Files
sentinel-home-ai/fam-edge/config/config.yaml
ericwyuan 02da23ef42 feat: 异步任务队列架构 - SQLite队列 + 速率限制 + NAS Poller
Edge端:
- 新增 SQLite 异步任务队列 (queue_manager + consumer)
- 新增 TokenBucket 速率限制器 (Gemini 1000 RPM, NVIDIA 40 RPM, burst 2x)
- 新增 /api/edge/video/enqueue + /api/edge/results 端点
- 消费者线程从队列消费任务,按速率限制调用AI模型
- orchestrator 集成 rate_limiter,Gemini优先→NVIDIA兜底

NAS端:
- Dispatcher 重构为 enqueue 模式(上传后立即返回,不等结果)
- 新增 Poller 线程(定期从Edge拉取结果写 MariaDB)
- app.py 启动 Poller,config.yaml 新增 poller 配置
- db_layer 更新 valid_stages 添加 'process'
2026-08-20 12:07:09 +08:00

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# FAM-Edge 配置文件 (Oracle 端) - 多模型池配置
# Tailscale: Oracle=100.74.137.126, NAS=100.70.234.39
#
# 异步队列模式:
# NAS 上传视频 → /api/edge/video/enqueue 入 SQLite 队列 → 消费者线程异步处理
# → NAS Poller 从 /api/edge/results 拉取结果
# 速率限制: Gemini 1000RPM x2 burst, NVIDIA 40RPM x2 burst
# NAS 端回调地址(旧 webhook 模式保留,异步模式不使用)
nas:
webhook_url: "http://100.70.234.39:8000/api/core/callback/event"
media_base_url: "http://100.70.234.39:8000/media"
media_token: "sentinel-media-2026"
# Oracle 端服务
server:
host: "0.0.0.0"
port: 5000
max_concurrent_tasks: 1
# 异步任务队列
queue:
db_path: "/opt/fam-edge/data/fam_queue.db"
upload_dir: "/tmp/fam_uploads"
poll_interval: 10 # 消费者轮询间隔(秒)
# API 速率限制 (RPM)burst_factor=2 表示突发容量为 2 倍 RPM
rate_limit:
gemini_rpm: 1000
nvidia_rpm: 40
burst_factor: 2
# 编排调度模式: fallback(顺序降级, 默认) | ensemble(并行交叉验证)
orchestrator:
mode: "fallback"
overall_timeout: 600
# 关键帧筛选参数(自适应:帧数随视频时长动态计算)
video:
candidate_per_minute: 2 # 每分钟粗抽候选帧数
candidate_min: 30 # 候选帧下限(短视频保底)
candidate_max: 120 # 候选帧上限(超长视频截断)
key_frame_interval_sec: 150 # 关键帧间隔每2.5分钟1张
min_key_frames: 5 # 关键帧下限(帧差不足时补足到此数)
max_key_frames_floor: 8 # 关键帧上限的下限(短视频保底)
max_key_frames_cap: 30 # 关键帧上限(超长视频截断)
mse_threshold: 500
jpeg_quality: 80
max_long_edge: 1024
# 超时(秒)
timeout:
download: 60
vlm_visual: 600
vlm_fusion: 300
callback: 30
overall: 1800
# 多模型池配置(新框架:本地大模型不参与视频分析,仅智能问答兜底)
#
# 视频分析链路(推送模式):
# 云端 VLM 直接产出结构化 JSON (global_summary / entities_json / frame_details)
# -> Edge 仅做格式化/校验 (format_cloud_result) -> 直接回写 NAS无本地融合步骤
# 视觉角色: Gemini(主) -> NVIDIA NIM(备) 顺序降级; 两云端全失败 -> 任务 FAILED 走重试
#
# 智能问答链路:
# Gemini -> NVIDIA -> 本地 Ollama (仅当两云端都失败才启用本地兜底)
models:
- provider: "gemini"
role: "vision"
enabled: true
model_name: "gemini-flash-latest" # v1beta 下 gemini-1.5-flash 会 404
api_key: "${GEMINI_API_KEY}"
timeout: 90
circuit_breaker:
enabled: true
threshold: 5
cooldown: 300
- provider: "nvidia"
role: "vision"
enabled: true
model_name: "meta/llama-3.2-11b-vision-instruct"
base_url: "https://integrate.api.nvidia.com/v1"
api_key: "${NVIDIA_API_KEY}"
timeout: 30
circuit_breaker:
enabled: true
threshold: 5
cooldown: 300
# 本地模型:纯文本 qwen2.5:7b仅参与智能问答作为 Gemini/NVIDIA 都失败时的兜底
- provider: "ollama"
role: "text"
usage: "qa_fallback"
enabled: true
model_name: "qwen2.5:7b"
base_url: "http://localhost:11434"
timeout: 120
num_predict: 512
circuit_breaker:
enabled: false