feat(scada): add project monitoring replay

2026-11-04 14:27:21 +08:00
parent f25423dec4
commit 26fa5d1909
53 changed files with 5153 additions and 20 deletions
@@ -0,0 +1,210 @@
+"""Deterministic monitoring summaries built only from frozen replay outputs."""
+from __future__ import annotations
+
+from datetime import datetime, timedelta
+from statistics import median
+from typing import Any
+from uuid import NAMESPACE_URL, uuid5
+
+import pandas as pd
+
+from app.domain.schemas.scada_replay import ScadaReplayCreate
+from app.services.burst_detection import (
+    TARGET_MU,
+    TARGET_N_ESTIMATORS,
+    TARGET_RANDOM_STATE,
+    TARGET_SCORE_THRESHOLD,
+    run_burst_detection,
+)
+
+
+def _time(value: str | datetime) -> datetime:
+    return value if isinstance(value, datetime) else datetime.fromisoformat(value)
+
+
+def _valid_rows(rows: list[dict], device_id: str) -> list[dict]:
+    return sorted(
+        (
+            row for row in rows
+            if row.get("device_id") == device_id
+            and row.get("quality") in {"valid", "corrected"}
+            and row.get("cleaned_value") is not None
+        ),
+        key=lambda row: _time(row["time"]),
+    )
+
+
+def _relative_indicator(
+    rows: list[dict],
+    config: ScadaReplayCreate,
+    device_ids: list[str],
+    period_start: datetime,
+    period_end: datetime,
+    *,
+    direction: str,
+) -> dict[str, Any]:
+    details: list[dict[str, Any]] = []
+    gaps: list[dict[str, str]] = []
+    for device_id in device_ids:
+        rule = next((
+            item for item in config.rules
+            if item.kind == "relative_change" and device_id in item.device_ids
+            and item.threshold is not None
+        ), None)
+        if rule is None:
+            gaps.append({"device_id": device_id, "reason": "relative_change_rule_missing"})
+            continue
+        valid = _valid_rows(rows, device_id)
+        baseline = [row for row in valid if _time(row["time"]) < period_start][-rule.window_samples:]
+        current = [row for row in valid if period_start <= _time(row["time"]) <= period_end]
+        if len(baseline) < rule.minimum_samples:
+            gaps.append({"device_id": device_id, "reason": "baseline_not_ready"})
+            continue
+        if not current:
+            gaps.append({"device_id": device_id, "reason": "period_reading_missing"})
+            continue
+        reference = median(float(row["cleaned_value"]) for row in baseline)
+        if reference == 0:
+            gaps.append({"device_id": device_id, "reason": "zero_baseline"})
+            continue
+        latest = current[-1]
+        value = float(latest["cleaned_value"])
+        change = (value - reference) / abs(reference) * 100
+        hit = change <= -rule.threshold if direction == "decrease" else change >= rule.threshold
+        details.append({
+            "device_id": device_id,
+            "observed_at": _time(latest["time"]).isoformat(),
+            "baseline": reference,
+            "value": value,
+            "change_percent": change,
+            "threshold_percent": rule.threshold,
+            "hit": hit,
+        })
+    return {
+        "status": "ready" if not gaps else "insufficient_data",
+        "hit": any(item["hit"] for item in details),
+        "hit_device_ids": [item["device_id"] for item in details if item["hit"]],
+        "devices": details,
+        "gaps": gaps,
+    }
+
+
+def _burst_detection(
+    rows: list[dict],
+    pressure_device_ids: list[str],
+    interval_seconds: int,
+    period_end: datetime,
+) -> dict[str, Any]:
+    if len(pressure_device_ids) < 5:
+        return {"status": "sensor_coverage_insufficient", "hit": False,
+                "required_sensors": 5, "available_sensors": len(pressure_device_ids)}
+    if interval_seconds % 60 or 86400 % interval_seconds:
+        return {"status": "unsupported_sampling_interval", "hit": False,
+                "interval_seconds": interval_seconds}
+    points_per_day = 86400 // interval_seconds
+    count = 15 * points_per_day
+    series_by_device: dict[str, dict[datetime, float]] = {}
+    common_times: set[datetime] | None = None
+    for device_id in pressure_device_ids:
+        values = {_time(row["time"]): float(row["cleaned_value"])
+                  for row in _valid_rows(rows, device_id) if _time(row["time"]) <= period_end}
+        series_by_device[device_id] = values
+        common_times = set(values) if common_times is None else common_times & set(values)
+    if not common_times:
+        return {"status": "baseline_not_ready", "hit": False, "required_samples": count}
+    target = max(common_times)
+    expected = [target - timedelta(seconds=interval_seconds * offset)
+                for offset in range(count - 1, -1, -1)]
+    incomplete = [device_id for device_id, values in series_by_device.items()
+                  if any(at not in values for at in expected)]
+    if incomplete:
+        return {"status": "baseline_not_ready", "hit": False, "required_samples": count,
+                "incomplete_device_ids": incomplete}
+    frame = pd.DataFrame({device_id: [series_by_device[device_id][at] for at in expected]
+                          for device_id in pressure_device_ids}, index=expected)
+    try:
+        result = run_burst_detection(
+            network="scada-replay",
+            username="scada-replay",
+            observed_pressure_data=frame,
+            points_per_day=points_per_day,
+            mu=TARGET_MU,
+            iforest_params={
+                "n_estimators": TARGET_N_ESTIMATORS,
+                "random_state": TARGET_RANDOM_STATE,
+                "contamination": "auto",
+            },
+            sensor_nodes=pressure_device_ids,
+        )
+    except (TypeError, ValueError) as exc:
+        return {"status": "analysis_failed", "hit": False, "reason": str(exc)[:300]}
+    latest = result["rows"][-1]
+    score = float(latest["Score"])
+    return {
+        "status": "ready",
+        "hit": score <= TARGET_SCORE_THRESHOLD,
+        "target_time": target.isoformat(),
+        "target_score": score,
+        "score_threshold": TARGET_SCORE_THRESHOLD,
+        "sensor_count": len(pressure_device_ids),
+        "reference_day_count": 14,
+        "latest_sensor_rankings": result["summary"].get("latest_sensor_rankings", []),
+    }
+
+
+def evaluate_monitoring_period(
+    document: dict,
+    rows: list[dict],
+    *,
+    period_start: datetime,
+    period_end: datetime,
+    batch_sequence: int,
+) -> dict[str, Any]:
+    config = ScadaReplayCreate.model_validate(document["config"])
+    monitoring = document.get("monitoring_config")
+    rule_version = (monitoring or {}).get("rule_version", "unconfigured")
+    monitoring_id = str(uuid5(
+        NAMESPACE_URL,
+        f"{document['replay_id']}:{period_end.isoformat()}:{rule_version}",
+    ))
+    base = {
+        "monitoring_id": monitoring_id,
+        "period_start": period_start.isoformat(),
+        "period_end": period_end.isoformat(),
+        "knowledge_cutoff": period_end.isoformat(),
+        "batch_sequence": batch_sequence,
+        "rule_version": rule_version,
+        "task_id": None,
+    }
+    if not monitoring:
+        return {**base, "status": "insufficient_data", "detail": "项目未配置 SCADA 监测设备角色",
+                "pressure": {"status": "unconfigured", "hit": False},
+                "outlet_flow": {"status": "unconfigured", "hit": False},
+                "burst_detection": {"status": "not_run", "hit": False}}
+    pressure_ids = monitoring.get("pressure_device_ids", [])
+    flow_ids = monitoring.get("outlet_flow_device_ids", [])
+    selected = set(config.device_ids)
+    role_gaps = sorted((set(pressure_ids) | set(flow_ids)) - selected)
+    if not pressure_ids or not flow_ids or role_gaps:
+        return {**base, "status": "insufficient_data", "detail": "监测角色为空或不属于本轮回放设备",
+                "role_gaps": role_gaps,
+                "pressure": {"status": "unconfigured", "hit": False},
+                "outlet_flow": {"status": "unconfigured", "hit": False},
+                "burst_detection": {"status": "not_run", "hit": False}}
+    pressure = _relative_indicator(rows, config, pressure_ids, period_start, period_end, direction="decrease")
+    outlet_flow = _relative_indicator(rows, config, flow_ids, period_start, period_end, direction="increase")
+    detector = _burst_detection(
+        rows, pressure_ids, int(document["monitoring_interval_seconds"]), period_end,
+    )
+    complete = pressure["status"] == outlet_flow["status"] == detector["status"] == "ready"
+    hits = (bool(detector["hit"]), bool(pressure["hit"]), bool(outlet_flow["hit"]))
+    if not complete:
+        status, detail = "insufficient_data", "监测数据、设备角色或爆管侦测基线不足"
+    elif all(hits):
+        status, detail = "suspected_burst", "爆管侦测、压力下降与出厂边界流量增加相互支持"
+    elif any(hits):
+        status, detail = "attention", "仅部分爆管相关指标命中，需要继续观察或人工核查"
+    else:
+        status, detail = "normal", "本周期爆管相关指标均未命中"
+    return {**base, "status": status, "detail": detail, "pressure": pressure,
+            "outlet_flow": outlet_flow, "burst_detection": detector}