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@@ -209,6 +209,7 @@ def diffmin(res):
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def res_output(TestOrg,scaler,model,group,end_time):
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df_res=pd.DataFrame(columns=['product_id', 'start_time', 'end_time', 'diff_min','soc','loss_sum','loss_max','diffV','downV','diffdownV','window_step'])
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+ diff=0
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test2=create_dataset(TestOrg)
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test3=cal_dataset(test2)
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newtest=timeserie(test3)
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@@ -225,40 +226,42 @@ def res_output(TestOrg,scaler,model,group,end_time):
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res=ref_test[(ref_test['test_loss_diff']>0.03) & (ref_test['test_loss_max']>0.03) & (ref_test['SOC[%]']>15) & (ref_test['window_step']>0) & (ref_test['最大电压下降']<-3) &((ref_test['test_loss_sum']>3) | (ref_test['SOC[%]']<90))]
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else:
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res=ref_test[(ref_test['test_loss_diff']>0.6) & (ref_test['test_loss_max']>0.6) & (ref_test['SOC[%]']>15) & (ref_test['window_step']>0) & (ref_test['电压下降低偏']>3.5) &((ref_test['test_loss_sum']>3) | (ref_test['SOC[%]']<90))]
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- res=res.reset_index()
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- for k in range(len(res)):
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- if res.loc[k,'最大电压下降']<-130:
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- sn=res.loc[k,'sn']
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- win=res.loc[k,'window_step']
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- index = res[(res["sn"]== sn)&(res["window_step"]== win)].index.tolist()[0]
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- res=res.drop([index-2,index-1,index])
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- maxsum=list(res['test_loss_sum'].groupby(res['n_split']).max())
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- maxmax=list(res['test_loss_max'].groupby(res['n_split']).max())
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- res_start=res.drop_duplicates(subset=['n_split'],keep='first',inplace=False)
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- res_end=res.drop_duplicates(subset=['n_split'],keep='last',inplace=False)
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- start=list(map(lambda x:str(x),list(res_start['时间戳'].values)))
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- end=list(map(lambda x:str(x),list(res_end['时间戳'].values)))
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- product_id=list(res_start['sn'].values)
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- df_res['product_id']=product_id
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- df_res['start_time']=start
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- df_res['end_time']=end
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- df_res['loss_sum']=list(map(lambda x:round(x,3),maxsum))
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- df_res['loss_max']=list(map(lambda x:round(x,3),maxmax))
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- soc=list(res_start['SOC[%]'].values)
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- df_res['SOC']=soc
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- df_res['diffV']=list(res_start['单体压差'].values)
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- df_res['downV']=list(res_start['最大电压下降'].values)
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- df_res['diffdownV']=list(res_start['电压下降低偏'].values)
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- #df_res['window_step']=list(res_start['window_step'].values)
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- diff_min=diffmin(df_res)
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- df_res['diff_min']=diff_min
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- df_res.reset_index(drop=True,inplace=True)
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- end=datetime.datetime.strptime(str(df_res.loc[len(df_res)-1,'end_time']),'%Y-%m-%d %H:%M:%S')
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- end_time=datetime.datetime.strptime(str(end_time),'%Y-%m-%d %H:%M:%S')
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- diff=(end_time-end).total_seconds()
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- if diff<600:
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- df_res.loc[len(df_res)-1,'end_time']='0000-00-00 00:00:00'
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+ if len(res)>0:
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+ res=res.reset_index()
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+ for k in range(len(res)):
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+ if res.loc[k,'最大电压下降']<-130:
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+ sn=res.loc[k,'sn']
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+ win=res.loc[k,'window_step']
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+ index = res[(res["sn"]== sn)&(res["window_step"]== win)].index.tolist()[0]
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+ res=res.drop([index-2,index-1,index])
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+
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+ maxsum=list(res['test_loss_sum'].groupby(res['n_split']).max())
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+ maxmax=list(res['test_loss_max'].groupby(res['n_split']).max())
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+ res_start=res.drop_duplicates(subset=['n_split'],keep='first',inplace=False)
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+ res_end=res.drop_duplicates(subset=['n_split'],keep='last',inplace=False)
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+ start=list(map(lambda x:str(x),list(res_start['时间戳'].values)))
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+ end=list(map(lambda x:str(x),list(res_end['时间戳'].values)))
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+ product_id=list(res_start['sn'].values)
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+ df_res['product_id']=product_id
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+ df_res['start_time']=start
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+ df_res['end_time']=end
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+ df_res['loss_sum']=list(map(lambda x:round(x,3),maxsum))
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+ df_res['loss_max']=list(map(lambda x:round(x,3),maxmax))
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+ soc=list(res_start['SOC[%]'].values)
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+ df_res['SOC']=soc
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+ df_res['diffV']=list(res_start['单体压差'].values)
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+ df_res['downV']=list(res_start['最大电压下降'].values)
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+ df_res['diffdownV']=list(res_start['电压下降低偏'].values)
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+ #df_res['window_step']=list(res_start['window_step'].values)
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+ diff_min=diffmin(df_res)
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+ df_res['diff_min']=diff_min
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+ df_res.reset_index(drop=True,inplace=True)
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+ end=datetime.datetime.strptime(str(df_res.loc[len(df_res)-1,'end_time']),'%Y-%m-%d %H:%M:%S')
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+ end_time=datetime.datetime.strptime(str(end_time),'%Y-%m-%d %H:%M:%S')
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+ diff=(end_time-end).total_seconds()
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+ if diff<600:
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+ df_res.loc[len(df_res)-1,'end_time']='0000-00-00 00:00:00'
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return df_res,diff
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##################################################################################################################
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