import numpy as np from stable_baselines3 import DQN from DQN_env import UFSuperCycleEnv from DQN_env import UFParams # 模型路径 MODEL_PATH = "dqn_model.zip" # 创建环境实例以获取观察空间和动作空间 def _get_model_spaces(): """获取模型的观察空间和动作空间""" env = UFSuperCycleEnv(UFParams()) obs_space = env.observation_space action_space = env.action_space env.close() return obs_space, action_space # 加载模型（只加载一次，提高效率） try: # 尝试直接加载 model = DQN.load(MODEL_PATH) except KeyError: # 如果失败，则提供观察空间和动作空间 obs_space, action_space = _get_model_spaces() model = DQN.load(MODEL_PATH, custom_objects={ 'observation_space': obs_space, 'action_space': action_space }) def run_uf_DQN_decide(uf_params, TMP0_value: float): """ 单步决策函数：输入原始 TMP0，预测并执行动作 参数: TMP0_value (float): 当前 TMP0 值（单位与环境一致） 返回: dict: 包含模型选择的动作、动作参数、新状态、奖励等 """ # 1. 实例化环境 base_params = uf_params env = UFSuperCycleEnv(base_params) # 2. 将输入的 TMP0 写入环境 env.current_params.TMP0 = TMP0_value # 3. 获取归一化状态 obs = env._get_obs().reshape(1, -1) # 4. 模型预测动作 action, _ = model.predict(obs, deterministic=True) # 5. 解析动作对应的 L_s 和 t_bw_s L_s, t_bw_s = env._get_action_values(action[0]) # 6. 在环境中执行该动作 next_obs, reward, terminated, truncated, info = env.step(action[0]) # 7. 整理结果 result = { "action": int(action[0]), "L_s": float(L_s), "t_bw_s": float(t_bw_s), "next_obs": next_obs, "reward": reward, "terminated": terminated, "truncated": truncated, "info": info } # 8. 关闭环境 env.close() return result def generate_plc_instructions(current_L_s, current_t_bw_s, model_prev_L_s, model_prev_t_bw_s, model_L_s, model_t_bw_s): """ 根据工厂当前值、模型上一轮决策值和模型当前轮决策值，生成PLC指令。 新增功能： 1. 处理None值情况：如果模型上一轮值为None，则使用工厂当前值； 如果工厂当前值也为None，则返回None并提示错误。 """ # 参数配置保持不变 params = UFParams( L_min_s=3600.0, L_max_s=6000.0, L_step_s=60.0, t_bw_min_s=40.0, t_bw_max_s=60.0, t_bw_step_s=2.0, ) # 参数解包 L_step_s = params.L_step_s t_bw_step_s = params.t_bw_step_s L_min_s = params.L_min_s L_max_s = params.L_max_s t_bw_min_s = params.t_bw_min_s t_bw_max_s = params.t_bw_max_s adjustment_threshold = 1.0 # 处理None值情况 if model_prev_L_s is None: if current_L_s is None: print("错误: 过滤时长的工厂当前值和模型上一轮值均为None") return None, None else: # 使用工厂当前值作为基准 effective_current_L = current_L_s source_L = "工厂当前值(模型上一轮值为None)" else: # 模型上一轮值不为None，继续检查工厂当前值 if current_L_s is None: effective_current_L = model_prev_L_s source_L = "模型上一轮值(工厂当前值为None)" else: effective_current_L = model_prev_L_s source_L = "模型上一轮值" # 对反洗时长进行同样的处理 if model_prev_t_bw_s is None: if current_t_bw_s is None: print("错误: 反洗时长的工厂当前值和模型上一轮值均为None") return None, None else: effective_current_t_bw = current_t_bw_s source_t_bw = "工厂当前值(模型上一轮值为None)" else: if current_t_bw_s is None: effective_current_t_bw = model_prev_t_bw_s source_t_bw = "模型上一轮值(工厂当前值为None)" else: effective_current_t_bw = model_prev_t_bw_s source_t_bw = "模型上一轮值" # 检测所有输入值是否在规定范围内（只对非None值进行检查） # 工厂当前值检查（警告） if current_L_s is not None and not (L_min_s <= current_L_s <= L_max_s): print(f"警告: 当前过滤时长 {current_L_s} 秒不在允许范围内 [{L_min_s}, {L_max_s}]") if current_t_bw_s is not None and not (t_bw_min_s <= current_t_bw_s <= t_bw_max_s): print(f"警告: 当前反洗时长 {current_t_bw_s} 秒不在允许范围内 [{t_bw_min_s}, {t_bw_max_s}]") # 模型上一轮决策值检查（警告） if model_prev_L_s is not None and not (L_min_s <= model_prev_L_s <= L_max_s): print(f"警告: 模型上一轮过滤时长 {model_prev_L_s} 秒不在允许范围内 [{L_min_s}, {L_max_s}]") if model_prev_t_bw_s is not None and not (t_bw_min_s <= model_prev_t_bw_s <= t_bw_max_s): print(f"警告: 模型上一轮反洗时长 {model_prev_t_bw_s} 秒不在允许范围内 [{t_bw_min_s}, {t_bw_max_s}]") # 模型当前轮决策值检查（错误） if model_L_s is None: raise ValueError("错误: 决策模型建议的过滤时长不能为None") elif not (L_min_s <= model_L_s <= L_max_s): raise ValueError(f"错误: 决策模型建议的过滤时长 {model_L_s} 秒不在允许范围内 [{L_min_s}, {L_max_s}]") if model_t_bw_s is None: raise ValueError("错误: 决策模型建议的反洗时长不能为None") elif not (t_bw_min_s <= model_t_bw_s <= t_bw_max_s): raise ValueError(f"错误: 决策模型建议的反洗时长 {model_t_bw_s} 秒不在允许范围内 [{t_bw_min_s}, {t_bw_max_s}]") print(f"过滤时长基准: {source_L}, 值: {effective_current_L}") print(f"反洗时长基准: {source_t_bw}, 值: {effective_current_t_bw}") # 使用选定的基准值进行计算调整 L_diff = model_L_s - effective_current_L L_adjustment = 0 if abs(L_diff) >= adjustment_threshold * L_step_s: if L_diff >= 0: L_adjustment = L_step_s else: L_adjustment = -L_step_s next_L_s = effective_current_L + L_adjustment t_bw_diff = model_t_bw_s - effective_current_t_bw t_bw_adjustment = 0 if abs(t_bw_diff) >= adjustment_threshold * t_bw_step_s: if t_bw_diff >= 0: t_bw_adjustment = t_bw_step_s else: t_bw_adjustment = -t_bw_step_s next_t_bw_s = effective_current_t_bw + t_bw_adjustment return next_L_s, next_t_bw_s from DQN_env import simulate_one_supercycle def calc_uf_cycle_metrics(p, TMP0, max_tmp_during_filtration, min_tmp_during_filtration, L_s: float, t_bw_s: float): """ 计算 UF 超滤系统的核心性能指标 参数: p (UFParams): UF 系统参数 L_s (float): 单次过滤时间（秒） t_bw_s (float): 单次反洗时间（秒） 返回: dict: { "k_bw_per_ceb": 小周期次数, "ton_water_energy_kWh_per_m3": 吨水电耗, "recovery": 回收率, "net_delivery_rate_m3ph": 净供水率 (m³/h), "daily_prod_time_h": 日均产水时间 (小时/天) "max_permeability": 全周期最高渗透率(lmh/bar) } """ # 将跨膜压差写入参数 p.TMP0 = TMP0 # 模拟该参数下的超级周期 feasible, info = simulate_one_supercycle(p, L_s, t_bw_s) # 获得模型模拟周期信息 k_bw_per_ceb = info["k_bw_per_ceb"] ton_water_energy_kWh_per_m3 = info["ton_water_energy_kWh_per_m3"] recovery = info["recovery"] net_delivery_rate_m3ph = info["net_delivery_rate_m3ph"] daily_prod_time_h = info["daily_prod_time_h"] # 获得模型模拟周期内最高跨膜压差/最低跨膜压差 if max_tmp_during_filtration is None: max_tmp_during_filtration = info["max_TMP_during_filtration"] if min_tmp_during_filtration is None: min_tmp_during_filtration = info["min_TMP_during_filtration"] # 计算最高渗透率 max_permeability = 100 * p.q_UF / (128*40) / min_tmp_during_filtration return { "k_bw_per_ceb": k_bw_per_ceb, "ton_water_energy_kWh_per_m3": ton_water_energy_kWh_per_m3, "recovery": recovery, "net_delivery_rate_m3ph": net_delivery_rate_m3ph, "daily_prod_time_h": daily_prod_time_h, "max_permeability": max_permeability } # ============================== # 示例调用 # ============================== if __name__ == "__main__": uf_params = UFParams() TMP0 = 0.03 # 原始 TMP0 model_decide_result = run_uf_DQN_decide(uf_params, TMP0) # 调用模型获得动作 model_L_s = model_decide_result['L_s'] # 获得模型决策产水时长 model_t_bw_s = model_decide_result['t_bw_s'] # 获得模型决策反洗时长 current_L_s = 3800 current_t_bw_s = 40 model_prev_L_s = 4040 model_prev_t_bw_s = 60 L_s, t_bw_s = generate_plc_instructions(current_L_s, current_t_bw_s, model_prev_L_s, model_prev_t_bw_s, model_L_s, model_t_bw_s) # 获取模型下发指令 L_s = 4100 t_bw_s = 96 max_tmp_during_filtration = 0.050176 # 新增工厂数据接口：周期最高/最低跨膜压差，无工厂数据接入时传入None，calc_uf_cycle_metrics()自动获取模拟周期中的跨膜压差最值 min_tmp_during_filtration = 0.012496 execution_result = calc_uf_cycle_metrics(uf_params, TMP0, max_tmp_during_filtration, min_tmp_during_filtration, L_s, t_bw_s) print("\n===== 单步决策结果 =====") print(f"模型选择的动作: {model_decide_result['action']}") print(f"模型选择的L_s: {model_L_s} 秒, 模型选择的t_bw_s: {model_t_bw_s} 秒") print(f"指令下发的L_s: {L_s} 秒, 指令下发的t_bw_s: {t_bw_s} 秒") print(f"指令对应的反洗次数: {execution_result['k_bw_per_ceb']}") print(f"指令对应的吨水电耗: {execution_result['ton_water_energy_kWh_per_m3']}") print(f"指令对应的回收率: {execution_result['recovery']}") print(f"指令对应的日均产水时间: {execution_result['daily_prod_time_h']}") print(f"指令对应的最高渗透率: {execution_result['max_permeability']}")