From 2dec3c73fbd85841c4fc3ee68fb8f07033bebfe7 Mon Sep 17 00:00:00 2001
From: chenjiale <cjl601523@gmail.com>
Date: Tue, 23 Sep 2025 15:06:28 +0800
Subject: [PATCH] Add compiled Python bytecode for config and recon modules

- Generated __pycache__ files for config.py and recon.py, containing the compiled bytecode.
- These changes improve the loading time of the modules by utilizing cached bytecode.
---
 AANN_Fit.py                                  |   3 -
 ANN_Test_offline.py                          |  94 ++++++++++---------
 ANN_Train_offline.py                         |  13 ++-
 PCA_Test.py                                  |  14 ---
 PCA_Test_offline.py                          |  10 --
 __pycache__/PCA_Test.cpython-310.pyc         | Bin 0 -> 7667 bytes
 __pycache__/PCA_Test.cpython-39.pyc          | Bin 0 -> 7985 bytes
 __pycache__/PCA_Test_offline.cpython-310.pyc | Bin 0 -> 7475 bytes
 __pycache__/app.cpython-310.pyc              | Bin 0 -> 13120 bytes
 __pycache__/app.cpython-39.pyc               | Bin 0 -> 14155 bytes
 __pycache__/config.cpython-310.pyc           | Bin 0 -> 233 bytes
 __pycache__/recon.cpython-310.pyc            | Bin 0 -> 8489 bytes
 app.py                                       |   8 --
 requirements.txt                             | Bin 7462 -> 454 bytes
 14 files changed, 55 insertions(+), 87 deletions(-)
 create mode 100644 __pycache__/PCA_Test.cpython-310.pyc
 create mode 100644 __pycache__/PCA_Test.cpython-39.pyc
 create mode 100644 __pycache__/PCA_Test_offline.cpython-310.pyc
 create mode 100644 __pycache__/app.cpython-310.pyc
 create mode 100644 __pycache__/app.cpython-39.pyc
 create mode 100644 __pycache__/config.cpython-310.pyc
 create mode 100644 __pycache__/recon.cpython-310.pyc

diff --git a/AANN_Fit.py b/AANN_Fit.py
index 9e371fd..d19074e 100644
--- a/AANN_Fit.py
+++ b/AANN_Fit.py
@@ -4,9 +4,6 @@ import config
 import json
 import sys
 import requests
-import datetime
-import jenkspy
-import xlrd
 import AANN_Fit
 import traceback
 
diff --git a/ANN_Test_offline.py b/ANN_Test_offline.py
index 1f9ee22..2993437 100644
--- a/ANN_Test_offline.py
+++ b/ANN_Test_offline.py
@@ -5,24 +5,26 @@
  @File    : ANN_test.py
  @Software: PyCharm
 """
+import json
 import os
 import time
-import json
-import requests
+
+import keras
 import numpy as np
+import requests
 import tensorflow as tf
-from tensorflow.keras import backend
-import PCA_Test_offline
+from keras import backend
+from keras.models import model_from_json
 from sklearn.preprocessing import MinMaxScaler
-from tensorflow.keras.models import load_model
-from tensorflow.keras.models import model_from_json
+
 import config
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
-def get_history_value(points,time1,interval,typedata):
-    url=f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat"
-    headers = {"Content-Type": "application/json;charset=utf-8"}#,"token":get_token()
+
+def get_history_value(points, time1, interval, typedata):
+    url = f"http://{config._EXA_IP}:9000/exawebapi/exatime/GetSamplingValueArrayFloat"
+    headers = {"Content-Type": "application/json;charset=utf-8"}  # ,"token":get_token()
     point_array = points.split(",")
     time_span = time1.split(";")
     value_array = []
@@ -33,7 +35,7 @@ def get_history_value(points,time1,interval,typedata):
             et = time_piece.split(",")[1]
             para = {"ItemName": item, "StartingTime": st, "TerminalTime": et, "SamplingPeriod": interval}
             response = requests.get(url, headers=headers, params=para)
-            value = eval(str(response.text).replace("\"","").replace("null","0"))
+            value = eval(str(response.text).replace("\"", "").replace("null", "0"))
             for row in value:
                 value_group.append(row[1])
         value_array.append(value_group)
@@ -43,19 +45,19 @@ def get_history_value(points,time1,interval,typedata):
     # data_y = [(valuetrs.T)[:, item[0]].tolist() for item in typeArr if item[1] == "1"]
     x_data = np.array(data_x).T
     # y_data = np.array(data_y).T
-    return x_data,valuetrs
+    return x_data, valuetrs
 
 
 def rmse(y_true, y_pred):
-    return backend.sqrt(backend.mean(tf.keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
+    return backend.sqrt(backend.mean(keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
 
 
-def main(mms1,mms2,x_data,origndata,filepath,weight):
-    #x_data=np.array(x_data)
+def main(mms1, mms2, x_data, origndata, filepath, weight):
+    # x_data=np.array(x_data)
     mms_x = MinMaxScaler()
     mms_y = MinMaxScaler()
     mms_x.data_max_ = np.array(mms1["data_max_"])
-    #mms_x.data_max_ = np.array(mms1.data_max_)
+    # mms_x.data_max_ = np.array(mms1.data_max_)
     mms_x.data_min_ = np.array(mms1["data_min_"])
     mms_x.data_range_ = np.array(mms1["data_range_"])
     mms_x.min_ = np.array(mms1["min_"])
@@ -66,46 +68,46 @@ def main(mms1,mms2,x_data,origndata,filepath,weight):
     mms_y.min_ = np.array(mms2["min_"])
     mms_y.scale_ = np.array(mms2["scale_"])
     for i in range(len(weight)):
-        weight[i]=np.array(weight[i])
+        weight[i] = np.array(weight[i])
     # 用对应的归一化信息将输入数据归一化，输出反归一化
     normal_data = mms_x.transform(x_data)
     t1 = time.time()
-    #model = load_model("my_model.h5")
-    #model=load_model(filepath)
+    # model = load_model("my_model.h5")
+    # model=load_model(filepath)
     try:
-        filepath=json.dumps(filepath)
-        model=model_from_json(filepath)
+        filepath = json.dumps(filepath)
+        model = model_from_json(filepath)
     except TypeError:
         model = model_from_json(json.loads(filepath))
     model.set_weights(weight)
     t2 = time.time()
     predict_data = model.predict(normal_data, batch_size=100)
-    t3=time.time()
+    t3 = time.time()
     y_normal = mms_y.transform(origndata)
     with tf.compat.v1.Session():
         spe = rmse(predict_data, y_normal).eval()
-        mse=tf.sqrt(tf.keras.losses.mean_squared_error(predict_data, y_normal)).eval()
+        mse = tf.sqrt(keras.losses.mean_squared_error(predict_data, y_normal)).eval()
     y_data = mms_y.inverse_transform(predict_data)
-    #return y_data
-    result={}
+    # return y_data
+    result = {}
     errorData = y_data - origndata
-    result["reconData"]=(y_data.T).tolist()
+    result["reconData"] = (y_data.T).tolist()
     result["errorData"] = (errorData.T).tolist()
     result["SPE"] = spe
-    result["mse"]=mse.tolist()
-    result["x"]=x_data.tolist()
-    result["time1"]=t2-t1
-    result["time2"]=t3-t2
+    result["mse"] = mse.tolist()
+    result["x"] = x_data.tolist()
+    result["time1"] = t2 - t1
+    result["time2"] = t3 - t2
     return result
 
 
-def test_offline_main(mms1,mms2,x_data,output_data, filepath,weight):
-    #x_data=np.array(x_data)
+def test_offline_main(mms1, mms2, x_data, output_data, filepath, weight):
+    # x_data=np.array(x_data)
     mms_x = MinMaxScaler()
     mms_y = MinMaxScaler()
     mms3 = MinMaxScaler()
     mms_x.data_max_ = np.array(mms1["data_max_"])
-    #mms_x.data_max_ = np.array(mms1.data_max_)
+    # mms_x.data_max_ = np.array(mms1.data_max_)
     mms_x.data_min_ = np.array(mms1["data_min_"])
     mms_x.data_range_ = np.array(mms1["data_range_"])
     mms_x.min_ = np.array(mms1["min_"])
@@ -116,21 +118,21 @@ def test_offline_main(mms1,mms2,x_data,output_data, filepath,weight):
     mms_y.min_ = np.array(mms2["min_"])
     mms_y.scale_ = np.array(mms2["scale_"])
     for i in range(len(weight)):
-        weight[i]=np.array(weight[i])
+        weight[i] = np.array(weight[i])
     # 用对应的归一化信息将输入数据归一化，输出反归一化
     normal_data = mms_x.transform(x_data)
-    #model = load_model("my_model.h5")
-    #model=load_model(filepath)
-    model=model_from_json(json.dumps(filepath))
+    # model = load_model("my_model.h5")
+    # model=load_model(filepath)
+    model = model_from_json(json.dumps(filepath))
     model.set_weights(weight)
     predict_data = model.predict(normal_data, batch_size=100)
     y_data = mms_y.inverse_transform(predict_data)
-    #return y_data
-    result={}
+    # return y_data
+    result = {}
     y_normal = mms3.fit_transform(output_data)
     with tf.compat.v1.Session():
         spe = rmse(predict_data, y_normal).eval()
-    errorData = output_data-np.array(y_data)
+    errorData = output_data - np.array(y_data)
     paraState = []
     temp_list = []
     for item in output_data[0]:
@@ -142,15 +144,17 @@ def test_offline_main(mms1,mms2,x_data,output_data, filepath,weight):
     result["FAI"] = [spe]
     return result
 
+
 def isnumber(limits):
-    flag=True
+    flag = True
     for item in limits:
-        item=item.replace("-","")
-        if(item.isdigit()==False):
-            flag=False
+        item = item.replace("-", "")
+        if (item.isdigit() == False):
+            flag = False
             break
     return flag
 
+
 def clean_main(info):
     try:
         datatype = info['type']
@@ -227,7 +231,7 @@ def clean_main(info):
 
 
 if __name__ == "__main__":
-    #info_str=r'{"model":"{\"filename\":{\"class_name\":\"Sequential\",\"config\":{\"name\":\"sequential\",\"layers\":[{\"class_name\":\"Dense\",\"config\":{\"name\":\"dense\",\"trainable\":true,\"batch_input_shape\":[null,2],\"dtype\":\"float32\",\"units\":5,\"activation\":\"sigmoid\",\"use_bias\":true,\"kernel_initializer\":{\"class_name\":\"GlorotUniform\",\"config\":{\"seed\":null,\"dtype\":\"float32\"}},\"bias_initializer\":{\"class_name\":\"Zeros\",\"config\":{\"dtype\":\"float32\"}},\"kernel_regularizer\":null,\"bias_regularizer\":null,\"activity_regularizer\":null,\"kernel_constraint\":null,\"bias_constraint\":null}},{\"class_name\":\"Dense\",\"config\":{\"name\":\"dense_1\",\"trainable\":true,\"dtype\":\"float32\",\"units\":1,\"activation\":\"linear\",\"use_bias\":true,\"kernel_initializer\":{\"class_name\":\"GlorotUniform\",\"config\":{\"seed\":null,\"dtype\":\"float32\"}},\"bias_initializer\":{\"class_name\":\"Zeros\",\"config\":{\"dtype\":\"float32\"}},\"kernel_regularizer\":null,\"bias_regularizer\":null,\"activity_regularizer\":null,\"kernel_constraint\":null,\"bias_constraint\":null}}]},\"keras_version\":\"2.2.4-tf\",\"backend\":\"tensorflow\"},\"mms1\":{\"data_max_\":[78.94322,81.25558],\"data_min_\":[64.52349,63.5361977],\"data_range_\":[14.419730000000001,17.719382299999992],\"min_\":[-4.47466700139323,-3.5856891975291956],\"scale_\":[0.06934942609882432,0.056435375853931456]},\"mms2\":{\"data_max_\":[82.4897156],\"data_min_\":[64.52349],\"data_range_\":[17.9662256],\"min_\":[-3.59137703358239],\"scale_\":[0.0556599934935694]},\"train_max\":[82.4897156,81.25558,80.96978,79.69671,77.449295,74.86414,71.75935,68.6935349,68.45969,71.23971,73.13635,74.24058,75.4357452,76.6438751,77.20247,77.69612,77.96892,78.16381,78.4236,78.68343,78.81335,78.90425,78.91726,78.7873459,78.709404,78.60549,78.59249,78.4236,78.306694,78.11184,77.94295,75.7994843,74.30555,74.95509,75.47472,75.57862,75.72154,75.61759,75.42274,75.16291,75.02003,74.8771057,74.7602,74.64329,74.44843,74.33152,74.1886,73.9937439,73.9158,73.8119,73.74692,73.61704,73.44816,73.2532654,73.14935,72.9415,72.79862,72.61673,72.4608459,72.31795,72.2010345,72.058136,71.92822,71.75935,71.5774841,71.38261,71.27868,71.09681,70.96692,70.837,70.7070847,70.60317,70.4862442,70.3433456,70.2654,70.23942,70.20045,70.1355057,70.0835342,70.00559,69.9406357,69.84971,69.70681,69.64185,69.56391,69.434,69.35606,69.33007,69.33007,69.2781143,69.3170853,69.23914,69.2911,69.39503,69.56391,69.60288,69.5379257,69.4989548,69.48597,69.434,69.33007,69.2261353,69.2261353,69.33007,69.3041,69.0962448,68.03099,67.0307159,66.4850845,66.0823746,65.7186356,65.41985,65.12107,64.77032,64.52349,64.6403961,65.66668,66.40714,66.88782,67.08267,67.22557,67.3295,67.55034,67.5763245,66.83584],\"train_min\":[78.94322,77.4752655,77.5532455,76.35808,73.96778,71.03185,67.7971649,64.6923752,66.44611,70.51223,72.79862,74.21461,75.3058243,76.2801361,76.8907,77.30642,77.61818,77.76106,77.9559555,78.18978,78.33266,78.4236,78.39764,78.345665,78.18978,78.07287,78.00789,77.87801,77.80003,77.67016,77.488266,75.56566,74.29255,74.55234,74.7732,74.95509,75.046,74.83814,74.63032,74.42246,74.25358,74.1366653,73.9937439,73.9028,73.7339554,73.5261,73.37022,73.20133,73.05841,72.9285,72.79862,72.72068,72.56476,72.43486,72.27898,72.07112,71.85028,71.655426,71.49952,71.35662,71.2527161,71.07082,70.8889542,70.77204,70.61616,70.46027,70.3563461,70.14849,70.04456,69.84971,69.7197952,69.62887,69.4989548,69.36903,69.25213,69.26511,69.26511,69.21316,69.07026,69.0183,68.8624,68.7844543,68.70651,68.6025848,68.55064,68.40773,68.34277,68.34277,68.25183,68.18689,68.12193,68.08296,68.12193,68.13492,68.1479,68.17389,68.10893,68.08296,68.03099,68.0180054,67.91408,67.73221,67.7971649,67.73221,67.70622,67.36847,66.38118,65.95248,65.48481,65.0691,64.74433,64.44555,64.14677,63.7830238,63.5361977,63.5361977,64.1077957,64.70536,65.1340561,65.41985,65.54975,65.79658,65.80958,65.96546,65.52378],\"train_ave\":[80.5497552,79.99547516666667,79.83093516666668,78.58383333333333,76.28879,73.58671,70.43862163333334,67.359815,67.78849666666666,70.99721666666666,73.02377333333332,74.23192333333333,75.39243823333334,76.52262876666667,77.09854666666666,77.56622,77.84768516666666,78.02956,78.22874983333332,78.46258666666667,78.60548,78.70073,78.71374,78.6054803,78.49722466666667,78.40629,78.35865333333332,78.20710150000001,78.11182466666666,77.96461333333333,77.79138866666666,75.72154286666667,74.30121666666666,74.82083999999999,75.24088,75.37077666666666,75.49636,75.35777333333334,75.15859999999999,74.91609333333334,74.76454666666667,74.63029223333334,74.50471463333334,74.39645999999999,74.2102718,74.06304666666666,73.91580666666665,73.72960593333333,73.63000333333333,73.51743333333333,73.43082,73.31825333333335,73.13636666666666,72.98046360000001,72.85922666666666,72.65137333333332,72.48250666666667,72.29629533333333,72.14040393333333,71.99750666666667,71.88492836666667,71.72903066666667,71.58179806666666,71.43024666666666,71.25704273333334,71.075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23:21:28,2020-02-05 23:21:28","interval":300000}'
+    # info_str=r'{"model":"{\"filename\":{\"class_name\":\"Sequential\",\"config\":{\"name\":\"sequential\",\"layers\":[{\"class_name\":\"Dense\",\"config\":{\"name\":\"dense\",\"trainable\":true,\"batch_input_shape\":[null,2],\"dtype\":\"float32\",\"units\":5,\"activation\":\"sigmoid\",\"use_bias\":true,\"kernel_initializer\":{\"class_name\":\"GlorotUniform\",\"config\":{\"seed\":null,\"dtype\":\"float32\"}},\"bias_initializer\":{\"class_name\":\"Zeros\",\"config\":{\"dtype\":\"float32\"}},\"kernel_regularizer\":null,\"bias_regularizer\":null,\"activity_regularizer\":null,\"kernel_constraint\":null,\"bias_constraint\":null}},{\"class_name\":\"Dense\",\"config\":{\"name\":\"dense_1\",\"trainable\":true,\"dtype\":\"float32\",\"units\":1,\"activation\":\"linear\",\"use_bias\":true,\"kernel_initializer\":{\"class_name\":\"GlorotUniform\",\"config\":{\"seed\":null,\"dtype\":\"float32\"}},\"bias_initializer\":{\"class_name\":\"Zeros\",\"config\":{\"dtype\":\"float32\"}},\"kernel_regularizer\":null,\"bias_regularizer\":null,\"activity_regularizer\":null,\"kernel_constraint\":null,\"bias_constraint\":null}}]},\"keras_version\":\"2.2.4-tf\",\"backend\":\"tensorflow\"},\"mms1\":{\"data_max_\":[78.94322,81.25558],\"data_min_\":[64.52349,63.5361977],\"data_range_\":[14.419730000000001,17.719382299999992],\"min_\":[-4.47466700139323,-3.5856891975291956],\"scale_\":[0.06934942609882432,0.056435375853931456]},\"mms2\":{\"data_max_\":[82.4897156],\"data_min_\":[64.52349],\"data_range_\":[17.9662256],\"min_\":[-3.59137703358239],\"scale_\":[0.0556599934935694]},\"train_max\":[82.4897156,81.25558,80.96978,79.69671,77.449295,74.86414,71.75935,68.6935349,68.45969,71.23971,73.13635,74.24058,75.4357452,76.6438751,77.20247,77.69612,77.96892,78.16381,78.4236,78.68343,78.81335,78.90425,78.91726,78.7873459,78.709404,78.60549,78.59249,78.4236,78.306694,78.11184,77.94295,75.7994843,74.30555,74.95509,75.47472,75.57862,75.72154,75.61759,75.42274,75.16291,75.02003,74.8771057,74.7602,74.64329,74.44843,74.33152,74.1886,73.9937439,73.9158,73.8119,73.74692,73.61704,73.44816,73.2532654,73.14935,72.9415,72.79862,72.61673,72.4608459,72.31795,72.2010345,72.058136,71.92822,71.75935,71.5774841,71.38261,71.27868,71.09681,70.96692,70.837,70.7070847,70.60317,70.4862442,70.3433456,70.2654,70.23942,70.20045,70.1355057,70.0835342,70.00559,69.9406357,69.84971,69.70681,69.64185,69.56391,69.434,69.35606,69.33007,69.33007,69.2781143,69.3170853,69.23914,69.2911,69.39503,69.56391,69.60288,69.5379257,69.4989548,69.48597,69.434,69.33007,69.2261353,69.2261353,69.33007,69.3041,69.0962448,68.03099,67.0307159,66.4850845,66.0823746,65.7186356,65.41985,65.12107,64.77032,64.52349,64.6403961,65.66668,66.40714,66.88782,67.08267,67.22557,67.3295,67.55034,67.5763245,66.83584],\"train_min\":[78.94322,77.4752655,77.5532455,76.35808,73.96778,71.03185,67.7971649,64.6923752,66.44611,70.51223,72.79862,74.21461,75.3058243,76.2801361,76.8907,77.30642,77.61818,77.76106,77.9559555,78.18978,78.33266,78.4236,78.39764,78.345665,78.18978,78.07287,78.00789,77.87801,77.80003,77.67016,77.488266,75.56566,74.29255,74.55234,74.7732,74.95509,75.046,74.83814,74.63032,74.42246,74.25358,74.1366653,73.9937439,73.9028,73.7339554,73.5261,73.37022,73.20133,73.05841,72.9285,72.79862,72.72068,72.56476,72.43486,72.27898,72.07112,71.85028,71.655426,71.49952,71.35662,71.2527161,71.07082,70.8889542,70.77204,70.61616,70.46027,70.3563461,70.14849,70.04456,69.84971,69.7197952,69.62887,69.4989548,69.36903,69.25213,69.26511,69.26511,69.21316,69.07026,69.0183,68.8624,68.7844543,68.70651,68.6025848,68.55064,68.40773,68.34277,68.34277,68.25183,68.18689,68.12193,68.08296,68.12193,68.13492,68.1479,68.17389,68.10893,68.08296,68.03099,68.0180054,67.91408,67.73221,67.7971649,67.73221,67.70622,67.36847,66.38118,65.95248,65.48481,65.0691,64.74433,64.44555,64.14677,63.7830238,63.5361977,63.5361977,64.1077957,64.70536,65.1340561,65.41985,65.54975,65.79658,65.80958,65.96546,65.52378],\"train_ave\":[80.5497552,79.99547516666667,79.83093516666668,78.58383333333333,76.28879,73.58671,70.43862163333334,67.359815,67.78849666666666,70.99721666666666,73.02377333333332,74.23192333333333,75.39243823333334,76.52262876666667,77.09854666666666,77.56622,77.84768516666666,78.02956,78.22874983333332,78.46258666666667,78.60548,78.70073,78.71374,78.6054803,78.49722466666667,78.40629,78.35865333333332,78.20710150000001,78.11182466666666,77.96461333333333,77.79138866666666,75.72154286666667,74.30121666666666,74.82083999999999,75.24088,75.37077666666666,75.49636,75.35777333333334,75.15859999999999,74.91609333333334,74.76454666666667,74.63029223333334,74.50471463333334,74.39645999999999,74.2102718,74.06304666666666,73.91580666666665,73.72960593333333,73.63000333333333,73.51743333333333,73.43082,73.31825333333335,73.13636666666666,72.98046360000001,72.85922666666666,72.65137333333332,72.48250666666667,72.29629533333333,72.14040393333333,71.99750666666667,71.88492836666667,71.72903066666667,71.58179806666666,71.43024666666666,71.25704273333334,71.07516333333332,70.97123536666666,70.78070333333334,70.65946666666667,70.50790333333333,70.37798819999999,70.27840333333334,70.15714773333333,70.01857373333333,69.92764333333334,69.91465,69.88867,69.82805713333333,69.74577613333334,69.67649333333333,69.58122379999999,69.48596143333333,69.37337666666667,69.2911016,69.21316333333334,69.08758,69.01396666666666,68.98365143333334,68.92735666666665,68.87106476666666,68.84508670000001,68.81044000000001,68.84075,68.89271333333333,68.97066000000001,68.97932333333333,68.94467333333334,68.88838493333334,68.88405859999999,68.82343513333332,68.70651813333335,68.5809332,68.60258483333332,68.60691666666668,68.59393333333334,68.2301916,67.31650666666667,66.67130393333333,66.13866816666666,65.7446164,65.39386706666666,65.09508333333333,64.79630333333334,64.44122126666666,64.19439256666666,64.27232996666667,65.0344619,65.66667333333334,66.0867187,66.34219183333333,66.480758,66.63665666666667,66.74491,66.82285816666666,66.24259666666667],\"pre_max\":[2.6586617944335984],\"pre_min\":[-5.7904892206054654],\"pre_ave\":[0.049737347943749455],\"pre_s\":[4.881696903414218],\"pre_s_ave\":4.881696903414218,\"limit\":0.2718421401094552,\"CleanOrNot\":true,\"BeforeCleanSamNum\":125,\"AfterCleanSamNum\":125}","type":"0,0,0","point":"JL_D1_10MILLA:SEP_TEMP.PNT,JL_D1_10FSSS20A:HFC10CT301.PNT,JL_D1_10FSSS20A:HFC10CT302.PNT","time":"2020-02-04 23:21:28,2020-02-05 23:21:28","interval":300000}'
     # info_str=r'{"model":"{\"filename\":{\"class_name\":\"Sequential\",\"config\":{\"name\":\"sequential\",\"layers\":[{\"class_name\":\"Dense\",\"config\":{\"name\":\"dense\",\"trainable\":true,\"batch_input_shape\":[null,2],\"dtype\":\"float32\",\"units\":6,\"activation\":\"sigmoid\",\"use_bias\":true,\"kernel_initializer\":{\"class_name\":\"GlorotUniform\",\"config\":{\"seed\":null,\"dtype\":\"float32\"}},\"bias_initializer\":{\"class_name\":\"Zeros\",\"config\":{\"dtype\":\"float32\"}},\"kernel_regularizer\":null,\"bias_regularizer\":null,\"activity_regularizer\":null,\"kernel_constraint\":null,\"bias_constraint\":null}},{\"class_name\":\"Dense\",\"config\":{\"name\":\"dense_1\",\"trainable\":true,\"dtype\":\"float32\",\"units\":1,\"activation\":\"linear\",\"use_bias\":true,\"kernel_initializer\":{\"class_name\":\"GlorotUniform\",\"config\":{\"seed\":null,\"dtype\":\"float32\"}},\"bias_initializer\":{\"class_name\":\"Zeros\",\"config\":{\"dtype\":\"float32\"}},\"kernel_regularizer\":null,\"bias_regularizer\":null,\"activity_regularizer\":null,\"kernel_constraint\":null,\"bias_constraint\":null}}]},\"keras_version\":\"2.2.4-tf\",\"backend\":\"tensorflow\"},\"mms1\":{\"data_max_\":[82.97037,87.59508],\"data_min_\":[46.5572548,53.2085228],\"data_range_\":[36.4131152,34.3865572],\"min_\":[-1.2785847776078219,-1.5473640612093613],\"scale_\":[0.027462632474795783,0.029081131739469402]},\"mms2\":{\"data_max_\":[88.43947],\"data_min_\":[53.2085228],\"data_range_\":[35.2309472],\"min_\":[-1.5102779524474435],\"scale_\":[0.028384136092713393]},\"train_max\":[88.43947,82.97037,87.59508],\"train_min\":[53.2085228,46.5572548,53.2085228],\"train_ave\":[73.95152649122672,69.7637995632764,72.95635137818323],\"pre_max\":[2.1512201489257876],\"pre_min\":[-9.576423670019537],\"pre_ave\":[-0.11074971603245708],\"pre_s\":[4.083063363267552],\"pre_s_ave\":4.083063363267552,\"limit\":0.11627729309035165,\"CleanOrNot\":true,\"BeforeCleanSamNum\":1288,\"AfterCleanSamNum\":1288}","type":"1,0,0","point":"JL_D1_10MILLA:SEP_TEMP.PNT,JL_D1_10FSSS20A:HFC10CT301.PNT,JL_D1_10FSSS20A:HFC10CT302.PNT","time":"2020-01-18 22:16:49,2020-01-23 09:33:25","interval":300000}'
     # info = json.loads(info_str)
     # points = info['point']
@@ -263,4 +267,4 @@ if __name__ == "__main__":
     # print("aaa")
     info = {}
     result = main(info["mms1"], info["mms2"], np.array(info["testDataX"]),
-                                   np.array(info["testDataY"]), info["filename"], info["weight"])
\ No newline at end of file
+                  np.array(info["testDataY"]), info["filename"], info["weight"])
diff --git a/ANN_Train_offline.py b/ANN_Train_offline.py
index 14ea0ec..fca85e8 100644
--- a/ANN_Train_offline.py
+++ b/ANN_Train_offline.py
@@ -17,10 +17,9 @@ import pandas as pd
 import requests
 import tensorflow as tf
 from sklearn.preprocessing import MinMaxScaler
-from tensorflow.keras import backend
-from tensorflow.keras import layers
-from tensorflow.keras.models import load_model
-from tensorflow.keras.models import model_from_json
+import keras
+from keras import backend
+from keras import layers
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
@@ -53,7 +52,7 @@ def get_history_value(points, time, interval, typedata):
 
 
 def rmse(y_true, y_pred):
-    return backend.sqrt(backend.mean(tf.keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
+    return backend.sqrt(backend.mean(keras.losses.mean_squared_error(y_true, y_pred), axis=-1))
 
 
 def TrainOffline(x_data, y_data, hidden_layers, epochsdata):
@@ -73,7 +72,7 @@ def TrainOffline(x_data, y_data, hidden_layers, epochsdata):
     x_train = x_normal
     y_train = y_normal
     # 构建网络结构
-    model = tf.keras.Sequential()
+    model = keras.Sequential()
     model.add(layers.Dense(units=hidden_layers[0], input_dim=x_data.shape[1], activation="sigmoid"))
     for i in range(len(hidden_layers) - 1):
         model.add(layers.Dense(units=hidden_layers[i + 1], activation="sigmoid"))
@@ -150,7 +149,7 @@ def Train(x_data, y_data, hidden_layers, valuetrs, epochsdata):
     x_train = x_normal
     y_train = y_normal
     # 构建网络结构
-    model = tf.keras.Sequential()
+    model = keras.Sequential()
     model.add(layers.Dense(units=hidden_layers[0], input_dim=x_data.shape[1], activation="sigmoid"))
     for i in range(len(hidden_layers) - 1):
         model.add(layers.Dense(units=hidden_layers[i + 1], activation="sigmoid"))
diff --git a/PCA_Test.py b/PCA_Test.py
index 7428622..29ec88c 100644
--- a/PCA_Test.py
+++ b/PCA_Test.py
@@ -5,24 +5,10 @@ PCA source code
 @author: liudiwei
 """
 
-import xlsxwriter as xw
 import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from scipy.stats import norm
-from scipy.stats.distributions import chi2
 import json
-import sys
 import pymssql
 import requests
-import datetime
-from scipy.stats import norm
-from scipy.stats import f
-from scipy.stats import chi2
-import jenkspy
-import xlrd
-import time
-# import PCA_Test_offline
 import config
 
 
diff --git a/PCA_Test_offline.py b/PCA_Test_offline.py
index f64e6c5..77887fe 100644
--- a/PCA_Test_offline.py
+++ b/PCA_Test_offline.py
@@ -7,21 +7,11 @@ PCA source code————最新更新—————————————
 
 
 import numpy as np
-import pandas as pd
-from scipy.stats import norm
-from scipy.stats.distributions import chi2
 import json
-import sys
 import pymssql
 import requests
-import datetime
-from scipy.stats import norm
-from scipy.stats import f
-from scipy.stats import chi2
 import jenkspy
-import xlrd
 import gc
-import time
 import pyodbc
 from recon import Lars, recon_fault_diagnosis_r, recon_fault_diagnosis_r_l, recon_fault_diagnosis_r_c
 import config
diff --git a/__pycache__/PCA_Test.cpython-310.pyc b/__pycache__/PCA_Test.cpython-310.pyc
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diff --git a/app.py b/app.py
index 066f061..46cee0b 100644
--- a/app.py
+++ b/app.py
@@ -1,20 +1,12 @@
 # -*- coding: utf-8 -*-
-import datetime
 import json
-import json
-import sys
 import traceback
 
 import PCA_Test
 import PCA_Test_offline
-import config
-import jenkspy
 import numpy as np
-import requests
-import xlrd
 from flask import Flask
 from flask import request
-from numba import jit
 
 app = Flask(__name__)
 
diff --git a/requirements.txt b/requirements.txt
index e3e77951f0ab98a0c9e6194c48306793d4b4515c..66dc9015a36f84b2b58821a6f11ccfc1b27844e5 100644
GIT binary patch
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