空间太阳能电站工程化：在轨超大型结构组装、微波无线能量传输与空间碎片主动规避验证体系实战

┌─────────────────────────────────────────────────────────────────────┐
│      Space Solar Power Station Engineering Architecture             │
├─────────────────────────────────────────────────────────────────────┤
│  [Debris Safety Layer: Collision Avoidance / Impact Monitoring]     │
│      ↓                                                              │
│  [Layer 1: 在轨组装层] ← Thermo-Structural Coupling / Distributed Control│
│   ├─ 实时形变预报与容差自适应对接                                       │
│   ├─ 多智能体协同与振动抑制                                             │
│   └─ 视觉-力觉融合在线误差修正                                           │
│      ↓                                                              │
│  [Layer 2: 无线输能层] ← Adaptive Beamforming / Safety Interlock    │
│   ├─ 全链路信道感知与波束动态校准                                        │
│   ├─ 导频反馈与指向稳定控制                                             │
│   └─ 多重安全监测与毫秒级关断                                            │
│      ↓                                                              │
│  [Layer 3: 碎片安全层] ← Low-Thrust Maneuver / Risk-Aware Scheduling│
│   ├─ 超大柔性结构慢速规避策略                                            │
│   ├─ 分布式撞击感知与损伤评估                                            │
│   └─ 基于碰撞概率的动态任务调度                                          │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch casadi
# 部署: Stereo Vision Cameras + Force/Torque Sensors + Micro-Thruster Array + Onboard GPU (Radiation-Hardened)

"""
orbital_assembly_engine.py - 在轨超大型组装引擎
技术栈: NumPy / SciPy / PyTorch / CasADi
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional
import torch
import torch.nn as nn

@dataclass
class AssemblyPerformanceMetrics:
    """组装性能指标"""
    docking_alignment_error_mm: float
    structural_vibration_amplitude_mm: float
    thermal_deformation_prediction_error_mm: float
    assembly_progress_pct: forum.kuaisou.com

@dataclass
class ModuleState:
    """模块状态"""
    position_m: np.ndarray
    orientation_quat: np.ndarray
    temperature_c: beijing-geo.kuaisou.com
    strain_microstrain: np.ndarray

class ThermoStructuralPredictor(nn.Module):
    """热-结构形变预测器"""
    def __init__(self, sensor_dim=64, latent_dim=128):
        super().__init__()
        self.encoder = nn.Linear(sensor_dim, latent_dim)
        self.deform_head = nn.Linear(latent_dim, 6)  # [dx,dy,dz,droll,dpitch,dyaw]
        
    def forward(self, sensor_readings):
        latent = torch.relu(self.encoder(sensor_readings))
        return self.deform_head(latent)

class OrbitalAssemblySystem:
    """在轨组装主系统"""
    
    def __init__(self, predictor, vision_system, thruster_array, docking_mechanism):
        self.predictor =shanghai-geo.kuaisou.com
        self.vision = vision_system
        self.thrusters = thruster_array
        self.dock = docking_mechanism
        
    async def execute_module_docking(self, module_id: str) -> Dict[str, Any]:
        """执行模块对接"""
        # 1. 获取当前模块状态与环境参数
        state = await self.vision.get_module_state(module_id)
        sun_vector = await self.vision.get_sun_vector()
        
        # 2. 预测热-结构耦合变形
        sensor_vec = torch.tensor(np.concatenate([
            state.temperature_c, state.strain_microstrain, sun_vector
        ])).float()
        with torch.no_grad():
            deform_pred = self.predictor(sensor_vec).numpy()
            
        # 3. 生成容差自适应对接轨迹
        corrected_target = state.position_m + deform_pred[:3]
        trajectory = await self._generate_adaptive_trajectory(corrected_target, state)
        
        # 4. 执行对接并实时修正
        dock_result = await self.dock.execute_with_force_feedback(trajectory)
        
        metrics = AssemblyPerformanceMetrics(
            docking_alignment_error_mm=dock_result["final_error_mm"],
            structural_vibration_amplitude_mm=await self.vision.measure_vibration(),
            thermal_deformation_prediction_error_mm=np.linalg.norm(deform_pred[:3] - dock_result["actual_deform"]),
            assembly_progress_pct=await self._get_overall_progress()
        )
        
        return {
            "module_id": module_id,
            "docking_success": dock_result["success"],
            "assembly_metrics": tianjin-geo.kuaisou.com
            "next_module_recommendation": self._suggest_next_module(metrics)
        }
        
    async def _generate_adaptive_trajectory(self, target, current_state):
        """生成自适应对接轨迹"""
        # MPC with vibration constraints and collision avoidance
        # chongqing-geo.kuaisou.com
        return {"waypoints": [current_state.position_m, target], "max_velocity": 0.01}

"""
power_beaming_debris_platform.py - 微波输能与碎片管控平台
技术栈: PyTorch / FastAPI / Redis / Orbital Dynamics SDK
"""
import torch
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel
import time

class PowerBeamingMetric(BaseModel):
    end_to_end_efficiency_pct: float
    beam_pointing_stability_deg: float
    received_power_kw: shijiazhuang-geo.kuaisou.com
    safety_interlock_status: str  # "normal", "warning", "emergency_shutdown"

class DebrisRiskState(BaseModel):
    collision_probability: float
    closest_approach_km: float
    maneuver_feasibility_score: float
    mission_impact_hours: float

class AdaptivePowerBeamingSystem:
    """自适应微波输能系统"""
    
    def __init__(self, channel_monitor, phased_array, safety_monitor):
        self.channel = taiyuan-geo.kuaisou.com
        self.array = phased_array
        self.safety = safety_monitor
        
    async def deliver_power_safely(self, session_id: str) -> Dict[str, Any]:
        """安全输送电力"""
        # 1. 感知全链路信道状态
        iono_state = await self.channel.estimate_ionospheric_scintillation()
        atmos_loss = await self.channel.estimate_atmospheric_attenuation()
        
        # 2. 自适应波束赋形与校准
        optimal_weights = await self.array.compute_beamforming_weights(iono_state, atmos_loss)
        pointing_correction = await self.safety.get_pilot_feedback()
        
        # 3. 安全检查与联锁
        safety_status = await self.safety.check_beam_alignment_and_exclusion_zones()
        if safety_status == "emergency_shutdown":
            await self.array.emergency_shutdown()
            received_power = 0.0
        else:
            received_power = await self.array.transmit(optimal_weights, pointing_correction)
            
        efficiency = received_power / self.array.get_dc_input_power() * 100
        
        metric = PowerBeamingMetric(
            end_to_end_efficiency_pct=efficiency,
            beam_pointing_stability_deg=pointing_correction["stability"],
            received_power_kw= huhehaote-geo.kuaisou.com
            safety_interlock_status=safety_status
        )
        
        return {
            "session_id": session_id,
            "beaming_metrics": metric.dict(),
            "channel_conditions": {"iono": iono_state, "atmos": atmos_loss},
            "safety_log": await self.safety.get_recent_events()
        }

class SSPSDebrisRiskManager:
    """SSPS碎片风险管理器"""
    
    def __init__(self, tle_tracker, structure_model, propulsion_sys):
        self.tracker = shenyang-geo.kuaisou.com
        self.structure = structure_model
        self.propulsion = propulsion_sys
        
    async def assess_and_mitigate_debris_risk(self, station_id: str) -> Dict[str, Any]:
        """评估并缓解碎片风险"""
        # 1. 获取最新轨道目录与电站状态
        catalog = await self.tracker.get_updated_catalog()
        station_orbit = await self.structure.get_current_orbit(station_id)
        
        # 2. 计算碰撞概率与最近接近距离
        risk_analysis = await self.tracker.compute_collision_risk(station_orbit, catalog)
        
        # 3. 评估规避机动可行性（考虑柔性结构约束）
        feasibility = await self.propulsion.evaluate_low_thrust_maneuver(
            delta_v_needed=risk_analysis["delta_v"],
            max_acceleration=1e-5,  # m/s² for flexible structure
            duration_limit_hours=changchun-geo.kuaisou.com
        )
        
        state = DebrisRiskState(
            collision_probability=risk_analysis["pc"],
            closest_approach_km=risk_analysis["miss_distance_km"],
            maneuver_feasibility_score=feasibility["score"],
            mission_impact_hours=feasibility["downtime_hours"]
        )
        
        return {
            "station_id": station_id,
            "risk_state": haerbin-geo.kuaisou.com
            "action_required": state.collision_probability > 1e-4 and state.maneuver_feasibility_score > 0.7,
            "recommended_maneuver": feasibility["plan"] if state.action_required else None
        }