compete/app/predict/tf_lstm_pre.py

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @FileName  :tf_lstm_pre.py
# @Time      :2025/2/13 10:52
# @Author    :David
# @Company: shenyang JY
import os.path

import numpy as np
import logging, argparse, traceback
from app.common.data_handler import DataHandler, write_number_to_file
from threading import Lock
import time, json

model_lock = Lock()
from itertools import chain
from app.common.logs import logger, params
from app.common.tf_lstm import TSHandler
from app.common.dbmg import MongoUtils
from copy import deepcopy

np.random.seed(42)  # NumPy随机种子

mgUtils = MongoUtils(logger)


def model_prediction(pre_data, input_file, cap):
    # 获取程序开始时间
    start_time = time.time()
    success = 0
    print("Program starts execution!")
    farm_id = input_file.split('/')[-2]
    output_file = input_file.replace('IN', 'OUT')
    file = 'DQYC_OUT_PREDICT_POWER.txt'
    status_file = 'STATUS.TXT'
    local_params = deepcopy(params)
    ts = TSHandler(logger, local_params)
    dh = DataHandler(logger, local_params)
    try:
        local_params['model_table'] += farm_id
        local_params['scaler_table'] += farm_id
        feature_scaler, target_scaler = mgUtils.get_scaler_model_from_mongo(local_params)
        ts.opt.cap = round(target_scaler.transform(np.array([[cap]]))[0, 0], 2)
        ts.get_model(local_params)
        dh.opt.features = json.loads(ts.model_params).get('Model').get('features', ','.join(ts.opt.features)).split(',')
        scaled_pre_x, pre_data = dh.pre_data_handler(pre_data, feature_scaler)

        success = 1
        # 更新算法状态：1. 启动成功
        write_number_to_file(os.path.join(output_file, status_file), 1, 1, 'rewrite')
        logger.info("算法启动成功")
        res = list(chain.from_iterable(target_scaler.inverse_transform([ts.predict(scaled_pre_x).flatten()])))
        pre_data['Power'] = res[:len(pre_data)]
        pre_data['PlantID'] = farm_id
        pre_data = pre_data[['PlantID', local_params['col_time'], 'Power']]

        pre_data.loc[:, 'Power'] = pre_data['Power'].round(2)
        pre_data.loc[pre_data['Power'] > cap, 'Power'] = cap
        pre_data.loc[pre_data['Power'] < 0, 'Power'] = 0
        pre_data.to_csv(os.path.join(output_file, file), sep=' ', index=False)
        # 更新算法状态：正常结束
        write_number_to_file(os.path.join(output_file, status_file), 2, 2)
        logger.info("算法正常结束")
    except Exception as e:
        # 如果算法状态没启动，不更新
        if success:
            write_number_to_file(os.path.join(output_file, status_file), 2, 3)
        my_exception = traceback.format_exc()
        my_exception.replace("\n", "\t")
        logger.info("算法状态异常：{}".format(my_exception))
    end_time = time.time()
    logger.info("lstm预测任务：用了 %s 秒 " % (end_time - start_time))


if __name__ == "__main__":
    print("Program starts execution!")
    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    logger = logging.getLogger("model_training_bp log")

    # serve(app, host="0.0.0.0", port=1010x, threads=4)
    print("server start!")

    # ------------------------测试代码------------------------
    args_dict = {"mongodb_database": 'david_test', 'scaler_table': 'j00083_scaler', 'model_name': 'bp1.0.test',
                 'model_table': 'j00083_model', 'mongodb_read_table': 'j00083_test', 'col_time': 'date_time', 'mongodb_write_table': 'j00083_rs',
                 'features': 'speed10,direction10,speed30,direction30,speed50,direction50,speed70,direction70,speed90,direction90,speed110,direction110,speed150,direction150,speed170,direction170'}
    args_dict['features'] = args_dict['features'].split(',')
    arguments.update(args_dict)
    dh = DataHandler(logger, arguments)
    ts = TSHandler(logger)
    opt = argparse.Namespace(**arguments)

    opt.Model['input_size'] = len(opt.features)
    pre_data = get_data_from_mongo(args_dict)
    feature_scaler, target_scaler = get_scaler_model_from_mongo(arguments)
    pre_x = dh.pre_data_handler(pre_data, feature_scaler, opt)
    ts.get_model(arguments)
    result = ts.predict(pre_x)
    result1 = list(chain.from_iterable(target_scaler.inverse_transform([result.flatten()])))
    pre_data['power_forecast'] = result1[:len(pre_data)]
    pre_data['farm_id'] = 'J00083'
    pre_data['cdq'] = 1
    pre_data['dq'] = 1
    pre_data['zq'] = 1
    pre_data.rename(columns={arguments['col_time']: 'date_time'}, inplace=True)
    pre_data = pre_data[['date_time', 'power_forecast', 'farm_id', 'cdq', 'dq', 'zq']]

    pre_data['power_forecast'] = pre_data['power_forecast'].round(2)
    pre_data.loc[pre_data['power_forecast'] > opt.cap, 'power_forecast'] = opt.cap
    pre_data.loc[pre_data['power_forecast'] < 0, 'power_forecast'] = 0

    insert_data_into_mongo(pre_data, arguments)