zlwl-algos-new/USER/shouxueqi/offline_data_split/import datetime #from datetime.py

import datetime #from datetime 
from multiprocessing import Pool
import json
import os
import time
import traceback
import warnings

from keras.models import load_model
import pickle
from sqlalchemy import text, delete, and_, or_, update
import pandas as pd

from ZlwlAlgosCommon.utils.ProUtils import *
from ZlwlAlgosCommon.service.iotp.IotpAlgoService import IotpAlgoService
#from DataSplit.COMMON.service.iotp.IotpAlgoService import IotpAlgoService #test_code
from ZlwlAlgosCommon.service.iotp.Beans import DataField
from ZlwlAlgosCommon.orm.models import *

 

from DataSplit.V_1_0_0  import data_status as ds  ##充电状态标准化程序
from DataSplit.V_1_0_0  import data_split  as dt ##分段函数程序
from DataSplit.V_1_0_0  import data_drive_stat as ddt ##行驶数据按行驶段汇总统计
from DataSplit.V_1_0_0  import data_charge_stat as dct ##充电数据按充电段汇总
from DataSplit.V_1_0_0  import data_stand_stat as dst ##静置数据按静置段汇总
from DataSplit.V_1_0_0  import data_drive_stat_period as ddtp ##行驶数据按充电周期汇总统计
from DataSplit.V_1_0_0  import trans_day  as trd  ##解决跨天的问题
from DataSplit.V_1_0_0  import stand_status as ss
def update_param(db_engine):
    # 从redis中获取参数，如果redis中获取不到，则去数据库中获取

    df_algo_adjustable_param = pd.read_sql("select * from algo_adjustable_param", db_engine)

    df_algo_list = pd.read_sql("select * from algo_list", db_engine)


    df_algo_pack_param = pd.read_sql("select* from algo_pack_param", db_engine)

    return df_algo_adjustable_param, df_algo_list, df_algo_pack_param
def main(): 
    process_num=1
    # 程序不能停止
    while(True):
        try:
           
            warnings.filterwarnings("ignore")
            try:   
                cleanUtils = CleanUtils()
                # 调用算法前的准备工作
                mysql_algo_conn = None
                mysql_algo_engine = None
                mysql_iotp_conn =  None
                mysql_iotp_engine= None
                kafka_consumer = None
                rc= None
                

                algo_list = ['socdiag','low_soc_diag','Sor_Diag','Li_Plted','Data_Split'] # 本调度所包含的算法名列表。
                
                loggers = sysUtils.get_loggers(algo_list, log_base_path, process_num) # 为每个算法分配一个logger
                logger_main.info(f"process-{process_num}: 配置中间件")
                
                # mysql
                mysql_algo_params = sysUtils.get_cf_param('mysql-algo')
                mysqlUtils = MysqlUtils()
                mysql_algo_engine, mysql_algo_Session= mysqlUtils.get_mysql_engine(mysql_algo_params)
                mysql_algo_conn = mysql_algo_engine.connect()
                
                
                mysql_iotp_data = sysUtils.get_cf_param('mysql-iotp')
                mysqlUtils = MysqlUtils()
                mysql_iotp_engine, mysql_iopt_Session= mysqlUtils.get_mysql_engine(mysql_iotp_data)
                mysql_iotp_conn = mysql_iotp_engine.connect()            
                
                




                #Hbase
                hbase_params = sysUtils.get_cf_param('hbase-datafactory')# test_code 
                iotp_service = IotpAlgoService(hbase_params=hbase_params)

                #redis
                redis_params = sysUtils.get_cf_param('redis')
                reidsUtils = RedisUtils()
                rc = reidsUtils.get_redis_conncect(redis_params)

            except Exception as e:
                logger_main.error(f'process-{process_num}: {e}')
                logger_main.error(f'process-{process_num}: {traceback.format_exc()}')
                cleanUtils.clean(mysql_algo_conn, mysql_algo_engine, mysql_iotp_conn, mysql_iotp_engine, kafka_consumer, rc)
            
            
            # 开始准备调度
            # for message in kafka_consumer:
                #KafkaConsumer.commit(self)
            try:
                logger_main.info(f'收到调度')
                if not mysql_algo_conn.closed:
                    mysql_algo_conn.close()
                mysql_algo_conn = mysql_algo_engine.connect() # 从连接池中获取一个myslq连接
                
                df_algo_adjustable_param, df_algo_list, df_algo_pack_param=update_param(mysql_algo_conn)
                df_algo_adjustable_param[df_algo_adjustable_param['pack_code']=='JX19220']
                df_algo_pack_param=df_algo_pack_param[df_algo_pack_param['pack_code']=='JX19220']
                df_algo_pack_param=json.loads(df_algo_pack_param.iloc[0]['param'])
                pack_code='JX19220'
                # schedule_params = json.loads(message.value)                    
                # if (schedule_params is None) or (schedule_params ==''):
                #     logger_main.info('{} kafka数据异常，跳过本次运算'.format(str(message.value)))
                #     continue
                # kafka 调度参数解析
                # df_snlist = pd.DataFrame(schedule_params['snlist'])
                # df_algo_adjustable_param = pd.DataFrame([(d['algo_id'], d['param'],d['param_ai']) for d in schedule_params['adjustable_param']], columns=['algo_id', 'param','param_ai'])
                # df_algo_pack_param = json.loads(schedule_params['pack_param'][0]['param'])
                # df_algo_pack_param = {k: eval(v) if isinstance(v, str) else v for k, v in df_algo_pack_param.items()}
                # df_algo_param = pd.DataFrame(schedule_params['algo_list'])
                # start_time = schedule_params['start_time']
                # end_time = schedule_params['end_time']
                # pack_code = schedule_params['pack_code']
                # cell_type = schedule_params['cell_type']
                mysql_algo_params = sysUtils.get_cf_param('mysql-algo')
                mysqlUtils = MysqlUtils()
                mysql_algo_engine, mysql_algo_Session= mysqlUtils.get_mysql_engine(mysql_algo_params)
                mysql_algo_conn = mysql_algo_engine.connect()
                df_snpk_list = pd.read_sql("select sn, imei,pack_model,device_cell_type,scrap_status from t_device", mysql_algo_conn)
                df_snpk_list=df_snpk_list[df_snpk_list['scrap_status']<4]
                df_snpk_list=df_snpk_list.rename(columns={'pack_model':'pack_code'})
                df_snpk_list=df_snpk_list[df_snpk_list['sn'].str.contains('N234P')]
                df_sn_list=list(df_snpk_list['sn'])
                sn_list_total=df_sn_list
                
                sn_list=sn_list_total
                
                start_time = "2023-03-25 00:00:00"
                end_time = "2023-06-25 00:00:00" 
                columns = [ DataField.time, DataField.sn, DataField.pack_crnt, DataField.pack_volt, DataField.pack_soc,  
                            DataField.cell_voltage_count, DataField.cell_temp_count, DataField.cell_voltage, DataField.cell_temp, 
                            DataField.other_temp_value, DataField.bms_sta, DataField.charge_sta,DataField.latitude,DataField.longitude,
                            DataField.mileage, DataField.accum_chg_wh, DataField.accum_dschg_wh, DataField.accum_chg_ah,DataField.accum_dschg_ah,DataField.vin]                
                for i in range(0,len(sn_list_total)):
                    sn_list=[sn_list_total[i]]
                # 取数
                    time_st = time.time()
                    logger_main.info(f"process-{process_num}: 开始取数，{start_time} ~ {end_time}\n{str(sn_list)}")

                    df_data = iotp_service.get_data(sn_list=sn_list, columns=columns, start_time=start_time, end_time=end_time)
                #df_data.to_excel('df_data_1.xlsx')
                    logger_main.info(f'process-{process_num},获取到{len(df_data)}条数据,取数d耗时:{time.time()-time_st}')
                    if len(df_data) == 0:
                        logger_main.info(f"process-{process_num}: 数据清洗耗时{time.time()-time_st}, 无有效数据，跳过本次运算")      
                        continue
                    # 将字符串转换成datetime对象
                    str_date = start_time
                    date_time =datetime.datetime.strptime(str_date, '%Y-%m-%d %H:%M:%S')
                    # 将datetime对象减去6小时
                    new_date_time = date_time - datetime.timedelta(hours=8)
                    # 将datetime对象转换成字符串
                    start_time_8h = new_date_time.strftime('%Y-%m-%d %H:%M:%S')
                    df_data_8h = iotp_service.get_data(sn_list=sn_list, columns=columns, start_time=start_time_8h, end_time=start_time)
                    logger_main.info(f'process-{process_num},获取到{len(df_data_8h)}条数据,取数耗时:{time.time()-time_st}') 

                    # 数据清洗
                    try:
                        time_st = time.time()
                        logger_main.info(f'process-{process_num}数据清洗') 
                        if len(df_data_8h)==0:
                            df_data_8h=pd.DataFrame(columns=['datatype','time','sn','pack_crnt','pack_volt','pack_soc',
                                                                'cell_voltage_count','cell_temp_count','cell_voltage','cell_temp','other_temp_value',
                                                                'bms_sta','charge_sta','latitude','longitude','mileage','accum_dschg_wh','accum_chg_wh','accum_chg_ah','accum_dschg_ah','vin'])
                            #df_data_8h = df_data_8h.drop(['latitude','longitude','mileage','accum_chg_wh''accum_dschg_wh','accum_dschg_ah','accum_chg_ah','vin'], axis=1, errors='ignore')
                        if len(df_data)==0:
                            df_data=pd.DataFrame(columns=['datatype','time','sn','pack_crnt','pack_volt','pack_soc',
                                                                'cell_voltage_count','cell_temp_count','cell_voltage','cell_temp','other_temp_value',
                                                                'bms_sta','charge_sta','latitude','longitude','mileage','accum_dschg_wh','accum_chg_wh','accum_chg_ah','accum_dschg_ah','vin'])
                            #df_data = df_data.drop(['latitude','longitude','mileage','accum_chg_wh','accum_dschg_wh','accum_dschg_ah','accum_chg_ah','vin'], axis=1, errors='ignore')
                        df_data_gps=iotp_service.gps_datacleaning(df_data)
                        df_data_gps_8h=iotp_service.gps_datacleaning(df_data_8h)
                        df_data_accum=iotp_service.accum_datacleaning(df_data)
                        df_data_accum_8h=iotp_service.accum_datacleaning(df_data_8h)
                        df_data_vin=iotp_service.vin_datacleaning(df_data)
                        df_data_vin_8h=iotp_service.vin_datacleaning(df_data_8h)                                                
                        df_data, df_table, cellvolt_name, celltemp_name = iotp_service.datacleaning(df_algo_pack_param,df_data)#进行数据清洗
                        df_data_8h, df_table_t, cellvolt_name_t, celltemp_name_t = iotp_service.datacleaning(df_algo_pack_param,df_data_8h)#进行数据清洗
                        #df_data.to_excel('df_data_2.xlsx')
                        if len(df_data_gps_8h)==0:
                            df_data_gps_8h=pd.DataFrame(columns=['sn','time','datatype','latitude','longitude','mileage'])
                        if len(df_data_gps)==0:
                            df_data_gps=pd.DataFrame(columns=['sn','time','datatype','latitude','longitude','mileage'])
                        if len(df_data_accum_8h)==0:
                            df_data_accum_8h=pd.DataFrame(columns=['sn','time','datatype','accum_chg_wh','accum_dschg_wh','accum_dschg_ah','accum_chg_ah'])
                        if len(df_data_accum)==0:
                            df_data_accum=pd.DataFrame(columns=['sn','time','datatype','accum_chg_wh','accum_dschg_wh','accum_dschg_ah','accum_chg_ah'])
                        if len(df_data_vin_8h)==0:
                            df_data_vin_8h=pd.DataFrame(columns=['sn','time','datatype','vin'])
                        if len(df_data_vin)==0:
                            df_data_vin=pd.DataFrame(columns=['sn','time','datatype','vin'])
                        try:
                            df_data_32h=pd.concat([df_data_8h,df_data])
                            df_data_32h=df_data_32h.reset_index(drop=True)
                            #df_data_32h.to_excel('data_32h.xlsx')
                            if len(df_data_32h)==0:
                                logger_main.info(f"process-{process_num}: 数据清洗耗时{time.time()-time_st}, 无有效数据，跳过本次运算")      
                                continue
                            else:   
                                df_data_gps_32h=pd.concat([df_data_gps_8h,df_data_gps])
                                df_data_gps_32h=df_data_gps_32h.reset_index(drop=True)
                                if len(df_data_gps_32h)==0:
                                    df_data_gps_32h=pd.DataFrame(columns=['sn','time','datatype','latitude','longitude','mileage'])
                                    
                                df_data_accum_32h=pd.concat([df_data_accum_8h,df_data_accum])
                                df_data_accum_32h=df_data_accum_32h.reset_index(drop=True)
                                if len(df_data_accum_32h)==0:
                                    df_data_accum_32h=pd.DataFrame(columns=['sn','time','datatype','accum_chg_wh','accum_dschg_wh','accum_dschg_ah','accum_chg_ah'])
                                df_data_vin_32h=pd.concat([df_data_vin_8h,df_data_vin])
                                df_data_vin_32h=df_data_vin_32h.reset_index(drop=True)
                                if len(df_data_vin_32h)==0:
                                    df_data_vin_32h=pd.DataFrame(columns=['sn','time','datatype','vin'])
                                ##先关联gps和accum
                                    
                                df_merge_ga = pd.merge(df_data_gps_32h, df_data_accum_32h,on=['sn','time','datatype'],how='outer')
                                df_merge_ga = pd.merge(df_merge_ga, df_data_vin_32h,on=['sn','time','datatype'],how='outer')
                                df_merge_ga=df_merge_ga.sort_values(["sn","time"],ascending = [True, True])
                                df_merge_ga_filled = df_merge_ga.groupby("sn").fillna(method='ffill')
                                df_merge_ga_filled= pd.concat([df_merge_ga[['sn']], df_merge_ga_filled], axis=1)
                                df_merge_ga_filled = df_merge_ga_filled.groupby("sn").fillna(method='bfill')
                                df_merge_ga= pd.concat([df_merge_ga[['sn']], df_merge_ga_filled], axis=1)                        
                                ##识别静置的情况，关联非静置数据用前值填充
                                df_merge_ga['time'] = pd.to_datetime(df_merge_ga['time'], format='%Y-%m-%d %H:%M:%S')
                                df_merge_bga = pd.merge(df_data_32h, df_merge_ga,  on=['sn','time','datatype'],how='outer')
                                #df_merge=ss.stand_status(df_merge_bga,s_order_delta=600,lon_delta=0.1)
                                df_merge=df_merge_bga.sort_values(["sn","time"],ascending = [True, True])
                                df_merge_filled = df_merge.groupby("sn").fillna(method='ffill')
                                df_merge = pd.concat([df_merge[['sn']], df_merge_filled], axis=1)
                                df_merge_filled = df_merge.groupby("sn").fillna(method='bfill')
                                df_merge = pd.concat([df_merge[['sn']], df_merge_filled], axis=1)
                                df_merge=df_merge.dropna(subset=['pack_crnt','latitude','longitude','vin'],axis=0,how='all')    
                                df_merge=df_merge[df_merge['datatype']==12]                    
                                #df_merge.to_excel('merged.xlsx')                        

                            
                            
                            
                            
                            
                            
                            
                        except Exception as e:
                            logger_main.error(f"process-{process_num}:32小时拼接出错")
                            logger_main.error(f"process-{process_num}:{e}")
                            logger_main.error(f"process-{process_num}:{traceback.format_exc()}")
                        if len(df_data) == 0:
                            logger_main.info(f"process-{process_num}: 数据清洗耗时{time.time()-time_st}, 无有效数据，跳过本次运算")      
                            continue
                        else:
                            logger_main.info(f"process-{process_num}: {pack_code}, time_type:{df_data.loc[0, 'time']} ~ {df_data.iloc[-1]['time']}, 数据清洗完成耗时{time.time()-time_st}")
                    except Exception as e:
                        logger_main.error(f"process-{process_num}:数据清洗出错")
                        logger_main.error(f"process-{process_num}:{e}")
                        logger_main.error(f"process-{process_num}:{traceback.format_exc()}")
                        continue



                    try:                   
                        loggers['Data_Split'].info(f'开始执行算法{pack_code}, time:{start_time}~{end_time},\n sn_list:{sn_list}')
                        df_merge=ds.data_status(df_merge,c_soc_dif_p=0.05,s_soc_dif_p=0,c_order_delta=1200,s_order_delta=300)
                        ##基于各个状态码，进行分段，分段函数
                        cell_type='L'
                        df_drive,df_charge,df_stand,df_data_split_rlt,df_rank_abnormal=dt.split(df_merge,celltemp_name,cellvolt_name,drive_interval_time_min=1200,charge_interval_time_min=1200,stand_interval_time_min=1200,single_num_min=3,drive_sts=3,charge_sts=[21,22],stand_sts=0,cell_type=cell_type,capacity=456)                            
                        #df_drive,df_charge,df_stand,df_data_split_rlt=dt.split(df_merge,celltemp_name,cellvolt_name,drive_interval_time_min=1200,charge_interval_time_min=1200,stand_interval_time_min=1200,single_num_min=3,drive_sts=3,charge_sts=[21,22],stand_sts=0,cell_type=cell_type,capacity=456)                    
                        df_data_split_rlt.to_excel(f'{sn_list[0]}_NEW.xlsx')
                        loggers['Data_Split'].info(f'算法运行完成{pack_code}，算法耗时{time.time()-time_st}')
                    except Exception as e:
                        loggers['Data_Split'].error('算法运行出错')
                        loggers['Data_Split'].error(str(e))
                        loggers['Data_Split'].error(traceback.format_exc()) 
                        df_drive=pd.DataFrame()
                        df_charge=pd.DataFrame()
                        df_stand=pd.DataFrame()
                        df_data_split_rlt=pd.DataFrame()

                    try:
                        if not df_data_split_rlt.empty:

                            time_record = time.time()
                            df_data_split_rlt.reset_index(drop = True, inplace = True)
                            #df_data_split_rlt.to_sql("algo_charge_info",con=mysql_algo_conn, if_exists="append",index=False)
                            #write_mysql_time = write_mysql_time + time.time()-time_record
                            logger_main.info(f'process-{process_num}数据分段统计入库{pack_code}完成')
                        logger_main.info(f"process-{process_num}: 结果入库耗时：{time.time()-time_st}")                    
                        
                    except Exception as e:
                        logger_main.error(f"process-{process_num}:数据分段结果入库出错")
                        logger_main.error(f"process-{process_num}:{e}")
                        logger_main.error(f"process-{process_num}:{traceback.format_exc()}")     


            except Exception as e:
                logger_main.error(f"process-{process_num}:获取原始数据出错")
                logger_main.error(f"process-{process_num}:{e}")
                logger_main.error(f"process-{process_num}:{traceback.format_exc()}")
                continue
                

                
     


            #算法5_日数据分段
 
                
                    
                    
                    

                        
        
        except Exception as e:
            logger_main.error(f'process-{process_num}: {e}')
            logger_main.error(f'process-{process_num}: {traceback.format_exc()}')
            cleanUtils.clean(mysql_algo_conn, mysql_algo_engine, mysql_iotp_conn, mysql_iotp_engine, kafka_consumer, rc)









if __name__ == '__main__':
   while(True):
        try:
            # 配置量
            
            cur_env = 'dev' # 设置运行环境
            app_path = "/home/shouxueqi/projects/zlwl-algos/" # 设置app绝对路径
            log_base_path = f"{os.path.dirname(os.path.abspath(__file__))}/log" # 设置日志路径

            app_name = "task_day_1" # 应用名
            
            sysUtils = SysUtils(cur_env, app_path)
            logger_main = sysUtils.get_logger(app_name, log_base_path)
            logger_main.info(f"本次主进程号: {os.getpid()}")
            # 读取配置文件 (该部分请不要修改)
            processes = int(sysUtils.env_params.get("PROCESS_NUM_PER_NODE", '1')) # 默认为1个进程
            pool = Pool(processes = int(processes))
            logger_main.info("开始分配子进程")
            main()

        except Exception as e:
            print(str(e))
            print(traceback.format_exc())
            logger_main.error(str(e))
            logger_main.error(traceback.format_exc())
        finally:
            handlers = logger_main.handlers.copy()
            for h in handlers:
                logger_main.removeHandler(h)
            pool.terminate()