TSPR层：状态表示与SAL更新（伪代码实现）

from dataclasses import dataclass, field
from typing import Dict, List
import numpy as np

@dataclass
class UserState:
    user_id: str
    intent_probs: np.ndarray          # shape (num_intents,), 例如 [0.2,0.7,0.1]
    profile: Dict[str, float]          # 兴趣标签权重 {"sports":0.8, "tech":0.3}
    session_cnt: int = 0
    last_active_ts: int = 0

@dataclass
class EnvState:
    time_of_day: int                   # 0-23
    device_type: str                   # "mobile"/"pc"
    network_quality: float             # 0-1

@dataclass
class TaskState:
    current_task: str                  # "recommend"/"qa"/"control"
    task_progress: float               # 0-1
    pending_actions: List[str] = field(default_factory=list)

@dataclass
class GlobalState:
    user: UserState
    env: EnvState
    task: TaskState
    version: int = 0                   # 用于乐观锁更新

// Java伪代码，实际可用PyFlink
public class StateUpdateFunction extends KeyedProcessFunction<String, FeedbackEvent, StateUpdateResult> {
    private transient ValueState<GlobalState> stateDescriptor;

    @Override
    public void processElement(FeedbackEvent event, Context ctx, Collector<StateUpdateResult> out) {
        GlobalState oldState = stateDescriptor.value();
        GlobalState newState = updateState(oldState, event);
        
        // 写Redis（异步）
        redisClient.hset("state:" + event.userId, newState);
        
        // 发布状态变更事件给下游（HIC/ACTION）
        out.collect(StateUpdateResult.of(event.userId, newState));
        
        stateDescriptor.update(newState);
    }
    
    private GlobalState updateState(GlobalState s, FeedbackEvent e) {
        // 贝叶斯更新意图概率
        float[] newIntentProbs = bayesianUpdate(s.user.intent_probs, e.observedIntentLikelihood);
        s.user.intent_probs = newIntentProbs;
        
        // 更新用户兴趣（指数衰减+增量）
        for (String tag : e.interactedTags) {
            s.user.profile[tag] = s.user.profile.getOrDefault(tag, 0.0) * 0.9 + 0.1;
        }
        s.user.session_cnt += 1;
        
        // 环境状态：直接覆盖
        s.env.network_quality = e.networkQuality;
        
        // 任务进度更新
        if (e.actionExecuted != null) {
            s.task.task_progress = Math.min(1.0, s.task.task_progress + 0.1);
        }
        s.version++;
        return s;
    }
}

# Key: state:{user_id}
# Hash field: global (整个JSON)
# 同时存储独立字段便于快速读取
HSET state:user123 global '{"user":{...},"env":{...},"task":{...},"version":5}'
HSET state:user123 intent_probs "0.2,0.7,0.1"
HSET state:user123 profile '{"sports":0.8,"tech":0.3}'

package hic

# 硬规则：禁止危险动作
default deny = false

deny {
    input.action.type == "control_robot"
    input.action.params.speed > 0.5
    msg = "Speed exceeds safety limit"
}

# 软规则：个性化推荐加权
default recommend_weight = 1.0

recommend_weight = weight {
    input.user.profile.sports > 0.6
    input.action.params.category == "sports"
    weight = 1.5
}

recommend_weight = weight {
    input.user.intent_probs[2] > 0.8   # 假设index2为"purchase"
    input.action.type == "recommend"
    weight = 1.2
}

# 最终决策分数 = LLM原始分 * recommend_weight，并检查deny

# 离线训练：基于历史反馈优化规则权重
def rel_offline_train(feedback_dataset):
    # 使用贝叶斯优化或Bandit算法
    from skopt import gp_minimize
    def objective(weights):
        # 模拟使用新权重在历史数据上的累计奖励
        return - simulate_reward(weights, feedback_dataset)
    
    # 初始权重（当前线上规则权重）
    initial_weights = [1.0, 1.2, 0.8]
    res = gp_minimize(objective, [(-0.5, 2.0)]*len(initial_weights), n_calls=50)
    new_weights = res.x
    return new_weights

# 安全上线流程
def rel_safe_deploy(new_rules_or_weights):
    # 1. 沙盒验证：用最近1天数据回测，检查指标不下降（p<0.05）
    if not sandbox_backtest(new_rules_or_weights):
        logging.error("Sandbox failed, abort deploy")
        return False
    
    # 2. 人工审核（对于权重变化>0.3或新增规则）
    if need_human_review(new_rules_or_weights):
        if not human_approval():
            return False
    
    # 3. 灰度发布：5%流量，监控核心KPI（转化率、错误率）
    for traffic in [0.05, 0.20, 0.50, 1.0]:
        deploy_to_traffic(traffic, new_rules_or_weights)
        if not observe_kpi_stable(hours=2):
            rollback()
            return False
    return True

{
  "type": "record",
  "name": "Feedback",
  "fields": [
    {"name": "feedback_id", "type": "string"},
    {"name": "user_id", "type": "string"},
    {"name": "action_id", "type": "string"},
    {"name": "reward", "type": "float"},
    {"name": "quality_score", "type": "float", "default": 1.0},
    {"name": "timestamp", "type": "long"}
  ]
}

-- 从原始反馈过滤低质量
INSERT INTO filtered_feedback
SELECT * FROM raw_feedback WHERE quality_score > 0.3;

-- SAL: 更新状态（使用自定义UDAF）
INSERT INTO state_update
SELECT user_id, update_state(collect_list(*)) FROM filtered_feedback
GROUP BY user_id;

-- REL: 每隔1000条反馈触发一次规则优化（窗口聚合）
SELECT rule_update_job(collect_list(*)) 
FROM filtered_feedback 
WHERE HOP_START(rowtime, INTERVAL '1' HOUR) % INTERVAL '1' DAY = 0;

# namespace: taios
apiVersion: v1
kind: Namespace
metadata:
  name: taios
---
# API Gateway (Kong/Ingress)
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: taios-gateway
  namespace: taios
spec:
  rules:
  - host: api.taios.example.com
    http:
      paths:
      - path: /v1/chat
        backend:
          service:
            name: web-layer
            port: 8080

apiVersion: apps/v1
kind: Deployment
metadata:
  name: llm-gateway
  namespace: taios
spec:
  replicas: 3
  selector:
    matchLabels:
      app: llm-gateway
  template:
    metadata:
      labels:
        app: llm-gateway
    spec:
      containers:
      - name: main
        image: taios/llm-gateway:v1.0
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: llm-secrets
              key: openai-key
        - name: MODEL_ROUTING_CONFIG
          value: "/config/routing.yaml"
        resources:
          limits:
            cpu: "2"
            memory: "4Gi"
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
        volumeMounts:
        - name: config
          mountPath: /config
      volumes:
      - name: config
        configMap:
          name: llm-routing-config

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: redis-cluster
  namespace: taios
spec:
  serviceName: redis-cluster
  replicas: 6
  selector:
    matchLabels:
      app: redis-cluster
  template:
    metadata:
      labels:
        app: redis-cluster
    spec:
      containers:
      - name: redis
        image: redis:7-alpine
        command: ["redis-server"]
        args: ["--cluster-enabled", "yes", "--appendonly", "yes"]
        ports:
        - containerPort: 6379
        volumeMounts:
        - name: data
          mountPath: /data
  volumeClaimTemplates:
  - metadata:
      name: data
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 100Gi

# 暴露的Metrics
- taios_llm_request_duration_seconds (histogram)
- taios_hic_rule_evaluation_total (counter, with rule_id, result)
- taios_sal_state_update_latency_ms (histogram)
- taios_rel_rule_deploy_total (counter, with status)
- taios_feedback_quality_score (gauge)

groups:
- name: taios_alerts
  rules:
  - alert: LLMHighLatency
    expr: histogram_quantile(0.99, taios_llm_request_duration_seconds) > 5
    for: 5m
    annotations:
      summary: "LLM p99 latency > 5s"
  - alert: RELRuleRollback
    expr: increase(taios_rel_rule_deploy_total{status="rollback"}[1h]) > 0
    annotations:
      summary: "Rule deployment rolled back due to KPI drop"