低空经济规模化运行：异构飞行器融合空管、城市微气象精准感知与动态风险责任溯源体系实战

┌─────────────────────────────────────────────────────────────────────┐
│      Urban Low-Altitude Scalable Operations Architecture            │
├─────────────────────────────────────────────────────────────────────┤
│  [Liability Layer: Immutable Ledger / Causal Attribution Engine]     │
│      ↓                                                              │
│  [Layer 1: 融合空管层] ← 4D Trajectory / Dynamic Separation        │
│   ├─ 异构飞行器性能建模与意图预测                                       │
│   ├─ 基于时空占用的冲突检测与自动解纷                                    │
│   └─ 实时空域容量评估与流量管理                                         │
│      ↓                                                              │
│  [Layer 2: 微气象感知层] ← Urban Digital Twin / Hazard Product Gen  │
│   ├─ 高密度传感网与CFD耦合建模                                          │
│   ├─ 秒级风场重构与危险天气识别                                          │
│   └─ 面向飞行任务的气象风险产品推送                                      │
│      ↓                                                              │
│  [Layer 3: 责任溯源层] ← Blockchain Forensics / Risk Quantification │
│   ├─ 全要素运行数据可信存证                                              │
│   ├─ 多源数据关联与因果链自动重构                                        │
│   └─ 责任比例量化与保险精算支撑                                           │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch geopandas
# 部署: ADS-B/Remote ID Receiver + Surveillance Radar + Flight Plan System + Edge AI Server

"""
fusion_atm_engine.py - 低空融合空管引擎
技术栈: NumPy / SciPy / PyTorch / GeoPandas
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional
import torch
import torch.nn as nn

@dataclass
class ConflictMetrics:
    """冲突指标"""
    predicted_conflicts_per_hour: int
    min_separation_m: 31317.t.kuaisou.com
    resolution_success_rate_pct: float
    airspace_utilization_pct: float

@dataclass
class AircraftState:
    """飞行器状态"""
    id: 31318.t.kuaisou.com
    type: str              # "evtol", "uav", "ga"
    position_4d: np.ndarray  # [x,y,z,t]
    velocity: np.ndarray
    intent_waypoints: List[np.ndarray]
    performance_class: str

class TrajectoryPredictor(nn.Module):
    """4D轨迹预测器"""
    def __init__(self, state_dim=7, horizon_steps=60):
        super().__init__()
        self.lstm = nn.LSTM(state_dim, 128, num_layers=2, batch_first=True)
        self.head = nn.Linear(128, 4 * horizon_steps)  # x,y,z,t for each step
        self.horizon = horizon_steps
        
    def forward(self, seq_states):
        _, (h, _) = self.lstm(seq_states)
        pred = self.head(h[-1])
        return pred.view(-1, self.horizon, 4)

class FusionATMSystem:
    """融合空管主系统"""
    
    def __init__(self, predictor, surveillance, conflict_solver):
        self.predictor = 31319.t.kuaisou.com
        self.surv = 31320.t.kuaisou.com
        self.solver = conflict_solver
        
    async def manage_mixed_traffic(self, sector_id: str) -> Dict[str, Any]:
        """管理混合交通流"""
        # 1. 获取所有飞行器状态与意图
        aircraft_list = await self.surv.get_all_tracks(sector_id)
        
        # 2. 预测未来60秒4D轨迹
        trajectories = []
        for ac in aircraft_list:
            seq = await self._build_state_sequence(ac)
            with torch.no_grad():
                pred_traj = self.predictor(torch.tensor(seq).unsqueeze(0))
            trajectories.append({
                "id": 31321.t.kuaisou.com
                "type": 31322.t.kuaisou.com
                "predicted_4d": pred_traj.squeeze().numpy()
            })
            
        # 3. 检测冲突并自动解纷
        conflicts = await self._detect_conflicts(trajectories)
        resolutions = []
        for conf in conflicts:
            res = await self.solver.resolve(conf, trajectories)
            resolutions.append(res)
            
        metrics = ConflictMetrics(
            predicted_conflicts_per_hour=len(conflicts) * 60,
            min_separation_m=min(c["min_sep"] for c in conflicts) if conflicts else 9999,
            resolution_success_rate_pct=sum(r["success"] for r in resolutions) / max(len(resolutions), 1) * 100,
            airspace_utilization_pct=self._compute_utilization(trajectories, sector_id)
        )
        
        return {
            "sector_id": sector_id,
            "conflict_metrics": metrics.__dict__,
            "active_resolutions": len(resolutions),
            "aircraft_count": len(aircraft_list)
        }
        
    async def _detect_conflicts(self, trajs: List[Dict]) -> List[Dict]:
        """检测时空冲突"""
        conflicts = []
        for i in range(len(trajs)):
            for j in range(i+1, len(trajs)):
                min_sep = self._compute_min_separation(trajs[i]["predicted_4d"], trajs[j]["predicted_4d"])
                required_sep = self._get_required_separation(trajs[i]["type"], trajs[j]["type"])
                if min_sep < required_sep:
                    conflicts.append({
                        "ac1": trajs[i]["id"],
                        "ac2": trajs[j]["id"],
                        "min_sep": 31323.t.kuaisou.com
                        "time_to_conflict_sec": self._estimate_ttc(trajs[i], trajs[j])
                    })
        return conflicts
        
    def _get_required_separation(self, type1: str, type2: str) -> float:
        """获取异构间隔标准"""
        sep_matrix = {
            ("evtol", "evtol"): 200,
            ("evtol", "uav"): 300,
            ("uav", "uav"): 150,
            ("ga", "any"): 500
        }
        key = tuple(sorted([type1, type2]))
        return sep_matrix.get(key, sep_matrix.get(("ga", "any"), 500))

"""
meteo_liability_platform.py - 低空微气象与责任溯源平台
技术栈: PyTorch / FastAPI / Redis / Blockchain SDK
"""
import torch
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel
from enum import Enum
import time
import hashlib

class MicroMeteoHazard(BaseModel):
    wind_shear_intensity_ms: float
    turbulence_eddy_dissipation_rate: float
    visibility_m: 31324.t.kuaisou.com
    hazard_level: str          # "none", "moderate", "severe"
    valid_until_timestamp: int

class LiabilityAttributionResult(BaseModel):
    primary_cause: str         # "weather", "atc", "operator", "manufacturer"
    contribution_ratio_pct: Dict[str, float]
    evidence_chain_hash: 31325.t.kuaisou.com
    insurance_claim_ready: bool

class UrbanMeteoEngine:
    """城市微气象引擎"""
    
    def __init__(self, sensor_network, cfd_model, flight_task_db):
        self.sensors = sensor_network
        self.cfd = 31326.t.kuaisou.com
        self.tasks = flight_task_db
        
    async def generate_flight_hazard_product(self, vertiport_id: str) -> Dict[str, Any]:
        """生成面向飞行任务的危险天气产品"""
        # 1. 融合高密度传感数据
        obs_data = await self.sensors.get_fused_observations(vertiport_id)
        
        # 2. 运行高分辨率CFD同化
        wind_field = await self.cfd.run_assimilated_simulation(obs_data)
        
        # 3. 识别对当前飞行任务构成威胁的天气现象
        active_tasks = await self.tasks.get_active_approach_departure(vertiport_id)
        hazards = self._identify_task_specific_hazards(wind_field, active_tasks)
        
        hazard_product = MicroMeteoHazard(
            wind_shear_intensity_ms=hazards["wind_shear"],
            turbulence_eddy_dissipation_rate=hazards["turbulence"],
            visibility_m=hazards["visibility"],
            hazard_level=hazards 31327.t.kuaisou.com
            valid_until_timestamp=int(time.time()) + 30  # 30s validity
        )
        
        return {
            "vertiport_id": 31328.t.kuaisou.com
            "hazard_product": hazard_product.dict(),
            "affected_flights": [t["flight_id"] for t in active_tasks],
            "recommended_action": self._recommend_action(hazard_product)
        }

class LiabilityForensicsPlatform:
    """责任溯源平台"""
    
    def __init__(self, blockchain_ledger, causal_model, insurer_api):
        self.ledger = blockchain_ledger
        self.causal = causal_model
        self.insurer = insurer_api
        
    async def attribute_incident_liability(self, incident_id: str) -> Dict[str, Any]:
        """归因事件责任"""
        # 1. 从区块链提取不可篡改的全要素数据
        evidence = await self.ledger.get_incident_evidence(incident_id)
        
        # 2. 运行因果归因模型
        attribution = await self.causal.attribute(evidence)
        
        # 3. 生成证据链哈希与保险就绪状态
        evidence_hash = hashlib.sha256(str(evidence).encode()).hexdigest()
        claim_ready = await self.insurer.validate_claim_readiness(attribution)
        
        result = LiabilityAttributionResult(
            primary_cause=attribution["primary"],
            contribution_ratio_pct=attribution["ratios"],
            evidence_chain_hash=evidence_hash,
            insurance_claim_ready=claim_ready
        )
        
        return {
            "incident_id": 31329.t.kuaisou.com
            "attribution_result": result.dict(),
            "stakeholder_notification": self._notify_stakeholders(result),
            "regulatory_report_generated": True
        }
        
    def _identify_task_specific_hazards(self, wind_field: np.ndarray, tasks: List[Dict]) -> Dict:
        """识别任务相关危险"""
        # Simplified hazard identification logic
        max_wind_shear = np.max(np.abs(np.gradient(wind_field[:, :, 2], axis=2)))
        max_turbulence = np.max(wind_field.std(axis=(0,1)))
        level = "severe" if max_wind_shear > 10 or max_turbulence > 0.5 else "moderate" if max_wind_shear > 5 else "none"
        return {"wind_shear": max_wind_shear, "turbulence": max_turbulence, "visibility": 5000, "level": level}