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@@ -36,11 +36,11 @@ def diag_cal():
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password='Qx@123456'
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#读取结果库数据......................................................
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- param='product_id,start_time,end_time,diff_min,SOC[%],AnoScoreV_sum_max,AnoScoreV_max_max,AnoScoreT_sum_max,AnoScoreT_max_max'
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+ param='product_id,start_time,end_time,diff_min,SOC,AnoScoreV_sum_max,AnoScoreV_max_max,AnoScoreT_sum_max,AnoScoreT_max_max'
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tablename='fault_detection'
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mysql = pymysql.connect (host=host, user=user, password=password, port=port, database=db)
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cursor = mysql.cursor()
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- sql = "select '{}' from {} where end_time='0000-00-00 00:00:00'".format(param,tablename)
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+ sql = "select {} from {} where end_time='0000-00-00 00:00:00'".format(param,tablename)
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cursor.execute(sql)
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res = cursor.fetchall()
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df_diag_ram= pd.DataFrame(res,columns=param.split(','))
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@@ -60,11 +60,13 @@ def diag_cal():
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data_bms = df_data['bms']
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data_bms['sn']=sn
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if len(data_bms)>0:
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+ logger.info("SN: {} 数据开始预处理".format(sn))
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data_stand=data_groups(data_bms,sn,start_time,end_time)
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df_stand=split(data_stand)
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res=pd.DataFrame()
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if len(df_stand)>0:
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#读取训练产出的缩放指标：均值&方差
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+ logger.info("SN: {} 数据开始模型预测".format(sn))
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scaler = pickle.load(open('LIB/MIDDLE/FaultDetection/V1_0_2/train_out/scalerV_'+group+'_10.pkl', 'rb'))
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scaler2 = pickle.load(open('LIB/MIDDLE/FaultDetection/V1_0_2/train_out/scalerT_'+group+'_10.pkl', 'rb'))
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#读取训练产出的模型状态空间：电压模型&温度模型
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