chrgV000

LIB/MIDDLE/InfoChrgDrive/Charge/V1_0_0/coreV0.py

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+
+import datetime
+import pandas as pd   
+import numpy as np
+from DataPreProcess import DataPreProcess
+import math
+from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import BatParam
+import requests
+import re
+
+
+def process(data_ori_temp,cellvolt_list,celltemp_name,sn):
+    data_ori_temp=data_ori_temp.drop(['GSM信号','故障等级','故障代码','开关状态','绝缘电阻','定位类型','速度[km/h]','有效位','外电压','总输出状态','上锁状态','加热状态','航向'],axis=1,errors='ignore')
+    if sn[:4]=='MGML':
+        for k in range(len(data_ori_temp)):
+            if data_ori_temp.loc[k,'经度']==0:
+                data_ori_temp.loc[k,'经度']=np.nan
+            if data_ori_temp.loc[k,'纬度']==0:
+                data_ori_temp.loc[k,'纬度']=np.nan
+        data_ori_temp=data_ori_temp.drop(['其他温度2','其他温度6'],axis=1)
+        celltemp_name.remove('其他温度2')
+        celltemp_name.remove('其他温度6')
+    data_ori_temp.fillna(method ='pad', inplace = True , axis = 0)
+    data_ori_delnone=data_ori_temp.fillna(method ='backfill', axis = 0)
+    if data_ori_delnone.loc[0,'经度'] is None:
+        data_ori_delnone['经度']=116.417
+        data_ori_delnone['纬度']=39.917
+    data_ori_delnone = data_ori_delnone.dropna(axis = 1)
+    for name_col in cellvolt_list:
+        data_ori_delnone = data_ori_delnone.drop(data_ori_delnone[(data_ori_delnone[name_col] < 2000)].index)
+    for name_te in celltemp_name:
+        data_ori_delnone = data_ori_delnone.drop(data_ori_delnone[(data_ori_delnone[name_te] < -20)].index)
+    df_data=data_ori_delnone.reset_index(drop = True)
+    return df_data
+
+def city(df_sts_chrg,gpscity):
+    listcity=[]
+    data_sta=df_sts_chrg.reset_index(drop=True)
+    for i in range(len(data_sta)):
+        dist=[]
+        for j in range(len(gpscity)):
+            x1=data_sta.loc[i,'经度']
+            x2=gpscity.loc[j,'经度']
+            y1=data_sta.loc[i,'纬度']
+            y2=gpscity.loc[j,'纬度']
+            distance=math.sqrt((x1-x2)**2+(y1-y2)**2)
+            dist.append(distance)
+        jmin=dist.index(np.min(dist))
+        city=gpscity.loc[jmin,'城市']
+        listcity.append(city)
+    data_sta['city']=listcity
+    return data_sta
+
+def gpstemp_new(data_sta):
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.183 Safari/537.36',
+    }
+    if data_sta.loc[0,'city'][:2]=='北京':
+        response = requests.get('http://www.weather.com.cn/html/weather/101010100.shtml',headers=headers)
+    elif data_sta.loc[0,'city'][:2]=='苏州':
+        response = requests.get('http://www.weather.com.cn/html/weather/101190401.shtml',headers=headers)
+    response.encoding = response.apparent_encoding
+    # 时间，需要反转一下，因为最后一条数据对应第一个
+    x_time = re.findall('"od21":"(.*?)"',response.text)
+    x_time.reverse()
+    # 温度
+    temp = re.findall('"od22":"(.*?)"',response.text)
+    temp.reverse()
+    data_sta['气温']=''
+    for i in range(len(data_sta)):
+        for time in x_time:
+            if str(data_sta.loc[i,'时间戳'])[11:13]==time:
+                k=x_time.index(time)
+                if temp[k]!='':
+                    temp0=float(temp[k])
+                    data_sta.loc[i,'气温']=temp0
+    data_sta['气温'].fillna(method='ffill') 
+    print(data_sta['气温'])
+    return data_sta
+
+def defcelltype(sn):
+    if 'PK500' in sn:
+        celltype=1 #6040三元电芯
+    elif 'PK502' in sn:
+        celltype=2 #4840三元电芯
+    elif 'K504B' in sn:
+        celltype=99 #60ah林磷酸铁锂电芯
+    elif 'MGMLXN750' in sn:
+        celltype=3 #力信50ah三元电芯
+    elif 'MGMCLN750' in sn: 
+        celltype=4 #CATL 50ah三元电芯
+    elif 'UD' in sn:
+        celltype=4 #CATL 50ah三元电芯
+    elif 'TJMCL' in sn: 
+        celltype=100 #金茂电芯
+    else:
+        print('SN：{}，未找到对应电池类型！！！'.format(sn))
+    return celltype
+
+def stat_chrg_st(df_splice_data,param):
+    cellvolt_list = [s for s in list(df_splice_data) if '单体电压' in s] 
+    celltemp_name = [s for s in list(df_splice_data) if '温度' in s] 
+    df_time_temp = pd.to_datetime(df_splice_data['时间戳'])
+    delta_time = round((df_time_temp.iloc[-1] - df_time_temp.iloc[0])/pd.Timedelta(1, 'hours'), 3)
+    df_soc_temp = df_splice_data['SOC[%]']
+    soc_st=df_soc_temp.iloc[0]
+    soc_end=df_soc_temp.iloc[-1]
+    delta_soc = round((df_soc_temp.iloc[-1] - df_soc_temp.iloc[0]), 3)
+    if delta_soc >= 3:
+        rate_chrg = round(delta_soc/(100*delta_time), 2)   #电流的等效倍率
+    else:
+        rate_chrg = 0
+    if rate_chrg > 0.3:
+        stat_flg = 1#快充
+    elif rate_chrg > 0:
+        stat_flg = 2#慢充
+    else:
+        stat_flg = 0
+    if soc_end > 98:
+        full_chg_flg = 1#满充
+    else:
+        full_chg_flg = 0
+    listc=list(df_splice_data['总电流[A]'])
+    for k in listc:
+        if k>300:
+            listc.remove(k)
+    max_crnt = np.max(listc)   #最大有效电流
+    rate_chrg2=round(max_crnt/param.Capacity,2)
+    df_volt = df_splice_data['总电压[V]']
+    df_volt_end = df_volt.iloc[-1]   #截止电压
+    df_volt_st = df_volt.iloc[0]   #起始电压
+    #delta_volt = round((df_volt.iloc[-1] - df_volt.iloc[0]), 3)   #压变
+    df_diffvolt = df_splice_data['单体压差']
+    diffvolt_end = df_diffvolt .iloc[-1]   #截止压差
+    diffvolt_st = df_diffvolt .iloc[0]   #起始压差
+    #delta_diffvolt = round((df_diffvolt.iloc[-1] - df_diffvolt.iloc[0]), 3)  #压差变化
+    df_tem_temp = df_splice_data[celltemp_name]
+    df_tem_max = df_tem_temp.max()  #每列最大
+    df_tem_min = df_tem_temp.min()  #每列最小
+    df_tem_mean = df_tem_temp.mean()    #每列均值
+    tem_delt = max(df_tem_max - df_tem_min)    #最大温变
+    tem_mean=round(np.mean(df_tem_mean),1)  #平均温度
+    df_tem_max2 = df_tem_temp.max(axis=1)  #每行最大
+    #temp_st_max=df_tem_max2.iloc[0]  #起始最大温度
+    #temp_end_max=df_tem_max2.iloc[-1]  #结束最大温度
+    df_tem_min2 = df_tem_temp.min(axis=1)  #每行最小
+    #temp_st_min=df_tem_min2.iloc[0]  #起始最小温度
+    #temp_end_min=df_tem_min2.iloc[-1]  #结束最小温度  
+    df_tem_mean = df_tem_temp.mean(axis=1)  #每行平均
+    temp_st_mean=df_tem_mean.iloc[0]  #起始平均温度
+    temp_end_mean=df_tem_mean.iloc[-1]  #结束平均温度   
+    difftem_max = max(df_tem_max2 - df_tem_min2)    #最大温差
+    df_ovchrg_data = df_splice_data.loc[(df_splice_data['总电流[A]'] >= param.FullChrgSoc) & \
+        (df_splice_data['总电压[V]'] >= param.PackFullChrgVolt) & \
+        (df_splice_data['SOC[%]'] >= param.FullChrgSoc) & \
+        (df_splice_data[cellvolt_list].max(axis = 1) >= 1000*param.CellFullChrgVolt) & \
+        (df_splice_data['总电流[A]'] < 0) & (df_splice_data['总电压[V]'] <= param.CellVoltNums*5) & \
+        (df_splice_data['SOC[%]'] <= 200)]
+    if not df_ovchrg_data.empty:
+        ovchrg_flg = 1
+        df_ovchrg_data.reset_index(drop = True, inplace = True)
+        df_ovchrg_time = df_ovchrg_data['时间戳']
+        df_ovchrg_crnt = df_ovchrg_data['总电流[A]']
+        np_dff_time = np.diff(df_ovchrg_time)/pd.Timedelta(1, 'hours')
+        np_dff_time_new = np.append(np_dff_time, 0)
+        np_chrg_ah = np.multiply(np.array(np_dff_time_new),np.array(df_ovchrg_crnt))
+        ovchrg_prop = round(100*np_chrg_ah.sum()/param.Capacity, 3)
+    else:
+        ovchrg_flg = 0
+        ovchrg_prop = 0
+    df_sts_chrg_temp = pd.DataFrame({"sn":[df_splice_data.loc[0,'sn']], "time_st":[df_time_temp.iloc[0]], "time_end":[df_time_temp.iloc[-1]], "status":[df_splice_data.loc[0,'data_status']],"delta_time":[delta_time],
+                                    "soc_st":[soc_st], "soc_end":[soc_end], "volt_st":df_volt_st,"volt_end":df_volt_end,"diffvolt_st":[diffvolt_st],"diffvolt_end":[diffvolt_end],"temp_max":[max(df_tem_max)],
+                                    "temp_min":[min(df_tem_min)], "temp_incr":[tem_delt],"temp_mean":[tem_mean],"temp_st_mean":[temp_st_mean],"temp_end_mean":[temp_end_mean],"difftem_max":[difftem_max],
+                                    "meancrnt":[rate_chrg], "max_meancrnt":[rate_chrg2],"sts_flg":[stat_flg], "full_chrg_flg":[full_chg_flg],"ovchrg_flg":[ovchrg_flg],"ovchrg_prop":[ovchrg_prop],
+                                    "gps_lon":[round(df_splice_data.loc[0, '经度'], 3)], "gps_lat":[round(df_splice_data.loc[0, '纬度'], 3)],"city":[df_splice_data.loc[0,'city']],"airtemp_st":[df_splice_data.loc[0,'气温']],
+                                    "airtemp_end":[df_splice_data.loc[len(df_splice_data)-1,'气温']]})
+    return df_sts_chrg_temp
+
+
+
+def gpstemp(data_sta2,temp):
+    data_sta=data_sta2.copy()
+    maxtemp1=[]
+    mintemp1=[]
+    maxtemp2=[]
+    mintemp2=[]
+    for k in range(len(data_sta)):
+        city=data_sta.loc[k,'city']
+        date1=str(data_sta.loc[k,'time_st'])[:10]
+        date2=str(data_sta.loc[k,'time_end'])[:10]
+        for l in range(len(temp)):
+            if (date1==temp.loc[l,'日期'][:10]) & (city==temp.loc[l,'城市']):
+                t1=float(temp.loc[l,'最高气温'][:-1])
+                t2=float(temp.loc[l,'最低气温'][:-1])
+                maxtemp1.append(t1)
+                mintemp1.append(t2)
+            if (date2==temp.loc[l,'日期'][:10]) & (city==temp.loc[l,'城市']):
+                t3=float(temp.loc[l,'最高气温'][:-1])
+                t4=float(temp.loc[l,'最低气温'][:-1])
+                maxtemp2.append(t3)
+                mintemp2.append(t4)
+            
+    data_sta['开始最高气温']=maxtemp1
+    data_sta['开始最低气温']=mintemp1
+    data_sta['结束最高气温']=maxtemp2
+    data_sta['结束最低气温']=mintemp2
+    return data_sta
+
+def gpstemp2(data_sta3):
+    data_sta=data_sta3.copy()
+    temp11=[]
+    temp22=[]
+    for k in range(len(data_sta)):
+        time1=str(data_sta.loc[k,'time_st'])[11:13]
+        time2=str(data_sta.loc[k,'time_end'])[11:13]    
+        if time1 in ['00','01','02','03','04','05']:
+            temp11.append(data_sta.loc[k,'开始最低气温'])
+        elif time1 in ['06','23','07','22']:
+            temp11.append(min(data_sta.loc[k,'开始最低气温']+2,data_sta.loc[k,'开始最高气温']))
+        elif time1 in ['08','09','21','20']:
+            temp11.append(min(data_sta.loc[k,'开始最低气温']+5,data_sta.loc[k,'开始最高气温']))
+        elif time1 in ['10','11','19','18']:
+            temp11.append(min(data_sta.loc[k,'开始最低气温']+9,data_sta.loc[k,'开始最高气温']))
+        else:
+            temp11.append(data_sta.loc[k,'开始最高气温'])
+        if time2 in ['00','01','02','03','04','05']:
+            temp22.append(data_sta.loc[k,'结束最低气温'])
+        elif time2 in ['06','23','07','22']:
+            temp22.append(min(data_sta.loc[k,'开始最低气温']+2,data_sta.loc[k,'开始最高气温']))
+        elif time2 in ['08','09','21','20']:
+            temp22.append(min(data_sta.loc[k,'开始最低气温']+5,data_sta.loc[k,'开始最高气温']))
+        elif time2 in ['10','11','19','18']:
+            temp22.append(min(data_sta.loc[k,'开始最低气温']+9,data_sta.loc[k,'开始最高气温']))
+        else:
+            temp22.append(data_sta.loc[k,'开始最高气温'])
+    data_sta['airtemp_st']=temp11
+    data_sta['airtemp_end']=temp22
+    return data_sta
+
+def newsplit(data_new):
+    data_split=pd.DataFrame()
+    splice_num = np.unique(data_new['data_split_by_status_time']) 
+    for item in splice_num: 
+        df_splice_data = data_new[data_new['data_split_by_status_time'] == item]
+        df_splice_data.reset_index(inplace = True, drop = True) 
+        data_split=data_split.append(df_splice_data.loc[[0,len(df_splice_data)-1],:])
+    data_split=data_split.reset_index(drop = True)
+    data_split2=data_split.copy()
+    for k in range(1,len(data_split)):
+        if (data_split.loc[k,'data_split_by_status_time']==data_split.loc[k-1,'data_split_by_status_time']) & \
+            (data_split.loc[k,'data_status']=='charge') & \
+            (data_split.loc[k,'SOC[%]']==data_split.loc[k-1,'SOC[%]']):
+            data_split2=data_split2.drop([k-1,k])
+        if (data_split.loc[k,'data_split_by_status_time']==data_split.loc[k-1,'data_split_by_status_time']) & \
+            (data_split.loc[k,'data_status']=='drive') & \
+            (round(data_split.loc[k,'纬度'],2)==round(data_split.loc[k-1,'纬度'],2)) & \
+            (round(data_split.loc[k,'经度'],2)==round(data_split.loc[k-1,'经度'],2)) :
+            data_split2=data_split2.drop([k-1,k])
+    data_split2=data_split2.reset_index(drop = True)
+    data_split3=data_split2.copy()
+    for k in range(1,len(data_split2)):
+        if (data_split2.loc[k,'data_split_by_status_time']!=data_split2.loc[k-1,'data_split_by_status_time']) & \
+            (data_split2.loc[k,'data_status']==data_split2.loc[k-1,'data_status']) & \
+            ((data_split2.loc[k,'时间戳']-data_split2.loc[k-1,'时间戳']).total_seconds()/60<10):
+            data_split3.loc[k+1,'data_split_by_status_time']=data_split3.loc[k-1,'data_split_by_status_time']
+            data_split3=data_split3.drop([k-1,k])
+    data_split3=data_split3.reset_index(drop = True)
+    return data_split3
+
+def chrgst_time(data_split):
+    for k in range(1,len(data_split)):
+        if (data_split.loc[k,'data_status']=='charge') & (data_split.loc[k-1,'data_status']=='stand'):
+            delta_time_f=(data_split.loc[k-1,'时间戳']-data_split.loc[k-2,'时间戳']).total_seconds()/60
+            data_split.loc[k,'stand_time']=delta_time_f
+        if (data_split.loc[k,'data_status']=='charge') & (data_split.loc[k-1,'data_status']=='drive'):
+            delta_time_f=0
+            data_split.loc[k,'stand_time']=delta_time_f
+        if (data_split.loc[k,'data_status']=='stand') & (data_split.loc[k-1,'data_status']=='charge'):
+            delta_time_b=(data_split.loc[k+1,'时间戳']-data_split.loc[k,'时间戳']).total_seconds()/60
+            data_split.loc[k-1,'stand_time']=delta_time_b
+        if (data_split.loc[k,'data_status']=='drive') & (data_split.loc[k-1,'data_status']=='charge'):
+            delta_time_b=0
+            data_split.loc[k-1,'stand_time']=delta_time_b     
+    return data_split
+
+def chrgdr(data_split):
+    chrg=pd.DataFrame()
+    dr=pd.DataFrame()
+    if len(data_split)>3:
+        if (data_split.loc[0,'data_status']=='charge')  & (data_split.loc[2,'data_status']=='stand'):
+            chrg=chrg.append(data_split.iloc[:4]) 
+        elif (data_split.loc[0,'data_status']=='charge') & (data_split.loc[2,'data_status']=='drive'):
+            chrg=chrg.append(data_split.iloc[:2]) 
+  
+        if (data_split.loc[len(data_split)-2,'data_status']=='charge') & (data_split.loc[len(data_split)-3,'data_status']=='stand'):
+            chrg=chrg.append(data_split.iloc[len(data_split)-4:]) 
+        elif (data_split.loc[len(data_split)-2,'data_status']=='charge') & (data_split.loc[len(data_split)-3,'data_status']=='drive'):
+            chrg=chrg.append(data_split.iloc[len(data_split)-2:]) 
+
+        if len(data_split)>5:
+            for k in range(2,len(data_split)-2):
+                if (data_split.loc[k,'data_status']=='charge') & (data_split.loc[k-1,'data_status']=='stand') & (data_split.loc[k+2,'data_status']=='stand'):
+                    chrg=chrg.append(data_split.iloc[k-2:k+4]) 
+                elif (data_split.loc[k,'data_status']=='charge') & (data_split.loc[k-1,'data_status']=='drive') & (data_split.loc[k+2,'data_status']=='stand'):
+                    chrg=chrg.append(data_split.iloc[k:k+4]) 
+                elif (data_split.loc[k,'data_status']=='charge') & (data_split.loc[k-1,'data_status']=='drive') & (data_split.loc[k+2,'data_status']=='drive'):
+                    chrg=chrg.append(data_split.iloc[k:k+2]) 
+                elif (data_split.loc[k,'data_status']=='charge') & (data_split.loc[k-1,'data_status']=='stand') & (data_split.loc[k+2,'data_status']=='drive'):
+                    chrg=chrg.append(data_split.iloc[k-2:k+2])        
+    else:
+        if data_split.loc[0,'data_status'] in ['charge','stand']:
+            chrg=chrg.append(data_split.iloc[:2]) 
+
+    return chrg
+
+def makedf_chrgdr(df_splice_chrg,data_new):
+    time1=df_splice_chrg.loc[0,'时间戳']
+    time2=df_splice_chrg.loc[1,'时间戳']
+    status=df_splice_chrg.loc[0,'data_status']
+    split=df_splice_chrg.loc[0,'data_split_by_status_time']
+    index1=data_new[(data_new['时间戳']==time1) & (data_new['data_status']==status)].index.tolist()[0]
+    index2=data_new[(data_new['时间戳']==time2) & (data_new['data_status']==status)].index.tolist()[-1]
+    df_chrg=data_new.iloc[index1:index2+1]
+    df_chrg['data_split_by_status_time']=split
+    df_chrg.reset_index(drop=True,inplace=True)
+    return df_chrg
+
+def change_new(df_sts_chrg,chrg_last):
+    time_last=chrg_last['time_end']
+    time_first=df_sts_chrg.loc[0,'time_st']
+    time_last=datetime.datetime.strptime(time_last,'%Y-%m-%d %H:%M:%S')
+    if (chrg_last['status']==df_sts_chrg.loc[0,'status']) & ((time_first-time_last).total_seconds()/60<10) & \
+        (round(chrg_last['gps_lat'],2)==round(df_sts_chrg.loc[0,'gps_lat'],2)) & \
+        (round(chrg_last['gps_lon'],2)==round(df_sts_chrg.loc[0,'gps_lon'],2)) :
+        chrg_last['time_end']=df_sts_chrg.loc[0,'time_end']
+        chrg_last['delta_time']=chrg_last['delta_time']+df_sts_chrg.loc[0,'delta_time']
+        chrg_last['soc_end']=df_sts_chrg.loc[0,'soc_end']
+        chrg_last['volt_end']=df_sts_chrg.loc[0,'volt_end']
+        chrg_last['diffvolt_end']=df_sts_chrg.loc[0,'diffvolt_end']
+        chrg_last['temp_max']=np.max([df_sts_chrg.loc[0,'temp_max'],chrg_last['temp_max']])
+        chrg_last['temp_min']=np.min([df_sts_chrg.loc[0,'temp_min'],chrg_last['temp_min']])
+        chrg_last['temp_incr']=df_sts_chrg.loc[0,'temp_incr']+chrg_last['temp_incr']
+        chrg_last['temp_mean']=round(np.mean([df_sts_chrg.loc[0,'temp_mean'],chrg_last['temp_mean']]),1)
+        #chrg_last['temp_end_max']=df_sts_chrg.loc[0,'temp_end_max']
+        #chrg_last['temp_end_min']=df_sts_chrg.loc[0,'temp_end_min']
+        chrg_last['temp_end_mean']=df_sts_chrg.loc[0,'temp_end_mean']
+        chrg_last['difftem_max']=np.max([df_sts_chrg.loc[0,'difftem_max'],chrg_last['difftem_max']])
+        delta_soc=chrg_last['soc_end']-chrg_last['soc_st']
+        if delta_soc >= 3:
+            rate_chrg =round(delta_soc/(100*chrg_last['delta_time']), 2)   #电流的等效倍率
+        else:
+            rate_chrg = 0
+        if rate_chrg > 0.3:
+            stat_flg = 1#快充
+        elif rate_chrg > 0:
+            stat_flg = 2#慢充
+        else:
+            stat_flg = 0
+        if chrg_last['soc_end'] > 98:
+            full_chg_flg = 1#满充
+        else:
+            full_chg_flg = 0
+        chrg_last['meancrnt']= rate_chrg
+        chrg_last['max_meancrnt']=np.max([df_sts_chrg.loc[0,'max_meancrnt'],chrg_last['max_meancrnt']])
+        chrg_last['sts_flg']= stat_flg
+        chrg_last['full_chrg_flg']= full_chg_flg
+        chrg_last['ovchrg_flg']= np.max([df_sts_chrg.loc[0,'ovchrg_flg'],chrg_last['ovchrg_flg']])
+        chrg_last['ovchrg_prop']=df_sts_chrg.loc[0,'ovchrg_prop']+chrg_last['ovchrg_prop']
+        chrg_last['standtime_b']=df_sts_chrg.loc[0,'standtime_b']
+        chrg_last['airtemp_end']=df_sts_chrg.loc[0,'airtemp_end']
+        chrg_last=pd.DataFrame(chrg_last).T
+        df_sts_chrg=df_sts_chrg.drop([0])
+
+    else:
+        chrg_last=pd.DataFrame()
+    return df_sts_chrg,chrg_last
+
+def sep_chrg_dr(df_merge,sn):
+    cellvolt_list = [s for s in list(df_merge) if '单体电压' in s] 
+    celltemp_name = [s for s in list(df_merge) if '温度' in s] 
+    df_data=process(df_merge,cellvolt_list,celltemp_name,sn)
+    celltype=defcelltype(sn)
+    param=BatParam.BatParam(celltype)       
+    df_data['总电流[A]']=param.PackCrntDec*df_data['总电流[A]']
+    data_new=DataPreProcess.data_split_by_status(df_data,df_data)
+    data_new=DataPreProcess.data_split_by_time(data_new,data_new) 
+    data_new=DataPreProcess.cal_stand_time(data_new,data_new)
+    data_new.reset_index(inplace = True, drop = True)
+    data_split=newsplit(data_new)
+    data_split=chrgst_time(data_split)
+    chrg=chrgdr(data_split)
+    return chrg,data_new,param
+
+def mkdf_chrg(chrg,data_new,param,gpscity):
+    splice_num= np.unique(chrg['data_split_by_status_time'])  
+    df_sts_chrg=pd.DataFrame()       
+    for item in splice_num: 
+        df_splice_chrg = chrg[chrg['data_split_by_status_time'] == item] 
+        df_splice_chrg.reset_index(inplace = True, drop = True)
+        df_chrg=makedf_chrgdr(df_splice_chrg,data_new)
+        data_sta=city(df_chrg,gpscity)
+        data_sta=data_sta.reset_index(drop=True)
+        data_sta2=gpstemp_new(data_sta)
+        df_sts_chrg_temp=stat_chrg_st(data_sta2,param)
+        if df_splice_chrg.loc[0,'data_status'] =='charge':
+            stand_dur_f=df_splice_chrg.loc[0,'stand_time']
+            stand_dur_b=df_splice_chrg.loc[1,'stand_time']
+        else:
+            stand_dur_f=0
+            stand_dur_b=0 
+        df_sts_chrg_temp['standtime_f']=round(stand_dur_f,3)
+        df_sts_chrg_temp['standtime_b']=round(stand_dur_b,3)
+        df_sts_chrg=df_sts_chrg.append(df_sts_chrg_temp)
+    return df_sts_chrg
+
+def pro_output(df_merge,sn,gpscity,chrg_last):
+    new=pd.DataFrame()
+    change=pd.DataFrame()
+    if len(df_merge)>0:
+        chrg,data_new,param=sep_chrg_dr(df_merge,sn)
+        if len(chrg)>0:
+            data_sta=mkdf_chrg(chrg,data_new,param,gpscity)
+            new=data_sta.copy() 
+            if len(chrg_last)>0:
+                new,change=change_new(data_sta,chrg_last)
+    return new,change

LIB/MIDDLE/InfoChrgDrive/Charge/gps.csv

@@ -0,0 +1,40 @@
+,城市,经度,纬度
+0,北京市市中⼼,116.417,39.917
+1,上海市区,121.433,34.5
+2,天津市区,117.2,39.133
+3,⾹港,114.1,22.2
+4,⼴州,113.233,23.167
+5,珠海,113.517,22.3
+6,深圳,114.067,22.617
+7,杭州,120.2,30.267
+8,重庆市区,106.45,29.567
+9,青岛,120.333,36.067
+10,厦门,118.1,24.467
+11,福州,119.3,26.083
+12,兰州,103.733,36.033
+13,贵阳,106.717,26.567
+14,长沙,113.0,28.217
+15,南京,118.783,32.05
+16,南昌,115.9,28.683
+17,沈阳,123.383,41.8
+18,太原,112.533,37.867
+19,成都,104.067,30.667
+20,拉萨,91.0,29.6
+21,乌鲁⽊齐,87.683,43.767
+22,昆明,102.733,25.05
+23,西安,108.95,34.267
+24,西宁,101.75,36.567
+25,银川,106.267,38.467
+26,兰浩特,122.083,46.067
+27,哈尔滨,126.633,45.75
+28,长春,125.35,43.883
+29,武汉,114.317,30.517
+30,郑州,113.65,34.767
+31,⽯家庄,114.483,38.033
+32,三亚,109.5,18.2
+33,海⼝,110.35,20.017
+34,澳门,113.5,22.2
+35,苏州,120.37,31.19
+36,苏州,120.37,31.19
+37,苏州,120.37,31.19
+38,苏州,120.37,31.19

LIB/MIDDLE/InfoChrgDrive/Charge/main_V0.py

@@ -0,0 +1,116 @@
+from coreV0 import *
+import pymysql
+import datetime
+import pandas as pd
+from LIB.BACKEND import DBManager
+from sqlalchemy import create_engine
+from urllib import parse
+import datetime, time
+from apscheduler.schedulers.blocking import BlockingScheduler
+from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import DBDownload
+#from LIB.MIDDLE.CellStateEstimation.Common.V1_0_1 import log
+from LIB.MIDDLE.CellStateEstimation.Common import log
+
+#...................................充电技术指标统计函数......................................................................................................................
+def diag_cal():
+    global SNnums
+
+    start=time.time()
+    now_time=datetime.datetime.now()
+    start_time=now_time-datetime.timedelta(hours=24)
+    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    end_time=now_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    #数据库配置
+    host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+    port=3306
+    db='qx_cas'
+    user='qx_read'
+    password='Qx@123456'
+
+    #读取结果库数据......................................................
+    param='sn,time_st,time_end,status,delta_time,soc_st,soc_end,volt_st,volt_end,diffvolt_st,diffvolt_end, \
+        temp_max,temp_min,temp_incr,temp_mean,temp_st_mean,temp_end_mean,difftem_max,meancrnt,max_meancrnt, \
+        sts_flg,full_chrg_flg,ovchrg_flg,ovchrg_prop,gps_lon,gps_lat,standtime_f,standtime_b,city,airtemp_st,airtemp_end'
+    tablename='algo_charge_info'
+    mysql = pymysql.connect (host=host, user=user, password=password, port=port, database=db)
+    cursor = mysql.cursor()
+    sql =  "select %s from %s where time_end='0000-00-00 00:00:00'" %(param,tablename)
+    cursor.execute(sql)
+    res = cursor.fetchall()
+    df_diag_ram= pd.DataFrame(res,columns=param.split(','))
+    
+
+    db_res_engine = create_engine(
+        "mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8".format(
+            user, parse.quote_plus(password), host, port, db
+        ))
+
+    mylog=log.Mylog('log_info_charge.txt','error')
+    mylog.logcfg()
+
+    for sn in SNnums:
+        try:
+            
+            #读取原始数据库数据........................................................................................................................................................
+            dbManager = DBManager.DBManager()
+            df_data = dbManager.get_data(sn=sn, start_time=start_time, end_time=end_time, data_groups=['bms','gps'])
+            df_bms = df_data['bms']
+            df_gps = df_data['gps']
+
+            #读取城市天气数据........................................................................................................................................................
+            gpscity=pd.read_csv('gps.csv')
+            
+
+            #调用主函数................................................................................................................................................................
+            
+            if not df_bms.empty:   #BMS数据非空
+                df_merge=pd.merge(df_bms,df_gps,how='outer',sort=True)
+                df_merge['sn']=sn
+                df_diag_ram_sn=pd.Series()
+                if not df_diag_ram.empty:   #结果库非空
+                    df_diag_ram_sn=df_diag_ram[df_diag_ram['sn']==sn]
+                    if not df_diag_ram_sn.empty:   #该sn相关结果非空
+                        df_diag_ram_sn.reset_index(inplace=True,drop=True)
+                df_diag_new,df_diag_change=pro_output(df_merge,sn,gpscity,df_diag_ram_sn)
+                if not df_diag_change.empty:   #需变更的结果非空
+                    cursor.execute("DELETE FROM algo_charge_info WHERE time_end = '0000-00-00 00:00:00' and sn='{}'".format(sn))
+                    mysql.commit()
+                    df_diag_change.to_sql("algo_charge_info",con=db_res_engine, if_exists="append",index=False)
+
+                #新增结果存入结果库.....................................................................
+                if not df_diag_new.empty:  #需新增的结果非空
+                    df_diag_new.to_sql("algo_charge_info",con=db_res_engine, if_exists="append",index=False)
+
+            end=time.time()
+            print(end-start)  
+            
+        except Exception as e:
+            print(repr(e))
+            mylog.logopt(e)
+
+    cursor.close()
+    mysql.close()
+
+#...............................................主函数起定时作用.......................................................................................................................
+if __name__ == "__main__":
+    
+    #excelpath=r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\sn-20210903.xlsx'
+    excelpath='sn-20210903.xlsx'
+    dataSOH = pd.read_excel('sn-20210903.xlsx',sheet_name='sn-20210903')
+    SNnums = list(dataSOH['sn'])
+    
+    mylog=log.Mylog('log_info_charge.txt','error')
+    mylog.logcfg()
+
+    diag_cal()
+    #定时任务.......................................................................................................................................................................
+    scheduler = BlockingScheduler()
+    scheduler.add_job(diag_cal, 'interval', hours=24)
+
+    try:  
+        scheduler.start()
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)