        if (mse1[i] < max[0]):
-            missNum += 1
-            missList.append(mse1[i])
+    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
 
 
@@ -404,16 +441,16 @@ if __name__ == '__main__':
     model.summary()
     early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, mode='min', verbose=1)
 
-    checkpoint = tf.keras.callbacks.ModelCheckpoint(
-        filepath=save_name,
-        monitor='val_loss',
-        verbose=1,
-        save_best_only=True,
-        mode='min',
-        period=1)
+    # checkpoint = tf.keras.callbacks.ModelCheckpoint(
+    #     filepath=save_name,
+    #     monitor='val_loss',
+    #     verbose=1,
+    #     save_best_only=True,
+    #     mode='min',
+    #     period=1)
 
-    history = model.fit(train_data_healthy[:30000,:,:], train_label1_healthy[:30000,:], epochs=10,
-                        batch_size=32,validation_split=0.2,shuffle=False, verbose=1,callbacks=[checkpoint,early_stop])
+    # history = model.fit(train_data_healthy[:30000,:,:], train_label1_healthy[:30000,:], epochs=10,
+    #                     batch_size=32,validation_split=0.2,shuffle=False, verbose=1,callbacks=[checkpoint,early_stop])
     # model.save(save_name)
 
     ## TODO testing
@@ -427,9 +464,19 @@ if __name__ == '__main__':
         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)
+              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)
 
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-3.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-3.py
index 3ab7d3b..55178c0 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-3.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-3.py
@@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_step_two_name = "./model/two_weight/{0}_weight_epoch6_99899_9996/weight".format(model_name,
+save_step_two_name = "./model/two_weight/{0}_weight_epoch0_9973_9994/weight".format(model_name,
                                                                                                          time_stamp,
                                                                                                          feature_num,
                                                                                                          batch_size,
                                                                                                          EPOCH)
 
-save_mse_name = "./mse/RNet_3/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_max_name = "./mse/RNet_3/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                              batch_size,
@@ -472,10 +472,7 @@ def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False
     total_result = np.reshape(total_result, [-1, ])
 
     #误报率，漏报率，准确性的计算
-
-
     if isSave:
-
         np.savetxt(save_mse_name, total_result, delimiter=',')
     if isPlot:
         plt.figure(1, figsize=(6.0, 2.68))
@@ -649,7 +646,8 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
 
 
 if __name__ == '__main__':
-    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    # total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = np.load("G:\data\SCADA数据\靖边8号处理后的数据\原始10SCADA数据/total_data.npy")
     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)
@@ -673,14 +671,14 @@ if __name__ == '__main__':
     ### unhealthy_data.shape: (16594,10)
     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))
 
     healthy_size, _, _ = train_data_healthy.shape
     unhealthy_size, _, _ = train_data_unhealthy.shape
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-34.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-34.py
index 311cc8a..0101b30 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-34.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-34.py
@@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_step_two_name = "./model/two_weight/{0}_weight/weight".format(model_name,
+save_step_two_name = "./model/two_weight/{0}_weight_epoch0_9965_9996_9989/weight".format(model_name,
                                                                                                          time_stamp,
                                                                                                          feature_num,
                                                                                                          batch_size,
                                                                                                          EPOCH)
 
-save_mse_name = "./mse/RNet_34/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_max_name = "./mse/RNet_34/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                              batch_size,
@@ -649,7 +649,8 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
 
 
 if __name__ == '__main__':
-    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    # total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = np.load("G:\data\SCADA数据\靖边8号处理后的数据\原始10SCADA数据/total_data.npy")
     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)
@@ -673,14 +674,14 @@ if __name__ == '__main__':
     ### unhealthy_data.shape: (16594,10)
     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))
 
     healthy_size, _, _ = train_data_healthy.shape
     unhealthy_size, _, _ = train_data_unhealthy.shape
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-35.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-35.py
index 8eb8ab0..20ddcd0 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-35.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-35.py
@@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_step_two_name = "./model/two_weight/{0}_weight/weight".format(model_name,
+save_step_two_name = "./model/two_weight/{0}_weight_epoch0_9974_9996_9990/weight".format(model_name,
                                                                                                          time_stamp,
                                                                                                          feature_num,
                                                                                                          batch_size,
                                                                                                          EPOCH)
 
-save_mse_name = "./mse/RNet_35/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_max_name = "./mse/RNet_35/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                              batch_size,
@@ -649,7 +649,8 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
 
 
 if __name__ == '__main__':
-    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    # total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = np.load("G:\data\SCADA数据\靖边8号处理后的数据\原始10SCADA数据/total_data.npy")
     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)
@@ -673,14 +674,14 @@ if __name__ == '__main__':
     ### unhealthy_data.shape: (16594,10)
     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))
 
     healthy_size, _, _ = train_data_healthy.shape
     unhealthy_size, _, _ = train_data_unhealthy.shape
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-4.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-4.py
index e98323a..1fc5360 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-4.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-4.py
@@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_step_two_name = "./model/two_weight/{0}_weight/weight".format(model_name,
+save_step_two_name = "./model/two_weight/{0}_weight_epoch0_9973_9996_9982/weight".format(model_name,
                                                                                                          time_stamp,
                                                                                                          feature_num,
                                                                                                          batch_size,
                                                                                                          EPOCH)
 
-save_mse_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_max_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                              batch_size,
@@ -649,7 +649,8 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
 
 
 if __name__ == '__main__':
-    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    # total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = np.load("G:\data\SCADA数据\靖边8号处理后的数据\原始10SCADA数据/total_data.npy")
     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)
@@ -673,14 +674,14 @@ if __name__ == '__main__':
     ### unhealthy_data.shape: (16594,10)
     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))
 
     healthy_size, _, _ = train_data_healthy.shape
     unhealthy_size, _, _ = train_data_unhealthy.shape
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-45.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-45.py
index 599bcf8..0cc43a6 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-45.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-45.py
@@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_step_two_name = "./model/two_weight/{0}_weight/weight".format(model_name,
+save_step_two_name = "./model/two_weight/{0}_weight_epoch0_9969_9992/weight".format(model_name,
                                                                                                          time_stamp,
                                                                                                          feature_num,
                                                                                                          batch_size,
                                                                                                          EPOCH)
 
-save_mse_name = "./mse/RNet_45/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_max_name = "./mse/RNet_45/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                              batch_size,
@@ -470,12 +470,8 @@ def showResult(step_two_model: Joint_Monitoring, test_data, isPlot: bool = False
         total_result.append(output4)
     total_result = np.reshape(total_result, [total_result.__len__(), -1])
     total_result = np.reshape(total_result, [-1, ])
-
-    #误报率，漏报率，准确性的计算
-
-
+    # 误报率，漏报率，准确性的计算
     if isSave:
-
         np.savetxt(save_mse_name, total_result, delimiter=',')
     if isPlot:
         plt.figure(1, figsize=(6.0, 2.68))
@@ -649,7 +645,9 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
 
 
 if __name__ == '__main__':
-    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    # total_data = loadData.execute(N=feature_num, file_name=file_name)
+
+    total_data=np.load("G:\data\SCADA数据\靖边8号处理后的数据\原始10SCADA数据/total_data.npy")
     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)
@@ -673,14 +671,14 @@ if __name__ == '__main__':
     ### unhealthy_data.shape: (16594,10)
     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))
 
     healthy_size, _, _ = train_data_healthy.shape
     unhealthy_size, _, _ = train_data_unhealthy.shape
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-5.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-5.py
index 9e71349..9207f90 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-5.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-5.py
@@ -42,18 +42,18 @@ save_name = "./model/weight/{0}/weight".format(model_name,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_step_two_name = "./model/two_weight/{0}_weight/weight".format(model_name,
+save_step_two_name = "./model/two_weight/{0}_weight_epoch1_9990_9992/weight".format(model_name,
                                                                                                          time_stamp,
                                                                                                          feature_num,
                                                                                                          batch_size,
                                                                                                          EPOCH)
 
-save_mse_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_result.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_max_name = "./mse/RNet_C/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                              batch_size,
@@ -649,7 +649,8 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
 
 
 if __name__ == '__main__':
-    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = np.load("G:\data\SCADA数据\靖边8号处理后的数据\原始10SCADA数据/total_data.npy")
+    # 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)
@@ -673,14 +674,14 @@ if __name__ == '__main__':
     ### unhealthy_data.shape: (16594,10)
     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))
 
     healthy_size, _, _ = train_data_healthy.shape
     unhealthy_size, _, _ = train_data_unhealthy.shape
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-L.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-L.py
index a214cb9..8903dad 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-L.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-L.py
@@ -48,6 +48,17 @@ save_step_two_name = "./model/two_weight/{0}_timestamp{1}_feature{2}_weight/weig
                                                                                            batch_size,
                                                                                            EPOCH)
 
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_mse.csv".format(model_name,
+                                                                              time_stamp,
+                                                                              feature_num,
+                                                                              batch_size,
+                                                                              EPOCH)
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_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,
@@ -557,11 +568,11 @@ def get_MSE(data, label, new_model, isStandard: bool = True, isPlot: bool = True
 def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data, unhealthy_label, isPlot: bool = False,
               isSave: bool = False, predictI: int = 1):
     # TODO 计算MSE确定阈值
-
+    # plt.ion()
     mseList, meanList, maxList = get_MSE(healthy_data, healthy_label, model)
     mse1List, _, _ = get_MSE(unhealthy_data, unhealthy_label, model, isStandard=False)
 
-    for mse, mean, max, mse1 in zip(mseList, meanList, maxList, mse1List):
+    for mse, mean, max, mse1, j in zip(mseList, meanList, maxList, mse1List, range(3)):
 
         # 误报率的计算
         total, = mse.shape
@@ -588,15 +599,22 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
         print("漏报率:", miss_rate)
 
         # 总体图
-        print("mse:", mse)
-        print("mse1:", mse1)
+        # print("mse:", mse)
+        # print("mse1:", mse1)
         print("============================================")
         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], ])
 
-        plt.figure(random.randint(1, 9))
+        if isSave:
+            save_mse_name1 = save_mse_name[:-4] + "_predict" + str(j + 1) + ".csv"
+            save_max_name1 = save_max_name[:-4] + "_predict" + str(j + 1) + ".csv"
+
+            np.savetxt(save_mse_name1, total_mse, delimiter=',')
+            np.savetxt(save_max_name1, total_max, delimiter=',')
+
+        plt.figure(random.randint(1, 100))
         plt.plot(total_max)
         plt.plot(total_mse)
         plt.plot(total_mean)
@@ -606,7 +624,8 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
 
 
 if __name__ == '__main__':
-    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    # total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data=np.load("G:\data\SCADA数据\靖边8号处理后的数据\原始10SCADA数据/total_data.npy")
     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)
@@ -618,7 +637,7 @@ if __name__ == '__main__':
     # train_step_one(train_data=train_data_healthy[:256, :, :], train_label1=train_label1_healthy[:256, :],
     #                train_label2=train_label2_healthy[:256, ])
     ####  模型训练
-    train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
+    # 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()
@@ -633,17 +652,18 @@ if __name__ == '__main__':
     all_data, _, _ = get_training_data_overlapping(
         total_data[healthy_size - 2 * unhealthy_size:unhealthy_date, :], is_Healthy=True)
 
+    ##出结果单次测试
     ##出结果单次测试
     # getResult(step_one_model,
     #           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,:])
-
+    #           unhealthy_data=train_data_unhealthy[:200, :], unhealthy_label=train_label1_unhealthy[:200, :],isSave=True)
+    #
     getResult(step_one_model, 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)
+              unhealthy_data=train_data_unhealthy, unhealthy_label=train_label1_unhealthy, isSave=True)
 
     # ###TODO 展示全部的结果
     # all_data, _, _ = get_training_data_overlapping(
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-MSE.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-MSE.py
index 625fd70..0f126c8 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-MSE.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-MSE.py
@@ -48,12 +48,12 @@ save_step_two_name = "./model/two_weight/{0}_timestamp{1}_feature{2}_weight/weig
                                                                                            EPOCH)
 
 
-save_mse_name = "./mse/RNet_MSE/{0}_timestamp{1}_feature{2}_mse.csv".format(model_name,
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_mse.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                               batch_size,
                                                                               EPOCH)
-save_max_name = "./mse/RNet_MSE/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
                                                                               time_stamp,
                                                                               feature_num,
                                                                              batch_size,
@@ -598,8 +598,8 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
         print("漏报率:", miss_rate)
 
         # 总体图
-        print("mse:", mse)
-        print("mse1:", mse1)
+        # print("mse:", mse)
+        # print("mse1:", mse1)
         print("============================================")
         total_mse = np.concatenate([mse, mse1], axis=0)
         total_max = np.broadcast_to(max[0], shape=[total_mse.shape[0], ])
@@ -625,6 +625,7 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
 
 if __name__ == '__main__':
     total_data = loadData.execute(N=feature_num, file_name=file_name)
+    np.save("G:\data\SCADA数据/total_data.npy",total_data)
     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)
@@ -636,7 +637,7 @@ if __name__ == '__main__':
     # train_step_one(train_data=train_data_healthy[:256, :, :], train_label1=train_label1_healthy[:256, :],
     #                train_label2=train_label2_healthy[:256, ])
     ####  模型训练
-    train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
+    # 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()
@@ -658,7 +659,7 @@ if __name__ == '__main__':
     #           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(step_one_model, 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)
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-S.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-S.py
index 4b046c0..eb985a6 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-S.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/RNet-S.py
@@ -36,27 +36,27 @@ K = 18
 namuda = 0.01
 '''保存名称'''
 # save_name = "E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\hard_model\weight\joint_timestamp120_feature10_weight_epoch11_0.0077/weight"
-save_name = "./model/weight/{0}/weight".format(model_name,
-                                                                              time_stamp,
-                                                                              feature_num,
-                                                                              batch_size,
-                                                                              EPOCH)
+save_name = "./model/weight/{0}_epoch3_2.47_1.63/weight".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/RNet_S/{0}_timestamp{1}_feature{2}_mse.csv".format(model_name,
-                                                                              time_stamp,
-                                                                              feature_num,
-                                                                              batch_size,
-                                                                              EPOCH)
-save_max_name = "./mse/RNet_S/{0}_timestamp{1}_feature{2}_max.csv".format(model_name,
-                                                                              time_stamp,
-                                                                              feature_num,
-                                                                             batch_size,
-                                                                              EPOCH)
+save_mse_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_mse.csv".format(model_name,
+                                                                       time_stamp,
+                                                                       feature_num,
+                                                                       batch_size,
+                                                                       EPOCH)
+save_max_name = "./mse/{0}/{0}_timestamp{1}_feature{2}_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,
@@ -246,9 +246,6 @@ def EWMA(data, K=K, namuda=namuda):
     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)
@@ -586,7 +583,7 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
     mseList, meanList, maxList = get_MSE(healthy_data, healthy_label, model)
     mse1List, _, _ = get_MSE(unhealthy_data, unhealthy_label, model, isStandard=False)
 
-    for mse, mean, max, mse1,j in zip(mseList, meanList, maxList, mse1List,range(3)):
+    for mse, mean, max, mse1, j in zip(mseList, meanList, maxList, mse1List, range(3)):
 
         # 误报率的计算
         total, = mse.shape
@@ -613,8 +610,8 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
         print("漏报率:", miss_rate)
 
         # 总体图
-        print("mse:", mse)
-        print("mse1:", mse1)
+        # print("mse:", mse)
+        # print("mse1:", mse1)
         print("============================================")
         total_mse = np.concatenate([mse, mse1], axis=0)
         total_max = np.broadcast_to(max[0], shape=[total_mse.shape[0], ])
@@ -622,12 +619,11 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
         total_mean = np.broadcast_to(mean[0], shape=[total_mse.shape[0], ])
 
         if isSave:
-            save_mse_name1=save_mse_name[:-4]+"_predict"+str(j+1)+".csv"
-            save_max_name1=save_max_name[:-4]+"_predict"+str(j+1)+".csv"
-
-            np.savetxt(save_mse_name1,total_mse, delimiter=',')
-            np.savetxt(save_max_name1,total_max, delimiter=',')
+            save_mse_name1 = save_mse_name[:-4] + "_predict" + str(j + 1) + ".csv"
+            save_max_name1 = save_max_name[:-4] + "_predict" + str(j + 1) + ".csv"
 
+            np.savetxt(save_mse_name1, total_mse, delimiter=',')
+            np.savetxt(save_max_name1, total_max, delimiter=',')
 
         plt.figure(random.randint(1, 100))
         plt.plot(total_max)
@@ -639,7 +635,8 @@ def getResult(model: tf.keras.Model, healthy_data, healthy_label, unhealthy_data
 
 
 if __name__ == '__main__':
-    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    # total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data=np.load("G:\data\SCADA数据\靖边8号处理后的数据\原始10SCADA数据/total_data.npy")
     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)
@@ -649,8 +646,7 @@ if __name__ == '__main__':
     #### TODO 第一步训练
     # 单次测试
     # train_step_one(train_data=train_data_healthy[:32, :, :], train_label1=train_label1_healthy[:32, :],train_label2=train_label2_healthy[:32, ])
-    train_step_one(train_data=train_data_healthy, train_label1=train_label1_healthy, train_label2=train_label2_healthy)
-
+    # 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()
@@ -674,7 +670,7 @@ if __name__ == '__main__':
 
     getResult(step_one_model, 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)
+              unhealthy_data=train_data_unhealthy, unhealthy_label=train_label1_unhealthy, isSave=True)
 
     ###TODO 展示全部的结果
     # all_data, _, _ = get_training_data_overlapping(
@@ -685,3 +681,6 @@ if __name__ == '__main__':
     # showResult(step_two_model, test_data=all_data, isPlot=True)
 
     pass
+
+
+
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/resnet_18.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/resnet_18.py
index 399720f..f97e229 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/resnet_18.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/self_try/compare/resnet_18.py
@@ -42,7 +42,11 @@ save_max_name = "./mse/ResNet/{0}_timestamp{1}_feature{2}_max.csv".format(model_
                                                                           feature_num,
                                                                           batch_size,
                                                                           EPOCH)
-
+save_mse_name1 = "./mse/ResNet/{0}_timestamp{1}_feature{2}_totalresult.csv".format(model_name,
+                                                                             time_stamp,
+                                                                             feature_num,
+                                                                             batch_size,
+                                                                             EPOCH)
 '''文件名'''
 file_name = "G:\data\SCADA数据\jb4q_8_delete_total_zero.csv"
 
@@ -232,13 +236,15 @@ def showResult(step_two_model: tf.keras.Model, test_data, isPlot: bool = False,
 
     size, length, dims = test_data.shape
     predict_label = step_two_model.predict(test_data)
-    total_result = np.reshape(total_result, [total_result.__len__(), -1])
-    total_result = np.reshape(total_result, [-1, ])
+    predict_label=predict_label.flatten()
+    # total_result = np.reshape(predict_label, [predict_label.__len__(), -1])
+    # total_data=np.flatten(total_result)
+    total_result = predict_label
 
     # 误报率，漏报率，准确性的计算
 
     if isSave:
-        np.savetxt(save_mse_name, total_result, delimiter=',')
+        np.savetxt(save_mse_name1, total_result, delimiter=',')
     if isPlot:
         plt.figure(1, figsize=(6.0, 2.68))
         plt.subplots_adjust(left=0.1, right=0.94, bottom=0.2, top=0.9, wspace=None,
@@ -351,7 +357,8 @@ def plot_confusion_matrix_accuracy(cls, true_labels, predict_labels):
 if __name__ == '__main__':
     # # 数据读入
     #
-    total_data = loadData.execute(N=feature_num, file_name=file_name)
+    # total_data = loadData.execute(N=feature_num, file_name=file_name)
+    total_data = np.load("G:\data\SCADA数据\靖边8号处理后的数据\原始10SCADA数据/total_data.npy")
     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)
@@ -394,9 +401,9 @@ if __name__ == '__main__':
     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)
+        total_data[0:, :], is_Healthy=True)
 
-    showResult(step_two_model, test_data=all_data, isPlot=True)
+    showResult(model, test_data=all_data, isPlot=True)
 
     # trained_data = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer('bn_last').output).predict(
     #     train_data)
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py
index a2766ac..328acb9 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py
@@ -4,6 +4,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import random
+import pandas as pd
+import seaborn as sns
+from condition_monitoring.data_deal.loadData import read_data
 
 '''
 @Author : dingjiawen
@@ -19,6 +22,7 @@ mse_file_name="E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_mon
 
 max_file_name="E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_D\RNet_D_timestamp120_feature10_max_predict1.csv"
 
+source_path = "G:\data\SCADA数据\jb4q_8_delete_total_zero.csv"
 
 
 def plot_result(result_data):
@@ -126,6 +130,46 @@ def plot_MSE(total_MSE,total_max):
     pass
 
 
+def plot_Corr(data, label):
+    parameters = {
+        'figure.dpi': 600,
+        'figure.figsize': (2.8, 2),
+        'savefig.dpi': 600,
+        'xtick.direction': 'inout',
+        'ytick.direction': 'inout',
+        'xtick.labelsize': 3,
+        'ytick.labelsize': 3,
+        '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
+
+    print("计算皮尔逊相关系数")
+    pd_data = pd.DataFrame(data)
+    person = pd_data.corr()
+    print(person)
+    # 画热点图heatmap
+    cmap = sns.heatmap(person, annot=True, xticklabels=label, yticklabels=label,annot_kws={
+        'fontsize':2.6
+    })
+
+    plt.title("Heatmap of correlation coefficient matrix", size=7, fontdict=font1)
+    plt.tick_params(bottom=False, top=False, left=False, right=False,direction='inout',length=2,width=0.5)
+    plt.yticks(rotation=90)
+
+    # 调整色带的标签：
+    cbar = cmap.collections[0].colorbar
+    cbar.ax.tick_params(labelsize=4, labelcolor="black",length=2,width=0.5)
+    cbar.ax.set_ylabel(ylabel="color scale", color="black", loc="center", fontdict=font1)
+    # plt.axis('off')  # 去坐标轴
+
+    plt.savefig('./corr.png')
+
+    plt.show()
+    pass
+
 def test_result(file_name:str=result_file_name):
     # result_data = np.recfromcsv(file_name)
     result_data = np.loadtxt(file_name, delimiter=",")
@@ -148,9 +192,24 @@ def test_mse(mse_file_name:str=mse_file_name,max_file_name:str=max_file_name):
     plot_MSE(mse_data,max_data)
 
 
-if __name__ == '__main__':
-    # test_mse()
-    test_result()
+
+
+def test_corr(file_name=source_path,N=10):
+    needed_data, label = read_data(file_name=file_name, isNew=False)
+    print(needed_data)
+    print(needed_data.shape)
+    # plot_original_data(needed_data)
+    person = plot_Corr(needed_data, label)
+    person = np.array(person)
+    pass
+
+
+
+
+if __name__ == '__main__':
+    # test_mse()
+    # test_result()
+    test_corr()
     pass
 
 
diff --git a/TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/compare/RNet_45.py b/TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/compare/RNet_45.py
index 60ca054..73b59ea 100644
--- a/TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/compare/RNet_45.py
+++ b/TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/compare/RNet_45.py
@@ -170,6 +170,7 @@ class Joint_Monitoring(keras.Model):
             # step three
             # 分类器
             concat3 = tf.concat([output2, output3], axis=1)
+            # concat3 = tf.concat([output1, output2, output3], axis=1)
             # dropout = tf.keras.layers.Dropout(0.25)(concat3)
             d4 = self.d4(concat3)
             d5 = self.d5(d4)
@@ -288,7 +289,8 @@ class Joint_Monitoring(keras.Model):
             # f3 = tf.reshape(p3, shape=[B, -1])
             # step three
             # 分类器
-            concat3 = tf.concat([output1, output2, output3], axis=1)
+            # concat3 = tf.concat([output1, output2, output3], axis=1)
+            concat3 = tf.concat([ output2, output3], axis=1)
             # dropout = tf.keras.layers.Dropout(0.25)(concat3)
             d4 = self.d4(concat3)
             d5 = self.d5(d4)