Memories

User and Workflow

User wants Chinese responses.
Project path for subagent headers is /Users/yoilun/Code/cold_display_guard.
Current workflow batch is v1.2 轨迹识别.
User requested following /Users/yoilun/Code/goal-subagents-workflow-prompt.md.
Every subagent task must begin with:

[项目: /Users/yoilun/Code/cold_display_guard]
[工作流批次: v1.2 轨迹识别]
[阶段: 阶段 x]
[角色: 对应智能体角色]

First implement lightweight motion trajectory detection without YOLO dependencies.
Preserve a stable disposal_evidence contract for a future trained YOLO product detector.
Keep ROI occupancy timing as the source of zone occupied/empty state.
Use trajectory evidence before generic trash-motion FIFO fallback.
Current runtime writes top-level disposal_evidence and nested diagnostics.trajectory into runtime diagnostics JSONL.

Stage 1 established the backend contract: Observation.disposal_evidence normalizes backend-neutral disposal evidence, and the engine can discard a pending batch only when evidence targets trash, meets confidence, and matches the pending source_zone_id.
Stage 2 added the lightweight motion trajectory runtime path: ROI occupancy still drives occupied/empty state, TrajectoryTracker emits source-zone-to-trash evidence, and generic trash-motion count remains as a fallback.
Stage 3 added diagnostics and tests for runtime evidence propagation, trajectory sampling interval behavior, capture-failure schema, and trajectory/yolo runtime config parsing.
Final review fixes: matched evidence now only subtracts the trash fallback budget by the number of batches it actually closed, and trajectory candidates reject outside-before-source motion with motion_started_outside_source.
2026-06-01 false events across zones 1-7 were caused by a global brightness/exposure drop around 04:55 and recovery around 08:16; the fix is a lighting-shift guard that freezes occupancy transitions when many regions shift brightness in the same direction.
Current v1.2 does not use YOLO. yolo_enabled, yolo_model_path, and yolo_min_confidence are reserved for a future trained model backend that should keep emitting the same disposal_evidence shape.

Remote deployment target is xiaozheng@192.168.5.206:/home/xiaozheng/cold_display_guard.
Preserve the remote config/example.toml; it may contain camera, calibration, threshold, and deployment-specific runtime settings that must not be overwritten blindly.
When syncing code remotely, verify that runtime diagnostics still show top-level disposal_evidence and diagnostics.trajectory before evaluating v1.2 trajectory behavior from logs/events.jsonl.
The latest v1.2 deployment was verified with cold-display-guard-runtime and cold-display-guard-api up, API health status=ok, and diagnostics schema showing has_disposal_evidence=True plus has_trajectory=True.