┌─────────────────────────────────────────────────────┐
│  L4 业务编排层：会议议程管理、参会人画像、组织知识库   │
├─────────────────────────────────────────────────────┤
│  L3 策略决策层：干预时机判定、干预强度分级、风险评估   │
├─────────────────────────────────────────────────────┤
│  L2 规划与纠偏层：意图识别 → 工具链规划 → 执行监控 → 纠偏重试 │
├─────────────────────────────────────────────────────┤
│  L1 感知理解层：ASR/VAD、视觉编码、屏幕内容OCR、多模态融合 │
└─────────────────────────────────────────────────────┘

# 伪代码：增量多模态意图编码器
class IncrementalMultimodalIntentEncoder:
    def __init__(self, window_ms=200, context_window=8192):
        self.audio_encoder = WhisperStreamingEncoder()
        self.visual_encoder = ViT_L14_Streaming()
        self.screen_encoder = OCR_VLM_Fusion()
        self.fusion = CrossModalAttentionFusion(d_model=1024)
        self.intent_head = IntentClassificationHead(num_classes=12)
        self.calibrator = TemperatureScalingCalibrator()
    
    def step(self, audio_chunk, video_frame, screen_diff, prev_state):
        # 1. 单模态增量编码
        a_feat = self.audio_encoder.encode_incremental(audio_chunk)
        v_feat = self.visual_encoder.encode_frame(video_frame)
        s_feat = self.screen_encoder.encode_diff(screen_diff)
        
        # 2. 跨模态注意力融合（含时序位置编码）
        fused = self.fusion(a_feat, v_feat, s_feat, prev_state.cross_modal_cache)
        
        # 3. 意图分布预测 + 置信度校准
        logits = self.intent_head(fused)
        probs = self.calibrator(logits)
        
        # 4. 输出结构化意图候选集
        return IntentCandidates(
            top_k=probs.topk(3),
            urgency=self.urgency_predictor(fused),
            context_update=fused.new_cache
        )

{
  "tool_id": "create_action_item",
  "description": "从会议上下文抽取行动项并写入任务系统",
  "input_schema": {
    "type": "object",
    "properties": {
      "assignee": {"type": "string", "source": "speaker_id|entity_linking"},
      "task": {"type": "string", "source": "llm_extraction"},
      "deadline": {"type": "string", "format": "date-time", "optional": true},
      "priority": {"type": "enum", "enum": ["P0","P1","P2"], "default": "P1"},
      "context_span": {"type": "object", "properties": {"start_ts": "number", "end_ts": "number"}}
    },
    "required": ["assignee", "task", "context_span"]
  },
  "output_schema": {"type": "object", "properties": {"task_id": "string", "status": "string"}},
  "side_effects": ["write_task_db", "notify_assignee"],
  "idempotency_key": "context_span_hash",
  "timeout_ms": 3000,
  "retry_policy": {"max_attempts": 2, "backoff_ms": 500},
  "preconditions": ["speaker_identified", "action_verb_detected"],
  "conflict_groups": ["create_calendar_event"]
}

class ParameterResolver:
    def __init__(self, kg_client, entity_linker, temporal_normalizer):
        self.kg = kg_client
        self.linker = entity_linker
        self.time_norm = temporal_normalizer
    
    def resolve(self, tool_schema, intent_ctx, meeting_state):
        resolved = {}
        for param, spec in tool_schema.input_schema.properties.items():
            if spec.get("source") == "speaker_id":
                resolved[param] = self._resolve_speaker(intent_ctx, meeting_state)
            elif spec.get("source") == "entity_linking":
                resolved[param] = self._link_entities(intent_ctx.mentions, meeting_state.kg_cache)
            elif spec.get("format") == "date-time":
                resolved[param] = self.time_norm.normalize(
                    intent_ctx.temporal_expressions, 
                    meeting_state.meeting_start_time
                )
            # ... 其他源类型
        return validated_params

工具执行失败/异常
       │
       ▼
┌──────────────────┐
│ 是否幂等/可重试?  │──否──▶ 标记链路失败，上报 L3，触发人工兜底/会后补偿
└────────┬─────────┘
         │是
         ▼
┌──────────────────┐
│ 重试次数 < 上限?  │──否──▶ 尝试替代工具（同功能不同实现）或参数回退
└────────┬─────────┘
         │是
         ▼
   指数退避重试
         │
         ▼
   成功? ──否──▶ 进入下一轮重试/替代
         │是
         ▼
   继续执行后续节点

# 奖励计算器（工程化版）
class ToolRewardCalculator:
    # 静态先验表（离线专家打分 + 历史采纳率统计，每日更新）
    STATIC_PRIORS = {
        "create_action_item": 0.92,
        "query_knowledge_base": 0.78,
        "send_clarification_card": 0.65,
        "trigger_topic_alert": 0.55,
        # ...
    }
    
    # 动态上下文特征权重（在线 LR 模型，特征维度 < 20）
    DYNAMIC_WEIGHTS = np.array([...]) 
    
    def compute(self, tool_id: str, ctx: PlanningContext) -> float:
        static = self.STATIC_PRIORS.get(tool_id, 0.5)
        # 动态特征：意图置信度、发言人权重、会议阶段、历史冲突度
        dyn_feat = self._extract_dynamic_features(ctx)  
        dynamic = sigmoid(np.dot(self.DYNAMIC_WEIGHTS, dyn_feat))
        # 加权融合，静态占比 0.7 保证稳定性
        return 0.7 * static + 0.3 * dynamic

class GuidedMCTSPlanner:
    def __init__(self, policy_net, max_nodes=200, time_budget_ms=45):
        self.policy_net = policy_net  # 轻量级 Transformer，输入：当前状态+候选工具集，输出：工具选择概率分布
        self.max_nodes = max_nodes
        self.time_budget = time_budget_ms
    
    def search(self, root_state: PlanningState) -> ToolChain:
        root = MCTSNode(root_state)
        start = time.monotonic()
        
        while (time.monotonic() - start) * 1000 < self.time_budget and root.visit_count < self.max_nodes:
            # 1. Selection: PUCT + 策略先验
            node = self._select(root)
            
            # 2. Expansion: 仅展开 Top-K 高先验动作
            if not node.is_terminal():
                priors = self.policy_net.predict(node.state)  # shape: [num_tools]
                valid_actions = node.get_valid_actions()
                top_k = min(4, len(valid_actions))  # 关键：限制展开宽度
                actions = valid_actions[np.argsort(priors[valid_actions])[-top_k:]]
                node.expand(actions, priors[actions])
            
            # 3. Simulation: 轻量级 Rollout（贪婪 + 启发式）
            reward = self._simulate(node)
            
            # 4. Backpropagation
            node.backpropagate(reward)
        
        return root.best_child(c_param=0.0).action_sequence  # 最终选访问次数最多路径

graph LR
    A[线上流量] --> B{分流器}
    B -->|影子模式 10%| C[新模型推理]
    B -->|主流量 90%| D[基线模型]
    C --> E[指标采集器<br/>延迟/准确率/采纳率/误触发]
    D --> E
    E --> F[自动化评测平台<br/>离线指标集+在线A/B]
    F --> G{发布决策}
    G -->|通过| H[灰度发布 1% -> 10% -> 100%]
    G -->|拒绝| I[回滚/进入实验池]
    H --> J[数据入湖]
    I --> J
    J --> K[每周训练调度]
    K --> L[SFT -> RM -> PPO/DPO]
    L --> M[模型注册中心]
    M --> A

# 熔断器状态机（工程实现片段）
class CircuitBreaker:
    STATES = ["CLOSED", "HALF_OPEN", "OPEN"]
    
    def __init__(self, failure_threshold=5, timeout_sec=30, half_open_max_calls=3):
        self.state = "CLOSED"
        self.failure_count = 0
        self.success_count = 0
        self.last_failure_time = 0
        self.config = ...
    
    def call(self, func, *args, **kwargs):
        if self.state == "OPEN":
            if time.time() - self.last_failure_time > self.config.timeout_sec:
                self.state = "HALF_OPEN"
                self.success_count = 0
            else:
                raise CircuitOpenError("熔断开启，快速失败")
        
        try:
            result = func(*args, **kwargs)
            self._on_success()
            return result
        except Exception as e:
            self._on_failure()
            raise
    
    def _on_success(self):
        self.failure_count = 0
        if self.state == "HALF_OPEN":
            self.success_count += 1
            if self.success_count >= self.config.half_open_max_calls:
                self.state = "CLOSED"
    
    def _on_failure(self):
        self.failure_count += 1
        self.last_failure_time = time.time()
        if self.failure_count >= self.config.failure_threshold:
            self.state = "OPEN"
            # 触发告警、切换降级模式
            alerting.fire("circuit_breaker_open", {"breaker": self.name})

┌─────────────────────────────────────────────────────────────┐
│ 租户控制面：配额管理、模型版本绑定、数据驻留策略、合规标签    │
├─────────────────────────────────────────────────────────────┤
│ 服务网格层：Sidecar 注入、mTLS、流量染色、限流熔断、可观测性  │
├─────────────────────────────────────────────────────────────┤
│ 计算/存储资源池：                                            │
│  ├─ 专属资源池（大客户）：独享 GPU/CPU、独立向量库、专属模型微调 │
│  ├─ 共享资源池（长尾客户）：多租户 GPU 分片、请求级隔离、冷启动优化 │
│  └─ 边缘/本地部署（私有化）：模型蒸馏包下发、离线推理、数据不出域 │
└─────────────────────────────────────────────────────────────┘

# Kubernetes ResourceQuota + MIG/Time-Slicing 配置示例
apiVersion: v1
kind: ResourceQuota
metadata:
  name: tenant-a-gpu-quota
spec:
  hard:
    nvidia.com/gpu: "2"          # 逻辑配额：2 张卡等价
    requests.nvidia.com/gpu-core: "140"  # 物理核心配额：140% (1.4 卡)
    requests.nvidia.com/gpu-memory: "32Gi" # 显存配额：32GB
---
# Pod 级资源请求（请求级调度器拦截注入）
resources:
  limits:
    nvidia.com/gpu-core: "70"    # 单请求限制 70% 核心
    nvidia.com/gpu-memory: "16Gi" # 单请求限制 16GB 显存
  requests:
    nvidia.com/gpu-core: "20"    # 保障 20% 核心
    nvidia.com/gpu-memory: "4Gi"  # 保障 4GB 显存

// Span Attributes 扩展规范 (Semantic Conventions for Meeting Intelligence)
{
  "meeting.id": "m_7x9k2p",
  "meeting.tenant_id": "t_enterprise_123",
  "meeting.stage": "topic_discussion",  // agenda_item | topic_discussion | wrap_up
  "pipeline.stage": "intent_recognition", // perception | intent | planning | execution | monitoring
  "intent.top1": "action_item_extraction",
  "intent.top1_confidence": 0.92,
  "intent.calibrated_confidence": 0.87,
  "intent.is_implicit": true,
  "planning.dag_nodes": 3,
  "planning.critical_path_latency_ms": 180,
  "planning.fallback_triggered": false,
  "tool_chain.id": "chain_act_001",
  "tool_chain.status": "success",
  "tool.create_action_item.latency_ms": 45,
  "tool.create_action_item.retry_count": 0,
  "user.feedback": "accepted", // accepted | dismissed | modified | complained
  "error.type": "NULL", // NULL | ASR_LOW_CONF | TOOL_TIMEOUT | PARAM_VALIDATION_FAILED
  "degradation.level": "L0"
}

┌──────────────┐      加密通道/可信执行环境 (TEE)      ┌──────────────┐
│  客户端/边缘  │ ◄─────────────────────────────────────► │   云端推理    │
│  (SGX/TrustZone)│  1. 加密音视频流 / 2. 加密模型权重      │  (GPU Cluster)│
└──────┬───────┘                                        └──────┬───────┘
       │                                                         │
       ▼                                                         ▼
┌──────────────┐                                        ┌──────────────┐
│ 本地敏感处理  │                                        │ 通用能力处理  │
│ • VAD/说话人分离        │                                        • 大模型推理 (意图/规划)     │
│ • 实体识别/脱敏 (NER)   │ ── 脱敏后文本/结构化特征 ──► │ • 知识库检索 (RAG)           │
│ • 热词 FST 构建         │                                        • 工具网关代理               │
│ • 关键状态本地持久化    │ ◄── 结构化决策指令/工具结果 ── │                             │
└──────────────┘                                        └──────────────┘