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+import pandas as pd
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+import pymysql
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+from sqlalchemy import create_engine
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+import datetime
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+import pdb
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+
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+#建立引擎
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+engine = create_engine(str(r"mysql+mysqldb://%s:" + '%s' + "@%s/%s") % ('root', 'pengmin', 'localhost', 'qixiangdb'))
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+#连接到qx数据库
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+conn_qx = pymysql.connect(
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+ host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com',
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+ user='qx_cas',
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+ password='Qx@123456',#Qx@123456
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+ database='qx_cas',
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+ charset='utf8'
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+ )
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+#连接到本地数据库，输出物
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+conn_local = pymysql.connect(
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+ host='localhost',
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+ user='root',
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+ password='pengmin',
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+ database='qixiangdb',
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+ charset='utf8'
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+ )
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+
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+#计算下一个soc
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+def getNextSoc(start_soc):
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+ '''输入当前的soc,寻找目标soc函数'''
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+ if start_soc>80:
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+ next_soc=80
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+ elif start_soc>60:
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+ next_soc=60
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+ elif start_soc>40:
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+ next_soc=40
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+ elif start_soc>20:
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+ next_soc=20
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+ else:
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+ next_soc=5#下一次目标soc
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+ return next_soc
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+#更新全部5个区间段的factor
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+def updtSnFct(sn_factor_df,end_soc,delta_range,range_soc):
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+ '''输入当前的soc区间段，里程变量量，soc变化量，输出新的df
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+ sn_factor_df为dataframe，delta_range单位为km，range_soc单位为km/persoc'''
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+ if end_soc==80:
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+ updtFctByCol(sn_factor_df,'a0',delta_range,range_soc)
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+ elif end_soc==60:
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+ updtFctByCol(sn_factor_df,'a1',delta_range,range_soc)
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+ elif end_soc==40:
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+ updtFctByCol(sn_factor_df,'a2',delta_range,range_soc)
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+ elif end_soc==20:
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+ updtFctByCol(sn_factor_df,'a3',delta_range,range_soc)
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+ elif end_soc<20:
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+ updtFctByCol(sn_factor_df,'a4',delta_range,range_soc)
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+ return sn_factor_df
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+#更新一列的factor
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+def updtFctByCol(sn_factor_df,colmun_name,delta_range,range_soc):
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+ '''更新制定列的factor，sn_factor_df为dataframe，新的系数更新到第一行。delta_range单位为km，
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+ range_soc单位为km/persoc，默认按照100km更新续驶里程权重'''
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+ range_soc_old=sn_factor_df.loc[0,colmun_name]#读取第0行的老factor
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+ debounce_range=100#更新权重
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+ new_factor=range_soc*((delta_range)/debounce_range)+range_soc_old*(1-delta_range/debounce_range)
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+ #在第1行，存储新的factor
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+ sn_factor_df.loc[1,colmun_name]=new_factor
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+ return sn_factor_df
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+#更新今日的factor
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+def updtTodayFct(factor_input,sn_day_df):
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+ '''更新今日的Factor***'''
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+ sn_factor_df_last=factor_input
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+ start_soc=sn_day_df.loc[0,'soc']#首行soc
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+ next_soc=getNextSoc(start_soc)#下一个目标soc
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+ start_range=sn_day_df.loc[0,'vehodo']#首行vehodo
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+ sn=sn_day_df.loc[0,'name']#sn号
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+
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+ for index in range(len(sn_day_df)-1):
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+ #寻找分割点，
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+ index_soc=sn_day_df.loc[index,'soc']#当前行soc
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+ next_index_soc=sn_day_df.loc[index+1,'soc']#下一行soc
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+
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+ if (index_soc>=next_soc)&(next_index_soc<next_soc):
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+ #当前行soc>目标soc，下一行低soc<目标soc，说明到达了分割点80-60-40-20
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+ delta_soc_tonext=start_soc-next_soc#两个距离点的soc差，单位为%
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+ delta_range_tonext=sn_day_df.loc[index,'vehodo']-start_range#两个时间点的距离差，单位为m
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+ delta_range_tonext_km=delta_range_tonext/1000#两个时间点的距离差，单位为km
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+ range_soc_tonext=(delta_range_tonext_km)/delta_soc_tonext#单位soc可行驶的公里数
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+
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+ # print(sn+'start_soc: '+str(start_soc),'next_soc: '+str(next_soc),'delta_vehodo； '+str(round(delta_range_tonext_km,3))
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+ # +'km'+' range_soc:'+str(round(range_soc_tonext,3)))#调试用语句，看单次factor变化量
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+
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+ if (delta_range_tonext_km>1)&(delta_range_tonext_km<5*delta_soc_tonext):
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+ #里程变化量>1km。且<5倍的soc变化量，大于此值认为不合理。
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+ sn_factor_df_last=updtSnFct(sn_factor_df_last,next_soc,delta_range_tonext_km,range_soc_tonext)
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+
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+ start_soc=next_index_soc#变更开始soc
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+ next_soc=getNextSoc(start_soc)#变更结束soc
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+ start_range=sn_day_df.loc[index+1,'vehodo']#变更开始里程
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+
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+ return sn_factor_df_last
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+#对driveinfo进行预处理
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+def snDayDfPreProcess(sn_day_df):
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+ '''预处理，判断是否在dirvemode，获取drivemode条件下的累计行驶距离。
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+ 增加delta_soc列，drive_flg列，vehodo列'''
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+ sn_day_df=sn_day_df.reset_index(drop=True)#重置index
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+ #增加列，计算delta_soc
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+ for index in range(len(sn_day_df)):
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+ if index==0:
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+ sn_day_df.loc[index,'delta_soc']=0
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+ else:
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+ sn_day_df.loc[index,'delta_soc']=sn_day_df.loc[index,'soc']-sn_day_df.loc[index-1,'soc']
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+ #增加列，判断是否在drive状态
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+ drive_flg=False
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+ accum_distance=0
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+ for index in range(len(sn_day_df)):
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+ if index==0:
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+ sn_day_df.loc[index,'drive_status']=drive_flg
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+ sn_day_df.loc[index,'vehodo']=0
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+ else:
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+ if (sn_day_df.loc[index,'delta_soc']<-0.1)|\
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+ ((sn_day_df.loc[index,'delta_soc']<=0)&(sn_day_df.loc[index,'distance']>500)):#soc处于下降状态，说明在drive
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+ drive_flg=True#置true
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+ elif sn_day_df.loc[index,'delta_soc']>0.1:#soc处于上升状态，说明不在drive
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+ drive_flg=False#置false
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+ accum_distance=0#清零
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+ sn_day_df.loc[index,'drive_flg']=drive_flg
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+ accum_distance+=sn_day_df.loc[index,'distance']#对行驶里程进行累加
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+ sn_day_df.loc[index,'vehodo']=accum_distance
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+ #筛选所有的drive信息行
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+ sn_day_drive_df=sn_day_df.loc[sn_day_df['drive_flg']==True,:]
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+ #按时间进行一次筛选,此处丢弃了晚上0点以后的行车数据
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+
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+ sn_day_drive_df=sn_day_drive_df.reset_index(drop=True)#重置index
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+
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+ return sn_day_drive_df
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+
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+
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+#更新所有sn,连读多天的的factor
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+def updtAllSnFct(start_date,end_date):
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+ '''计算开始时间到结束时间的，所有sn的factor'''
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+ start_date_datetime=datetime.datetime.strptime(start_date,'%Y-%m-%d')#开始时间
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+ end_date_datetime=datetime.datetime.strptime(end_date,'%Y-%m-%d')#开始时间
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+ delta_day=(end_date_datetime-start_date_datetime).days#间隔天数
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+ i=1
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+ while i<=delta_day:
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+ end_date=(start_date_datetime+datetime.timedelta(days=i)).strftime("%Y-%m-%d")
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+ updtAllSnTodayFct(start_date,end_date)#调用函数
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+ # print('update all sn factor from '+start_date+" to "+end_date)
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+ start_date=end_date
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+ i+=1#自加
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+#更新所有sn,一天的factor
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+def updtAllSnTodayFct(start_date,end_date):
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+ ''''更新今天所有sn的factorx信息，start_date和end_date相隔一天。此处还可优化'''
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+ conn_local = pymysql.connect(
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+ host='localhost',
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+ user='root',
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+ password='pengmin',
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+ database='qixiangdb',
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+ charset='utf8'
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+ )
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+
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+ start_date_str="'"+start_date+"'"
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+ end_date_str="'"+end_date+"'"
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+ sql_cmd="select * from drive_info where time between "+start_date_str+" and "+end_date_str+" and distance!=0;"
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+ range_soc_df = pd.read_sql(sql_cmd, conn_qx)#使用read_sql方法查询qx数据库
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+
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+ #筛选出所有当日数据之后，筛选当日有更新的sn
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+ today_sn_list=range_soc_df['name'].unique().tolist()#[:100]#先一次更新5个
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+ #建立空的dataframe，用于承接所有更新的factor信息
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+ today_sn_fct_df=pd.DataFrame([],columns=['sn','date','a0','a1','a2','a3','a4'])
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+
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+ for sn in today_sn_list:
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+ #寻找factor_df，里面是否有sn号，如果没有sn对应信息，则新增信息。
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+ sn_str="'"+sn+"'"
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+ update_today_factor_flg=True
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+ sql_cmd3="select sn,date,a0,a1,a2,a3,a4 from tb_sn_factor where date="+start_date_str+" and sn="+sn_str
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+ factor_today_df=pd.read_sql(sql_cmd3, conn_local)#使用read_sql方法查询local数据库
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+ if len(factor_today_df)>=1:
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+ print(sn+' '+start_date_str+' factor exist in table! Factor not update.')
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+ update_today_factor_flg=False
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+
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+ sql_cmd2="select sn,date,a0,a1,a2,a3,a4 from tb_sn_factor where date<"+start_date_str+" and sn="+sn_str
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+ #此处可以限定每次查询的数量，例如不高于5行
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+ factor_df=pd.read_sql(sql_cmd2, conn_local)#使用read_sql方法查询local数据库
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+
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+ #按照sn号和日期进行去重，避免运行时重复产生factor数据，保留第一次出现的行。
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+ factor_df=factor_df.drop_duplicates(subset=['sn','date'],keep='first')
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+
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+ if len(factor_df)==0:
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+ #如果没有搜索到factor历史数据,则声明一个新的进行初始化
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+ start_date_datetime=datetime.datetime.strptime(start_date,'%Y-%m-%d')
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+ yesterday=(start_date_datetime+datetime.timedelta(days=-1)).strftime("%Y-%m-%d")
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+ #为sn申请一个新的factor，初始值为1
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+ factor_df=pd.DataFrame({'sn':sn,'date':yesterday,'a0':[1],'a1':[1],'a2':[1],'a3':[1],'a4':[1]})
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+ sn_factor_df=factor_df.loc[factor_df['sn']==sn,:]#筛选sn对应的factor
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+ sn_factor_df=sn_factor_df.sort_values(by='date',ascending='True')#按照日期排序
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+
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+ sn_factor_df_last=sn_factor_df.tail(1).copy()#寻找最后一行，代表最近日期
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+ sn_factor_df_last=sn_factor_df_last.append(sn_factor_df_last)#新增加一行,用于存储新的factor
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+ sn_factor_df_last=sn_factor_df_last.reset_index(drop=True)#重置index
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+ sn_factor_df_last.loc[1,'date']=start_date#更改后一行的date为当前日期
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+ #筛选对应车辆的信息
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+ condition_sn=(range_soc_df['name']==sn)
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+ sn_day_df=range_soc_df.loc[condition_sn,:].copy()
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+ sn_day_df=sn_day_df.reset_index(drop=True)
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+ #使用updtTodayFct函数更新今天的factor
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+ if len(sn_day_df)>=2:
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+ #使用process函数，进行预处理
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+ sn_day_df=snDayDfPreProcess(sn_day_df)#预处理函数
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+ # 临时措施，删除每天晚上0点以后的数据，5点以前的数据，防止对驾驶cycle判断产生影响。
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+ day_start_time=datetime.datetime.strptime(start_date,'%Y-%m-%d')
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+ day_morning_time=day_start_time+datetime.timedelta(hours=5)
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+ morning_time_str=day_morning_time.strftime('%Y-%m-%d %H:%M:%S')
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+ sn_day_df=sn_day_df.loc[sn_day_df['time']>morning_time_str,:]#去除掉了每天晚上0点以后的数据，短期措施
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+ sn_day_df=sn_day_df.reset_index(drop=True)#重置index
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+
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+ if len(sn_day_df)>=2:
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+ sn_factor_df_new=updtTodayFct(sn_factor_df_last,sn_day_df)#
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+ if (len(sn_factor_df_new)>=2)&(update_today_factor_flg):#如果factor
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+ today_sn_fct_df=today_sn_fct_df.append(sn_factor_df_new.loc[1,:])#筛选第一行，进行拼接，最后写入到数据库中
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+
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+ #将today_sn_fct_df写入到数据库中，今天所有factor更新的系数，一次写入。
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+ if len(today_sn_fct_df)>=1:
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+ today_sn_fct_df.to_sql('tb_sn_factor',con=engine,chunksize=10000,if_exists='append',index=False)
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+
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+#更新一个sn，连续多天的factor
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+def updtOneSnFct(sn,start_date,end_date):
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+ '''计算开始时间到结束时间的，一个sn的所有factor。
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+ 重复多次调用，updtOneSnTodayFct。
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+ '''
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+ start_date_datetime=datetime.datetime.strptime(start_date,'%Y-%m-%d')#开始时间
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+ end_date_datetime=datetime.datetime.strptime(end_date,'%Y-%m-%d')#开始时间
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+ delta_day=(end_date_datetime-start_date_datetime).days#间隔天数
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+ i=1
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+ while i<=delta_day:
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+ end_date=(start_date_datetime+datetime.timedelta(days=i)).strftime("%Y-%m-%d")
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+ # print('update one '+sn+'factor from '+start_date+" to "+end_date)
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+ updtOneSnTodayFct(sn,start_date,end_date)#调用函数,更新当日的factor。
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+ start_date=end_date
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+ i+=1#自加
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+#更新一个sn,一天的factor
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+def updtOneSnTodayFct(sn,start_date,end_date):
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+ '''更新一个sn,一天的factor。'''
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+ #重新建立连接,更新数据库
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+ conn_local = pymysql.connect(
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+ host='localhost',
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+ user='root',
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+ password='pengmin',
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+ database='qixiangdb',
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+ charset='utf8'
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+ )
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+
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+ start_date_str="'"+start_date+"'"
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+ end_date_str="'"+end_date+"'"
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+ sn_str="'"+sn+"'"
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+ sql_cmd="select * from drive_info where time between "+start_date_str+" and "+end_date_str+\
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+ " and distance!=0 and name="+sn_str
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+ range_soc_df = pd.read_sql(sql_cmd, conn_qx)#使用read_sql方法查询qx数据库
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+
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+ if len(range_soc_df)>0:
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+ #筛选出所有当日数据之后，筛选当日有更新的sn
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+ today_sn_list=range_soc_df['name'].unique().tolist()
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+ #建立空的dataframe，用于承接所有更新的factor信息
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+ today_sn_fct_df=pd.DataFrame([],columns=['sn','date','a0','a1','a2','a3','a4'])
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+
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+ for sn in today_sn_list:
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+ #寻找factor_df，里面是否有sn号，如果没有sn对应信息，则新增信息。
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+ sn_str="'"+sn+"'"
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+
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+ update_today_factor_flg=True
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+ sql_cmd3="select sn,date,a0,a1,a2,a3,a4 from tb_sn_factor where date="+start_date_str+" and sn="+sn_str
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+ factor_today_df=pd.read_sql(sql_cmd3, conn_local)#使用read_sql方法查询local数据库
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+ if len(factor_today_df)>=1:
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+ print(sn+' '+start_date_str+' factor exist in table! Factor not update.')
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+ update_today_factor_flg=False
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+
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+ sql_cmd2="select sn,date,a0,a1,a2,a3,a4 from tb_sn_factor where date<="+start_date_str+" and sn="+sn_str
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+ factor_df=pd.read_sql(sql_cmd2, conn_local)#使用read_sql方法查询local数据库
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+ #按照sn号和日期进行去重，避免运行时重复产生factor数据，保留第一次出现的行。
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+ factor_df=factor_df.drop_duplicates(subset=['sn','date'],keep='first')
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+ # pdb.set_trace()
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+ if len(factor_df)==0:
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+ #如果没有搜索到factor历史数据,则声明一个新的进行初始化
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+ start_date_datetime=datetime.datetime.strptime(start_date,'%Y-%m-%d')
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+ yesterday=(start_date_datetime+datetime.timedelta(days=-1)).strftime("%Y-%m-%d")
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+ factor_df=pd.DataFrame({'sn':sn,'date':yesterday,'a0':[1],'a1':[1],'a2':[1],'a3':[1],'a4':[1]})
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+ today_sn_fct_df=today_sn_fct_df.append(factor_df.loc[0,:])#将初始化的行记录到数据库
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+
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+ sn_factor_df=factor_df.loc[factor_df['sn']==sn,:]#筛选sn对应的factor
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+ sn_factor_df=sn_factor_df.sort_values(by='date',ascending='True')#按照日期排序
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+
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+ sn_factor_df_last=sn_factor_df.tail(1).copy()#寻找最后一行，代表最近日期
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+ sn_factor_df_last=sn_factor_df_last.append(sn_factor_df_last)#新增加一行,用于存储新的factor
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+ sn_factor_df_last=sn_factor_df_last.reset_index(drop=True)#重置index
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+ sn_factor_df_last.loc[1,'date']=start_date#更改后一行的date为当前日期
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+ #筛选对应车辆的信息
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+ condition_sn=(range_soc_df['name']==sn)
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+ sn_day_df=range_soc_df.loc[condition_sn,:].copy()
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+ sn_day_df=sn_day_df.reset_index(drop=True)
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+ #使用updtTodayFct函数更新今天的factor
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+ if len(sn_day_df)>=2:
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+ #使用process函数，进行预处理
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+ sn_day_df=snDayDfPreProcess(sn_day_df)#!!!!!!!!!!!增加
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+ # 临时措施，删除每天晚上0点以后的数据，5点以前的数据，防止对驾驶cycle判断产生影响。
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+ day_start_time=datetime.datetime.strptime(start_date,'%Y-%m-%d')
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+ day_morning_time=day_start_time+datetime.timedelta(hours=5)
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+ morning_time_str=day_morning_time.strftime('%Y-%m-%d %H:%M:%S')
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+ sn_day_df=sn_day_df.loc[sn_day_df['time']>morning_time_str,:]#去除掉了每天晚上0点以后的数据，短期措施
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+ sn_day_df=sn_day_df.reset_index(drop=True)#重置index
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+
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+ if len(sn_day_df)>=2:
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+ sn_factor_df_new=updtTodayFct(sn_factor_df_last,sn_day_df)#更新fator的主函数
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+
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+ if (len(sn_factor_df_new)>=2)&(update_today_factor_flg):#如果今日factor没有更新
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+ today_sn_fct_df=today_sn_fct_df.append(sn_factor_df_new.loc[1,:])#筛选第一行，进行拼接，最后写入到数据库中
|
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+
|
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+ # #将today_sn_fct_df写入到数据库中
|
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+ if len(today_sn_fct_df)>=1:
|
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|
+ today_sn_fct_df.to_sql('tb_sn_factor',con=engine,chunksize=10000,if_exists='append',index=False)
|
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+ # print(sn+' factor will be update in table tb_sn_factor!')
|
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+ return sn_factor_df_new
|
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+
|
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+
|
|
|
+#更新最新的factor，一天调用一次。
|
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+def updtNewestFctTb():
|
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|
+
|
|
|
+ '''更新tb_sn_factor_newest，只保留最新日期的factor。
|
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+ 从tb_sn_factor中，筛选最新的日期。
|
|
|
+ 函数每天运行一次，从tb_sn_factor中筛选最新日期的factor。'''
|
|
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+
|
|
|
+ current_time=datetime.datetime.now()#当前时间
|
|
|
+ current_time_str=current_time.strftime('%Y-%m-%d %H:%M:%S')#时间格式化为字符串,年-月-日 时-分-秒
|
|
|
+ current_time_str="'"+current_time_str+"'"
|
|
|
+
|
|
|
+ sql_cmd_4="select sn,date,a0,a1,a2,a3,a4 from tb_sn_factor where date<"+current_time_str
|
|
|
+ factor_all_df = pd.read_sql(sql_cmd_4, conn_local)#使用read_sql方法查询qx数据库
|
|
|
+ #筛选今天之前的所有factor，只保留最近的一天。
|
|
|
+ sn_list=factor_all_df['sn'].unique().tolist()#筛选sn序列
|
|
|
+ newest_sn_fct_df=pd.DataFrame([],columns=['sn','date','a0','a1','a2','a3','a4'])#声明空df
|
|
|
+
|
|
|
+ for sn in sn_list:
|
|
|
+ condition_sn=(factor_all_df['sn']==sn)
|
|
|
+ factor_pick_df=factor_all_df.loc[condition_sn,:]#按照sn进行筛选
|
|
|
+ factor_pick_df=factor_pick_df.sort_values(by='date')#按照日期排序
|
|
|
+ factor_last_df=factor_pick_df.tail(1)#选择最后日期
|
|
|
+ newest_sn_fct_df=newest_sn_fct_df.append(factor_last_df)#拼接到空df中
|
|
|
+
|
|
|
+ #按照日期排序，只保留最近的一天,输出factor_unique_df，方法为replace。
|
|
|
+ #本函数，每天需要运行一次，用于更新factor。
|
|
|
+ newest_sn_fct_df.to_sql('tb_sn_factor_newest',con=engine,chunksize=10000,\
|
|
|
+ if_exists='replace',index=False)
|
|
|
+#使用factor和soc推荐剩余续驶里程
|
|
|
+def calDistFromFct(input_df):
|
|
|
+ '''根据sn-time-soc-a0-a1-a2-a3-a4，使用factor正向计算计算VehElecRng。'''
|
|
|
+ row_df=input_df.copy()
|
|
|
+ soc=row_df['soc']#获取soc
|
|
|
+ factor=[]
|
|
|
+ factor.append(row_df['a4'])#0~20之间的factor
|
|
|
+ factor.append(row_df['a3'])#20~40之间的factor
|
|
|
+ factor.append(row_df['a2'])#40~60之间的factor
|
|
|
+ factor.append(row_df['a1'])#60~80之间的factor
|
|
|
+ factor.append(row_df['a0'])#80~100之间的factor
|
|
|
+
|
|
|
+ gap=20
|
|
|
+ yushu=soc%gap#余数部分
|
|
|
+ zhengshu=soc//gap#整数部分
|
|
|
+ i=0
|
|
|
+ range=0
|
|
|
+ while i<zhengshu:
|
|
|
+ dur_factor=factor[i]#当前权重
|
|
|
+ range+=dur_factor*gap#分段累加里程
|
|
|
+ i=i+1
|
|
|
+ if yushu>0.01:#避免soc=100时报错
|
|
|
+ range=range+yushu*factor[zhengshu]#最后把余项对应的里程加上
|
|
|
+ row_df['vehelecrng']=range#给VehElecRng列赋值
|
|
|
+ return row_df
|
|
|
+#更新当前时间对应的里程，每5min调用一次
|
|
|
+def updtVehElecRng(input_time='2021-07-29 12:01:00'):
|
|
|
+ '''更新续驶里程，到tb_sn_factor_soc_range。
|
|
|
+ 部署时设置每5min更新一次。
|
|
|
+ '''
|
|
|
+ #设置一个时间作为结束时间
|
|
|
+ # current_time=datetime.datetime.now()
|
|
|
+ current_time_raw=input_time#当前时间
|
|
|
+ current_time=datetime.datetime.strptime(current_time_raw,'%Y-%m-%d %H:%M:%S')#字符串转时间
|
|
|
+
|
|
|
+ #结束时间往前4min,59s，作为起始时间
|
|
|
+ before6min_time_str=(current_time+datetime.timedelta(minutes=-4,seconds=-59)).strftime('%Y-%m-%d %H:%M:%S')#6min前
|
|
|
+ before6min_time_str="'"+before6min_time_str+"'"
|
|
|
+ current_time_str=current_time.strftime('%Y-%m-%d %H:%M:%S')#时间格式化为字符串
|
|
|
+ current_time_str="'"+current_time_str+"'"
|
|
|
+
|
|
|
+ #从drive_info里面读取，该时间段内的name,time,soc三列
|
|
|
+ sql_cmd="select name,time,soc from drive_info where time between "+before6min_time_str+" and "+current_time_str
|
|
|
+ print(sql_cmd)
|
|
|
+ range_soc_df = pd.read_sql(sql_cmd, conn_qx)#使用read_sql方法查询qx数据库
|
|
|
+ range_soc_df.rename(columns={'name':'sn'},inplace=True)#将name列重命名为sn列
|
|
|
+
|
|
|
+ #任务2，从tb_sn_factor_newest里面读取最新的factor，获取距离今天最近的一个factor list
|
|
|
+ sql_cmd_1="select sn,a0,a1,a2,a3,a4 from tb_sn_factor_newest"
|
|
|
+ print(sql_cmd_1)
|
|
|
+ sn_factor_newest_df_raw = pd.read_sql(sql_cmd_1, conn_local)#使用read_sql方法查询qx数据库
|
|
|
+
|
|
|
+ #任务3，将range_soc_df和sn_factor_newest_df_raw，双表合并成为一个新表格。
|
|
|
+ sn_soc_factor_df=pd.merge(range_soc_df,sn_factor_newest_df_raw,how='left',on='sn')
|
|
|
+ sn_soc_factor_df.fillna(1,inplace=True)#如果range_soc_df中有sn号，但sn_factor_newest_df_raw中没有。用1填充。
|
|
|
+ # sn_soc_factor_df.head()
|
|
|
+ #填充完成后，sn-time-soc-a0-a1-a2-a3-a4都已经齐全。
|
|
|
+
|
|
|
+ #任务4，调用函数，将VehElecRng计算出来
|
|
|
+ sn_soc_factor_range_df=pd.DataFrame([],columns=['sn','time','soc','a0','a1','a2','a3','a4','vehelecrng'])
|
|
|
+ for index in sn_soc_factor_df.index.tolist():
|
|
|
+ input_df=sn_soc_factor_df.loc[index,:]#挑选
|
|
|
+ sn_soc_factor_range_row=calDistFromFct(input_df)#计算VehElecRng
|
|
|
+ sn_soc_factor_range_df=sn_soc_factor_range_df.append(sn_soc_factor_range_row)#拼接
|
|
|
+
|
|
|
+ ##任务5，将sn_soc_factor_range_df写入到tb_sn_factor_soc_range中,使用替换关系。
|
|
|
+ sn_soc_factor_range_df.to_sql('tb_sn_factor_soc_range',con=engine,chunksize=10000,\
|
|
|
+ if_exists='replace',index=False)
|
|
|
+
|
