energy-data-trans/etl/wind_power/wave/WaveTrans_1.py

import json
import multiprocessing
import traceback

from conf.constants import ParallelProcessing
from etl.common.WaveData import WaveData
from service.plt_service import get_all_wind
from service.trans_conf_service import update_trans_status_running, update_trans_transfer_progress, \
    update_trans_status_success, update_trans_status_error
from service.trans_service import get_wave_conf, save_df_to_db, get_or_create_wave_table, \
    get_wave_data, delete_exist_wave_data
from utils.file.trans_methods import *
from utils.log.trans_log import set_trance_id, info, error
from utils.systeminfo.sysinfo import get_available_cpu_count_with_percent

exec("from os.path import *")
exec("import re")


class WaveTrans(object):
    """波形数据转换类"""

    def __init__(self, id: int, wind_farm_code: str, wind_farm_name: str, read_dir: str):
        """
        初始化波形数据转换类
        
        Args:
            id: 任务ID
            wind_farm_code: 风电场编码
            read_dir: 读取目录
        """
        self.id = id
        self.wind_farm_code = wind_farm_code
        self.wind_farm_name = wind_farm_name
        self.read_dir = read_dir
        self.begin = datetime.datetime.now()

        self.engine_count = 0
        self.min_date = None
        self.max_date = None
        self.data_count = 0

    def get_data_exec(self, func_code: str, filepath: str, measupoint_names: List[str]) -> Optional[WaveData]:
        """
        执行数据获取函数

        Args:
            func_code: 函数代码
            filepath: 文件路径
            measupoint_names: 测量点名称列表

        Returns:
            WaveData: 波形数据对象 / None
        """
        exec(func_code)
        return locals()['get_data'](filepath, measupoint_names)

    def del_exists_data(self, df: pd.DataFrame):
        """
        删除已存在的数据
        
        Args:
            df: 数据帧
        """
        min_date, max_date = df['time_stamp'].min(), df['time_stamp'].max()
        db_df = get_wave_data(self.wind_farm_code + '_wave', min_date, max_date)

        exists_df = pd.merge(db_df, df,
                             on=['wind_turbine_name', 'time_stamp', 'sampling_frequency', 'mesure_point_name'],
                             how='inner')
        ids = [int(i) for i in exists_df['id'].to_list()]
        if ids:
            delete_exist_wave_data(self.wind_farm_code + "_wave", ids)

    def run(self):
        """运行波形数据转换"""
        update_trans_status_running(self.id)
        trance_id = '-'.join([self.wind_farm_code, 'wave'])
        set_trance_id(trance_id)

        wave_conf = get_wave_conf(self.wind_farm_code)

        filter_types = wave_conf.get("filter_types", "txt,csv")
        filter_types = filter_types.replace("，", ",")

        all_files = read_files(self.read_dir, [str(i).strip() for i in filter_types.split(",")])

        wind_turbine_name_set = set()
        if len(all_files) > 0:
            update_trans_transfer_progress(self.id, 5)

            # 最大取系统cpu的 1/2
            split_count = get_available_cpu_count_with_percent(1 / 2)
            # 限制最大进程数
            split_count = min(split_count, ParallelProcessing.MAX_PROCESSES)

            all_wind, _ = get_all_wind(self.wind_farm_code, False)

            get_or_create_wave_table(self.wind_farm_code + '_wave', self.wind_farm_name)

            base_param_exec = wave_conf.get('base_param_exec', '')
            map_dict = {}
            if base_param_exec:
                base_param_exec = base_param_exec.replace('\r\n', '\n').replace('\t', '    ')
                info(base_param_exec)
                if 'import ' in base_param_exec:
                    raise Exception("方法不支持import方法")

            mesure_poins = [key for key, value in wave_conf.items() if str(key).startswith('conf_') and value]
            for point in mesure_poins:
                point_names = wave_conf[point].strip().split('|')
                for name in point_names:
                    map_dict[name] = point.replace('conf_', '')

            # 优化批次大小
            batch_size = split_count * 10
            all_array = split_array(all_files, batch_size)
            total_index = len(all_array)

            for index, now_array in enumerate(all_array):
                index_begin = datetime.datetime.now()
                with multiprocessing.Pool(split_count,maxtasksperchild=5) as pool:
                    try:
                        file_datas = pool.starmap(self.get_data_exec,
                                                  [(base_param_exec, i, list(map_dict.keys())) for i in now_array])
                        info(f'总数:{len(now_array)},返回个数{len(file_datas)}')
                    except Exception as e:
                        message = str(e)
                        error(traceback.format_exc())
                        update_trans_status_error(self.id, message[0:len(message) if len(message) < 100 else 100])
                        raise e

                update_trans_transfer_progress(self.id, 20 + int(index / total_index * 60))
                info("读取文件耗时:", datetime.datetime.now() - self.begin)

                result_list = list()
                for file_data in file_datas:
                    if file_data:
                        wind_turbine_name, time_stamp, sampling_frequency, rotational_speed, mesure_point_name, type, mesure_data = \
                            file_data[0], file_data[1], file_data[2], file_data[3], file_data[4], file_data[5], \
                                file_data[6]

                        if mesure_point_name in map_dict:
                            wind_turbine_name_set.add(wind_turbine_name)
                            if self.min_date is None or self.min_date > time_stamp:
                                self.min_date = time_stamp
                            if self.max_date is None or self.max_date < time_stamp:
                                self.max_date = time_stamp

                            result_list.append(
                                [wind_turbine_name, time_stamp, rotational_speed, sampling_frequency, mesure_point_name,
                                 type,
                                 mesure_data])

                if result_list:
                    self.data_count += len(result_list)
                    df = pd.DataFrame(result_list,
                                      columns=['wind_turbine_name', 'time_stamp', 'rotational_speed',
                                               'sampling_frequency',
                                               'mesure_point_name', 'type', 'mesure_data'])
                    df['time_stamp'] = pd.to_datetime(df['time_stamp'], errors='coerce')
                    df['mesure_point_name'] = df['mesure_point_name'].map(map_dict)
                    df.dropna(subset=['mesure_point_name'], inplace=True)
                    df['wind_turbine_number'] = df['wind_turbine_name'].map(all_wind).fillna(df['wind_turbine_name'])

                    # 批量处理JSON序列化
                    df['mesure_data'] = df['mesure_data'].apply(lambda x: json.dumps(x))

                    df.sort_values(by=['time_stamp', 'mesure_point_name'], inplace=True)

                    self.del_exists_data(df)

                    save_df_to_db(self.wind_farm_code + '_wave', df, batch_count=400)
                info(f"总共{total_index}组,当前{index + 1}", "本次写入耗时:", datetime.datetime.now() - index_begin,
                     "总耗时:", datetime.datetime.now() - self.begin)

        update_trans_status_success(self.id, len(wind_turbine_name_set), None,
                                    self.min_date, self.max_date, self.data_count)

        info("总耗时:", datetime.datetime.now() - self.begin)