feat: 修复 AI 风险语义并隔离 Mock 数据

ai-service/app/config.py

@@ -5,6 +5,7 @@ MODEL_MODE = os.getenv("MODEL_MODE", "mock")
 MODEL_PATH = os.getenv("MODEL_PATH", "models/best.onnx")
 # YOLO 类别（二分类训练基线：healthy/sick；7 类病种扩展后再调整）
 MODEL_LABELS = os.getenv("MODEL_LABELS", "healthy,sick").split(",")
+MODEL_VERSION = os.getenv("MODEL_VERSION", "mock-2026.08.14" if MODEL_MODE == "mock" else "best.onnx")
 # mock 模式返回的固定结果
 MOCK_CLASS = os.getenv("MOCK_CLASS", "healthy")
 MOCK_CONFIDENCE = float(os.getenv("MOCK_CONFIDENCE", "0.95"))

ai-service/app/detector.py

@@ -5,6 +5,52 @@ from abc import ABC, abstractmethod
 from PIL import Image
 
 
+def _label_set(labels) -> set[str]:
+    return {label.strip().lower() for label in labels if label.strip()}
+
+
+def abnormal_probability(detections: list[dict], labels: tuple[str, ...] | list[str]) -> float:
+    """单帧异常概率：取异常类检测的最高置信度，避免多框求和造成虚高。"""
+    abnormal = _label_set(labels) - {"healthy"}
+    best = 0.0
+    for detection in detections:
+        class_name = str(detection.get("class_name", "")).strip().lower()
+        if class_name in ("", "healthy", "unknown"):
+            continue
+        if abnormal and class_name not in abnormal:
+            continue
+        try:
+            confidence = float(detection.get("confidence", 0))
+        except (TypeError, ValueError):
+            confidence = 0.0
+        best = max(best, min(1.0, max(0.0, confidence)))
+    return best
+
+
+def detection_status(detections: list[dict], labels: tuple[str, ...] | list[str]) -> str:
+    """空检测或 unknown 类不再被当作 healthy。"""
+    if not detections:
+        return "unknown"
+    abnormal = _label_set(labels) - {"healthy"}
+    healthy_seen = False
+    unknown_seen = False
+    for detection in detections:
+        class_name = str(detection.get("class_name", "")).strip().lower()
+        if abnormal and class_name in abnormal:
+            return "abnormal"
+        if not abnormal and class_name not in ("", "healthy", "unknown"):
+            return "abnormal"
+        if class_name == "healthy":
+            healthy_seen = True
+        elif class_name in ("", "unknown"):
+            unknown_seen = True
+        else:
+            unknown_seen = True
+    if healthy_seen and not unknown_seen:
+        return "healthy"
+    return "unknown"
+
+
 class Detector(ABC):
     @abstractmethod
     def detect(self, image_bytes: bytes) -> list[dict]:

ai-service/app/main.py

@@ -6,7 +6,7 @@ import time
 from fastapi import FastAPI, File, HTTPException, Request, UploadFile
 
 from . import config
-from .detector import MockDetector, ONNXDetector
+from .detector import MockDetector, ONNXDetector, abnormal_probability, detection_status
 from .stream_tasks import StreamTaskWorker, is_allowed_stream_ref
 
 app = FastAPI(title="Silk AI Service", version="0.1.0")
@@ -36,7 +36,12 @@ else:
 
 @app.get("/health")
 def health():
-    return {"status": "ok", "model": config.MODEL_MODE}
+    return {
+        "status": "ok",
+        "model": config.MODEL_MODE,
+        "modelVersion": config.MODEL_VERSION,
+        "isMock": isinstance(detector, MockDetector),
+    }
 
 
 @app.post("/detect")
@@ -50,6 +55,10 @@ async def detect(file: UploadFile = File(...)):
         raise HTTPException(status_code=400, detail=str(exc)) from exc
     return {
         "model": config.MODEL_MODE,
+        "modelVersion": config.MODEL_VERSION,
+        "isMock": isinstance(detector, MockDetector),
+        "status": detection_status(detections, config.MODEL_LABELS),
+        "abnormalProbability": abnormal_probability(detections, config.MODEL_LABELS),
         "detections": [
             {
                 "bbox": d["bbox"],
@@ -112,6 +121,8 @@ def metrics():
             gpu = None
     return {
         "model": config.MODEL_MODE,
+        "modelVersion": config.MODEL_VERSION,
+        "isMock": isinstance(detector, MockDetector),
         "uptimeSeconds": int(time.time() - START_TIME),
         "requests": reqs,
         "avgLatencyMs": round(avg, 2),