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+#!/usr/bin/env python
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+# -*- coding: utf-8 -*-
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+# time: 2023/3/2 10:28
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+# file: config.py
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+# author: David
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+# company: shenyang JY
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+
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+"""
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+模型调参及系统功能配置
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+"""
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+import argparse
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+import pandas as pd
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+from pathlib import Path
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+from app.common.logs import params, logger
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+
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+""""
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+调用思路
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+ xxxx 1. 从入口参数中获取IN OUT文件位置 xxxx
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+ 2. 按照训练和预测加载和解析数据
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+ 3. 对数据进行预处理
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+ 4. 执行训练，保存模型，输出状态
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+ 5. 执行预测，输出结果，输出状态
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+"""
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+
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+def material(input_file, isDq=True):
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+ basi, station_info_w, station_info_d_w, station_info_s, station_info_d_s, nwp_w, nwp_s, nwp_w_h, nwp_s_h, power = (
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+ 'DQYC_IN_BASIC.txt', 'DQYC_IN_PLANT_WIND.txt', 'DQYC_IN_PLANT_DETAIL_WIND.txt', 'DQYC_IN_PLANT_SOLAR.txt',
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+ 'DQYC_IN_PLANT_DETAIL_SOLAR.txt', 'DQYC_IN_FORECAST_WEATHER_WIND.txt', 'DQYC_IN_FORECAST_WEATHER_SOLAR.txt',
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+ 'DQYC_IN_FORECAST_WEATHER_WIND_H.txt', 'DQYC_IN_FORECAST_WEATHER_SOLAR_H.txt', 'DQYC_IN_HISTORY_POWER_LONG.txt')
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+ basi_area = 'DQYC_AREA_IN_BASIC'
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+ nwp_v, nwp_v_h = 'DQYC_IN_FORECAST_WEATHER.txt', 'DQYC_IN_FORECAST_WEATHER_H.txt' # 多版本气象
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+ nwp_own, nwp_own_h = 'DQYC_IN_FORECAST_WEATHER_OWNER.txt', 'DQYC_IN_FORECAST_WEATHER_OWNER_H.txt', # 自有气象
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+ env_wf, env_sf = 'DQYC_IN_ACTUAL_WEATHER_WIND', 'DQYC_IN_ACTUAL_WEATHER_SOLAR' # 实测气象
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+ input_file = Path(input_file)
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+ env_w, env_s = None, None
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+ basic = pd.read_csv(input_file / basi, sep=r'\s+', header=0)
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+ power = pd.read_csv(input_file / power, sep=r'\s+', header=0)
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+ plant_type = int(basic.loc[basic['PropertyID'].to_list().index(('PlantType')), 'Value'])
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+ if isDq:
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+ nwp_v = pd.read_csv(input_file / '0' / nwp_v, sep=r'\s+', header=0)
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+ nwp_v_h = pd.read_csv(input_file / '0' / nwp_v_h, sep=r'\s+', header=0)
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+ nwp_own = pd.read_csv(input_file / '1' / nwp_own, sep=r'\s+', header=0)
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+ nwp_own_h = pd.read_csv(input_file / '1' / nwp_own_h, sep=r'\s+', header=0)
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+ if params['switch_nwp_owner']:
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+ nwp_v, nwp_v_h = nwp_own, nwp_own_h
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+ # 如果是风电
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+ if plant_type == 0:
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+ station_info = pd.read_csv(input_file / station_info_w, sep=r'\s+', header=0)
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+ station_info_d = pd.read_csv(input_file / station_info_d_w, sep=r'\s+', header=0)
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+ nwp = pd.read_csv(input_file / nwp_w, sep=r'\s+', header=0)
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+ nwp_h = pd.read_csv(input_file / nwp_w_h, sep=r'\s+', header=0)
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+ if (input_file / env_wf).exists():
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+ env_w = pd.read_csv(input_file / env_wf, sep=r'\s+', header=0)
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+ return station_info, station_info_d, nwp, nwp_h, power, nwp_v, nwp_v_h, env_w
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+ # 如果是光伏
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+ elif plant_type == 1:
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+ station_info = pd.read_csv(input_file / station_info_s, sep=r'\s+', header=0)
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+ station_info_d = pd.read_csv(input_file / station_info_d_s, sep=r'\s+', header=0)
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+ nwp = pd.read_csv(input_file / nwp_s, sep=r'\s+', header=0)
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+ nwp_h = pd.read_csv(input_file / nwp_s_h, sep=r'\s+', header=0)
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+ if (input_file / env_sf).exists():
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+ env_s = pd.read_csv(input_file / env_sf, sep=r'\s+', header=0)
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+ return station_info, station_info_d, nwp, nwp_h, power, nwp_v, nwp_v_h, env_s
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+ else:
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+ # 区域级预测待定，可能需要遍历获取场站数据
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+ basic_area = pd.read_csv(input_file / basi_area, sep=r'\s+', header=0)
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+ return basic_area
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+
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+def clean_power(power, env, plant_id):
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+ env_power = pd.merge(env, power, on=params['col_time'])
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+ if 'HubSpeed' in env.columns.tolist():
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+ from app.common.limited_power_wind import LimitPower
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+ lp = LimitPower(logger, params, env_power)
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+ power = lp.clean_limited_power(plant_id, True)
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+ elif 'Irradiance' in env.columns.tolist():
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+ from app.common.limited_power_solar import LimitPower
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+ lp = LimitPower(logger, params, env_power)
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+ power = lp.clean_limited_power(plant_id, True)
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+ return power
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+
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+
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+def input_file_handler(input_file: str, model_name: str):
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+ # DQYC：短期预测，qy：区域级
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+ if 'dqyc' in input_file.lower():
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+ station_info, station_info_d, nwp, nwp_h, power, nwp_v, nwp_v_h, env = material(input_file, True)
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+ cap = round(float(station_info['PlantCap'][0]), 2)
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+ plant_id = int(station_info['PlantID'][0])
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+ # 含有model，训练
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+ if 'model' in input_file.lower():
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+ if env is not None and params['clean_power']: # 进行限电清洗
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+ power = clean_power(power, env, plant_id)
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+ train_data = pd.merge(nwp_v_h, power, on=params['col_time'])
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+ if model_name == 'fmi':
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+ from app.model.tf_fmi_train import model_training
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+ elif model_name == 'cnn':
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+ from app.model.tf_cnn_train import model_training
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+ else:
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+ from app.model.tf_lstm_train import model_training
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+ model_training(train_data, input_file, cap)
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+ # 含有predict，预测
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+ else:
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+ logger.info("训练路径错误！")
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+ else:
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+ # 区域级预测：未完
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+ basic_area = material(input_file, False)
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+
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+def main():
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+ # 创建解析器对象
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+ parser = argparse.ArgumentParser(description="程序描述")
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+ # 创建
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+ # 添加参数
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+ parser.add_argument("input_file", help="输入文件路径") # 第一个位置参数
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+
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+ parser.add_argument("--model_name", default="lstm", help='选择短期模型') # 第二个位置参数
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+ # 解析参数
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+ args = parser.parse_args()
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+
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+ # 使用参数
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+ print(f"文件: {args.input_file}")
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+ input_file_handler(args.input_file, args.model_name)
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+
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+
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+if __name__ == "__main__":
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+ main()
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David