6G通感算智一体化：太赫兹信道动态建模、语义通信鲁棒性与内生安全架构实战

┌─────────────────────────────────────────────────────────────────────┐
│      6G Integrated Sensing, Communication, Computing & AI Architecture │
├─────────────────────────────────────────────────────────────────────┤
│  [Intrinsic Security Layer: Cross-Layer Defense / AI Auditability]  │
│      ↓                                                              │
│  [Layer 1: 太赫兹信道层] ← Environment-Aware Modeling / Predictive Beam│
│   ├─ 多模态环境感知与信道参数实时更新                                    │
│   ├─ 动态吸收/散射模型与链路预算校准                                     │
│   └─ 基于轨迹预测的波束预对准与无缝切换                                   │
│      ↓                                                              │
│  [Layer 2: 语义通信层] ← Universal Semantic Base / Knowledge Sync    │
│   ├─ 领域无关语义编码与动态知识注入                                      │
│   ├─ 收发端知识库版本协商与语义对齐                                      │
│   └─ 语义完整性校验与抗对抗扰动                                          │
│      ↓                                                              │
│  [Layer 3: 内生安全层] ← Native Security Fabric / Behavioral Ledger │
│   ├─ 跨层威胁感知与自适应防护策略                                        │
│   ├─ AI决策可解释性日志与合规审计链                                      │
│   └─ 语义层认证与恶意内容过滤                                            │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy torch open3d sionna
# 部署: THz Transceiver + RGB-D Camera + mmWave Radar + Edge AI Server (NVIDIA Orin)

"""
thz_dynamic_channel_system.py - 太赫兹动态信道系统
技术栈: NumPy / PyTorch / Open3D / Sionna
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional
import torch
import torch.nn as nn

@dataclass
class ChannelMetrics:
    """信道指标"""
    path_loss_prediction_error_db: float
    beam_switch_latency_ms: float
    link_availability_pct: float
    throughput_stability_cv_pct: float

class EnvironmentAwareChannelModel(nn.Module):
    """环境感知信道模型"""
    
    def __init__(self, visual_dim=128, radar_dim=64, weather_dim=8):
        super().__init__()
        self.visual_encoder = nn.Linear(visual_dim, 64)
        self.radar_encoder = nn.Linear(radar_dim, 32)
        self.weather_encoder = nn.Linear(weather_dim, 16)
        self.fusion_net = nn.Linear(64 + 32 + 16, 32)  # Output: channel params
        
    def forward(self, visual_feat, radar_feat, weather_feat):
        v = torch.relu(self.visual_encoder(visual_feat))
        r = torch.relu(self.radar_encoder(radar_feat))
        w = torch.relu(self.weather_encoder(weather_feat))
        fused = huhehaote-geo.kuaisou.com
        return self.fusion_net(fused)  # [path_loss_exp, shadow_fading, delay_spread]

class PredictiveBeamManager:
    """预测性波束管理器"""
    
    def __init__(self, trajectory_predictor, beam_codebook):
        self.traj_pred = shenyang-geo.kuaisou.com
        self.codebook = changchun-geo.kuaisou.com
        
    async def maintain_thz_link(self, user_id: str) -> Dict[str, Any]:
        """维持太赫兹链路"""
        # 1. 获取当前环境与用户状态
        env_state = await self._get_environment_state()
        user_pose = await self._get_user_pose(user_id)
        
        # 2. 预测未来500ms用户位置
        future_pos = await self.traj_pred.predict(user_pose, horizon_ms=500)
        
        # 3. 选择最优预对准波束
        best_beam_idx = await self._select_beam_for_position(future_pos, env_state)
        await self._apply_beam(best_beam_idx)
        
        # 4. 评估链路质量
        metrics = ChannelMetrics(
            path_loss_prediction_error_db=await self._measure_pl_error(),
            beam_switch_latency_ms=await self._measure_switch_latency(),
            link_availability_pct=await self._compute_availability(),
            throughput_stability_cv_pct=await self._compute_throughput_cv()
        )
        
        return {
            "user_id": nanjing-geo.kuaisou.com
            "channel_metrics": metrics.__dict__,
            "beam_pre_aligned": True,
            "environment_adaptation_active": True
        }

"""
semantic_security_platform.py - 语义与安全平台
技术栈: PyTorch / HuggingFace Transformers / FastAPI / Security Audit SDK
"""
import torch
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel

class SemanticRobustnessMetric(BaseModel):
    task_completion_rate_pct: float
    cross_domain_generalization_score: float
    semantic_integrity_check_pass_rate_pct: float
    bandwidth_saving_ratio_pct: float

class IntrinsicSecurityState(BaseModel):
    cross_layer_threat_detection_rate_pct: float
    ai_decision_audit_coverage_pct: float
    semantic_attack_mitigation_success_rate_pct: float
    compliance_with_itu_r_m_2516: bool

class RobustSemanticCommunicator:
    """鲁棒语义通信器"""
    
    def __init__(self, universal_encoder, knowledge_sync, integrity_checker):
        self.encoder = universal_encoder
        self.sync = knowledge_sync
        self.checker = integrity_checker
        
    async def transmit_semantic_message(self, msg_id: str, content: str, receiver_id: str) -> Dict[str, Any]:
        """传输语义消息"""
        # 1. 提取领域无关语义表示
        universal_repr = await self.encoder.encode_universal(content)
        
        # 2. 同步接收端知识库版本
        kb_version = await self.sync.negotiate_knowledge_base(receiver_id)
        domain_adapter = await self._load_domain_adapter(kb_version)
        
        # 3. 生成带完整性保护的语义包
        semantic_packet = await self.checker.sign_and_encrypt(universal_repr, domain_adapter)
        
        # 4. 评估传输效果
        task_success = await self._evaluate_task_completion(msg_id, receiver_id)
        generalization = await self._assess_cross_domain_performance(content)
        
        metric = SemanticRobustnessMetric(
            task_completion_rate_pct=task_success * 100,
            cross_domain_generalization_score=generalization,
            semantic_integrity_check_pass_rate_pct=await self._compute_integrity_pass_rate(),
            bandwidth_saving_ratio_pct=await self._compute_bandwidth_saving()
        )
        
        return {
            "msg_id": msg_id,
            "semantic_metrics": metric.dict(),
            "knowledge_synced": kb_version is not None,
            "integrity_verified": True
        }

class SixGIntrinsicSecurityVerifier:
    """6G内生安全验证器"""
    
    def __init__(self, threat_monitor, audit_logger, compliance_checker):
        self.monitor = threat_monitor
        self.logger = audit_logger
        self.compliance = compliance_checker
        
    async def verify_intrinsic_security(self, network_slice_id: str) -> Dict[str, Any]:
        """验证内生安全"""
        # 1. 检测跨层威胁
        threats = await self.monitor.detect_cross_layer_threats(network_slice_id)
        detection_rate = sum(t["detected"] for t in threats) / len(threats) * 100
        
        # 2. 检查AI决策审计覆盖率
        audit_coverage = await self.logger.compute_ai_decision_coverage(network_slice_id)
        
        # 3. 评估语义攻击缓解效果
        mitigation_rate = await self._evaluate_semantic_attack_mitigation(network_slice_id)
        
        # 4. 检查ITU-R M.2516合规性
        compliant = await self.compliance.check_itu_r_m_2516(network_slice_id)
        
        state = IntrinsicSecurityState(
            cross_layer_threat_detection_rate_pct=detection_rate,
            ai_decision_audit_coverage_pct=audit_coverage,
            semantic_attack_mitigation_success_rate_pct=mitigation_rate,
            compliance_with_itu_r_m_2516=compliant
        )
        
        return {
            "network_slice_id": network_slice_id,
            "security_state": state.dict(),
            "secure_deployment_ready": compliant and detection_rate >= 99 and audit_coverage >= 95,
            "security_gaps": self._identify_gaps(state)
        }