data_analyze_platform/LIB/MIDDLE/odo/UpdtFct.py

import pandas as pd
import pymysql
from sqlalchemy import create_engine
import datetime

#建立引擎
engine = create_engine(str(r"mysql+mysqldb://%s:" + '%s' + "@%s/%s") % ('root', 'pengmin', 'localhost', 'qixiangdb'))

conn_qx = pymysql.connect(
        host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com',
        user='qx_cas',
        password='Qx@123456',#Qx@123456
        database='qx_cas',
        charset='utf8'
    )

conn_local = pymysql.connect(
        host='localhost',
        user='root',
        password='pengmin',
        database='qixiangdb',
        charset='utf8'
    )

def getNextSoc(start_soc):
    '''输入当前的soc,寻找目标soc函数'''
    if start_soc>80:
        next_soc=80
    elif start_soc>60:
        next_soc=60
    elif start_soc>40:
        next_soc=40
    elif start_soc>20:
        next_soc=20
    else:
        next_soc=1
    return next_soc

def updtSnFct(sn_factor_df,end_soc,delta_range,range_soc):
    '''输入当前的soc区间段，里程变量量，soc变化量，输出新的df
    sn_factor_df为dataframe，delta_range单位为km，range_soc单位为km/persoc'''
    if end_soc==80:
        updtFctByCol(sn_factor_df,'a0',delta_range,range_soc)
    elif end_soc==60:
        updtFctByCol(sn_factor_df,'a1',delta_range,range_soc)
    elif end_soc==40:
        updtFctByCol(sn_factor_df,'a2',delta_range,range_soc)
    elif end_soc==20:
        updtFctByCol(sn_factor_df,'a3',delta_range,range_soc)
    elif end_soc<20:
        updtFctByCol(sn_factor_df,'a4',delta_range,range_soc)
    return sn_factor_df

def updtFctByCol(sn_factor_df,colmun_name,delta_range,range_soc):
    '''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，
    range_soc单位为km/persoc，默认按照100km更新续驶里程权重'''
    range_soc_old=sn_factor_df.loc[0,colmun_name]#读取第0行的老factor
    debounce_range=200#更新权重
    new_factor=range_soc*((delta_range)/debounce_range)+range_soc_old*(1-(delta_range)/debounce_range)
    #在第1行，存储新的factor
    sn_factor_df.loc[1,colmun_name]=new_factor
    return sn_factor_df

def updtTodayFct(factor_input,sn_day_df):
    '''更新今日的Factor***'''
    sn_factor_df_last=factor_input
    start_soc=sn_day_df.loc[0,'soc']
    next_soc=getNextSoc(start_soc)
    start_range=sn_day_df.loc[0,'vehodo']
    sn=sn_day_df.loc[0,'name']

    for index in range(len(sn_day_df)-1):
    #寻找分割点，
        index_soc=sn_day_df.loc[index,'soc']#当前行soc
        next_index_soc=sn_day_df.loc[index+1,'soc']#下一行soc

        if (index_soc>=next_soc)&(next_index_soc<next_soc):#当前行高，下一行低
            delta_soc_tonext=start_soc-next_soc#两个距离点的soc差，单位为%
            delta_range_tonext=sn_day_df.loc[index,'vehodo']-start_range#两个时间点的距离差，单位为m
            delta_range_tonext_km=delta_range_tonext/1000#两个时间点的距离差，单位为km
            range_soc_tonext=(delta_range_tonext/1000)/delta_soc_tonext#单位soc可行驶的公里数
            print(sn+'start_soc: '+str(start_soc),'next_soc: '+str(next_soc),'delta_vehodo； '+str(round(delta_range_tonext_km,3))
            +'km'+' range_soc:'+str(round(range_soc_tonext,3)))

            if (delta_range_tonext_km)>1:
                sn_factor_df_last=updtSnFct(sn_factor_df_last,next_soc,delta_range_tonext_km,range_soc_tonext)
            
            start_soc=next_index_soc#变更开始soc
            next_soc=getNextSoc(start_soc)#变更结束soc
            start_range=sn_day_df.loc[index+1,'vehodo']#变更开始里程    

    return sn_factor_df_last

def snDayDfPreProcess(sn_day_df):
    '''预处理，判断是否在dirvemode，获取drivemode条件下的累计行驶距离。
    增加delta_soc列，drive_flg列，vehodo列'''
    sn_day_df=sn_day_df.reset_index(drop=True)#重置index
    #增加列，计算delta_soc
    for index in range(len(sn_day_df)):
        if index==0:
            sn_day_df.loc[index,'delta_soc']=0
        else:
            sn_day_df.loc[index,'delta_soc']=sn_day_df.loc[index,'soc']-sn_day_df.loc[index-1,'soc']
    #增加列，判断是否在drive状态
    drive_flg=False
    accum_distance=0
    for index in range(len(sn_day_df)):
        if index==0:
            sn_day_df.loc[index,'drive_status']=drive_flg
            sn_day_df.loc[index,'vehodo']=0
        else:
            if (sn_day_df.loc[index,'delta_soc']<-0.1)|\
                ((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
                drive_flg=True#置true
            elif sn_day_df.loc[index,'delta_soc']>0.1:#soc处于上升状态，说明不在drive
                drive_flg=False#置false
                accum_distance=0#清零
            sn_day_df.loc[index,'drive_flg']=drive_flg
            accum_distance+=sn_day_df.loc[index,'distance']#对行驶里程进行累加
            sn_day_df.loc[index,'vehodo']=accum_distance
    #筛选所有的drive信息行
    sn_day_drive_df=sn_day_df.loc[sn_day_df['drive_flg']==True,:]
    sn_day_drive_df=sn_day_drive_df.reset_index(drop=True)#重置index
    
    return sn_day_drive_df 

def updtAllSnFct(start_date,end_date):
    '''计算开始时间到结束时间的，所有sn的factor'''
    start_date_datetime=datetime.datetime.strptime(start_date,'%Y-%m-%d')#开始时间
    end_date_datetime=datetime.datetime.strptime(end_date,'%Y-%m-%d')#开始时间
    delta_day=(end_date_datetime-start_date_datetime).days#间隔天数
    i=1
    while i<=delta_day:
        end_date=(start_date_datetime+datetime.timedelta(days=i)).strftime("%Y-%m-%d")
        updtAllSnTodayFct(start_date,end_date)#调用函数
        print('update all sn factor from '+start_date+" to "+end_date)
        start_date=end_date
        i+=1#自加

def updtAllSnTodayFct(start_date,end_date):
    ''''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'''
    start_date_str="'"+start_date+"'"
    end_date_str="'"+end_date+"'"
    sql_cmd="select * from drive_info where time between "+start_date_str+" and "+end_date_str+" and distance!=0;"
    range_soc_df = pd.read_sql(sql_cmd, conn_qx)#使用read_sql方法查询qx数据库

    #筛选出所有当日数据之后，筛选当日有更新的sn
    today_sn_list=range_soc_df['name'].unique().tolist()#[:100]#先一次更新5个
    #建立空的dataframe，用于承接所有更新的factor信息
    today_sn_fct_df=pd.DataFrame([],columns=['sn','date','a0','a1','a2','a3','a4'])

    for sn in today_sn_list:
        #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。
        sn_str="'"+sn+"'"
        sql_cmd2="select sn,date,a0,a1,a2,a3,a4 from tb_sn_factor where date<"+start_date_str+" and sn="+sn_str
        #此处可以限定每次查询的数量，例如不高于5行
        factor_df=pd.read_sql(sql_cmd2, conn_local)#使用read_sql方法查询local数据库

        #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。
        factor_df=factor_df.drop_duplicates(subset=['sn','date'],keep='first')

        if len(factor_df)==0:
            #如果没有搜索到factor历史数据,则声明一个新的进行初始化
            start_date_datetime=datetime.datetime.strptime(start_date,'%Y-%m-%d')
            yesterday=(start_date_datetime+datetime.timedelta(days=-1)).strftime("%Y-%m-%d")
            #为sn申请一个新的factor，初始值为1
            factor_df=pd.DataFrame({'sn':sn,'date':yesterday,'a0':[1],'a1':[1],'a2':[1],'a3':[1],'a4':[1]})
        sn_factor_df=factor_df.loc[factor_df['sn']==sn,:]#筛选sn对应的factor
        sn_factor_df=sn_factor_df.sort_values(by='date',ascending='True')#按照日期排序

        sn_factor_df_last=sn_factor_df.tail(1).copy()#寻找最后一行，代表最近日期
        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
        sn_factor_df_last=sn_factor_df_last.reset_index(drop=True)#重置index
        sn_factor_df_last.loc[1,'date']=start_date#更改后一行的date为当前日期
        #筛选对应车辆的信息
        condition_sn=(range_soc_df['name']==sn)
        sn_day_df=range_soc_df.loc[condition_sn,:].copy()
        sn_day_df=sn_day_df.reset_index(drop=True)
        #使用updtTodayFct函数更新今天的factor
        if len(sn_day_df)>=2:
            #使用process函数，进行预处理
            sn_day_df=snDayDfPreProcess(sn_day_df)#预处理函数
            if len(sn_day_df)>=2:
                sn_factor_df_new=updtTodayFct(sn_factor_df_last,sn_day_df)#
                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
    
    #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。
    if len(today_sn_fct_df)>=1:
        today_sn_fct_df.to_sql('tb_sn_factor',con=engine,chunksize=10000,if_exists='append',index=False)

def updtOneSnFct(sn,start_date,end_date):
    '''计算开始时间到结束时间的，一个sn的所有factor'''
    start_date_datetime=datetime.datetime.strptime(start_date,'%Y-%m-%d')#开始时间
    end_date_datetime=datetime.datetime.strptime(end_date,'%Y-%m-%d')#开始时间
    delta_day=(end_date_datetime-start_date_datetime).days#间隔天数
    i=1
    while i<=delta_day:
        end_date=(start_date_datetime+datetime.timedelta(days=i)).strftime("%Y-%m-%d")
        updtOneSnTodayFct(sn,start_date,end_date)#调用函数
        print('update one sn factor from '+start_date+" to "+end_date)
        start_date=end_date
        i+=1#自加

def updtOneSnTodayFct(sn,start_date,end_date):
    start_date_str="'"+start_date+"'"
    end_date_str="'"+end_date+"'"
    sn_str="'"+sn+"'"
    sql_cmd="select * from drive_info where time between "+start_date_str+" and "+end_date_str+\
    " and distance!=0 and name="+sn_str
    range_soc_df = pd.read_sql(sql_cmd, conn_qx)#使用read_sql方法查询qx数据库

    if len(range_soc_df)>0:
        #筛选出所有当日数据之后，筛选当日有更新的sn
        today_sn_list=range_soc_df['name'].unique().tolist()
        #建立空的dataframe，用于承接所有更新的factor信息
        today_sn_fct_df=pd.DataFrame([],columns=['sn','date','a0','a1','a2','a3','a4'])

        for sn in today_sn_list:
            #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。
            sn_str="'"+sn+"'"
            sql_cmd2="select sn,date,a0,a1,a2,a3,a4 from tb_sn_factor where date<"+start_date_str+" and sn="+sn_str
            factor_df=pd.read_sql(sql_cmd2, conn_local)#使用read_sql方法查询local数据库

            #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。
            factor_df=factor_df.drop_duplicates(subset=['sn','date'],keep='first')

            if len(factor_df)==0:
                #如果没有搜索到factor历史数据,则声明一个新的进行初始化
                start_date_datetime=datetime.datetime.strptime(start_date,'%Y-%m-%d')
                yesterday=(start_date_datetime+datetime.timedelta(days=-1)).strftime("%Y-%m-%d")
                factor_df=pd.DataFrame({'sn':sn,'date':yesterday,'a0':[1],'a1':[1],'a2':[1],'a3':[1],'a4':[1]})
                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

            sn_factor_df=factor_df.loc[factor_df['sn']==sn,:]#筛选sn对应的factor
            
            sn_factor_df=sn_factor_df.sort_values(by='date',ascending='True')#按照日期排序
            sn_factor_df_last=sn_factor_df.tail(1).copy()#寻找最后一行，代表最近日期
            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
            sn_factor_df_last=sn_factor_df_last.reset_index(drop=True)#重置index
            sn_factor_df_last.loc[1,'date']=start_date#更改后一行的date为当前日期
            #筛选对应车辆的信息
            condition_sn=(range_soc_df['name']==sn)
            sn_day_df=range_soc_df.loc[condition_sn,:].copy()
            sn_day_df=sn_day_df.reset_index(drop=True)
            #使用updtTodayFct函数更新今天的factor
            if len(sn_day_df)>=2:
                #使用process函数，进行预处理
                sn_day_df=snDayDfPreProcess(sn_day_df)#!!!!!!!!!!!增加
                if len(sn_day_df)>=2:
                    sn_factor_df_new=updtTodayFct(sn_factor_df_last,sn_day_df)#
                    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
        
        # #将today_sn_fct_df写入到数据库中
        if len(today_sn_fct_df)>=1:
            today_sn_fct_df.to_sql('tb_sn_factor',con=engine,chunksize=10000,if_exists='append',index=False)
            # print(sn+' factor will be update in table tb_sn_factor!')
        return today_sn_fct_df





# def updtASnTodayFct(start_date,end_date,today_sn_list):

#     sql_cmd="select * from qixiang_test where time>='"+start_date+"' and time<='"+end_date+"'"
#     range_soc_df = pd.read_sql(sql_cmd, conn)#使用read_sql方法查询数据库

#     sql_cmd2="select sn,date,a0,a1,a2,a3,a4 from tb_sn_factor where date<'"+start_date+"'"
#     factor_df=pd.read_sql(sql_cmd2, conn)#使用read_sql方法查询数据库

#     #筛选出所有当日数据之后，筛选当日有更新的sn
#     # today_sn_list=range_soc_df['sn'].unique().tolist()
#     # today_sn_list=today_sn_list[:10]#更新若干个
#     #建立空的dataframe，用于承接所有更新的factor信息
#     today_sn_fct_df=pd.DataFrame([],columns=['sn','date','a0','a1','a2','a3','a4'])

#     for sn in today_sn_list:
#         sn_factor_df=factor_df.loc[factor_df['sn']==sn,:]#筛选sn对应的factor
#         sn_factor_df=sn_factor_df.sort_values(by='date',ascending='True')#按照日期排序
#         sn_factor_df_last=sn_factor_df.tail(1).copy()#寻找最后一行，代表最近日期
#         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
#         sn_factor_df_last=sn_factor_df_last.reset_index(drop=True)#重置index
#         sn_factor_df_last.loc[1,'date']=start_date#更改后一行的date为当前日期
#         #筛选对应车辆的信息
#         condition_sn=(range_soc_df['sn']==sn)
#         sn_day_df=range_soc_df.loc[condition_sn,:].copy()
#         sn_day_df=sn_day_df.reset_index(drop=True)
#         #使用updtTodayFct函数更新今天的factor
#         sn_factor_df_new=updtTodayFct(sn_factor_df_last,sn_day_df)
#         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
    
#     #将today_sn_fct_df写入到数据库中
#     today_sn_fct_df.to_sql('tb_sn_factor',con=engine,chunksize=10000,if_exists='append',index=False)