orchestrator: 全专家失败时不调用 aggregator provider

src/llm/agents/orchestrator.py

@@ -259,7 +259,8 @@ async def predict_match_multi(
             "match %s: 所有 %d 位专家均无有效数据,跳过终裁,标记 degraded",
             match_id, len(reports),
         )
-        aggregator_provider = await _agent_provider("aggregator", tier="aggregator")
+        # 无有效专家时不调用 aggregator provider,避免多余开销
+        # model 使用 settings 默认值占位(无实际 LLM 调用)
         final = {
             "pred_home_goals": None,
             "pred_away_goals": None,
@@ -272,6 +273,7 @@ async def predict_match_multi(
         }
         agg_prompt_tokens = 0
         agg_completion_tokens = 0
+        aggregator_model = settings.LLM_MODEL  # 占位,无实际 LLM 调用
 
     latency_ms = int((time.perf_counter() - start) * 1000)
 
@@ -297,17 +299,19 @@ async def predict_match_multi(
                 raise RuntimeError(f"终裁输出校验失败: {e}")
             agent_weights = validate_agent_weights(final.get("agent_weights"))
             pred_status = "success"
+            model_name = aggregator_provider.model
         else:
             # 无有效报告:跳过严格校验,直接构造降级结果
             validated = None  # type: ignore
             agent_weights = {}
             pred_status = "degraded"
+            model_name = aggregator_model
 
         pred = await _upsert_prediction(
             session,
             match_id=match_id,
             provider_name=settings.LLM_PROVIDER,
-            model=aggregator_provider.model,
+            model=model_name,
             mode="multi",
             run_type="backtest" if backtest else "live",
             values={