┌─────────────────────────────────────────────────────────────┐
│                      云端管理平台                             │
│  设备注册/固件OTA/策略下发/全域监控/数据分析/租户隔离          │
└──────────────────────────┬──────────────────────────────────┘
                           │ MQTT/HTTPS (TLS加密)
┌──────────────────────────▼──────────────────────────────────┐
│                    边缘网关 / 会议室中控主机                   │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌────────┐  │
│  │ 协议适配层   │ │ 场景规则引擎 │ │ 本地媒体服务 │ │ AI推理 │  │
│  │ (Modbus/    │ │ (Drools/    │ │ (SFU/录制/  │ │ (TensorRT/│
│  │  KNX/ONVIF/ │ │  自研DSL)   │ │  转码)      │ │  ONNX)   │
│  │  BLE/RS485) │ │             │ │             │ │          │  │
│  └─────────────┘ └─────────────┘ └─────────────┘ └────────┘  │
└──────────────────────────┬──────────────────────────────────┘
                           │ 本地总线 (CAN/RS485/以太网/蓝牙Mesh)
┌──────────────────────────▼──────────────────────────────────┐
│                      终端感控层                                │
│  摄像头阵列 | 阵列麦克风 | 环境传感器 | 智能面板 | 执行器(灯/幕/空调) │
└─────────────────────────────────────────────────────────────┘

# 设备物模型示例（JSON Schema 简化版）
device_model:
  identifier: "ptz_camera_4k_01"
  name: "4K 智能云台摄像头"
  category: "video_input"
  properties:
    - {id: "pan", type: "int", range: [-170, 170], unit: "degree", rw: "rw"}
    - {id: "tilt", type: "int", range: [-30, 90], unit: "degree", rw: "rw"}
    - {id: "zoom", type: "int", range: [1, 30], unit: "x", rw: "rw"}
    - {id: "tracking_mode", type: "enum", enum: ["off", "speaker", "frame", "auto"], rw: "rw"}
    - {id: "firmware_version", type: "string", rw: "r"}
  events:
    - {id: "tracking_lost", params: [{name: "last_known_pos", type: "object"}]}
  services:
    - {id: "preset_recall", input: {preset_id: "int"}}
    - {id: "auto_focus_trigger", input: {}}

type DeviceDriver interface {
    // 协议探测与设备发现
    Probe(ctx context.Context, conn Connection) (DeviceInfo, error)
    // 属性读写
    ReadProperty(ctx context.Context, prop string) (interface{}, error)
    WriteProperty(ctx context.Context, prop string, val interface{}) error
    // 服务调用
    CallService(ctx context.Context, svc string, input map[string]interface{}) (output map[string]interface{}, error)
    // 事件订阅
    SubscribeEvent(event string, handler EventHandler) error
    // 生命周期
    Start() error
    Stop() error
}

{
  "rule_id": "meeting_start_auto_env",
  "trigger": { "type": "event", "source": "calendar", "event": "meeting_start" },
  "conditions": [
    { "type": "device_prop", "device": "illuminance_sensor_01", "prop": "lux", "op": "<", "value": 300 }
  ],
  "actions": [
    { "device": "light_group_main", "service": "set_brightness", "params": { "level": 80, "ct": 4000 } },
    { "device": "curtain_01", "service": "set_position", "params": { "pos": 40 } },
    { "device": "ptz_camera_01", "service": "set_tracking_mode", "params": { "mode": "speaker" } },
    { "device": "ac_01", "service": "set_mode", "params": { "mode": "auto", "target_temp": 24 } }
  ],
  "rollback_on_end": true
}

音频流 (8ch 环形阵列麦克风)          视频流 (4K 主摄 + 广角全景)
        │                                    │
        ▼                                    ▼
┌───────────────┐                   ┌───────────────┐
│  GCC-PHAT /   │                   │  YOLOv8-Pose  │
│  SRP-PHAT     │                   │  + ByteTrack  │
│  声源DOA估计   │                   │  人体/人脸/   │
│  (方位角/仰角) │                   │  手势检测跟踪  │
└───────┬───────┘                   └───────┬───────┘
        │                                    │
        └──────────────┬─────────────────────┘
                       ▼
            ┌─────────────────────┐
            │  贝叶斯融合 /       │
            │  卡尔曼滤波器        │
            │  (时空对齐/置信度    │
            │   加权/轨迹平滑)     │
            └──────────┬──────────┘
                       ▼
            ┌─────────────────────┐
            │  发言人状态机判定    │
            │  (单人/多人/白板/    │
            │   无人/离席)         │
            └──────────┬──────────┘
                       ▼
            ┌─────────────────────┐
            │  PTZ 控制策略生成    │
            │  (平滑插值/预测/     │
            │   构图规则/切镜逻辑)  │
            └─────────────────────┘

class PTZController:
    def __init__(self):
        self.pid_pan = PID(Kp=0.8, Ki=0.01, Kd=0.15, output_limit=100)  # deg/s
        self.pid_tilt = PID(Kp=0.8, Ki=0.01, Kd=0.15, output_limit=100)
        self.pid_zoom = PID(Kp=0.5, Ki=0.0, Kd=0.1, output_limit=5)    # x/s
        self.target_hist = deque(maxlen=5)  # 目标位置历史，用于预测

    def update(self, target_spherical, dt):
        # 1. 运动预测（简单线性外推）
        if len(self.target_hist) >= 2:
            v = (self.target_hist[-1] - self.target_hist[-2]) / dt
            pred_target = target_spherical + v * 0.2  # 预测 200ms
        else:
            pred_target = target_spherical
        self.target_hist.append(target_spherical)

        # 2. PID 计算角速度指令
        pan_speed = self.pid_pan.compute(pred_target.azimuth - self.curr_azimuth, dt)
        tilt_speed = self.pid_tilt.compute(pred_target.elevation - self.curr_elevation, dt)
        
        # 3. 变焦控制：目标人高占比 -> 期望焦距
        desired_zoom = self.calc_zoom_for_body_ratio(pred_target, target_body_ratio=0.6)
        zoom_speed = self.pid_zoom.compute(desired_zoom - self.curr_zoom, dt)

        # 4. 发送指令（协议：Pelco-D / VISCA over IP / ONVIF PTZ）
        self.send_ptz_cmd(pan_speed, tilt_speed, zoom_speed)

[全景固定] --(检测到发言人)--> [主摄跟踪] --(发言人走向白板)--> [辅摄白板预位] --(发言人返回)--> [主摄跟踪]
      ^                                                                       |
      |--(会议结束/无人>5min)-------------------------------------------------|

# 会议室拓扑模型
RoomTopology:
  room_id: "conf_room_A_301"
  space_type: "large_conference"  # small_huddle / medium_board / large_training / auditorium
  geometry:
    width_m: 12.5
    depth_m: 8.0
    height_m: 3.2
    coordinate_system: "ENU"  # East-North-Up, 原点在门口地面中心
  zones:
    - {zone_id: "speaker_area", polygon: [[1,2],[3,2],[3,4],[1,4]], semantic: "primary_speaker"}
    - {zone_id: "whiteboard_wall", polygon: [[0,4],[12,4],[12,5],[0,5]], semantic: "presentation"}
    - {zone_id: "audience_area", polygon: [[1,5],[11,5],[11,7.5],[1,7.5]], semantic: "attendee"}
  devices:
    - {device_id: "cam_main_01", type: "ptz_camera", pose: {x:6, y:0.5, z:2.8, yaw:180, pitch:-15, roll:0}, fov_h: 70, fov_v: 40, intrinsic_calibrated: true}
    - {device_id: "cam_pano_01", type: "panoramic_camera", pose: {x:6, y:4, z:2.9, yaw:0, pitch:-90, roll:0}, fov_h: 180, fov_v: 180}
    - {device_id: "mic_array_01", type: "mic_array_8ch", pose: {x:6, y:2, z:1.2}, geometry: "circular", radius_m: 0.045}
    - {device_id: "light_group_01", type: "dali_light_group", zones: ["speaker_area", "whiteboard_wall", "audience_area"]}
    - {device_id: "env_sensor_01", type: "multi_env_sensor", pose: {x:11, y:7, z:1.5}, measures: ["lux", "temp", "hum", "co2", "voc", "noise_db"]}

┌──────────────┐     ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  边缘侧采集   │────▶│  数据清洗与   │────▶│  自动/半自动  │────▶│  版本化数据   │
│  (困难样本/   │     │  脱敏合规     │     │  标注管线     │     │  集 (DVC/    │
│   新场景触发)  │     │  (人脸模糊/   │     │  (SAM辅助/    │     │  LakeFS)      │
│               │     │   音频加噪)   │     │   主动学习)   │     │               │
└──────────────┘     └──────────────┘     └──────────────┘     └──────┬───────┘
                                                                        │
┌──────────────┐     ┌──────────────┐     ┌──────────────┐            │
│  灰度发布/   │◀────│  自动化训练   │◀────│  实验追踪与   │◀───────────┘
│  A/B 测试    │     │  (NAS/超参搜索)│     │  模型注册表   │
│  (影子模式)  │     │  知识蒸馏/量化  │     │  (MLflow)     │
└──────────────┘     └──────────────┘     └──────────────┘

# 伪代码：异构推理流水线调度器
class InferencePipeline:
    def __init__(self, devices):
        # 设备拓扑：NPU -> GPU(DSP) -> CPU
        self.stages = [
            Stage("preprocess", device="DSP", ops=["resize", "normalize", "nv12_to_rgb"]),
            Stage("detect", device="NPU", model="yolov8n_pose_int8.rknn", batch=4),
            Stage("track", device="CPU", algo="bytetrack", affinity="core_6_7"),  # 绑核隔离
            Stage("fusion", device="CPU", algo="kalman_fuse", input=["detect", "audio_doa"]),
            Stage("ptz_ctrl", device="CPU", algo="pid_predict", output="serial/can"),
        ]
        self.buffer_pool = RingBuffer(capacity=3)  # 三级流水线缓冲，吸收抖动

    def run(self, frame_packet):
        ctx = PipelineContext(frame_packet)
        for stage in self.stages:
            # 非阻塞提交，回调驱动下一阶段
            stage.async_exec(ctx, callback=self._on_stage_done)
        return ctx.future_result

原始信号 (8ch x 48kHz)
       │
       ▼
┌──────────────────┐
│  硬件级 AEC 参考  │◀── 扬声器回放信号 (软件/硬件环回)
│  (立体声/多声道)  │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│  子带域自适应滤波 │  (Partitioned Block Frequency Domain Adaptive Filter - PBFDAF)
│  (多通道 AEC)     │  延迟补偿 < 5ms, ERLE > 35dB
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│  多通道降噪       │
│  (MCWF / DNN-based)│  保留方向性语音，抑制扩散噪声/空调风噪
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│  波束成形         │
│  (MVDR / LCMV /   │  形成 3-5 个固定波束覆盖全场 + 1 个自适应波束指向 DOA
│   Neural Beamformer)│
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│  声源定位 (SRP-PHAT│
│  / TOPS / DNN-DOA) │  输出: {azimuth, elevation, confidence, vad_flag}
└────────┬─────────┘
         │
         ▼
   下游任务 (ASR/跟焦融合/录制)

# 自愈规则 CRD 示例
apiVersion: iot.example.com/v1alpha1
kind: SelfHealingRule
metadata:
  name: gateway-gpu-memory-leak
spec:
  target: "edge-gateway-*"
  detection:
    type: "promql"
    query: 'container_memory_working_set_bytes{container="ai-inference",job="edge-gateway"} / container_spec_memory_limit_bytes > 0.9'
    for: "5m"
  diagnosis:
    - script: "nsenter -t $(pidof ai-inference) -- gdb -batch -ex 'bt' -p $(pidof ai-inference)"
      timeout: "30s"
    - script: "curl -s localhost:9090/debug/pprof/heap | go tool pprof -top"
  remediation:
    - action: "rolling_restart_pod"
      params: {max_surge: 1, max_unavailable: 0}
      condition: "diagnosis contains 'memory leak in tensorrt context'"
    - action: "patch_configmap"
      params: {key: "TRT_WORKSPACE_SIZE", value: "512MB"}  # 降低工作区
    - action: "create_incident"
      params: {title: "GPU Memory Leak Auto-Mitigated", severity: "P2"}