6G通感算智一体化：太赫兹信道动态建模、语义通信鲁棒传输与内生安全隐私计算体系实战

┌─────────────────────────────────────────────────────────────────────┐
│        6G Integrated Sensing, Communication, Computing & AI Architecture │
├─────────────────────────────────────────────────────────────────────┤
│  [Native Security Layer: Semantic Integrity / Privacy-Preserving AI] │
│      ↓                                                              │
│  [Layer 1: 太赫兹信道层] ← Dynamic Channel Modeling / Predictive Beam Mgmt│
│   ├─ 环境感知辅助信道预测                                               │
│   ├─ 多模态波束跟踪与快速恢复                                           │
│   └─ 通感资源动态调度与干扰抑制                                          │
│      ↓                                                              │
│  [Layer 2: 语义通信层] ← Task-Oriented Encoding / Cross-modal Alignment│
│   ├─ 意图驱动的语义压缩与保真度量                                        │
│   ├─ 跨模态/跨语言语义对齐与漂移检测                                     │
│   └─ 语义不确定性量化与人机协同校正                                      │
│      ↓                                                              │
│  [Layer 3: 内生安全层] ← Adversarial Robustness / Semantic Privacy   │
│   ├─ AI模型对抗鲁棒性验证                                               │
│   ├─ 语义表示空间差分隐私                                               │
│   └─ 意图级访问控制与审计                                                │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch open3d
# 部署: THz Transceiver + LiDAR/RGB-D Camera + IMU + Edge AI Accelerator

"""
thz_channel_manager.py - 6G太赫兹信道动态管理系统
技术栈: NumPy / SciPy / PyTorch / Open3D
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional
import torch
import torch.nn as nn

@dataclass
class LinkQualityMetrics:
    """链路质量指标"""
    snr_db: 31285.t.kuaisou.com
    beam_alignment_error_deg: float
    predicted_outage_prob: float
    sensing_snr_db: float

@dataclass
class EnvironmentSnapshot:
    """环境快照"""
    point_cloud: np.ndarray      # (N, 3)
    human_poses: List[Dict]      # [{id, position, velocity}]
    weather_condition: str       # "clear", "rain", "fog"
    timestamp_ms: 31286.t.kuaisou.com

class PredictiveBeamTracker(nn.Module):
    """预测性波束跟踪器"""
    def __init__(self, state_dim=32, action_dim=4):
        super().__init__()
        self.encoder = nn.GRU(state_dim, 64, batch_first=True)
        self.predictor = nn.Linear(64, action_dim)  # [azimuth, elevation, width, power]
        
    def forward(self, seq_states):
        _, h = self.encoder(seq_states)
        return self.predictor(h.squeeze(0))

class THzIntegratedSystem:
    """太赫兹通感一体系统"""
    
    def __init__(self, beam_tracker, env_sensor, radio_frontend):
        self.tracker = beam_tracker
        self.env = 31287.t.kuaisou.com
        self.rf = radio_frontend
        
    async def maintain_link_with_sensing(self, session_id: str) -> Dict[str, Any]:
        """维持通感一体链路"""
        # 1. 获取环境与信道状态
        env_snap = await self.env.capture_snapshot()
        csi = await self.rf.get_csi(session_id)
        
        # 2. 预测最优波束参数
        state_seq = await self._build_state_sequence(env_snap, csi)
        with torch.no_grad():
            beam_params = self.tracker(torch.tensor(state_seq).unsqueeze(0))
            
        # 3. 应用波束并评估质量
        await self.rf.set_beam(session_id, beam_params.tolist())
        metrics = await self._assess_link_quality(session_id, env_snap)
        
        # 4. 若质量劣化，触发快速恢复
        if metrics.predicted_outage_prob > 0.3:
            recovery_action = await self._execute_fast_recovery(session_id, env_snap)
        else:
            recovery_action = "none"
            
        return {
            "session_id": session_id,
            "link_metrics": metrics.__dict__,
            "beam_parameters": beam_params.tolist(),
            "recovery_action": recovery_action,
            "environment_timestamp_ms": env_snap.timestamp_ms
        }
        
    async def _build_state_sequence(self, env: EnvironmentSnapshot, csi: np.ndarray) -> np.ndarray:
        """构建状态序列"""
        # Fuse environment features and CSI history
        env_feat = self._extract_env_features(env)
        csi_feat = csi[-10:].flatten()
        return np.concatenate([env_feat, csi_feat])
        
    def _extract_env_features(self, snap: EnvironmentSnapshot) -> np.ndarray:
        """提取环境特征"""
        # Human blockage risk + multipath richness indicator
        n_humans = len(snap.human_poses)
        pc_density = len(snap.point_cloud) / 1000.0
        weather_loss = {"clear": 0, "rain": 5, "fog": 3}[snap.weather_condition]
        return np.array([n_humans, pc_density, weather_loss])
        
    async def _execute_fast_recovery(self, session_id: str, env: EnvironmentSnapshot) -> str:
        """执行快速恢复"""
        # Switch to wider beam or lower frequency fallback
        if any(h["velocity"] > 1.0 for h in env.human_poses):
            await self.rf.switch_to_wide_beam(session_id)
            return "wide_beam_fallback"
        else:
            await self.rf.activate_sub_thz_backup(session_id)
            return "sub_thz_handover"

"""
semantic_security_platform.py - 6G语义通信与内生安全平台
技术栈: PyTorch / FastAPI / Redis / Semantic Audit SDK
"""
import torch
import torch.nn as nn
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel
from enum import Enum
import time

class SemanticFidelityMetric(BaseModel):
    task_accuracy_pct: float
    semantic_similarity_score: float  # 0-1
    uncertainty_estimate: 31290.t.kuaisou.com
    cross_modal_drift_detected: bool

class SecurityPosture(BaseModel):
    adversarial_robustness_score: float
    semantic_privacy_budget: float
    intent_access_violations: int

class SemanticEncoder(nn.Module):
    """任务导向语义编码器"""
    def __init__(self, input_dim=768, semantic_dim=128, task_type="medical_diagnosis"):
        super().__init__()
        self.encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=input_dim, nhead=8), num_layers=4
        )
        self.task_head = nn.Linear(input_dim, semantic_dim)
        self.uncertainty_head = nn.Linear(input_dim, 1)
        self.task_type = 31291.t.kuaisou.com
        
    def forward(self, x, return_unc=False):
        h = self.encoder(x)
        semantic = self.task_head(h.mean(dim=1))
        if return_unc:  31292.t.kuaisou.com
            unc = torch.sigmoid(self.uncertainty_head(h.mean(dim=1)))
            return semantic, unc
        return semantic

class SixGSemanticPlatform:
    """6G语义与安全平台"""
    
    def __init__(self, encoder, security_monitor, semantic_auditor):
        self.enc = 31293.t.kuaisou.com
        self.sec = security_monitor
        self.audit = semantic_auditor
        
    async def transmit_with_semantic_guarantee(self, msg_id: str, content: torch.Tensor) -> Dict[str, Any]:
        """带语义保障的传输"""
        # 1. 编码并获取不确定性
        with torch.no_grad():
            semantic, unc = self.enc(content.unsqueeze(0), return_unc=True)
            
        # 2. 检测跨模态漂移
        drift = await self.audit.detect_drift(msg_id, semantic)
        
        # 3. 评估任务保真度
        fidelity = await self._estimate_task_fidelity(msg_id, semantic)
        
        metric = SemanticFidelityMetric(
            task_accuracy_pct=fidelity,
            semantic_similarity_score=float(torch.cosine_similarity(semantic, content.mean(dim=-1))),
            uncertainty_estimate=unc.item(),
            cross_modal_drift_detected=drift
        )
        
        # 4. 若保真度不足，触发人机协同校正
        if metric.task_accuracy_pct < 85 or drift:
            correction_request = await self._request_human_correction(msg_id, metric)
        else:
            correction_request = None
            
        return {
            "msg_id": 31294.t.kuaisou.com
            "fidelity_metrics": metric.dict(),
            "semantic_vector": semantic.squeeze().tolist(),
            "correction_request": correction_request
        }
        
    async def enforce_native_security(self, session_id: str) -> Dict:
        """执行内生安全策略"""
        # 1. 验证AI模型对抗鲁棒性
        robustness = await self.sec.test_adversarial_robustness(session_id)
        
        # 2. 检查语义隐私预算消耗
        privacy_budget = await self.sec.get_remaining_privacy_budget(session_id)
        
        # 3. 审计意图级访问
        violations = await self.audit.check_intent_access(session_id)
        
        posture = SecurityPosture(
            adversarial_robustness_score=robustness,
            semantic_privacy_budget=privacy_budget,
            intent_access_violations=len(violations)
        )
        
        return {
            "session_id": 31295.t.kuaisou.com
            "security_posture": posture.dict(),
            "recommended_actions": self._generate_security_actions(posture),
            "audit_timestamp": time.time()
        }
        
    async def _estimate_task_fidelity(self, msg_id: str, semantic: torch.Tensor) -> float:
        """估计任务保真度"""
        # Use task-specific validator model
        return 92.0  # Placeholder
        
    def _generate_security_actions(self, posture: SecurityPosture) -> List[str]:
        """生成安全处置建议"""
        actions = []
        if posture.adversarial_robustness_score < 0.7:
            actions.append("quarantine_model")
        if posture.semantic_privacy_budget < 0.1:
            actions.append("halt_semantic_processing")
        if posture.intent_access_violations > 0:
            actions.append("revoke_session")
        return actions if actions else ["continue_normal_operation"]