User: 下载所用到的核素文件python代码实现
```python
import os
import gymnasium as gym
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
import openmc
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
#plt.style.use(plt.style.available)
openmc.config['cross_sections'] = '/home/unknowxxs/python/endfb-viii.0-hdf5/cross_sections.xml'
class NuclearReactorEnv(gym.Env):
    def __init__(self):
        super().__init__()
        self.action_space = gym.spaces.Discrete(11)  # 控制棒位置，0-10
        
        # 观察空间：温度、功率、k_eff、控制棒位置、燃料燃耗
        self.observation_space = gym.spaces.Box(
            low=np.array([500, 0, 0.5, 0, 0]),
            high=np.array([1500, 3000, 1.5, 1, 100]),
            dtype=np.float32
        )
        
        self.max_steps = 1000
        self.current_step = 0
        self._setup_model()
        
    def _setup_model(self):
        # 设置材料
        # UO2燃料
        self.fuel = openmc.Material(name='fuel')
        self.fuel.add_nuclide('U235', 0.05)
        self.fuel.add_nuclide('U238', 0.95)
        self.fuel.add_nuclide('O16', 2.0)
        self.fuel.set_density('g/cm3', 10.0)
        self.fuel.temperature = 900  # K

        # 水冷却剂
        self.water = openmc.Material(name='water')
        self.water.add_nuclide('H1', 2)
        self.water.add_nuclide('O16', 1)
        self.water.set_density('g/cm3', 1.0)
        self.water.temperature = 600  # K

        # 控制棒 (B4C)
        self.control_rod = openmc.Material(name='control_rod')
        self.control_rod.add_nuclide('B10', 0.8)
        self.control_rod.add_nuclide('B11', 0.2)
        self.control_rod.add_nuclide('C12', 1.0)
        self.control_rod.set_density('g/cm3', 2.52)

        # 创建材料集合
        self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])
        
        # 创建几何体
        # 简化的圆柱形反应堆堆芯
        cylinder = openmc.ZCylinder(r=100)
        fuel_region = -cylinder
        
        fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)
        
        # 创建universe和geometry
        root_universe = openmc.Universe(cells=[fuel_cell])
        self.geometry = openmc.Geometry(root_universe)
        
        # 设置模拟参数
        self.settings = openmc.Settings()
        self.settings.batches = 100
        self.settings.inactive = 10
        self.settings.particles = 1000
        self.settings.temperature = {'method': 'interpolation'}
        
    def init_visualization(self):
        """初始化可视化图表"""
        plt.ion()  # 开启交互模式
        self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))
        self.fig.suptitle('核反应堆运行状态监测', fontsize=16)
        
        # 初始化数据存储
        self.history = {
            'temperature': [],
            'power': [],
            'k_eff': [],
            'burnup': [],
            'control_rod': [],
            'steps': []
        }
        
        # 配置子图
        self.lines = {}
        
        # 温度和功率图
        self.axs[0, 0].set_title('温度和功率变化')
        self.axs[0, 0].set_xlabel('步数')
        self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='温度 (K)')
        self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='功率 (MW)')
        self.axs[0, 0].legend()
        
        # k_eff图
        self.axs[0, 1].set_title('k_eff变化')
        self.axs[0, 1].set_xlabel('步数')
        self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')
        self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='临界值')
        self.axs[0, 1].legend()
        
        # 控制棒位置图
        self.axs[1, 0].set_title('控制棒位置')
        self.axs[1, 0].set_xlabel('步数')
        self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='控制棒位置')
        self.axs[1, 0].legend()
        
        # 燃耗图
        self.axs[1, 1].set_title('燃料燃耗')
        self.axs[1, 1].set_xlabel('步数')
        self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='燃耗 (MWd/kgU)')
        self.axs[1, 1].legend()
        
        plt.tight_layout()

    def update_visualization(self):
        """更新可视化图表"""
        # 更新历史数据
        self.history['temperature'].append(self.temperature)
        self.history['power'].append(self.power)
        self.history['k_eff'].append(self.k_eff)
        self.history['burnup'].append(self.burnup)
        self.history['control_rod'].append(self.control_rod_pos)
        self.history['steps'].append(self.current_step)
        
        # 更新图表
        steps = self.history['steps']
        
        # 更新温度和功率
        self.lines['temp'].set_data(steps, self.history['temperature'])
        self.lines['power'].set_data(steps, self.history['power'])
        self.axs[0, 0].relim()
        self.axs[0, 0].autoscale_view()
        
        # 更新k_eff
        self.lines['k_eff'].set_data(steps, self.history['k_eff'])
        self.axs[0, 1].relim()
        self.axs[0, 1].autoscale_view()
        
        # 更新控制棒位置
        self.lines['control'].set_data(steps, self.history['control_rod'])
        self.axs[1, 0].relim()
        self.axs[1, 0].autoscale_view()
        
        # 更新燃耗
        self.lines['burnup'].set_data(steps, self.history['burnup'])
        self.axs[1, 1].relim()
        self.axs[1, 1].autoscale_view()
        
        plt.draw()
        plt.pause(0.01)

    def reset(self, seed=None):
        super().reset(seed=seed)
        self.current_step = 0
        self.temperature = 600  # K
        self.power = 1000      # MW
        self.k_eff = 1.0
        self.burnup = 0        # MWd/kgU
        self.control_rod_pos = 0.5
        
        # 初始化可视化
        self.init_visualization()
        
        return self._get_observation(), {}
    
    def _get_observation(self):
        return np.array([
            self.temperature,
            self.power,
            self.k_eff,
            self.control_rod_pos,
            self.burnup
        ], dtype=np.float32)
    
    def step(self, action):
        self.current_step += 1
        
        # 更新控制棒位置
        self.control_rod_pos = action / 10.0
        
        # 运行OpenMC模拟
        try:
            # 更新材料温度
            self.fuel.temperature = self.temperature
            
            # 运行临界计算
           # os.remove('/home/unknowxxs/python/statepoint.50.h5')
            result = openmc.run()
         
            
            # 获取k_eff
            with openmc.StatePoint(result) as sp:
                self.k_eff = sp.k_effective.nominal_value
                
            # 更新系统状态
            self._update_state()
            
            # 更新可视化
            self.update_visualization()
            
            # 计算奖励
            reward = self._calculate_reward()
            
            # 检查是否结束
            done = self.current_step >= self.max_steps or self._is_unsafe()
            
        except Exception as e:
            print(f"模拟过程出错: {e}")
            reward = -1000
            done = True
        
        return self._get_observation(), reward, done, False, {}
    
    def _update_state(self):
        # 基于k_eff更新功率
        power_factor = (self.k_eff - 1.0) * 5000
        self.power = np.clip(self.power + power_factor, 0, 3000)
        
        # 更新温度
        delta_temp = (self.power - 1000) * 0.1
        self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)
        
        # 更新燃耗
        self.burnup += self.power * 0.001
        
    def _calculate_reward(self):
        reward = 0
        
        # 奖励稳定的运行状态
        reward -= abs(self.power - 1000) * 0.01  # 目标功率1000MW
        reward -= abs(self.k_eff - 1.0) * 100    # 目标k_eff=1
        reward -= abs(self.temperature - 900) * 0.1  # 目标温度900K
        
        # 惩罚不安全状态
        if self._is_unsafe():
            reward -= 1000
            
        return reward
    
    def _is_unsafe(self):
        return (self.temperature > 1400 or 
                self.power > 2500 or 
                self.k_eff > 1.2)

def train_agent():
    env = make_vec_env(NuclearReactorEnv, n_envs=4)
    
    model = PPO(
        "MlpPolicy",
        env,
        verbose=1,
        learning_rate=0.0003,
        n_steps=2048,
        batch_size=64,
        n_epochs=10
    )
    
    model.learn(total_timesteps=10)
    model.save("ppo_nuclear_reactor")
    
    return model

def evaluate_agent(model, num_episodes=5):
    env = NuclearReactorEnv()
    
    for episode in range(num_episodes):
        obs, _ = env.reset()
        episode_reward = 0
        done = False
        
        while not done:
            action, _ = model.predict(obs)
            obs, reward, done, _, _ = env.step(action)
            episode_reward += reward
            
            # 打印当前状态
            print(f"步骤 {env.current_step}:")
            print(f"温度: {env.temperature:.1f}K")
            print(f"功率: {env.power:.1f}MW")
            print(f"k_eff: {env.k_eff:.3f}")
            print(f"控制棒位置: {env.control_rod_pos:.2f}")
            print(f"燃耗: {env.burnup:.2f}MWd/kgU")
            print("------------------------")
        
        plt.savefig(f'episode_{episode+1}_results.png')
        print(f"Episode {episode + 1}: 总奖励 = {episode_reward:.2f}\n")
    
    plt.close('all')

model = train_agent()
print("\n评估核反应堆控制性能：")
evaluate_agent(model)

```
Assistant: The provided Python code is a simulation of a nuclear reactor environment using reinforcement learning, specifically with the PPO algorithm from Stable Baselines3. The code integrates OpenMC, a tool for Monte Carlo particle transport simulation, to simulate the reactor's behavior based on defined material compositions and geometry.

Here's a summary and explanation of the code:

1. **Environment Setup**:  
   The class `NuclearReactorEnv` extends `gym.Env` to create a custom environment. This environment models the behavior of a simplified cylindrical nuclear reactor core composed of UO2 fuel, a water moderator, and a B4C control rod.

2. **Simulation Using OpenMC**:  
   The environment uses OpenMC to perform criticality calculations. Materials are defined, and a simple cylindrical geometry is created. OpenMC parameters like temperature and material compositions are updated based on the current state in each step.

3. **Reinforcement Learning**:  
   - The action space is discrete, representing different positions of the control rod (0-10).
   - The observation space consists of temperature, power, \(k_{eff}\), control rod position, and fuel burnup.
   - The environment aims to maintain reactor stability around specific target values.

4. **Training and Evaluation**:  
   - The `train_agent` function uses PPO to train an agent with the custom environment.
   - The `evaluate_agent` function simulates episodes using the trained agent to monitor performance and stability.

In the code, you should verify paths, such as the cross-section file specified in `openmc.config['cross_sections']`, and ensure they point to the correct location on your machine.

Here's how you might integrate this code into your project:

1. **Environment Dependencies**:
   Make sure you've installed the required packages:
   - OpenMC: `pip install openmc`
   - Stable Baselines3: `pip install stable-baselines3[extra]`
   - Other necessary libraries: `gym`, `numpy`, `matplotlib`

2. **Verify Paths and Resources**:
   - Ensure the file paths you’ve used (like for cross sections) are accurate.
   - OpenMC requires nuclear data in HDF5 format; ensure you have these datasets correctly installed.

3. **Simulation and Visualization**:
   - The `init_visualization` and `update_visualization` methods handle real-time plotting of the simulation's progress.
   - Adjust `model.learn(total_timesteps=10)` to a higher value (e.g., `total_timesteps=100000`) for meaningful training results, depending on computational resources.

4. **Debug and Testing**:
   - Test the environment independently of the learning to ensure OpenMC is set up correctly.
   - Introduce error handling or logging to troubleshoot issues (e.g., OpenMC execution errors).

5. **Expand Functionality**:
   - The given simulation is highly simplified. Consider extending the geometry or including more detailed physics to better match real-world scenarios.
   - Experiment with reward structures or action/observation spaces to optimize learning and reflect realistic control scenarios.

Ensure you’re operating in a suitable computational environment, as Monte Carlo simulations can be resource-intensive, especially when integrating with RL training loops.
User: 用中文
```python
import os
import gymnasium as gym
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
import openmc
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
#plt.style.use(plt.style.available)
openmc.config['cross_sections'] = '/home/unknowxxs/python/endfb-viii.0-hdf5/cross_sections.xml'
class NuclearReactorEnv(gym.Env):
    def __init__(self):
        super().__init__()
        self.action_space = gym.spaces.Discrete(11)  # 控制棒位置，0-10
        
        # 观察空间：温度、功率、k_eff、控制棒位置、燃料燃耗
        self.observation_space = gym.spaces.Box(
            low=np.array([500, 0, 0.5, 0, 0]),
            high=np.array([1500, 3000, 1.5, 1, 100]),
            dtype=np.float32
        )
        
        self.max_steps = 1000
        self.current_step = 0
        self._setup_model()
        
    def _setup_model(self):
        # 设置材料
        # UO2燃料
        self.fuel = openmc.Material(name='fuel')
        self.fuel.add_nuclide('U235', 0.05)
        self.fuel.add_nuclide('U238', 0.95)
        self.fuel.add_nuclide('O16', 2.0)
        self.fuel.set_density('g/cm3', 10.0)
        self.fuel.temperature = 900  # K

        # 水冷却剂
        self.water = openmc.Material(name='water')
        self.water.add_nuclide('H1', 2)
        self.water.add_nuclide('O16', 1)
        self.water.set_density('g/cm3', 1.0)
        self.water.temperature = 600  # K

        # 控制棒 (B4C)
        self.control_rod = openmc.Material(name='control_rod')
        self.control_rod.add_nuclide('B10', 0.8)
        self.control_rod.add_nuclide('B11', 0.2)
        self.control_rod.add_nuclide('C12', 1.0)
        self.control_rod.set_density('g/cm3', 2.52)

        # 创建材料集合
        self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])
        
        # 创建几何体
        # 简化的圆柱形反应堆堆芯
        cylinder = openmc.ZCylinder(r=100)
        fuel_region = -cylinder
        
        fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)
        
        # 创建universe和geometry
        root_universe = openmc.Universe(cells=[fuel_cell])
        self.geometry = openmc.Geometry(root_universe)
        
        # 设置模拟参数
        self.settings = openmc.Settings()
        self.settings.batches = 100
        self.settings.inactive = 10
        self.settings.particles = 1000
        self.settings.temperature = {'method': 'interpolation'}
        
    def init_visualization(self):
        """初始化可视化图表"""
        plt.ion()  # 开启交互模式
        self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))
        self.fig.suptitle('核反应堆运行状态监测', fontsize=16)
        
        # 初始化数据存储
        self.history = {
            'temperature': [],
            'power': [],
            'k_eff': [],
            'burnup': [],
            'control_rod': [],
            'steps': []
        }
        
        # 配置子图
        self.lines = {}
        
        # 温度和功率图
        self.axs[0, 0].set_title('温度和功率变化')
        self.axs[0, 0].set_xlabel('步数')
        self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='温度 (K)')
        self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='功率 (MW)')
        self.axs[0, 0].legend()
        
        # k_eff图
        self.axs[0, 1].set_title('k_eff变化')
        self.axs[0, 1].set_xlabel('步数')
        self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')
        self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='临界值')
        self.axs[0, 1].legend()
        
        # 控制棒位置图
        self.axs[1, 0].set_title('控制棒位置')
        self.axs[1, 0].set_xlabel('步数')
        self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='控制棒位置')
        self.axs[1, 0].legend()
        
        # 燃耗图
        self.axs[1, 1].set_title('燃料燃耗')
        self.axs[1, 1].set_xlabel('步数')
        self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='燃耗 (MWd/kgU)')
        self.axs[1, 1].legend()
        
        plt.tight_layout()

    def update_visualization(self):
        """更新可视化图表"""
        # 更新历史数据
        self.history['temperature'].append(self.temperature)
        self.history['power'].append(self.power)
        self.history['k_eff'].append(self.k_eff)
        self.history['burnup'].append(self.burnup)
        self.history['control_rod'].append(self.control_rod_pos)
        self.history['steps'].append(self.current_step)
        
        # 更新图表
        steps = self.history['steps']
        
        # 更新温度和功率
        self.lines['temp'].set_data(steps, self.history['temperature'])
        self.lines['power'].set_data(steps, self.history['power'])
        self.axs[0, 0].relim()
        self.axs[0, 0].autoscale_view()
        
        # 更新k_eff
        self.lines['k_eff'].set_data(steps, self.history['k_eff'])
        self.axs[0, 1].relim()
        self.axs[0, 1].autoscale_view()
        
        # 更新控制棒位置
        self.lines['control'].set_data(steps, self.history['control_rod'])
        self.axs[1, 0].relim()
        self.axs[1, 0].autoscale_view()
        
        # 更新燃耗
        self.lines['burnup'].set_data(steps, self.history['burnup'])
        self.axs[1, 1].relim()
        self.axs[1, 1].autoscale_view()
        
        plt.draw()
        plt.pause(0.01)

    def reset(self, seed=None):
        super().reset(seed=seed)
        self.current_step = 0
        self.temperature = 600  # K
        self.power = 1000      # MW
        self.k_eff = 1.0
        self.burnup = 0        # MWd/kgU
        self.control_rod_pos = 0.5
        
        # 初始化可视化
        self.init_visualization()
        
        return self._get_observation(), {}
    
    def _get_observation(self):
        return np.array([
            self.temperature,
            self.power,
            self.k_eff,
            self.control_rod_pos,
            self.burnup
        ], dtype=np.float32)
    
    def step(self, action):
        self.current_step += 1
        
        # 更新控制棒位置
        self.control_rod_pos = action / 10.0
        
        # 运行OpenMC模拟
        try:
            # 更新材料温度
            self.fuel.temperature = self.temperature
            
            # 运行临界计算
           # os.remove('/home/unknowxxs/python/statepoint.50.h5')
            result = openmc.run()
         
            
            # 获取k_eff
            with openmc.StatePoint(result) as sp:
                self.k_eff = sp.k_effective.nominal_value
                
            # 更新系统状态
            self._update_state()
            
            # 更新可视化
            self.update_visualization()
            
            # 计算奖励
            reward = self._calculate_reward()
            
            # 检查是否结束
            done = self.current_step >= self.max_steps or self._is_unsafe()
            
        except Exception as e:
            print(f"模拟过程出错: {e}")
            reward = -1000
            done = True
        
        return self._get_observation(), reward, done, False, {}
    
    def _update_state(self):
        # 基于k_eff更新功率
        power_factor = (self.k_eff - 1.0) * 5000
        self.power = np.clip(self.power + power_factor, 0, 3000)
        
        # 更新温度
        delta_temp = (self.power - 1000) * 0.1
        self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)
        
        # 更新燃耗
        self.burnup += self.power * 0.001
        
    def _calculate_reward(self):
        reward = 0
        
        # 奖励稳定的运行状态
        reward -= abs(self.power - 1000) * 0.01  # 目标功率1000MW
        reward -= abs(self.k_eff - 1.0) * 100    # 目标k_eff=1
        reward -= abs(self.temperature - 900) * 0.1  # 目标温度900K
        
        # 惩罚不安全状态
        if self._is_unsafe():
            reward -= 1000
            
        return reward
    
    def _is_unsafe(self):
        return (self.temperature > 1400 or 
                self.power > 2500 or 
                self.k_eff > 1.2)

def train_agent():
    env = make_vec_env(NuclearReactorEnv, n_envs=4)
    
    model = PPO(
        "MlpPolicy",
        env,
        verbose=1,
        learning_rate=0.0003,
        n_steps=2048,
        batch_size=64,
        n_epochs=10
    )
    
    model.learn(total_timesteps=10)
    model.save("ppo_nuclear_reactor")
    
    return model

def evaluate_agent(model, num_episodes=5):
    env = NuclearReactorEnv()
    
    for episode in range(num_episodes):
        obs, _ = env.reset()
        episode_reward = 0
        done = False
        
        while not done:
            action, _ = model.predict(obs)
            obs, reward, done, _, _ = env.step(action)
            episode_reward += reward
            
            # 打印当前状态
            print(f"步骤 {env.current_step}:")
            print(f"温度: {env.temperature:.1f}K")
            print(f"功率: {env.power:.1f}MW")
            print(f"k_eff: {env.k_eff:.3f}")
            print(f"控制棒位置: {env.control_rod_pos:.2f}")
            print(f"燃耗: {env.burnup:.2f}MWd/kgU")
            print("------------------------")
        
        plt.savefig(f'episode_{episode+1}_results.png')
        print(f"Episode {episode + 1}: 总奖励 = {episode_reward:.2f}\n")
    
    plt.close('all')

model = train_agent()
print("\n评估核反应堆控制性能：")
evaluate_agent(model)

```
Assistant: 这段代码用Python来模拟一个核反应堆的环境，并使用稳定基线3（Stable Baselines3）的PPO算法进行强化学习。通过OpenMC进行蒙特卡罗粒子输运模拟，模拟反应堆的行为。以下是代码的详细解释：

### 核心功能：

1. **环境设置 (`NuclearReactorEnv` 类)**：
   - 定义了控制棒的位置（动作空间为离散的0-10）。
   - 观察空间包含温度、功率、k_eff、控制棒位置和燃料燃耗。
   - 实现在OpenMC中的几何设计和材料设置，包括UO2燃料、水冷却剂和B4C控制棒。
   - 进行OpenMC的设置，定义核材料、几何形状以及模拟参数。

2. **强化学习和模拟**：
   - 使用PPO算法训练代理，通过控制棒的位置改变来达到稳定的反应堆状态。
   - 奖励函数设计旨在维持系统的稳定性，目标是功率1000MW、k_eff为1.0和温度900K。
   - 评估函数展示了每回合的模拟结果，并保存图像。

3. **数据可视化**：
   - 实时绘制温度、功率、k_eff、控制棒位置和燃耗的变化趋势。
   - 使用Matplotlib进行交互式图表更新。

### 使用指南：

1. **环境依赖**：
   - 需要安装OpenMC、Stable Baselines3、Gym、NumPy和Matplotlib。
   - 使用命令安装依赖：`pip install openmc stable-baselines3[extra] gym numpy matplotlib`

2. **文件路径验证**：
   - 确保OpenMC交叉截面数据的路径(如`cross_sections.xml`)正确。

3. **训练与评估**：
   - 默认训练时间步数设置过低以便快速演示，可以根据计算资源增加`model.learn(total_timesteps=1000000)`。
   - 评估函数每回合生成结果图，存储在当前目录。

4. **调试与测试**：
   - 独立测试环境设置，确保OpenMC正常运行。
   - 处理模拟中可能出现的错误信息。

该代码提供了一个基础的模拟反应堆环境和控制策略优化模型，通过强化学习实现复杂系统的自适应控制。这是一个基础模型，可以用于理解和扩展到更复杂的核反应堆模拟场景。
User: 下载所需核素文件
```python
import os
import gymnasium as gym
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
import openmc
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
#plt.style.use(plt.style.available)
openmc.config['cross_sections'] = '/home/unknowxxs/python/endfb-viii.0-hdf5/cross_sections.xml'
class NuclearReactorEnv(gym.Env):
    def __init__(self):
        super().__init__()
        self.action_space = gym.spaces.Discrete(11)  # 控制棒位置，0-10
        
        # 观察空间：温度、功率、k_eff、控制棒位置、燃料燃耗
        self.observation_space = gym.spaces.Box(
            low=np.array([500, 0, 0.5, 0, 0]),
            high=np.array([1500, 3000, 1.5, 1, 100]),
            dtype=np.float32
        )
        
        self.max_steps = 1000
        self.current_step = 0
        self._setup_model()
        
    def _setup_model(self):
        # 设置材料
        # UO2燃料
        self.fuel = openmc.Material(name='fuel')
        self.fuel.add_nuclide('U235', 0.05)
        self.fuel.add_nuclide('U238', 0.95)
        self.fuel.add_nuclide('O16', 2.0)
        self.fuel.set_density('g/cm3', 10.0)
        self.fuel.temperature = 900  # K

        # 水冷却剂
        self.water = openmc.Material(name='water')
        self.water.add_nuclide('H1', 2)
        self.water.add_nuclide('O16', 1)
        self.water.set_density('g/cm3', 1.0)
        self.water.temperature = 600  # K

        # 控制棒 (B4C)
        self.control_rod = openmc.Material(name='control_rod')
        self.control_rod.add_nuclide('B10', 0.8)
        self.control_rod.add_nuclide('B11', 0.2)
        self.control_rod.add_nuclide('C12', 1.0)
        self.control_rod.set_density('g/cm3', 2.52)

        # 创建材料集合
        self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])
        
        # 创建几何体
        # 简化的圆柱形反应堆堆芯
        cylinder = openmc.ZCylinder(r=100)
        fuel_region = -cylinder
        
        fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)
        
        # 创建universe和geometry
        root_universe = openmc.Universe(cells=[fuel_cell])
        self.geometry = openmc.Geometry(root_universe)
        
        # 设置模拟参数
        self.settings = openmc.Settings()
        self.settings.batches = 100
        self.settings.inactive = 10
        self.settings.particles = 1000
        self.settings.temperature = {'method': 'interpolation'}
        
    def init_visualization(self):
        """初始化可视化图表"""
        plt.ion()  # 开启交互模式
        self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))
        self.fig.suptitle('核反应堆运行状态监测', fontsize=16)
        
        # 初始化数据存储
        self.history = {
            'temperature': [],
            'power': [],
            'k_eff': [],
            'burnup': [],
            'control_rod': [],
            'steps': []
        }
        
        # 配置子图
        self.lines = {}
        
        # 温度和功率图
        self.axs[0, 0].set_title('温度和功率变化')
        self.axs[0, 0].set_xlabel('步数')
        self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='温度 (K)')
        self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='功率 (MW)')
        self.axs[0, 0].legend()
        
        # k_eff图
        self.axs[0, 1].set_title('k_eff变化')
        self.axs[0, 1].set_xlabel('步数')
        self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')
        self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='临界值')
        self.axs[0, 1].legend()
        
        # 控制棒位置图
        self.axs[1, 0].set_title('控制棒位置')
        self.axs[1, 0].set_xlabel('步数')
        self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='控制棒位置')
        self.axs[1, 0].legend()
        
        # 燃耗图
        self.axs[1, 1].set_title('燃料燃耗')
        self.axs[1, 1].set_xlabel('步数')
        self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='燃耗 (MWd/kgU)')
        self.axs[1, 1].legend()
        
        plt.tight_layout()

    def update_visualization(self):
        """更新可视化图表"""
        # 更新历史数据
        self.history['temperature'].append(self.temperature)
        self.history['power'].append(self.power)
        self.history['k_eff'].append(self.k_eff)
        self.history['burnup'].append(self.burnup)
        self.history['control_rod'].append(self.control_rod_pos)
        self.history['steps'].append(self.current_step)
        
        # 更新图表
        steps = self.history['steps']
        
        # 更新温度和功率
        self.lines['temp'].set_data(steps, self.history['temperature'])
        self.lines['power'].set_data(steps, self.history['power'])
        self.axs[0, 0].relim()
        self.axs[0, 0].autoscale_view()
        
        # 更新k_eff
        self.lines['k_eff'].set_data(steps, self.history['k_eff'])
        self.axs[0, 1].relim()
        self.axs[0, 1].autoscale_view()
        
        # 更新控制棒位置
        self.lines['control'].set_data(steps, self.history['control_rod'])
        self.axs[1, 0].relim()
        self.axs[1, 0].autoscale_view()
        
        # 更新燃耗
        self.lines['burnup'].set_data(steps, self.history['burnup'])
        self.axs[1, 1].relim()
        self.axs[1, 1].autoscale_view()
        
        plt.draw()
        plt.pause(0.01)

    def reset(self, seed=None):
        super().reset(seed=seed)
        self.current_step = 0
        self.temperature = 600  # K
        self.power = 1000      # MW
        self.k_eff = 1.0
        self.burnup = 0        # MWd/kgU
        self.control_rod_pos = 0.5
        
        # 初始化可视化
        self.init_visualization()
        
        return self._get_observation(), {}
    
    def _get_observation(self):
        return np.array([
            self.temperature,
            self.power,
            self.k_eff,
            self.control_rod_pos,
            self.burnup
        ], dtype=np.float32)
    
    def step(self, action):
        self.current_step += 1
        
        # 更新控制棒位置
        self.control_rod_pos = action / 10.0
        
        # 运行OpenMC模拟
        try:
            # 更新材料温度
            self.fuel.temperature = self.temperature
            
            # 运行临界计算
           # os.remove('/home/unknowxxs/python/statepoint.50.h5')
            result = openmc.run()
         
            
            # 获取k_eff
            with openmc.StatePoint(result) as sp:
                self.k_eff = sp.k_effective.nominal_value
                
            # 更新系统状态
            self._update_state()
            
            # 更新可视化
            self.update_visualization()
            
            # 计算奖励
            reward = self._calculate_reward()
            
            # 检查是否结束
            done = self.current_step >= self.max_steps or self._is_unsafe()
            
        except Exception as e:
            print(f"模拟过程出错: {e}")
            reward = -1000
            done = True
        
        return self._get_observation(), reward, done, False, {}
    
    def _update_state(self):
        # 基于k_eff更新功率
        power_factor = (self.k_eff - 1.0) * 5000
        self.power = np.clip(self.power + power_factor, 0, 3000)
        
        # 更新温度
        delta_temp = (self.power - 1000) * 0.1
        self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)
        
        # 更新燃耗
        self.burnup += self.power * 0.001
        
    def _calculate_reward(self):
        reward = 0
        
        # 奖励稳定的运行状态
        reward -= abs(self.power - 1000) * 0.01  # 目标功率1000MW
        reward -= abs(self.k_eff - 1.0) * 100    # 目标k_eff=1
        reward -= abs(self.temperature - 900) * 0.1  # 目标温度900K
        
        # 惩罚不安全状态
        if self._is_unsafe():
            reward -= 1000
            
        return reward
    
    def _is_unsafe(self):
        return (self.temperature > 1400 or 
                self.power > 2500 or 
                self.k_eff > 1.2)

def train_agent():
    env = make_vec_env(NuclearReactorEnv, n_envs=4)
    
    model = PPO(
        "MlpPolicy",
        env,
        verbose=1,
        learning_rate=0.0003,
        n_steps=2048,
        batch_size=64,
        n_epochs=10
    )
    
    model.learn(total_timesteps=10)
    model.save("ppo_nuclear_reactor")
    
    return model

def evaluate_agent(model, num_episodes=5):
    env = NuclearReactorEnv()
    
    for episode in range(num_episodes):
        obs, _ = env.reset()
        episode_reward = 0
        done = False
        
        while not done:
            action, _ = model.predict(obs)
            obs, reward, done, _, _ = env.step(action)
            episode_reward += reward
            
            # 打印当前状态
            print(f"步骤 {env.current_step}:")
            print(f"温度: {env.temperature:.1f}K")
            print(f"功率: {env.power:.1f}MW")
            print(f"k_eff: {env.k_eff:.3f}")
            print(f"控制棒位置: {env.control_rod_pos:.2f}")
            print(f"燃耗: {env.burnup:.2f}MWd/kgU")
            print("------------------------")
        
        plt.savefig(f'episode_{episode+1}_results.png')
        print(f"Episode {episode + 1}: 总奖励 = {episode_reward:.2f}\n")
    
    plt.close('all')

model = train_agent()
print("\n评估核反应堆控制性能：")
evaluate_agent(model)

```
Assistant: