energy-data-trans/utils/tmp_util/神木_完整度_10分.py

# coding=utf-8

import datetime
import multiprocessing
import os
import sys

sys.path.insert(0, os.path.abspath(__file__).split("utils")[0])

import pandas as pd

from utils.file.trans_methods import read_file_to_df, read_excel_files


def get_time_space_count(start_time: datetime.datetime, end_time: datetime.datetime, time_space=1):
    """
    获取俩个时间之间的个数
    :return: 查询时间间隔
    """
    delta = end_time - start_time
    total_seconds = delta.days * 24 * 60 * 60 + delta.seconds

    return abs(int(total_seconds / time_space)) + 1


def save_percent(value, save_decimal=7):
    return round(value, save_decimal) * 100


def read_and_select(file_path, read_cols_bak):
    try:
        read_cols = read_cols_bak[0:len(read_cols_bak)]
        result_df = pd.DataFrame()
        df = read_file_to_df(file_path, read_cols=read_cols)
        wind_name = df['名称'].values[0]
        df['时间'] = pd.to_datetime(df['时间'])
        count = get_time_space_count(df['时间'].min(), df['时间'].max(), 600)
        repeat_time_count = df.shape[0] - len(df['时间'].unique())
        print(wind_name, count, repeat_time_count)
        result_df['风机号'] = [wind_name]
        result_df['重复率'] = [save_percent(repeat_time_count / count)]
        result_df['重复次数'] = [repeat_time_count]
        result_df['总记录数'] = [count]

        read_cols.remove('名称')
        for read_col in read_cols:

            if read_col != '时间':
                df[read_col] = pd.to_numeric(df[read_col], errors='coerce')
            else:
                df[read_col] = pd.to_datetime(df[read_col], errors='coerce')

        group_df = df.groupby(by=['名称']).count()
        group_df.reset_index(inplace=True)
        count_df = pd.DataFrame(group_df)
        total_count = count_df[read_cols].values[0].sum()
        print(wind_name, total_count, count * len(read_cols))
        result_df['平均缺失率,单位%'] = [save_percent(1 - total_count / (count * len(read_cols)))]
        result_df['缺失数值'] = [
            '-'.join([f'{col_name}_{str(count - i)}' for col_name, i in zip(read_cols, count_df[read_cols].values[0])])]
        del group_df

        error_fengsu_count = df.query("(风速 < 0) | (风速 > 80)").shape[0]
        error_yougong_gonglv = df.query("(发电机有功功率 < -200) | (发电机有功功率 > 2500)").shape[0]

        result_df['平均异常率'] = [save_percent((error_fengsu_count + error_yougong_gonglv) / (2 * count))]
    except Exception as e:
        print(file_path)
        raise e

    return result_df


if __name__ == '__main__':
    read_cols_str = '名称,时间,发电机有功功率,发电机转速,发电机驱动端轴承温度,发电机非驱动端轴承温度,发电机定子U相线圈温度,发电机定子V相线圈温度,发电机定子W相线圈温度,实际扭矩,设定扭矩,仪表盘风速,舱内温度,控制柜内温度,舱外温度,风向,风速,机舱风向夹角,1#桨叶片角度,1#桨设定角度,2#桨叶片角度,2#桨设定角度,3#桨叶片角度,3#桨设定角度,1#桨电机温度,2#桨电机温度,3#桨电机温度,轮毂内温度,齿轮箱油泵吸油口油压,齿轮箱分配器位置油压,偏航液压刹车系统蓄能罐压力,主轴转速,齿轮箱油路入口温度,齿轮箱中间轴驱动端轴承温度,齿轮箱中间轴非驱动端轴承温度,齿轮箱油池温度,主轴承外圈温度,可利用率,机舱位置,总扭缆角度'
    read_cols = [i for i in read_cols_str.split(",") if i]
    read_dir = r'D:\data\tmp_data\10分'

    files = read_excel_files(read_dir)

    with multiprocessing.Pool(4) as pool:
        dfs = pool.starmap(read_and_select, [(os.path.join(read_dir, i), read_cols) for i in files])

    df = pd.concat(dfs, ignore_index=True)
    df.sort_values(by=['风机号'], inplace=True)

    df.to_csv("神木风电场-10分钟.csv", encoding='utf8', index=False)