DataNalysis/DataBase/db-wind/main.py

import pandas as pd
import os
from datetime import timedelta
from inputData import DataBase
import Arg
#——————————————————————————机头风速-99和连续异常值清洗代码——————————————————————————————
def mark_abnormal_streaks(df, columns, min_streak):
    abnormal_mask = pd.Series(False, index=df.index)
    streak_start = None
    for i in range(len(df)):
        if i == 0 or any(df.at[i - 1, col] != df.at[i, col] for col in columns):
            streak_start = i

        if i - streak_start >= min_streak - 1:
            abnormal_mask[i - min_streak + 1:i + 1] = True

    return abnormal_mask


def remove_abnormal_values(df, N):
    # 标记C_ACTIVE_POWER为-99的行为异常值
    abnormal_mask1 = df['C_ACTIVE_POWER'] == -99
    count_abnormal1 = abnormal_mask1.sum()

    # 标记C_WS, A, B连续5行不变的行为异常值
    columns = ['C_WS', 'C_WD', 'C_ACTIVE_POWER']
    abnormal_mask2 = mark_abnormal_streaks(df, columns, N)
    count_abnormal2 = abnormal_mask2.sum()
    # 获得所有异常值的布尔掩码
    abnormal_mask = abnormal_mask1 | abnormal_mask2
    # 获取连续异常值具体数值
    removed_continuous_values = {column: df.loc[abnormal_mask2, column].unique() for column in columns}
    # 剔除异常值
    df_clean = df[~abnormal_mask]
    total_removed = abnormal_mask.sum()
    return df_clean, count_abnormal1, count_abnormal2, total_removed, removed_continuous_values


def process_csv_files(input_dir, output_dir,M,N):  # MBD:没有考虑时间重复
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    for i in arg.turbineloc:
        input_file = os.path.join(input_dir, f"turbine-{i}.csv")
        output_file = os.path.join(output_dir, f"turbine-{i}.csv")

        # 读取csv文件
        df = pd.read_csv(input_file)

        # 剔除异常值，并获取异常值统计信息
        df_clean, count_abnormal1, count_abnormal2, total_removed, removed_continuous_values = remove_abnormal_values(df,N)

        # 输出异常值统计信息
        print(f"处理文件：{input_file}")
        print(f"剔除 -99 点异常值数量：{count_abnormal1}")
        print(f"剔除连续异常值数量：{count_abnormal2}")
        print(f"总共剔除数据量：{total_removed}")
        print(f"剔除的连续异常值具体数值：{removed_continuous_values}\n")

        # 保存处理过的CSV文件
        df_clean.to_csv(output_file, index=False)
#——————————————————————————风机单机时间对齐——————————————————————————————
def TimeMerge(input_dir, output_dir,M):
    # 读取所有CSV文件
    files = [os.path.join(input_dir, f"turbine-{i}.csv") for i in arg.turbineloc]
    dataframes = [pd.read_csv(f) for f in files]

    # 获取C_TIME列的交集
    c_time_intersection = set(dataframes[0]["C_TIME"])
    for df in dataframes[1:]:
        c_time_intersection.intersection_update(df["C_TIME"])

    # 只保留C_TIME交集中的数据
    filtered_dataframes = [df[df["C_TIME"].isin(c_time_intersection)] for df in dataframes]

    # 将每个过滤后的DataFrame写入新的CSV文件
    os.makedirs(output_dir, exist_ok=True)
    for (filtered_df, i) in zip(filtered_dataframes, arg.turbineloc):
        if i == 144:
            filtered_df['C_ACTIVE_POWER'] /= 1000
        filtered_df.to_csv(os.path.join(output_dir, f"turbine-{i}.csv"), index=False)

#——————————————————————————风机缺失点处理——————————————————————————————
def MissingPointProcessing(input_dir,output_dir,M,N):

    # 存储数据的列表

    # 读取M个文件
    for k in arg.turbineloc:
        file_name = input_dir + '/' + f"turbine-{k}.csv"
        # file_name = os.path.join(input_dir, f"turbine-{k}.csv")
    # 读取CSV文件
        data = pd.read_csv(file_name, parse_dates=['C_TIME'])

    # 计算时间差
        data['time_diff'] = data['C_TIME'].diff().dt.total_seconds()

    # 找出缺失的时间点
        missing_data_points = data[data['time_diff'] > 900]

    # 存储填充的时间和值
        filled_data = []

    # 输出缺失的开始时刻和数量
        print("缺失的开始时刻:")
        for index, row in missing_data_points.iterrows():
            missing_start = row['C_TIME'] - timedelta(seconds=row['time_diff'])
            missing_count = int(row['time_diff'] // 900) - 1

            # 如果缺失的点数小于N个，则进行填充 MBD:填充代码比较啰嗦
            if missing_count <= N:
                prev_values = data.iloc[index - 1][['C_WS', 'C_WD', 'C_ACTIVE_POWER']]
                next_values = row[['C_WS', 'C_WD', 'C_ACTIVE_POWER']]

                for i in range(1, missing_count + 1):
                    t = i / (missing_count + 1)
                    filled_time = missing_start + timedelta(minutes=15 * i)

                    filled_values = {
                        'C_TIME': filled_time,
                        'C_WS': prev_values['C_WS'] + (next_values['C_WS'] - prev_values['C_WS']) * t,
                        'C_WD': prev_values['C_WD']+(next_values['C_WD']-prev_values['C_WD'])*t,
                        'C_ACTIVE_POWER': prev_values['C_ACTIVE_POWER'] + (
                                    next_values['C_ACTIVE_POWER'] - prev_values['C_ACTIVE_POWER']) * t,
                    }

                    # 将角度值限制在-180到180的范围内
                    filled_values['C_WD'] = (filled_values['C_WD'] + 180) % 360 - 180

                    filled_data.append(filled_values)
                    print(f"填充的时间: {filled_time}, 填充的值: {filled_values}")

            print(f"{missing_start} - 缺失的点的数量: {missing_count}")

        # 将填充的数据插入原始数据中
        filled_df = pd.DataFrame(filled_data)
        data = pd.concat([data, filled_df], ignore_index=True)
        # 对数据按时间排序并重置索引
        data = data.sort_values(by='C_TIME').reset_index(drop=True)

        # 输出总缺失点数
        missing_data_points = data[data['time_diff'] > 900]
        print(f"总缺失点数: {int(missing_data_points['time_diff'].sum() // 900) - len(missing_data_points)}")
        data.drop(columns=['time_diff'], inplace=True)
        os.makedirs(output_dir, exist_ok=True)
        output_path_name = os.path.join(output_dir, f"turbine-{k}.csv")
        print(output_path_name)
    # 保存插值后的文件
        data.to_csv(output_path_name, index=False)
#——————————————————————————风机单机连续时间段分割——————————————————————————————
def top_n_continuous_periods(data, n):
    continuous_periods = []
    continuous_start = data['C_TIME'].iloc[0]
    continuous_count = 1

    for i in range(1, len(data)):
        if data['time_diff'].iloc[i] == 900:
            continuous_count += 1
        else:
            continuous_periods.append({
                'start': continuous_start,
                'end': data['C_TIME'].iloc[i - 1],
                'count': continuous_count
            })
            continuous_start = data['C_TIME'].iloc[i]
            continuous_count = 1

    continuous_periods.append({
        'start': continuous_start,
        'end': data['C_TIME'].iloc[-1],
        'count': continuous_count
    })
    continuous_periods.sort(key=lambda x: x['count'], reverse=True)
    return continuous_periods[:n]
def Continuous_Data(input_dir,output_dir,M,TopN):
    # 读取CSV文件
    for k in arg.turbineloc:
        path_dir = f"turbine-{k}.csv"
        input_path = os.path.join(input_dir, path_dir)
        data = pd.read_csv(input_path, parse_dates=['C_TIME'])
        data = data.sort_values(by='C_TIME').reset_index(drop=True)
    # 计算时间差
        data['time_diff'] = data['C_TIME'].diff().dt.total_seconds()
    # 获取Top N连续的数据段
        top_n = TopN
        top_n_periods = top_n_continuous_periods(data, top_n)
        data.drop(columns=['time_diff'], inplace=True)
    # 输出Top N连续的数据的数量、开始时间和最后的时间
        print(f"Top {top_n} 连续的数据段:")
        for i, period in enumerate(top_n_periods):
            print(f"{i + 1}. 开始时间: {period['start']} - 结束时间: {period['end']} - 数据量: {period['count']}")
            output_file = f"turbine-{k}_{period['count']}.csv"
            mask = (data['C_TIME'] >= period['start']) & (data['C_TIME'] <= period['end'])
            filtered_df = data.loc[mask]
            # 更新文件名中的period['count']为数据集大小
            output_file = output_file.replace(str(period['count']), str(filtered_df.shape[0]))
            output_folder = f"Continuous_Turbine_Data_{period['count']}_{period['start'].strftime('%y-%m-%d-%H-%M')}_{period['end'].strftime('%y-%m-%d-%H-%M')}"
            output_folder = os.path.join(output_dir, output_folder)
            if not os.path.exists(output_folder):
                os.makedirs(output_folder)
            # 保存截取的数据到新的csv文件
            # filtered_df.to_csv(output_dir, index=False)
            filtered_df.to_csv(os.path.join(output_folder, output_file), index=False)
            print(f"Processed {input_path}")


if __name__ == "__main__":
    import datetime
    arg = Arg.Arg()
    db = DataBase(arg=arg)
    db.data_process()
    # input_dir = "../data/314/turbine-15"  # 输入文件夹路径
    # output_dir = "../data/314/output_clean_csv_files"  # 输出文件夹路径
    # 对机头风速连续异常值和-99进行清洗,第三个参数是连续5个值不变以后就认为异常
    # 这步会生成一个"output_clean_csv_files"文件夹，里面包含全部单机的数据，存储的机头风速只清理了-99，参数50是风机数量+1，风机参数5就是连续5个点的认为是异常值，全部剔除。
    # process_csv_files(input_dir, output_dir, 50, 5)
    # output_dir_time_Merge = "../data/314/output_filtered_csv_files"
    # 这步会生成一个"output_filtered_csv_files"文件夹，在上一步的基础上，对齐了全部风机的时间，只各自保留了交集。
    # TimeMerge(output_dir,output_dir_time_Merge,50)
    # output_complete_data = "../data_mts/complete_data"
    # 这步会生成一个"complete_data"文件夹，在上一步的基础上，填充了10个时间点之内的缺失。
    # MissingPointProcessing(output_dir_time_Merge,output_complete_data,50,10)
    # continuous_time = "../data_mts/continuous_data"
    # # 这步会生成一个"Continuous_data"文件夹，在上一步的基础上，取Top10个连续时间段最长的单机数据。
    # Continuous_Data(output_complete_data, continuous_time, 50, 10)