diff --git a/Leecode/src/main/java/com/markilue/leecode/greedy/FindMinArrowShots.java b/Leecode/src/main/java/com/markilue/leecode/greedy/FindMinArrowShots.java
new file mode 100644
index 0000000..81cdf3d
--- /dev/null
+++ b/Leecode/src/main/java/com/markilue/leecode/greedy/FindMinArrowShots.java
@@ -0,0 +1,73 @@
+package com.markilue.leecode.greedy;
+
+import org.junit.Test;
+
+import java.util.Arrays;
+import java.util.Comparator;
+
+/**
+ * @BelongsProject: Leecode
+ * @BelongsPackage: com.markilue.leecode.greedy
+ * @Author: markilue
+ * @CreateTime: 2022-11-17  20:29
+ * @Description: TODO  力扣452题  用最少的数量的箭引爆气球：
+ * 有一些球形气球贴在一堵用 XY 平面表示的墙面上。墙面上的气球记录在整数数组 points ，其中points[i] = [xstart, xend] 表示水平直径在 xstart 和 xend之间的气球。你不知道气球的确切 y 坐标。
+ * 一支弓箭可以沿着 x 轴从不同点 完全垂直 地射出。在坐标 x 处射出一支箭，若有一个气球的直径的开始和结束坐标为 xstart，xend， 且满足  xstart ≤ x ≤ xend，则该气球会被 引爆 。可以射出的弓箭的数量 没有限制 。 弓箭一旦被射出之后，可以无限地前进。
+ * 给你一个数组 points ，返回引爆所有气球所必须射出的 最小 弓箭数 。
+ * @Version: 1.0
+ */
+public class FindMinArrowShots {
+
+    @Test
+    public void test() {
+        int[][] points = {{1, 2}, {3, 4},{5, 6},{7, 8}};
+        int[][] points1 = {{10, 16}, {2, 8},{1, 6},{7, 12}};
+        int[][] points2 = {{1, 2}, {2, 3},{3, 4},{4, 5}};
+        int[][] points3 = {{1, 4}, {1, 3},{3, 4},{4, 5}};
+        int[][] points4 = {{-2147483646, -2147483645}, {2147483646, 2147483647}};
+        int[][] points5 = {{9,12}, {1, 10},{4, 11},{8, 12}, {3, 9}, {6, 9},{6, 7}};
+//        System.out.println((long)(-2147483646)-(long)(2147483647)); //-4294967293
+//        System.out.println((int)((long)(-2147483646)-(long)(2147483647)));//3
+//        System.out.println((int)(-2147483646)-(int)(2147483647));  //3
+        System.out.println(findMinArrowShots(points));
+    }
+
+    /**
+     * 本人思路：先根据xstart进行排序，然后寻找第一个比xend大的xstart则result+1以此类推
+     * 排序的时间复杂度为O(nlogn),所以总复杂度为O(nlogn)
+     * 速度击败12.72%，内存击败88.71%
+     * @param points
+     * @return
+     */
+    public int findMinArrowShots(int[][] points) {
+
+        //按xstart进行排序
+        Arrays.sort(points, new Comparator<int[]>() {
+            @Override
+            public int compare(int[] o1, int[] o2) {
+                //警惕两数相减超过int范围
+                return o1[0] == o2[0] ? (o1[1] > o2[1]?1:-1) : (o1[0] > o2[0]?1:-1);
+            }
+        });
+
+
+        //遍历points，找到比xend大的xstart就+1
+        int result=1;
+        int lastEnd=points[0][1];
+        for (int i = 1; i < points.length; i++) {
+
+            if(points[i][0]>lastEnd){
+                result++;
+                lastEnd=points[i][1];
+            }
+            //判断第二个数是不是比lastEnd还小，如果还小就是子集，那么就还得该lastEnd
+            if(points[i][1]<lastEnd){
+                lastEnd=points[i][1];
+            }
+
+        }
+
+        return result;
+
+    }
+}
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/plot_sigmoid.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/plot_sigmoid.py
new file mode 100644
index 0000000..ff9c22a
--- /dev/null
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/plot_sigmoid.py
@@ -0,0 +1,50 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+
+'''
+@Author : dingjiawen
+@Date : 2022/11/14 10:43
+@Usage :  画sigmoid
+@Desc :
+'''
+
+
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import tensorflow.keras
+import matplotlib.pyplot as plt
+
+
+def sigmoid(x):
+    y = 1 / (1 + np.exp(-x))
+    # dy=y*(1-y)
+    return y
+    pass
+
+
+def plot_sigmoid():
+    parameters = {
+        'figure.dpi': 600,
+        'figure.figsize': (8, 6),
+        'savefig.dpi': 600,
+        'xtick.direction': 'in',
+        'ytick.direction': 'in',
+        'xtick.labelsize': 15,
+        'ytick.labelsize': 15,
+        'legend.fontsize': 15,
+    }
+    plt.rcParams.update(parameters)
+    plt.figure()
+    x=np.arange(-10,10,0.02)
+    y=sigmoid(x)
+    # 画出 y=1 这条水平线
+    plt.axhline(0.5, c='red', label='Failure threshold', lw=2)
+    plt.plot(x,y,c='black',lw=2)
+    plt.show()
+
+
+if __name__ == '__main__':
+    plot_sigmoid()
\ 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
index 2f88923..db646b7 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py
@@ -47,24 +47,20 @@ def plot_result_banda(result_data):
         'savefig.dpi': 600,
         'xtick.direction': 'in',
         'ytick.direction': 'in',
-        'xtick.labelsize': 5,
-        'ytick.labelsize': 5,
+        'xtick.labelsize': 6,
+        'ytick.labelsize': 6,
         'legend.fontsize': 5,
+        'font.family': 'Times New Roman',
     }
     plt.rcParams.update(parameters)
     fig, ax = plt.subplots(1, 1)
     plt.rc('font', family='Times New Roman')  # 全局字体样式
     font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5}  # 设置坐标标签的字体大小，字体
+    font2 = {'family': 'Times New Roman', 'weight': 'normal','size':7}  # 设置坐标标签的字体大小，字体
     plt.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.01, label="predict")
     # 画出 y=1 这条水平线
     plt.axhline(0.5, c='red', label='Failure threshold', lw=1)
     # 箭头指向上面的水平线
-    # plt.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 / 3-50, c='blue', ls='-.', lw=0.5, label='real fault')
     # plt.axvline(415548, c='blue', ls='-.', lw=0.5, label='real fault')
     # plt.xticks(range(6), ('06/09/17', '12/09/17', '18/09/17', '24/09/17', '29/09/17'))  # 设置x轴的标尺
@@ -89,24 +85,27 @@ def plot_result_banda(result_data):
     # pad调整label与坐标轴之间的距离
     plt.tick_params(bottom=True, top=False, left=True, right=False, direction='inout', length=2, width=0.5, pad=1)
     # plt.yticks([index  for index in indices1], classes1)
-    plt.ylabel('Confidence', fontsize=5)
-    plt.xlabel('Time', fontsize=5)
+    plt.ylabel('Confidence',fontdict=font2)
+    plt.xlabel('Time',fontdict=font2)
     plt.tight_layout()
     # plt.legend(loc='best', edgecolor='black', fontsize=4)
     plt.legend(loc='upper right', frameon=False, fontsize=4.5)
     # plt.grid()
 
     # 局部方法图
+
     axins = inset_axes(ax, width="40%", height="30%", loc='lower left',
                        bbox_to_anchor=(0.1, 0.1, 1, 1),
                        bbox_transform=ax.transAxes)
-    axins.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.001, label="predict")
+    axins.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.005, label="predict")
     axins.axvline(result_data.shape[0] * 2 / 3-50, c='blue', ls='-.', lw=0.5, label='real fault')
     plt.axhline(0.5, c='red', label='Failure threshold', lw=0.5)
     # 设置放大区间
     # 设置放大区间
     zone_left = int(result_data.shape[0] * 2 / 3 -160)
     zone_right = int(result_data.shape[0] * 2 / 3) + 40
+    # zone_left = int(result_data.shape[0] * 2 / 3 +250)
+    # zone_right = int(result_data.shape[0] * 2 / 3) + 450
     x = list(range(result_data.shape[0]))
 
     # 坐标轴的扩展比例（根据实际数据调整）
@@ -140,27 +139,22 @@ def plot_result(result_data):
         'savefig.dpi': 600,
         'xtick.direction': 'in',
         'ytick.direction': 'in',
-        'xtick.labelsize': 5,
-        'ytick.labelsize': 5,
+        'xtick.labelsize': 6,
+        'ytick.labelsize': 6,
         'legend.fontsize': 5,
+        'font.family': 'Times New Roman',
     }
     plt.rcParams.update(parameters)
     fig, ax = plt.subplots(1, 1)
     plt.rc('font', family='Times New Roman')  # 全局字体样式
     font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 5}  # 设置坐标标签的字体大小，字体
+    font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 7}  # 设置坐标标签的字体大小，字体
     plt.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.01, label="predict")
     # 画出 y=1 这条水平线
     plt.axhline(0.5, c='red', label='Failure threshold', lw=1)
-    # 箭头指向上面的水平线
-    # plt.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 / 3-50, c='blue', ls='-.', lw=0.5, label='real fault')
-    # plt.axvline(415548, c='blue', ls='-.', lw=0.5, label='real fault')
-    # plt.xticks(range(6), ('06/09/17', '12/09/17', '18/09/17', '24/09/17', '29/09/17'))  # 设置x轴的标尺
+
+    plt.axvline(result_data.shape[0] * 2 / 3-15, c='blue', ls='-.', lw=0.5, label='real fault')
     plt.text(result_data.shape[0] * 5 / 6, 0.4, "Fault", fontsize=5, color='black', verticalalignment='top',
              horizontalalignment='center',
              bbox={'facecolor': 'grey',
@@ -182,8 +176,8 @@ def plot_result(result_data):
     # pad调整label与坐标轴之间的距离
     plt.tick_params(bottom=True, top=False, left=True, right=False, direction='inout', length=2, width=0.5, pad=1)
     # plt.yticks([index  for index in indices1], classes1)
-    plt.ylabel('Confidence', fontsize=5)
-    plt.xlabel('Time', fontsize=5)
+    plt.ylabel('Confidence', fontdict=font2)
+    plt.xlabel('Time', fontdict=font2)
     plt.tight_layout()
     # plt.legend(loc='best', edgecolor='black', fontsize=4)
     plt.legend(loc='best', frameon=False, fontsize=4.5)
@@ -193,8 +187,8 @@ def plot_result(result_data):
     axins = inset_axes(ax, width="40%", height="30%", loc='lower left',
                        bbox_to_anchor=(0.1, 0.1, 1, 1),
                        bbox_transform=ax.transAxes)
-    axins.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.001, label="predict")
-    axins.axvline(result_data.shape[0] * 2 / 3-50, c='blue', ls='-.', lw=0.5, label='real fault')
+    axins.scatter(list(range(result_data.shape[0])), result_data, c='black', s=0.005, label="predict")
+    axins.axvline(result_data.shape[0] * 2 / 3-15, c='blue', ls='-.', lw=0.5, label='real fault')
     plt.axhline(0.5, c='red', label='Failure threshold', lw=0.5)
     # 设置放大区间
     # 设置放大区间
@@ -587,6 +581,48 @@ def plot_mse(file_name_mse="../others_idea/mse",data:str=''):
 
     plt.show()
 
+def plot_mse_single(file_name_mse="../self_try/compare/mse/JM_banda/banda_joint_result_predict1.csv"):
+    mse = np.loadtxt(file_name_mse, delimiter=",")
+
+    print("mse:",mse.shape)
+
+    need_shape=int(mse.shape[0]*2/3)
+    # mse = mse[2000:2300]
+    # mse = mse[1800:2150]
+    # mse = mse[ need_shape+100:need_shape+377]
+    mse = mse[ need_shape-300:need_shape-10]
+
+    parameters = {
+        'figure.dpi': 600,
+        'figure.figsize': (2.8, 2),
+        'savefig.dpi': 600,
+        'xtick.direction': 'in',
+        'ytick.direction': 'in',
+        'xtick.labelsize': 6,
+        'ytick.labelsize': 6,
+        'legend.fontsize': 5,
+        'font.family':'Times New Roman'
+    }
+    plt.rcParams.update(parameters)
+    font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 7}  # 设置坐标标签的字体大小，字体
+
+    plt.figure()
+    plt.rc('font', family='Times New Roman')  # 全局字体样式#画混淆矩阵
+    indices = [mse.shape[0] * i / 4 for i in range(5)]
+    # classes = ['07:21','08:21', '09:21', '10:21', '11:21']
+    classes = ['01:58','02:58', '03:58', '04:58', '05:58']
+
+    plt.xticks([index  for index in indices], classes, rotation=25)  # 设置横坐标方向，rotation=45为45度倾斜
+    # pad调整label与坐标轴之间的距离
+    plt.tick_params(bottom=True, top=False, left=True, right=False, direction='inout', length=2, width=0.5, pad=1)
+    plt.ylabel('Residual', fontdict=font2)
+    plt.xlabel('Time', fontdict=font2)
+    plt.tight_layout()
+    plt.plot(mse[:], lw=0.5)
+
+
+
+    plt.show()
 
 
 def plot_3d():
@@ -646,7 +682,7 @@ def test_result(file_name: str = result_file_name):
     # result_data = np.concatenate([result_data, data], axis=0)
     print(result_data)
     print(result_data.shape)
-    plot_result_banda(result_data)
+    plot_result(result_data)
 
 
 def test_mse(mse_file_name: str = mse_file_name, max_file_name: str = max_file_name):
@@ -739,17 +775,17 @@ def test_model_visualization(model_name=file_name):
 if __name__ == '__main__':
     # test_mse(fi)
     # test_result(
-    #     file_name='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\ResNet\ResNet_banda_result3.csv')
+    #     file_name='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\ResNet\ResNet_timestamp120_feature10_result.csv')
     # test_corr()
     # acc()
     # list = [3.77, 2.64, 2.35, 2.05, 1.76, 1.09, 0.757, 0.82, 1.1, 0.58, 0, 0.03, 0.02]
     # test_bar(list)
 
-    # list=[99.99,98.95,99.95,96.1,95,99.65,76.25,72.64,75.87,68.74]
-    # plot_FNR1(list)
+    list=[98.56,98.95,99.95,96.1,95,99.65,76.25,72.64,75.87,68.74]
+    plot_FNR1(list)
     # #
-    list=[3.43,1.99,1.92,2.17,1.63,1.81,1.78,1.8,0.6]
-    plot_FNR2(list)
+    # list=[3.43,1.99,1.92,2.17,1.63,1.81,1.78,1.8,0.6]
+    # plot_FNR2(list)
 
     # 查看网络某一层的权重
     # test_model_visualization(model_name = "E:\跑模型\论文写作/SE.txt")
@@ -759,8 +795,8 @@ if __name__ == '__main__':
     # test_model_weight_l(file_name)
 
     # 单独预测图
-    plot_mse('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/banda_joint_result_predict3.csv',data='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/raw_data.csv')
-
+    # plot_mse('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/banda_joint_result_predict3.csv',data='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/raw_data.csv')
+    # plot_mse_single('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_D/banda\RNet_D_banda_mse_predict1.csv')
     #画3d图
     # plot_3d()
 
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 a630945..ca3e9f2 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
@@ -283,76 +283,7 @@ def showResult(step_two_model: tf.keras.Model, test_data, isPlot: bool = False,
     return total_result
 
 
-def plot_result(result_data):
-    parameters = {
-        'figure.dpi': 600,
-        'figure.figsize': (5, 5),
-        'savefig.dpi': 600,
-        'xtick.direction': 'in',
-        'ytick.direction': 'in',
-        'xtick.labelsize': 20,
-        'ytick.labelsize': 20,
-        'legend.fontsize': 11.3,
-    }
-    plt.rcParams.update(parameters)
-    plt.figure()
-    plt.rc('font', family='Times New Roman')  # 全局字体样式#画混淆矩阵
 
-    plt.scatter(list(range(result_data.shape[0])), result_data, c='black', s=10)
-    # 画出 y=1 这条水平线
-    plt.axhline(0.5, c='red', label='Failure threshold')
-    # 箭头指向上面的水平线
-    # plt.arrow(35000, 0.9, 33000, 0.75, head_width=0.02, head_length=0.1, shape="full", fc='red', ec='red',
-    #           alpha=0.9, overhang=0.5)
-    plt.text(test_data.shape[0] * 2 / 3 + 1000, 0.7, "Truth Fault", fontsize=10, color='red',
-             verticalalignment='top')
-    plt.axvline(test_data.shape[0] * 2 / 3, c='blue', ls='-.')
-    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
-
-
-    indices = range(result_data.shape[0])
-    classes = ['N', 'IF', 'OF' 'TRC', 'TSP']  # for i in range(cls):
-    # 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(indices. classes,rotation=45)#设置横坐标方向，rotation=45为45度倾斜# plt.yticks(indices, classes, rotation=45)
-    plt.xticks([index + 0.5 for index in indices], classes, rotation=45)  # 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=45)
-    plt.ylabel('Actual label', fontsize=20)
-    plt.xlabel('Predicted label', fontsize=20)
-    plt.tight_layout()
-    plt.show()
-    pass
-
-
-def plot_confusion_matrix_accuracy(cls, true_labels, predict_labels):
-    sns.set(font_scale=1.5)
-    parameters = {
-        'figure.dpi': 600,
-        'figure.figsize': (5, 5),
-        'savefig.dpi': 600,
-        'xtick.direction': 'in',
-        'ytick.direction': 'in',
-        'xtick.labelsize': 20,
-        'ytick.labelsize': 20,
-        'legend.fontsize': 11.3,
-    }
-    plt.rcParams.update(parameters)
-    plt.figure()
-    plt.rc('font', family='Times New Roman')  # 全局字体样式#画混淆矩阵
-    confusion = confusion_matrix(true_labels, predict_labels)
-    # confusion = confusion.astype('float') / confusion.sum(axis=1)[: np.newaxis] plt.figure()
-    # sns.heatmap(confusion, annot=True,fmt="d",cmap="Greens")
-    sns.heatmap(confusion, annot=True, fmt="d", cmap="Blues", vmax=100, vmin=0, cbar=None, square=True)
-    indices = range(len(confusion))
-    classes = ['N', 'IF', 'OF' 'TRC', 'TSP']  # for i in range(cls):
-    # classes.append(str(i))
-    # 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(indices. classes,rotation=45)#设置横坐标方向，rotation=45为45度倾斜# plt.yticks(indices, classes, rotation=45)
-    plt.xticks([index + 0.5 for index in indices], classes, rotation=45)  # 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=45)
-    plt.ylabel('Actual label', fontsize=20)
-    plt.xlabel('Predicted label', fontsize=20)
-    plt.tight_layout()
-    plt.show()
 
 
 if __name__ == '__main__':
diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py
index 328acb9..8a3d6e5 100644
--- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py
+++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/test.py
@@ -204,12 +204,21 @@ def test_corr(file_name=source_path,N=10):
     pass
 
 
+def plot_raw_data():
+    # data, label = read_data(file_name='G:\data\SCADA数据\jb4q_8_delete_total_zero.csv', isNew=False)
+    # data = np.loadtxt('G:\data\SCADA数据/normalization.csv',delimiter=',')
+    data = pd.read_csv('G:\data\SCADA数据/normalization.csv')
+    print(data.shape)
+    print(data)
+    plt.plot(data)
+    plt.show()
 
 
 if __name__ == '__main__':
     # test_mse()
     # test_result()
-    test_corr()
+    # test_corr()
+    plot_raw_data()
     pass
 
 
diff --git a/TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/Joint_Monitoring_banda.py b/TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/Joint_Monitoring_banda.py
index 3135b3c..f3acba7 100644
--- a/TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/Joint_Monitoring_banda.py
+++ b/TensorFlow_eaxmple/Model_train_test/model/Joint_Monitoring/Joint_Monitoring_banda.py
@@ -28,12 +28,12 @@ class Joint_Monitoring(keras.Model):
         super(Joint_Monitoring, self).__init__()
         # step one
         self.RepDCBlock1 = RevConvBlock(num=3, kernel_size=5)
-        self.conv1 = tf.keras.layers.Conv1D(filters=conv_filter, kernel_size=1, strides=2, padding='SAME')
+        self.conv1 = tf.keras.layers.Conv1D(filters=conv_filter, kernel_size=1, strides=2, padding='SAME',kernel_initializer=0.7,bias_initializer=1)
         self.upsample1 = tf.keras.layers.UpSampling1D(size=2)
 
         self.DACU2 = DynamicChannelAttention()
         self.RepDCBlock2 = RevConvBlock(num=3, kernel_size=3)
-        self.conv2 = tf.keras.layers.Conv1D(filters=2 * conv_filter, kernel_size=1, strides=2, padding='SAME')
+        self.conv2 = tf.keras.layers.Conv1D(filters=2 * conv_filter, kernel_size=1, strides=2, padding='SAME',kernel_initializer=0.7,bias_initializer=1)
         self.upsample2 = tf.keras.layers.UpSampling1D(size=2)
 
         self.DACU3 = DynamicChannelAttention()
@@ -382,16 +382,18 @@ class Joint_Monitoring(keras.Model):
 
 class RevConv(keras.layers.Layer):
 
-    def __init__(self, kernel_size=3):
+    def __init__(self, kernel_size=3,dilation_rate=2):
         # 调用父类__init__()方法
         super(RevConv, self).__init__()
         self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
 
     def get_config(self):
         # 自定义层里面的属性
         config = (
             {
-                'kernel_size': self.kernel_size
+                'kernel_size': self.kernel_size,
+                'dilation_rate': self.dilation_rate
             }
         )
         base_config = super(RevConv, self).get_config()
@@ -402,10 +404,10 @@ class RevConv(keras.layers.Layer):
         _, _, output_dim = input_shape[0], input_shape[1], input_shape[2]
         self.conv1 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=self.kernel_size, strides=1,
                                             padding='causal',
-                                            dilation_rate=4)
+                                            dilation_rate=self.dilation_rate)
 
         self.conv2 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=1, strides=1, padding='causal',
-                                            dilation_rate=4)
+                                            dilation_rate=self.dilation_rate)
         # self.b2 = tf.keras.layers.BatchNormalization()
 
         # self.b3 = tf.keras.layers.BatchNormalization()
@@ -440,7 +442,7 @@ class RevConvBlock(keras.layers.Layer):
         self.kernel_size = kernel_size
         self.L = []
         for i in range(num):
-            RepVGG = RevConv(kernel_size=kernel_size)
+            RepVGG = RevConv(kernel_size=kernel_size,dilation_rate=(i+1)*2)
             self.L.append(RepVGG)
 
     def get_config(self):