优化传感器布置算法，修复数据库更新逻辑
This commit is contained in:
+475
-179
@@ -1,18 +1,435 @@
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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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from app.algorithms._utils import fill_time_gaps
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ID_LIKE_COLUMNS = {
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"id",
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"device_id",
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"node_id",
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"sensor_id",
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"monitor_id",
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"junction_id",
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}
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def _normalize_time_frame(data: pd.DataFrame) -> pd.DataFrame:
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"""返回按时间排序的副本，并尽量将 time 列解析为时间类型。"""
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data = data.copy()
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if "time" in data.columns:
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data["time"] = pd.to_datetime(data["time"], errors="coerce")
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data = data.sort_values(["time"]).reset_index(drop=True)
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return data
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def _select_pressure_columns(data: pd.DataFrame) -> tuple[list[str], list[str]]:
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"""区分需要清洗的数值列与需要原样保留的列。"""
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value_cols: list[str] = []
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keep_cols: list[str] = []
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for col in data.columns:
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if col == "time":
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continue
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col_key = col.lower()
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if col_key in ID_LIKE_COLUMNS or col_key.endswith("_id"):
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keep_cols.append(col)
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continue
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numeric = pd.to_numeric(data[col], errors="coerce")
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if numeric.notna().sum() == 0 or numeric.nunique(dropna=True) <= 1:
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keep_cols.append(col)
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else:
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value_cols.append(col)
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return value_cols, keep_cols
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def _robust_scale(values: pd.Series) -> float:
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"""基于 MAD 计算稳健尺度。"""
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series = pd.to_numeric(values, errors="coerce").dropna()
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if series.empty:
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return 1.0
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median = series.median()
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mad = (series - median).abs().median()
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if pd.notna(mad) and mad > 0:
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return float(1.4826 * mad)
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iqr = series.quantile(0.75) - series.quantile(0.25)
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if pd.notna(iqr) and iqr > 0:
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return float(iqr / 1.349)
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std = series.std()
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if pd.notna(std) and std > 0:
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return float(std)
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return 1.0
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def _shrink_toward_baseline(observed: float, baseline: float, scale: float) -> float:
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"""把观测值向基线值收缩，scale 越小，修复越强。"""
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if pd.isna(observed):
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return baseline
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if pd.isna(baseline):
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return observed
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diff = observed - baseline
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weight = scale / (abs(diff) + scale)
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return float(baseline + diff * weight)
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def _infer_time_frequency(time_values: pd.Series | pd.Index) -> pd.Timedelta:
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"""从时间序列中推断采样频率，失败时默认 15 分钟。"""
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parsed = pd.to_datetime(pd.Series(time_values), errors="coerce").dropna().sort_values()
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if len(parsed) < 2:
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return pd.Timedelta(minutes=15)
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diffs = parsed.diff().dropna()
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diffs = diffs[diffs > pd.Timedelta(0)]
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if diffs.empty:
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return pd.Timedelta(minutes=15)
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mode = diffs.mode()
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return mode.iloc[0] if not mode.empty else diffs.median()
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def _build_local_pressure_baseline(series: pd.Series) -> pd.Series:
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"""基于局部插值与中值滤波构造平滑基线。"""
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baseline = _safe_time_interpolate(series)
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baseline = baseline.rolling(window=5, center=True, min_periods=1).median()
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baseline = _safe_time_interpolate(baseline)
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return baseline.ffill().bfill()
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def _build_seasonal_pressure_baseline(series: pd.Series) -> pd.Series:
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"""按一天内的同一时刻构造季节性基线，适合日周期压力数据。"""
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if not isinstance(series.index, pd.DatetimeIndex):
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return pd.Series(np.nan, index=series.index, dtype=float)
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slot_labels = pd.Series(series.index.strftime("%H:%M:%S"), index=series.index)
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return series.groupby(slot_labels).transform("median")
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def _detect_pressure_spikes(series: pd.Series, local_baseline: pd.Series) -> pd.Series:
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"""识别单点异常上升/下降尖峰，避免过度修正正常波动。"""
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residual = series - local_baseline
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neighbor_center = (series.shift(1) + series.shift(-1)) / 2
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curvature = series - neighbor_center
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residual_scale = max(_robust_scale(residual), 1e-6)
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curvature_scale = max(_robust_scale(curvature), 1e-6)
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direction_flip = ((series - series.shift(1)) * (series.shift(-1) - series) < 0).fillna(False)
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return (
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residual.abs() > 3.5 * residual_scale
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) & (
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curvature.abs() > 3.0 * curvature_scale
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) & direction_flip
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def _fill_pressure_gaps(
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original: pd.Series,
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repaired: pd.Series,
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local_baseline: pd.Series,
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seasonal_baseline: pd.Series,
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) -> pd.Series:
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"""短缺口用局部插值，长缺口优先使用同一时刻的季节性轨迹。"""
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missing_mask = original.isna()
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if not missing_mask.any():
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return repaired
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gap_groups = (missing_mask != missing_mask.shift(fill_value=False)).cumsum()
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gap_lengths = missing_mask.groupby(gap_groups).transform("sum").where(missing_mask, 0)
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filled = repaired.copy()
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short_gap_mask = missing_mask & (gap_lengths < 4)
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long_gap_mask = missing_mask & ~short_gap_mask
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filled[short_gap_mask] = local_baseline[short_gap_mask]
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long_gap_fill = seasonal_baseline.where(seasonal_baseline.notna(), local_baseline)
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filled[long_gap_mask] = long_gap_fill[long_gap_mask]
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return filled
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def _clean_pressure_series(series: pd.Series) -> pd.Series:
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"""清洗单个压力时间序列。"""
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series = pd.to_numeric(series, errors="coerce").astype(float)
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local_baseline = _build_local_pressure_baseline(series)
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spike_mask = _detect_pressure_spikes(series, local_baseline)
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repaired = series.copy()
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repaired[spike_mask] = local_baseline[spike_mask]
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seasonal_baseline = _build_seasonal_pressure_baseline(repaired)
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repaired = _fill_pressure_gaps(series, repaired, local_baseline, seasonal_baseline)
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if repaired.isna().any():
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repaired = repaired.where(repaired.notna(), local_baseline)
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return repaired.ffill().bfill()
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def _format_time_column(data: pd.DataFrame) -> pd.DataFrame:
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"""统一输出时间格式，方便下游直接按 ISO 字符串解析。"""
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if "time" not in data.columns:
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return data
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formatted = data.copy()
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time_values = pd.to_datetime(formatted["time"], errors="coerce")
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if time_values.isna().all():
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return formatted
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if time_values.dt.tz is not None:
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time_strings = time_values.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
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time_strings = time_strings.str.replace(
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r"([+-]\d{2})(\d{2})$",
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r"\1:\2",
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regex=True,
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)
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else:
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time_strings = time_values.dt.strftime("%Y-%m-%dT%H:%M:%S")
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formatted["time"] = time_strings.where(time_values.notna(), formatted["time"])
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return formatted
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def _expand_snapshot_time_grid(data: pd.DataFrame, freq: pd.Timedelta) -> pd.DataFrame:
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"""仅补齐时间轴，不提前填充值，避免长缺口丢失原始形状特征。"""
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expanded = data.copy()
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expanded["time"] = pd.to_datetime(expanded["time"], errors="coerce")
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expanded = expanded.dropna(subset=["time"]).sort_values("time")
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if expanded.empty:
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return data
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indexed = expanded.set_index("time")
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full_index = pd.date_range(indexed.index.min(), indexed.index.max(), freq=freq)
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indexed = indexed.reindex(full_index)
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indexed.index.name = "time"
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return indexed.reset_index()
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def _safe_datetime_index(values: pd.Series | pd.Index | list[object]) -> pd.DatetimeIndex | None:
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"""尽量把时间值标准化为 DatetimeIndex；失败则返回 None。"""
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parsed = pd.to_datetime(values, errors="coerce")
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try:
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datetime_index = pd.DatetimeIndex(parsed)
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except (TypeError, ValueError):
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return None
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if datetime_index.isna().all():
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return None
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return datetime_index
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def _safe_time_interpolate(series: pd.Series) -> pd.Series:
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"""仅在索引确实是 DatetimeIndex 时使用 time interpolation。"""
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if isinstance(series.index, pd.DatetimeIndex):
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return series.interpolate(method="time", limit_direction="both")
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return series.interpolate(limit_direction="both")
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def _detect_long_form_identifier(data: pd.DataFrame, value_cols: list[str], keep_cols: list[str]) -> str | None:
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"""识别 time/id/value 长表结构。"""
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if "time" not in data.columns or len(value_cols) != 1:
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return None
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identifier_candidates = [
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col
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for col in keep_cols
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if col.lower() in ID_LIKE_COLUMNS or col.lower().endswith("_id")
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]
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if len(identifier_candidates) != 1:
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return None
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if not data["time"].duplicated().any():
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return None
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return identifier_candidates[0]
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def _clean_long_form_pressure(
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data: pd.DataFrame,
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value_col: str,
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identifier_col: str,
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keep_cols: list[str],
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fill_gaps: bool,
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) -> pd.DataFrame:
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"""按测点拆分 long-form 压力数据，再逐列清洗后恢复原结构。"""
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data = _normalize_time_frame(data)
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wide_df = (
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data[[identifier_col, "time", value_col]]
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.pivot(index="time", columns=identifier_col, values=value_col)
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.reset_index()
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)
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sensor_cols = [col for col in wide_df.columns if col != "time"]
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cleaned_wide = _clean_snapshot_pressure(wide_df, sensor_cols, keep_cols=[], fill_gaps=fill_gaps)
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cleaned_long = cleaned_wide.melt(
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id_vars="time",
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var_name=identifier_col,
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value_name=value_col,
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)
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passthrough_cols = [col for col in keep_cols if col != identifier_col]
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if passthrough_cols:
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metadata = data[[identifier_col] + passthrough_cols].drop_duplicates(subset=[identifier_col])
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cleaned_long = cleaned_long.merge(metadata, on=identifier_col, how="left")
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try:
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cleaned_long[identifier_col] = cleaned_long[identifier_col].astype(data[identifier_col].dtype)
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except (TypeError, ValueError):
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pass
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cleaned_long = cleaned_long.sort_values(["time", identifier_col]).reset_index(drop=True)
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ordered_cols = ["time", identifier_col] + passthrough_cols + [value_col]
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cleaned_long = cleaned_long[[col for col in ordered_cols if col in cleaned_long.columns]]
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return cleaned_long
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def _build_time_slot_frame(
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data: pd.DataFrame, value_col: str, expected_slots: int
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) -> pd.DataFrame:
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"""把重复时间点整理成 time x slot 的矩阵。"""
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grouped = data.groupby("time", sort=True)
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times = list(grouped.groups.keys())
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slot_frame = pd.DataFrame(index=pd.Index(times, name="time"), columns=range(expected_slots), dtype=float)
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for time_value, group in grouped:
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values = pd.to_numeric(group[value_col], errors="coerce").tolist()
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for slot_idx, value in enumerate(values[:expected_slots]):
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slot_frame.loc[time_value, slot_idx] = value
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return slot_frame
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def _slot_baseline(slot_frame: pd.DataFrame) -> pd.DataFrame:
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"""对每个槽位做时间插值和平滑，得到基线轨迹。"""
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baseline = pd.DataFrame(index=slot_frame.index, columns=slot_frame.columns, dtype=float)
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for col in slot_frame.columns:
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series = slot_frame[col].astype(float)
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series = _safe_time_interpolate(series)
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series = series.rolling(window=5, center=True, min_periods=1).median()
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series = _safe_time_interpolate(series).ffill().bfill()
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baseline[col] = series
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return baseline
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def _choose_insertion_position(
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observed: list[float], baseline_row: pd.Series, expected_slots: int
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) -> int:
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"""为少一个观测值的时间组选择最合理的插入位置。"""
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missing_count = expected_slots - len(observed)
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if missing_count <= 0:
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return 0
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best_pos = 0
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best_cost = float("inf")
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for insert_pos in range(expected_slots):
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cost = 0.0
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obs_idx = 0
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for slot_idx in range(expected_slots):
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if slot_idx == insert_pos:
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continue
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obs_value = observed[obs_idx]
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base_value = float(baseline_row.iloc[slot_idx])
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if pd.notna(obs_value) and pd.notna(base_value):
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cost += abs(obs_value - base_value)
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obs_idx += 1
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if cost < best_cost:
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best_cost = cost
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best_pos = insert_pos
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return best_pos
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def _clean_repeated_timestamp_pressure(
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data: pd.DataFrame, value_col: str, keep_cols: list[str]
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) -> pd.DataFrame:
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"""针对同一时间点重复采样的压力数据进行修复。"""
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data = _normalize_time_frame(data)
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grouped_sizes = data.groupby("time").size()
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if grouped_sizes.empty:
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return data
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expected_slots = int(grouped_sizes.mode().iloc[0]) if not grouped_sizes.mode().empty else int(grouped_sizes.max())
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expected_slots = max(expected_slots, int(grouped_sizes.max()))
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slot_frame = _build_time_slot_frame(data, value_col, expected_slots)
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baseline_frame = _slot_baseline(slot_frame)
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residuals = slot_frame - baseline_frame
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slot_scales = {
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col: max(_robust_scale(residuals[col]), 1e-6) for col in residuals.columns
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}
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cleaned_rows: list[dict[str, object]] = []
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grouped = data.groupby("time", sort=True)
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for time_value, group in grouped:
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observed_values = pd.to_numeric(group[value_col], errors="coerce").tolist()
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baseline_row = baseline_frame.loc[time_value]
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insert_pos = _choose_insertion_position(observed_values, baseline_row, expected_slots)
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cleaned_values: list[float] = []
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obs_idx = 0
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for slot_idx in range(expected_slots):
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if slot_idx == insert_pos and len(observed_values) < expected_slots:
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cleaned_values.append(float(baseline_row.iloc[slot_idx]))
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continue
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if obs_idx >= len(observed_values):
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cleaned_values.append(float(baseline_row.iloc[slot_idx]))
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continue
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observed = observed_values[obs_idx]
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baseline = float(baseline_row.iloc[slot_idx])
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cleaned_values.append(
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_shrink_toward_baseline(observed, baseline, slot_scales.get(slot_idx, 1.0))
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)
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obs_idx += 1
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# 其余字段原样保留；常量列（如 id）直接复制第一条记录即可
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template_row = group.iloc[0].to_dict()
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for slot_idx, cleaned_value in enumerate(cleaned_values):
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row = dict(template_row)
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row["time"] = time_value
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row[value_col] = cleaned_value
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cleaned_rows.append(row)
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cleaned_df = pd.DataFrame(cleaned_rows)
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cleaned_df = cleaned_df.sort_values(["time"]).reset_index(drop=True)
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ordered_cols = ["time"] + keep_cols + [value_col]
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ordered_cols = [col for col in ordered_cols if col in cleaned_df.columns]
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remaining_cols = [col for col in cleaned_df.columns if col not in ordered_cols]
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cleaned_df = cleaned_df[ordered_cols + remaining_cols]
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return _format_time_column(cleaned_df)
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def _clean_snapshot_pressure(
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data: pd.DataFrame, value_cols: list[str], keep_cols: list[str], fill_gaps: bool
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) -> pd.DataFrame:
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"""针对单条时间序列或多列快照数据进行稳健修复。"""
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data = _normalize_time_frame(data)
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if fill_gaps and "time" in data.columns:
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freq = _infer_time_frequency(data["time"])
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data = _expand_snapshot_time_grid(data, freq)
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data["time"] = pd.to_datetime(data["time"], errors="coerce")
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data = data.sort_values(["time"]).reset_index(drop=True)
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cleaned_df = data.copy()
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time_index = (
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_safe_datetime_index(cleaned_df["time"])
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if "time" in cleaned_df.columns
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else None
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)
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if time_index is None:
|
||||
time_index = pd.RangeIndex(start=0, stop=len(cleaned_df))
|
||||
for col in value_cols:
|
||||
series = pd.Series(
|
||||
pd.to_numeric(cleaned_df[col], errors="coerce").to_numpy(),
|
||||
index=time_index,
|
||||
dtype=float,
|
||||
)
|
||||
cleaned_df[col] = _clean_pressure_series(series).to_numpy()
|
||||
|
||||
ordered_cols = ["time"] + keep_cols + value_cols
|
||||
ordered_cols = [col for col in ordered_cols if col in cleaned_df.columns]
|
||||
remaining_cols = [col for col in cleaned_df.columns if col not in ordered_cols]
|
||||
cleaned_df = cleaned_df[ordered_cols + remaining_cols]
|
||||
return _format_time_column(cleaned_df)
|
||||
|
||||
|
||||
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）。
|
||||
读取输入 CSV，基于时间结构进行稳健修复。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。
|
||||
|
||||
@@ -24,80 +441,38 @@ def clean_pressure_data_km(
|
||||
# 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")
|
||||
data = _normalize_time_frame(data)
|
||||
value_cols, keep_cols = _select_pressure_columns(data)
|
||||
has_repeated_time = "time" in data.columns and data["time"].duplicated().any()
|
||||
identifier_col = _detect_long_form_identifier(data, value_cols, keep_cols)
|
||||
|
||||
# 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
|
||||
if identifier_col is not None:
|
||||
data_repaired = _clean_long_form_pressure(
|
||||
data,
|
||||
value_cols[0],
|
||||
identifier_col,
|
||||
keep_cols,
|
||||
fill_gaps,
|
||||
)
|
||||
elif has_repeated_time and len(value_cols) == 1:
|
||||
data_repaired = _clean_repeated_timestamp_pressure(data, value_cols[0], keep_cols)
|
||||
else:
|
||||
data_repaired = _clean_snapshot_pressure(data, value_cols, keep_cols, fill_gaps)
|
||||
|
||||
# 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）
|
||||
# 可选可视化（只展示首个数值列）
|
||||
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）")
|
||||
if show_plot and value_cols:
|
||||
plot_col = value_cols[0]
|
||||
if "time" in data_repaired.columns:
|
||||
x = pd.to_datetime(data_repaired["time"], errors="coerce")
|
||||
else:
|
||||
x = np.arange(len(data_repaired))
|
||||
plt.figure(figsize=(12, 6))
|
||||
plt.plot(x, pd.to_numeric(data_repaired[plot_col], errors="coerce"), label="cleaned")
|
||||
plt.xlabel("时间" if "time" in data_repaired.columns else "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.title(f"{plot_col} 清洗结果")
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
@@ -110,9 +485,6 @@ def clean_pressure_data_km(
|
||||
# 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
|
||||
@@ -126,10 +498,10 @@ def clean_pressure_data_km(
|
||||
return os.path.abspath(output_path)
|
||||
|
||||
|
||||
def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> dict:
|
||||
def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> pd.DataFrame:
|
||||
"""
|
||||
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。
|
||||
接收一个 DataFrame 数据结构，使用时间感知的稳健修复方法清洗压力数据。
|
||||
返回清洗后的 DataFrame。
|
||||
|
||||
Args:
|
||||
data: 输入 DataFrame（可包含 time 列）
|
||||
@@ -137,113 +509,37 @@ def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> di
|
||||
"""
|
||||
# 使用传入的 DataFrame
|
||||
data = data.copy()
|
||||
data = _normalize_time_frame(data)
|
||||
value_cols, keep_cols = _select_pressure_columns(data)
|
||||
has_repeated_time = "time" in data.columns and data["time"].duplicated().any()
|
||||
identifier_col = _detect_long_form_identifier(data, value_cols, keep_cols)
|
||||
|
||||
# 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
|
||||
)
|
||||
if identifier_col is not None:
|
||||
data_repaired = _clean_long_form_pressure(
|
||||
data,
|
||||
value_cols[0],
|
||||
identifier_col,
|
||||
keep_cols,
|
||||
fill_gaps=True,
|
||||
)
|
||||
elif has_repeated_time and len(value_cols) == 1:
|
||||
data_repaired = _clean_repeated_timestamp_pressure(data, value_cols[0], keep_cols)
|
||||
else:
|
||||
data_repaired = _clean_snapshot_pressure(data, value_cols, keep_cols, fill_gaps=True)
|
||||
|
||||
# 保存 time 列用于最后合并
|
||||
time_col_series = None
|
||||
if "time" in data_filled.columns:
|
||||
time_col_series = data_filled["time"]
|
||||
|
||||
# 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）")
|
||||
if show_plot and value_cols:
|
||||
plt.rcParams["font.sans-serif"] = ["SimHei"]
|
||||
plt.rcParams["axes.unicode_minus"] = False
|
||||
plot_col = value_cols[0]
|
||||
x = pd.to_datetime(data_repaired["time"], errors="coerce") if "time" in data_repaired.columns else np.arange(len(data_repaired))
|
||||
plt.figure(figsize=(12, 6))
|
||||
plt.plot(x, pd.to_numeric(data_repaired[plot_col], errors="coerce"), label="cleaned")
|
||||
plt.xlabel("时间" if "time" in data_repaired.columns else "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.title(f"{plot_col} 清洗结果")
|
||||
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()
|
||||
|
||||
# 将 time 列添加回结果
|
||||
if time_col_series is not None:
|
||||
data_repaired.insert(0, "time", time_col_series)
|
||||
|
||||
# 返回清洗后的字典
|
||||
return data_repaired
|
||||
|
||||
|
||||
|
||||
@@ -6,104 +6,66 @@ import sklearn.cluster
|
||||
import os
|
||||
|
||||
|
||||
|
||||
class QD_KMeans(object):
|
||||
def __init__(self, wn, num_monitors):
|
||||
# self.inp = inp
|
||||
self.cluster_num = num_monitors # 聚类中心个数，也即测压点个数
|
||||
self.wn=wn
|
||||
self.cluster_num = num_monitors # 聚类中心个数，也即测压点个数
|
||||
self.wn = wn
|
||||
self.monitor_nodes = []
|
||||
self.coords = []
|
||||
self.junction_nodes = {} # Added missing initialization
|
||||
|
||||
|
||||
def get_junctions_coordinates(self):
|
||||
|
||||
for junction_name in self.wn.junction_name_list:
|
||||
|
||||
for junction_name in self.wn.junction_name_list:
|
||||
junction = self.wn.get_node(junction_name)
|
||||
self.junction_nodes[junction_name] = junction.coordinates
|
||||
self.coords.append(junction.coordinates )
|
||||
self.coords.append(junction.coordinates)
|
||||
|
||||
# print(f"Total junctions: {self.junction_coordinates}")
|
||||
# print(f"Total junctions: {self.junction_coordinates}")
|
||||
|
||||
def select_monitoring_points(self):
|
||||
if not self.coords: # Add check if coordinates are collected
|
||||
self.get_junctions_coordinates()
|
||||
coords = np.array(self.coords)
|
||||
coords_normalized = (coords - coords.min(axis=0)) / (coords.max(axis=0) - coords.min(axis=0))
|
||||
kmeans = sklearn.cluster.KMeans(n_clusters= self.cluster_num, random_state=42)
|
||||
kmeans.fit(coords_normalized)
|
||||
coords_normalized = (coords - coords.min(axis=0)) / (
|
||||
coords.max(axis=0) - coords.min(axis=0)
|
||||
)
|
||||
kmeans = sklearn.cluster.KMeans(n_clusters=self.cluster_num, random_state=42)
|
||||
kmeans.fit(coords_normalized)
|
||||
|
||||
for center in kmeans.cluster_centers_:
|
||||
distances = np.sum((coords_normalized - center) ** 2, axis=1)
|
||||
nearest_node = self.wn.junction_name_list[np.argmin(distances)]
|
||||
self.monitor_nodes.append(nearest_node)
|
||||
self.monitor_nodes.append(nearest_node)
|
||||
|
||||
return self.monitor_nodes
|
||||
|
||||
|
||||
def visualize_network(self):
|
||||
"""Visualize network with monitoring points"""
|
||||
ax=wntr.graphics.plot_network(self.wn,
|
||||
node_attribute=self.monitor_nodes,
|
||||
node_size=30,
|
||||
title='Optimal sensor')
|
||||
plt.show()
|
||||
ax = wntr.graphics.plot_network(
|
||||
self.wn,
|
||||
node_attribute=self.monitor_nodes,
|
||||
node_size=30,
|
||||
title="Optimal sensor",
|
||||
)
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
def kmeans_sensor_placement(name: str, sensor_num: int, min_diameter: int) -> list:
|
||||
inp_name = f'./db_inp/{name}.db.inp'
|
||||
wn= wntr.network.WaterNetworkModel(inp_name)
|
||||
wn_cluster=QD_KMeans(wn, sensor_num)
|
||||
inp_name = f"./db_inp/{name}.db.inp"
|
||||
wn = wntr.network.WaterNetworkModel(inp_name)
|
||||
wn_cluster = QD_KMeans(wn, sensor_num)
|
||||
|
||||
# Select monitoring pointse
|
||||
sensor_ids= wn_cluster.select_monitoring_points()
|
||||
sensor_ids = wn_cluster.select_monitoring_points()
|
||||
|
||||
# wn_cluster.visualize_network()
|
||||
|
||||
return sensor_ids
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
#sensorindex = get_ID(name='suzhouhe_2024_cloud_0817', sensor_num=30, min_diameter=500)
|
||||
sensorindex = kmeans_sensor_placement(name='szh', sensor_num=50, min_diameter=300)
|
||||
# sensorindex = get_ID(name='suzhouhe_2024_cloud_0817', sensor_num=30, min_diameter=500)
|
||||
sensorindex = kmeans_sensor_placement(name="szh", sensor_num=50, min_diameter=300)
|
||||
print(sensorindex)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -20,6 +20,7 @@ import geopandas as gpd
|
||||
from sklearn.metrics import pairwise_distances
|
||||
import app.services.project_info as project_info
|
||||
|
||||
|
||||
# 2025/03/12
|
||||
# Step1: 获取节点坐标
|
||||
def getCoor(wn: wntr.network.WaterNetworkModel) -> pandas.DataFrame:
|
||||
@@ -31,7 +32,7 @@ def getCoor(wn: wntr.network.WaterNetworkModel) -> pandas.DataFrame:
|
||||
# site: pandas.Series
|
||||
# index：节点名称（wn.node_name_list）
|
||||
# values：每个节点的坐标，格式为 tuple（如 (x, y) 或 (x, y, z)）
|
||||
site = wn.query_node_attribute('coordinates')
|
||||
site = wn.query_node_attribute("coordinates")
|
||||
# Coor: pandas.Series
|
||||
# index：与site相同（节点名称）。
|
||||
# values：坐标转换为numpy.ndarray（如array([10.5, 20.3])）
|
||||
@@ -43,9 +44,9 @@ def getCoor(wn: wntr.network.WaterNetworkModel) -> pandas.DataFrame:
|
||||
x.append(Coor.values[i][0]) # 将 x 坐标存入 x 列表。
|
||||
y.append(Coor.values[i][1]) # 将 y 坐标存入 y 列表
|
||||
# xy: dict[str, list], x、y 坐标的字典
|
||||
xy = {'x': x, 'y': y}
|
||||
xy = {"x": x, "y": y}
|
||||
# Coor_node: pandas.DataFrame, 存储节点 x, y 坐标的 DataFrame
|
||||
Coor_node = pd.DataFrame(xy, index=wn.node_name_list, columns=['x', 'y'])
|
||||
Coor_node = pd.DataFrame(xy, index=wn.node_name_list, columns=["x", "y"])
|
||||
return Coor_node
|
||||
|
||||
|
||||
@@ -87,23 +88,23 @@ def skater_partition(G, n_clusters):
|
||||
字典形式的聚类结果，键为区域编号，值为该区域内的节点列表。
|
||||
"""
|
||||
# 1. 获取所有节点坐标，假设每个节点都有 'pos' 属性
|
||||
pos = nx.get_node_attributes(G, 'pos')
|
||||
pos = nx.get_node_attributes(G, "pos")
|
||||
nodes = list(G.nodes())
|
||||
# 构造坐标数组：每行为 [x, y]
|
||||
coords = np.array([pos[node] for node in nodes])
|
||||
|
||||
# 2. 构造 GeoDataFrame：创建 DataFrame 并生成 geometry 列
|
||||
df = pd.DataFrame(coords, columns=['x', 'y'], index=nodes)
|
||||
df = pd.DataFrame(coords, columns=["x", "y"], index=nodes)
|
||||
# 利用 shapely 的 Point 构造空间位置
|
||||
df['geometry'] = df.apply(lambda row: Point(row['x'], row['y']), axis=1)
|
||||
gdf = gpd.GeoDataFrame(df, geometry='geometry')
|
||||
df["geometry"] = df.apply(lambda row: Point(row["x"], row["y"]), axis=1)
|
||||
gdf = gpd.GeoDataFrame(df, geometry="geometry")
|
||||
|
||||
# 3. 构造空间权重矩阵，使用 4 近邻方法（k=4，可根据实际情况调整）
|
||||
w = ps.weights.KNN.from_array(coords, k=4)
|
||||
w.transform = 'R'
|
||||
w.transform = "R"
|
||||
|
||||
# 4. 调用 SKATER：新版本 API 要求传入 gdf, w 以及 attrs_name（这里使用 'x' 和 'y' 作为属性）
|
||||
skater = Skater(gdf, w, attrs_name=['x', 'y'], n_clusters=n_clusters)
|
||||
skater = Skater(gdf, w, attrs_name=["x", "y"], n_clusters=n_clusters)
|
||||
skater.solve()
|
||||
|
||||
# 5. 获取聚类标签，构造成字典格式
|
||||
@@ -133,24 +134,24 @@ def spectral_partition(G, n_clusters):
|
||||
键为聚类标签，值为该聚类对应的节点列表。
|
||||
"""
|
||||
# 1. 获取节点空间坐标，注意保证每个节点都有 'pos' 属性
|
||||
pos_dict = nx.get_node_attributes(G, 'pos')
|
||||
pos_dict = nx.get_node_attributes(G, "pos")
|
||||
nodes = list(G.nodes())
|
||||
coords = np.array([pos_dict[node] for node in nodes])
|
||||
|
||||
# 2. 计算节点之间的欧氏距离矩阵
|
||||
D = pairwise_distances(coords, metric='euclidean')
|
||||
D = pairwise_distances(coords, metric="euclidean")
|
||||
|
||||
# 3. 计算 sigma 值：这里取所有距离的均值，当然也可以根据实际情况调整
|
||||
sigma = np.mean(D)
|
||||
|
||||
# 4. 构造相似度矩阵：使用高斯核函数
|
||||
# A(i, j) = exp( -d(i,j)^2 / (2*sigma^2) )
|
||||
A = np.exp(- (D ** 2) / (2 * sigma ** 2))
|
||||
A = np.exp(-(D**2) / (2 * sigma**2))
|
||||
|
||||
# 5. 使用谱聚类进行图分区
|
||||
clustering = SpectralClustering(n_clusters=n_clusters,
|
||||
affinity='precomputed',
|
||||
random_state=0)
|
||||
clustering = SpectralClustering(
|
||||
n_clusters=n_clusters, affinity="precomputed", random_state=0
|
||||
)
|
||||
labels = clustering.fit_predict(A)
|
||||
|
||||
# 6. 构造字典形式的分区结果
|
||||
@@ -160,6 +161,7 @@ def spectral_partition(G, n_clusters):
|
||||
|
||||
return groups
|
||||
|
||||
|
||||
# 2025/03/12
|
||||
# Step3: wn_func类，水力计算
|
||||
# wn_func 主要用于计算：
|
||||
@@ -181,7 +183,7 @@ class wn_func(object):
|
||||
self.results = wntr.sim.EpanetSimulator(wn).run_sim() # 存储运行结果
|
||||
self.wn = wn
|
||||
# self.q：pandas.DataFrame,管道流量，索引为时间步长，列为管道名称
|
||||
self.q = self.results.link['flowrate']
|
||||
self.q = self.results.link["flowrate"]
|
||||
# ReservoirIndex / Tankindex: list[str],水库 / 水箱节点名称列表
|
||||
ReservoirIndex = wn.reservoir_name_list
|
||||
Tankindex = wn.tank_name_list
|
||||
@@ -191,7 +193,7 @@ class wn_func(object):
|
||||
# self.nodes: list[str],所有节点的名称
|
||||
self.nodes = wn.node_name_list
|
||||
# self.coordinates：pandas.Series,节点坐标，索引为节点名，值为 (x, y) 坐标的 tuple
|
||||
self.coordinates = wn.query_node_attribute('coordinates')
|
||||
self.coordinates = wn.query_node_attribute("coordinates")
|
||||
# allpumps / allvalves: list[str],所有泵/阀门名称列表
|
||||
allpumps = wn.pump_name_list
|
||||
allvalves = wn.valve_name_list
|
||||
@@ -222,17 +224,27 @@ class wn_func(object):
|
||||
# 泵的起终点、tank、reservoir
|
||||
# self.delnodes: list[str],需要删除的节点（包括水库、泵、阀门连接的节点）
|
||||
self.delnodes = list(
|
||||
set(ReservoirIndex).union(Tankindex, pumpstnode, pumpednode, valvestnode, valveednode, Reservoirednode))
|
||||
set(ReservoirIndex).union(
|
||||
Tankindex,
|
||||
pumpstnode,
|
||||
pumpednode,
|
||||
valvestnode,
|
||||
valveednode,
|
||||
Reservoirednode,
|
||||
)
|
||||
)
|
||||
# 泵、起终点为tank、reservoir的管道
|
||||
# self.delpipes: list[str],需要删除的管道（包括水库、泵、阀门连接的管道）
|
||||
self.delpipes = list(set(wn.pump_name_list).union(wn.valve_name_list).union(Reservoirpipe))
|
||||
self.delpipes = list(
|
||||
set(wn.pump_name_list).union(wn.valve_name_list).union(Reservoirpipe)
|
||||
)
|
||||
self.pipes = [pipe for pipe in wn.pipe_name_list if pipe not in self.delpipes]
|
||||
# self.L: list[float],所有管道的长度（以米为单位）
|
||||
self.L = wn.query_link_attribute('length')[self.pipes].tolist()
|
||||
self.L = wn.query_link_attribute("length")[self.pipes].tolist()
|
||||
self.n = len(self.nodes)
|
||||
self.m = len(self.pipes)
|
||||
# self.unit_headloss: list[float],单位水头损失（headloss 数据的第一行，单位：米/km）
|
||||
self.unit_headloss = self.results.link['headloss'].iloc[0, :].tolist()
|
||||
self.unit_headloss = self.results.link["headloss"].iloc[0, :].tolist()
|
||||
##
|
||||
self.delnodes1 = list(set(ReservoirIndex).union(Tankindex))
|
||||
|
||||
@@ -245,7 +257,9 @@ class wn_func(object):
|
||||
end_node = wn.links[pipe].end_node.name
|
||||
self.less_than_min_diameter_junction_list.extend([start_node, end_node])
|
||||
# 去重
|
||||
self.less_than_min_diameter_junction_list = list(set(self.less_than_min_diameter_junction_list))
|
||||
self.less_than_min_diameter_junction_list = list(
|
||||
set(self.less_than_min_diameter_junction_list)
|
||||
)
|
||||
|
||||
# Step3.2: 计算水力距离
|
||||
def CtoS(self):
|
||||
@@ -266,7 +280,7 @@ class wn_func(object):
|
||||
q = self.q
|
||||
L = self.L
|
||||
# H1：pandas.DataFrame,水头数据，索引为时间步长，列为节点名
|
||||
H1 = self.results.node['head'].T
|
||||
H1 = self.results.node["head"].T
|
||||
# hh：list[float],计算管道两端水头之差
|
||||
hh = []
|
||||
# 水头损失
|
||||
@@ -280,8 +294,18 @@ class wn_func(object):
|
||||
# headloss：pandas.DataFrame,管道水头损失矩阵
|
||||
headloss = pd.DataFrame(hh, index=pipes).T
|
||||
# s1:管道阻力系数，s2：将管道阻力系数与管道的起始节点和终止节点对应
|
||||
hf = pd.DataFrame(np.array([0] * (n ** 2)).reshape(n, n), index=nodes, columns=nodes, dtype=float)
|
||||
weightL = pd.DataFrame(np.array([0] * (n ** 2)).reshape(n, n), index=nodes, columns=nodes, dtype=float)
|
||||
hf = pd.DataFrame(
|
||||
np.array([0] * (n**2)).reshape(n, n),
|
||||
index=nodes,
|
||||
columns=nodes,
|
||||
dtype=float,
|
||||
)
|
||||
weightL = pd.DataFrame(
|
||||
np.array([0] * (n**2)).reshape(n, n),
|
||||
index=nodes,
|
||||
columns=nodes,
|
||||
dtype=float,
|
||||
)
|
||||
# s2为对应管道起始节点与终止节点的粗糙度系数矩阵，index代表起始节点，columns代表终止节点
|
||||
G = nx.DiGraph()
|
||||
for i in range(0, m):
|
||||
@@ -298,11 +322,16 @@ class wn_func(object):
|
||||
weightL.loc[b, a] = headloss.loc[0, pipe] * L[i]
|
||||
G.add_weighted_edges_from([(b, a, weightL.loc[b, a])])
|
||||
|
||||
hydraulicL = pd.DataFrame(np.array([0] * (n ** 2)).reshape(n, n), index=nodes, columns=nodes, dtype=float)
|
||||
hydraulicL = pd.DataFrame(
|
||||
np.array([0] * (n**2)).reshape(n, n),
|
||||
index=nodes,
|
||||
columns=nodes,
|
||||
dtype=float,
|
||||
)
|
||||
|
||||
for a in nodes:
|
||||
if a in G.nodes:
|
||||
d = nx.shortest_path_length(G, source=a, weight='weight')
|
||||
d = nx.shortest_path_length(G, source=a, weight="weight")
|
||||
for b in list(d.keys()):
|
||||
hydraulicL.loc[a, b] = d[b]
|
||||
|
||||
@@ -331,11 +360,17 @@ class wn_func(object):
|
||||
for t in self.wn.tanks():
|
||||
self.nonjunc_index.append(t[0])
|
||||
# Conn：numpy.matrix，节点-管道连接矩阵，起点 -1，终点 1
|
||||
Conn = np.mat(np.zeros([n, m - p - v])) # 节点和管道的关系矩阵，行为节点，列为管道，起点为-1，终点为1
|
||||
Conn = np.mat(
|
||||
np.zeros([n, m - p - v])
|
||||
) # 节点和管道的关系矩阵，行为节点，列为管道，起点为-1，终点为1
|
||||
# NConn：numpy.matrix，节点-节点连接矩阵，有管道相连的地方设为 1
|
||||
NConn = np.mat(np.zeros([n, n])) # 节点之间的关系，之间有管道为1，反之为0
|
||||
# pipes：list[str]，去除泵和阀门的管道列表
|
||||
pipes = [pipe for pipe in self.wn.pipes() if pipe not in self.wn.pumps() and pipe not in self.wn.valves()]
|
||||
pipes = [
|
||||
pipe
|
||||
for pipe in self.wn.pipes()
|
||||
if pipe not in self.wn.pumps() and pipe not in self.wn.valves()
|
||||
]
|
||||
for pipe_name, pipe in pipes:
|
||||
start = self.wn.node_name_list.index(pipe.start_node_name)
|
||||
end = self.wn.node_name_list.index(pipe.end_node_name)
|
||||
@@ -345,12 +380,21 @@ class wn_func(object):
|
||||
NConn[start, end] = 1
|
||||
NConn[end, start] = 1
|
||||
self.A = Conn
|
||||
link_name_list = [link for link in self.wn.link_name_list if
|
||||
link not in self.wn.pump_name_list and link not in self.wn.valve_name_list]
|
||||
self.A2 = pd.DataFrame(self.A, index=self.wn.node_name_list, columns=link_name_list)
|
||||
link_name_list = [
|
||||
link
|
||||
for link in self.wn.link_name_list
|
||||
if link not in self.wn.pump_name_list
|
||||
and link not in self.wn.valve_name_list
|
||||
]
|
||||
self.A2 = pd.DataFrame(
|
||||
self.A, index=self.wn.node_name_list, columns=link_name_list
|
||||
)
|
||||
self.A2 = self.A2.drop(self.delnodes)
|
||||
for pipe in self.delpipes:
|
||||
if pipe not in self.wn.pump_name_list and pipe not in self.wn.valve_name_list:
|
||||
if (
|
||||
pipe not in self.wn.pump_name_list
|
||||
and pipe not in self.wn.valve_name_list
|
||||
):
|
||||
self.A2 = self.A2.drop(columns=pipe)
|
||||
self.junc_list = self.A2.index
|
||||
self.A2 = np.mat(self.A2) # 节点管道关系
|
||||
@@ -372,10 +416,10 @@ class wn_func(object):
|
||||
except EpanetException:
|
||||
pass
|
||||
finally:
|
||||
h = result.link['headloss'][self.pipes].values[0]
|
||||
q = result.link['flowrate'][self.pipes].values[0]
|
||||
l = self.wn.query_link_attribute('length')[self.pipes]
|
||||
C = self.wn.query_link_attribute('roughness')[self.pipes]
|
||||
h = result.link["headloss"][self.pipes].values[0]
|
||||
q = result.link["flowrate"][self.pipes].values[0]
|
||||
l = self.wn.query_link_attribute("length")[self.pipes]
|
||||
C = self.wn.query_link_attribute("roughness")[self.pipes]
|
||||
# headloss：numpy.ndarray,水头损失数组
|
||||
headloss = np.array(h)
|
||||
# 调整流量方向
|
||||
@@ -393,7 +437,7 @@ class wn_func(object):
|
||||
try:
|
||||
det = np.linalg.det(X)
|
||||
except RuntimeError as e:
|
||||
sign, logdet = slogdet(X) # 防止溢出
|
||||
sign, logdet = slogdet(X) # 防止溢出
|
||||
det = sign * np.exp(logdet)
|
||||
if det != 0:
|
||||
J_H_Cw = X.I * A * S
|
||||
@@ -430,7 +474,10 @@ class Sensorplacement(wn_func):
|
||||
"""
|
||||
Sensorplacement 类继承了 wn_func 类，并且用于计算和优化传感器布置的位置。
|
||||
"""
|
||||
def __init__(self, wn: wntr.network.WaterNetworkModel, sensornum: int, min_diameter: int):
|
||||
|
||||
def __init__(
|
||||
self, wn: wntr.network.WaterNetworkModel, sensornum: int, min_diameter: int
|
||||
):
|
||||
"""
|
||||
|
||||
:param wn: 由wntr生成的模型
|
||||
@@ -442,7 +489,9 @@ class Sensorplacement(wn_func):
|
||||
|
||||
# 1.某个节点到所有节点的加权距离之和
|
||||
# 2.某个节点到该组内所有节点的加权距离之和
|
||||
def sensor(self, SS: pandas.DataFrame, G: networkx.Graph, group: dict[int, list[str]]):
|
||||
def sensor(
|
||||
self, SS: pandas.DataFrame, G: networkx.Graph, group: dict[int, list[str]]
|
||||
):
|
||||
"""
|
||||
sensor 方法是用来根据灵敏度矩阵 SS 和加权图 G 来确定传感器布置位置的
|
||||
:param SS: 灵敏度矩阵，每个节点的行和列代表不同节点，矩阵元素表示节点间的灵敏度。SS.iloc[i, :] 表示第 i 行对应节点 i 到所有其他节点的灵敏度
|
||||
@@ -527,7 +576,7 @@ def get_ID(name: str, sensor_num: int, min_diameter: int) -> list[str]:
|
||||
:return: 测压点节点ID
|
||||
"""
|
||||
# inp_file_real：str,输入文件名，表示原始水力模型文件的路径,该文件格式为 EPANET 输入文件（.inp），包含管网的结构信息、节点、管道、泵等数据
|
||||
inp_file_real = f'./db_inp/{name}.db.inp'
|
||||
inp_file_real = f"./db_inp/{name}.db.inp"
|
||||
# sensornum：int,需要布置的传感器数量
|
||||
# sensornum = sensor_num
|
||||
# wn_real：wntr.network.WaterNetworkModel,加载 EPANET 水力模型
|
||||
@@ -538,7 +587,7 @@ def get_ID(name: str, sensor_num: int, min_diameter: int) -> list[str]:
|
||||
results_real = sim_real.run_sim()
|
||||
|
||||
# real_C：list[float]，包含所有管道粗糙度的列表
|
||||
real_C = wn_real.query_link_attribute('roughness').tolist()
|
||||
real_C = wn_real.query_link_attribute("roughness").tolist()
|
||||
# wn_fun1：wn_func（继承自 object），创建 wn_func 类的实例，传入 wn_real 水力模型对象。wn_func 用于计算管网相关的水力属性，比如水力距离、灵敏度等
|
||||
wn_fun1 = wn_func(wn_real, min_diameter=min_diameter)
|
||||
# nodes：list[str]，管网的节点名称列表
|
||||
@@ -598,7 +647,6 @@ def get_ID(name: str, sensor_num: int, min_diameter: int) -> list[str]:
|
||||
sensorindex, sensorindex_2 = wn_fun.sensor(SS, G, group) # 初始的sensorindex
|
||||
# print(str(sensor_num), "个测压点，测压点位置：", sensorindex)
|
||||
|
||||
|
||||
# 重新打开数据库
|
||||
# if is_project_open(name=name):
|
||||
# close_project(name=name)
|
||||
@@ -637,7 +685,7 @@ def get_ID(name: str, sensor_num: int, min_diameter: int) -> list[str]:
|
||||
return sensorindex
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
sensorindex = get_ID(name=project_info.name, sensor_num=20, min_diameter=300)
|
||||
|
||||
print(sensorindex)
|
||||
|
||||
@@ -6,7 +6,6 @@ import shutil
|
||||
import threading
|
||||
from fastapi import APIRouter, HTTPException, File, UploadFile, Query, Path, Body
|
||||
from fastapi.responses import PlainTextResponse
|
||||
import app.infra.db.influxdb.api as influxdb_api
|
||||
import app.services.simulation as simulation
|
||||
import app.services.globals as globals
|
||||
from app.services.tjnetwork import (
|
||||
@@ -28,8 +27,7 @@ from app.algorithms.sensor import (
|
||||
pressure_sensor_placement_sensitivity,
|
||||
pressure_sensor_placement_kmeans,
|
||||
)
|
||||
import app.algorithms.cleaning.flow as flow_data_clean
|
||||
import app.algorithms.cleaning.pressure as pressure_data_clean
|
||||
|
||||
from app.services.network_import import network_update
|
||||
from app.services.simulation_ops import (
|
||||
project_management,
|
||||
|
||||
@@ -89,12 +89,17 @@ class ScadaRepository:
|
||||
if field not in valid_fields:
|
||||
raise ValueError(f"Invalid field: {field}")
|
||||
|
||||
query = sql.SQL(
|
||||
update_query = sql.SQL(
|
||||
"UPDATE scada.scada_data SET {} = %s WHERE time = %s AND device_id = %s"
|
||||
).format(sql.Identifier(field))
|
||||
insert_query = sql.SQL(
|
||||
"INSERT INTO scada.scada_data (time, device_id, {}) VALUES (%s, %s, %s)"
|
||||
).format(sql.Identifier(field))
|
||||
|
||||
async with conn.cursor() as cur:
|
||||
await cur.execute(query, (value, time, device_id))
|
||||
await cur.execute(update_query, (value, time, device_id))
|
||||
if cur.rowcount == 0:
|
||||
await cur.execute(insert_query, (time, device_id, value))
|
||||
|
||||
@staticmethod
|
||||
async def delete_scada_by_id_time_range(
|
||||
|
||||
@@ -0,0 +1,108 @@
|
||||
import importlib.util
|
||||
import sys
|
||||
import types
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def _load_pressure_cleaning_module():
|
||||
project_root = Path(__file__).resolve().parents[2]
|
||||
utils_path = project_root / "app" / "algorithms" / "_utils.py"
|
||||
pressure_path = project_root / "app" / "algorithms" / "cleaning" / "pressure.py"
|
||||
|
||||
app_module = sys.modules.setdefault("app", types.ModuleType("app"))
|
||||
algorithms_module = sys.modules.setdefault(
|
||||
"app.algorithms",
|
||||
types.ModuleType("app.algorithms"),
|
||||
)
|
||||
setattr(app_module, "algorithms", algorithms_module)
|
||||
|
||||
utils_spec = importlib.util.spec_from_file_location("app.algorithms._utils", utils_path)
|
||||
assert utils_spec and utils_spec.loader
|
||||
utils_module = importlib.util.module_from_spec(utils_spec)
|
||||
sys.modules["app.algorithms._utils"] = utils_module
|
||||
utils_spec.loader.exec_module(utils_module)
|
||||
|
||||
pressure_spec = importlib.util.spec_from_file_location(
|
||||
"tests_pressure_under_test",
|
||||
pressure_path,
|
||||
)
|
||||
assert pressure_spec and pressure_spec.loader
|
||||
pressure_module = importlib.util.module_from_spec(pressure_spec)
|
||||
pressure_spec.loader.exec_module(pressure_module)
|
||||
return pressure_module
|
||||
|
||||
|
||||
def test_clean_pressure_data_df_km_repairs_long_form_pressure_series():
|
||||
module = _load_pressure_cleaning_module()
|
||||
repo_root = Path(__file__).resolve().parents[3]
|
||||
|
||||
raw_df = pd.read_csv(repo_root / "data" / "node_simulation.csv")
|
||||
noisy_df = pd.read_csv(repo_root / "data" / "node_simulation_noisy.csv")
|
||||
cleaned_df = module.clean_pressure_data_df_km(noisy_df)
|
||||
|
||||
for df in (raw_df, noisy_df, cleaned_df):
|
||||
df["time"] = pd.to_datetime(df["time"])
|
||||
|
||||
assert len(cleaned_df) == len(raw_df)
|
||||
assert set(cleaned_df.columns) == {"time", "id", "pressure"}
|
||||
assert cleaned_df["pressure"].isna().sum() == 0
|
||||
|
||||
noisy_joined = raw_df.merge(noisy_df, on=["time", "id"], how="inner", suffixes=("_raw", "_noisy"))
|
||||
cleaned_joined = raw_df.merge(
|
||||
cleaned_df,
|
||||
on=["time", "id"],
|
||||
how="inner",
|
||||
suffixes=("_raw", "_clean"),
|
||||
)
|
||||
|
||||
noisy_rmse = float(
|
||||
np.sqrt(np.mean((noisy_joined["pressure_raw"] - noisy_joined["pressure_noisy"]) ** 2))
|
||||
)
|
||||
cleaned_rmse = float(
|
||||
np.sqrt(np.mean((cleaned_joined["pressure_raw"] - cleaned_joined["pressure_clean"]) ** 2))
|
||||
)
|
||||
noisy_mae = float(
|
||||
np.mean(np.abs(noisy_joined["pressure_raw"] - noisy_joined["pressure_noisy"]))
|
||||
)
|
||||
cleaned_mae = float(
|
||||
np.mean(np.abs(cleaned_joined["pressure_raw"] - cleaned_joined["pressure_clean"]))
|
||||
)
|
||||
|
||||
assert cleaned_rmse < 0.35
|
||||
assert cleaned_rmse < noisy_rmse * 0.5
|
||||
assert cleaned_mae < noisy_mae
|
||||
|
||||
repaired_gap = cleaned_df[
|
||||
(cleaned_df["id"] == 170490)
|
||||
& (cleaned_df["time"] == pd.Timestamp("2026-01-01T05:00:00+08:00"))
|
||||
]["pressure"].iloc[0]
|
||||
assert abs(repaired_gap - 30.62433433532715) < 1.0
|
||||
|
||||
spike_row = cleaned_df[
|
||||
(cleaned_df["id"] == 42563)
|
||||
& (cleaned_df["time"] == pd.Timestamp("2026-01-01T03:45:00+08:00"))
|
||||
]["pressure"].iloc[0]
|
||||
assert abs(spike_row - 28.018701553344727) < 2.0
|
||||
|
||||
|
||||
def test_clean_pressure_data_df_km_accepts_single_sensor_wide_frame_with_utc_strings():
|
||||
module = _load_pressure_cleaning_module()
|
||||
repo_root = Path(__file__).resolve().parents[3]
|
||||
|
||||
noisy_df = pd.read_csv(repo_root / "data" / "node_simulation_noisy.csv")
|
||||
single_sensor = (
|
||||
noisy_df[noisy_df["id"] == 170490][["time", "pressure"]]
|
||||
.rename(columns={"pressure": "170490"})
|
||||
.copy()
|
||||
)
|
||||
single_sensor["time"] = (
|
||||
pd.to_datetime(single_sensor["time"], utc=True).dt.strftime("%Y-%m-%dT%H:%M:%SZ")
|
||||
)
|
||||
|
||||
cleaned_df = module.clean_pressure_data_df_km(single_sensor)
|
||||
|
||||
assert len(cleaned_df) == 192
|
||||
assert cleaned_df["170490"].isna().sum() == 0
|
||||
@@ -0,0 +1,87 @@
|
||||
from datetime import datetime, timezone
|
||||
import importlib.util
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
def _load_scada_repository():
|
||||
module_path = (
|
||||
Path(__file__).resolve().parents[2]
|
||||
/ "app"
|
||||
/ "infra"
|
||||
/ "db"
|
||||
/ "timescaledb"
|
||||
/ "repositories"
|
||||
/ "scada.py"
|
||||
)
|
||||
spec = importlib.util.spec_from_file_location("tests_scada_repo_under_test", module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
assert spec and spec.loader
|
||||
spec.loader.exec_module(module)
|
||||
return module.ScadaRepository
|
||||
|
||||
|
||||
class _FakeCursor:
|
||||
def __init__(self, initial_rowcount: int):
|
||||
self.initial_rowcount = initial_rowcount
|
||||
self.rowcount = 0
|
||||
self.calls: list[tuple[str, tuple]] = []
|
||||
|
||||
async def __aenter__(self):
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type, exc, tb):
|
||||
return False
|
||||
|
||||
async def execute(self, query, params):
|
||||
self.calls.append((str(query), params))
|
||||
if len(self.calls) == 1:
|
||||
self.rowcount = self.initial_rowcount
|
||||
else:
|
||||
self.rowcount = 1
|
||||
|
||||
|
||||
class _FakeConnection:
|
||||
def __init__(self, initial_rowcount: int):
|
||||
self.cursor_instance = _FakeCursor(initial_rowcount)
|
||||
|
||||
def cursor(self):
|
||||
return self.cursor_instance
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_update_scada_field_inserts_when_update_hits_no_rows():
|
||||
ScadaRepository = _load_scada_repository()
|
||||
conn = _FakeConnection(initial_rowcount=0)
|
||||
point_time = datetime(2026, 1, 1, 0, 0, tzinfo=timezone.utc)
|
||||
|
||||
await ScadaRepository.update_scada_field(
|
||||
conn,
|
||||
point_time,
|
||||
"170490",
|
||||
"cleaned_value",
|
||||
26.5,
|
||||
)
|
||||
|
||||
assert len(conn.cursor_instance.calls) == 2
|
||||
assert "UPDATE scada.scada_data SET" in conn.cursor_instance.calls[0][0]
|
||||
assert "INSERT INTO scada.scada_data" in conn.cursor_instance.calls[1][0]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_update_scada_field_skips_insert_when_update_succeeds():
|
||||
ScadaRepository = _load_scada_repository()
|
||||
conn = _FakeConnection(initial_rowcount=1)
|
||||
point_time = datetime(2026, 1, 1, 0, 0, tzinfo=timezone.utc)
|
||||
|
||||
await ScadaRepository.update_scada_field(
|
||||
conn,
|
||||
point_time,
|
||||
"170490",
|
||||
"cleaned_value",
|
||||
26.5,
|
||||
)
|
||||
|
||||
assert len(conn.cursor_instance.calls) == 1
|
||||
assert "UPDATE scada.scada_data SET" in conn.cursor_instance.calls[0][0]
|
||||
Reference in New Issue
Block a user