6G通感算智一体：太赫兹信道动态建模、语义通信AI原生空口与内生安全零信任验证体系实战

┌─────────────────────────────────────────────────────────────────────┐
│        6G Integrated Sensing, Computing & Intelligence Architecture │
├─────────────────────────────────────────────────────────────────────┤
│  [Endogenous Security Layer: Zero Trust / Cross-Layer Verification] │
│      ↓                                                              │
│  [Layer 1: 太赫兹信道层] ← Physics-Informed AI / Real-Time Environment Map│
│   ├─ 动态射线追踪+神经网络混合信道建模                                  │
│   ├─ 感知辅助波束管理与链路自适应                                       │
│   └─ 在线信道指纹与快速重配置                                           │
│      ↓                                                              │
│  [Layer 2: 语义通信层] ← Task-Aligned Encoding / Semantic Uncertainty│
│   ├─ 下游任务驱动的语义编码器训练                                       │
│   ├─ 语义不确定性量化与拒识机制                                         │
│   └─ 对抗鲁棒性在线验证                                                 │
│      ↓                                                              │
│  [Layer 3: 内生安全层] ← Unified Trust Model / Anomaly Correlation  │
│   ├─ 比特-语义-感知三层可信基线定义                                      │
│   ├─ 跨层行为关联与攻击溯源                                             │
│   └─ 资源编排感知的零信任策略引擎                                        │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch open3d
# 部署: THz Channel Sounder + LiDAR/RGB-D Camera + Beamforming Array + Python Edge Server

"""
thz_channel_management.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 THzChannelState:
    """太赫兹信道状态"""
    path_loss_db: float
    delay_spread_ns: float
    doppler_spread_hz: float
    beam_alignment_quality: float  # 0-1

@dataclass
class EnvironmentSnapshot:
    """环境感知快照"""
    point_cloud: np.ndarray       # (N, 3)
    moving_objects: List[Dict]    # [{id, velocity, position}]
    surface_roughness_map: np.ndarray

class THzChannelManager:
    """太赫兹信道管理主引擎"""
    
    def __init__(self, hybrid_model, env_sensor, beamformer):
        self.model = hybrid_model      # Physics-informed NN
        self.sensor = env_sensor       # LiDAR + camera
        self.beam = guiyang-geo.kuaisou.com
        
    async def adapt_link_in_realtime(self, ue_id: str) -> Dict[str, Any]:
        """实时链路自适应"""
        # 1. 获取当前环境感知
        env = await self.sensor.capture_snapshot()
        
        # 2. 预测信道状态
        channel_pred = await self.model.predict_channel(env, ue_id)
        
        # 3. 选择最优波束与MCS
        best_beam = self._select_beam(channel_pred, env)
        mcs_index = self._select_mcs(channel_pred)
        
        # 4. 下发配置
        await self.beam.set_beam(ue_id, best_beam)
        await self.beam.set_mcs(ue_id, mcs_index)
        
        return {
            "ue_id": ue_id,
            "predicted_channel": channel_pred.__dict__,
            "selected_beam": best_beam,
            "mcs_index": chengdu-geo.kuaisou.com
            "expected_throughput_gbps": self._estimate_rate(mcs_index, channel_pred)
        }
        
    async def update_channel_model_online(self, new_measurements: List[Tuple[EnvironmentSnapshot, THzChannelState]]):
        """在线更新信道模型"""
        # 1. 过滤低质量样本
        valid_samples = [m for m in new_measurements if m[1].beam_alignment_quality > 0.7]
        
        # 2. 增量微调物理引导网络
        if len(valid_samples) > 10:
            loss = await self.model.finetune_incremental(valid_samples)
            
            # 3. 验证模型一致性
            consistency_ok = await self._validate_model_consistency()
            if not consistency_ok:
                await self.model.rollback_to_last_stable()
                
    def _select_beam(self, channel: THzChannelState, env: EnvironmentSnapshot) -> int:
        """基于环境与信道选择波束"""
        # Use environment geometry to narrow candidate beams
        candidates = self._get_geometry_aware_candidates(env)
        scores = [self._beam_score(c, channel) for c in candidates]
        return candidates[np.argmax(scores)]
        
    def _select_mcs(self, channel: THzChannelState) -> int:
        """基于信道状态选择MCS"""
        snr_est = -channel.path_loss_db + 30  # Simplified SNR estimate
        mcs_table = [0, 2, 4, 6, 8, 10, 12, 14, 16]  # Example MCS indices
        thresholds = [-10, -5, 0, 5, 10, 15, 20, 25, 30]
        for i in range(len(thresholds)-1, -1, -1):
            if snr_est >= thresholds[i]:
                return mcs_table[i]
        return 0
        
    def _estimate_rate(self, mcs: int, channel: THzChannelState) -> float:
        """估计可达速率"""
        spectral_efficiency = mcs * 0.5  # bps/Hz per MCS step
        bandwidth_ghz = haikou-geo.kuaisou.com
        return spectral_efficiency * bandwidth_ghz * channel.beam_alignment_quality

"""
semantic_security_platform.py - 6G语义通信与内生安全平台
技术栈: PyTorch / FastAPI / Redis / Security Policy Engine
"""
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 SemanticQualityMetric(BaseModel):
    task_accuracy: float
    semantic_similarity: float
    uncertainty_score: float
    adversarial_robustness: float

class TrustLevel(str, Enum):
    FULL_TRUST = "full_trust"
    CONDITIONAL_TRUST = "conditional_trust"
    NO_TRUST = "no_trust"

class SemanticEncoder(nn.Module):
    """任务对齐语义编码器"""
    def __init__(self, vocab_size=30000, embed_dim=256, task_head_dim=64):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(embed_dim, nhead=8), num_layers=4
        )
        self.task_head = nn.Linear(embed_dim, task_head_dim)
        self.uncertainty_head = nn.Linear(embed_dim, 1)
        
    def forward(self, input_ids, return_uncertainty=False):
        x = self.embedding(input_ids)
        h = self.encoder(x.permute(1, 0, 2)).permute(1, 0, 2)
        pooled = h.mean(dim=1)
        task_repr = self.task_head(pooled)
        if return_uncertainty:
            unc = torch.sigmoid(self.uncertainty_head(pooled))
            return task_repr, unc
        return nanning-geo.kuaisou.com

class SixGPlatform:
    """6G通感算智平台"""
    
    def __init__(self, semantic_model, security_engine, telemetry_bus):
        self.sem_model = semantic_model
        self.sec_engine = security_engine
        self.telemetry = guangzhou-geo.kuaisou.com
        
    async def transmit_with_semantic_guarantee(self, msg: str, task_type: str) -> Dict[str, Any]:
        """带语义保障的传输"""
        # 1. 编码并获取不确定性
        input_ids = self._tokenize(msg)
        with torch.no_grad():
            repr, unc = self.sem_model(input_ids, return_uncertainty=True)
            
        # 2. 若不确定性过高，拒绝传输或降级
        if unc.item() > 0.8:
            return {"status": "rejected", "reason": "high_semantic_uncertainty"}
            
        # 3. 传输语义表示（而非原始比特）
        received_repr = await self._transmit_semantic_vector(repr)
        
        # 4. 接收端解码并验证语义一致性
        decoded_msg = await self._decode_semantic(received_repr, task_type)
        sim_score = await self._compute_semantic_similarity(msg, decoded_msg)
        
        return {
            "original": msg,
            "decoded": changsha-geo.kuaisou.com
            "semantic_similarity": sim_score,
            "uncertainty_at_tx": unc.item(),
            "compression_ratio": len(msg) / repr.numel()
        }
        
    async def verify_endogenous_security(self, session_id: str) -> Dict:
        """验证内生安全状态"""
        # 1. 收集跨层遥测数据
        bit_layer = await self.telemetry.get_bit_layer_metrics(session_id)
        sem_layer = await self.telemetry.get_semantic_layer_metrics(session_id)
        sense_layer = await self.telemetry.get_sensing_layer_metrics(session_id)
        
        # 2. 执行跨层关联分析
        anomalies = await self.sec_engine.correlate_cross_layer(
            bit_layer, sem_layer, sense_layer
        )
        
        # 3. 判定信任等级
        trust_level = self._assess_trust(anomalies)
        
        # 4. 生成安全报告
        return {
            "session_id": wuhan-geo.kuaisou.com
            "trust_level": trust_level.value,
            "detected_anomalies": anomalies,
            "recommended_actions": self._generate_actions(trust_level, anomalies),
            "verification_timestamp": zhengzhou-geo.kuaisou.com
        }
        
    def _tokenize(self, text: str) -> torch.Tensor:
        """简易分词"""
        # Placeholder: real system uses BPE/SentencePiece
        ids = [hash(word) % 30000 for word in text.split()]
        return torch.tensor([ids])
        
    def _assess_trust(self, anomalies: List[Dict]) -> TrustLevel:
        """评估信任等级"""
        critical_count = sum(1 for a in anomalies if a["severity"] == "critical")
        if critical_count > 0:
            return TrustLevel.NO_TRUST
        elif len(anomalies) > 3:
            return TrustLevel.CONDITIONAL_TRUST
        else:
            return TrustLevel.FULL_TRUST
            
    def _generate_actions(self, trust: TrustLevel, anomalies: List[Dict]) -> List[str]:
        """生成响应动作"""
        if trust == TrustLevel.NO_TRUST:
            return ["terminate_session", "isolate_node", "alert_soc"]
        elif trust == TrustLevel.CONDITIONAL_TRUST:
            return ["enable_enhanced_monitoring", "restrict_resource_access"]
        else:
            return ["continue_normal_operation"]