深海资源开发工程化：万米级装备结构完整性、极端环境通信导航与海洋生态扰动最小化验证体系实战

┌─────────────────────────────────────────────────────────────────────┐
│      Deep-Sea Resource Development Engineering Architecture         │
├─────────────────────────────────────────────────────────────────────┤
│  [Eco-Coexistence Layer: Plume Modeling / Baseline Monitoring]      │
│      ↓                                                              │
│  [Layer 1: 结构耐久层] ← Digital Twin / Fatigue Inversion          │
│   ├─ 制造缺陷-服役载荷耦合建模                                        │
│   ├─ 嵌入式传感与损伤在线反演                                          │
│   └─ 剩余寿命预测与维护优化                                            │
│      ↓                                                              │
│  [Layer 2: 极端通信层] ← Adaptive Acoustic / Semantic Prioritization│
│   ├─ 时变信道感知与调制编码自适应                                       │
│   ├─ 多模态融合与关键信息提取                                           │
│   └─ 通信中断自主决策与状态缓存                                         │
│      ↓                                                              │
│  [Layer 3: 生态共生层] ← High-Res Baseline / Dynamic Impact Assessment│
│   ├─ 原位生态监测与基线动态更新                                         │
│   ├─ 羽流-生物响应耦合建模                                             │
│   └─ 实时扰动预警与作业参数联动调节                                      │
└─────────────────────────────────────────────────────────────────────┘

pip install numpy scipy pytorch fenics
# 部署: Fiber Bragg Grating Sensors + Strain Gauges + Ultrasonic NDE + Edge Computing Node (Pressure-Tolerant)

"""
pressure_hull_health_engine.py - 耐压结构健康管理系统
技术栈: NumPy / SciPy / PyTorch / FEniCS
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional
import torch
import torch.nn as nn

@dataclass
class StructuralHealthMetrics:
    """结构健康指标"""
    fatigue_damage_accumulation_pct: float
    crack_growth_rate_mm_per_cycle: float
    remaining_life_cycles: 31360.t.kuaisou.com
    inspection_urgency_level: str  # "routine", "enhanced", "immediate"

@dataclass
class LoadHistory:
    """载荷历史"""
    pressure_profile_mpa: np.ndarray
    temperature_profile_c: np.ndarray
    vibration_spectrum_g: np.ndarray
    cycle_count: 31361.t.kuaisou.com

class FatigueDamageInverter(nn.Module):
    """疲劳损伤反演器"""
    def __init__(self, sensor_dim=32, latent_dim=64):
        super().__init__()
        self.encoder = nn.Linear(sensor_dim, latent_dim)
        self.damage_head = nn.Linear(latent_dim, 3)  # [damage, crack_rate, uncertainty]
        
    def forward(self, sensor_readings):
        latent = torch.relu(self.encoder(sensor_readings))
        return self.damage_head(latent)

class PressureHullHealthSystem:
    """耐压壳健康主系统"""
    
    def __init__(self, inverter, digital_twin, nde_system):
        self.inverter = 31362.t.kuaisou.com
        self.dt = digital_twin
        self.nde = nde_system
        
    async def assess_hull_integrity_in_situ(self, dive_id: str) -> Dict[str, Any]:
        """原位评估壳体完整性"""
        # 1. 获取嵌入式传感器读数
        fbgs, strains = await self.dt.get_embedded_sensor_data(dive_id)
        sensor_vec = torch.tensor(np.concatenate([fbgs, strains])).float()
        
        # 2. 反演当前损伤状态
        with torch.no_grad():
            damage_est = self.inverter(sensor_vec)
            damage_pct, crack_rate, uncertainty = damage_est.tolist()
            
        # 3. 结合数字孪生预测剩余寿命
        load_hist = await self.dt.get_load_history(dive_id)
        remaining = self.dt.predict_remaining_life(damage_pct, crack_rate, load_hist)
        
        urgency = "immediate" if uncertainty > 0.3 or remaining < 50 else \
                  "enhanced" if remaining < 200 else "routine"
                  
        metrics = StructuralHealthMetrics(
            fatigue_damage_accumulation_pct=damage_pct * 100,
            crack_growth_rate_mm_per_cycle=crack_rate,
            remaining_life_cycles=int(remaining),
            inspection_urgency_level=urgency
        )
        
        return {
            "dive_id": 31363.t.kuaisou.com
            "health_metrics": metrics.__dict__,
            "digital_twin_sync_status": await self.dt.sync_status(),
            "nde_recommendation": self._generate_nde_plan(metrics)
        }
        
    def _generate_nde_plan(self, metrics: StructuralHealthMetrics) -> Dict:
        """生成无损检测计划"""
        if metrics.inspection_urgency_level == "immediate":
            return {"method": "phased_array_ultrasonic", "coverage": "100%", "priority": "before_next_dive"}
        elif metrics.inspection_urgency_level == "enhanced":
            return {"method": "guided_wave", "coverage": "critical_zones", "priority": "within_5_dives"}
        else:
            return {"method": "visual_inspection", "coverage": "external", "priority": "scheduled"}

"""
comm_eco_platform.py - 深海通信与生态评估平台
技术栈: PyTorch / FastAPI / Redis / Oceanographic SDK
"""
import torch
import numpy as np
from typing import Dict, List, Optional, Any
from pydantic import BaseModel
import time

class CommunicationPerformanceMetric(BaseModel):
    effective_bandwidth_bps: float
    packet_error_rate_pct: float
    command_latency_sec: 31369.t.kuaisou.com
    autonomy_level: str  # "full_remote", "supervised", "autonomous"

class EcoDisturbanceState(BaseModel):
    plume_extent_km: 31364.t.kuaisou.com
    benthic_community_stress_index: float
    baseline_deviation_sigma: float
    operational_adjustment_required: bool

class AdaptiveDeepSeaCommSystem:
    """自适应深海通信系统"""
    
    def __init__(self, channel_estimator, semantic_encoder, autonomy_manager):
        self.channel = channel_estimator
        self.semantic = semantic_encoder
        self.autonomy = autonomy_manager
        
    async def maintain_communication_link(self, mission_id: str) -> Dict[str, Any]:
        """维持通信链路"""
        # 1. 估计当前信道状态
        csi = await self.channel.estimate_time_varying_channel(mission_id)
        
        # 2. 自适应调制编码与语义压缩
        mod_scheme = self._select_modulation(csi)
        priority_data = await self.semantic.extract_task_critical_info(mission_id)
        
        # 3. 若链路质量过低，提升自主等级
        if csi["snr_db"] < 5 or csi["delay_sec"] > 15:
            autonomy_level = "autonomous"
            await self.autonomy.activate_contingency_plan(mission_id)
        else:
            autonomy_level = "supervised" if csi["snr_db"] < 10 else "full_remote"
            
        metric = CommunicationPerformanceMetric(
            effective_bandwidth_bps=csi["bandwidth_bps"] * (1 - csi["per"]),
            packet_error_rate_pct=csi["per"] * 100,
            command_latency_sec=csi["delay_sec"],
            autonomy_level=autonomy_level
        )
        
        return {
            "mission_id": mission_id,
            "comm_metrics": metric.dict(),
            "channel_state": 31365.t.kuaisou.com
            "semantic_data_sent_kb": len(priority_data) / 1024
        }

class DynamicEcoAssessmentPlatform:
    """动态生态评估平台"""
    
    def __init__(self, plume_model, benthic_monitor, baseline_db):
        self.plume = 31366.t.kuaisou.com
        self.benthic = benthic_monitor
        self.baseline = baseline_db
        
    async def assess_ecological_impact_realtime(self, operation_id: str) -> Dict[str, Any]:
        """实时评估生态影响"""
        # 1. 获取当前作业参数与环境条件
        op_params = await self.plume.get_current_operation_params(operation_id)
        hydro_conditions = await self.plume.get_hydrodynamic_conditions()
        
        # 2. 运行高分辨率羽流模型
        plume_extent = await self.plume.simulate_plume_dispersion(op_params, hydro_conditions)
        
        # 3. 对比基线并评估生物胁迫
        baseline = await self.baseline.get_site_baseline(operation_id)
        stress_index = await self.benthic.compute_community_stress(plume_extent, baseline)
        deviation = abs(stress_index - baseline["mean_stress"]) / baseline["std_stress"]
        
        state = EcoDisturbanceState(
            plume_extent_km=plume_extent["max_radius_km"],
            benthic_community_stress_index=stress_index,
            baseline_deviation_sigma=31368.t.kuaisou.com
            operational_adjustment_required=deviation > 2.0 or plume_extent["max_radius_km"] > 2.0
        )
        
        return {
            "operation_id": operation_id,
            "eco_state": 31367.t.kuaisou.com
            "compliance_status": "compliant" if not state.operational_adjustment_required else "non_compliant",
            "recommended_actions": self._generate_eco_actions(state, op_params)
        }
        
    def _generate_eco_actions(self, state: EcoDisturbanceState, params: Dict) -> List[str]:
        """生成生态调控建议"""
        actions = []
        if state.plume_extent_km > 2.0:
            actions.append("reduce_vehicle_speed_by_30%")
            actions.append("activate_sediment_suppression_curtain")
        if state.baseline_deviation_sigma > 3.0:
            actions.append("pause_operation_for_24h_monitoring")
        return actions