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+# Moon Colony Logistics（地月运输建模与对比）
+
+本目录包含针对 **MCM 2026 Problem B**（见 `prob/en_B.md`）的一组可运行脚本：用**简化的轨道力学 + 火箭方程**估算把 **1 亿吨（100 million metric tons）**物资送到月球轨道/基地的**时间线**与**能量消耗**，并对比：
+
+- **纯火箭方案**（10 个既有发射场，每站每日 1 发）
+- **纯太空电梯方案**（3 个电梯港口持续转运）
+- **混合方案**（电梯优先，剩余由火箭补齐，且随着工期变长更多任务转移到低纬发射场以降低纬度惩罚）
+
+> 本项目的“能量”口径：  
+> - **火箭**：用推进剂化学能近似 \(E \approx m_\text{fuel}\cdot e_s\)（`e_s` 为燃料比能，默认 LH2/LOX 约 15.5 MJ/kg）。  
+> - **太空电梯**：用“提升能 + 顶端火箭段能量”的**总比能量**近似（默认 157.2 MJ/kg，即 157.2 GJ/吨）。  
+> 这是一种用于**对比量级与趋势**的工程近似，不等同于严格的热力学效率或完整任务能量收支。
+
+---
+
+### 目录结构与脚本索引
+
+#### 核心脚本
+
+- **`specific_energy_comparison.py`**  
+  地面发射 vs 太空电梯：比能量、燃料比、纬度影响（包含指定发射场列表）。
+- **`rocket_launch_scenario.py`**  
+  纯火箭方案（10 发射场 * 每日 1 发）排布与能量-工期曲线；并做发射频率敏感性分析。
+- **`combined_scenario.py`**  
+  混合方案（电梯优先 + 火箭补齐），输出组合方案能量-工期曲线，并生成 **100–300 年**区间三方案同图对比。
+- **`launch_capacity_analysis.py`**  
+  （可选）对历史火箭年发射量做 Logistic / Gompertz / Richards 拟合与预测，生成 `launch_capacity_*.png` 与 `launch_capacity_results.csv`。
+
+#### 主要输出图表（均在本目录）
+
+| 图表文件 | 含义 |
+|---|---|
+| `specific_energy_comparison.png` | 地面 vs 电梯的比能量/燃料比对比，含 ΔV 敏感性 |
+| `latitude_effects.png` | 月球任务场景下：纬度→自转速度损失→燃料/能量增加趋势（并标注指定发射场） |
+| `rocket_launch_timeline.png` | 纯火箭方案：完成年限 vs 总能量（工期变长→更多低纬→能量略降） |
+| `rocket_comprehensive.png` | 纯火箭方案综合图：能量趋势 + 分配饼图等 |
+| `launch_frequency_analysis.png` | 发射频率敏感性：每站每日发射数提升→完工年限显著下降 |
+| `combined_scenario_analysis.png` | 混合方案：总能量/电梯能量/火箭能量随工期变化 + 电梯占比 + 需要启用的火箭站点数 |
+| `scenario_comparison.png` | elevator-only / rocket-only / combined 的完工时间与能量柱状对比 |
+| **`three_scenarios_comparison.png`** | **100–300 年区间**：纯火箭、纯电梯、混合方案的“年份-能量”折线同图对比 |
+| `launch_capacity_analysis.png` `launch_capacity_physical.png` `launch_capacity_predictions.png` | （可选）火箭发射能力上限/趋势拟合与预测 |
+
+#### 数据文件（如存在）
+
+- `launch_capacity_results.csv`
+- `rocket_launch_counts.csv`
+- `launch_sites.csv`
+- `rocket launch counts.txt`
+
+---
+
+### 图表总览（本目录所有 PNG）
+
+为便于写报告/插图，这里把当前目录下的所有 PNG 统一列出（按主题分组）。如果你只想看关键结论，优先看 **`three_scenarios_comparison.png`**。
+
+<details>
+<summary><strong>1) 比能量与纬度效应（地面 vs 电梯、发射场纬度）</strong></summary>
+
+#### `specific_energy_comparison.png`
+
+![specific_energy_comparison](specific_energy_comparison.png)
+
+#### `latitude_effects.png`
+
+![latitude_effects](latitude_effects.png)
+
+</details>
+
+<details>
+<summary><strong>2) 纯火箭方案（排布与敏感性）</strong></summary>
+
+#### `rocket_launch_timeline.png`
+
+![rocket_launch_timeline](rocket_launch_timeline.png)
+
+#### `rocket_comprehensive.png`
+
+![rocket_comprehensive](rocket_comprehensive.png)
+
+#### `launch_distribution_min.png`
+
+![launch_distribution_min](launch_distribution_min.png)
+
+#### `launch_distribution_mid.png`
+
+![launch_distribution_mid](launch_distribution_mid.png)
+
+#### `launch_frequency_analysis.png`
+
+![launch_frequency_analysis](launch_frequency_analysis.png)
+
+</details>
+
+<details>
+<summary><strong>3) 混合方案与三方案对比（核心结论图）</strong></summary>
+
+#### `combined_scenario_analysis.png`
+
+![combined_scenario_analysis](combined_scenario_analysis.png)
+
+#### `scenario_comparison.png`
+
+![scenario_comparison](scenario_comparison.png)
+
+#### `three_scenarios_comparison.png`（100–300 年折线同图对比）
+
+![three_scenarios_comparison](three_scenarios_comparison.png)
+
+</details>
+
+<details>
+<summary><strong>4)（可选）火箭发射能力上限/趋势拟合</strong></summary>
+
+#### `launch_capacity_analysis.png`
+
+![launch_capacity_analysis](launch_capacity_analysis.png)
+
+#### `launch_capacity_physical.png`
+
+![launch_capacity_physical](launch_capacity_physical.png)
+
+#### `launch_capacity_predictions.png`
+
+![launch_capacity_predictions](launch_capacity_predictions.png)
+
+</details>
+
+<details>
+<summary><strong>5) 其它历史遗留图</strong></summary>
+
+本目录还包含 `earth_moon_transfer_analysis.png`（早期“地月转移：载荷–燃料/能量关系”图）。当前目录下未发现对应的 `earth_moon_transfer.py` 源码文件；如果你需要我把该脚本补回并与现有口径统一，我可以再加一个文件。
+
+#### `earth_moon_transfer_analysis.png`
+
+![earth_moon_transfer_analysis](earth_moon_transfer_analysis.png)
+
+</details>
+
+### 依赖安装
+
+建议使用 Python 3.10+。
+
+```bash
+pip install numpy matplotlib pandas
+```
+
+如需运行 `launch_capacity_analysis.py`（含曲线拟合），还需要：
+
+```bash
+pip install scipy
+```
+
+---
+
+### 数学模型（简化版）概览
+
+#### 1）火箭方程：燃料-载荷的线性比例（在固定 ΔV 与 Isp 下）
+
+火箭方程：
+\[
+\Delta V = v_e \ln\left(\frac{m_0}{m_f}\right),\quad v_e = I_{sp} g_0
+\]
+
+当任务 ΔV、发动机 \(I_{sp}\) 固定时，质量比 \(R=\exp(\Delta V/v_e)\) 为常数。若进一步采用结构系数近似：
+\[
+m_s=\alpha m_\text{fuel}
+\]
+则可解得燃料与载荷满足：
+\[
+\boxed{m_\text{fuel}=k\,m_p}\quad(\text{其中 }k\text{ 为常数，取决于 }\Delta V, I_{sp}, \alpha)
+\]
+
+这解释了为什么在同一任务与发动机参数下，“载荷–燃料”是近似**线性**关系。
+
+#### 2）能量口径（用于对比）
+
+- 火箭推进剂能量（近似）：
+\[
+E_\text{rocket}\approx m_\text{fuel}\,e_s
+\]
+其中 \(e_s\) 为燃料比能（默认 15.5 MJ/kg）。
+
+- 太空电梯总比能量（本项目用常数近似）：
+\[
+E_\text{elevator}\approx \epsilon_\text{elevator}\,m_p
+\]
+默认 \(\epsilon_\text{elevator}=157.2\text{ MJ/kg}\)（可在脚本中改）。
+
+#### 3）纬度导致的自转“损失”近似
+
+地球表面自转线速度：
+\[
+v(\varphi)=\omega R_E\cos(\varphi)
+\]
+相对赤道的速度差近似为 ΔV 惩罚：
+\[
+\Delta v_\text{loss}=v(0^\circ)-v(\varphi)
+\]
+
+在月球任务场景下（允许进入与发射场纬度相匹配的倾角、避免昂贵的纯平面变轨），纬度主要通过该项影响火箭能耗。
+
+---
+
+### 如何运行（会生成图片）
+
+#### 1）比能量与纬度效应
+
+```bash
+python specific_energy_comparison.py
+```
+
+输出（示例）会包括不同发射场的 ΔV 损失、燃料比、能量（MJ/kg）等，并生成：
+
+- `specific_energy_comparison.png`
+- `latitude_effects.png`
+
+#### 2）纯火箭方案：工期-能量与排布
+
+```bash
+python rocket_launch_scenario.py
+```
+
+会生成：
+
+- `rocket_launch_timeline.png`
+- `rocket_comprehensive.png`
+- `launch_distribution_min.png`、`launch_distribution_mid.png`
+- `launch_frequency_analysis.png`
+
+**部分运行输出（示例）**：
+
+```text
+Total payload: 100 million metric tons
+Payload per launch: 125 metric tons
+Total launches needed: 800,000
+Number of launch sites: 10
+Minimum completion time: 219.2 years
+
+Energy reduction (Extended vs Minimum time): 2.4%
+```
+
+解释：
+- 由于“每站每日 1 发、载荷 125 吨”的硬约束，纯火箭最短也要约 **219 年**完成。
+- 工期拉长后，可把更多发射集中到低纬站点，**能量仅小幅下降（约几个百分点）**——纬度惩罚相对火箭整体能耗并不占主导。
+
+#### 3）混合方案 + 100–300 年三方案同图对比
+
+```bash
+python combined_scenario.py
+```
+
+会生成：
+
+- `combined_scenario_analysis.png`
+- `scenario_comparison.png`
+- **`three_scenarios_comparison.png`（100–300 年折线同图对比）**
+
+**部分运行输出（示例）**：
+
+```text
+Elevator-only completion: 186.2 years
+Minimum completion (all systems): 100.7 years
+
+1. Minimum Time (~101 years): Total energy: 31537 PJ
+4. Elevator Only (186 years): Total energy: 15720 PJ
+Rocket Only (219 years): Total energy: 50609 PJ
+```
+
+解释：
+- **混合方案**把最短工期从电梯单独的 ~186 年压缩到 ~101 年（靠火箭补齐），但能量高于纯电梯。
+- 当工期 ≥ 186 年时，电梯即可独立完成，混合方案会自动退化为纯电梯曲线（能耗趋于同一水平）。
+- 纯火箭能耗显著更高（本模型下约为纯电梯的 **3×** 量级）。
+
+---
+
+### 发射场（10 个）与纬度效应（月球任务场景）
+
+本项目按纬度从低到高优先分配火箭发射（“低纬优先”贪心策略），站点列表与纬度如下（与题目一致）：
+
+| 发射场 | 近似纬度 | 备注 |
+|---|---:|---|
+| French Guiana（Kourou） | 5.2° | 低纬优势明显 |
+| Satish Dhawan（India） | 13.7° | 低纬 |
+| Texas（Boca Chica） | 26.0° | 美国 |
+| Florida（Cape Canaveral） | 28.5° | 美国 |
+| California（Vandenberg） | 34.7° | 美国 |
+| Virginia（Wallops） | 37.8° | 美国 |
+| Taiyuan（China） | 38.8° | 中国 |
+| Mahia（New Zealand） | 39.3° | 新西兰（南半球，按 \(|\varphi|\) 处理） |
+| Kazakhstan（Baikonur） | 45.6° | 哈萨克斯坦 |
+| Alaska（Kodiak） | 57.4° | 高纬，惩罚最大 |
+
+对应曲线可在下图直观看到：
+
+![latitude_effects](latitude_effects.png)
+
+---
+
+### 三方案“年份–能量”折线对比（100–300 年）
+
+下图是你要求的 **100–300 年**区间三条折线同图（不可完成的年份用空缺表示）：
+
+![three_scenarios_comparison](three_scenarios_comparison.png)
+
+读图要点：
+- **100–186 年**：只有混合方案可完成（电梯能力不足、火箭也不够快）。
+- **186–219 年**：电梯可完成；火箭仍无法完成；混合与电梯曲线重合（优先用电梯）。
+- **≥219 年**：三种方案都可完成，但纯火箭能耗最高。
+
+为了写报告时更快引用，下面给出几组关键年份的能量读数（与脚本运行输出一致，单位 PJ）：
+
+| 年份 | 混合方案 | 纯电梯 | 纯火箭 |
+|---:|---:|---:|---:|
+| 110 | 29852 | N/A | N/A |
+| 150 | 22257 | N/A | N/A |
+| 186 | 15720 | 15720 | N/A |
+| 220 | 15720 | 15720 | 50605 |
+| 300 | 15720 | 15720 | 50187 |
+
+---
+
+### 可调参数（最常改的地方）
+
+#### 火箭相关（`rocket_launch_scenario.py` / `combined_scenario.py`）
+
+- `TOTAL_PAYLOAD`：总物资（吨）
+- `PAYLOAD_PER_LAUNCH`：单次发射载荷（吨，默认 125）
+- `ISP`、`ALPHA`、`NUM_STAGES`：推进系统与结构参数
+- `SPECIFIC_FUEL_ENERGY`：燃料比能（J/kg）
+- `DELTA_V_BASE`：赤道基准 ΔV（m/s）
+- `LAUNCH_SITES`：发射场纬度列表（可自行替换/增减）
+
+#### 太空电梯相关（`combined_scenario.py`）
+
+- `NUM_ELEVATORS`：电梯数量（默认 3）
+- `ELEVATOR_CAPACITY_PER_YEAR`：**每个电梯年运力（吨/年）**  
+  > 题面是 **179,000 t/year**；若你要用 **17,900 t/year**，直接改此值即可。
+- `ELEVATOR_SPECIFIC_ENERGY`：电梯总比能量（J/吨），默认 157.2 GJ/吨
+
+---
+
+### 重要假设与局限（写报告时建议明确）
+
+- **脉冲变轨/固定 ΔV**：将复杂轨道机动用固定 ΔV 预算概括。
+- **恒定比冲/结构系数**：忽略发动机工况变化、分级差异、载具设计差异。
+- **“能量”是工程近似口径**：火箭取推进剂化学能；电梯取提升+顶端段合并比能量。
+- **完美运行**：未考虑失败率、维护停机、窗口约束、天气/法规等运营因素。
+- **排布算法为贪心**：火箭发射按“低纬优先”分配以降低纬度惩罚；未做更复杂的成本/容量优化（可扩展为线性规划/整数规划）。
+
+---
+
+### 一句话结论（便于写 Summary）
+
+- **纯火箭**在“10 站 * 每日 1 发 * 125 吨/发”的约束下，最短约 **219 年**，且能耗约 **5×10⁴ PJ** 量级。  
+- **纯电梯**能耗最低（约 **1.57×10⁴ PJ**），但最短约 **186 年**。  
+- **混合方案**能把工期压到约 **101 年**，能耗介于两者之间，并随着工期增加逐步趋近纯电梯能耗。
+
--- a/p1/combined_scenario.py
+++ b/p1/combined_scenario.py
@@ -0,0 +1,705 @@
+"""
+Moon Colony Construction - Combined Scenario Analysis
+(Space Elevator + Traditional Rockets)
+
+Problem: Deliver 100 million metric tons to Moon
+- 3 Space Elevators: 179,000 metric tons/year each (priority)
+- 10 Rocket launch sites: max 1 launch/day each, 125 tons/launch
+- Optimize: Use elevators first (lower specific energy), then rockets
+
+Key: As timeline extends:
+1. More payload handled by elevators (lower energy)
+2. Remaining rocket launches shift to lower-latitude sites
+"""
+
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+from dataclasses import dataclass
+from typing import List, Tuple, Dict
+import pandas as pd
+
+# 设置字体
+rcParams['font.sans-serif'] = ['Arial Unicode MS', 'DejaVu Sans', 'SimHei']
+rcParams['axes.unicode_minus'] = False
+
+# ============== 物理常数 ==============
+G0 = 9.81  # m/s²
+OMEGA_EARTH = 7.27e-5  # rad/s
+R_EARTH = 6.371e6  # m
+
+# ============== 任务参数 ==============
+TOTAL_PAYLOAD = 100e6  # 100 million metric tons
+
+# ============== 太空电梯参数 ==============
+NUM_ELEVATORS = 3
+ELEVATOR_CAPACITY_PER_YEAR = 179000  # metric tons per elevator per year
+TOTAL_ELEVATOR_CAPACITY = NUM_ELEVATORS * ELEVATOR_CAPACITY_PER_YEAR  # 537,000 tons/year
+
+# 太空电梯能量参数 (从之前分析)
+# 电梯提升能量: ~88.7 MJ/kg
+# 顶端推进能量: ~68.5 MJ/kg
+# 总计: ~157.2 MJ/kg = 157.2 TJ/1000 tons
+ELEVATOR_SPECIFIC_ENERGY = 157.2e9  # J per metric ton (157.2 MJ/kg * 1000 kg)
+
+# ============== 火箭参数 ==============
+PAYLOAD_PER_LAUNCH = 125  # metric tons per launch
+ISP = 450  # 比冲 (秒)
+SPECIFIC_FUEL_ENERGY = 15.5e6  # J/kg
+ALPHA = 0.10  # 结构系数
+NUM_STAGES = 3
+DELTA_V_BASE = 13300  # m/s (赤道发射到月球)
+
+# 火箭比能量 (从之前分析: ~492.7 MJ/kg = 492.7 TJ/1000 tons for equator)
+# 实际会因纬度变化
+
+
+# ============== 发射场定义 ==============
+@dataclass
+class LaunchSite:
+    name: str
+    short_name: str
+    latitude: float
+    max_launches_per_day: int = 1
+    
+    @property
+    def abs_latitude(self) -> float:
+        return abs(self.latitude)
+    
+    @property
+    def rotation_velocity(self) -> float:
+        return OMEGA_EARTH * R_EARTH * np.cos(np.radians(self.abs_latitude))
+    
+    @property
+    def delta_v_loss(self) -> float:
+        v_equator = OMEGA_EARTH * R_EARTH
+        return v_equator - self.rotation_velocity
+    
+    @property
+    def total_delta_v(self) -> float:
+        return DELTA_V_BASE + self.delta_v_loss
+
+
+# 10个发射场 (按纬度排序)
+LAUNCH_SITES = sorted([
+    LaunchSite("Kourou (French Guiana)", "Kourou", 5.2),
+    LaunchSite("Satish Dhawan (India)", "SDSC", 13.7),
+    LaunchSite("Boca Chica (Texas)", "Texas", 26.0),
+    LaunchSite("Cape Canaveral (Florida)", "Florida", 28.5),
+    LaunchSite("Vandenberg (California)", "California", 34.7),
+    LaunchSite("Wallops (Virginia)", "Virginia", 37.8),
+    LaunchSite("Taiyuan (China)", "Taiyuan", 38.8),
+    LaunchSite("Mahia (New Zealand)", "Mahia", 39.3),
+    LaunchSite("Baikonur (Kazakhstan)", "Baikonur", 45.6),
+    LaunchSite("Kodiak (Alaska)", "Alaska", 57.4),
+], key=lambda x: x.abs_latitude)
+
+
+# ============== 核心计算函数 ==============
+
+def fuel_ratio_multistage(delta_v: float) -> float:
+    """多级火箭燃料/载荷比"""
+    ve = ISP * G0
+    delta_v_per_stage = delta_v / NUM_STAGES
+    R_stage = np.exp(delta_v_per_stage / ve)
+    
+    denominator = 1 - ALPHA * (R_stage - 1)
+    if denominator <= 0:
+        return np.inf
+    
+    k_stage = (R_stage - 1) / denominator
+    
+    total_fuel_ratio = 0
+    remaining_ratio = 1.0
+    
+    for _ in range(NUM_STAGES):
+        fuel_this_stage = remaining_ratio * k_stage
+        total_fuel_ratio += fuel_this_stage
+        remaining_ratio *= (1 + k_stage * (1 + ALPHA))
+    
+    return total_fuel_ratio
+
+
+def rocket_energy_per_ton(site: LaunchSite) -> float:
+    """火箭发射每吨载荷的能量消耗 (J/ton)"""
+    k = fuel_ratio_multistage(site.total_delta_v)
+    fuel_per_ton = k * 1000  # kg fuel per metric ton payload
+    return fuel_per_ton * SPECIFIC_FUEL_ENERGY
+
+
+def rocket_energy_per_launch(site: LaunchSite) -> float:
+    """单次火箭发射的能量消耗 (J)"""
+    return rocket_energy_per_ton(site) * PAYLOAD_PER_LAUNCH
+
+
+# ============== 组合方案计算 ==============
+
+def calculate_combined_scenario(completion_years: float) -> Dict:
+    """
+    计算给定完成年限下的组合方案
+    
+    策略:
+    1. 太空电梯满负荷运行
+    2. 剩余载荷由火箭发射，优先低纬度站点
+    
+    返回:
+        方案详情字典
+    """
+    # 太空电梯运输量
+    elevator_payload = min(TOTAL_ELEVATOR_CAPACITY * completion_years, TOTAL_PAYLOAD)
+    elevator_energy = elevator_payload * ELEVATOR_SPECIFIC_ENERGY
+    
+    # 剩余需要火箭运输的载荷
+    remaining_payload = TOTAL_PAYLOAD - elevator_payload
+    
+    if remaining_payload <= 0:
+        # 完全由电梯完成
+        return {
+            'completion_years': completion_years,
+            'elevator_payload': elevator_payload,
+            'elevator_energy_PJ': elevator_energy / 1e15,
+            'rocket_payload': 0,
+            'rocket_energy_PJ': 0,
+            'total_energy_PJ': elevator_energy / 1e15,
+            'rocket_launches': 0,
+            'sites_used': 0,
+            'elevator_fraction': 1.0,
+            'rocket_distribution': [],
+        }
+    
+    # 需要火箭发射
+    rocket_launches_needed = int(np.ceil(remaining_payload / PAYLOAD_PER_LAUNCH))
+    
+    # 按纬度优先分配火箭发射
+    days_available = completion_years * 365
+    max_launches_per_site = int(days_available)
+    
+    rocket_distribution = []
+    remaining_launches = rocket_launches_needed
+    rocket_energy = 0
+    sites_used = 0
+    
+    for site in LAUNCH_SITES:
+        if remaining_launches <= 0:
+            rocket_distribution.append((site, 0))
+        else:
+            allocated = min(remaining_launches, max_launches_per_site)
+            rocket_distribution.append((site, allocated))
+            rocket_energy += rocket_energy_per_launch(site) * allocated
+            remaining_launches -= allocated
+            if allocated > 0:
+                sites_used += 1
+    
+    # 检查是否能在规定时间内完成
+    total_rocket_capacity = len(LAUNCH_SITES) * max_launches_per_site * PAYLOAD_PER_LAUNCH
+    if remaining_payload > total_rocket_capacity:
+        # 无法完成
+        return None
+    
+    rocket_payload = rocket_launches_needed * PAYLOAD_PER_LAUNCH
+    total_energy = elevator_energy + rocket_energy
+    
+    return {
+        'completion_years': completion_years,
+        'elevator_payload': elevator_payload,
+        'elevator_energy_PJ': elevator_energy / 1e15,
+        'rocket_payload': rocket_payload,
+        'rocket_energy_PJ': rocket_energy / 1e15,
+        'total_energy_PJ': total_energy / 1e15,
+        'rocket_launches': rocket_launches_needed,
+        'sites_used': sites_used,
+        'elevator_fraction': elevator_payload / TOTAL_PAYLOAD,
+        'rocket_distribution': rocket_distribution,
+    }
+
+
+def analyze_combined_timeline() -> pd.DataFrame:
+    """
+    分析不同完成年限下的组合方案
+    """
+    # 计算关键时间点
+    # 1. 仅电梯完成所需时间
+    elevator_only_years = TOTAL_PAYLOAD / TOTAL_ELEVATOR_CAPACITY
+    
+    # 2. 最短时间（电梯+全部火箭站点满负荷）
+    rocket_capacity_per_year = len(LAUNCH_SITES) * 365 * PAYLOAD_PER_LAUNCH
+    total_capacity_per_year = TOTAL_ELEVATOR_CAPACITY + rocket_capacity_per_year
+    min_years = TOTAL_PAYLOAD / total_capacity_per_year
+    
+    print(f"Elevator-only completion: {elevator_only_years:.1f} years")
+    print(f"Minimum completion (all systems): {min_years:.1f} years")
+    print(f"Elevator capacity: {TOTAL_ELEVATOR_CAPACITY:,} tons/year")
+    print(f"Rocket capacity: {rocket_capacity_per_year:,} tons/year")
+    
+    # 分析范围
+    years_range = np.linspace(min_years, elevator_only_years * 1.1, 200)
+    
+    results = []
+    for years in years_range:
+        scenario = calculate_combined_scenario(years)
+        if scenario is not None:
+            results.append(scenario)
+    
+    return pd.DataFrame(results)
+
+
+# ============== 可视化 ==============
+
+def plot_combined_analysis(
+    save_path: str = '/Volumes/Files/code/mm/20260130_b/combined_scenario_analysis.png'
+):
+    """
+    绘制组合方案分析图
+    """
+    df = analyze_combined_timeline()
+    
+    # 关键时间点
+    elevator_only_years = TOTAL_PAYLOAD / TOTAL_ELEVATOR_CAPACITY
+    rocket_capacity_per_year = len(LAUNCH_SITES) * 365 * PAYLOAD_PER_LAUNCH
+    total_capacity_per_year = TOTAL_ELEVATOR_CAPACITY + rocket_capacity_per_year
+    min_years = TOTAL_PAYLOAD / total_capacity_per_year
+    
+    fig, axes = plt.subplots(2, 2, figsize=(16, 14))
+    
+    # ========== 图1: 完成年份 vs 总能量消耗 ==========
+    ax1 = axes[0, 0]
+    
+    ax1.plot(df['completion_years'], df['total_energy_PJ'], 'b-', linewidth=2.5, 
+             label='Total Energy')
+    ax1.plot(df['completion_years'], df['elevator_energy_PJ'], 'g--', linewidth=2, 
+             label='Elevator Energy')
+    ax1.plot(df['completion_years'], df['rocket_energy_PJ'], 'r--', linewidth=2, 
+             label='Rocket Energy')
+    
+    # 标记关键点
+    ax1.axvline(x=min_years, color='orange', linestyle=':', linewidth=1.5,
+                label=f'Min time: {min_years:.0f} years')
+    ax1.axvline(x=elevator_only_years, color='purple', linestyle=':', linewidth=1.5,
+                label=f'Elevator-only: {elevator_only_years:.0f} years')
+    
+    ax1.set_xlabel('Completion Time (years)', fontsize=12)
+    ax1.set_ylabel('Energy Consumption (PJ)', fontsize=12)
+    ax1.set_title('Combined Scenario: Completion Time vs Energy\n'
+                  '(Space Elevator + Rockets)', fontsize=14)
+    ax1.legend(loc='upper right')
+    ax1.grid(True, alpha=0.3)
+    ax1.set_xlim(min_years * 0.95, elevator_only_years * 1.05)
+    
+    # ========== 图2: 电梯 vs 火箭载荷比例 ==========
+    ax2 = axes[0, 1]
+    
+    ax2.fill_between(df['completion_years'], 0, df['elevator_fraction'] * 100, 
+                     alpha=0.7, color='green', label='Space Elevator')
+    ax2.fill_between(df['completion_years'], df['elevator_fraction'] * 100, 100, 
+                     alpha=0.7, color='red', label='Rockets')
+    
+    ax2.axvline(x=min_years, color='orange', linestyle=':', linewidth=1.5)
+    ax2.axvline(x=elevator_only_years, color='purple', linestyle=':', linewidth=1.5)
+    
+    ax2.set_xlabel('Completion Time (years)', fontsize=12)
+    ax2.set_ylabel('Payload Share (%)', fontsize=12)
+    ax2.set_title('Payload Distribution: Elevator vs Rockets', fontsize=14)
+    ax2.legend(loc='center right')
+    ax2.set_ylim(0, 100)
+    ax2.set_xlim(min_years * 0.95, elevator_only_years * 1.05)
+    ax2.grid(True, alpha=0.3)
+    
+    # ========== 图3: 使用的火箭发射场数量 ==========
+    ax3 = axes[1, 0]
+    
+    ax3.plot(df['completion_years'], df['sites_used'], 'r-', linewidth=2)
+    ax3.fill_between(df['completion_years'], 0, df['sites_used'], alpha=0.3, color='red')
+    
+    ax3.axvline(x=min_years, color='orange', linestyle=':', linewidth=1.5)
+    ax3.axvline(x=elevator_only_years, color='purple', linestyle=':', linewidth=1.5)
+    
+    # 标注发射场
+    for i, site in enumerate(LAUNCH_SITES):
+        ax3.axhline(y=i+1, color='gray', linestyle=':', alpha=0.2)
+        ax3.text(df['completion_years'].max() * 0.99, i+1 + 0.15, 
+                f'{site.short_name} ({site.abs_latitude:.0f}°)', 
+                fontsize=8, ha='right', va='bottom')
+    
+    ax3.set_xlabel('Completion Time (years)', fontsize=12)
+    ax3.set_ylabel('Number of Rocket Sites Used', fontsize=12)
+    ax3.set_title('Rocket Launch Sites Required\n(Longer timeline → fewer high-lat sites)', fontsize=14)
+    ax3.set_ylim(0, 11)
+    ax3.set_xlim(min_years * 0.95, elevator_only_years * 1.05)
+    ax3.grid(True, alpha=0.3)
+    
+    # ========== 图4: 能量效率对比 ==========
+    ax4 = axes[1, 1]
+    
+    # 计算平均比能量
+    avg_specific_energy = df['total_energy_PJ'] * 1e15 / TOTAL_PAYLOAD / 1e9  # GJ/ton
+    
+    ax4.plot(df['completion_years'], avg_specific_energy, 'b-', linewidth=2)
+    
+    # 参考线
+    elevator_specific = ELEVATOR_SPECIFIC_ENERGY / 1e9  # GJ/ton
+    ax4.axhline(y=elevator_specific, color='green', linestyle='--', 
+                label=f'Elevator only: {elevator_specific:.1f} GJ/ton')
+    
+    # 平均火箭比能量 (赤道)
+    rocket_specific_equator = rocket_energy_per_ton(LAUNCH_SITES[0]) / 1e9
+    ax4.axhline(y=rocket_specific_equator, color='red', linestyle='--', 
+                label=f'Rocket (equator): {rocket_specific_equator:.1f} GJ/ton')
+    
+    ax4.axvline(x=min_years, color='orange', linestyle=':', linewidth=1.5)
+    ax4.axvline(x=elevator_only_years, color='purple', linestyle=':', linewidth=1.5)
+    
+    ax4.set_xlabel('Completion Time (years)', fontsize=12)
+    ax4.set_ylabel('Average Specific Energy (GJ/ton)', fontsize=12)
+    ax4.set_title('Energy Efficiency vs Timeline\n(Longer time → more elevator → lower energy)', fontsize=14)
+    ax4.legend()
+    ax4.set_xlim(min_years * 0.95, elevator_only_years * 1.05)
+    ax4.grid(True, alpha=0.3)
+    
+    plt.suptitle(f'Combined Scenario: 100M tons to Moon\n'
+                 f'3 Elevators ({TOTAL_ELEVATOR_CAPACITY:,} t/yr) + 10 Rocket Sites',
+                 fontsize=15, y=1.02)
+    
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    print(f"\n组合方案分析图已保存至: {save_path}")
+    
+    return df
+
+
+def plot_scenario_comparison(
+    save_path: str = '/Volumes/Files/code/mm/20260130_b/scenario_comparison.png'
+):
+    """
+    对比三种方案：仅电梯、仅火箭、组合
+    """
+    # 关键时间点分析
+    elevator_only_years = TOTAL_PAYLOAD / TOTAL_ELEVATOR_CAPACITY
+    rocket_capacity_per_year = len(LAUNCH_SITES) * 365 * PAYLOAD_PER_LAUNCH
+    rocket_only_min_years = TOTAL_PAYLOAD / rocket_capacity_per_year
+    combined_min_years = TOTAL_PAYLOAD / (TOTAL_ELEVATOR_CAPACITY + rocket_capacity_per_year)
+    
+    # 各方案能量
+    elevator_only_energy = TOTAL_PAYLOAD * ELEVATOR_SPECIFIC_ENERGY / 1e15  # PJ
+    
+    # 火箭方案 (平均所有站点)
+    avg_rocket_energy_per_ton = sum(rocket_energy_per_ton(s) for s in LAUNCH_SITES) / len(LAUNCH_SITES)
+    rocket_only_energy = TOTAL_PAYLOAD * avg_rocket_energy_per_ton / 1e15  # PJ
+    
+    # 组合方案 (最短时间 - 加余量)
+    combined_scenario = calculate_combined_scenario(combined_min_years * 1.01)
+    combined_min_energy = combined_scenario['total_energy_PJ'] if combined_scenario else elevator_only_energy
+    
+    # 组合方案 (仅电梯时间)
+    combined_extended = calculate_combined_scenario(elevator_only_years * 0.95)
+    combined_extended_energy = combined_extended['total_energy_PJ'] if combined_extended else elevator_only_energy
+    
+    fig, axes = plt.subplots(1, 2, figsize=(14, 6))
+    
+    # ========== 图1: 完成时间对比 ==========
+    ax1 = axes[0]
+    
+    scenarios = ['Elevator\nOnly', 'Rocket\nOnly', 'Combined\n(Min Time)', 'Combined\n(Extended)']
+    times = [elevator_only_years, rocket_only_min_years, combined_min_years, elevator_only_years * 0.99]
+    colors = ['green', 'red', 'blue', 'purple']
+    
+    bars = ax1.bar(scenarios, times, color=colors, alpha=0.7)
+    
+    ax1.set_ylabel('Completion Time (years)', fontsize=12)
+    ax1.set_title('Completion Time Comparison', fontsize=14)
+    ax1.grid(True, alpha=0.3, axis='y')
+    
+    for bar, t in zip(bars, times):
+        ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 2, 
+                f'{t:.0f}y', ha='center', fontsize=11, fontweight='bold')
+    
+    # ========== 图2: 能量消耗对比 ==========
+    ax2 = axes[1]
+    
+    energies = [elevator_only_energy, rocket_only_energy, combined_min_energy, combined_extended_energy]
+    
+    bars = ax2.bar(scenarios, energies, color=colors, alpha=0.7)
+    
+    ax2.set_ylabel('Total Energy (PJ)', fontsize=12)
+    ax2.set_title('Energy Consumption Comparison', fontsize=14)
+    ax2.grid(True, alpha=0.3, axis='y')
+    
+    for bar, e in zip(bars, energies):
+        ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 500, 
+                f'{e:.0f}', ha='center', fontsize=11, fontweight='bold')
+    
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    print(f"方案对比图已保存至: {save_path}")
+    
+    return {
+        'elevator_only': {'years': elevator_only_years, 'energy_PJ': elevator_only_energy},
+        'rocket_only': {'years': rocket_only_min_years, 'energy_PJ': rocket_only_energy},
+        'combined_min': {'years': combined_min_years, 'energy_PJ': combined_min_energy},
+        'combined_extended': {'years': elevator_only_years * 0.99, 'energy_PJ': combined_extended_energy},
+    }
+
+
+def print_summary(df: pd.DataFrame):
+    """打印分析摘要"""
+    elevator_only_years = TOTAL_PAYLOAD / TOTAL_ELEVATOR_CAPACITY
+    rocket_capacity_per_year = len(LAUNCH_SITES) * 365 * PAYLOAD_PER_LAUNCH
+    combined_min_years = TOTAL_PAYLOAD / (TOTAL_ELEVATOR_CAPACITY + rocket_capacity_per_year)
+    
+    print("\n" + "=" * 90)
+    print("COMBINED SCENARIO (SPACE ELEVATOR + ROCKETS) ANALYSIS")
+    print("=" * 90)
+    
+    print(f"\nSystem Capacities:")
+    print(f"  - 3 Space Elevators: {TOTAL_ELEVATOR_CAPACITY:,} tons/year")
+    print(f"  - 10 Rocket Sites: {rocket_capacity_per_year:,} tons/year (@ 1 launch/day each)")
+    print(f"  - Combined: {TOTAL_ELEVATOR_CAPACITY + rocket_capacity_per_year:,} tons/year")
+    
+    print(f"\nSpecific Energy:")
+    print(f"  - Elevator: {ELEVATOR_SPECIFIC_ENERGY/1e9:.1f} GJ/ton ({ELEVATOR_SPECIFIC_ENERGY/1e9/rocket_energy_per_ton(LAUNCH_SITES[0])*1e9*100:.1f}% of rocket)")
+    print(f"  - Rocket (Kourou): {rocket_energy_per_ton(LAUNCH_SITES[0])/1e9:.1f} GJ/ton")
+    print(f"  - Rocket (Alaska): {rocket_energy_per_ton(LAUNCH_SITES[-1])/1e9:.1f} GJ/ton")
+    
+    print(f"\nKey Scenarios:")
+    
+    # 最短时间 (加一点余量确保可以完成)
+    min_scenario = calculate_combined_scenario(combined_min_years * 1.01)
+    print(f"\n  1. Minimum Time (~{combined_min_years:.0f} years):")
+    print(f"     - Elevator payload: {min_scenario['elevator_payload']/1e6:.1f}M tons ({min_scenario['elevator_fraction']*100:.1f}%)")
+    print(f"     - Rocket payload: {min_scenario['rocket_payload']/1e6:.1f}M tons")
+    print(f"     - Rocket launches: {min_scenario['rocket_launches']:,}")
+    print(f"     - Sites used: {min_scenario['sites_used']}")
+    print(f"     - Total energy: {min_scenario['total_energy_PJ']:.0f} PJ")
+    
+    # 中等时间
+    mid_years = (combined_min_years + elevator_only_years) / 2
+    mid_scenario = calculate_combined_scenario(mid_years)
+    print(f"\n  2. Medium Timeline ({mid_years:.0f} years):")
+    print(f"     - Elevator payload: {mid_scenario['elevator_payload']/1e6:.1f}M tons ({mid_scenario['elevator_fraction']*100:.1f}%)")
+    print(f"     - Rocket payload: {mid_scenario['rocket_payload']/1e6:.1f}M tons")
+    print(f"     - Rocket launches: {mid_scenario['rocket_launches']:,}")
+    print(f"     - Sites used: {mid_scenario['sites_used']}")
+    print(f"     - Total energy: {mid_scenario['total_energy_PJ']:.0f} PJ")
+    
+    # 接近仅电梯
+    extended_years = elevator_only_years * 0.95
+    extended_scenario = calculate_combined_scenario(extended_years)
+    print(f"\n  3. Extended Timeline ({extended_years:.0f} years):")
+    print(f"     - Elevator payload: {extended_scenario['elevator_payload']/1e6:.1f}M tons ({extended_scenario['elevator_fraction']*100:.1f}%)")
+    print(f"     - Rocket payload: {extended_scenario['rocket_payload']/1e6:.1f}M tons")
+    print(f"     - Rocket launches: {extended_scenario['rocket_launches']:,}")
+    print(f"     - Sites used: {extended_scenario['sites_used']}")
+    print(f"     - Total energy: {extended_scenario['total_energy_PJ']:.0f} PJ")
+    
+    # 仅电梯
+    print(f"\n  4. Elevator Only ({elevator_only_years:.0f} years):")
+    elevator_energy = TOTAL_PAYLOAD * ELEVATOR_SPECIFIC_ENERGY / 1e15
+    print(f"     - Elevator payload: 100M tons (100%)")
+    print(f"     - Rocket launches: 0")
+    print(f"     - Total energy: {elevator_energy:.0f} PJ")
+    
+    print("\n" + "=" * 90)
+    print("ENERGY SAVINGS ANALYSIS")
+    print("=" * 90)
+    
+    rocket_only_energy = TOTAL_PAYLOAD * sum(rocket_energy_per_ton(s) for s in LAUNCH_SITES) / len(LAUNCH_SITES) / 1e15
+    
+    print(f"\n  Baseline (Rocket Only): {rocket_only_energy:.0f} PJ")
+    print(f"  Combined (Min Time): {min_scenario['total_energy_PJ']:.0f} PJ (saves {(1-min_scenario['total_energy_PJ']/rocket_only_energy)*100:.0f}%)")
+    print(f"  Combined (Extended): {extended_scenario['total_energy_PJ']:.0f} PJ (saves {(1-extended_scenario['total_energy_PJ']/rocket_only_energy)*100:.0f}%)")
+    print(f"  Elevator Only: {elevator_energy:.0f} PJ (saves {(1-elevator_energy/rocket_only_energy)*100:.0f}%)")
+    
+    print("=" * 90)
+
+
+def plot_three_scenarios_comparison(
+    year_min: float = 100,
+    year_max: float = 300,
+    save_path: str = '/Volumes/Files/code/mm/20260130_b/three_scenarios_comparison.png'
+):
+    """
+    绘制三种方案在指定年份范围内的能量消耗对比折线图
+    
+    方案:
+    1. 纯火箭方案 (Rocket Only)
+    2. 纯电梯方案 (Elevator Only)  
+    3. 混合方案 (Combined)
+    """
+    years = np.linspace(year_min, year_max, 200)
+    
+    # 计算各方案的能量
+    rocket_energy = []
+    elevator_energy = []
+    combined_energy = []
+    
+    # 关键时间点
+    elevator_only_min_years = TOTAL_PAYLOAD / TOTAL_ELEVATOR_CAPACITY  # ~186 years
+    rocket_only_min_years = TOTAL_PAYLOAD / (len(LAUNCH_SITES) * 365 * PAYLOAD_PER_LAUNCH)  # ~219 years
+    combined_min_years = TOTAL_PAYLOAD / (TOTAL_ELEVATOR_CAPACITY + len(LAUNCH_SITES) * 365 * PAYLOAD_PER_LAUNCH)  # ~101 years
+    
+    for y in years:
+        # ===== 纯火箭方案 =====
+        if y >= rocket_only_min_years:
+            # 可以完成，优先使用低纬度站点
+            total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
+            days_available = y * 365
+            max_per_site = int(days_available)
+            
+            energy = 0
+            remaining = total_launches
+            for site in LAUNCH_SITES:
+                if remaining <= 0:
+                    break
+                allocated = min(remaining, max_per_site)
+                energy += rocket_energy_per_launch(site) * allocated
+                remaining -= allocated
+            
+            rocket_energy.append(energy / 1e15)
+        else:
+            # 无法完成
+            rocket_energy.append(np.nan)
+        
+        # ===== 纯电梯方案 =====
+        if y >= elevator_only_min_years:
+            # 可以完成
+            energy = TOTAL_PAYLOAD * ELEVATOR_SPECIFIC_ENERGY
+            elevator_energy.append(energy / 1e15)
+        else:
+            # 无法完成
+            elevator_energy.append(np.nan)
+        
+        # ===== 混合方案 =====
+        if y >= combined_min_years:
+            scenario = calculate_combined_scenario(y)
+            if scenario:
+                combined_energy.append(scenario['total_energy_PJ'])
+            else:
+                combined_energy.append(np.nan)
+        else:
+            combined_energy.append(np.nan)
+    
+    # 创建图表
+    fig, ax = plt.subplots(figsize=(14, 8))
+    
+    # 绘制三条折线
+    ax.plot(years, rocket_energy, 'r-', linewidth=2.5, label='Rocket Only', marker='', markersize=4)
+    ax.plot(years, elevator_energy, 'g-', linewidth=2.5, label='Elevator Only', marker='', markersize=4)
+    ax.plot(years, combined_energy, 'b-', linewidth=2.5, label='Combined (Elevator + Rocket)', marker='', markersize=4)
+    
+    # 标记关键时间点
+    ax.axvline(x=combined_min_years, color='blue', linestyle=':', alpha=0.7, linewidth=1.5)
+    ax.axvline(x=elevator_only_min_years, color='green', linestyle=':', alpha=0.7, linewidth=1.5)
+    ax.axvline(x=rocket_only_min_years, color='red', linestyle=':', alpha=0.7, linewidth=1.5)
+    
+    # 添加标注
+    y_pos = ax.get_ylim()[1] * 0.95
+    ax.annotate(f'Combined\nMin: {combined_min_years:.0f}y', 
+                xy=(combined_min_years, y_pos), fontsize=9, ha='center',
+                bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.8))
+    ax.annotate(f'Elevator\nMin: {elevator_only_min_years:.0f}y', 
+                xy=(elevator_only_min_years, y_pos * 0.85), fontsize=9, ha='center',
+                bbox=dict(boxstyle='round', facecolor='lightgreen', alpha=0.8))
+    ax.annotate(f'Rocket\nMin: {rocket_only_min_years:.0f}y', 
+                xy=(rocket_only_min_years, y_pos * 0.75), fontsize=9, ha='center',
+                bbox=dict(boxstyle='round', facecolor='lightsalmon', alpha=0.8))
+    
+    # 填充区域表示节能效果
+    # Combined比Rocket节省的能量
+    rocket_arr = np.array(rocket_energy)
+    combined_arr = np.array(combined_energy)
+    valid_mask = ~np.isnan(rocket_arr) & ~np.isnan(combined_arr)
+    ax.fill_between(years[valid_mask], combined_arr[valid_mask], rocket_arr[valid_mask], 
+                    alpha=0.2, color='purple', label='Energy saved by Combined vs Rocket')
+    
+    ax.set_xlabel('Completion Time (years)', fontsize=13)
+    ax.set_ylabel('Total Energy Consumption (PJ)', fontsize=13)
+    ax.set_title('Moon Colony Construction: Energy vs Completion Time\n'
+                 '(100 Million Metric Tons Payload Comparison)', fontsize=15)
+    ax.legend(loc='upper right', fontsize=11)
+    ax.grid(True, alpha=0.3)
+    ax.set_xlim(year_min, year_max)
+    
+    # 添加数据表格
+    # 选择几个关键年份的数据
+    key_years = [110, 150, 186, 220, 250]
+    table_data = []
+    for ky in key_years:
+        idx = np.argmin(np.abs(years - ky))
+        table_data.append([
+            f'{ky}',
+            f'{combined_energy[idx]:.0f}' if not np.isnan(combined_energy[idx]) else 'N/A',
+            f'{elevator_energy[idx]:.0f}' if not np.isnan(elevator_energy[idx]) else 'N/A',
+            f'{rocket_energy[idx]:.0f}' if not np.isnan(rocket_energy[idx]) else 'N/A',
+        ])
+    
+    # 在图表下方添加表格
+    table = ax.table(
+        cellText=table_data,
+        colLabels=['Year', 'Combined (PJ)', 'Elevator (PJ)', 'Rocket (PJ)'],
+        loc='bottom',
+        bbox=[0.15, -0.25, 0.7, 0.18],
+        cellLoc='center'
+    )
+    table.auto_set_font_size(False)
+    table.set_fontsize(10)
+    table.scale(1, 1.5)
+    
+    # 调整布局给表格留空间
+    plt.subplots_adjust(bottom=0.25)
+    
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    print(f"\n三方案对比图已保存至: {save_path}")
+    
+    # 打印关键数据
+    print("\n" + "=" * 70)
+    print("THREE SCENARIOS ENERGY COMPARISON (100-300 years)")
+    print("=" * 70)
+    print(f"\n{'Year':<10} {'Combined':>15} {'Elevator':>15} {'Rocket':>15}")
+    print(f"{'':10} {'(PJ)':>15} {'(PJ)':>15} {'(PJ)':>15}")
+    print("-" * 55)
+    
+    for ky in [110, 130, 150, 186, 200, 220, 250, 300]:
+        idx = np.argmin(np.abs(years - ky))
+        c = f'{combined_energy[idx]:.0f}' if not np.isnan(combined_energy[idx]) else 'N/A'
+        e = f'{elevator_energy[idx]:.0f}' if not np.isnan(elevator_energy[idx]) else 'N/A'
+        r = f'{rocket_energy[idx]:.0f}' if not np.isnan(rocket_energy[idx]) else 'N/A'
+        print(f"{ky:<10} {c:>15} {e:>15} {r:>15}")
+    
+    print("=" * 70)
+    
+    return fig
+
+
+# ============== 主程序 ==============
+
+if __name__ == "__main__":
+    print("=" * 90)
+    print("MOON COLONY: COMBINED SCENARIO ANALYSIS")
+    print("(Space Elevator System + Traditional Rockets)")
+    print("=" * 90)
+    
+    # 主分析
+    df = plot_combined_analysis()
+    
+    # 打印摘要
+    print_summary(df)
+    
+    # 方案对比
+    print("\n\n正在生成方案对比图...")
+    comparison = plot_scenario_comparison()
+    
+    print("\n" + "=" * 90)
+    print("FINAL COMPARISON")
+    print("=" * 90)
+    print(f"\n{'Scenario':<25} {'Years':>12} {'Energy (PJ)':>15} {'vs Rocket Only':>18}")
+    print("-" * 70)
+    for name, data in comparison.items():
+        rocket_energy_val = comparison['rocket_only']['energy_PJ']
+        savings = (1 - data['energy_PJ'] / rocket_energy_val) * 100 if rocket_energy_val > 0 else 0
+        print(f"{name:<25} {data['years']:>12.0f} {data['energy_PJ']:>15.0f} {savings:>17.0f}%")
+    print("=" * 90)
+    
+    # 生成三方案对比折线图 (100-300年)
+    print("\n\n正在生成三方案对比折线图 (100-300年)...")
+    plot_three_scenarios_comparison(year_min=100, year_max=300)
--- a/p1/combined_scenario_analysis.png
+++ b/p1/combined_scenario_analysis.png
--- a/p1/earth_moon_transfer_analysis.png
+++ b/p1/earth_moon_transfer_analysis.png
--- a/p1/latitude_effects.png
+++ b/p1/latitude_effects.png
--- a/p1/launch_capacity_analysis.png
+++ b/p1/launch_capacity_analysis.png
--- a/p1/launch_capacity_analysis.py
+++ b/p1/launch_capacity_analysis.py
@@ -0,0 +1,785 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+火箭发射数上限预测分析
+对比 Logistic、Gompertz、Richards 三种增长模型
+分析不同历史数据窗口对预测的影响
+"""
+
+import pandas as pd
+import numpy as np
+from scipy.optimize import curve_fit
+from scipy.stats import pearsonr
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings('ignore')
+
+plt.rcParams["font.sans-serif"] = ["Arial Unicode MS", "SimHei", "DejaVu Sans"]
+plt.rcParams["axes.unicode_minus"] = False
+
+
+# ============================================================
+# 1. 增长模型定义
+# ============================================================
+
+def logistic(t, K, r, t0):
+    """
+    Logistic 增长模型（对称S曲线）
+    N(t) = K / (1 + exp(-r*(t - t0)))
+    
+    参数:
+        K  : 环境容量/饱和上限
+        r  : 增长率
+        t0 : 拐点时间（增长最快的时刻，此时 N = K/2）
+    """
+    return K / (1 + np.exp(-r * (t - t0)))
+
+
+def gompertz(t, K, b, c):
+    """
+    Gompertz 增长模型（非对称S曲线，早期增长较快）
+    N(t) = K * exp(-b * exp(-c*t))
+    
+    参数:
+        K : 饱和上限
+        b : 位移参数
+        c : 增长率参数
+    拐点在 N = K/e ≈ 0.368K 处
+    """
+    return K * np.exp(-b * np.exp(-c * t))
+
+
+def richards(t, K, r, t0, v):
+    """
+    Richards 曲线（广义 Logistic，可调节对称性）
+    N(t) = K / (1 + exp(-r*(t - t0)))^(1/v)
+    
+    参数:
+        K  : 饱和上限
+        r  : 增长率
+        t0 : 拐点时间
+        v  : 形状参数（v=1 时退化为 Logistic）
+    """
+    exp_term = np.exp(-r * (t - t0))
+    # 避免数值溢出
+    exp_term = np.clip(exp_term, 1e-10, 1e10)
+    return K / np.power(1 + exp_term, 1/v)
+
+
+# ============================================================
+# 2. 数据加载与预处理
+# ============================================================
+
+def load_data(filepath="rocket_launch_counts.csv"):
+    """加载并清洗火箭发射数据"""
+    df = pd.read_csv(filepath)
+    df = df.rename(columns={"YDate": "year", "Total": "launches"})
+    df["year"] = pd.to_numeric(df["year"], errors="coerce")
+    df["launches"] = pd.to_numeric(df["launches"], errors="coerce")
+    df = df.dropna(subset=["year", "launches"])
+    # 只保留完整年份数据（截至2025年，2026年数据不完整）
+    df = df[(df["year"] >= 1957) & (df["year"] <= 2025)]
+    df = df.astype({"year": int, "launches": int})
+    df = df.sort_values("year").reset_index(drop=True)
+    return df
+
+
+# ============================================================
+# 3. 模型拟合
+# ============================================================
+
+def fit_model(model_func, t, y, model_name, p0, bounds):
+    """通用模型拟合函数"""
+    try:
+        popt, pcov = curve_fit(model_func, t, y, p0=p0, bounds=bounds, maxfev=50000)
+        perr = np.sqrt(np.diag(pcov))
+        y_pred = model_func(t, *popt)
+        
+        # 计算拟合优度
+        ss_res = np.sum((y - y_pred) ** 2)
+        ss_tot = np.sum((y - np.mean(y)) ** 2)
+        r_squared = 1 - (ss_res / ss_tot)
+        rmse = np.sqrt(np.mean((y - y_pred) ** 2))
+        
+        return {
+            "success": True,
+            "params": popt,
+            "param_err": perr,
+            "r_squared": r_squared,
+            "rmse": rmse,
+            "y_pred": y_pred
+        }
+    except Exception as e:
+        return {
+            "success": False,
+            "error": str(e)
+        }
+
+
+def fit_all_models(data, base_year=1957):
+    """对数据拟合所有三种模型"""
+    years = data["year"].values
+    launches = data["launches"].values
+    t = (years - base_year).astype(float)
+    
+    results = {}
+    
+    # Logistic 模型 - 提高上限边界以避免撞边界
+    p0_log = [2000.0, 0.08, 70.0]
+    bounds_log = ([300, 0.01, 0], [500000, 1.0, 300])
+    res = fit_model(logistic, t, launches, "Logistic", p0_log, bounds_log)
+    if res["success"]:
+        K, r, t0 = res["params"]
+        res["K"] = K
+        res["inflection_year"] = base_year + t0
+        res["inflection_value"] = K / 2
+    results["Logistic"] = res
+    
+    # Gompertz 模型
+    p0_gom = [2000.0, 5.0, 0.03]
+    bounds_gom = ([300, 0.5, 0.005], [500000, 50.0, 0.5])
+    res = fit_model(gompertz, t, launches, "Gompertz", p0_gom, bounds_gom)
+    if res["success"]:
+        K, b, c = res["params"]
+        res["K"] = K
+        # Gompertz 拐点：t = ln(b)/c, 值为 K/e
+        res["inflection_year"] = base_year + np.log(b) / c
+        res["inflection_value"] = K / np.e
+    results["Gompertz"] = res
+    
+    # Richards 模型
+    p0_ric = [2000.0, 0.08, 70.0, 2.0]
+    bounds_ric = ([300, 0.01, 0, 0.1], [500000, 1.0, 300, 10.0])
+    res = fit_model(richards, t, launches, "Richards", p0_ric, bounds_ric)
+    if res["success"]:
+        K, r, t0, v = res["params"]
+        res["K"] = K
+        res["inflection_year"] = base_year + t0
+        res["inflection_value"] = K / (v + 1) ** (1/v)
+        res["v"] = v
+    results["Richards"] = res
+    
+    return results, years, launches, t, base_year
+
+
+def predict_future(results, base_year, years_future):
+    """生成未来预测"""
+    t_future = years_future - base_year
+    predictions = {}
+    
+    for model_name, res in results.items():
+        if not res["success"]:
+            continue
+        
+        params = res["params"]
+        if model_name == "Logistic":
+            pred = logistic(t_future, *params)
+        elif model_name == "Gompertz":
+            pred = gompertz(t_future, *params)
+        elif model_name == "Richards":
+            pred = richards(t_future, *params)
+        
+        predictions[model_name] = np.maximum(pred, 0)
+    
+    return predictions
+
+
+# ============================================================
+# 4. 可视化
+# ============================================================
+
+def plot_model_comparison(df, results_dict, save_path="launch_capacity_analysis.png"):
+    """绘制模型对比图"""
+    fig, axes = plt.subplots(2, 2, figsize=(16, 12))
+    
+    # 颜色方案
+    colors = {
+        "Logistic": "#E74C3C",
+        "Gompertz": "#3498DB", 
+        "Richards": "#27AE60"
+    }
+    
+    # 子图1: 历史数据 + 三种模型拟合（全部数据）
+    ax1 = axes[0, 0]
+    results, years, launches, t, base_year = results_dict["全部数据"]
+    ax1.scatter(years, launches, color="gray", s=30, alpha=0.7, label="历史数据", zorder=3)
+    
+    years_curve = np.arange(years.min(), 2101)
+    t_curve = years_curve - base_year
+    
+    for model_name, res in results.items():
+        if not res["success"]:
+            continue
+        params = res["params"]
+        if model_name == "Logistic":
+            pred = logistic(t_curve, *params)
+        elif model_name == "Gompertz":
+            pred = gompertz(t_curve, *params)
+        elif model_name == "Richards":
+            pred = richards(t_curve, *params)
+        
+        ax1.plot(years_curve, pred, color=colors[model_name], lw=2,
+                label=f"{model_name} (K={res['K']:.0f}, R²={res['r_squared']:.3f})")
+    
+    ax1.axvline(2025, color="gray", ls="--", lw=1, alpha=0.5)
+    ax1.set_xlabel("年份", fontsize=11)
+    ax1.set_ylabel("年发射次数", fontsize=11)
+    ax1.set_title("全部历史数据 (1957-2025) 模型拟合", fontsize=12)
+    ax1.legend(loc="upper left", fontsize=9)
+    ax1.grid(True, alpha=0.3)
+    ax1.set_xlim(1955, 2105)
+    ax1.set_ylim(0, None)
+    
+    # 子图2: 近15年数据拟合
+    ax2 = axes[0, 1]
+    results, years, launches, t, base_year = results_dict["近15年"]
+    ax2.scatter(years, launches, color="gray", s=40, alpha=0.8, label="历史数据", zorder=3)
+    
+    years_curve = np.arange(2010, 2101)
+    t_curve = years_curve - base_year
+    
+    for model_name, res in results.items():
+        if not res["success"]:
+            continue
+        params = res["params"]
+        if model_name == "Logistic":
+            pred = logistic(t_curve, *params)
+        elif model_name == "Gompertz":
+            pred = gompertz(t_curve, *params)
+        elif model_name == "Richards":
+            pred = richards(t_curve, *params)
+        
+        ax2.plot(years_curve, pred, color=colors[model_name], lw=2,
+                label=f"{model_name} (K={res['K']:.0f})")
+    
+    ax2.axvline(2025, color="gray", ls="--", lw=1, alpha=0.5)
+    ax2.set_xlabel("年份", fontsize=11)
+    ax2.set_ylabel("年发射次数", fontsize=11)
+    ax2.set_title("近15年数据 (2010-2025) 模型拟合", fontsize=12)
+    ax2.legend(loc="upper left", fontsize=9)
+    ax2.grid(True, alpha=0.3)
+    ax2.set_xlim(2008, 2105)
+    ax2.set_ylim(0, None)
+    
+    # 子图3: 只展示 Richards 模型的K值（因为其他模型撞边界）
+    ax3 = axes[1, 0]
+    windows = ["全部数据", "近25年", "近15年", "近10年"]
+    x = np.arange(len(windows))
+    
+    k_values = []
+    for window in windows:
+        if window in results_dict:
+            res = results_dict[window][0].get("Richards", {})
+            k_values.append(res.get("K", 0) if res.get("success", False) else 0)
+        else:
+            k_values.append(0)
+    
+    bars = ax3.bar(x, k_values, 0.6, color=colors["Richards"], alpha=0.8, edgecolor='black')
+    
+    # 添加数值标签
+    for bar, k in zip(bars, k_values):
+        if k > 0:
+            ax3.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 500, 
+                    f'{k:.0f}', ha='center', va='bottom', fontsize=10, fontweight='bold')
+    
+    ax3.set_xlabel("数据窗口", fontsize=11)
+    ax3.set_ylabel("预测饱和上限 K (次/年)", fontsize=11)
+    ax3.set_title("Richards 模型: 不同数据窗口的饱和上限预测\n(Logistic/Gompertz 撞边界，不适用)", fontsize=11)
+    ax3.set_xticks(x)
+    ax3.set_xticklabels(windows)
+    ax3.grid(True, alpha=0.3, axis='y')
+    ax3.set_ylim(0, max(k_values) * 1.15 if k_values else 10000)
+    
+    # 子图4: Richards 模型拐点年份
+    ax4 = axes[1, 1]
+    inflection_years = []
+    inflection_values = []
+    for window in windows:
+        if window in results_dict:
+            res = results_dict[window][0].get("Richards", {})
+            if res.get("success", False):
+                inflection_years.append(res.get("inflection_year", 0))
+                inflection_values.append(res.get("inflection_value", 0))
+            else:
+                inflection_years.append(0)
+                inflection_values.append(0)
+        else:
+            inflection_years.append(0)
+            inflection_values.append(0)
+    
+    bars = ax4.bar(x, inflection_years, 0.6, color=colors["Richards"], alpha=0.8, edgecolor='black')
+    
+    # 添加标签（拐点年份和对应发射数）
+    for bar, year, val in zip(bars, inflection_years, inflection_values):
+        if year > 0:
+            ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5,
+                    f'{year:.0f}\n({val:.0f}次/年)', ha='center', va='bottom', fontsize=9)
+    
+    ax4.axhline(2025, color="red", ls="--", lw=2, alpha=0.8, label="当前年份 (2025)")
+    ax4.set_xlabel("数据窗口", fontsize=11)
+    ax4.set_ylabel("拐点年份", fontsize=11)
+    ax4.set_title("Richards 模型: 预测拐点年份\n(增长最快的时刻)", fontsize=11)
+    ax4.set_xticks(x)
+    ax4.set_xticklabels(windows)
+    ax4.legend(loc="lower right", fontsize=9)
+    ax4.grid(True, alpha=0.3, axis='y')
+    valid_years = [y for y in inflection_years if y > 0]
+    if valid_years:
+        ax4.set_ylim(2020, max(valid_years) + 15)
+    
+    plt.suptitle("火箭年发射数上限预测 - 多模型对比分析", fontsize=14, fontweight='bold', y=1.02)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches="tight")
+    plt.close()
+    print(f"图表已保存: {save_path}")
+
+
+def plot_physical_vs_model(results_dict, physical_limits, save_path="launch_capacity_physical.png"):
+    """绘制物理约束与模型预测对比图"""
+    fig, axes = plt.subplots(1, 2, figsize=(14, 6))
+    
+    # 左图：物理约束与模型预测K值对比
+    ax1 = axes[0]
+    
+    # 物理约束
+    phys_scenarios = list(physical_limits.keys())
+    phys_values = list(physical_limits.values())
+    x_phys = np.arange(len(phys_scenarios))
+    bars1 = ax1.barh(x_phys, phys_values, 0.6, color='#3498DB', alpha=0.7, label='物理约束估计')
+    
+    # 添加数值标签
+    for bar, val in zip(bars1, phys_values):
+        ax1.text(val + 50, bar.get_y() + bar.get_height()/2, 
+                f'{val:,}', va='center', fontsize=10)
+    
+    # 添加模型预测（Richards）
+    richards_k = []
+    for window in ["近25年", "近15年", "近10年"]:
+        if window in results_dict:
+            res = results_dict[window][0].get("Richards", {})
+            if res.get("success", False):
+                richards_k.append(res["K"])
+    
+    if richards_k:
+        avg_richards = np.mean(richards_k)
+        ax1.axvline(avg_richards, color='#E74C3C', ls='--', lw=2, 
+                   label=f'Richards模型平均 K={avg_richards:,.0f}')
+    
+    ax1.set_yticks(x_phys)
+    ax1.set_yticklabels(phys_scenarios)
+    ax1.set_xlabel("年发射上限 (次/年)", fontsize=11)
+    ax1.set_title("物理约束 vs 模型预测 (10个发射场)", fontsize=12)
+    ax1.legend(loc="lower right", fontsize=9)
+    ax1.grid(True, alpha=0.3, axis='x')
+    ax1.set_xlim(0, max(phys_values) * 1.3)
+    
+    # 右图：带物理约束的时间预测
+    ax2 = axes[1]
+    
+    # 获取近15年Richards预测
+    if "近15年" in results_dict:
+        results, years, launches, t, base_year = results_dict["近15年"]
+        res = results.get("Richards", {})
+        
+        if res["success"]:
+            years_future = np.arange(2010, 2101)
+            t_future = years_future - base_year
+            pred = richards(t_future, *res["params"])
+            
+            ax2.scatter(years, launches, color="gray", s=40, alpha=0.7, label="历史数据", zorder=3)
+            ax2.plot(years_future, pred, color='#27AE60', lw=2, label=f"Richards (K={res['K']:,.0f})")
+            
+            # 添加物理约束线
+            phys_colors = ['#E74C3C', '#F39C12', '#3498DB', '#9B59B6', '#1ABC9C']
+            for i, (scenario, limit) in enumerate(physical_limits.items()):
+                ax2.axhline(limit, color=phys_colors[i % len(phys_colors)], 
+                           ls='--', lw=1.5, alpha=0.7, label=f'{scenario}: {limit:,}')
+            
+            ax2.axvline(2025, color="gray", ls=":", lw=1, alpha=0.5)
+            ax2.set_xlabel("年份", fontsize=11)
+            ax2.set_ylabel("年发射次数", fontsize=11)
+            ax2.set_title("Richards预测 + 物理约束线", fontsize=12)
+            ax2.legend(loc="upper left", fontsize=8)
+            ax2.grid(True, alpha=0.3)
+            ax2.set_xlim(2008, 2105)
+            ax2.set_ylim(0, max(physical_limits.values()) * 1.2)
+    
+    plt.suptitle("物理约束分析 (10个发射场)", fontsize=14, fontweight='bold', y=1.02)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches="tight")
+    plt.close()
+    print(f"图表已保存: {save_path}")
+
+
+def plot_future_predictions(results_dict, save_path="launch_capacity_predictions.png"):
+    """绘制未来预测图"""
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6))
+    
+    colors = {
+        "Logistic": "#E74C3C",
+        "Gompertz": "#3498DB", 
+        "Richards": "#27AE60"
+    }
+    
+    # 左图：基于全部数据的预测
+    ax1 = axes[0]
+    results, years, launches, t, base_year = results_dict["全部数据"]
+    years_future = np.arange(1957, 2151)
+    predictions = predict_future(results, base_year, years_future)
+    
+    ax1.scatter(years, launches, color="gray", s=20, alpha=0.6, label="历史数据", zorder=3)
+    for model_name, pred in predictions.items():
+        ax1.plot(years_future, pred, color=colors[model_name], lw=2, label=model_name)
+    
+    ax1.axvline(2025, color="gray", ls="--", lw=1, alpha=0.5)
+    ax1.axvspan(2025, 2150, alpha=0.05, color="blue")
+    ax1.set_xlabel("年份", fontsize=11)
+    ax1.set_ylabel("年发射次数", fontsize=11)
+    ax1.set_title("基于全部历史数据的预测 (至2150年)", fontsize=12)
+    ax1.legend(loc="upper left", fontsize=10)
+    ax1.grid(True, alpha=0.3)
+    ax1.set_xlim(1955, 2155)
+    ax1.set_ylim(0, None)
+    
+    # 右图：基于近15年数据的预测
+    ax2 = axes[1]
+    results, years, launches, t, base_year = results_dict["近15年"]
+    years_future = np.arange(2010, 2151)
+    predictions = predict_future(results, base_year, years_future)
+    
+    ax2.scatter(years, launches, color="gray", s=40, alpha=0.7, label="历史数据", zorder=3)
+    for model_name, pred in predictions.items():
+        ax2.plot(years_future, pred, color=colors[model_name], lw=2, label=model_name)
+    
+    ax2.axvline(2025, color="gray", ls="--", lw=1, alpha=0.5)
+    ax2.axvspan(2025, 2150, alpha=0.05, color="blue")
+    ax2.set_xlabel("年份", fontsize=11)
+    ax2.set_ylabel("年发射次数", fontsize=11)
+    ax2.set_title("基于近15年数据的预测 (至2150年)", fontsize=12)
+    ax2.legend(loc="upper left", fontsize=10)
+    ax2.grid(True, alpha=0.3)
+    ax2.set_xlim(2008, 2155)
+    ax2.set_ylim(0, None)
+    
+    plt.suptitle("火箭年发射数长期预测", fontsize=14, fontweight='bold', y=1.02)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches="tight")
+    plt.close()
+    print(f"图表已保存: {save_path}")
+
+
+# ============================================================
+# 5. 主程序
+# ============================================================
+
+def bootstrap_K_estimate(data, model_func, p0, bounds, n_bootstrap=500, base_year=1957):
+    """Bootstrap 估计 K 值的不确定性"""
+    years = data["year"].values
+    launches = data["launches"].values
+    t = (years - base_year).astype(float)
+    n = len(t)
+    
+    K_samples = []
+    for _ in range(n_bootstrap):
+        # 重采样
+        idx = np.random.choice(n, n, replace=True)
+        t_boot = t[idx]
+        y_boot = launches[idx]
+        
+        try:
+            popt, _ = curve_fit(model_func, t_boot, y_boot, p0=p0, bounds=bounds, maxfev=10000)
+            K_samples.append(popt[0])  # K 是第一个参数
+        except:
+            pass
+    
+    if len(K_samples) > 10:
+        return {
+            "mean": np.mean(K_samples),
+            "median": np.median(K_samples),
+            "std": np.std(K_samples),
+            "ci_5": np.percentile(K_samples, 5),
+            "ci_95": np.percentile(K_samples, 95),
+            "samples": K_samples
+        }
+    return None
+
+
+def calculate_aic_bic(y_true, y_pred, n_params):
+    """计算 AIC 和 BIC"""
+    n = len(y_true)
+    ss_res = np.sum((y_true - y_pred) ** 2)
+    mse = ss_res / n
+    
+    # Log-likelihood (假设高斯误差)
+    log_likelihood = -n/2 * (np.log(2 * np.pi) + np.log(mse) + 1)
+    
+    aic = 2 * n_params - 2 * log_likelihood
+    bic = n_params * np.log(n) - 2 * log_likelihood
+    
+    return aic, bic
+
+
+def main():
+    print("=" * 70)
+    print("火箭年发射数上限预测分析")
+    print("模型: Logistic, Gompertz, Richards")
+    print("=" * 70)
+    
+    # 加载数据
+    df = load_data()
+    print(f"\n数据范围: {df['year'].min()} - {df['year'].max()}")
+    print(f"数据点数: {len(df)}")
+    print(f"最近5年发射数:")
+    for _, row in df.tail(5).iterrows():
+        print(f"  {int(row['year'])}: {int(row['launches'])} 次")
+    
+    # 计算增长率
+    recent = df[df["year"] >= 2020].copy()
+    if len(recent) > 1:
+        growth_rates = recent["launches"].pct_change().dropna() * 100
+        print(f"\n2020年以来年均增长率: {growth_rates.mean():.1f}%")
+    
+    # 定义不同的数据窗口
+    end_year = 2025
+    windows = {
+        "全部数据": df[df["year"] <= end_year].copy(),
+        "近25年": df[(df["year"] >= 2000) & (df["year"] <= end_year)].copy(),
+        "近15年": df[(df["year"] >= 2010) & (df["year"] <= end_year)].copy(),
+        "近10年": df[(df["year"] >= 2015) & (df["year"] <= end_year)].copy(),
+    }
+    
+    # 存储所有结果
+    all_results = {}
+    
+    # 对每个窗口进行拟合
+    for window_name, data in windows.items():
+        print(f"\n{'='*50}")
+        print(f"数据窗口: {window_name} ({data['year'].min()}-{data['year'].max()}, n={len(data)})")
+        print("=" * 50)
+        
+        results, years, launches, t, base_year = fit_all_models(data)
+        all_results[window_name] = (results, years, launches, t, base_year)
+        
+        for model_name, res in results.items():
+            if res["success"]:
+                print(f"\n{model_name} 模型:")
+                print(f"  饱和上限 K     = {res['K']:.0f} 次/年")
+                print(f"  拐点年份       = {res['inflection_year']:.1f}")
+                print(f"  拐点处发射数   = {res['inflection_value']:.0f} 次/年")
+                print(f"  R² (拟合优度)  = {res['r_squared']:.4f}")
+                print(f"  RMSE          = {res['rmse']:.2f}")
+                if model_name == "Richards":
+                    print(f"  形状参数 v     = {res.get('v', 'N/A'):.3f}")
+            else:
+                print(f"\n{model_name} 模型: 拟合失败 - {res.get('error', 'Unknown')}")
+    
+    # 生成未来预测表
+    print("\n" + "=" * 70)
+    print("未来预测 (基于近15年数据)")
+    print("=" * 70)
+    
+    results, years, launches, t, base_year = all_results["近15年"]
+    future_years = np.array([2030, 2040, 2050, 2075, 2100])
+    predictions = predict_future(results, base_year, future_years)
+    
+    print(f"\n{'年份':<10}", end="")
+    for model in ["Logistic", "Gompertz", "Richards"]:
+        print(f"{model:<15}", end="")
+    print()
+    print("-" * 55)
+    
+    for i, year in enumerate(future_years):
+        print(f"{year:<10}", end="")
+        for model in ["Logistic", "Gompertz", "Richards"]:
+            if model in predictions:
+                print(f"{predictions[model][i]:<15.0f}", end="")
+            else:
+                print(f"{'N/A':<15}", end="")
+        print()
+    
+    # 饱和上限汇总
+    print("\n" + "=" * 70)
+    print("饱和上限 K 汇总 (次/年)")
+    print("=" * 70)
+    print(f"\n{'数据窗口':<20}", end="")
+    for model in ["Logistic", "Gompertz", "Richards"]:
+        print(f"{model:<15}", end="")
+    print()
+    print("-" * 65)
+    
+    for window_name in ["全部数据", "近25年", "近15年", "近10年"]:
+        results = all_results[window_name][0]
+        print(f"{window_name:<20}", end="")
+        for model in ["Logistic", "Gompertz", "Richards"]:
+            if model in results and results[model]["success"]:
+                print(f"{results[model]['K']:<15.0f}", end="")
+            else:
+                print(f"{'N/A':<15}", end="")
+        print()
+    
+    # 物理约束分析
+    physical_limits = analyze_physical_constraints(n_sites=10)
+    
+    # 绘制图表
+    print("\n" + "=" * 70)
+    print("生成图表...")
+    plot_model_comparison(df, all_results)
+    plot_future_predictions(all_results)
+    plot_physical_vs_model(all_results, physical_limits)
+    
+    # 保存详细结果到CSV
+    save_results_csv(all_results)
+    
+    print("\n" + "=" * 70)
+    print("分析完成!")
+    print("=" * 70)
+    
+    # Bootstrap 不确定性分析
+    print("\n" + "=" * 70)
+    print("Bootstrap 不确定性分析 (基于近15年数据, 500次重采样)")
+    print("=" * 70)
+    
+    data_15 = windows["近15年"]
+    base_year = 1957
+    
+    # Richards 模型 Bootstrap
+    p0_ric = [2000.0, 0.08, 70.0, 2.0]
+    bounds_ric = ([300, 0.01, 0, 0.1], [500000, 1.0, 300, 10.0])
+    
+    boot_result = bootstrap_K_estimate(data_15, richards, p0_ric, bounds_ric, n_bootstrap=500, base_year=base_year)
+    if boot_result:
+        print(f"\nRichards 模型 K 值 Bootstrap 估计:")
+        print(f"  均值:          {boot_result['mean']:.0f} 次/年")
+        print(f"  中位数:        {boot_result['median']:.0f} 次/年")
+        print(f"  标准差:        {boot_result['std']:.0f}")
+        print(f"  90% 置信区间:  [{boot_result['ci_5']:.0f}, {boot_result['ci_95']:.0f}] 次/年")
+    
+    # AIC/BIC 模型选择
+    print("\n" + "=" * 70)
+    print("模型选择 (AIC/BIC, 基于近15年数据)")
+    print("=" * 70)
+    
+    results_15, years_15, launches_15, t_15, _ = all_results["近15年"]
+    print(f"\n{'模型':<12} {'AIC':<12} {'BIC':<12} {'参数数':<8}")
+    print("-" * 44)
+    
+    for model_name, res in results_15.items():
+        if res["success"]:
+            n_params = len(res["params"])
+            aic, bic = calculate_aic_bic(launches_15, res["y_pred"], n_params)
+            print(f"{model_name:<12} {aic:<12.1f} {bic:<12.1f} {n_params:<8}")
+    
+    # 结论
+    print("\n" + "=" * 70)
+    print("📊 分析结论与建议")
+    print("=" * 70)
+    
+    # 收集有效的K值（排除撞边界的）
+    valid_k_values = []
+    for window_name, (results, _, _, _, _) in all_results.items():
+        for model_name, res in results.items():
+            if res["success"] and res["K"] < 100000:  # 排除撞边界的
+                valid_k_values.append((window_name, model_name, res["K"]))
+    
+    print("\n1. 模型适用性分析:")
+    print("-" * 50)
+    print("   • Logistic/Gompertz: 数据处于快速增长期，无法确定上限")
+    print("     (模型撞到边界，说明增长远未饱和)")
+    print("   • Richards 曲线: 额外的形状参数使其能给出更合理估计")
+    print("     (推荐用于当前阶段的上限预测)")
+    
+    print("\n2. 合理的上限估计 (Richards 模型):")
+    print("-" * 50)
+    for window_name, model_name, k in valid_k_values:
+        if model_name == "Richards" and window_name != "全部数据":
+            print(f"   • {window_name}: K ≈ {k:.0f} 次/年")
+    
+    # Richards 结果的平均值
+    richards_k = [k for w, m, k in valid_k_values if m == "Richards" and w != "全部数据"]
+    if richards_k:
+        print(f"\n   📌 综合估计: {int(np.mean(richards_k)):,} - {int(np.max(richards_k)):,} 次/年")
+    
+    print("\n3. 物理约束参考 (基于10个主要发射场):")
+    print("-" * 50)
+    print("   • 主要发射场数量: 10个")
+    print("   • 假设每发射场年最大容量:")
+    print("     - 保守估计: 50次/年 → 总上限 500 次/年")
+    print("     - 中等估计: 100次/年 → 总上限 1,000 次/年")
+    print("     - 乐观估计: 200次/年 → 总上限 2,000 次/年")
+    print("     - 极限估计 (SpaceX级别): 400次/年 → 总上限 4,000 次/年")
+    print("   • 2025年实际数据: 329次 (10发射场平均 33次/场)")
+    print("   • SpaceX Starship 单型号目标: 1,000+ 次/年")
+    
+    print("\n4. 预测不确定性:")
+    print("-" * 50)
+    print("   • 当前处于指数增长早期，上限估计高度不确定")
+    print("   • 建议每年用新数据更新预测")
+    print("   • 需关注技术突破（可重复使用、星舰等）对上限的影响")
+    
+    if boot_result:
+        print(f"\n   Bootstrap 90%置信区间: {boot_result['ci_5']:.0f} - {boot_result['ci_95']:.0f} 次/年")
+
+
+def analyze_physical_constraints(n_sites=10):
+    """
+    基于发射场物理约束的上限分析
+    
+    参数:
+        n_sites: 发射场数量
+    """
+    print("\n" + "=" * 70)
+    print(f"物理约束分析 (基于 {n_sites} 个发射场)")
+    print("=" * 70)
+    
+    # 不同情景下的单发射场年容量
+    scenarios = {
+        "保守 (当前技术)": 50,
+        "中等 (技术进步)": 100,
+        "乐观 (快速周转)": 200,
+        "极限 (SpaceX级)": 400,
+        "理论极限 (每天1发)": 365,
+    }
+    
+    print(f"\n单发射场年发射容量假设:")
+    print("-" * 50)
+    
+    results = {}
+    for scenario, capacity_per_site in scenarios.items():
+        total_capacity = n_sites * capacity_per_site
+        results[scenario] = total_capacity
+        print(f"  {scenario:<20}: {capacity_per_site:>3} 次/场/年 → 总上限 {total_capacity:>5,} 次/年")
+    
+    # 带置信度的综合估计
+    print(f"\n综合估计 ({n_sites} 发射场):")
+    print("-" * 50)
+    print(f"  50% 置信区间: {n_sites * 50:,} - {n_sites * 100:,} 次/年")
+    print(f"  90% 置信区间: {n_sites * 30:,} - {n_sites * 200:,} 次/年")
+    print(f"  理论最大值:   {n_sites * 365:,} 次/年 (每天每场1发)")
+    
+    return results
+
+
+def save_results_csv(all_results, filepath="launch_capacity_results.csv"):
+    """保存详细结果到CSV"""
+    rows = []
+    for window_name, (results, _, _, _, _) in all_results.items():
+        for model_name, res in results.items():
+            if res["success"]:
+                row = {
+                    "数据窗口": window_name,
+                    "模型": model_name,
+                    "饱和上限K": res["K"],
+                    "拐点年份": res["inflection_year"],
+                    "拐点发射数": res["inflection_value"],
+                    "R²": res["r_squared"],
+                    "RMSE": res["rmse"]
+                }
+                rows.append(row)
+    
+    df_results = pd.DataFrame(rows)
+    df_results.to_csv(filepath, index=False, encoding="utf-8-sig")
+    print(f"详细结果已保存: {filepath}")
+
+
+if __name__ == "__main__":
+    main()
--- a/p1/launch_capacity_physical.png
+++ b/p1/launch_capacity_physical.png
--- a/p1/launch_capacity_predictions.png
+++ b/p1/launch_capacity_predictions.png
--- a/p1/launch_capacity_results.csv
+++ b/p1/launch_capacity_results.csv
@@ -0,0 +1,13 @@
+数据窗口,模型,饱和上限K,拐点年份,拐点发射数,R²,RMSE
+全部数据,Logistic,405.92414403335295,2099.062819804582,202.96207201667647,0.07766486971853037,46.74814967377199
+全部数据,Gompertz,401.0329954671314,2051.6490738033563,147.53179426375786,0.07545958812791254,46.80400317046386
+全部数据,Richards,687.2209173212347,2256.999921701128,512.1739060128197,0.08220581306057739,46.632929826767096
+近25年,Logistic,499998.291933841,2107.433566292766,249999.1459669205,0.8047914079948382,29.605437976518306
+近25年,Gompertz,499999.9999896808,2216.1161350271577,183939.72058192495,0.779423273804876,31.47037146055304
+近25年,Richards,16788.895636148314,2070.4580199940424,10907.704452410562,0.8048416215235632,29.60163002650332
+近15年,Logistic,499999.9999933337,2082.166434872205,249999.99999666685,0.8979462268050302,23.639528906752357
+近15年,Gompertz,499999.9999999759,2153.207304429069,183939.72058571232,0.8798702599574121,25.647766611285153
+近15年,Richards,32126.622670684326,2061.1007442806736,20365.326256859138,0.8979860420054704,23.63491710078375
+近10年,Logistic,499999.9996950232,2069.5299620988108,249999.9998475116,0.9632290653308331,15.01250946110567
+近10年,Gompertz,499999.99999999994,2122.2979572477343,183939.72058572114,0.9542285701514692,16.74935931143396
+近10年,Richards,23814.980027911413,2051.157710362777,15373.812459292129,0.9632499410027958,15.00824738946148
--- a/p1/launch_distribution_mid.png
+++ b/p1/launch_distribution_mid.png
--- a/p1/launch_distribution_min.png
+++ b/p1/launch_distribution_min.png
--- a/p1/launch_frequency_analysis.png
+++ b/p1/launch_frequency_analysis.png
--- a/p1/launch_sites.csv
+++ b/p1/launch_sites.csv
@@ -0,0 +1,24 @@
+英文名,发射场,发射场所在国家,2023年,2024年,2025年
+Florida,卡角太空基地（卡纳维拉尔角空军基地）,美国,59,71,82
+California,范登堡太空基地（范登堡旧金山基地）,美国,30,46,66
+Florida,肯尼迪航天中心,美国,13,23,27
+Virginia,沃洛普斯飞行中心,美国,3,1,
+Texas,博卡奇卡星星基地,美国,2,4,
+,酒泉卫星发射场,中国,36,21,33
+,西昌卫星发射场,中国,15,19,18
+Taiyuan Satellite Launch Center (China),太原卫星发射场,中国,4,13,
+,文昌航天发射场,中国,9,8,
+,海南商业航天发射场,中国,0,1,
+,广东阳江,中国,2,2,
+,山东海阳,中国,1,4,
+,普列谢茨克航天发射场,俄罗斯,7,5,
+,东方航天发射场,俄罗斯,3,4,
+Kazakhstan,拜科努尔航天发射场,哈萨克斯坦,9,8,
+Mahia Peninsula (New Zealand),欧尼努伊站,新西兰,7,13,
+Satish Dhawan Space Centre (India),萨迪什·达万航天中心,印度,7,5,
+French Guiana,圭亚那航天中心,法国,3,3,
+,种子岛航天中心,日本,3,5,
+,纪伊航天港,日本,0,2,
+,西海卫星发射场,朝鲜,3,1,
+,沙赫鲁德航天中心,伊朗,2,2,
+,塞姆南航天中心,伊朗,0,2,
--- a/counts.txt
+++ b/counts.txt
@@ -0,0 +1,78 @@
+# Bin     YDate                     ValMin                    ValMax                US     SU     RU     CN     F      J      AU     D      UK     I      I-ELDO IN     BR     KR     I-ESA  KP     IR     IL     CYM    NZ     Total
+# ---     ----                      ----                      ----                  (1)    (2)    (3)    (4)    (5)    (6)    (7)    (8)    (9)    (10)   (11)   (12)   (13)   (14)   (15)   (16)   (17)   (18)   (19)   (20)   -----
+     0   1956                     1956                     1957                        0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0            0
+     1   1957                     1957                     1958                        1      2      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0            3
+     2   1958                     1958                     1959                       23      5      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0           28
+     3   1959                     1959                     1960                       19      4      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0           23
+     4   1960                     1960                     1961                       29      9      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0           38
+     5   1961                     1961                     1962                       41      9      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0           50
+     6   1962                     1962                     1963                       59     22      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0           81
+     7   1963                     1963                     1964                       46     23      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0           69
+     8   1964                     1964                     1965                       64     36      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0          100
+     9   1965                     1965                     1966                       70     53      0      0      1      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0          124
+    10   1966                     1966                     1967                       77     51      0      0      1      2      0      0      0      0      0      0      0      0      0      0      0      0      0      0          131
+    11   1967                     1967                     1968                       61     74      0      0      2      1      0      0      0      1      0      0      0      0      0      0      0      0      0      0          139
+    12   1968                     1968                     1969                       48     79      0      0      0      0      0      0      0      0      1      0      0      0      0      0      0      0      0      0          128
+    13   1969                     1969                     1970                       41     82      0      0      0      1      0      0      0      0      1      0      0      0      0      0      0      0      0      0          125
+    14   1970                     1970                     1971                       29     87      0      1      2      2      0      0      1      1      1      0      0      0      0      0      0      0      0      0          124
+    15   1971                     1971                     1972                       33     91      0      1      2      2      0      0      1      2      1      0      0      0      0      0      0      0      0      0          133
+    16   1972                     1972                     1973                       32     79      0      0      0      1      0      0      0      1      0      0      0      0      0      0      0      0      0      0          113
+    17   1973                     1973                     1974                       25     89      0      1      1      0      0      0      0      0      0      0      0      0      0      0      0      0      0      0          116
+    18   1974                     1974                     1975                       23     85      0      2      0      1      0      0      0      2      0      0      0      0      0      0      0      0      0      0          113
+    19   1975                     1975                     1976                       30     93      0      3      3      2      0      0      0      1      0      0      0      0      0      0      0      0      0      0          132
+    20   1976                     1976                     1977                       26    100      0      3      0      2      0      0      0      0      0      0      0      0      0      0      0      0      0      0          131
+    21   1977                     1977                     1978                       26    102      0      0      0      2      0      0      0      0      0      0      0      0      0      0      0      0      0      0          130
+    22   1978                     1978                     1979                       33     91      0      1      0      3      0      0      0      0      0      0      0      0      0      0      0      0      0      0          128
+    23   1979                     1979                     1980                       16     89      0      1      0      2      0      0      0      0      0      1      0      0      1      0      0      0      0      0          110
+    24   1980                     1980                     1981                       15     89      0      0      0      2      0      0      0      0      0      1      0      0      1      0      0      0      0      0          108
+    25   1981                     1981                     1982                       19    100      0      1      0      3      0      0      0      0      0      1      0      0      2      0      0      0      0      0          126
+    26   1982                     1982                     1983                       18    108      0      1      0      1      0      0      0      0      0      0      0      0      1      0      0      0      0      0          129
+    27   1983                     1983                     1984                       22    100      0      1      0      3      0      0      0      0      0      1      0      0      2      0      0      0      0      0          129
+    28   1984                     1984                     1985                       22     97      0      3      3      3      0      0      0      0      0      0      0      0      1      0      0      0      0      0          129
+    29   1985                     1985                     1986                       18    100      0      1      4      2      0      0      0      0      0      0      0      0      0      0      0      0      0      0          125
+    30   1986                     1986                     1987                        9     94      0      2      3      2      0      0      0      0      0      0      0      0      0      0      0      0      0      0          110
+    31   1987                     1987                     1988                        9     97      0      2      2      3      0      0      0      0      0      1      0      0      0      0      0      0      0      0          114
+    32   1988                     1988                     1989                       11     94      0      4      7      2      0      0      0      1      0      1      0      0      0      0      0      1      0      0          121
+    33   1989                     1989                     1990                       18     75      0      0      7      2      0      0      0      0      0      0      0      0      0      0      0      0      0      0          102
+    34   1990                     1990                     1991                       27     79      0      5      6      3      0      0      0      0      0      0      0      0      0      0      0      1      0      0          121
+    35   1991                     1991                     1992                       19     61      0      1      8      2      0      0      0      0      0      0      0      0      0      0      0      0      0      0           91
+    36   1992                     1992                     1993                       29      0     55      4      7      1      0      0      0      0      0      1      0      0      0      0      0      0      0      0           97
+    37   1993                     1993                     1994                       25      0     48      1      7      1      0      0      0      0      0      1      0      0      0      0      0      0      0      0           83
+    38   1994                     1994                     1995                       27      0     49      5      8      2      0      0      0      0      0      2      0      0      0      0      0      0      0      0           93
+    39   1995                     1995                     1996                       30      0     33      3     11      2      0      0      0      0      0      0      0      0      0      0      0      1      0      0           80
+    40   1996                     1996                     1997                       33      0     27      4     10      1      0      0      0      0      0      1      0      0      1      0      0      0      0      0           77
+    41   1997                     1997                     1998                       38      0     29      6     11      2      0      0      0      0      0      1      1      0      1      0      0      0      0      0           89
+    42   1998                     1998                     1999                       36      0     25      6     11      2      0      0      0      0      0      0      0      0      0      1      0      1      0      0           82
+    43   1999                     1999                     2000                       31      0     22      4     16      1      0      0      0      0      0      1      1      0      0      0      0      0      2      0           78
+    44   2000                     2000                     2001                       29      0     32      5     16      1      0      0      0      0      0      0      0      0      0      0      0      0      2      0           85
+    45   2001                     2001                     2002                       24      0     23      1      8      1      0      0      0      0      0      2      0      0      0      0      0      0      0      0           59
+    46   2002                     2002                     2003                       18      0     25      5     12      3      0      0      0      0      0      1      0      0      0      0      0      1      0      0           65
+    47   2003                     2003                     2004                       26      0     19      7      6      3      0      0      0      0      0      2      0      0      0      0      0      0      0      0           63
+    48   2004                     2004                     2005                       19      0     23      8      3      0      0      0      0      0      0      1      0      0      0      0      0      1      0      0           55
+    49   2005                     2005                     2006                       16      0     23      6      6      2      0      0      0      0      0      1      0      0      2      0      0      0      0      0           56
+    50   2006                     2006                     2007                       23      0     23      6      7      6      0      0      0      0      0      1      0      0      0      0      0      0      0      0           66
+    51   2007                     2007                     2008                       20      0     23     10      9      2      0      0      0      0      0      3      0      0      0      0      0      1      0      0           68
+    52   2008                     2008                     2009                       20      0     26     11      7      1      0      0      0      0      0      3      0      0      0      0      1      0      0      0           69
+    53   2009                     2009                     2010                       25      0     32      6      7      3      0      0      0      0      0      2      0      1      0      1      1      0      0      0           78
+    54   2010                     2010                     2011                       15      0     30     15      7      2      0      0      0      0      0      3      0      1      0      0      0      1      0      0           74
+    55   2011                     2011                     2012                       19      0     30     19      9      3      0      0      0      0      0      3      0      0      0      0      1      0      0      0           84
+    56   2012                     2012                     2013                       16      0     23     19     10      2      0      0      0      0      0      2      0      0      1      2      3      0      0      0           78
+    57   2013                     2013                     2014                       20      0     31     15      7      3      0      0      0      0      0      3      0      1      1      0      0      0      0      0           81
+    58   2014                     2014                     2015                       24      0     32     16     10      4      0      0      0      0      0      4      0      0      1      0      0      1      0      0           92
+    59   2015                     2015                     2016                       20      0     26     19      9      4      0      0      0      0      0      5      0      0      3      0      1      0      0      0           87
+    60   2016                     2016                     2017                       22      0     17     22     11      4      0      0      0      0      0      7      0      0      0      1      0      1      0      0           85
+    61   2017                     2017                     2018                       29      0     19     18     11      7      0      0      0      0      0      5      0      0      0      0      0      0      0      1           90
+    62   2018                     2018                     2019                       31      0     17     39     11      6      0      0      0      0      0      7      0      0      0      0      0      0      0      3          114
+    63   2019                     2019                     2020                       21      0     22     34      9      2      0      0      0      0      0      6      0      0      0      0      2      0      0      6          102
+    64   2020                     2020                     2021                       37      0     12     39     10      4      0      0      0      0      0      2      0      0      0      0      2      1      0      7          114
+    65   2021                     2021                     2022                       45      0     16     56     15      3      0      0      0      0      0      2      0      1      0      0      2      0      0      6          146
+    66   2022                     2022                     2023                       78      0     21     64      5      1      0      0      0      0      0      5      0      1      1      0      1      0      0      9          186
+    67   2023                     2023                     2024                      109      0     19     67      3      3      0      0      0      0      0      7      0      2      0      3      2      1      0      7          223
+    68   2024                     2024                     2025                      145      0     17     68      2      7      0      0      0      0      0      5      0      0      1      1      4      0      0     13          263
+    69   2025                     2025                     2026                      181      0     17     92      7      4      1      1      0      0      0      5      0      2      0      0      1      1      0     17          329
+    70   2026                     2026                     2027                       12      0      0      7      0      0      0      0      0      0      0      1      0      0      0      0      0      0      0      2           22
+    71 Total                    Total                    Total                      2352   2449    886    741    350    142      1      1      2      9      4    101      2      9     20      9     21     13      4     71         7187
+# Bin     YDate                     ValMin                    ValMax                US     SU     RU     CN     F      J      AU     D      UK     I      I-ELDO IN     BR     KR     I-ESA  KP     IR     IL     CYM    NZ     Total
+# ---     ----                      ----                      ----                  (1)    (2)    (3)    (4)    (5)    (6)    (7)    (8)    (9)    (10)   (11)   (12)   (13)   (14)   (15)   (16)   (17)   (18)   (19)   (20)   -----
+# Data last updated: 2026 Jan 30 0451:24
+https://planet4589.org/space/stats/launches.html
--- a/p1/rocket_comprehensive.png
+++ b/p1/rocket_comprehensive.png
--- a/p1/rocket_launch_counts.csv
+++ b/p1/rocket_launch_counts.csv
@@ -0,0 +1,73 @@
+Bin,YDate,ValMin,ValMax,US,SU,RU,CN,F,J,AU,D,UK,I,I-ELDO,IN,BR,KR,I-ESA,KP,IR,IL,CYM,NZ,Total
+0,1956,1956,1957,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+1,1957,1957,1958,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3
+2,1958,1958,1959,23,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28
+3,1959,1959,1960,19,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23
+4,1960,1960,1961,29,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,38
+5,1961,1961,1962,41,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,50
+6,1962,1962,1963,59,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,81
+7,1963,1963,1964,46,23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,69
+8,1964,1964,1965,64,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,100
+9,1965,1965,1966,70,53,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,124
+10,1966,1966,1967,77,51,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,131
+11,1967,1967,1968,61,74,0,0,2,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,139
+12,1968,1968,1969,48,79,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,128
+13,1969,1969,1970,41,82,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,125
+14,1970,1970,1971,29,87,0,1,2,2,0,0,1,1,1,0,0,0,0,0,0,0,0,0,124
+15,1971,1971,1972,33,91,0,1,2,2,0,0,1,2,1,0,0,0,0,0,0,0,0,0,133
+16,1972,1972,1973,32,79,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,113
+17,1973,1973,1974,25,89,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,116
+18,1974,1974,1975,23,85,0,2,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,113
+19,1975,1975,1976,30,93,0,3,3,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,132
+20,1976,1976,1977,26,100,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,131
+21,1977,1977,1978,26,102,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,130
+22,1978,1978,1979,33,91,0,1,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,128
+23,1979,1979,1980,16,89,0,1,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,110
+24,1980,1980,1981,15,89,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,108
+25,1981,1981,1982,19,100,0,1,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,126
+26,1982,1982,1983,18,108,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,129
+27,1983,1983,1984,22,100,0,1,0,3,0,0,0,0,0,1,0,0,2,0,0,0,0,0,129
+28,1984,1984,1985,22,97,0,3,3,3,0,0,0,0,0,0,0,0,1,0,0,0,0,0,129
+29,1985,1985,1986,18,100,0,1,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,125
+30,1986,1986,1987,9,94,0,2,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,110
+31,1987,1987,1988,9,97,0,2,2,3,0,0,0,0,0,1,0,0,0,0,0,0,0,0,114
+32,1988,1988,1989,11,94,0,4,7,2,0,0,0,1,0,1,0,0,0,0,0,1,0,0,121
+33,1989,1989,1990,18,75,0,0,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,102
+34,1990,1990,1991,27,79,0,5,6,3,0,0,0,0,0,0,0,0,0,0,0,1,0,0,121
+35,1991,1991,1992,19,61,0,1,8,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,91
+36,1992,1992,1993,29,0,55,4,7,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,97
+37,1993,1993,1994,25,0,48,1,7,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,83
+38,1994,1994,1995,27,0,49,5,8,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,93
+39,1995,1995,1996,30,0,33,3,11,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,80
+40,1996,1996,1997,33,0,27,4,10,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,77
+41,1997,1997,1998,38,0,29,6,11,2,0,0,0,0,0,1,1,0,1,0,0,0,0,0,89
+42,1998,1998,1999,36,0,25,6,11,2,0,0,0,0,0,0,0,0,0,1,0,1,0,0,82
+43,1999,1999,2000,31,0,22,4,16,1,0,0,0,0,0,1,1,0,0,0,0,0,2,0,78
+44,2000,2000,2001,29,0,32,5,16,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,85
+45,2001,2001,2002,24,0,23,1,8,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,59
+46,2002,2002,2003,18,0,25,5,12,3,0,0,0,0,0,1,0,0,0,0,0,1,0,0,65
+47,2003,2003,2004,26,0,19,7,6,3,0,0,0,0,0,2,0,0,0,0,0,0,0,0,63
+48,2004,2004,2005,19,0,23,8,3,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,55
+49,2005,2005,2006,16,0,23,6,6,2,0,0,0,0,0,1,0,0,2,0,0,0,0,0,56
+50,2006,2006,2007,23,0,23,6,7,6,0,0,0,0,0,1,0,0,0,0,0,0,0,0,66
+51,2007,2007,2008,20,0,23,10,9,2,0,0,0,0,0,3,0,0,0,0,0,1,0,0,68
+52,2008,2008,2009,20,0,26,11,7,1,0,0,0,0,0,3,0,0,0,0,1,0,0,0,69
+53,2009,2009,2010,25,0,32,6,7,3,0,0,0,0,0,2,0,1,0,1,1,0,0,0,78
+54,2010,2010,2011,15,0,30,15,7,2,0,0,0,0,0,3,0,1,0,0,0,1,0,0,74
+55,2011,2011,2012,19,0,30,19,9,3,0,0,0,0,0,3,0,0,0,0,1,0,0,0,84
+56,2012,2012,2013,16,0,23,19,10,2,0,0,0,0,0,2,0,0,1,2,3,0,0,0,78
+57,2013,2013,2014,20,0,31,15,7,3,0,0,0,0,0,3,0,1,1,0,0,0,0,0,81
+58,2014,2014,2015,24,0,32,16,10,4,0,0,0,0,0,4,0,0,1,0,0,1,0,0,92
+59,2015,2015,2016,20,0,26,19,9,4,0,0,0,0,0,5,0,0,3,0,1,0,0,0,87
+60,2016,2016,2017,22,0,17,22,11,4,0,0,0,0,0,7,0,0,0,1,0,1,0,0,85
+61,2017,2017,2018,29,0,19,18,11,7,0,0,0,0,0,5,0,0,0,0,0,0,0,1,90
+62,2018,2018,2019,31,0,17,39,11,6,0,0,0,0,0,7,0,0,0,0,0,0,0,3,114
+63,2019,2019,2020,21,0,22,34,9,2,0,0,0,0,0,6,0,0,0,0,2,0,0,6,102
+64,2020,2020,2021,37,0,12,39,10,4,0,0,0,0,0,2,0,0,0,0,2,1,0,7,114
+65,2021,2021,2022,45,0,16,56,15,3,0,0,0,0,0,2,0,1,0,0,2,0,0,6,146
+66,2022,2022,2023,78,0,21,64,5,1,0,0,0,0,0,5,0,1,1,0,1,0,0,9,186
+67,2023,2023,2024,109,0,19,67,3,3,0,0,0,0,0,7,0,2,0,3,2,1,0,7,223
+68,2024,2024,2025,145,0,17,68,2,7,0,0,0,0,0,5,0,0,1,1,4,0,0,13,263
+69,2025,2025,2026,181,0,17,92,7,4,1,1,0,0,0,5,0,2,0,0,1,1,0,17,329
+70,2026,2026,2027,12,0,0,7,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,22
+71,Total,Total,Total,2352,2449,886,741,350,142,1,1,2,9,4,101,2,9,20,9,21,13,4,71,7187
--- a/p1/rocket_launch_scenario.py
+++ b/p1/rocket_launch_scenario.py
@@ -0,0 +1,678 @@
+"""
+Moon Colony Construction - Rocket Launch Scenario Analysis
+
+Problem: Deliver 100 million metric tons to Moon using traditional rockets
+- 10 launch sites, each max 1 launch per day
+- Rocket payload: 100-150 metric tons (use 125 tons average)
+- Optimize launch distribution to minimize energy consumption
+- Plot completion year vs total energy consumption
+
+Key insight: As completion timeline extends, more launches can be allocated 
+to low-latitude sites, reducing the latitude-induced energy penalty.
+"""
+
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+from dataclasses import dataclass
+from typing import List, Tuple
+import pandas as pd
+
+# 设置字体
+rcParams['font.sans-serif'] = ['Arial Unicode MS', 'DejaVu Sans', 'SimHei']
+rcParams['axes.unicode_minus'] = False
+
+# ============== 物理常数 ==============
+G0 = 9.81  # m/s²
+OMEGA_EARTH = 7.27e-5  # rad/s
+R_EARTH = 6.371e6  # m
+
+# ============== 任务参数 ==============
+TOTAL_PAYLOAD = 100e6  # 100 million metric tons = 100e9 kg 的物资，但单位是metric tons
+PAYLOAD_PER_LAUNCH = 125  # metric tons per launch (average of 100-150)
+TOTAL_LAUNCHES_NEEDED = TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH  # 800,000 launches
+
+# 火箭参数
+ISP = 450  # 比冲 (秒) - 液氧液氢
+SPECIFIC_FUEL_ENERGY = 15.5e6  # J/kg 燃料比能量
+ALPHA = 0.10  # 结构系数
+NUM_STAGES = 3  # 3级火箭
+
+# 地面发射基准 ΔV (赤道发射到月球轨道)
+DELTA_V_BASE = 13300  # m/s (LEO + TLI + LOI)
+
+
+# ============== 发射场定义 ==============
+@dataclass
+class LaunchSite:
+    name: str
+    short_name: str
+    latitude: float  # degrees
+    max_launches_per_day: int = 1
+    
+    @property
+    def abs_latitude(self) -> float:
+        return abs(self.latitude)
+    
+    @property
+    def rotation_velocity(self) -> float:
+        """地球自转速度 (m/s)"""
+        return OMEGA_EARTH * R_EARTH * np.cos(np.radians(self.abs_latitude))
+    
+    @property
+    def delta_v_loss(self) -> float:
+        """相对赤道的ΔV损失 (m/s)"""
+        v_equator = OMEGA_EARTH * R_EARTH  # ~465 m/s
+        return v_equator - self.rotation_velocity
+    
+    @property
+    def total_delta_v(self) -> float:
+        """总ΔV需求 (m/s)"""
+        return DELTA_V_BASE + self.delta_v_loss
+
+
+# 10个发射场 (按纬度排序)
+LAUNCH_SITES = [
+    LaunchSite("Kourou (French Guiana)", "Kourou", 5.2),
+    LaunchSite("Satish Dhawan (India)", "SDSC", 13.7),
+    LaunchSite("Boca Chica (Texas)", "Texas", 26.0),
+    LaunchSite("Cape Canaveral (Florida)", "Florida", 28.5),
+    LaunchSite("Vandenberg (California)", "California", 34.7),
+    LaunchSite("Wallops (Virginia)", "Virginia", 37.8),
+    LaunchSite("Taiyuan (China)", "Taiyuan", 38.8),
+    LaunchSite("Mahia (New Zealand)", "Mahia", 39.3),
+    LaunchSite("Baikonur (Kazakhstan)", "Baikonur", 45.6),
+    LaunchSite("Kodiak (Alaska)", "Alaska", 57.4),
+]
+
+# 按纬度排序
+LAUNCH_SITES = sorted(LAUNCH_SITES, key=lambda x: x.abs_latitude)
+
+
+# ============== 核心计算函数 ==============
+
+def fuel_ratio_multistage(delta_v: float, isp: float = ISP, alpha: float = ALPHA, 
+                          num_stages: int = NUM_STAGES) -> float:
+    """
+    多级火箭燃料/载荷比
+    """
+    ve = isp * G0
+    delta_v_per_stage = delta_v / num_stages
+    R_stage = np.exp(delta_v_per_stage / ve)
+    
+    denominator = 1 - alpha * (R_stage - 1)
+    if denominator <= 0:
+        return np.inf
+    
+    k_stage = (R_stage - 1) / denominator
+    
+    # 多级计算
+    total_fuel_ratio = 0
+    remaining_ratio = 1.0
+    
+    for _ in range(num_stages):
+        fuel_this_stage = remaining_ratio * k_stage
+        total_fuel_ratio += fuel_this_stage
+        remaining_ratio *= (1 + k_stage * (1 + alpha))
+    
+    return total_fuel_ratio
+
+
+def energy_per_launch(site: LaunchSite, payload_tons: float = PAYLOAD_PER_LAUNCH) -> float:
+    """
+    计算单次发射的能量消耗 (Joules)
+    
+    参数:
+        site: 发射场
+        payload_tons: 载荷 (metric tons)
+    返回:
+        能量消耗 (J)
+    """
+    delta_v = site.total_delta_v
+    k = fuel_ratio_multistage(delta_v)
+    
+    payload_kg = payload_tons * 1000  # convert to kg
+    fuel_kg = k * payload_kg
+    
+    energy = fuel_kg * SPECIFIC_FUEL_ENERGY
+    return energy
+
+
+def fuel_per_launch(site: LaunchSite, payload_tons: float = PAYLOAD_PER_LAUNCH) -> float:
+    """
+    计算单次发射的燃料消耗 (metric tons)
+    """
+    delta_v = site.total_delta_v
+    k = fuel_ratio_multistage(delta_v)
+    return k * payload_tons
+
+
+# ============== 发射排布优化 ==============
+
+def calculate_launch_distribution(
+    total_launches: int,
+    completion_years: float,
+    sites: List[LaunchSite] = LAUNCH_SITES
+) -> List[Tuple[LaunchSite, int]]:
+    """
+    计算给定完成年限下的发射分配
+    
+    策略：优先使用低纬度发射场，直到达到其最大容量
+    
+    参数:
+        total_launches: 总发射次数
+        completion_years: 完成年限
+        sites: 发射场列表 (已按纬度排序)
+    
+    返回:
+        [(发射场, 分配的发射次数), ...]
+    """
+    days_available = completion_years * 365
+    max_launches_per_site = days_available  # 每站每天最多1发
+    
+    distribution = []
+    remaining_launches = total_launches
+    
+    # 按纬度从低到高分配
+    for site in sites:
+        if remaining_launches <= 0:
+            distribution.append((site, 0))
+        else:
+            allocated = min(remaining_launches, max_launches_per_site)
+            distribution.append((site, int(allocated)))
+            remaining_launches -= allocated
+    
+    return distribution
+
+
+def calculate_total_energy(distribution: List[Tuple[LaunchSite, int]]) -> dict:
+    """
+    计算发射分配的总能量消耗
+    
+    返回:
+        包含各项统计的字典
+    """
+    total_energy = 0
+    total_fuel = 0
+    total_launches = 0
+    site_details = []
+    
+    for site, num_launches in distribution:
+        if num_launches > 0:
+            energy = energy_per_launch(site) * num_launches
+            fuel = fuel_per_launch(site) * num_launches
+            total_energy += energy
+            total_fuel += fuel
+            total_launches += num_launches
+            
+            site_details.append({
+                'site': site.name,
+                'short_name': site.short_name,
+                'latitude': site.abs_latitude,
+                'launches': num_launches,
+                'energy_PJ': energy / 1e15,  # PetaJoules
+                'fuel_Mt': fuel / 1e6,  # Million metric tons
+            })
+    
+    return {
+        'total_energy_PJ': total_energy / 1e15,
+        'total_fuel_Mt': total_fuel / 1e6,
+        'total_launches': total_launches,
+        'sites': site_details
+    }
+
+
+def analyze_completion_timeline(
+    years_range: np.ndarray,
+    total_payload: float = TOTAL_PAYLOAD,
+    payload_per_launch: float = PAYLOAD_PER_LAUNCH
+) -> pd.DataFrame:
+    """
+    分析不同完成年限下的能量消耗
+    
+    返回:
+        DataFrame with analysis results
+    """
+    total_launches = int(total_payload / payload_per_launch)
+    
+    results = []
+    for years in years_range:
+        distribution = calculate_launch_distribution(total_launches, years)
+        stats = calculate_total_energy(distribution)
+        
+        # 计算使用了多少个发射场
+        sites_used = sum(1 for _, n in distribution if n > 0)
+        
+        # 计算低纬度发射场的比例
+        low_lat_launches = sum(n for site, n in distribution if site.abs_latitude < 30)
+        low_lat_ratio = low_lat_launches / total_launches if total_launches > 0 else 0
+        
+        results.append({
+            'years': years,
+            'total_launches': total_launches,
+            'sites_used': sites_used,
+            'total_energy_PJ': stats['total_energy_PJ'],
+            'total_fuel_Mt': stats['total_fuel_Mt'],
+            'low_lat_ratio': low_lat_ratio,
+            'energy_per_launch_TJ': stats['total_energy_PJ'] * 1000 / total_launches,
+        })
+    
+    return pd.DataFrame(results)
+
+
+# ============== 可视化 ==============
+
+def plot_timeline_analysis(
+    save_path: str = '/Volumes/Files/code/mm/20260130_b/rocket_launch_timeline.png'
+):
+    """
+    绘制完成年份-能量消耗关系图
+    """
+    # 计算最短完成时间
+    total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
+    num_sites = len(LAUNCH_SITES)
+    min_years = total_launches / (num_sites * 365)
+    
+    print(f"Total payload: {TOTAL_PAYLOAD/1e6:.0f} million metric tons")
+    print(f"Payload per launch: {PAYLOAD_PER_LAUNCH} metric tons")
+    print(f"Total launches needed: {total_launches:,}")
+    print(f"Number of launch sites: {num_sites}")
+    print(f"Minimum completion time: {min_years:.1f} years")
+    
+    # 分析范围：从最短时间到5倍最短时间
+    years_range = np.linspace(min_years, min_years * 5, 100)
+    
+    # 运行分析
+    df = analyze_completion_timeline(years_range)
+    
+    # 创建图表
+    fig, axes = plt.subplots(2, 2, figsize=(16, 14))
+    
+    # ========== 图1: 完成年份 vs 总能量消耗 ==========
+    ax1 = axes[0, 0]
+    ax1.plot(df['years'], df['total_energy_PJ'], 'b-', linewidth=2)
+    ax1.fill_between(df['years'], df['total_energy_PJ'], alpha=0.3)
+    
+    # 标记关键点
+    min_idx = df['total_energy_PJ'].idxmin()
+    ax1.axvline(x=df.loc[min_idx, 'years'], color='r', linestyle='--', 
+                label=f'Optimal: {df.loc[min_idx, "years"]:.1f} years')
+    ax1.axhline(y=df.loc[min_idx, 'total_energy_PJ'], color='r', linestyle=':', alpha=0.5)
+    
+    # 标记最短时间点
+    ax1.axvline(x=min_years, color='g', linestyle='--', 
+                label=f'Minimum time: {min_years:.1f} years')
+    
+    ax1.set_xlabel('Completion Time (years)', fontsize=12)
+    ax1.set_ylabel('Total Energy Consumption (PJ)', fontsize=12)
+    ax1.set_title('Completion Timeline vs Total Energy\n(100 Million Metric Tons to Moon)', fontsize=14)
+    ax1.legend()
+    ax1.grid(True, alpha=0.3)
+    
+    # ========== 图2: 使用的发射场数量 ==========
+    ax2 = axes[0, 1]
+    ax2.plot(df['years'], df['sites_used'], 'g-', linewidth=2, marker='o', 
+             markevery=10, markersize=6)
+    
+    ax2.set_xlabel('Completion Time (years)', fontsize=12)
+    ax2.set_ylabel('Number of Launch Sites Used', fontsize=12)
+    ax2.set_title('Launch Sites Required vs Timeline', fontsize=14)
+    ax2.set_ylim(0, 11)
+    ax2.grid(True, alpha=0.3)
+    
+    # 添加发射场名称标注
+    for i, site in enumerate(LAUNCH_SITES):
+        ax2.axhline(y=i+1, color='gray', linestyle=':', alpha=0.3)
+        ax2.text(df['years'].max() * 0.98, i+1 + 0.1, site.short_name, 
+                fontsize=8, ha='right', va='bottom')
+    
+    # ========== 图3: 低纬度发射比例 ==========
+    ax3 = axes[1, 0]
+    ax3.plot(df['years'], df['low_lat_ratio'] * 100, 'm-', linewidth=2)
+    ax3.fill_between(df['years'], df['low_lat_ratio'] * 100, alpha=0.3, color='magenta')
+    
+    ax3.set_xlabel('Completion Time (years)', fontsize=12)
+    ax3.set_ylabel('Low Latitude (<30°) Launch Ratio (%)', fontsize=12)
+    ax3.set_title('Proportion of Launches from Low-Latitude Sites', fontsize=14)
+    ax3.set_ylim(0, 105)
+    ax3.grid(True, alpha=0.3)
+    
+    # ========== 图4: 平均每发能量 ==========
+    ax4 = axes[1, 1]
+    ax4.plot(df['years'], df['energy_per_launch_TJ'], 'r-', linewidth=2)
+    
+    # 计算赤道和最高纬度的能量参考线
+    energy_equator = energy_per_launch(LAUNCH_SITES[0]) / 1e12  # TJ
+    energy_max_lat = energy_per_launch(LAUNCH_SITES[-1]) / 1e12  # TJ
+    
+    ax4.axhline(y=energy_equator, color='g', linestyle='--', 
+                label=f'Equator baseline: {energy_equator:.1f} TJ')
+    ax4.axhline(y=energy_max_lat, color='orange', linestyle='--', 
+                label=f'Alaska (57°): {energy_max_lat:.1f} TJ')
+    
+    ax4.set_xlabel('Completion Time (years)', fontsize=12)
+    ax4.set_ylabel('Average Energy per Launch (TJ)', fontsize=12)
+    ax4.set_title('Energy Efficiency vs Timeline\n(Longer timeline → more low-lat launches)', fontsize=14)
+    ax4.legend()
+    ax4.grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    print(f"\n图表已保存至: {save_path}")
+    
+    return df, fig
+
+
+def plot_launch_distribution(
+    years: float,
+    save_path: str = '/Volumes/Files/code/mm/20260130_b/launch_distribution.png'
+):
+    """
+    绘制特定年限下的发射分配详情
+    """
+    total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
+    distribution = calculate_launch_distribution(total_launches, years)
+    stats = calculate_total_energy(distribution)
+    
+    fig, axes = plt.subplots(1, 2, figsize=(14, 6))
+    
+    # ========== 图1: 发射次数分配 ==========
+    ax1 = axes[0]
+    sites = [s.short_name for s, _ in distribution]
+    launches = [n for _, n in distribution]
+    latitudes = [s.abs_latitude for s, _ in distribution]
+    
+    colors = plt.cm.RdYlGn_r(np.array(latitudes) / 60)
+    bars = ax1.barh(sites, launches, color=colors)
+    
+    ax1.set_xlabel('Number of Launches', fontsize=12)
+    ax1.set_ylabel('Launch Site', fontsize=12)
+    ax1.set_title(f'Launch Distribution ({years:.1f} years completion)\n'
+                  f'Total: {total_launches:,} launches', fontsize=13)
+    ax1.grid(True, alpha=0.3, axis='x')
+    
+    # 添加数值标签
+    for bar, n in zip(bars, launches):
+        if n > 0:
+            ax1.text(bar.get_width() + 1000, bar.get_y() + bar.get_height()/2,
+                    f'{n:,}', va='center', fontsize=9)
+    
+    # ========== 图2: 能量消耗分配 ==========
+    ax2 = axes[1]
+    
+    site_names = [d['short_name'] for d in stats['sites']]
+    energies = [d['energy_PJ'] for d in stats['sites']]
+    
+    colors = plt.cm.RdYlGn_r(np.array([d['latitude'] for d in stats['sites']]) / 60)
+    bars = ax2.barh(site_names, energies, color=colors)
+    
+    ax2.set_xlabel('Energy Consumption (PJ)', fontsize=12)
+    ax2.set_ylabel('Launch Site', fontsize=12)
+    ax2.set_title(f'Energy Distribution\n'
+                  f'Total: {stats["total_energy_PJ"]:.1f} PJ', fontsize=13)
+    ax2.grid(True, alpha=0.3, axis='x')
+    
+    # 添加数值标签
+    for bar, e in zip(bars, energies):
+        if e > 0:
+            ax2.text(bar.get_width() + 0.5, bar.get_y() + bar.get_height()/2,
+                    f'{e:.1f}', va='center', fontsize=9)
+    
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    print(f"分配详情图已保存至: {save_path}")
+    
+    return distribution, stats
+
+
+def print_analysis_summary(df: pd.DataFrame):
+    """打印分析摘要"""
+    print("\n" + "=" * 80)
+    print("ROCKET LAUNCH SCENARIO ANALYSIS SUMMARY")
+    print("=" * 80)
+    
+    total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
+    
+    print(f"\nMission Parameters:")
+    print(f"  - Total payload: {TOTAL_PAYLOAD/1e6:.0f} million metric tons")
+    print(f"  - Payload per launch: {PAYLOAD_PER_LAUNCH} metric tons")
+    print(f"  - Total launches required: {total_launches:,}")
+    print(f"  - Number of launch sites: {len(LAUNCH_SITES)}")
+    
+    # 最短时间场景
+    min_years = df['years'].min()
+    min_time_row = df[df['years'] == min_years].iloc[0]
+    print(f"\nMinimum Time Scenario ({min_years:.1f} years):")
+    print(f"  - All {int(min_time_row['sites_used'])} sites at full capacity")
+    print(f"  - Total energy: {min_time_row['total_energy_PJ']:.1f} PJ")
+    print(f"  - Total fuel: {min_time_row['total_fuel_Mt']:.1f} million metric tons")
+    print(f"  - Low-latitude ratio: {min_time_row['low_lat_ratio']*100:.1f}%")
+    
+    # 最优能量场景 (只使用最低纬度站点)
+    max_years = df['years'].max()
+    max_time_row = df[df['years'] == max_years].iloc[0]
+    print(f"\nExtended Timeline Scenario ({max_years:.1f} years):")
+    print(f"  - Using {int(max_time_row['sites_used'])} lowest-latitude sites")
+    print(f"  - Total energy: {max_time_row['total_energy_PJ']:.1f} PJ")
+    print(f"  - Total fuel: {max_time_row['total_fuel_Mt']:.1f} million metric tons")
+    print(f"  - Low-latitude ratio: {max_time_row['low_lat_ratio']*100:.1f}%")
+    
+    # 能量节省
+    energy_saving = (1 - max_time_row['total_energy_PJ'] / min_time_row['total_energy_PJ']) * 100
+    print(f"\nEnergy Savings (Extended vs Minimum time):")
+    print(f"  - Energy reduction: {energy_saving:.1f}%")
+    print(f"  - Time trade-off: {max_years - min_years:.1f} additional years")
+    
+    print("\n" + "=" * 80)
+    print("Launch Site Details:")
+    print("=" * 80)
+    print(f"\n{'Site':<25} {'Latitude':>10} {'ΔV Loss':>10} {'Fuel Ratio':>12} {'Energy/Launch':>14}")
+    print(f"{'':25} {'(°)':>10} {'(m/s)':>10} {'':>12} {'(TJ)':>14}")
+    print("-" * 80)
+    
+    for site in LAUNCH_SITES:
+        k = fuel_ratio_multistage(site.total_delta_v)
+        e = energy_per_launch(site) / 1e12
+        print(f"{site.name:<25} {site.abs_latitude:>10.1f} {site.delta_v_loss:>10.0f} {k:>12.1f} {e:>14.1f}")
+    
+    print("=" * 80)
+
+
+def analyze_launch_frequency_scenarios(
+    save_path: str = '/Volumes/Files/code/mm/20260130_b/launch_frequency_analysis.png'
+):
+    """
+    分析不同发射频率场景下的完成时间和能量消耗
+    """
+    total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
+    num_sites = len(LAUNCH_SITES)
+    
+    # 不同发射频率场景
+    frequencies = [1, 2, 4, 8, 12, 24]  # launches per day per site
+    
+    results = []
+    for freq in frequencies:
+        # 最短完成时间
+        min_years = total_launches / (num_sites * freq * 365)
+        
+        # 分析该时间下的能量（所有站点满负荷）
+        distribution = calculate_launch_distribution(total_launches, min_years)
+        stats = calculate_total_energy(distribution)
+        
+        results.append({
+            'frequency': freq,
+            'min_years': min_years,
+            'total_energy_PJ': stats['total_energy_PJ'],
+            'total_fuel_Mt': stats['total_fuel_Mt'],
+        })
+    
+    df = pd.DataFrame(results)
+    
+    # 绘图
+    fig, axes = plt.subplots(1, 2, figsize=(14, 6))
+    
+    # 图1: 发射频率 vs 完成时间
+    ax1 = axes[0]
+    ax1.bar(range(len(frequencies)), df['min_years'], color='steelblue', alpha=0.8)
+    ax1.set_xticks(range(len(frequencies)))
+    ax1.set_xticklabels([f'{f}/day' for f in frequencies])
+    ax1.set_xlabel('Launch Frequency (per site)', fontsize=12)
+    ax1.set_ylabel('Minimum Completion Time (years)', fontsize=12)
+    ax1.set_title('Completion Time vs Launch Frequency\n(10 sites, 800,000 launches)', fontsize=13)
+    ax1.grid(True, alpha=0.3, axis='y')
+    
+    # 添加数值标签
+    for i, (freq, years) in enumerate(zip(frequencies, df['min_years'])):
+        ax1.text(i, years + 2, f'{years:.1f}y', ha='center', fontsize=10, fontweight='bold')
+    
+    # 图2: 能量消耗比较
+    ax2 = axes[1]
+    ax2.bar(range(len(frequencies)), df['total_energy_PJ'], color='coral', alpha=0.8)
+    ax2.set_xticks(range(len(frequencies)))
+    ax2.set_xticklabels([f'{f}/day' for f in frequencies])
+    ax2.set_xlabel('Launch Frequency (per site)', fontsize=12)
+    ax2.set_ylabel('Total Energy (PJ)', fontsize=12)
+    ax2.set_title('Total Energy Consumption\n(Same for all frequencies at max capacity)', fontsize=13)
+    ax2.grid(True, alpha=0.3, axis='y')
+    
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    print(f"\n发射频率分析图已保存至: {save_path}")
+    
+    return df
+
+
+def plot_comprehensive_analysis(
+    save_path: str = '/Volumes/Files/code/mm/20260130_b/rocket_comprehensive.png'
+):
+    """
+    综合分析图：不同时间线下的能量消耗和发射分配
+    """
+    total_launches = int(TOTAL_PAYLOAD / PAYLOAD_PER_LAUNCH)
+    num_sites = len(LAUNCH_SITES)
+    min_years = total_launches / (num_sites * 365)
+    
+    # 选择几个关键时间点
+    key_years = [min_years, min_years*1.5, min_years*2, min_years*3, min_years*5]
+    
+    fig, axes = plt.subplots(2, 3, figsize=(18, 12))
+    
+    # 第一行：能量趋势
+    ax_main = axes[0, 0]
+    years_range = np.linspace(min_years, min_years * 5, 100)
+    df = analyze_completion_timeline(years_range)
+    
+    ax_main.plot(df['years'], df['total_energy_PJ'], 'b-', linewidth=2)
+    ax_main.fill_between(df['years'], df['total_energy_PJ'], alpha=0.3)
+    
+    # 标记关键点
+    for y in key_years[1:]:
+        ax_main.axvline(x=y, color='gray', linestyle='--', alpha=0.5)
+    
+    ax_main.set_xlabel('Completion Time (years)', fontsize=11)
+    ax_main.set_ylabel('Total Energy (PJ)', fontsize=11)
+    ax_main.set_title('Energy vs Timeline\n(Longer time → Lower energy)', fontsize=12)
+    ax_main.grid(True, alpha=0.3)
+    
+    # 能量效率
+    ax_eff = axes[0, 1]
+    ax_eff.plot(df['years'], df['energy_per_launch_TJ'], 'r-', linewidth=2)
+    ax_eff.set_xlabel('Completion Time (years)', fontsize=11)
+    ax_eff.set_ylabel('Energy per Launch (TJ)', fontsize=11)
+    ax_eff.set_title('Average Energy Efficiency', fontsize=12)
+    ax_eff.grid(True, alpha=0.3)
+    
+    # 发射场使用数量
+    ax_sites = axes[0, 2]
+    ax_sites.plot(df['years'], df['sites_used'], 'g-', linewidth=2)
+    ax_sites.set_xlabel('Completion Time (years)', fontsize=11)
+    ax_sites.set_ylabel('Sites Used', fontsize=11)
+    ax_sites.set_title('Number of Active Sites', fontsize=12)
+    ax_sites.set_ylim(0, 11)
+    ax_sites.grid(True, alpha=0.3)
+    
+    # 第二行：三个时间点的发射分配饼图
+    scenario_years = [min_years, min_years*2, min_years*5]
+    scenario_names = ['Minimum Time', '2x Minimum', '5x Minimum']
+    
+    for idx, (years, name) in enumerate(zip(scenario_years, scenario_names)):
+        ax = axes[1, idx]
+        distribution = calculate_launch_distribution(total_launches, years)
+        
+        # 只显示有发射的站点
+        labels = [s.short_name for s, n in distribution if n > 0]
+        sizes = [n for _, n in distribution if n > 0]
+        latitudes = [s.abs_latitude for s, n in distribution if n > 0]
+        
+        colors = plt.cm.RdYlGn_r(np.array(latitudes) / 60)
+        
+        wedges, texts, autotexts = ax.pie(sizes, labels=labels, autopct='%1.0f%%',
+                                          colors=colors, pctdistance=0.75)
+        
+        # 计算该场景的能量
+        stats = calculate_total_energy(distribution)
+        
+        ax.set_title(f'{name}\n({years:.0f} years, {stats["total_energy_PJ"]:.0f} PJ)', 
+                    fontsize=11)
+    
+    plt.suptitle(f'Rocket Launch Scenario: {TOTAL_PAYLOAD/1e6:.0f}M tons to Moon\n'
+                 f'({total_launches:,} launches needed)', fontsize=14, y=1.02)
+    
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=150, bbox_inches='tight')
+    print(f"\n综合分析图已保存至: {save_path}")
+    
+    return fig
+
+
+# ============== 主程序 ==============
+
+if __name__ == "__main__":
+    print("=" * 80)
+    print("MOON COLONY ROCKET LAUNCH SCENARIO ANALYSIS")
+    print("Mission: Deliver 100 million metric tons to Moon")
+    print("Constraint: 10 launch sites, max 1 launch/day each")
+    print("=" * 80)
+    
+    # 运行时间线分析
+    df, fig = plot_timeline_analysis()
+    
+    # 打印摘要
+    print_analysis_summary(df)
+    
+    # 绘制综合分析图
+    plot_comprehensive_analysis()
+    
+    # 发射频率场景分析
+    print("\n" + "=" * 80)
+    print("LAUNCH FREQUENCY SENSITIVITY ANALYSIS")
+    print("=" * 80)
+    freq_df = analyze_launch_frequency_scenarios()
+    print("\nIf launch frequency can be increased:")
+    print(f"{'Frequency':>15} {'Min Years':>15} {'Energy (PJ)':>15}")
+    print("-" * 50)
+    for _, row in freq_df.iterrows():
+        print(f"{row['frequency']:>12}/day {row['min_years']:>15.1f} {row['total_energy_PJ']:>15.0f}")
+    
+    # 关键结论
+    print("\n" + "=" * 80)
+    print("KEY FINDINGS")
+    print("=" * 80)
+    print(f"""
+    1. MINIMUM COMPLETION TIME: {df['years'].min():.0f} years
+       (All 10 sites at 1 launch/day capacity)
+    
+    2. TOTAL ENERGY REQUIRED: ~{df['total_energy_PJ'].mean():.0f} PJ
+       (~{df['total_fuel_Mt'].mean():.0f} million metric tons of fuel)
+    
+    3. LATITUDE EFFECT: 
+       - Energy savings from using only low-lat sites: ~2-3%
+       - Trade-off: Significantly longer timeline
+    
+    4. CONCLUSION: 
+       Traditional rockets alone are IMPRACTICAL for this mission.
+       219+ years is far beyond reasonable project timeline.
+       Space Elevator System is essential for feasibility.
+    """)
--- a/p1/rocket_launch_timeline.png
+++ b/p1/rocket_launch_timeline.png
--- a/p1/rocket_launch_to_csv.py
+++ b/p1/rocket_launch_to_csv.py
@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+"""将 rocket launch counts.txt 数据导出为 CSV 文件"""
+
+import csv
+import re
+
+
+def parse_header(header_line: str) -> list[str]:
+    """从 # 开头的表头行解析列名"""
+    # 去掉开头的 # 和多余空格，按空白分割
+    clean = header_line.lstrip("#").strip()
+    # 列名之间可能有多空格，统一按空白分割
+    return re.split(r"\s{2,}", clean)
+
+
+def parse_data_line(line: str) -> list[str]:
+    """解析数据行，按空白分割"""
+    return line.split()
+
+
+def main():
+    input_file = "rocket launch counts.txt"
+    output_file = "rocket_launch_counts.csv"
+
+    headers = None
+    rows = []
+
+    with open(input_file, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.rstrip("\n")
+            if not line.strip():
+                continue
+            if line.startswith("#"):
+                # 第二行是分隔线，第一行是列名
+                if "Bin" in line and "YDate" in line and headers is None:
+                    headers = parse_header(line)
+                continue
+            # 数据行
+            values = parse_data_line(line)
+            if len(values) >= 4:  # 至少包含 Bin, YDate, ValMin, ValMax
+                rows.append(values)
+
+    # 如果没有成功解析表头，使用默认列名
+    if headers is None or len(headers) != len(rows[0]) if rows else True:
+        headers = [
+            "Bin", "YDate", "ValMin", "ValMax",
+            "US", "SU", "RU", "CN", "F", "J", "AU", "D", "UK", "I",
+            "I-ELDO", "IN", "BR", "KR", "I-ESA", "KP", "IR", "IL", "CYM", "NZ",
+            "Total"
+        ]
+
+    with open(output_file, "w", encoding="utf-8", newline="") as out:
+        writer = csv.writer(out)
+        writer.writerow(headers)
+        writer.writerows(rows)
+
+    print(f"已导出 {len(rows)} 行数据到 {output_file}")
+
+
+if __name__ == "__main__":
+    main()
--- a/p1/scenario_comparison.png
+++ b/p1/scenario_comparison.png
--- a/p1/specific_energy_comparison.png
+++ b/p1/specific_energy_comparison.png
--- a/p1/specific_energy_comparison.py
+++ b/p1/specific_energy_comparison.py
--- a/p1/three_scenarios_comparison.png
+++ b/p1/three_scenarios_comparison.png