修复数据清洗index越界错误；重命名压力流量清洗方法

2026-02-02 14:15:54 +08:00
parent c3c26fb107
commit 3c7e2c5806
10 changed files with 116 additions and 60 deletions
--- a/app/algorithms/api_ex/pressure_data_clean.py
+++ b/app/algorithms/api_ex/pressure_data_clean.py
@@ -0,0 +1,344 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+from sklearn.impute import SimpleImputer
+import os
+
+
+def fill_time_gaps(
+    data: pd.DataFrame,
+    time_col: str = "time",
+    freq: str = "1min",
+    short_gap_threshold: int = 10,
+) -> pd.DataFrame:
+    """
+    补齐缺失时间戳并填补数据缺口。
+
+    Args:
+        data: 包含时间列的 DataFrame
+        time_col: 时间列名（默认 'time'）
+        freq: 重采样频率（默认 '1min'）
+        short_gap_threshold: 短缺口阈值（分钟），<=此值用线性插值，>此值用前向填充
+
+    Returns:
+        补齐时间后的 DataFrame（保留原时间列格式）
+    """
+    if time_col not in data.columns:
+        raise ValueError(f"时间列 '{time_col}' 不存在于数据中")
+
+    # 解析时间列并设为索引
+    data = data.copy()
+    data[time_col] = pd.to_datetime(data[time_col], utc=True)
+    data_indexed = data.set_index(time_col)
+
+    # 生成完整时间范围
+    full_range = pd.date_range(
+        start=data_indexed.index.min(), end=data_indexed.index.max(), freq=freq
+    )
+
+    # 重索引以补齐缺失时间点
+    data_reindexed = data_indexed.reindex(full_range)
+
+    # 按列处理缺口
+    for col in data_reindexed.columns:
+        # 识别缺失值位置
+        is_missing = data_reindexed[col].isna()
+
+        # 计算连续缺失的长度
+        missing_groups = (is_missing != is_missing.shift()).cumsum()
+        gap_lengths = is_missing.groupby(missing_groups).transform("sum")
+
+        # 短缺口：线性插值
+        short_gap_mask = is_missing & (gap_lengths <= short_gap_threshold)
+        if short_gap_mask.any():
+            data_reindexed.loc[short_gap_mask, col] = (
+                data_reindexed[col]
+                .interpolate(method="linear", limit_area="inside")
+                .loc[short_gap_mask]
+            )
+
+        # 长缺口：前向填充
+        long_gap_mask = is_missing & (gap_lengths > short_gap_threshold)
+        if long_gap_mask.any():
+            data_reindexed.loc[long_gap_mask, col] = (
+                data_reindexed[col].ffill().loc[long_gap_mask]
+            )
+
+    # 重置索引并恢复时间列（保留原格式）
+    data_result = data_reindexed.reset_index()
+    data_result.rename(columns={"index": time_col}, inplace=True)
+
+    # 保留时区信息
+    data_result[time_col] = data_result[time_col].dt.strftime("%Y-%m-%dT%H:%M:%S%z")
+    # 修正时区格式（Python的%z输出为+0000，需转为+00:00）
+    data_result[time_col] = data_result[time_col].str.replace(
+        r"(\+\d{2})(\d{2})$", r"\1:\2", regex=True
+    )
+
+    return data_result
+
+
+def clean_pressure_data_km(
+    input_csv_path: str, show_plot: bool = False, fill_gaps: bool = True
+) -> str:
+    """
+    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）。
+    原始数据在 sheet 'raw_pressure_data'，处理后数据在 sheet 'cleaned_pressusre_data'。
+    返回输出文件的绝对路径。
+
+    Args:
+        input_csv_path: CSV 文件路径
+        show_plot: 是否显示可视化
+        fill_gaps: 是否先补齐时间缺口（默认 True）
+    """
+    # 读取 CSV
+    input_csv_path = os.path.abspath(input_csv_path)
+    data = pd.read_csv(input_csv_path, header=0, index_col=None, encoding="utf-8")
+
+    # 补齐时间缺口（如果数据包含 time 列）
+    if fill_gaps and "time" in data.columns:
+        data = fill_time_gaps(
+            data, time_col="time", freq="1min", short_gap_threshold=10
+        )
+
+    # 分离时间列和数值列
+    time_col_data = None
+    if "time" in data.columns:
+        time_col_data = data["time"]
+        data = data.drop(columns=["time"])
+    # 标准化
+    data_norm = (data - data.mean()) / data.std()
+
+    # 聚类与异常检测
+    k = 3
+    kmeans = KMeans(n_clusters=k, init="k-means++", n_init=50, random_state=42)
+    clusters = kmeans.fit_predict(data_norm)
+    centers = kmeans.cluster_centers_
+
+    distances = np.linalg.norm(data_norm.values - centers[clusters], axis=1)
+    threshold = distances.mean() + 3 * distances.std()
+
+    anomaly_pos = np.where(distances > threshold)[0]
+    anomaly_indices = data.index[anomaly_pos]
+
+    anomaly_details = {}
+    for pos in anomaly_pos:
+        row_norm = data_norm.iloc[pos]
+        cluster_idx = clusters[pos]
+        center = centers[cluster_idx]
+        diff = abs(row_norm - center)
+        main_sensor = diff.idxmax()
+        anomaly_details[data.index[pos]] = main_sensor
+
+    # 修复：滚动平均（窗口可调）
+    data_rolled = data.rolling(window=13, center=True, min_periods=1).mean()
+    data_repaired = data.copy()
+    for pos in anomaly_pos:
+        label = data.index[pos]
+        sensor = anomaly_details[label]
+        data_repaired.loc[label, sensor] = data_rolled.loc[label, sensor]
+
+    # 可选可视化（使用位置作为 x 轴）
+    plt.rcParams["font.sans-serif"] = ["SimHei"]
+    plt.rcParams["axes.unicode_minus"] = False
+
+    if show_plot and len(data.columns) > 0:
+        n = len(data)
+        time = np.arange(n)
+        plt.figure(figsize=(12, 8))
+        for col in data.columns:
+            plt.plot(time, data[col].values, marker="o", markersize=3, label=col)
+        for pos in anomaly_pos:
+            sensor = anomaly_details[data.index[pos]]
+            plt.plot(pos, data.iloc[pos][sensor], "ro", markersize=8)
+        plt.xlabel("时间点（序号）")
+        plt.ylabel("压力监测值")
+        plt.title("各传感器折线图（红色标记主要异常点）")
+        plt.legend()
+        plt.show()
+
+        plt.figure(figsize=(12, 8))
+        for col in data_repaired.columns:
+            plt.plot(
+                time, data_repaired[col].values, marker="o", markersize=3, label=col
+            )
+        for pos in anomaly_pos:
+            sensor = anomaly_details[data.index[pos]]
+            plt.plot(pos, data_repaired.iloc[pos][sensor], "go", markersize=8)
+        plt.xlabel("时间点（序号）")
+        plt.ylabel("修复后压力监测值")
+        plt.title("修复后各传感器折线图（绿色标记修复值）")
+        plt.legend()
+        plt.show()
+
+    # 保存到 Excel：两个 sheet
+    input_dir = os.path.dirname(os.path.abspath(input_csv_path))
+    input_base = os.path.splitext(os.path.basename(input_csv_path))[0]
+    output_filename = f"{input_base}_cleaned.xlsx"
+    output_path = os.path.join(input_dir, output_filename)
+
+    # 如果原始数据包含时间列，将其添加回结果
+    data_for_save = data.copy()
+    data_repaired_for_save = data_repaired.copy()
+    if time_col_data is not None:
+        data_for_save.insert(0, "time", time_col_data)
+        data_repaired_for_save.insert(0, "time", time_col_data)
+
+    if os.path.exists(output_path):
+        os.remove(output_path)  # 覆盖同名文件
+    with pd.ExcelWriter(output_path, engine="openpyxl") as writer:
+        data_for_save.to_excel(writer, sheet_name="raw_pressure_data", index=False)
+        data_repaired_for_save.to_excel(
+            writer, sheet_name="cleaned_pressusre_data", index=False
+        )
+
+    # 返回输出文件的绝对路径
+    return os.path.abspath(output_path)
+
+
+def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> dict:
+    """
+    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。
+    返回清洗后的字典数据结构。
+
+    Args:
+        data: 输入 DataFrame（可包含 time 列）
+        show_plot: 是否显示可视化
+    """
+    # 使用传入的 DataFrame
+    data = data.copy()
+
+    # 补齐时间缺口（如果启用且数据包含 time 列）
+    data_filled = fill_time_gaps(
+        data, time_col="time", freq="1min", short_gap_threshold=10
+    )
+    # 移除 time 列用于后续清洗
+    data_filled = data_filled.drop(columns=["time"])
+
+    # 标准化（使用填充后的数据）
+    data_norm = (data_filled - data_filled.mean()) / data_filled.std()
+
+    # 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
+    imputer = SimpleImputer(
+        strategy="constant", fill_value=0, keep_empty_features=True
+    )  # 用 0 填充 NaN，包括全 NaN，并保留空特征
+    data_norm = pd.DataFrame(
+        imputer.fit_transform(data_norm),
+        columns=data_norm.columns,
+        index=data_norm.index,
+    )
+
+    # 聚类与异常检测
+    k = 3
+    kmeans = KMeans(n_clusters=k, init="k-means++", n_init=50, random_state=42)
+    clusters = kmeans.fit_predict(data_norm)
+    centers = kmeans.cluster_centers_
+
+    distances = np.linalg.norm(data_norm.values - centers[clusters], axis=1)
+    threshold = distances.mean() + 3 * distances.std()
+
+    anomaly_pos = np.where(distances > threshold)[0]
+    anomaly_indices = data_filled.index[anomaly_pos]
+
+    anomaly_details = {}
+    for pos in anomaly_pos:
+        row_norm = data_norm.iloc[pos]
+        cluster_idx = clusters[pos]
+        center = centers[cluster_idx]
+        diff = abs(row_norm - center)
+        main_sensor = diff.idxmax()
+        anomaly_details[data_filled.index[pos]] = main_sensor
+
+    # 修复：滚动平均（窗口可调）
+    data_rolled = data_filled.rolling(window=13, center=True, min_periods=1).mean()
+    data_repaired = data_filled.copy()
+    for pos in anomaly_pos:
+        label = data_filled.index[pos]
+        sensor = anomaly_details[label]
+        data_repaired.loc[label, sensor] = data_rolled.loc[label, sensor]
+
+    # 可选可视化（使用位置作为 x 轴）
+    plt.rcParams["font.sans-serif"] = ["SimHei"]
+    plt.rcParams["axes.unicode_minus"] = False
+
+    if show_plot and len(data.columns) > 0:
+        n = len(data)
+        time = np.arange(n)
+        n_filled = len(data_filled)
+        time_filled = np.arange(n_filled)
+        plt.figure(figsize=(12, 8))
+        for col in data.columns:
+            plt.plot(
+                time, data[col].values, marker="o", markersize=3, label=col, alpha=0.5
+            )
+        for col in data_filled.columns:
+            plt.plot(
+                time_filled,
+                data_filled[col].values,
+                marker="x",
+                markersize=3,
+                label=f"{col}_filled",
+                linestyle="--",
+            )
+        for pos in anomaly_pos:
+            sensor = anomaly_details[data_filled.index[pos]]
+            plt.plot(pos, data_filled.iloc[pos][sensor], "ro", markersize=8)
+        plt.xlabel("时间点（序号）")
+        plt.ylabel("压力监测值")
+        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）")
+        plt.legend()
+        plt.show()
+
+        plt.figure(figsize=(12, 8))
+        for col in data_repaired.columns:
+            plt.plot(
+                time_filled, data_repaired[col].values, marker="o", markersize=3, label=col
+            )
+        for pos in anomaly_pos:
+            sensor = anomaly_details[data_filled.index[pos]]
+            plt.plot(pos, data_repaired.iloc[pos][sensor], "go", markersize=8)
+        plt.xlabel("时间点（序号）")
+        plt.ylabel("修复后压力监测值")
+        plt.title("修复后各传感器折线图（绿色标记修复值）")
+        plt.legend()
+        plt.show()
+
+    # 返回清洗后的字典
+    return data_repaired
+
+
+# 测试
+# if __name__ == "__main__":
+#     # 默认使用脚本目录下的 pressure_raw_data.csv
+#     script_dir = os.path.dirname(os.path.abspath(__file__))
+#     default_csv = os.path.join(script_dir, "pressure_raw_data.csv")
+#     out_path = clean_pressure_data_km(default_csv, show_plot=False)
+#     print("保存路径：", out_path)
+
+# 测试 clean_pressure_data_dict_km 函数
+if __name__ == "__main__":
+    import random
+
+    # 读取 szh_pressure_scada.csv 文件
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    csv_path = os.path.join(script_dir, "szh_pressure_scada.csv")
+    data = pd.read_csv(csv_path, header=0, index_col=None, encoding="utf-8")
+
+    # 排除 Time 列，随机选择 5 列
+    columns_to_exclude = ["Time"]
+    available_columns = [col for col in data.columns if col not in columns_to_exclude]
+    selected_columns = random.sample(available_columns, 5)
+
+    # 将选中的列转换为字典
+    data_dict = {col: data[col].tolist() for col in selected_columns}
+
+    print("选中的列:", selected_columns)
+    print("原始数据长度:", len(data_dict[selected_columns[0]]))
+
+    # 调用函数进行清洗
+    cleaned_dict = clean_pressure_data_df_km(data_dict, show_plot=True)
+
+    print("清洗后的字典键:", list(cleaned_dict.keys()))
+    print("清洗后的数据长度:", len(cleaned_dict[selected_columns[0]]))
+    print("测试完成：函数运行正常")