from sklearn.preprocessing import StandardScaler import keras import os import pandas as pd import numpy as np from LIB.BACKEND import DataPreProcess import datetime #数据预处理 #删除采样异常点 def delete(data_bms): listV=[s for s in list(data_bms) if '单体电压' in s] listT=[s for s in list(data_bms) if '单体温度' in s] listT2=[s for s in list(data_bms) if '其他温度' in s] #data_bms2=data_bms.copy() for i in range(1,len(listV)+1): data_bms=data_bms[(data_bms['单体电压'+str(i)]>1000) & (data_bms['单体电压'+str(i)]<6000)] for i in range(1,len(listT)+1): data_bms=data_bms[(data_bms['单体温度'+str(i)]>-20) & (data_bms['单体温度'+str(i)]<100)] #for i in range(1,len(listT2)+1): #data_bms=data_bms[(data_bms['其他温度'+str(1)]>-20) & (data_bms['其他温度'+str(1)]<100)] #data_outliers=data_bms2.iloc[list(set(list(data_bms2.index)).difference(set(list(data_bms.index))))] data_bms=data_bms.reset_index(drop=True) return data_bms #构建时间序列&选取静置状态 def data_groups(data_bms,sn,start_time,end_time): data_bms=data_bms.drop(['GSM信号','外电压','开关状态','故障等级','故障代码','绝缘电阻','上锁状态','加热状态','单体均衡状态','总输出状态'],axis=1,errors='ignore') data_set=pd.DataFrame() start_time=start_time[:17]+'00' end_time=end_time[:17]+'00' data_set['时间戳'] = pd.date_range(start=start_time, end=end_time, freq='T') #每分钟一条记录 #给数据重建新特征：充放电状态，序列 if len(data_bms['总电流[A]']==0)>0: if sn[:4] in ['MGMC','UD02']: #data_bms=rest_stscs_v1.cell_statistic.rest_sta(data_bms) data_bms=DataPreProcess.DataPreProcess.data_split_by_status_forMGMCUD02(DataPreProcess, data_bms, drive_interval_threshold=120, charge_interval_threshold=300,drive_stand_threshold=120, charge_stand_threshold=300) else: data_bms=DataPreProcess.DataPreProcess.data_split_by_status(DataPreProcess, data_bms, drive_interval_threshold=120, charge_interval_threshold=300,drive_stand_threshold=120, charge_stand_threshold=300) else: data_bms['data_split_by_status']=1 data_bms['data_status']='work' #构建等差时间序列 data_bms['时间戳']=pd.to_datetime(data_bms['时间戳']) for i in range(len(data_bms)): data_bms.loc[i,'时间戳'] = data_bms.loc[i,'时间戳'].replace(second=0) data_bms.drop_duplicates(subset='时间戳',keep='last',inplace=False) data_bms2=pd.merge(data_set,data_bms,on='时间戳',how='left') data_bms2=data_bms2.fillna(method='ffill') data_bms2=data_bms2.fillna(method='bfill') data_bms2.drop_duplicates(subset='时间戳',keep='last',inplace=True) data_bms2=data_bms2.reset_index() #删除无用特征 data_bms2=data_bms2.drop(['Unnamed: 0','level_0','index','Unnamed: 0.1','充电状态','data_split_by_crnt'],axis=1,errors='ignore') #按状态分表 data_stand=data_bms2[data_bms2['data_status']=='stand'] return data_stand #标记时段 def split(data0): data0=data0.reset_index(drop=True) data0=data0.drop(['Unnamed: 0','Unnamed: 0.1'],axis=1,errors='ignore') data0['n_split']=np.nan data1=data0.copy() data1.drop_duplicates(subset=['data_split_by_status'],keep='first',inplace=True) data1['n_split']=range(1,len(data1)+1) data0.loc[data1.index,'n_split']=list(data1['n_split']) data0['n_split']=list(data0['n_split'].fillna(method='ffill')) time=list(map(lambda x: str(x),list(data0['时间戳']))) data0['时间戳']=time return data0 #################################################################################################################### #每10min一条记录：平均 def create_dataset(data_set): #X为dataframe，y为serie data_set=data_set.drop(['总电流[A]','SOH[%]','data_status','data_split_by_status'],axis=1,errors='ignore') time=list(map(lambda x: x[:15]+'0'+x[16:],list(data_set['时间戳']))) data_set['时间戳']=time List_n_split=sorted(list(set(data_set['n_split']))) data_set2=pd.DataFrame() for k in List_n_split: dataset=data_set[data_set['n_split']==k] if len(dataset)>10: dataset=dataset.reset_index(drop=True) sn=list(dataset['sn'].values)[0] dataset=dataset.drop(['sn','n_split'],axis=1) dataset2=dataset.groupby(dataset['时间戳']).mean() dataset2=dataset2.reset_index() dataset2['sn']=sn dataset2['n_split']=k data_set2=data_set2.append(dataset2) return data_set2 # 计算各单体电压下降量 def cal_dataset(df_stand): #X为dataframe，y为serie List_n_split=sorted(list(set(df_stand['n_split']))) listV=[s for s in list(df_stand) if '单体电压' in s] listT=[s for s in list(df_stand) if '温度' in s] newdataset=pd.DataFrame() for k in List_n_split: dataset=df_stand[df_stand['n_split']==k] dataset=dataset.reset_index(drop=True) dataset2=dataset[listV] dataset3=dataset2.diff() #periods=1, axis=0 dataset3['最大电压下降']=dataset3[listV].min(axis=1) dataset3['平均电压下降']=dataset3[listV].mean(axis=1) dataset3['电压下降低偏']=dataset3[listV].mean(axis=1)-dataset3[listV].min(axis=1) dataset3=dataset3.drop(listV+['平均电压下降'],axis=1) dataset4=dataset.drop(listT+listV+['总电压[V]'],axis=1) dataset5=pd.merge(dataset4,dataset3,left_index=True,right_index=True) dataset5=dataset5.dropna(axis=0) newdataset=newdataset.append(dataset5) return newdataset #每1hour一条记录：总和 def timeserie(data_set): #X为dataframe，y为serie List_n_split=sorted(list(set(data_set['n_split']))) time=list(map(lambda x: x[:14]+'00'+x[16:],list(data_set['时间戳']))) data_set['时间戳']=time data_set2=pd.DataFrame() for k in List_n_split: dataset=data_set[data_set['n_split']==k] if len(dataset)>10: dataset=dataset.reset_index(drop=True) sn=list(dataset['sn'].values)[0] soc=list(dataset['SOC[%]'].values)[0] dataset=dataset.drop(['sn','n_split'],axis=1) dataset2=dataset.groupby(dataset['时间戳']).sum() dataset2=dataset2.reset_index() dataset2['sn']=sn dataset2['n_split']=k dataset2['SOC[%]']=soc data_set2=data_set2.append(dataset2) return data_set2 def makescaler_test(scaler,data_test): data_test=data_test.reset_index(drop=True) data_test_pro=data_test.drop(['n_split','时间戳','sn','SOC[%]'],axis=1) test_sc=scaler.transform(np.array(data_test_pro)) test_sc=pd.DataFrame(test_sc) test_sc['n_split']=data_test['n_split'].values return test_sc #滑窗 def create_win(data_set,data_train,time_steps=5): #X为dataframe，y为serie a,b=[],[] index=pd.DataFrame() List_n_split=sorted(list(set(data_set['n_split']))) for k in List_n_split: dataset=data_set[data_set['n_split']==k] datatrain=data_train[data_train['n_split']==k] if len(dataset)>time_steps: dataset2=dataset.reset_index(drop=True) dataset=dataset.drop(['n_split'],axis=1) dataX, dataY = [], [] index_step=[] for i in range(len(dataset)-time_steps): v1 = dataset.iloc[i:(i+time_steps)].values v2 = dataset.iloc[i+time_steps] dataX.append(v1) dataY.append(v2) index_step.append(i) dataset3=dataset2.iloc[:len(dataset2)-time_steps] newdatatrain=datatrain[:len(dataset3)] newdatatrain2=newdatatrain.copy() newdatatrain2['window_step']=index_step dataX2=np.array(dataX) dataY2=np.array(dataY) a.append(dataX2) b.append(dataY2) index=index.append(newdatatrain2) aa=np.vstack(a) bb=np.vstack(b) return aa,bb,index def pred(Test,model): test_pred = model.predict(Test) test_loss = np.mean(np.abs(test_pred - Test), axis=1) return test_loss def ref(test_loss,new_test): test_loss_sum=test_loss.sum(axis=1) test_loss_max=test_loss.max(axis=1) ref_test=new_test.reset_index(drop=True) ref_test['test_loss_sum']=test_loss_sum ref_test['test_loss_max']=test_loss_max ref_test['test_loss压差']=test_loss[:,0] ref_test['test_loss降幅']=test_loss[:,1] ref_test['test_loss降差']=test_loss[:,2] return ref_test def difftime(delta): seconds = delta.total_seconds() minutes = seconds/60 return minutes def diffmin(res): start=list(res['start_time']) end=list(res['end_time']) start=list(map(lambda x: datetime.datetime.strptime(str(x),'%Y-%m-%d %H:%M:%S'),start)) end=list(map(lambda x: datetime.datetime.strptime(str(x),'%Y-%m-%d %H:%M:%S'),end)) diff=np.array(end)-np.array(start) diff_min=list(map(lambda x: difftime(x),diff)) return diff_min def res_output(TestOrg,scaler,model,group,end_time): df_res=pd.DataFrame(columns=['product_id', 'start_time', 'end_time', 'diff_min','soc','loss_sum','loss_max','diffV','downV','diffdownV','window_step']) diff=0 test2=create_dataset(TestOrg) test3=cal_dataset(test2) newtest=timeserie(test3) if len(newtest)>0: test_sc=makescaler_test(scaler,newtest) Test,y_test,win_test=create_win(test_sc,newtest,time_steps=3) test_loss=pred(Test,model) ref_test=ref(test_loss,win_test) ref_test['test_loss_diff']=list(map(lambda x: x[0]-x[1], zip(list(ref_test['test_loss_sum']), list(ref_test['test_loss_max'])))) if group=='MGMCL': res=ref_test[(ref_test['test_loss_max']>0.04) & (ref_test['SOC[%]']>15) & (ref_test['test_loss_sum']>0.06) & (ref_test['window_step']>0) & (ref_test['最大电压下降']<-3)] elif group=='PK504': 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))] else: 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))] if len(res)>0: res=res.reset_index() for k in range(len(res)): if res.loc[k,'最大电压下降']<-130: sn=res.loc[k,'sn'] win=res.loc[k,'window_step'] index = res[(res["sn"]== sn)&(res["window_step"]== win)].index.tolist()[0] res=res.drop([index-2,index-1,index],errors='ignore') if len(res)>0: maxsum=list(res['test_loss_sum'].groupby(res['n_split']).max()) maxmax=list(res['test_loss_max'].groupby(res['n_split']).max()) res_start=res.drop_duplicates(subset=['n_split'],keep='first',inplace=False) res_end=res.drop_duplicates(subset=['n_split'],keep='last',inplace=False) start=list(map(lambda x:str(x),list(res_start['时间戳'].values))) end=list(map(lambda x:str(x),list(res_end['时间戳'].values))) product_id=list(res_start['sn'].values) df_res['product_id']=product_id df_res['start_time']=start df_res['end_time']=end df_res['loss_sum']=list(map(lambda x:round(x,3),maxsum)) df_res['loss_max']=list(map(lambda x:round(x,3),maxmax)) soc=list(res_start['SOC[%]'].values) df_res['SOC']=soc df_res['diffV']=list(res_start['单体压差'].values) df_res['downV']=list(res_start['最大电压下降'].values) df_res['diffdownV']=list(res_start['电压下降低偏'].values) #df_res['window_step']=list(res_start['window_step'].values) diff_min=diffmin(df_res) df_res['diff_min']=diff_min df_res.reset_index(drop=True,inplace=True) end=datetime.datetime.strptime(str(df_res.loc[len(df_res)-1,'end_time']),'%Y-%m-%d %H:%M:%S') end_time=datetime.datetime.strptime(str(end_time),'%Y-%m-%d %H:%M:%S') diff=(end_time-end).total_seconds() if diff<600: df_res.loc[len(df_res)-1,'end_time']='0000-00-00 00:00:00' return df_res,diff ################################################################################################################## def arrange(result,result_final,start_time,diff): result=result.reset_index(drop=True) start=datetime.datetime.strptime(str(result.loc[0,'start_time']),'%Y-%m-%d %H:%M:%S') start_time=datetime.datetime.strptime(str(start_time),'%Y-%m-%d %H:%M:%S') diff_time=(start-start_time).total_seconds() if diff_time<600: result_final['end_time']=result.loc[0,'end_time'] diff_min_org=result_final['diff_min'] diff_min_new=result.loc[0,'diff_min'] result_final['diff_min']=diff_min_org+(diff_time+diff)/60+diff_min_new result=result.drop(0) return result,result_final