ultrafiltration_model/DQN_env.py

import os
import time
import random
import numpy as np
import gymnasium as gym
from gymnasium import spaces
from stable_baselines3 import DQN
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.callbacks import BaseCallback
from typing import Dict, Tuple, Optional
import torch
import torch.nn as nn
from dataclasses import dataclass, asdict
from save_uf_models import TMPIncreaseModel, TMPDecreaseModel  # 导入模型类
import copy


# ==== 定义膜的基础运行参数 ====
@dataclass
class UFParams:
    # —— 膜与运行参数 ——
    q_UF: float = 360.0  # 过滤进水流量（m^3/h）
    TMP0: float = 0.03  # 初始TMP（MPa）
    TMP_max: float = 0.06  # TMP硬上限（MPa）

    # —— 膜污染动力学 ——
    alpha: float = 1e-6  # TMP增长系数
    belta: float = 1.1  # 幂指数

    # —— 反洗参数（固定） ——
    q_bw_m3ph: float = 1000.0  # 物理反洗流量（m^3/h）

    # —— CEB参数（固定） ——
    T_ceb_interval_h: float = 48.0  # 固定每 k 小时做一次CEB
    v_ceb_m3: float = 30.0  # CEB用水体积（m^3）
    t_ceb_s: float = 40 * 60.0  # CEB时长（s）
    phi_ceb: float = 1.0  # CEB去除比例（简化：完全恢复到TMP0）

    # —— 约束与收敛 ——
    dTMP: float = 0.001  # 单次产水结束时，相对TMP0最大升幅（MPa）

    # —— 搜索范围（秒） ——
    L_min_s: float = 3800.0  # 过滤时长下限（s）
    L_max_s: float = 6000.0  # 过滤时长上限（s）
    t_bw_min_s: float = 40.0  # 物洗时长下限（s）
    t_bw_max_s: float = 60.0  # 物洗时长上限（s）

    # —— 物理反洗恢复函数参数 ——
    phi_bw_min: float = 0.7  # 物洗去除比例最小值
    phi_bw_max: float = 1.0  # 物洗去除比例最大值
    L_ref_s: float = 4000.0  # 过滤时长影响时间尺度
    tau_bw_s: float = 20.0  # 物洗时长影响时间尺度
    gamma_t: float = 1.0  # 物洗时长作用指数

    # —— 网格 ——
    L_step_s: float = 60.0  # 过滤时长步长（s）
    t_bw_step_s: float = 5.0  # 物洗时长步长（s）

    # 多目标加权及高TMP惩罚
    w_rec: float = 0.8  # 回收率权重
    w_rate: float = 0.2  # 净供水率权重
    w_headroom: float = 0.2  # 贴边惩罚权重
    r_headroom: float = 2.0  # 贴边惩罚幂次
    headroom_hardcap: float = 0.98  # 超过此比例直接视为不可取

# ==== 定义强化学习超参数 ====
@dataclass
class DQNParams:
    """
    DQN 超参数定义类
    用于统一管理模型训练参数
    """
    # 学习率，控制神经网络更新步长
    learning_rate: float = 1e-4

    # 经验回放缓冲区大小（步数）
    buffer_size: int = 10000

    # 学习开始前需要收集的步数
    learning_starts: int = 200

    # 每次从经验池中采样的样本数量
    batch_size: int = 32

    # 折扣因子，越接近1越重视长期奖励
    gamma: float = 0.95

    # 每隔多少步训练一次
    train_freq: int = 4

    # 目标网络更新间隔
    target_update_interval: int = 2000

    # 初始探索率 ε
    exploration_initial_eps: float = 1.0

    # 从初始ε衰减到最终ε所占的训练比例
    exploration_fraction: float = 0.3

    # 最终探索率 ε
    exploration_final_eps: float = 0.02

    # 日志备注（用于区分不同实验）
    remark: str = "default"

# ==== 加载模拟环境模型 ====
# 初始化模型
model_fp = TMPIncreaseModel()
model_bw = TMPDecreaseModel()

# 加载参数
model_fp.load_state_dict(torch.load("uf_fp.pth"))
model_bw.load_state_dict(torch.load("uf_bw.pth"))

# 切换到推理模式
model_fp.eval()
model_bw.eval()


def _delta_tmp(p, L_h: float) -> float:
    """
    过滤时段TMP上升量：调用 uf_fp.pth 模型
    """
    return model_fp(p, L_h)

def phi_bw_of(p, L_s: float, t_bw_s: float) -> float:
    """
    物洗去除比例：调用 uf_bw.pth 模型
    """
    return model_bw(p, L_s, t_bw_s)

def _tmp_after_ceb(p, L_s: float, t_bw_s: float) -> float:
    """
    计算化学清洗(CEB)后的TMP，当前为恢复初始跨膜压差
    """
    return p.TMP0

def _v_bw_m3(p, t_bw_s: float) -> float:
    """
    物理反洗水耗
    """
    return float(p.q_bw_m3ph * (float(t_bw_s) / 3600.0))

def simulate_one_supercycle(p: UFParams, L_s: float, t_bw_s: float):
    """
    返回 (是否可行, 指标字典)
    - 支持动态CEB次数：48h固定间隔
    - 增加日均产水时间和吨水电耗
    - 增加最小TMP记录
    """
    L_h = float(L_s) / 3600.0  # 小周期过滤时间(h)

    tmp = p.TMP0
    max_tmp_during_filtration = tmp
    min_tmp_during_filtration = tmp  # 新增：初始化最小TMP
    max_residual_increase = 0.0

    # 小周期总时长(h)
    t_small_cycle_h = (L_s + t_bw_s) / 3600.0

    # 计算超级周期内CEB次数
    k_bw_per_ceb = int(np.floor(p.T_ceb_interval_h / t_small_cycle_h))
    if k_bw_per_ceb < 1:
        k_bw_per_ceb = 1  # 至少一个小周期

    # ton水电耗查表
    energy_lookup = {
        3600: 0.1034, 3660: 0.1031, 3720: 0.1029, 3780: 0.1026,
        3840: 0.1023, 3900: 0.1021, 3960: 0.1019, 4020: 0.1017,
        4080: 0.1015, 4140: 0.1012, 4200: 0.1011
    }

    for _ in range(k_bw_per_ceb):
        tmp_run_start = tmp

        # 过滤阶段TMP增长
        dtmp = _delta_tmp(p, L_h)
        tmp_peak = tmp_run_start + dtmp

        # 约束1：峰值不得超过硬上限
        if tmp_peak > p.TMP_max + 1e-12:
            return False, {"reason": "TMP_max violated during filtration", "TMP_peak": tmp_peak}

        # 更新最大和最小TMP
        if tmp_peak > max_tmp_during_filtration:
            max_tmp_during_filtration = tmp_peak
        if tmp_run_start < min_tmp_during_filtration:  # 新增：记录运行开始时的最小TMP
            min_tmp_during_filtration = tmp_run_start

        # 物理反洗
        phi = phi_bw_of(p, L_s, t_bw_s)
        tmp_after_bw = tmp_peak - phi * (tmp_peak - tmp_run_start)

        # 约束2：单次残余增量控制
        residual_inc = tmp_after_bw - tmp_run_start
        if residual_inc > p.dTMP + 1e-12:
            return False, {
                "reason": "residual TMP increase after BW exceeded dTMP",
                "residual_increase": residual_inc,
                "limit_dTMP": p.dTMP
            }
        if residual_inc > max_residual_increase:
            max_residual_increase = residual_inc

        tmp = tmp_after_bw

    # CEB
    tmp_after_ceb = p.TMP0

    # 体积与回收率
    V_feed_super = k_bw_per_ceb * p.q_UF * L_h
    V_loss_super = k_bw_per_ceb * _v_bw_m3(p, t_bw_s) + p.v_ceb_m3
    V_net = max(0.0, V_feed_super - V_loss_super)
    recovery = max(0.0, V_net / max(V_feed_super, 1e-12))

    # 时间与净供水率
    T_super_h = k_bw_per_ceb * (L_s + t_bw_s) / 3600.0 + p.t_ceb_s / 3600.0
    net_delivery_rate_m3ph = V_net / max(T_super_h, 1e-12)

    # 贴边比例与硬限
    headroom_ratio = max_tmp_during_filtration / max(p.TMP_max, 1e-12)
    if headroom_ratio > p.headroom_hardcap + 1e-12:
        return False, {"reason": "headroom hardcap exceeded", "headroom_ratio": headroom_ratio}

    # —— 新增指标 1：日均产水时间（h/d） ——
    daily_prod_time_h = k_bw_per_ceb * L_h / T_super_h * 24.0

    # —— 新增指标 2：吨水电耗（kWh/m³） ——
    closest_L = min(energy_lookup.keys(), key=lambda x: abs(x - L_s))
    ton_water_energy = energy_lookup[closest_L]

    info = {
        "recovery": recovery,
        "V_feed_super_m3": V_feed_super,
        "V_loss_super_m3": V_loss_super,
        "V_net_super_m3": V_net,
        "supercycle_time_h": T_super_h,
        "net_delivery_rate_m3ph": net_delivery_rate_m3ph,
        "max_TMP_during_filtration": max_tmp_during_filtration,
        "min_TMP_during_filtration": min_tmp_during_filtration,  # 新增：最小TMP
        "max_residual_increase_per_run": max_residual_increase,
        "phi_bw_effective": phi,
        "TMP_after_ceb": tmp_after_ceb,
        "headroom_ratio": headroom_ratio,
        "daily_prod_time_h": daily_prod_time_h,
        "ton_water_energy_kWh_per_m3": ton_water_energy,
        "k_bw_per_ceb": k_bw_per_ceb
    }

    return True, info

def _score(p: UFParams, rec: dict) -> float:
    """综合评分：越大越好。通过非线性放大奖励差异，强化区分好坏动作"""

    # —— 无量纲化净供水率 ——
    rate_norm = rec["net_delivery_rate_m3ph"] / max(p.q_UF, 1e-12)

    # —— TMP soft penalty (sigmoid) ——
    tmp_ratio = rec["max_TMP_during_filtration"] / max(p.TMP_max, 1e-12)
    k = 10.0
    headroom_penalty = 1.0 / (1.0 + np.exp(-k * (tmp_ratio - 1.0)))

    # —— 基础 reward（0.6~0.9左右）——
    base_reward = (
        p.w_rec * rec["recovery"]
        + p.w_rate * rate_norm
        - p.w_headroom * headroom_penalty
    )

    # —— 非线性放大：平方映射 + 缩放 ——
    # 目的是放大好坏动作差异，同时限制最大值，避免 TD-error 过大
    amplified_reward = (base_reward - 0.5) ** 2 * 5.0

    # —— 可选：保留符号，区分负奖励
    if base_reward < 0.5:
        amplified_reward = -amplified_reward

    return amplified_reward


def set_global_seed(seed: int):
    """固定全局随机种子，保证训练可复现"""
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)  # 如果使用GPU
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

class UFSuperCycleEnv(gym.Env):
    """超滤系统环境（超级周期级别决策）"""

    metadata = {"render_modes": ["human"]}

    def __init__(self, base_params, max_episode_steps: int = 20):
        super(UFSuperCycleEnv, self).__init__()

        self.base_params = base_params
        self.current_params = copy.deepcopy(base_params)
        self.max_episode_steps = max_episode_steps
        self.current_step = 0

        # 计算离散动作空间
        self.L_values = np.arange(
            self.base_params.L_min_s,
            self.base_params.L_max_s + self.base_params.L_step_s,
            self.base_params.L_step_s
        )
        self.t_bw_values = np.arange(
            self.base_params.t_bw_min_s,
            self.base_params.t_bw_max_s + self.base_params.t_bw_step_s,
            self.base_params.t_bw_step_s
        )

        self.num_L = len(self.L_values)
        self.num_bw = len(self.t_bw_values)

        # 单一离散动作空间
        self.action_space = spaces.Discrete(self.num_L * self.num_bw)

        # 状态空间增加 TMP0, 上一次动作(L_s, t_bw_s), 本周期最高 TMP
        # 状态归一化均在 _get_obs 内处理
        self.observation_space = spaces.Box(
            low=np.zeros(4, dtype=np.float32),
            high=np.ones(4, dtype=np.float32),
            dtype=np.float32
        )

        # 初始化状态
        self.last_action = (self.base_params.L_min_s, self.base_params.t_bw_min_s)
        self.max_TMP_during_filtration = self.current_params.TMP0
        self.reset(seed=None)

    def _get_obs(self):
        TMP0 = self.current_params.TMP0
        TMP0_norm = (TMP0 - 0.01) / (0.05 - 0.01)

        L_s, t_bw_s = self.last_action
        L_norm = (L_s - self.base_params.L_min_s) / (self.base_params.L_max_s - self.base_params.L_min_s)
        t_bw_norm = (t_bw_s - self.base_params.t_bw_min_s) / (self.base_params.t_bw_max_s - self.base_params.t_bw_min_s)

        max_TMP_norm = (self.max_TMP_during_filtration - 0.01) / (0.05 - 0.01)

        return np.array([TMP0_norm, L_norm, t_bw_norm, max_TMP_norm], dtype=np.float32)

    def _get_action_values(self, action):
        L_idx = action // self.num_bw
        t_bw_idx = action % self.num_bw
        return self.L_values[L_idx], self.t_bw_values[t_bw_idx]

    def reset(self, seed=None, options=None):
        super().reset(seed=seed)
        self.current_params.TMP0 = np.random.uniform(0.01, 0.03)
        self.current_step = 0
        self.last_action = (self.base_params.L_min_s, self.base_params.t_bw_min_s)
        self.max_TMP_during_filtration = self.current_params.TMP0
        return self._get_obs(), {}

    def step(self, action):
        self.current_step += 1
        L_s, t_bw_s = self._get_action_values(action)
        L_s = np.clip(L_s, self.base_params.L_min_s, self.base_params.L_max_s)
        t_bw_s = np.clip(t_bw_s, self.base_params.t_bw_min_s, self.base_params.t_bw_max_s)

        # 模拟超级周期
        feasible, info = simulate_one_supercycle(self.current_params, L_s, t_bw_s)

        if feasible:
            reward = _score(self.current_params, info)
            self.current_params.TMP0 = info["TMP_after_ceb"]
            self.max_TMP_during_filtration = info["max_TMP_during_filtration"]
            terminated = False
        else:
            reward = -20
            terminated = True

        truncated = self.current_step >= self.max_episode_steps
        self.last_action = (L_s, t_bw_s)
        next_obs = self._get_obs()

        info["feasible"] = feasible
        info["step"] = self.current_step

        return next_obs, reward, terminated, truncated, info


class UFEpisodeRecorder:
    """记录episode中的决策和结果"""

    def __init__(self):
        self.episode_data = []
        self.current_episode = []

    def record_step(self, obs, action, reward, done, info):
        """记录一步"""
        step_data = {
            "obs": obs.copy(),
            "action": action.copy(),
            "reward": reward,
            "done": done,
            "info": info.copy() if info else {}
        }
        self.current_episode.append(step_data)

        if done:
            self.episode_data.append(self.current_episode)
            self.current_episode = []

    def get_episode_stats(self, episode_idx=-1):
        """获取episode统计信息"""
        if not self.episode_data:
            return {}

        episode = self.episode_data[episode_idx]
        total_reward = sum(step["reward"] for step in episode)
        avg_recovery = np.mean([step["info"].get("recovery", 0) for step in episode if "recovery" in step["info"]])
        feasible_steps = sum(1 for step in episode if step["info"].get("feasible", False))

        return {
            "total_reward": total_reward,
            "avg_recovery": avg_recovery,
            "feasible_steps": feasible_steps,
            "total_steps": len(episode)
        }


class UFTrainingCallback(BaseCallback):
    """
    PPO 训练回调，用于记录每一步的数据到 recorder。
    相比原来的 RecordingCallback，更加合理和健壮：
    1. 不依赖环境内部 last_* 属性
    2. 使用 PPO 提供的 obs、actions、rewards、dones、infos
    3. 自动处理 episode 结束时的统计
    """

    def __init__(self, recorder, verbose=0):
        super(UFTrainingCallback, self).__init__(verbose)
        self.recorder = recorder

    def _on_step(self) -> bool:
        try:
            new_obs = self.locals.get("new_obs")
            actions = self.locals.get("actions")
            rewards = self.locals.get("rewards")
            dones = self.locals.get("dones")
            infos = self.locals.get("infos")

            if len(new_obs) > 0:
                step_obs = new_obs[0]
                step_action = actions[0] if actions is not None else None
                step_reward = rewards[0] if rewards is not None else 0.0
                step_done = dones[0] if dones is not None else False
                step_info = infos[0] if infos is not None else {}

                # 打印当前 step 的信息
                if self.verbose:
                    print(f"[Step {self.num_timesteps}] 动作={step_action}, 奖励={step_reward:.3f}, Done={step_done}")

                # 记录数据
                self.recorder.record_step(
                    obs=step_obs,
                    action=step_action,
                    reward=step_reward,
                    done=step_done,
                    info=step_info,
                )

        except Exception as e:
            if self.verbose:
                print(f"[Callback Error] {e}")

        return True




class DQNTrainer:
    def __init__(self, env, params, callback=None):
        self.env = env
        self.params = params
        self.callback = callback
        self.log_dir = self._create_log_dir()
        self.model = self._create_model()

    def _create_log_dir(self):
        timestamp = time.strftime("%Y%m%d-%H%M%S")
        log_name = (
            f"DQN_lr{self.params.learning_rate}_buf{self.params.buffer_size}_bs{self.params.batch_size}"
            f"_gamma{self.params.gamma}_exp{self.params.exploration_fraction}"
            f"_{self.params.remark}_{timestamp}"
        )
        log_dir = os.path.join("./uf_dqn_tensorboard", log_name)
        os.makedirs(log_dir, exist_ok=True)
        return log_dir

    def _create_model(self):
        return DQN(
            policy="MlpPolicy",
            env=self.env,
            learning_rate=self.params.learning_rate,
            buffer_size=self.params.buffer_size,  # 大缓冲保证经验多样性
            learning_starts=self.params.learning_starts,
            batch_size=self.params.batch_size,
            gamma=self.params.gamma,
            train_freq=self.params.train_freq,
            target_update_interval=1,
            tau=0.005,
            exploration_initial_eps=self.params.exploration_initial_eps,
            exploration_fraction=self.params.exploration_fraction,
            exploration_final_eps=self.params.exploration_final_eps,
            verbose=1,
            tensorboard_log=self.log_dir
            # 不再指定 replay_buffer_class，默认使用 ReplayBuffer
        )

    def train(self, total_timesteps: int):
        if self.callback:
            self.model.learn(total_timesteps=total_timesteps, callback=self.callback)
        else:
            self.model.learn(total_timesteps=total_timesteps)
        print(f"模型训练完成，日志保存在：{self.log_dir}")

    def save(self, path=None):
        if path is None:
            path = os.path.join(self.log_dir, "dqn_model.zip")
        self.model.save(path)
        print(f"模型已保存到：{path}")

    def load(self, path):
        self.model = DQN.load(path, env=self.env)
        print(f"模型已从 {path} 加载")


def train_uf_rl_agent(params: UFParams, total_timesteps: int = 10000, seed: int = 2025):
    set_global_seed(seed)
    recorder = UFEpisodeRecorder()
    callback = UFTrainingCallback(recorder, verbose=1)

    def make_env():
        env = UFSuperCycleEnv(params)
        env = Monitor(env)
        return env

    env = DummyVecEnv([make_env])

    dqn_params = DQNParams()
    trainer = DQNTrainer(env, dqn_params, callback=callback)
    trainer.train(total_timesteps)
    trainer.save()

    stats = callback.recorder.get_episode_stats()
    print(f"训练完成 - 总奖励: {stats.get('total_reward', 0):.2f}, 平均回收率: {stats.get('avg_recovery', 0):.3f}")

    return trainer.model


# 训练和测试示例
if __name__ == "__main__":
    # 初始化参数
    params = UFParams()

    # 训练RL代理
    print("开始训练RL代理...")
    train_uf_rl_agent(params, total_timesteps=50000)