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@@ -55,7 +55,7 @@ for k in range(l):
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df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms'])
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data_test = df_data['bms']
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data_test=data_test[data_test['SOC[%]']>20]
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- if len(data_test)>5:
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+ if len(data_test)>0:
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pca1 = joblib.load('pca1_'+sn+'.m')
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pca2 = joblib.load('pca2_'+sn+'.m')
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res1 = pd.read_csv('res1_'+sn+'.csv',encoding='gbk')
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@@ -64,13 +64,17 @@ for k in range(l):
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outliers1=detect_outliers(res1,pred1,threshold=30)
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outliers2=detect_outliers(res2,pred2,threshold=16)
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if (len(outliers1)>0) & (len(outliers2)>0):
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- outliers=check_anomaly(outliers1,outliers2,res2)
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+ outliers=check_anomaly(outliers1,outliers2)
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if len(outliers)>5:
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+ outliers.to_csv('outliers'+sn+'.csv',encoding='gbk')
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outliers['sn']=sn
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anomalies=anomalies.append(outliers)
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+ anomalies.to_csv('anomalies.csv',encoding='gbk')
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if df_diag_ram_sn.empty:
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product_id=sn
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+ outliers=outliers.reset_index()
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start_time=outliers.loc[0,'时间']
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+ start_time=start_time[:14]+start_time[16]+'0:00'
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if outliers.loc[-1,'时间'] == pred1.loc[-1,'时间']:
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end_time='0000-00-00 00:00:00'
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else:
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