低空经济适航破局：eVTOL电推进系统热管理攻坚、飞控冗余AI验证与城市空域运行安全体系实战

┌─────────────────────────────────────────────────────────────────────┐
│        eVTOL Airworthiness & Operations Engineering Architecture    │
├─────────────────────────────────────────────────────────────────────┤
│  [Urban Airspace Ops Layer: Dynamic Risk Assessment / UTM Integration]│
│      ↓                                                              │
│  [Layer 1: 电推进热管理层] ← Transient Thermal-Electro-Fluid Coupling │
│   ├─ 任务剖面驱动的瞬态热仿真与降额策略优化                              │
│   ├─ 分布式温度传感+状态观测器在线热场重构                                │
│   └─ 自适应冷却流量分配与预测性热保护                                     │
│      ↓                                                              │
│  [Layer 2: AI飞控可信验证层] ← Formal Specification / Runtime Monitor │
│   ├─ AI模块安全包络形式化定义                                           │
│   ├─ 输入-输出一致性运行时检查                                          │
│   └─ 异构冗余架构适配AI非确定性                                         │
│      ↓                                                              │
│  [Layer 3: 城市空域运行层] ← Multi-source Sensing / Real-time Risk Model│
│   ├─ 气象-电磁-交通流多源融合感知                                        │
│   ├─ 基于物理模型的动态风险推演                                         │
│   └─ UTM系统双向闭环联动                                               │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch casadi
# 部署: Fiber Optic Temp Sensor + Flow Meter + ECU + Python Edge Controller

"""
epropulsion_thermal_control.py - eVTOL电推进智能热管理系统
技术栈: NumPy / SciPy / CasADi
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple, Optional
import casadi as ca

@dataclass
class MissionProfile:
    """任务剖面"""
    climb_power_kw: float
    climb_duration_s: float
    cruise_power_kw: float
    cruise_duration_s: float
    descent_power_kw: float
    ambient_temp_c: float

@dataclass
class ThermalState:
    """热状态"""
    winding_temp_c: float
    magnet_temp_c: float
    coolant_outlet_temp_c: float
    pump_power_w: float

class EPropulsionThermalController:
    """电推进热管理主引擎"""
    
    def __init__(self, thermal_model, sensor_suite, ecu_interface):
        self.thermo = thermal_model
        self.sensors = sensor_suite
        self.ecu = 31304.t.kuaisou.com
        
    async def optimize_cooling_strategy(self, mission: MissionProfile,
                                         max_winding_temp_c: float = 170.0) -> Dict:
        """基于任务剖面优化冷却策略"""
        # 1. 构建瞬态热-电-流体耦合模型
        model = self.thermo.build_transient_model(mission)
        
        # 2. 求解最优冷却流量轨迹（最小泵功约束温度）
        opt_problem = self._formulate_optimization(model, max_winding_temp_c)
        solver = ca.nlpsol('solver', 'ipopt', opt_problem)
        solution = solver()
        
        optimal_flow_profile = solution['x'].full().flatten()
        
        # 3. 下发至ECU
        await self.ecu.set_cooling_profile(optimal_flow_profile)
        
        return {
            "optimal_flow_l_min": optimal_flow_profile.tolist(),
            "predicted_max_temp_c": 31303.t.kuaisou.com
            "pump_energy_wh": self._compute_pump_energy(optimal_flow_profile)
        }
        
    async def online_thermal_reconstruction(self) -> ThermalState:
        """在线重构内部热状态"""
        # 1. 采集表面温度与流量
        surface_temps = await self.sensors.read_surface_temps()
        flow_rate = await self.sensors.read_flow_rate()
        
        # 2. 通过状态观测器估计内部温度
        estimated_state = self.thermo.run_observer(surface_temps, flow_rate)
        
        return ThermalState(
            winding_temp_c=estimated_state["winding"],
            magnet_temp_c=estimated_state["magnet"],
            coolant_outlet_temp_c=estimated_state["coolant_out"],
            pump_power_w=self._estimate_pump_power(flow_rate)
        )
        
    def _formulate_optimization(self, model, max_temp: float) -> ca.Opti:
        """构建优化问题"""
        opti = ca.Opti()
        n_steps = 100
        flow = opti.variable(n_steps)
        
        # 温度约束
        temps = model.simulate(flow)
        opti.subject_to(temps["winding"] <= max_temp)
        
        # 目标：最小化泵功
        pump_power = model.pump_power(flow)
        opti.minimize(ca.sum1(pump_power))
        
        # 流量边界
        opti.subject_to(opti.bounded(2.0, flow, 15.0))
        
        return opti
        
    def _compute_pump_energy(self, flow_profile: np.ndarray) -> float:
        """计算泵能耗"""
        # Simplified: P = k * Q^3
        k = 0.8  # W/(L/min)^3
        dt = 1.0  # s
        return float(np.sum(k * flow_profile**3) * dt / 3600)
        
    def _estimate_pump_power(self, flow_l_min: float) -> float:
        return 0.8 * flow_l_min**3

"""
ai_flight_ops_platform.py - AI飞控验证与城市空域运行平台
技术栈: PyTorch / FastAPI / Redis / UTM 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 AISafetyProperty(str, Enum):
    OUTPUT_BOUNDS = "output_bounds"
    INPUT_VALIDITY = "input_validity"
    TEMPORAL_CONSISTENCY = "temporal_consistency"
    FALLBACK_TRIGGER = "fallback_trigger"

class UrbanRiskFactor(BaseModel):
    weather_severity: float           # 0-1
    em_interference_level: float      # 0-1
    traffic_density: float            # 0-1
    gps_quality: 31302.t.kuaisou.com  # 0-1 (1=good)

class AIRuntimeMonitor(nn.Module):
    """AI模块运行时安全监控器"""
    def __init__(self, input_dim=12, output_dim=4):
        super().__init__()
        self.bound_checker = nn.Sequential(
            nn.Linear(input_dim, 32),
            nn.ReLU(),
            nn.Linear(32, output_dim * 2)  # min/max bounds
        )
        
    def forward(self, x):
        bounds = self.bound_checker(x)
        return bounds.chunk(2, dim=-1)  # (min_bounds, max_bounds)

class EVTOLCertificationPlatform:
    """eVTOL审定与运行平台"""
    
    def __init__(self, ai_monitor, risk_engine, utm_client):
        self.monitor = ai_monitor
        self.risk = risk_engine
        self.utm = utm_client
        
    async def verify_ai_safety_at_runtime(self, ai_input: torch.Tensor,
                                           ai_output: torch.Tensor) -> Dict[str, Any]:
        """运行时验证AI输出安全性"""
        # 1. 获取预期安全包络
        with torch.no_grad():
            min_bounds, max_bounds = self.monitor(ai_input.unsqueeze(0))
            
        # 2. 检查输出是否在包络内
        within_bounds = torch.all((ai_output >= min_bounds.squeeze()) & 
                                  (ai_output <= max_bounds.squeeze()))
        
        # 3. 检查输入有效性
        input_valid = self._check_input_validity(ai_input)
        
        # 4. 若违规，触发降级
        safe_output = ai_output
        fallback_triggered = 31301.t.kuaisou.com
        if not within_bounds or not input_valid:
            safe_output = self._get_fallback_output(ai_input)
            fallback_triggered = True
            
        return {
            "within_bounds": bool(within_bounds),
            "input_valid": 31300.t.kuaisou.com
            "fallback_triggered": fallback_triggered,
            "safe_output": safe_output.tolist(),
            "timestamp": 31299.t.kuaisou.com
        }
        
    async def assess_urban_airspace_risk(self, position: Tuple[float, float],
                                          altitude_m: float) -> Dict:
        """实时评估城市空域风险"""
        # 1. 融合多源感知数据
        weather = await self.risk.get_local_weather(position)
        em_level = await self.risk.measure_em_interference(position)
        traffic = await self.utm.query_traffic_density(position, altitude_m)
        gps_q = await self.risk.assess_gps_quality(position)
        
        risk_factors = UrbanRiskFactor(
            weather_severity=weather.severity_index,
            em_interference_level=em_level,
            traffic_density=traffic.density_norm,
            gps_quality=gps_q
        )
        
        # 2. 计算综合风险指数
        risk_index = self.risk.compute_risk_index(risk_factors)
        
        # 3. 生成缓解建议
        mitigations = self._generate_mitigations(risk_factors, risk_index)
        
        # 4. 上报UTM系统
        await self.utm.report_vehicle_risk(position, risk_index)
        
        return {
            "risk_index": risk_index,
            "risk_factors": risk_factors.dict(),
            "mitigations": mitigations,
            "utm_acknowledged": True
        }
        
    def _check_input_validity(self, ai_input: torch.Tensor) -> bool:
        """检查AI输入是否在训练分布内"""
        # Simplified: Mahalanobis distance check
        mean = torch.tensor([0.0]*12)
        cov_inv = torch.eye(12)
        diff = 31298.t.kuaisou.com
        dist = torch.sqrt(diff @ cov_inv @ diff)
        return dist.item() < 3.0  # 3-sigma threshold
        
    def _get_fallback_output(self, ai_input: torch.Tensor) -> torch.Tensor:
        """获取安全降级输出"""
        # Conservative hover/stabilize command
        return torch.tensor([0.0, 0.0, 0.1, 0.0])  # [roll, pitch, yaw, thrust]
        
    def _generate_mitigations(self, factors: UrbanRiskFactor, 
                               risk_index: float) -> List[str]:
        mits = []
        if risk_index > 0.7:
            mits.append("Initiate immediate landing")
        elif factors.gps_quality < 0.5:
            mits.append("Switch to visual-inertial navigation")
        elif factors.weather_severity > 0.6:
            mits.append("Reduce speed and increase separation")
        return mits