python_files/cppc_python脚本/服务器脚本应急/test_FlowByHour_All.py

# -*- codeing = utf-8 -*-
# @Time : 2022/7/13 9:51
# @Author : Clown
# @File : test_FlowByHour_All.py
# @Software : PyCharm
# -*- codeing = utf-8 -*-
# @Time : 2022/7/5 17:45
# @Author : Clown
# @File : test_FlowByHour_M.py
# @Software : PyCharm


import pymysql
import json
import requests
import csv
import os
import time
from datetime import datetime,timedelta
import sys
import traceback
from all_key_table import update_key_value_pair
import schedule

def linkTomySql(host,passwd,db_name):
    '''连接至数据库返回【db】,v2新增local_infile=1 打开文件导入权限'''
    try:
        #本地连接为：localhost  服务器连接为：124.222.188.59
        db = pymysql.connect(
                        host=host, user="root",
                        passwd=passwd,
                        db=db_name,
                        charset='utf8mb4',
                        local_infile=1,
                        port=63306)
        print('\nconnect to mysql server 成功')
        print('---------------------------------------')

    except:
        print("\ncould not connect to mysql server")
        db = "连接失败"
    return db

def read_key_value_pair(db,brand_name,wm_plate,owner):
    '''按条件读取，数据库中all_key_table表里的key_value_pair字段中的值，以键值对的形式输出
       db:数据库，
       brand_name：品牌名，
       wm_plate:外卖平台MEITUAN或ELEME，
       owner:账号权限all或one
    '''
    cursor = db.cursor()
    sql = f'SELECT key_value_pair FROM all_key_table WHERE brand_name = "{brand_name}" AND wm_plate = "{wm_plate}" AND owner = "{owner}";'
    cursor.execute(sql)
    pair = json.loads(cursor.fetchall()[0][0])
    return pair

def loadDataInfile(file_path ,csv_name,db,table_name):
    '''v2新增 执行csv文件导入数据库，注意ENCLOSED BY '"' 防止误判'''
    cursor = db.cursor()
    sql0 = f"SET global local_infile = 1;"
    cursor.execute(sql0)
    db.commit()
    sql1 = f'''LOAD DATA LOCAL INFILE '{file_path+'/'+csv_name}' REPLACE INTO TABLE {table_name} FIELDS TERMINATED BY ','   ENCLOSED BY '"'   LINES TERMINATED BY '\r\n'   IGNORE 1 LINES ;'''
    cursor.execute(sql1)
    db.commit()
    sql2= f"SET global local_infile = 0;"
    cursor.execute(sql2)
    db.commit()
    cursor.close()

def update_scriptrunninglogs(log_date,log_time,running_Time,operation_results,table_name,db):
    '''将脚本执行情况发送至【脚本执行日志】表
    配套内容
    from datetime import datetime
    import sys
    import traceback
    time_begin = time.time()
    log_date = datetime.now().strftime("%Y-%m-%d")  # 日志记录日期
    log_time = datetime.now().strftime('%H:%M:%S')  # 日志记录时间
    df_name = '数据库表名'
    try:
        operation_results = '执行成功'
    except Exception as e:
        operation_results = traceback.format_exc()
    running_Time = time.time()  - time_begin  # 脚本运行时长
    '''
    try:
        sql = "INSERT INTO scriptrunninglogs_test VALUES (%s,%s,%s,%s,%s,%s,%s)"#date, time, running_Time, script_name, operation_results, table_name, id
        script_name = os.path.basename(sys.argv[0])
        value = (log_date,log_time,running_Time,script_name,operation_results,table_name,0)
        cursor = db.cursor()
        cursor.execute(sql, value)  # 执行sql语句
        db.commit()
        cursor.close()
    except:
        result = "数据库连接失败"
        print(result)

def reset_mysql_table(db,table_name):
    cursor = db.cursor()
    sql = f'DELETE FROM {table_name} WHERE log_date = CURRENT_DATE() '
    cursor.execute(sql)
    db.commit()
    print(f'【{table_name}】当日数据重置成功，可导入新数据')

def get_shops_info_to_list(db,brand_name,wm_plate,key_name):
    '''获取门店信息表【shops_info_to_list】中的信息，
    并返回表单shops_info_df【shop_id，shop_name，update_datetime，info_for_script】
    db：数据库信息
    brand_name：品牌
    wm_plate：外卖平台
    key_name：关键信息字段名，如无填‘’，如有填对应键值对的key
    '''
    cursor = db.cursor()
    if key_name == '':
        sql = f'SELECT shop_id,shop_name,update_datetime FROM shops_info_for_script WHERE brand_name = "{brand_name}" AND wm_plate = "{wm_plate}";'
        cursor.execute(sql)
        shops_info = cursor.fetchall()
        shops_info_df = []
        for shop_info in shops_info:
            shop_info_dict = {'shop_id':shop_info[0],
                              'shop_name':shop_info[1],
                              'update_datetime':shop_info[2],
                              'info_for_script':''}
            shops_info_df.append(shop_info_dict)
        return shops_info_df
    else:
        sql = f'SELECT shop_id,shop_name,update_datetime,info_for_script -> "$.{key_name}" FROM shops_info_for_script WHERE brand_name = "{brand_name}" AND wm_plate = "{wm_plate}";'
        cursor.execute(sql)
        shops_info = cursor.fetchall()
        shops_info_df = []
        for shop_info in shops_info:
            shop_info_dict = {'shop_id':shop_info[0],
                              'shop_name':shop_info[1],
                              'update_datetime':shop_info[2],
                              f'{key_name}':shop_info[3]}
            shops_info_df.append(shop_info_dict)
        return shops_info_df

def test_FlowByHour_M(file_path,brand_name,mt_pair,shops_info_df):
    '''
            return: error or (csv_name_storage, csv_name_application)
            v2优化：增加批量错误判断，方便终止此函数
    '''

    #创建储存表
    csv_name_storage = 'test_FlowByHour_M_storage.csv'
    title_storage = ['script_name', 'brand_name', 'shop_id', 'wm_plate', 'date', 'time', 'data_details', 'id']
    f_storage = open(file_path+'/'+csv_name_storage, mode='w', encoding='utf-8-sig',newline="")
    writer_storage = csv.writer(f_storage)
    writer_storage.writerow(title_storage)

    #创建生产表
    csv_name_application = 'test_FlowByHour_M_application.csv'
    title_application = ['id', 'log_date', 'date_of_data', 'brand_name', 'shop_id', 'shop_name', 'timeSign',
                         'today_exp_pv', 'today_exp_uv', 'today_clk_pv', 'today_clk_uv', 'today_valid_order_cnt', 'today_valid_order_user_cnt', 'today_clk_exp_rate', 'today_ord_clk_rate',
                         'last_day_exp_pv', 'last_day_exp_uv', 'last_day_clk_pv', 'last_day_clk_uv', 'last_day_valid_order_cnt', 'last_day_valid_order_user_cnt', 'last_day_clk_exp_rate', 'last_day_ord_clk_rate',
                         'totalExposureAmount', 'uvCount', 'totalCost', 'averageCost']
                       #['id', '记录日期',  '数据所在日期',   '门店id',  '店名',       '时段',      '曝光次数',       '点击率%',    '点击次数',     '单次点击花费', '时段竞价花费']
    f_application = open(file_path + '/' + csv_name_application, mode='w', encoding='utf-8-sig', newline="")
    writer_application = csv.writer(f_application)
    writer_application.writerow(title_application)

    #通用参数
    script_name = 'test_FlowByHour_M'
    wm_plate = 'MEITUAN'
    date = datetime.now().strftime('%Y-%m-%d')
    time = datetime.now().strftime('%H:%M:%S')
    beginTime = (datetime.today()).strftime('%Y-%m-%d') #2022-06-23
    endTime = (datetime.today() + timedelta(days=-1)).strftime('%Y-%m-%d') #2022-06-23

    timeSign = datetime.now().strftime('%H:%M')  #20220706优化

    # shops_cnt = len(shops_info_df)  # v2优化
    shops_cnt = 1
    check_error_cnt = 0  # v2优化

    acctId = mt_pair['data']['session']['acctId']
    token = mt_pair['data']['session']['token']

    #门店信息
    # for shop_info in shops_info_df:
    # shop_id = shop_info['shop_id']
    # shop_name = shop_info['shop_name']
    # info_update_datetime = shop_info['update_datetime']
    shop_id = 8038750
    shop_name = '浆小白·粉面粥·豆浆夜市(周浦万达店)'
    info_update_datetime = datetime.today()

    #联网获取信息
    url_flow = f'https://waimaieapp.meituan.com/bizdata/flow/single/pc/overview?acctId={acctId}&wmPoiId={shop_id}&durationType=5&tabType=3&circleType=1'

    cookie = f'acctId={acctId}; ' \
             f'token={token}; ' \
             f'wmPoiId={shop_id}; '
    headers_flow = {
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36',
        'cookie': cookie}

    url_wager = f'https://waimaieapp.meituan.com/ad/v3/statistics/cpc/today/info?acctId={acctId}&wmPoiId={shop_id}&token={token}&platform=0&bizad_cityId=310100&bizad_second_city_id=310100&bizad_third_city_id=310115'

    headers_wager = {
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36'}

    if int(info_update_datetime.strftime("%Y%m%d")) >= int(datetime.strptime(beginTime,"%Y-%m-%d").strftime("%Y%m%d")):

        try:
            data_details_flow = requests.get(url_flow, headers= headers_flow).json()
            data_details_wager = requests.get(url_wager, headers= headers_wager).json()
            data_details = {"data_flow":data_details_flow,
                            "data_wager":data_details_wager}
            #储存信息至csv存储表
            row_storage = [script_name, brand_name, shop_id, wm_plate, date, time, json.dumps(data_details, ensure_ascii= False), 0]
            #解析数据存储至csv生产表

            try:

                data_all_flow = data_details_flow['data']
                today_exp_pv = data_all_flow['exposureCnt']['base'] #曝光次数
                today_exp_uv = data_all_flow['exposureNum']['base'] #曝光人数
                today_clk_pv = data_all_flow['visitCnt']['base'] #进店次数
                today_clk_uv = data_all_flow['visitNum']['base'] #进店人数
                today_valid_order_cnt = data_all_flow['orderCnt']['base'] #下单次数
                today_valid_order_user_cnt = data_all_flow['orderNum']['base'] #下单人数
                today_clk_exp_rate = data_all_flow['visitRate']['base'] #进店转化率
                today_ord_clk_rate = data_all_flow['orderRate']['base'] #下单转化率

                last_day_exp_pv = today_exp_pv - data_all_flow['exposureCnt']['baseDelta']  # 曝光次数
                last_day_exp_uv = today_exp_uv - data_all_flow['exposureNum']['baseDelta']  # 曝光人数
                last_day_clk_pv = today_clk_pv - data_all_flow['visitCnt']['baseDelta']  # 进店次数
                last_day_clk_uv = today_clk_uv - data_all_flow['visitNum']['baseDelta']  # 进店人数
                last_day_valid_order_cnt = today_valid_order_cnt - data_all_flow['orderCnt']['baseDelta']  # 下单次数
                last_day_valid_order_user_cnt = today_valid_order_user_cnt - data_all_flow['orderNum']['baseDelta']  # 下单人数
                last_day_clk_exp_rate = today_clk_exp_rate - data_all_flow['visitRate']['baseDelta']  # 进店转化率
                last_day_ord_clk_rate = today_ord_clk_rate - data_all_flow['orderRate']['baseDelta']  # 下单转化率

                data_all_wager = data_details_wager['data']
                totalExposureAmount = data_all_wager['showCount']  #累计曝光次数
                uvCount = data_all_wager['clickCount'] #竞价进店次数
                totalCost = data_all_wager['cost'] #累计竞价花费
                averageCost = data_all_wager['avgPrice'] #单次点击花费


                # 解析数据存储至csv生产表
                row_application = [0,date, beginTime, brand_name, shop_id, shop_name, timeSign,
                                   today_exp_pv, today_exp_uv, today_clk_pv, today_clk_uv, today_valid_order_cnt, today_valid_order_user_cnt, today_clk_exp_rate, today_ord_clk_rate,
                                   last_day_exp_pv, last_day_exp_uv, last_day_clk_pv, last_day_clk_uv, last_day_valid_order_cnt, last_day_valid_order_user_cnt, last_day_clk_exp_rate, last_day_ord_clk_rate,
                                   totalExposureAmount, uvCount, totalCost, averageCost]
                writer_application.writerow(row_application)

            except:
                # 解析数据存储至csv生产表
                row_application = [0, date, beginTime, brand_name, shop_id, shop_name, timeSign, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] #20220706优化
                writer_application.writerow(row_application)

        except:
            check_error_cnt += 1  # v2优化
            data_details = {'result': '此门店未打开营业or账户号已无效or相关界面无参数，请核实!'}
            # 储存信息至csv存储表
            row_storage = [script_name, brand_name, shop_id, wm_plate, date, time, json.dumps(data_details, ensure_ascii=False), 0]
            # 解析数据存储至csv生产表
            row_application = [0, date, beginTime, brand_name, shop_id, shop_name, timeSign, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] #20220706优化
            writer_application.writerow(row_application)

    else:
        check_error_cnt += 1  # v2优化
        data_details = {'result':'此门店未打开营业or账户号已无效or相关界面无参数，请核实!'}
        # 储存信息至csv存储表
        row_storage = [script_name, brand_name, shop_id, wm_plate, date, time,json.dumps(data_details, ensure_ascii=False), 0]
        # 解析数据存储至csv生产表
        row_application = [0, date, beginTime, brand_name, shop_id, shop_name, timeSign, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] #20220706优化
        writer_application.writerow(row_application)
    writer_storage.writerow(row_storage)


    if shops_cnt == check_error_cnt:#v2优化
        e = 0/0                     #v2优化
    else:                           #v2优化
        print(f'{brand_name}\n【{csv_name_storage}】\n【{csv_name_application}】\n加载成功！\n')
        return csv_name_storage,csv_name_application

def running_m():
    # 日志参数
    time_begin = time.time()
    log_date = datetime.now().strftime("%Y-%m-%d")  # 日志记录日期
    log_time = datetime.now().strftime('%H:%M:%S')  # 日志记录时间
    # 通用参数
    host = 'localhost'
    passwd = '111???clown'
    db_name = 'hexingxing'
    # brand_names = ['粥小鲜','浆小白']
    wm_plate = 'MEITUAN'
    owner = 'all'
    key_name = ''
    file_path = 'C:/Users/ClownHe/Desktop/goods'
    table_name = '[test_flowbyhour_m] Storage And App'
    table_name_storage = 'data_storage_test_flowbyhour_m'
    table_name_app = 'data_app_test_flowbyhour_m'
    operation_results = '执行成功'
    brand_name = '浆小白'
    # 脚本执行
    db = linkTomySql(host, passwd, db_name)

    # for brand_name in brand_names:

    mt_pair = read_key_value_pair(db, brand_name, wm_plate, owner)

    # shops_info_df = get_shops_info_to_list(db,brand_name,wm_plate,key_name)
    shops_info_df = 0

    try:
        try:
            csv_name_storage, csv_name_application = test_FlowByHour_M(file_path, brand_name, mt_pair,
                                                                                 shops_info_df)

        except Exception as x:
            print('重载关键表',x)
            update_key_value_pair(db, brand_name, wm_plate, owner, '')  # 报错更新【all_key_table】
            csv_name_storage, csv_name_application = test_FlowByHour_M(file_path, brand_name, mt_pair,
                                                                                 shops_info_df)
            operation_results = operation_results + f',更新【{brand_name}】【{wm_plate}】【all_key_table】'

        loadDataInfile(file_path, csv_name_storage, db, table_name_storage)
        print(f'【{csv_name_storage}】导入成功')

        loadDataInfile(file_path, csv_name_application, db, table_name_app)
        print(f'【{csv_name_application}】导入成功')

        print(f'本次脚本执行总用时{(time.time() - time_begin) / 60} min')

    except Exception as e:

        operation_results = traceback.format_exc()

    running_Time = time.time() - time_begin  # 脚本运行时长
    db = linkTomySql(host, passwd, db_name)
    update_scriptrunninglogs(log_date, log_time, running_Time, operation_results, table_name, db)
    print('Working done!')
    time_kill = time.time() - time_begin
    return time_kill

def test_FlowByHour_E(file_path,brand_name,elm_pair,shops_info_df):
    '''
            return: error or (csv_name_storage, csv_name_application)
            v2优化：增加批量错误判断，方便终止此函数
    '''

    #创建储存表
    csv_name_storage = 'test_FlowByHour_E_storage.csv'
    title_storage = ['script_name', 'brand_name', 'shop_id', 'wm_plate', 'date', 'time', 'data_details', 'id']
    f_storage = open(file_path+'/'+csv_name_storage, mode='w', encoding='utf-8-sig',newline="")
    writer_storage = csv.writer(f_storage)
    writer_storage.writerow(title_storage)

    #创建生产表
    csv_name_application = 'test_FlowByHour_E_application.csv'
    title_application = ['id', 'log_date', 'date_of_data', 'brand_name', 'shop_id', 'shop_name', 'timeSign',
                         'today_exp_pv', 'today_exp_uv', 'today_clk_pv', 'today_clk_uv', 'today_valid_order_cnt', 'today_valid_order_user_cnt', 'today_clk_exp_rate', 'today_ord_clk_rate',
                         'last_day_exp_pv', 'last_day_exp_uv', 'last_day_clk_pv', 'last_day_clk_uv', 'last_day_valid_order_cnt', 'last_day_valid_order_user_cnt', 'last_day_clk_exp_rate', 'last_day_ord_clk_rate',
                         'totalExposureAmount', 'uvCount', 'totalCost', 'averageCost']
                       #['id', '记录日期',  '数据所在日期',   '门店id',  '店名',       '时段',      '曝光次数',       '点击率%',    '点击次数',     '单次点击花费', '时段竞价花费']
    f_application = open(file_path + '/' + csv_name_application, mode='w', encoding='utf-8-sig', newline="")
    writer_application = csv.writer(f_application)
    writer_application.writerow(title_application)

    #通用参数
    script_name = 'test_FlowByHour_E'
    wm_plate = 'ELEME'
    date = datetime.now().strftime('%Y-%m-%d')
    time = datetime.now().strftime('%H:%M:%S')
    beginTime = (datetime.today()).strftime('%Y-%m-%d') #2022-06-23
    endTime = (datetime.today() + timedelta(days=-1)).strftime('%Y-%m-%d') #2022-06-23

    timeSign = datetime.now().strftime('%H:%M')  #20220706优化

    # shops_cnt = len(shops_info_df)  # v2优化
    shops_cnt = 1
    check_error_cnt = 0  # v2优化

    ksid = elm_pair['data']['session']['ksid']

    url_flow = 'https://httpizza.ele.me/napos-miracle-stats/SingleFlowService.realtimeFlowSummary'

    url_wager = 'https://app-api.shop.ele.me/alchemy/invoke/?method=wager.getUVSummary'

    headers = {
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36',
        'content-type': 'application/json'}

    #门店信息
    # for shop_info in shops_info_df:
    # shop_id = shop_info['shop_id']
    # shop_name = shop_info['shop_name']
    # info_update_datetime = shop_info['update_datetime']
    shop_id = 173858169
    shop_name = '浆小白·粉面粥·豆浆夜市(周浦万达店)'
    info_update_datetime = datetime.today()

    #联网获取信息
    json_params_flow = {"ksid":ksid,"requestParam":{"shopId":shop_id,"contrastType":"yesterday"}}

    json_params_wager = {"id":"3FC49D97DC28490990FC243CE038B9CA|1657016560816",
                         "metas":{"appName":"bs-river-bid",
                                  "appVersion":"4.4.0",
                                  "ksid":ksid},
                         "service":"wager",
                         "method":"getUVSummary",
                         "params":{"restaurantId":shop_id,
                                   "type":None,
                                   "beginTime":f"{datetime.today().strftime('%Y-%m-%d')}",
                                   "endTime":f"{datetime.today().strftime('%Y-%m-%d')}"},
                         "ncp":"2.0.0"}

    if int(info_update_datetime.strftime("%Y%m%d")) >= int(datetime.strptime(beginTime,"%Y-%m-%d").strftime("%Y%m%d")):

        try:
            data_details_flow = requests.post(url_flow, json= json_params_flow, headers= headers).json()
            data_details_wager = requests.post(url_wager, json= json_params_wager, headers= headers).json()
            data_details = {"data_flow":data_details_flow,
                            "data_wager":data_details_wager}
            #储存信息至csv存储表
            row_storage = [script_name, brand_name, shop_id, wm_plate, date, time, json.dumps(data_details, ensure_ascii= False), 0]
            #解析数据存储至csv生产表

            try:

                data_all_flow = data_details_flow['data']['result'][0]
                today_exp_pv = data_all_flow['today_exp_pv'] #曝光次数
                today_exp_uv = data_all_flow['today_exp_uv'] #曝光人数
                today_clk_pv = data_all_flow['today_clk_pv'] #进店次数
                today_clk_uv = data_all_flow['today_clk_uv'] #进店人数
                today_valid_order_cnt = data_all_flow['today_valid_order_cnt'] #下单次数
                today_valid_order_user_cnt = data_all_flow['today_valid_order_user_cnt'] #下单人数
                today_clk_exp_rate = data_all_flow['today_clk_exp_rate'] #进店转化率
                today_ord_clk_rate = data_all_flow['today_ord_clk_rate'] #下单转化率

                last_day_exp_pv = data_all_flow['last_day_exp_pv']  # 曝光次数
                last_day_exp_uv = data_all_flow['last_day_exp_uv']  # 曝光人数
                last_day_clk_pv = data_all_flow['last_day_clk_pv']  # 进店次数
                last_day_clk_uv = data_all_flow['last_day_clk_uv']  # 进店人数
                last_day_valid_order_cnt = data_all_flow['last_day_valid_order_cnt']  # 下单次数
                last_day_valid_order_user_cnt = data_all_flow['last_day_valid_order_user_cnt']  # 下单人数
                last_day_clk_exp_rate = data_all_flow['last_day_clk_exp_rate']  # 进店转化率
                last_day_ord_clk_rate = data_all_flow['last_day_ord_clk_rate']  # 下单转化率

                data_all_wager = data_details_wager['result']
                totalExposureAmount = data_all_wager['totalExposureAmount']  #累计曝光次数
                uvCount = data_all_wager['uvCount'] #竞价进店次数
                totalCost = data_all_wager['totalCost'] #累计竞价花费
                averageCost = data_all_wager['averageCost'] #单次点击花费


                # 解析数据存储至csv生产表
                row_application = [0,date, beginTime, brand_name, shop_id, shop_name, timeSign,
                                   today_exp_pv, today_exp_uv, today_clk_pv, today_clk_uv, today_valid_order_cnt, today_valid_order_user_cnt, today_clk_exp_rate, today_ord_clk_rate,
                                   last_day_exp_pv, last_day_exp_uv, last_day_clk_pv, last_day_clk_uv, last_day_valid_order_cnt, last_day_valid_order_user_cnt, last_day_clk_exp_rate, last_day_ord_clk_rate,
                                   totalExposureAmount, uvCount, totalCost, averageCost]
                writer_application.writerow(row_application)

            except:
                # 解析数据存储至csv生产表
                row_application = [0, date, beginTime, brand_name, shop_id, shop_name, timeSign, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] #20220706优化
                writer_application.writerow(row_application)

        except:
            check_error_cnt += 1  # v2优化
            data_details = {'result': '此门店未打开营业or账户号已无效or相关界面无参数，请核实!'}
            # 储存信息至csv存储表
            row_storage = [script_name, brand_name, shop_id, wm_plate, date, time, json.dumps(data_details, ensure_ascii=False), 0]
            # 解析数据存储至csv生产表
            row_application = [0, date, beginTime, brand_name, shop_id, shop_name, timeSign, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] #20220706优化
            writer_application.writerow(row_application)

    else:
        check_error_cnt += 1  # v2优化
        data_details = {'result':'此门店未打开营业or账户号已无效or相关界面无参数，请核实!'}
        # 储存信息至csv存储表
        row_storage = [script_name, brand_name, shop_id, wm_plate, date, time,json.dumps(data_details, ensure_ascii=False), 0]
        # 解析数据存储至csv生产表
        row_application = [0, date, beginTime, brand_name, shop_id, shop_name, timeSign, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] #20220706优化
        writer_application.writerow(row_application)
    writer_storage.writerow(row_storage)


    if shops_cnt == check_error_cnt:#v2优化
        e = 0/0                     #v2优化
    else:                           #v2优化
        print(f'{brand_name}\n【{csv_name_storage}】\n【{csv_name_application}】\n加载成功！\n')
        return csv_name_storage,csv_name_application

def running_e():
    # 日志参数
    time_begin = time.time()
    log_date = datetime.now().strftime("%Y-%m-%d")  # 日志记录日期
    log_time = datetime.now().strftime('%H:%M:%S')  # 日志记录时间
    # 通用参数
    host = 'localhost'
    passwd = '111???clown'
    db_name = 'hexingxing'
    # brand_names = ['粥小鲜','浆小白']
    wm_plate = 'ELEME'
    owner = 'all'
    key_name = ''
    file_path = 'C:/Users/ClownHe/Desktop/goods'
    table_name = '[test_flowbyhour_e] Storage And App'
    table_name_storage = 'data_storage_test_flowbyhour_e'
    table_name_app = 'data_app_test_flowbyhour_e'
    operation_results = '执行成功'
    brand_name = '浆小白'
    # 脚本执行
    db = linkTomySql(host, passwd, db_name)

    # for brand_name in brand_names:

    elm_pair = read_key_value_pair(db, brand_name, wm_plate, owner)

    # shops_info_df = get_shops_info_to_list(db,brand_name,wm_plate,key_name)
    shops_info_df = 0

    try:
        try:
            csv_name_storage, csv_name_application = test_FlowByHour_E(file_path, brand_name, elm_pair,
                                                                                 shops_info_df)

        except Exception as x:
            print('重载关键表',x)
            update_key_value_pair(db, brand_name, wm_plate, owner, '')  # 报错更新【all_key_table】
            csv_name_storage, csv_name_application = test_FlowByHour_E(file_path, brand_name, elm_pair,
                                                                                 shops_info_df)
            operation_results = operation_results + f',更新【{brand_name}】【{wm_plate}】【all_key_table】'

        loadDataInfile(file_path, csv_name_storage, db, table_name_storage)
        print(f'【{csv_name_storage}】导入成功')

        loadDataInfile(file_path, csv_name_application, db, table_name_app)
        print(f'【{csv_name_application}】导入成功')

        print(f'本次脚本执行总用时{(time.time() - time_begin) / 60} min')

    except Exception as e:

        operation_results = traceback.format_exc()

    running_Time = time.time() - time_begin  # 脚本运行时长
    db = linkTomySql(host, passwd, db_name)
    update_scriptrunninglogs(log_date, log_time, running_Time, operation_results, table_name, db)
    print('Working done!')
    time_kill = time.time() - time_begin
    return time_kill

if __name__ == '__main__':

    # schedule.every().hour.at("00:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("05:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("10:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("15:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("20:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("25:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("30:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("35:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("40:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("45:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("50:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().hour.at("57:10").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().minute.at(":05").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().minute.at(":15").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().minute.at(":25").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().minute.at(":35").do(running)  # 每个小时的n分n秒执行一次
    # schedule.every().minute.at(":45").do(running)  # 每个小时的n分n秒执行一次
    schedule.every().minute.at(":10").do(running_m)  # 每个小时的n分n秒执行一次
    schedule.every().minute.at(":15").do(running_e)
    while True:
        schedule.run_pending()  # 运行所有可运行的任务