218 lines
12 KiB
YAML
218 lines
12 KiB
YAML
input:
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dir: ./videos
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recursive: true
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extensions: [".mp4", ".mov", ".mkv", ".avi", ".flv", ".ts", ".m4v"]
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source:
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mode: local
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output:
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dir: ./outputs/local-batch
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overwrite: false
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resume: true
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keep_frames: true
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hik_cloud:
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api_base_url: https://api2.hik-cloud.com
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download_path: /v1/carrier/cstorage/open/play/download
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access_token: null
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access_token_env: HIK_CLOUD_ACCESS_TOKEN
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chunk_seconds: 600
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timeout_seconds: 60
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download_timeout_seconds: 600
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devices:
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- device_serial: EXAMPLE_DEVICE_SERIAL
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channel_no: 1
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name: example-device
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time_ranges:
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- begin: "2026-02-03 09:00:00"
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end: "2026-02-03 10:00:00"
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ffprobe:
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timeout_seconds: 30
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ffmpeg:
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prefer_nvdec: true
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allow_cpu_fallback: false
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hwaccel: cuda
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codec_decoders:
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h264: h264_cuvid
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hevc: hevc_cuvid
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frame_fps: 1
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frame_width: 640
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jpeg_quality: 4
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timeout_seconds_per_video: 3600
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clip:
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length_seconds: 10
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stride_seconds: 10
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frames_per_clip: 8
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min_frames_per_clip: 4
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vlm:
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api_base_url: http://localhost:8679
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chat_completions_path: /v1/chat/completions
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model: memai-zhengxin-v3-20260413
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timeout_seconds: 120
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max_tokens: 1024
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temperature: 0
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batch_size: 1
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image_transport: data_uri
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retries: 1
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prompt:
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system: >-
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You are an AI quality inspector and store monitoring assistant for a fried chicken cutlet production line and storefront.
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Your task is to analyze a short multi-frame video clip and output one strict JSON object. Preserve the existing action, quality, safety, people, guest, and timestamp fields, and additionally detect QSC violation events.
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Use only visual evidence from the provided frames. Do not guess hidden facts. If something is not clearly visible, output an empty value, unknown, or [] according to the schema.
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All top-level keys below are REQUIRED in every response. Do not omit any key.
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### 1. Action
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Identify the primary food-operation action.
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Valid values: Action_Defrost / Action_Breading / Action_Resting / Action_Start_Frying / End_Frying / Action_Triming / Action_Cutting / Action_Seasoning / Action_Serving / Action_Idle.
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If no clear food-operation action is detected, output Action_Idle.
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### 2. quality_status
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Choose based on the action:
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- Action_Breading: fully_covered | uneven
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- Action_Resting: stacked | qualified
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- Action_Start_Frying / End_Frying: standard_time | early_retrieval | overcooked | double_fried
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- Action_Cutting: complete_cut | linked | dusted_before_cut
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- Action_Seasoning: coverage_high | missed | single_side_dusted
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- Other actions: qualified
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If no ingredient is visible or the action has no applicable status, output an empty string.
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### 3. error_type
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Short description of legacy SOP operation anomaly only. Examples: dusted_before_cut, single_side_dusted, double_fried.
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If the operation is normal or no legacy SOP error is visible, output an empty string.
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QSC violations such as no mask, no hat, no gloves, tobacco, or foot-picking must be reported in qsc_events, not in error_type, unless they are also directly related to the legacy SOP operation.
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### 4. 安全隐患
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Chinese description of visible safety hazards in the scene. Example: 油锅附近有易燃物. If none, output an empty string.
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### 5. 人物位置
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Chinese sentence describing where people are and how they are moving. Example: 员工在油锅边操作，顾客在收银台前等待. If no people are visible, output an empty string.
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### 6. 总结
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Chinese sentence summarizing the scene and visible person count. Example: 画面中有2人，1名员工在操作台处理食物，1名顾客在收银台前等待. If no people are visible, output 无.
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### 7. 时间
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The timestamp overlaid on the original video frame, in format YYYY-MM-DD HH:MM:SS. If the timestamp is not visible or cannot be read, output an empty string.
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### 8. employees
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Array of employee objects. If no employees are visible, output [].
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Each employee object must contain:
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- status: 1 if working at equipment, food, packing, counter, or operation table; 2 if standing idle, waiting, or passing by
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- warning: 0 if no visible hazard; 1 if hazard present
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- position: one of YZL_1 / LCCZT_1 / SYJ / DPL / BSZSG / DCGZT / KLJ / UNKNOWN
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Position codes: YZL_1 = oil fryer area; LCCZT_1 = cooling or operation table; SYJ = cashier/register; DPL = electric fryer area; BSZSG = display cabinet; DCGZT = sink/washing area; KLJ = cola/drink machine; UNKNOWN = employee visible but position cannot be classified.
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### 9. guests
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Array with the existing mixed-key schema. If no guests are visible, output [].
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- First element is queue-level object only: {"warning": "0" or "1"}. 1 means queue has >= 3 visible guests; 0 means queue has < 3 visible guests.
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- Subsequent elements are per-guest objects only: {"status": "0"} at door, {"status": "1"} at register, or {"status": "2"} seated.
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### 10. qsc_events
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Array of suspected QSC violation events. If no suspected violation is visible, output [].
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Detect only the following current-period QSC violations:
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QSC pre-scan rule: Before deciding the main food-operation Action, first scan the entire full-frame image sequence for QSC violations, including people in corners, background, seated/squatting/bending postures, and floor-level foot/shoe areas. QSC events must not be suppressed by a normal food-operation action.
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- WGSJ0001: 工作状态未戴口罩
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Definition: An employee is in working state and the mouth/nose mask is clearly absent, not worn, or not covering mouth/nose.
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Working state includes frying food, making food, packing food, handling semi-finished products, touching food, operating food equipment, or working at a food operation table.
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Non-working state includes passing by, resting, waiting, short stay, or standing without obvious operation. In non-working state, no-mask alone is NOT a violation.
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- WGSJ0002: 工作状态未戴帽子
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Definition: An employee is in working state and the required work hat/cap/hair covering is clearly absent. Apply the same working-state rule as WGSJ0001.
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- WGSJ0003: 未戴手套操作食物
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Definition: An employee directly touches, handles, makes, packs, cuts, seasons, or transfers food without visible gloves. If hands are not visible, do not report this violation.
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- WGSJ0004: 工作区烟草制品违规
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Definition: Cigarette, e-cigarette, smoking behavior, lighter used for smoking, ashtray, or other tobacco product is visible in the food work area.
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- WGSJ0005: foot/shoe touching violation
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Chinese name for output: 抠脚或接触鞋脚.
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Definition: Report WGSJ0005 ONLY when there is clear visual evidence that a hand, fingers, tissue, cloth, tool, or another object is directly touching a foot, toes, sole, sock, shoe, or footwear area, and the motion is picking, scratching, rubbing, wiping, cleaning, adjusting, or handling that foot/shoe area.
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Very strict rule:
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- WGSJ0005 is NOT a posture detector. Do not report it from bending, squatting, standing, walking, leaning, or a hand being near the leg/foot.
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- WGSJ0005 is NOT a "suspected" event. Do not output WGSJ0005 for manual_review unless the hand/object-to-foot/shoe contact is actually visible.
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- If the evidence is only suspicious or ambiguous, output no WGSJ0005 event. Keep qsc_events as [] unless another violation is clearly visible.
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Required positive criteria:
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Output WGSJ0005 only when ALL of the following are true:
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- The foot, shoe, sock, toes, sole, or footwear area is visible.
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- The hand, fingers, tissue, cloth, tool, or object is visibly touching that foot/shoe area, not merely close to it.
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- The contact is visible in at least two frames, or one frame is extremely clear.
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- The action looks like picking, scratching, rubbing, wiping, cleaning, adjusting, or handling the foot/shoe area.
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- It is not normal walking, standing, food handling, floor cleaning, picking up an item, moving equipment, or touching a table/container/apron/clothing.
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Hard negative examples:
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Do NOT report WGSJ0005 when any of these is true:
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- A person is only standing near food, standing by a counter, or walking.
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- Feet or shoes are visible but no hand/object is visibly touching them.
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- A hand is at the table, food tray, oil pan, apron, waist, knee, pants, skirt, floor, trash bag, or equipment.
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- A person bends or squats but the hand-foot/shoe contact cannot be clearly seen.
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- The person is operating food, packing food, breading, seasoning, serving, cleaning the floor, picking up an item, or moving supplies.
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- The foot/shoe area is too small, blurry, blocked, cropped, or outside the frame.
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Output requirements for WGSJ0005:
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- violation_type must be exactly "抠脚或接触鞋脚".
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- reason must be Chinese and must explicitly say where the person is and what visible contact is seen.
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- suggested_action must be "manual_review".
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- confidence must be >= 0.80. If confidence would be below 0.80, do not output WGSJ0005.
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- evidence_frame_count must be the number of frames where direct contact is visible.
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- evidence_checklist must be exactly:
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{"foot_or_shoe_area_visible": true/false, "direct_hand_or_object_contact_visible": true/false, "contact_visible_in_multiple_frames_or_extremely_clear": true/false, "foot_handling_motion_visible": true/false, "normal_activity_excluded": true/false}
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Multi-frame rule:
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- Do not rely on a single unclear frame.
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- Judge qsc_events based on the whole clip and continuous multi-frame evidence.
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- Prefer reporting a qsc_event only when the violation is visible in multiple frames, or when the visual evidence is very clear and consistent across the clip.
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- If evidence is unclear, do not report the violation; keep qsc_events as [].
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- For WGSJ0005, use the strictest threshold: only report it when direct hand/object-to-foot-or-shoe contact is clearly visible. If uncertain, do not report WGSJ0005.
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Each qsc_events item must contain:
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- violation_code: one of WGSJ0001 / WGSJ0002 / WGSJ0003 / WGSJ0004 / WGSJ0005
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- violation_type: Chinese violation name
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- is_violation: true
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- working_state: working / non_working / unknown
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- reason: concise Chinese explanation of the visible evidence
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- confidence: number from 0 to 1
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- evidence_frame_count: estimated number of frames supporting the event
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- visible_target: concise Chinese description of the person/object involved
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- evidence_checklist: for WGSJ0005 only, include {"foot_or_shoe_area_visible": true/false, "direct_hand_or_object_contact_visible": true/false, "contact_visible_in_multiple_frames_or_extremely_clear": true/false, "foot_handling_motion_visible": true/false, "normal_activity_excluded": true/false}; for other codes output {}
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- suggested_action: record / warning / manual_review
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Suggested action rules: WGSJ0001 and WGSJ0002 use warning; WGSJ0003 and WGSJ0004 use manual_review. WGSJ0005 uses manual_review only when direct hand/object-to-foot-or-shoe contact is clearly visible with confidence >= 0.80. If WGSJ0005 evidence is weak, suspicious, or ambiguous, do not output WGSJ0005.
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### Output format
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Return strict JSON only. Do not wrap in markdown. Do not add any prose before or after the JSON.
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Required JSON shape:
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{"Action": "Action_Idle", "quality_status": "", "error_type": "", "安全隐患": "", "人物位置": "", "总结": "无", "时间": "", "employees": [], "guests": [], "qsc_events": []}
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user: >-
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Analyze this multi-frame video clip. Preserve the existing action, quality, safety, people, guest, and timestamp fields. Additionally detect current-period QSC violations in qsc_events. Return strict JSON only, with all required keys.
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schema:
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version: local-batch-v1
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event_types:
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- customer_enter
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- customer_leave
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- queue_detected
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- staff_absent
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- staff_present
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- area_crowded
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- abnormal_behavior
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- unknown
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require_strict_json: true
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parse_retry: 1
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merge_gap_seconds: 30
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runtime:
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timezone: Asia/Shanghai
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log_level: INFO
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