feat: 手术视频消耗、待确认与持久化改造

- 新增 Alembic 初始迁移、领域明细模型及归档持久化与重试链路\n- 拆分视频会话注册表、分类处理、推理时间窗聚合与流处理\n- 消耗日志：TSV/Markdown 含 top2/top3；item_id 优先产品编码；待确认记「待确认」行，语音确认后落正式行并更新汇总\n- 待确认时内存/DB 明细为占位行，确认后替换；拒绝时移除占位\n- 分类 probs 先 detach/cpu 再转 NumPy，修复 MPS/CUDA 上推理被静默跳过\n- 补充集成测试、归档与设备张量等单测

Made-with: Cursor

app/services/consumable_vision_algorithm.py

@@ -20,7 +20,11 @@ from ultralytics import YOLO
 
 from app.config import Settings, settings
 
-os.environ["YOLO_CONFIG_DIR"] = "/tmp"
+
+def _ensure_yolo_config_dir() -> None:
+    """Ultralytics 需要可写 YOLO_CONFIG_DIR；仅在未设置时给一个安全默认，不覆盖用户配置。"""
+    if not os.environ.get("YOLO_CONFIG_DIR"):
+        os.environ["YOLO_CONFIG_DIR"] = "/tmp"
 
 
 def resolve_inference_device(explicit: str) -> str | None:
@@ -184,40 +188,62 @@ def pad_box(
     return nx1, ny1, nx2, ny2
 
 
+def _probs_data_to_numpy1d(raw) -> np.ndarray:
+    """分类 logits/probs 向量 → 1D float64 NumPy 数组。
+
+    PyTorch 张量若在 ``cuda``、``mps`` 等设备上，**必须先** ``.cpu()`` 再转 NumPy：
+    NumPy 只支持 CPU（主机）内存，没有 CUDA/MPS 后端；``np.asarray(cuda_tensor)`` /
+    ``tensor.numpy()``（设备上）都会失败。``.cpu()`` 会做一次设备→主机的拷贝（已是 CPU
+    时开销很小），因此 CUDA 与 MPS 共用同一路径即可。
+    """
+    if raw is None:
+        return np.zeros((0,), dtype=np.float64)
+    x = raw
+    if hasattr(x, "detach"):
+        x = x.detach()
+    if hasattr(x, "cpu"):
+        x = x.cpu()
+    if hasattr(x, "numpy"):
+        # torch.Tensor / ultralytics BaseTensor 等
+        x = x.numpy()
+    return np.asarray(x, dtype=np.float64).reshape(-1)
+
+
 def cls_top3_from_result(
     cls: YOLO, r, name_to_code: dict[str, str]
 ) -> ClsTop3 | None:
     pr = r[0].probs
-    if pr is None or not hasattr(pr, "top5") or not pr.top5:
+    if pr is None:
         return None
-    t5i = list(pr.top5)
-    tc = pr.top5conf
-    if tc is None:
+    arr = _probs_data_to_numpy1d(pr.data)
+    if arr.size == 0:
         return None
+    order = np.argsort(-arr, kind="stable")
+    t5i = [int(order[i]) for i in range(min(5, int(order.size)))]
 
-    def _ci(i: int) -> float:
-        if i < 0 or i >= len(tc):
+    def _conf_for_idx(idx: int) -> float:
+        if idx < 0 or idx >= arr.size:
             return 0.0
         try:
-            v = tc[i]
+            v = arr[idx]
             return float(v.item() if hasattr(v, "item") else v)
         except (IndexError, ValueError, TypeError):
             return 0.0
 
-    t1i = int(pr.top1)
-    c1 = _ci(0) if t5i and int(t5i[0]) == t1i else float(
-        pr.top1conf.item() if hasattr(pr.top1conf, "item") else pr.top1conf
-    )
+    t1i = int(t5i[0])
+    c1 = _conf_for_idx(t1i)
     n1 = str(cls.names.get(t1i, "")).strip()
 
     n2 = n3 = ""
     c2 = c3 = 0.0
     if len(t5i) > 1:
-        n2 = str(cls.names.get(int(t5i[1]), "")).strip()
-        c2 = _ci(1)
+        i2 = int(t5i[1])
+        n2 = str(cls.names.get(i2, "")).strip()
+        c2 = _conf_for_idx(i2)
     if len(t5i) > 2:
-        n3 = str(cls.names.get(int(t5i[2]), "")).strip()
-        c3 = _ci(2)
+        i3 = int(t5i[2])
+        n3 = str(cls.names.get(i3, "")).strip()
+        c3 = _conf_for_idx(i3)
 
     def _pid(label: str) -> str:
         lb = (label or "").strip()
@@ -283,12 +309,50 @@ class ConsumableVisionAlgorithmService:
     """手部检测（可选）+ 耗材分类；供 CameraSessionManager 在视频线程中调用。"""
 
     def __init__(self, app_settings: Settings | None = None) -> None:
+        _ensure_yolo_config_dir()
         self._s = app_settings or settings
         self._det: YOLO | None = None
         self._cls: YOLO | None = None
         self._det_lock = Lock()
         self._cls_lock = Lock()
 
+    def effective_candidate_consumables(self, requested: list[str]) -> list[str]:
+        """请求体中的耗材子集；未提供（缺省或仅空白）时用目录 Excel 全部商品名，无目录则用分类模型全部类名。"""
+        out: list[str] = []
+        seen: set[str] = set()
+        for c in requested:
+            n = _norm_product_name((c or "").strip())
+            if not n or n in seen:
+                continue
+            seen.add(n)
+            out.append(n)
+        if out:
+            return out
+
+        xlsx_raw = (self._s.consumable_catalog_xlsx_path or "").strip()
+        if xlsx_raw:
+            path = Path(xlsx_raw).expanduser()
+            if path.is_file():
+                try:
+                    full = load_name_to_product_code(path)
+                except Exception as exc:
+                    logger.warning("读取耗材目录 Excel 失败，回退到模型类名: {}", exc)
+                else:
+                    if full:
+                        return sorted(full.keys())
+                    logger.warning("耗材目录 Excel 无有效行，回退到模型类名")
+            else:
+                logger.warning(
+                    "耗材目录 Excel 路径已配置但文件不存在: {}，回退到模型类名",
+                    path,
+                )
+
+        cls_model = self._get_cls()
+        labels = sorted(
+            {str(v).strip() for v in cls_model.names.values() if str(v).strip()}
+        )
+        return labels
+
     def build_name_mapping(
         self, candidate_consumables: list[str]
     ) -> dict[str, str]: