DEB 降级为模型集群中的普通一员，AI 不再照搬 DEB 做预测

此前 DEB 以独立字段 deb.prediction 传给 AI，提示词又要求"必须综合 DEB"，导致 AI 直接输出 DEB 值而非独立判断。改动： - 移除 AI 输入中的独立 deb 字段，DEB 只作为 model_cluster.sources 中的一条记录 (model: "DEB (fusion)")，与其他模型平等 - 提示词改为：以模型集群集中区间为基线，用报文观测信号独立判断上修/下修/维持 - 流式/非流式提示词均强调"不要直接照搬 DEB 的值，差异是正常的" - 回退路径改用模型集群中位数作为默认预测，DEB 仅作为备选参考
2026-05-06 16:03:22 +08:00
parent f9d0ea31bc
commit eb42d644a3
4 changed files with 83 additions and 47 deletions
@@ -354,18 +354,18 @@ def _build_city_ai_prompt(data: Dict[str, Any]) -> Dict[str, Any]:
            "icao": risk.get("icao") or airport_current.get("station_code") or airport_primary.get("station_code"),
            "distance_km": risk.get("distance_km"),
        },
-        "deb": {
-            "prediction": ((daily_entry.get("deb") or {}).get("prediction") if isinstance(daily_entry.get("deb"), dict) else None)
-            or ((data.get("deb") or {}).get("prediction") if isinstance(data.get("deb"), dict) else None),
-            "weights_info": ((data.get("deb") or {}).get("weights_info") if isinstance(data.get("deb"), dict) else None),
-        },
        "model_cluster": {
            "sources": [
-                {"model": str(name), "value": value}
-                for name, value in (models or {}).items()
-                if _safe_float(value) is not None
+                *([
+                    {"model": "DEB (fusion)", "value": ((daily_entry.get("deb") or {}).get("prediction") if isinstance(daily_entry.get("deb"), dict) else None) or ((data.get("deb") or {}).get("prediction") if isinstance(data.get("deb"), dict) else None)}
+                ] if (((daily_entry.get("deb") or {}).get("prediction") if isinstance(daily_entry.get("deb"), dict) else None) or ((data.get("deb") or {}).get("prediction") if isinstance(data.get("deb"), dict) else None)) is not None else []),
+                *[
+                    {"model": str(name), "value": value}
+                    for name, value in (models or {}).items()
+                    if _safe_float(value) is not None
+                ],
            ],
-            "model_count": len(model_values),
+            "model_count": len(model_values) + (1 if (((daily_entry.get("deb") or {}).get("prediction") if isinstance(daily_entry.get("deb"), dict) else None) or ((data.get("deb") or {}).get("prediction") if isinstance(data.get("deb"), dict) else None)) is not None else 0),
            "min": min(model_values) if model_values else None,
            "max": max(model_values) if model_values else None,
            "spread": (max(model_values) - min(model_values)) if len(model_values) >= 2 else None,