Kunpeng
25fa0c5825
Backend:
- Persist alert events and robot delivery counters in SQLite (data/app.db,
YOLO_DB_PATH override); EventStore keeps its interface, DeliveryStatsStore
uses read/write-through UPSERT; in-memory fallback remains for standalone
AlertManager use
- Serve /api/detect from a sync endpoint and serialize YOLO inference with a
lock, so concurrent requests no longer block the event loop
- Unify single-image and session detection: requests without session_id
share the fixed single-image session and run full frame confirmation
- Store UTC Z-suffixed timestamps for sortable string comparison; close
executor and database on shutdown via FastAPI lifespan
Frontend:
- Split app.js into ES modules (main.js + modules/{dom,api,ui,charts,store}
+ views/{dashboard,inspection,events,robots,robotCards}), no build step
- Escape all server data interpolated into innerHTML; guard missing
event.classes; replace lazy element ID list with memoized qs()
- Remove hardcoded fake stats (trend badge, device donut segment)
Docs:
- Rewrite README: accurate weight policy (only trained best.pt committed,
no Git LFS), SQLite persistence, single-worker note, data asset inventory
- Align models/pretrained/README.md with actual files; add YOLO_DB_PATH to
.env.example; add persistence unit tests; ruff clean
Pretrained Models
本目录保存本地缓存的基础模型权重，供首次训练或实验对照使用。这些文件由 .gitignore 排除、不提交到 Git，克隆后可从 Ultralytics 官方源重新下载。
当前本地权重：
yolo11s.pt— 训练默认起点（DEFAULT_PRETRAINED_MODEL）yolo26s.ptyolov8n.ptyolov8s.pt
TrainConfig.model_weights 的默认值是已训练好的 runs/detect/smoke_fire_yolo11s_v1-4/weights/best.pt，因此 fire-yolo train 默认执行微调（fine-tune）而不是从头训练。