商业航天"可回收"首飞与低轨星座组网加速：当太空从"国家工程"变为"工业流水线"，一场关于"发射成本摩尔定律"的轨道争夺战进入决胜局

from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Tuple
import math

class RocketReusability(Enum):
    """火箭复用等级"""
    EXPENDABLE = "一次性（Expendable）"
    PARTIAL_REUSE = "部分复用（一级回收）"
    FULL_REUSE = "完全复用（一级+二级/上面级回收）"

class PropellantType(Enum):
    """推进剂类型"""
    KEROSENE_LOX = "煤油/液氧"
    METHANE_LOX = "液氧甲烷"
    HYDROGEN_LOX = "液氢/液氧"
    SOLID = "固体"

@dataclass
class LaunchVehicle:
    """运载火箭规格"""
    vehicle_id: str
    name: 31275.t.kuaisou.com
    manufacturer: str
    propellant: PropellantType
    reusability: RocketReusability
    leo_capacity_kg: float              # LEO运力（kg）
    sso_capacity_kg: float              # SSO运力（kg）
    first_stage_reuse_count: int        # 一级已复用次数
    turnaround_days: int                # 周转天数（回收→再发射）
    refurbishment_cost_pct: float       # 翻新成本占新造成本比例（%）
    launch_price_million_usd: float     # 单次发射报价（百万美元）
    success_rate_pct: float             # 历史成功率（%）

class LaunchCostMooreLawModeler:
    """发射成本摩尔定律建模器"""

    # 各推进剂类型的理论成本下限（$/kg to LEO，完全复用状态下）
    THEORETICAL_FLOOR_USD_KG = {
        PropellantType.KEROSENE_LOX: 800,
        PropellantType.METHANE_LOX: 500,
        PropellantType.HYDROGEN_LOX: 1200,
        PropellantType.SOLID: 5000,
    }

    # 复用次数与成本下降的经验系数
    REUSE_COST_DECAY_FACTOR = 0.35  # 每翻倍复用，成本降35%

    def model_cost_curve(
        self, vehicle: LaunchVehicle
    ) -> Dict[str, any]:
        """建模单型火箭的成本下降曲线"""
        floor = self.THEORETICAL_FLOOR_USD_KG.get(vehicle.propellant, 1000)

        # 当前单位成本
        current_cost_per_kg = (
            vehicle.launch_price_million_usd * 1e6 / vehicle.leo_capacity_kg
            if vehicle.leo_capacity_kg > 0 else float('inf')
        )

        # 基于复用次数的预测成本
        reuse_multiplier = max(vehicle.first_stage_reuse_count, 1)
        predicted_cost_per_kg = current_cost_per_kg * (
            (1 - self.REUSE_COST_DECAY_FACTOR) ** math.log2(reuse_multiplier)
        )

        # 距理论下限的差距
        gap_to_floor_pct = (
            (predicted_cost_per_kg - floor) / floor * 100
            if floor > 0 else float('inf')
        )

        # 盈亏平衡复用次数估算
        breakeven_reuses = self._estimate_breakeven(
            vehicle.launch_price_million_usd,
            vehicle.refurbishment_cost_pct,
            vehicle.leo_capacity_kg,
            floor,
        )

        # 年化发射频次对成本的影响
        annual_launches_possible = 365 / max(vehicle.turnaround_days, 1)
        utilization_economy = min(annual_launches_possible / 20, 1.0)  # 20次/年为满产基准

        effective_cost_per_kg = predicted_cost_per_kg / max(utilization_economy, 0.3)

        return {
            "vehicle": 31274.t.kuaisou.com
            "propellant": vehicle.propellant.value,
            "reusability": vehicle.reusability.value,
            "current_metrics": {
                "launch_price_m_usd": vehicle.launch_price_million_usd,
                "leo_capacity_kg": vehicle.leo_capacity_kg,
                "cost_per_kg_usd": round(current_cost_per_kg, 0),
                "reuse_count": vehicle.first_stage_reuse_count,
            },
            "cost_projection": {
                "predicted_cost_per_kg_usd": round(predicted_cost_per_kg, 0),
                "theoretical_floor_usd_kg": floor,
                "gap_to_floor_pct": round(gap_to_floor_pct, 1),
                "effective_cost_with_utilization_usd_kg": round(effective_cost_per_kg, 0),
            },
            "economic_viability": {
                "breakeven_reuse_count": breakeven_reuses,
                "current_vs_breakeven": (
                    "已越过盈亏平衡点" if vehicle.first_stage_reuse_count >= breakeven_reuses
                    else f"距盈亏平衡还需{breakeven_reuses - vehicle.first_stage_reuse_count}次复用"
                ),
                "annual_launch_capacity": annual_launches_possible,
                "utilization_economy_factor": round(utilization_economy, 2),
            },
            "competitive_position": self._assess_competitive_position(
                effective_cost_per_kg, vehicle.success_rate_pct, gap_to_floor_pct
            ),
        }

    def _estimate_breakeven(
        self,
        launch_price_m_usd: float,
        refurb_pct: float,
        capacity_kg: float,
        floor_usd_kg: float,
    ) -> int:
        """估算盈亏平衡复用次数"""
        new_build_cost = launch_price_m_usd * 1e6 / 0.3  # 假设毛利率30%
        refurb_cost = new_build_cost * refurb_pct / 100
        revenue_per_launch = launch_price_m_usd * 1e6

        cumulative_cost = new_build_cost
        cumulative_revenue = 0
        for n in range(1, 51):
            cumulative_cost += refurb_cost
            cumulative_revenue += revenue_per_launch
            if cumulative_revenue >= cumulative_cost:
                return n
        return 50  # 超过50次仍未回本

    def _assess_competitive_position(
        self, cost_usd_kg: float, success_rate: float, gap_pct: float
    ) -> Dict[str, str]:
        """评估市场竞争力"""
        if cost_usd_kg < 1500 and success_rate > 95:
            return {"tier": "T0", "verdict": "全球第一梯队，具备价格主导权"}
        elif cost_usd_kg < 3000 and success_rate > 90:
            return {"tier": "T1", "verdict": "具备国际竞争力，可承接主流商业订单"}
        elif cost_usd_kg < 5000 and success_rate > 85:
            return {"tier": "T2", "verdict": "国内主力，国际市场需差异化竞争"}
        else:
            return {"tier": "T3", "verdict": "成本偏高，需加速复用验证或聚焦细分市场"}

    def compare_constellation_launch_needs(
        self,
        constellation_size: int,
        satellite_mass_kg: float,
        orbit_altitude_km: float,
        deployment_timeline_years: int,
    ) -> Dict[str, any]:
        """评估星座组网对发射能力的需求"""
        total_mass_kg = constellation_size * satellite_mass_kg
        annual_mass_kg = total_mass_kg / deployment_timeline_years

        # 考虑补网需求（年均5%替换率）
        replenishment_mass_kg = constellation_size * satellite_mass_kg * 0.05
        sustained_annual_mass_kg = annual_mass_kg + replenishment_mass_kg

        # 所需等效LEO运力（考虑轨道高度折算）
        altitude_penalty = 1 + (orbit_altitude_km - 500) / 2000  # 简化折算
        required_leo_equivalent_kg_year = sustained_annual_mass_kg * altitude_penalty

        return {
            "constellation_size": constellation_size,
            "satellite_mass_kg": satellite_mass_kg,
            "orbit_km": 31273.t.kuaisou.com
            "deployment_years": deployment_timeline_years,
            "launch_demand": {
                "total_mass_mt": round(total_mass_kg / 1000, 1),
                "annual_deployment_mass_mt": round(annual_mass_kg / 1000, 1),
                "annual_replenishment_mass_mt": round(replenishment_mass_kg / 1000, 1),
                "sustained_annual_leo_equiv_mt": round(required_leo_equivalent_kg_year / 1000, 1),
            },
            "fleet_requirement": {
                "min_vehicles_needed": math.ceil(
                    required_leo_equivalent_kg_year / (15000 * 20)  # 假设15t LEO运力×20次/年
                ),
                "note": "基于15吨级LEO运力、年发射20次的可回收火箭估算",
            },
            "strategic_implication": (
                "星座组网速度受制于发射频次而非卫星产能。"
                "可回收火箭的快速周转能力是决定组网进度的关键瓶颈。"
            ),
        }

from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Tuple
import math

class ConstellationApplication(Enum):
    """星座应用场景"""
    BROADBAND_INTERNET = "宽带互联网接入"
    IOT_BACKHAUL = "物联网回传"
    NAVIGATION_AUGMENTATION = "导航增强"
    REMOTE_SENSING = "遥感观测"
    DIRECT_TO_CELL = "手机直连卫星"
    GOVERNMENT_MILITARY = "政府/军事专用"

class MarketRegion(Enum):
    """目标市场区域"""
    CHINA_DOMESTIC = "中国境内"
    SOUTHEAST_ASIA = "东南亚"
    MIDDLE_EAST = "中东"
    AFRICA = "非洲"
    LATIN_AMERICA = "拉丁美洲"
    GLOBAL_MARITIME = "全球海事"
    GLOBAL_AVATION = "全球航空"

@dataclass
class SatelliteConstellation:
    """低轨星座规格"""
    constellation_id: str
    name: 31272.t.kuaisou.com
    operator: str
    planned_satellites: int             # 规划卫星总数
    deployed_satellites: int            # 已部署卫星数
    orbit_altitude_km: float            # 轨道高度（km）
    inclination_deg: float              # 轨道倾角（度）
    bandwidth_gbps: float               # 系统总带宽（Gbps）
    terminal_price_usd: float           # 用户终端售价（美元）
    monthly_subscription_usd: float     # 月服务费（美元）
    active_subscribers: int             # 活跃用户数
    annual_revenue_million_usd: float   # 年收入（百万美元）
    annual_opex_million_usd: float      # 年运维支出（百万美元）

class ConstellationBusinessModelEvaluator:
    """星座商业模型评估器"""

    # 各应用场景的单位带宽价值（$/Gbps/月）
    BANDWIDTH_VALUE_USD_GBPS_MONTH = {
        ConstellationApplication.BROADBAND_INTERNET: 50,
        ConstellationApplication.IOT_BACKHAUL: 20,
        ConstellationApplication.NAVIGATION_AUGMENTATION: 200,
        ConstellationApplication.REMOTE_SENSING: 150,
        ConstellationApplication.DIRECT_TO_CELL: 300,
        ConstellationApplication.GOVERNMENT_MILITARY: 500,
    }

    # 各区域市场的渗透率天花板（%）
    MARKET_PENETRATION_CAP = {
        MarketRegion.CHINA_DOMESTIC: 0.02,      # 地面网络发达，卫星为补充
        MarketRegion.SOUTHEAST_ASIA: 0.08,
        MarketRegion.MIDDLE_EAST: 0.06,
        MarketRegion.AFRICA: 0.12,
        MarketRegion.LATIN_AMERICA: 0.07,
        MarketRegion.GLOBAL_MARITIME: 0.25,
        MarketRegion.GLOBAL_AVATION: 0.15,
    }

    def evaluate_business_model(
        self, constellation: SatelliteConstellation
    ) -> Dict[str, any]:
        """评估星座商业模型健康度"""
        # 用户ARPU与LTV
        arpu_monthly = constellation.monthly_subscription_usd
        churn_rate_monthly = 0.03  # 假设月流失率3%
        ltv_months = 1 / churn_rate_monthly if churn_rate_monthly > 0 else 999
        customer_ltv = arpu_monthly * ltv_months

        # 终端补贴回收周期
        terminal_payback_months = (
            constellation.terminal_price_usd / arpu_monthly
            if arpu_monthly > 0 else float('inf')
        )

        # 年度收入vs运维支出
        revenue_gap = constellation.annual_revenue_million_usd - constellation.annual_opex_million_usd
        burn_rate_months = (
            constellation.annual_opex_million_usd / max(constellation.annual_revenue_million_usd, 0.1) * 12
        )

        # 星座利用率
        utilization_pct = (
            constellation.deployed_satellites / constellation.planned_satellites * 100
            if constellation.planned_satellites > 0 else 0
        )

        # 单星收入效率
        revenue_per_satellite_year = (
            constellation.annual_revenue_million_usd * 1e6 / constellation.deployed_satellites
            if constellation.deployed_satellites > 0 else 0
        )

        # 盈亏平衡用户数
        monthly_opex = constellation.annual_opex_million_usd * 1e6 / 12
        breakeven_subscribers = math.ceil(monthly_opex / arpu_monthly) if arpu_monthly > 0 else float('inf')

        return {
            "constellation": constellation.name,
            "operator": constellation.operator,
            "deployment_status": {
                "planned": constellation.planned_satellites,
                "deployed": constellation.deployed_satellites,
                "completion_pct": round(utilization_pct, 1),
            },
            "unit_economics": {
                "arpu_usd_month": arpu_monthly,
                "terminal_price_usd": constellation.terminal_price_usd,
                "terminal_payback_months": round(terminal_payback_months, 1),
                "customer_ltv_usd": round(customer_ltv, 0),
                "ltv_terminal_ratio": round(customer_ltv / max(constellation.terminal_price_usd, 1), 1),
            },
            "financial_health": {
                "annual_revenue_m_usd": constellation.annual_revenue_million_usd,
                "annual_opex_m_usd": constellation.annual_opex_million_usd,
                "net_cashflow_m_usd": round(revenue_gap, 1),
                "burn_rate_multiple": round(burn_rate_months, 1),
                "breakeven_subscribers": 31271.t.kuaisou.com
                "current_vs_breakeven_pct": round(
                    constellation.active_subscribers / max(breakeven_subscribers, 1) * 100, 1
                ),
            },
            "operational_efficiency": {
                "revenue_per_satellite_usd_year": round(revenue_per_satellite_year, 0),
                "active_subscribers": constellation.active_subscribers,
                "subscribers_per_satellite": round(
                    constellation.active_subscribers / max(constellation.deployed_satellites, 1), 1
                ),
            },
            "business_verdict": self._generate_business_verdict(
                revenue_gap, terminal_payback_months,
                constellation.active_subscribers, breakeven_subscribers,
                utilization_pct
            ),
        }

    def _generate_business_verdict(
        self,
        net_cashflow: float,
        terminal_payback: float,
        current_subs: int,
        breakeven_subs: int,
        completion_pct: float,
    ) -> str:
        """生成商业模型评估结论"""
        if net_cashflow > 0 and terminal_payback < 18:
            return "商业模型健康，已实现正向现金流，终端回收周期合理"
        elif current_subs >= breakeven_subs * 0.7 and completion_pct > 50:
            return "接近盈亏平衡，组网进度支撑商业化，需加速用户获取"
        elif completion_pct < 30:
            return "组网早期，商业验证窗口尚未打开，需控制烧钱节奏"
        elif terminal_payback > 36:
            return "终端定价过高或ARPU过低，需调整产品策略或开拓高价值场景"
        else:
            return "商业模型存在结构性风险，需重新评估目标市场与定价体系"

    def assess_regional_market_fit(
        self,
        constellation: SatelliteConstellation,
        region: MarketRegion,
        application: ConstellationApplication,
    ) -> Dict[str, any]:
        """评估特定区域-场景的市场适配性"""
        penetration_cap = self.MARKET_PENETRATION_CAP.get(region, 0.05)
        bandwidth_value = self.BANDWIDTH_VALUE_USD_GBPS_MONTH.get(application, 50)

        # 区域人口/终端基数估算（简化）
        regional_base_millions = {
            MarketRegion.CHINA_DOMESTIC: 1400,
            MarketRegion.SOUTHEAST_ASIA: 680,
            MarketRegion.MIDDLE_EAST: 450,
            MarketRegion.AFRICA: 1400,
            MarketRegion.LATIN_AMERICA: 660,
            MarketRegion.GLOBAL_MARITIME: 50,   # 船舶数量（万艘）
            MarketRegion.GLOBAL_AVATION: 30,    # 商用飞机数量（万架）
        }
        base = regional_base_millions.get(region, 100)
        addressable_subscribers = base * 1e6 * penetration_cap

        # 潜在年收入
        potential_annual_revenue_m_usd = (
            addressable_subscribers * constellation.monthly_subscription_usd * 12 / 1e6
        )

        # 带宽价值匹配度
        system_bandwidth_utilization = min(
            potential_annual_revenue_m_usd * 1e6 / 12 / max(constellation.bandwidth_gbps, 1) / bandwidth_value,
            1.0
        )

        return {
            "region": 31270.t.kuaisou.com
            "application": application.value,
            "market_potential": {
                "addressable_subscribers": round(addressable_subscribers, 0),
                "penetration_cap_pct": round(penetration_cap * 100, 1),
                "potential_annual_revenue_m_usd": round(potential_annual_revenue_m_usd, 1),
            },
            "bandwidth_fit": {
                "value_usd_gbps_month": bandwidth_value,
                "utilization_ratio": round(system_bandwidth_utilization, 2),
                "verdict": (
                    "带宽充裕，可激进拓展" if system_bandwidth_utilization < 0.5
                    else "带宽匹配" if system_bandwidth_utilization < 0.8
                    else "带宽紧张，需优先保障高价值用户"
                ),
            },
            "go_to_market_note": (
                f"{region.value}市场{application.value}场景的渗透率天花板为{penetration_cap*100:.1f}%，"
                f"建议终端定价≤{constellation.terminal_price_usd}美元以确保竞争力"
            ),
        }