feat: 修复 AI 风险语义并隔离 Mock 数据

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]: