数据链路 P1 修复：ETag 缓存 + 校准漂移检测

P1-5 ETag 支持：
- 后端新增 _etag_middleware：GET /api/* 自动返回 ETag (MD5)
- 支持 If-None-Match 请求头，匹配时返回 304 + 30s Cache-Control
- 前端 cache: no-store → default，浏览器自动处理 ETag/304 节省带宽

P1-6 校准漂移检测：
- probability_calibration.py 新增 check_calibration_drift()
- 对比最近 200 条 daily_records 的 CRPS 与校准基线
- 漂移 >15% 时返回 warning 提示重新训练
- 集成到 /api/system/status 的 probability.drift 字段

Tested: python -m ruff check ., npx tsc --noEmit

frontend/lib/dashboard-client.ts

@@ -68,7 +68,7 @@ async function fetchJson<T>(url: string, options?: { timeoutMs?: number }): Prom
   try {
     response = await fetchBackendApi(url, {
       headers,
-      cache: "no-store",
+      cache: "default",
       signal: controller?.signal,
     });
   } catch (error) {
@@ -238,7 +238,7 @@ export const dashboardClient = {
     void fetch(`/api/system/priority-warm?${params.toString()}`, {
       method: "POST",
       headers: { Accept: "application/json" },
-      cache: "no-store",
+      cache: "default",
       keepalive: true,
     }).catch(() => {});
   },
@@ -418,7 +418,7 @@ export const dashboardClient = {
     }).then((headers) => fetchBackendApi("/api/scan/terminal/ai", {
       method: "POST",
       headers,
-      cache: "no-store",
+      cache: "default",
       body: JSON.stringify({
         filters: payload.filters,
         snapshot_id: snapshotId || null,

src/analysis/probability_calibration.py

@@ -760,3 +760,75 @@ def default_calibration_payload(
             "reason": reason,
         },
     }
+
+
+def check_calibration_drift(records: list[dict[str, Any]], calibration_path: str | None = None) -> dict[str, Any]:
+    """Compare recent CRPS against calibration baseline to detect drift.
+
+    Returns a dict with keys: drifted (bool), current_crps (float),
+    baseline_crps (float), delta_pct (float), sample_count (int), warning (str|None).
+    A positive delta_pct means the model is performing worse than baseline.
+    """
+    import json
+    import os
+
+    if len(records) < 5:
+        return {"drifted": False, "sample_count": len(records), "warning": "Insufficient samples"}
+
+    path = calibration_path or DEFAULT_CALIBRATION_FILE
+    baseline_crps = None
+    try:
+        with open(path, "r", encoding="utf-8") as fh:
+            json.load(fh)  # calibration file
+        eval_path = os.path.join(
+            os.path.dirname(path) if os.path.dirname(path) else os.path.join(os.path.dirname(DEFAULT_CALIBRATION_FILE)),
+            "evaluation_report.json",
+        )
+        with open(eval_path, "r", encoding="utf-8") as fh:
+            report = json.load(fh)
+        baseline_crps = float(report.get("metrics", {}).get("selected_mean_crps") or 0) or None
+    except Exception:
+        pass
+
+    current_crps_values: list[float] = []
+    for r in records:
+        actual = float(r.get("actual_high") or r.get("observed") or 0)
+        mu = float(r.get("deb_prediction") or r.get("mu") or 0)
+        sigma = float(r.get("sigma") or r.get("ensemble_std") or 2.0)
+        if actual and mu and sigma > 0:
+            current_crps_values.append(_gaussian_crps(actual, mu, sigma))
+
+    if not current_crps_values:
+        return {"drifted": False, "sample_count": 0, "warning": "No valid records for CRPS"}
+
+    current_crps = round(sum(current_crps_values) / len(current_crps_values), 6)
+
+    if baseline_crps is None or baseline_crps <= 0:
+        return {
+            "drifted": False,
+            "current_crps": current_crps,
+            "baseline_crps": baseline_crps,
+            "sample_count": len(records),
+            "warning": "No baseline available",
+        }
+
+    delta_pct = round((current_crps - baseline_crps) / baseline_crps * 100, 1)
+    DRIFT_THRESHOLD_PCT = 15.0  # warn if CRPS degraded by >15%
+
+    if delta_pct > DRIFT_THRESHOLD_PCT:
+        return {
+            "drifted": True,
+            "current_crps": current_crps,
+            "baseline_crps": baseline_crps,
+            "delta_pct": delta_pct,
+            "sample_count": len(records),
+            "warning": f"CRPS degraded {delta_pct}% vs baseline; consider re-running fit_calibration()",
+        }
+
+    return {
+        "drifted": False,
+        "current_crps": current_crps,
+        "baseline_crps": baseline_crps,
+        "delta_pct": delta_pct,
+        "sample_count": len(records),
+    }

web/analysis_service.py

@@ -2069,9 +2069,8 @@ def _analyze(
             local_date=local_date_str,
             peak_status=peak_status,
         )
-        if lgbm_val is not None:
-            # LGBM is kept as an independent reference, not fed back into DEB
-            # to avoid circular dependency (DEB → LGBM training → DEB)
+        # LGBM is kept as an independent reference (lgbm.prediction),
+        # not fed back into DEB to avoid circular dependency
 
     deviation_monitor = _build_deviation_monitor(
         current_temp=cur_temp,

web/core.py

@@ -30,6 +30,7 @@ from src.utils.metrics import (
 )
 from src.analysis.probability_calibration import (
     DEFAULT_CALIBRATION_FILE,
+    check_calibration_drift,
     resolve_probability_engine_mode,
 )
 from src.analysis.probability_rollout import build_rollout_report
@@ -132,6 +133,40 @@ async def _metrics_middleware(request: Request, call_next):
         status=status_code,
     )
     return response
+
+
+@app.middleware("http")
+async def _etag_middleware(request: Request, call_next):
+    """Add ETag to GET /api/* responses; return 304 on If-None-Match hit."""
+    response = await call_next(request)
+
+    if request.method != "GET" or response.status_code != 200:
+        return response
+
+    path = request.url.path
+    if not path.startswith("/api/") or path.endswith("/stream"):
+        return response
+
+    body = getattr(response, "body", None) or b""
+    if not body:
+        return response
+
+    try:
+        import hashlib
+        etag = hashlib.md5(body).hexdigest()
+    except Exception:
+        return response
+
+    etag_value = f'"{etag}"'
+    if_none_match = request.headers.get("If-None-Match", "")
+    if if_none_match == etag_value:
+        from fastapi.responses import Response
+        return Response(status_code=304, headers={"ETag": etag_value})
+
+    response.headers["ETag"] = etag_value
+    response.headers["Cache-Control"] = "private, max-age=30"
+    return response
+
 _PROBABILITY_SHADOW_REPORT = os.path.join(
     _PROJECT_ROOT,
     "artifacts",
@@ -453,11 +488,34 @@ def _probability_summary() -> Dict[str, Any]:
         _PROBABILITY_EVALUATION_REPORT,
         _PROBABILITY_SHADOW_REPORT,
     )
+    drift = {"drifted": False, "sample_count": 0, "warning": "not checked"}
+    try:
+        import sqlite3
+        conn = sqlite3.connect(_account_db.db_path)
+        rows = conn.execute(
+            "SELECT city, target_date, actual_high, deb_prediction, extra_json FROM daily_records_store ORDER BY target_date DESC LIMIT 200"
+        ).fetchall()
+        conn.close()
+        records: list[dict[str, Any]] = []
+        for row in rows:
+            rec = {"city": row[0], "target_date": row[1], "actual_high": row[2], "deb_prediction": row[3]}
+            try:
+                extra = json.loads(row[4] or "{}") if row[4] else {}
+                rec["mu"] = extra.get("mu")
+                rec["sigma"] = extra.get("sigma")
+            except Exception:
+                pass
+            records.append(rec)
+        if records:
+            drift = check_calibration_drift(records)
+    except Exception:
+        pass
     return {
         "engine_mode": resolve_probability_engine_mode(),
         "calibration_file": os.getenv("POLYWEATHER_PROBABILITY_CALIBRATION_FILE")
         or DEFAULT_CALIBRATION_FILE,
         "rollout": rollout,
+        "drift": drift,
     }