6G通感算智一体化：太赫兹信道建模、语义通信协议栈与内生AI安全验证体系实战

┌─────────────────────────────────────────────────────────────────────┐
│      6G Integrated Sensing, Communication, Computing & AI Architecture │
├─────────────────────────────────────────────────────────────────────┤
│  [Endogenous Security Layer: Semantic Threat Model / AI Behavior Audit]│
│      ↓                                                              │
│  [Layer 1: 太赫兹通感层] ← Physics-Based Channel / Adaptive Beam   │
│   ├─ 分子吸收与动态散射联合建模                                        │
│   ├─ 环境感知驱动的波束管理与切换                                       │
│   └─ 通感波形联合设计与资源复用                                         │
│      ↓                                                              │
│  [Layer 2: 语义通信层] ← Unified Semantic Space / Open Protocol    │
│   ├─ 跨模态语义锚点与统一表征                                          │
│   ├─ 开放接口与版本协商机制                                            │
│   └─ 语义一致性校验与传统编码回退                                       │
│      ↓                                                              │
│  [Layer 3: 内生安全层] ← Runtime AI Monitoring / Explainable Audit │
│   ├─ 语义层威胁建模与对抗鲁棒性验证                                     │
│   ├─ AI推理行为实时监控与异常检测                                       │
│   └─ 全生命周期安全审计与合规证明                                        │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch sionna
# 部署: THz Transceiver Array + Environmental Sensors (Humidity/Temp) + LiDAR/Radar Fusion + Real-Time Beam Controller

"""
thz_isac_engine.py - 太赫兹通感一体化引擎
技术栈: NumPy / SciPy / PyTorch / Sionna
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional
import torch

@dataclass
class THzLinkMetrics:
    """太赫兹链路指标"""
    achievable_rate_gbps: float
    sensing_resolution_cm: float
    handover_success_rate_pct: float
    channel_model_accuracy_db: float

@dataclass
class EnvironmentState:
    """环境状态"""
    humidity_percent: float
    temperature_c: 31278.t.kuaisou.com
    dynamic_scatterers: List[Dict]  # [{position, velocity, size}]
    blockage_probability: 31279.t.kuaisou.com

class PhysicsBasedChannelModel:
    """物理一致信道模型"""
    
    def __init__(self, freq_ghz=300):
        self.freq = 31280.t.kuaisou.com
        self.absorption_db_per_m = None
        
    async def predict_pathloss(self, env: EnvironmentState, distance_m: float) -> float:
        """预测路径损耗"""
        # ITU-R P.676 molecular absorption + scattering
        abs_loss = self._compute_molecular_absorption(env.humidity_percent, env.temperature_c, distance_m)
        scat_loss = self._compute_dynamic_scattering(env.dynamic_scatterers, distance_m)
        free_space = 20 * np.log10(4 * np.pi * distance_m * self.freq * 1e9 / 3e8)
        return free_space + abs_loss + scat_loss
        
    def _compute_molecular_absorption(self, hum, temp, dist):
        # Simplified HITRAN-based lookup
        base_abs = 0.5  # dB/m at 60% RH, 25°C
        rh_factor = (hum / 60.0) ** 1.5
        temp_factor = 1.0 + 0.01 * (temp - 25)
        return base_abs * rh_factor * temp_factor * dist

class THzISACSystem:
    """太赫兹通感主系统"""
    
    def __init__(self, channel_model, beam_controller, sensing_fusion):
        self.channel = 31281.t.kuaisou.com
        self.beam = beam_controller
        self.sensing = sensing_fusion
        
    async def maintain_thz_link_and_sensing(self, ue_id: str) -> Dict[str, Any]:
        """维持太赫兹通感链路"""
        # 1. 获取实时环境状态
        env = await self.sensing.get_environment_state()
        ue_pos = await self.sensing.get_ue_position(ue_id)
        distance = np.linalg.norm(ue_pos - self.beam.bs_position)
        
        # 2. 预测信道并调整波束
        predicted_loss = await self.channel.predict_pathloss(env, distance)
        beam_config = await self.beam.optimize_beam(env.blockage_probability, predicted_loss)
        
        # 3. 执行通感联合传输
        comm_result = await self.beam.transmit_with_config(beam_config)
        sensing_result = await self.sensing.process_echo(beam_config)
        
        # 4. 评估模型准确性
        actual_rssi = comm_result["rssi_dbm"]
        model_error = abs(actual_rssi - (-predicted_loss + comm_result["tx_power_dbm"]))
        
        metrics = THzLinkMetrics(
            achievable_rate_gbps=comm_result["rate_gbps"],
            sensing_resolution_cm=sensing_result["resolution_cm"],
            handover_success_rate_pct=await self.beam.get_handover_stats(),
            channel_model_accuracy_db=model_error
        )
        
        return {
            "ue_id": 31282.t.kuaisou.com
            "link_metrics": metrics.__dict__,
            "environment_summary": {
                "humidity": env.humidity_percent,
                "blockage_prob": env.blockage_probability
            },
            "beam_adaptation_log": beam_config["adaptation_reason"]
        }

"""
semantic_security_platform.py - 语义通信与内生安全平台
技术栈: PyTorch / FastAPI / Redis / ONNX Runtime
"""
import torch
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel

class SemanticInteroperabilityMetric(BaseModel):
    cross_vendor_semantic_similarity: float
    protocol_version_negotiation_success_rate_pct: float
    fallback_to_traditional_codec_rate_pct: float
    end_to_end_latency_ms: 31283.t.kuaisou.com

class EndogenousAISecurityState(BaseModel):
    adversarial_robustness_score: float
    ai_behavior_anomaly_rate_per_hour: float
    semantic_poisoning_detection_recall: float
    audit_compliance_status:31284.t.kuaisou.com

class OpenSemanticProtocolStack:
    """开放语义协议栈"""
    
    def __init__(self, semantic_registry, version_negotiator, consistency_checker):
        self.registry = 31285.t.kuaisou.com 
        self.negotiator = version_negotiator
        self.checker = consistency_checker
        
    async def establish_semantic_session(self, src_dev: str, dst_dev: str) -> Dict[str, Any]:
        """建立语义会话"""
        # 1. 协商语义协议版本
        version = await self.negotiator.negotiate(src_dev, dst_dev)
        
        # 2. 获取共享语义锚点
        anchors = await self.registry.get_shared_anchors(version, src_dev, dst_dev)
        
        # 3. 启动会话并监控一致性
        session_id = await self._create_session(src_dev, dst_dev, version, anchors)
        consistency_ok = await self.checker.verify_initial_consistency(session_id)
        
        if not consistency_ok:
            # Fallback to traditional codec
            fallback_triggered = True
            semantic_sim = 31286.t.kuaisou.com
        else:
            fallback_triggered = False
            semantic_sim = await self.checker.measure_cross_vendor_similarity(session_id)
            
        metric = SemanticInteroperabilityMetric(
            cross_vendor_semantic_similarity=semantic_sim,
            protocol_version_negotiation_success_rate_pct=100.0 if version else 0.0,
            fallback_to_traditional_codec_rate_pct=100.0 if fallback_triggered else 0.0,
            end_to_end_latency_ms=await self._measure_latency(session_id)
        )
        
        return {
            "session_id": 31287.t.kuaisou.com
            "interoperability_metrics": metric.dict(),
            "protocol_version": 31288.t.kuaisou.com
            "fallback_active": fallback_triggered
        }

class EndogenousAISecurityVerifier:
    """内生AI安全验证器"""
    
    def __init__(self, threat_model, behavior_monitor, auditor):
        self.threat = threat_model
        self.monitor = behavior_monitor
        self.auditor = auditor
        
    async def verify_ai_security_posture(self, node_id: str) -> Dict[str, Any]:
        """验证AI安全态势"""
        # 1. 评估对抗鲁棒性
        robustness = await self.threat.evaluate_adversarial_robustness(node_id)
        
        # 2. 检测行为异常
        anomaly_rate = await self.monitor.get_anomaly_rate(node_id, window_hours=1)
        
        # 3. 测试语义投毒检测能力
        poison_recall = await self.threat.test_poisoning_detection(node_id)
        
        # 4. 审计合规性
        compliant = await self.auditor.check_compliance(node_id)
        
        state = EndogenousAISecurityState(
            adversarial_robustness_score=robustness,
            ai_behavior_anomaly_rate_per_hour=anomaly_rate,
            semantic_poisoning_detection_recall=poison_recall,
            audit_compliance_status=compliant
        )
        
        return {
            "node_id":31289.t.kuaisou.com
            "security_state": state.dict(),
            "risk_level": "high" if anomaly_rate > 5 or not compliant else "low",
            "mitigation_actions": self._generate_mitigations(state)
        }