356 lines
13 KiB
Python
356 lines
13 KiB
Python
import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.cluster import KMeans
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from sklearn.impute import SimpleImputer
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import os
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def fill_time_gaps(
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data: pd.DataFrame,
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time_col: str = "time",
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freq: str = "1min",
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short_gap_threshold: int = 10,
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) -> pd.DataFrame:
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"""
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补齐缺失时间戳并填补数据缺口。
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Args:
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data: 包含时间列的 DataFrame
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time_col: 时间列名（默认 'time'）
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freq: 重采样频率（默认 '1min'）
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short_gap_threshold: 短缺口阈值（分钟），<=此值用线性插值，>此值用前向填充
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Returns:
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补齐时间后的 DataFrame（保留原时间列格式）
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"""
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if time_col not in data.columns:
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raise ValueError(f"时间列 '{time_col}' 不存在于数据中")
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# 解析时间列并设为索引
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data = data.copy()
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data[time_col] = pd.to_datetime(data[time_col], utc=True)
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data_indexed = data.set_index(time_col)
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# 生成完整时间范围
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full_range = pd.date_range(
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start=data_indexed.index.min(), end=data_indexed.index.max(), freq=freq
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)
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# 重索引以补齐缺失时间点，同时保留原始时间戳
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combined_index = data_indexed.index.union(full_range).sort_values().unique()
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data_reindexed = data_indexed.reindex(combined_index)
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# 按列处理缺口
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for col in data_reindexed.columns:
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# 识别缺失值位置
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is_missing = data_reindexed[col].isna()
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# 计算连续缺失的长度
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missing_groups = (is_missing != is_missing.shift()).cumsum()
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gap_lengths = is_missing.groupby(missing_groups).transform("sum")
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# 短缺口：时间插值
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short_gap_mask = is_missing & (gap_lengths <= short_gap_threshold)
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if short_gap_mask.any():
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data_reindexed.loc[short_gap_mask, col] = (
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data_reindexed[col]
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.interpolate(method="time", limit_area="inside")
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.loc[short_gap_mask]
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)
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# 长缺口：前向填充
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long_gap_mask = is_missing & (gap_lengths > short_gap_threshold)
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if long_gap_mask.any():
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data_reindexed.loc[long_gap_mask, col] = (
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data_reindexed[col].ffill().loc[long_gap_mask]
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)
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# 重置索引并恢复时间列（保留原格式）
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data_result = data_reindexed.reset_index()
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data_result.rename(columns={"index": time_col}, inplace=True)
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# 保留时区信息
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data_result[time_col] = data_result[time_col].dt.strftime("%Y-%m-%dT%H:%M:%S%z")
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# 修正时区格式（Python的%z输出为+0000，需转为+00:00）
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data_result[time_col] = data_result[time_col].str.replace(
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r"(\+\d{2})(\d{2})$", r"\1:\2", regex=True
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)
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return data_result
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def clean_pressure_data_km(
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input_csv_path: str, show_plot: bool = False, fill_gaps: bool = True
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) -> str:
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"""
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读取输入 CSV，基于 KMeans 检测异常并用滚动平均修复。输出为 <input_basename>_cleaned.xlsx（同目录）。
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原始数据在 sheet 'raw_pressure_data'，处理后数据在 sheet 'cleaned_pressusre_data'。
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返回输出文件的绝对路径。
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Args:
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input_csv_path: CSV 文件路径
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show_plot: 是否显示可视化
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fill_gaps: 是否先补齐时间缺口（默认 True）
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"""
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# 读取 CSV
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input_csv_path = os.path.abspath(input_csv_path)
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data = pd.read_csv(input_csv_path, header=0, index_col=None, encoding="utf-8")
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# 补齐时间缺口（如果数据包含 time 列）
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if fill_gaps and "time" in data.columns:
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data = fill_time_gaps(
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data, time_col="time", freq="1min", short_gap_threshold=10
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)
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# 分离时间列和数值列
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time_col_data = None
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if "time" in data.columns:
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time_col_data = data["time"]
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data = data.drop(columns=["time"])
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# 标准化
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data_norm = (data - data.mean()) / data.std()
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# 聚类与异常检测
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k = 3
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kmeans = KMeans(n_clusters=k, init="k-means++", n_init=50, random_state=42)
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clusters = kmeans.fit_predict(data_norm)
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centers = kmeans.cluster_centers_
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distances = np.linalg.norm(data_norm.values - centers[clusters], axis=1)
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threshold = distances.mean() + 3 * distances.std()
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anomaly_pos = np.where(distances > threshold)[0]
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anomaly_indices = data.index[anomaly_pos]
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anomaly_details = {}
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for pos in anomaly_pos:
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row_norm = data_norm.iloc[pos]
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cluster_idx = clusters[pos]
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center = centers[cluster_idx]
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diff = abs(row_norm - center)
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main_sensor = diff.idxmax()
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anomaly_details[data.index[pos]] = main_sensor
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# 修复：滚动平均（窗口可调）
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data_rolled = data.rolling(window=13, center=True, min_periods=1).mean()
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data_repaired = data.copy()
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for pos in anomaly_pos:
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label = data.index[pos]
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sensor = anomaly_details[label]
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data_repaired.loc[label, sensor] = data_rolled.loc[label, sensor]
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# 可选可视化（使用位置作为 x 轴）
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plt.rcParams["font.sans-serif"] = ["SimHei"]
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plt.rcParams["axes.unicode_minus"] = False
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if show_plot and len(data.columns) > 0:
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n = len(data)
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time = np.arange(n)
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plt.figure(figsize=(12, 8))
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for col in data.columns:
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plt.plot(time, data[col].values, marker="o", markersize=3, label=col)
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for pos in anomaly_pos:
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sensor = anomaly_details[data.index[pos]]
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plt.plot(pos, data.iloc[pos][sensor], "ro", markersize=8)
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plt.xlabel("时间点（序号）")
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plt.ylabel("压力监测值")
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plt.title("各传感器折线图（红色标记主要异常点）")
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plt.legend()
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plt.show()
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plt.figure(figsize=(12, 8))
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for col in data_repaired.columns:
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plt.plot(
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time, data_repaired[col].values, marker="o", markersize=3, label=col
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)
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for pos in anomaly_pos:
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sensor = anomaly_details[data.index[pos]]
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plt.plot(pos, data_repaired.iloc[pos][sensor], "go", markersize=8)
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plt.xlabel("时间点（序号）")
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plt.ylabel("修复后压力监测值")
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plt.title("修复后各传感器折线图（绿色标记修复值）")
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plt.legend()
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plt.show()
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# 保存到 Excel：两个 sheet
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input_dir = os.path.dirname(os.path.abspath(input_csv_path))
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input_base = os.path.splitext(os.path.basename(input_csv_path))[0]
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output_filename = f"{input_base}_cleaned.xlsx"
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output_path = os.path.join(input_dir, output_filename)
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# 如果原始数据包含时间列，将其添加回结果
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data_for_save = data.copy()
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data_repaired_for_save = data_repaired.copy()
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if time_col_data is not None:
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data_for_save.insert(0, "time", time_col_data)
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data_repaired_for_save.insert(0, "time", time_col_data)
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if os.path.exists(output_path):
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os.remove(output_path) # 覆盖同名文件
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with pd.ExcelWriter(output_path, engine="openpyxl") as writer:
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data_for_save.to_excel(writer, sheet_name="raw_pressure_data", index=False)
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data_repaired_for_save.to_excel(
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writer, sheet_name="cleaned_pressusre_data", index=False
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)
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# 返回输出文件的绝对路径
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return os.path.abspath(output_path)
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def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> dict:
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"""
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接收一个 DataFrame 数据结构，使用KMeans聚类检测异常并用滚动平均修复。
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返回清洗后的字典数据结构。
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Args:
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data: 输入 DataFrame（可包含 time 列）
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show_plot: 是否显示可视化
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"""
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# 使用传入的 DataFrame
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data = data.copy()
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# 补齐时间缺口（如果启用且数据包含 time 列）
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data_filled = fill_time_gaps(
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data, time_col="time", freq="1min", short_gap_threshold=10
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)
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# 保存 time 列用于最后合并
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time_col_series = None
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if "time" in data_filled.columns:
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time_col_series = data_filled["time"]
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# 移除 time 列用于后续清洗
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data_filled = data_filled.drop(columns=["time"])
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# 标准化（使用填充后的数据）
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data_norm = (data_filled - data_filled.mean()) / data_filled.std()
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# 添加：处理标准化后的 NaN（例如，标准差为0的列），防止异常数据，时间段内所有数据都相同导致计算结果为 NaN
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imputer = SimpleImputer(
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strategy="constant", fill_value=0, keep_empty_features=True
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) # 用 0 填充 NaN，包括全 NaN，并保留空特征
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data_norm = pd.DataFrame(
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imputer.fit_transform(data_norm),
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columns=data_norm.columns,
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index=data_norm.index,
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)
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# 聚类与异常检测
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k = 3
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kmeans = KMeans(n_clusters=k, init="k-means++", n_init=50, random_state=42)
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clusters = kmeans.fit_predict(data_norm)
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centers = kmeans.cluster_centers_
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distances = np.linalg.norm(data_norm.values - centers[clusters], axis=1)
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threshold = distances.mean() + 3 * distances.std()
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anomaly_pos = np.where(distances > threshold)[0]
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anomaly_indices = data_filled.index[anomaly_pos]
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anomaly_details = {}
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for pos in anomaly_pos:
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row_norm = data_norm.iloc[pos]
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cluster_idx = clusters[pos]
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center = centers[cluster_idx]
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diff = abs(row_norm - center)
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main_sensor = diff.idxmax()
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anomaly_details[data_filled.index[pos]] = main_sensor
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# 修复：滚动平均（窗口可调）
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data_rolled = data_filled.rolling(window=13, center=True, min_periods=1).mean()
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data_repaired = data_filled.copy()
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for pos in anomaly_pos:
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label = data_filled.index[pos]
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sensor = anomaly_details[label]
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data_repaired.loc[label, sensor] = data_rolled.loc[label, sensor]
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# 可选可视化（使用位置作为 x 轴）
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plt.rcParams["font.sans-serif"] = ["SimHei"]
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plt.rcParams["axes.unicode_minus"] = False
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if show_plot and len(data.columns) > 0:
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n = len(data)
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time = np.arange(n)
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n_filled = len(data_filled)
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time_filled = np.arange(n_filled)
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plt.figure(figsize=(12, 8))
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for col in data.columns:
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plt.plot(
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time, data[col].values, marker="o", markersize=3, label=col, alpha=0.5
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)
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for col in data_filled.columns:
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plt.plot(
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time_filled,
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data_filled[col].values,
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marker="x",
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markersize=3,
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label=f"{col}_filled",
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linestyle="--",
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)
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for pos in anomaly_pos:
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sensor = anomaly_details[data_filled.index[pos]]
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plt.plot(pos, data_filled.iloc[pos][sensor], "ro", markersize=8)
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plt.xlabel("时间点（序号）")
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plt.ylabel("压力监测值")
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plt.title("各传感器折线图（红色标记主要异常点，虚线为0值填充后）")
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plt.legend()
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plt.show()
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plt.figure(figsize=(12, 8))
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for col in data_repaired.columns:
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plt.plot(
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time_filled, data_repaired[col].values, marker="o", markersize=3, label=col
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)
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for pos in anomaly_pos:
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sensor = anomaly_details[data_filled.index[pos]]
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plt.plot(pos, data_repaired.iloc[pos][sensor], "go", markersize=8)
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plt.xlabel("时间点（序号）")
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plt.ylabel("修复后压力监测值")
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plt.title("修复后各传感器折线图（绿色标记修复值）")
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plt.legend()
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plt.show()
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# 将 time 列添加回结果
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if time_col_series is not None:
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data_repaired.insert(0, "time", time_col_series)
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# 返回清洗后的字典
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return data_repaired
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# 测试
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# if __name__ == "__main__":
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# # 默认使用脚本目录下的 pressure_raw_data.csv
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# script_dir = os.path.dirname(os.path.abspath(__file__))
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# default_csv = os.path.join(script_dir, "pressure_raw_data.csv")
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# out_path = clean_pressure_data_km(default_csv, show_plot=False)
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# print("保存路径：", out_path)
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# 测试 clean_pressure_data_dict_km 函数
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if __name__ == "__main__":
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import random
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# 读取 szh_pressure_scada.csv 文件
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script_dir = os.path.dirname(os.path.abspath(__file__))
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csv_path = os.path.join(script_dir, "szh_pressure_scada.csv")
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data = pd.read_csv(csv_path, header=0, index_col=None, encoding="utf-8")
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# 排除 Time 列，随机选择 5 列
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columns_to_exclude = ["Time"]
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available_columns = [col for col in data.columns if col not in columns_to_exclude]
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selected_columns = random.sample(available_columns, 5)
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# 将选中的列转换为字典
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data_dict = {col: data[col].tolist() for col in selected_columns}
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print("选中的列:", selected_columns)
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print("原始数据长度:", len(data_dict[selected_columns[0]]))
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# 调用函数进行清洗
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cleaned_dict = clean_pressure_data_df_km(data_dict, show_plot=True)
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print("清洗后的字典键:", list(cleaned_dict.keys()))
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print("清洗后的数据长度:", len(cleaned_dict[selected_columns[0]]))
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print("测试完成：函数运行正常")
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