feat：真实工厂数据测试版调用代码

- 新增状态构建类DQNStateBuilder（models/uf-rl/uf_train/rl_model/DQN/dqn_statebuilder.py）
- 新增全状态决策脚本（models/uf-rl/uf_train/rl_model/DQN/run_dqn_deicde_totalstate.py）
- 示例文件位于models/uf-rl/uf_train/rl_model/DQN/online_datasets

models/uf-rl/config/dqn_config.yaml

@@ -0,0 +1,25 @@
+# ==================== DQN 超参数配置 ====================
+
+# ===== 神经网络参数 =====
+learning_rate: 1.0e-4
+
+# ===== 经验回放参数 =====
+buffer_size: 100000
+learning_starts: 10000
+batch_size: 32
+
+# ===== 强化学习核心参数 =====
+gamma: 0.95
+train_freq: 4
+
+# ===== 目标网络更新参数 =====
+target_update_interval: 1
+tau: 0.005
+
+# ===== 探索策略（ε-greedy） =====
+exploration_initial_eps: 1.0
+exploration_fraction: 0.3
+exploration_final_eps: 0.02
+
+# ===== 实验标识 =====
+remark: "default"

models/uf-rl/config/env_config.yaml

@@ -0,0 +1,124 @@
+UFState:
+  # ===== 膜动态运行参数 =====
+  q_UF: 360.0
+  TMP: 0.03
+  temp: 25.0
+  R: 200.0
+
+  # ===== 膜阻力模型参数 =====
+  nuK: 170.0
+  slope: 2.0
+  power: 1.032
+  ceb_removal: 100.0
+
+
+UFPhysicsParams:
+  # ===== TMP 全局约束 =====
+  global_TMP_hard_limit: 0.08
+  global_TMP_soft_limit: 0.06
+
+  # ===== 物理反洗参数 =====
+  tau_bw_s: 20.0
+  gamma_t: 1.0
+  q_bw_m3ph: 1000.0
+
+  # ===== CEB 化学反洗参数 =====
+  T_ceb_interval_h: 48.0
+  v_ceb_m3: 20.0
+  t_ceb_s: 2400.0   # 40 * 60
+
+  # ===== 膜组件参数 =====
+  A: 5120.0   # 128 * 40
+
+  # ===== 吨水电耗查找表 =====
+  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
+    4260: 0.1008
+    4320: 0.1007
+    4380: 0.1005
+    4440: 0.1003
+    4500: 0.1001
+    4560: 0.0999
+    4620: 0.0998
+    4680: 0.0996
+    4740: 0.0995
+    4800: 0.0993
+
+
+UFActionSpec:
+  # ===== 动作空间范围 =====
+  L_min_s: 3800.0
+  L_max_s: 4800.0
+  t_bw_min_s: 40.0
+  t_bw_max_s: 60.0
+
+  # ===== 动作离散化步长 =====
+  L_step_s: 60.0
+  t_bw_step_s: 5.0
+
+
+UFRewardParams:
+  # ===== TMP 安全与惩罚 =====
+  global_TMP_hard_limit: 0.08
+  global_TMP_soft_limit: 0.06
+  w_tmp_hard: 5.0
+  w_tmp: 1.5
+  p: 3.0
+  w_trend: 1.0
+
+  # ===== 回收率 =====
+  k_rec: 5.0
+  rec_low: 0.92
+  rec_high: 0.99
+  w_rec: 1.0
+
+  # ===== 残余污染 =====
+  k_res: 10.0
+  residual_ref_ratio: null
+  w_res: 2.0
+
+  # ===== 吨水电耗 =====
+  k_energy: 5.0
+  energy_low: 0.0993
+  energy_high: 0.1034
+  energy_ref: 0.1011
+  w_energy: 1.0
+
+
+UFStateBounds:
+  # ===== 流量初始化约束 =====
+  q_UF_min: 210.0
+  q_UF_max: 380.0
+
+  # ===== 温度初始化约束 =====
+  temp_min: 16.0
+  temp_max: 32.0
+
+  # ===== TMP 初始化约束 =====
+  TMP0_min: 0.01
+  TMP0_max: 0.045
+  global_TMP_hard_limit: 0.08
+
+  # ===== 短期污染参数 =====
+  nuK_min: 40.0
+  nuK_max: 260.0
+
+  # ===== 长期污染参数 =====
+  slope_min: 0.03
+  slope_max: 27.0
+  power_min: 0.4
+  power_max: 2.2
+
+  # ===== CEB 去除能力 =====
+  ceb_removal_min: 40.0
+  ceb_removal_max: 250.0

models/uf-rl/uf_data_process/uf_analyze_config.yaml → models/uf-rl/config/uf_analyze_config.yaml

@@ -16,7 +16,7 @@ UF:
 
 Params:
   # 稳定段提取
-  min_stable_points: 40
+  min_stable_points: 30
   initial_points: 10
 
   # 阻力趋势计算

models/uf-rl/uf_train/env/env_params.py

@@ -283,44 +283,34 @@ class UFRewardParams:
     """
     【奖励函数与安全约束参数】用于 reward 计算和 episode 终止判断。
     """
-
-    # ------------------ TMP 限值 ------------------
+    # TMP 限值
     global_TMP_hard_limit: float = 0.08
     # TMP 硬上限（MPa）
     # 说明：超过此值将导致 episode 失败，需立即停机
+    global_TMP_soft_limit: float = 0.06     # TMP 软上限 (MPa)
+    w_tmp_hard: float = 5.0      # TMP超硬限固定惩罚
+    w_tmp: float = 1.5            # TMP软限惩罚
+    p: float = 3                  # TMP软限非线性指数
+    w_trend: float = 1.0          # TMP趋势惩罚权重
 
-    global_TMP_soft_limit: float = 0.06
-    # TMP 软上限 (MPa)
-    # 说明：此上限用于指导奖励函数中膜阻力允许上升值，
-    # 越接近该上限，系统对膜阻力上升控制的更严格
-
-    # ------------------ 回收率奖励参数 ------------------
+    # 回收率
     k_rec: float = 5.0
-    # 回收率敏感度系数（控制回收率奖励的陡峭程度）
-
     rec_low: float = 0.92
     rec_high: float = 0.99
-    # 回收率正常范围，用于归一化奖励
+    w_rec: float = 1.0             # 回收率权重
 
-    # ------------------ 残余污染奖励参数 ------------------
+    # 残余污染
     k_res: float = 10.0
-    # 残余污染敏感度系数（控制污染惩罚的陡峭程度）
-
-    rr0: float = 0.08
-    # 残余污染比例参考值（原设计参考点）
-    # 在新设计中，每步允许上升比例由 1/max_episode_steps 决定，可覆盖原值
+    residual_ref_ratio: float = None   # 动态=1/max_episode_steps
+    w_res: float = 2.0                 # 残余污染权重
 
-    # ------------------ 吨水电耗奖励参数 ------------------
+    # 吨水电耗
     k_energy: float = 5.0
-    # 吨水电耗敏感度系数（控制电耗奖励/惩罚陡峭程度）
-
     energy_low: float = 0.0993
     energy_high: float = 0.1034
     energy_ref: float = 0.1011
-    # 电耗参考值
-    # energy_low: 低耗电，奖励较高
-    # energy_high: 高耗电，奖励为负
-    # energy_ref: 平衡点，对应奖励为 0
+    w_energy: float = 1.0              # 能耗权重
+
 
 
 @dataclass(frozen=True)

models/uf-rl/uf_train/env/env_reset.py

@@ -165,9 +165,21 @@ class ResetSampler:
 
     def _get_sampling_config(self, progress: float) -> dict:
         progress = np.clip(progress, 0.0, 1.0)
+
+        # -------------------------
+        # 阶段权重设计（非线性 + 提高虚拟工况）
+        # -------------------------
+        w_real = (1.0 - progress) ** 1.2  # 历史工况逐渐衰减
+        w_perturb = 0.5 * progress  # 周边扰动按线性增加
+        w_virtual = 0.3 * progress ** 1.5  # 虚拟工况加快增长，后期最大约 0.3
+
+        # perturb 扰动幅度
+        perturb_scale = 0.02 + 0.04 * progress
+
         return dict(
-            w_real=1.0 - 0.7 * progress,
-            w_perturb=0.5 * progress,
-            w_virtual=0.2 * progress,
-            perturb_scale=0.02 + 0.04 * progress,
+            w_real=w_real,
+            w_perturb=w_perturb,
+            w_virtual=w_virtual,
+            perturb_scale=perturb_scale,
         )
+

models/uf-rl/uf_train/env/uf_env.py

@@ -94,6 +94,7 @@ class UFSuperCycleEnv(gym.Env):
 
         self.current_step = 0
 
+
         # -------- 动作空间 --------
         self.action_spec = action_spec
 
@@ -220,6 +221,7 @@ class UFSuperCycleEnv(gym.Env):
             raise RuntimeError("无法生成可行初始状态")
 
         self.current_step = 0
+        self.tmp_over_limit_flag = False
         self.last_action = None
         self.max_TMP_during_filtration = self.state.TMP
 
@@ -301,28 +303,29 @@ class UFSuperCycleEnv(gym.Env):
         # 根据 info 判断是否成功
         feasible = self.physics.is_dead_cycle(info)  # True 表示成功循环，False 表示失败
 
-        if feasible:
-            # 每步奖励
-            reward, rec_reward, energy_reward, res_penalty = self._calculate_reward(info)
-            info["rec_reward"] = rec_reward
-            info["energy_reward"] = energy_reward
-            info["res_penalty"] = res_penalty
+        if info["max_TMP_during_filtration"] >= self.reward_params.global_TMP_hard_limit:
+            self.tmp_over_limit_flag = True
 
-            self.state = next_state
-            terminated = False
-        else:
-            # 中途失败惩罚
-            reward = -10
-            info["rec_reward"] = None
-            info["energy_reward"] = None
-            info["res_penalty"] = None
-            terminated = True
+        # ================== 孤立观察下一周期 ==================
+        info_next = None
+        if info["max_TMP_during_filtration"] > self.reward_params.global_TMP_soft_limit:
+            info_next, _ = self.physics.simulate_one_supercycle(state=next_state,L_s=L_s,t_bw_s=t_bw_s)
+
+
+        reward, tmp_penalty, rec_reward, energy_reward, res_penalty = self._calculate_reward(info, info_next)
+        info["tmp_penalty"] = tmp_penalty
+        info["rec_reward"] = rec_reward
+        info["energy_reward"] = energy_reward
+        info["res_penalty"] = res_penalty
+
+        self.state = next_state
+        terminated = False
 
         # 判断是否到达最大步数
         truncated = self.current_step >= self.max_episode_steps
 
         self.last_action = (L_s, t_bw_s)
-        next_obs = self.get_obs()
+        next_obs = self.get_obs(next_state)
 
         info["feasible"] = feasible
         info["step"] = self.current_step
@@ -351,7 +354,7 @@ class UFSuperCycleEnv(gym.Env):
 
         return next_obs, reward, terminated, truncated, info
 
-    def _calculate_reward(self, info: dict) -> float:
+    def _calculate_reward(self, info: dict, info_next) -> float:
         """
         计算强化学习奖励函数（扩展版）
 
@@ -387,6 +390,29 @@ class UFSuperCycleEnv(gym.Env):
             - 低点 = 0.0993 kWh/m³
         - 回收率参考点保持原有设计
         """
+
+        #新增：将TMP超限改为持续惩罚
+        # ========== TMP 超标风险惩罚项 ==========
+        tmp = info["max_TMP_during_filtration"]
+        tmp_soft = self.reward_params.global_TMP_soft_limit
+        tmp_hard = self.reward_params.global_TMP_hard_limit
+
+        if self.tmp_over_limit_flag:
+            tmp_state_penalty = -self.reward_params.w_tmp_hard
+        elif tmp <= tmp_soft:
+            tmp_state_penalty = 0.0
+        elif tmp < tmp_hard:
+            x = (tmp - tmp_soft) / (tmp_hard - tmp_soft)
+            tmp_state_penalty = -self.reward_params.w_tmp * x ** self.reward_params.p
+
+        # -------- TMP 趋势惩罚 --------
+        tmp_trend_penalty = 0.0
+        if info_next is not None:
+            delta_tmp = info_next["max_TMP_during_filtration"] - tmp
+            tmp_trend_penalty = -self.reward_params.w_trend * delta_tmp
+
+        tmp_penalty = tmp_state_penalty + tmp_trend_penalty
+
         # ========== 提取性能指标 ==========
         recovery = info["recovery"]  # 回收率 [0-1]
 
@@ -435,12 +461,12 @@ class UFSuperCycleEnv(gym.Env):
 
         # ========== 组合奖励 ==========
         # 简单线性组合三项（为污染项加权）
-        total_reward = rec_reward + 2.0 * res_penalty + energy_reward
+        total_reward = rec_reward + 2.0 * res_penalty + energy_reward + tmp_penalty
 
 
         # 可选：添加平移项使特定点的奖励为零（当前未使用）
         # total_reward -= offset
 
-        return total_reward, rec_reward, energy_reward, res_penalty
+        return total_reward, tmp_penalty, rec_reward, energy_reward, res_penalty
 
 

models/uf-rl/uf_train/rl_model/DQN/dqn_statebuilder.py

@@ -0,0 +1,261 @@
+import re
+from pathlib import Path
+from typing import Dict, Any
+import yaml
+import numpy as np
+import pandas as pd
+from oauthlib.uri_validate import segment
+
+# -------------------------------
+# 引入环境状态模板（最终输出）
+# -------------------------------
+from uf_train.env.env_params import UFState
+
+# -------------------------------
+# 引入分析类
+# -------------------------------
+from uf_data_process.load import UFConfigLoader
+from uf_data_process.label import UFEventClassifier, PostBackwashInletMarker
+from uf_data_process.filter import ConstantFlowFilter, EventQualityFilter, InletSegmentFilter
+from uf_data_process.calculate import UFResistanceCalculator, UFResistanceAnalyzer
+from uf_data_process.fit import ShortTermCycleFoulingFitter, LongTermFoulingFitter
+
+class DQNStateBuilder:
+    """
+    在 DQN 决策前构建状态的工具类
+
+    相关数据：
+        * CSV1 = 上一完整化学周期
+        * CSV2 = 新周期初始进水段
+    """
+
+    def __init__(self, config_path: str):
+        """
+        Parameters
+        ----------
+        config_path : str
+            uf_analyze_config.yaml 路径
+        """
+        self.cfg = UFConfigLoader(config_path)
+        self.uf_cfg = self.cfg.uf
+        self.params = self.cfg.params
+
+        self.units = self.uf_cfg["units"]
+        self.area_m2 = self.uf_cfg["area_m2"]
+
+        self.scale_factor = self.params.get("scale_factor", 1e10)
+        self.segment_head_n = self.params.get("segment_head_n", 10)
+        self.segment_tail_n = self.params.get("segment_tail_n", 10)
+
+    # ======================================================================
+    # 对外主接口
+    # ======================================================================
+
+    def build_from_csv_pair(
+        self,
+        prev_cycle_csv: str,
+        init_cycle_csv: str,
+    ) -> UFState:
+        """
+        使用【上一完整化学周期 CSV】+【当前周期初始 CSV】构建 UFState
+        """
+
+        df_prev = pd.read_csv(prev_cycle_csv)
+        df_init = pd.read_csv(init_cycle_csv)
+
+        # 自动识别 UF 单元编号（UF1 / UF2 / ...）
+        unit_id = self._infer_unit_id(df_prev)
+
+        # 分别处理两个 CSV
+        prev_features = self._analyze_previous_cycle_csv(df_prev, unit_id)
+        init_features = self._analyze_init_cycle_csv(df_init, unit_id)
+
+        # 化学清洗去除阻力（上一周期末 - 当前初始）
+        ceb_removal = max(
+            prev_features["R_end"] - init_features["R_start"],
+            0.0
+        )
+
+        # 构建 UFState
+        state = UFState(
+            q_UF=init_features["q_mean"],
+            TMP=init_features["tmp_mean"],
+            temp=init_features["temp_mean"],
+            R=init_features["R_start"],
+            nuK=prev_features["nuK"],
+            slope=prev_features["slope"],
+            power=prev_features["power"],
+            ceb_removal=ceb_removal,
+        )
+
+        return state
+
+    # ======================================================================
+    # 上一完整化学周期分析
+    # ======================================================================
+
+    def _analyze_previous_cycle_csv(
+        self,
+        df: pd.DataFrame,
+        unit_id: str,
+    ) -> Dict[str, float]:
+        """
+        上一完整化学周期分析逻辑
+
+        步骤：
+        1. 事件标注
+        2. 进水段过滤（质量过滤）
+        3. 膜阻力计算
+        4. 提取周期末稳定阻力
+        5. 拟合 nuK
+        6. 拟合长期不可逆污染（slope / power）
+        """
+
+        # -------- 事件标注 --------
+        df = self._label_events(df, unit_id)
+
+
+        # -------- 保留过滤进水段 --------
+        inlet_filter = InletSegmentFilter(
+            control_col=f"C.M.{unit_id}_DB@word_control",
+            stable_value=self.uf_cfg["stable_inlet_code"],
+            min_points=self.params["min_stable_points"],
+        )
+        segments = inlet_filter.extract(df)
+
+        quality_filter = EventQualityFilter(
+            min_points=self.params["min_stable_points"]
+        )
+        segments = quality_filter.filter(segments)
+
+        if len(segments) == 0:
+            raise ValueError("上一周期无有效稳定进水段，无法构建状态")
+
+        # -------- 3️⃣ 膜阻力计算 --------
+        res_calc = UFResistanceCalculator(
+            units=[unit_id],
+            area_m2=self.area_m2,
+            scale_factor=self.scale_factor,
+        )
+        segments = res_calc.calculate_for_segments(
+            segments,
+            temp_col=self.uf_cfg["temp_col"],
+            flow_col=self.uf_cfg["flow_col_template"].format(unit=unit_id),
+        )
+
+
+
+        # -------- 膜阻力统计 --------
+        res_col = f"{unit_id}_R_scaled"
+        ura = UFResistanceAnalyzer(
+            resistance_col=res_col,
+            head_n=self.segment_head_n,
+            tail_n=self.segment_tail_n
+        )
+        segments = ura.analyze_segments(segments)
+        df_all = segments[-1]
+        R_end = df_all["R_scaled_end"].iloc[0]
+
+        # ===== 确保 time 为 datetime =====
+        for i, seg in enumerate(segments):
+            if not pd.api.types.is_datetime64_any_dtype(seg["time"]):
+                seg = seg.copy()
+                seg["time"] = pd.to_datetime(seg["time"], errors="coerce")
+                seg = seg.dropna(subset=["time"])
+                segments[i] = seg
+
+        # -------- 5️⃣ 短期污染拟合（nuK）--------
+        st_fitter = ShortTermCycleFoulingFitter(unit_id)
+        nuK, _ = st_fitter.fit_cycle(segments)
+
+        # -------- 6️⃣ 长期不可逆污染拟合 --------
+        lt_fitter = LongTermFoulingFitter(unit_id)
+        slope, power, _ = lt_fitter.fit_cycle(segments)
+
+        return {
+            "R_end": R_end,
+            "nuK": float(nuK),
+            "slope": float(slope),
+            "power": float(power),
+        }
+
+    # ======================================================================
+    # 当前周期初始进水段分析
+    # ======================================================================
+
+    def _analyze_init_cycle_csv(
+        self,
+        df: pd.DataFrame,
+        unit_id: str,
+    ) -> Dict[str, float]:
+        """
+        当前周期初始进水段分析
+
+        特点：
+        - 不切段
+        - 不过滤
+        - 只计算均值
+        """
+
+        # -------- 1️⃣ 事件标注 --------
+        df = self._label_events(df, unit_id)
+
+        # -------- 2️⃣ 仅保留进水行 --------
+        df = df[df["event_type"] == "inlet"].copy()
+
+        if df.empty:
+            raise ValueError("初始 CSV 中无进水数据")
+
+        # -------- 3️⃣ 膜阻力计算 --------
+        res_calc = UFResistanceCalculator(
+            units=[unit_id],
+            area_m2=self.area_m2,
+            scale_factor=self.scale_factor,
+        )
+        segments = [df]
+        segments = res_calc.calculate_for_segments(
+            segments,
+            temp_col=self.uf_cfg["temp_col"],
+            flow_col=self.uf_cfg["flow_col_template"].format(unit=unit_id),
+        )
+        df = segments[-1]
+
+        flow_col = self.uf_cfg["flow_col_template"].format(unit=unit_id)
+        temp_col = self.uf_cfg["temp_col"]
+        press_col = f"C.M.{unit_id}_DB@press_PV"
+        res_col = f"{unit_id}_R_scaled"
+
+        return {
+            "q_mean": float(df[flow_col].mean()),
+            "tmp_mean": float(df[press_col].mean()),
+            "temp_mean": float(df[temp_col].mean()),
+            "R_start": float(df[res_col].mean()),
+        }
+
+    # ======================================================================
+    # 工具函数
+    # ======================================================================
+
+    def _infer_unit_id(self, df: pd.DataFrame) -> str:
+        """
+        根据列名自动识别 UF 单元编号
+        """
+        for unit in self.units:
+            key = f"C.M.{unit}_FT_JS@out"
+            if key in df.columns:
+                return unit
+        raise ValueError("无法从 CSV 列名识别 UF 单元编号")
+
+    def _label_events(self, df: pd.DataFrame, unit_id: str) -> pd.DataFrame:
+        """
+        为 DataFrame 标注 event_type
+        """
+        clf = UFEventClassifier(
+            unit_name=unit_id,
+            inlet_codes=self.uf_cfg["inlet_codes"],
+            physical_code=self.uf_cfg["physical_bw_code"],
+            chemical_code=self.uf_cfg["chemical_bw_code"],
+        )
+        df = clf.classify(df)
+        df = clf.segment(df)
+        return df

models/uf-rl/uf_train/rl_model/DQN/online_datasets/UF1_init_cycle.csv

@@ -0,0 +1,11 @@
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models/uf-rl/uf_train/rl_model/DQN/online_datasets/UF1_prev_cycle.csv

@@ -0,0 +1,2511 @@
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+2024-09-28 08:30:00,0.0010228814,0.1433698983,64.0,29.8432170339

models/uf-rl/uf_train/rl_model/DQN/run_dqn_decide.py

@@ -214,7 +214,7 @@ def run_dqn_decide(
 if __name__ == "__main__":
 
     MODEL_PATH = "model/dqn_model.zip"
-    TMP0 = 0.03  # 原始 TMP0
+    TMP0 = 0.019  # 原始 TMP0
     q_UF = 300 # 进水流量
     temp = 20.0 #进水温度
     current_state = UFState(TMP=TMP0, q_UF=q_UF, temp=temp)

models/uf-rl/uf_train/rl_model/DQN/run_dqn_deicde_totalstate.py

@@ -0,0 +1,267 @@
+"""
+run_dqn_decide_totalstate.py
+
+全状态UF 超滤 DQN 决策主入口（Inference / Online Assist）
+
+要求数据：
+1. UF1_prev_cycle.csv 示例数据，实际路径可更改，水厂上一周期完整数据，必须包含上一周期全部进水数据，且不能包含其他周期进水数据，分辨率1min，至少包含时间，进水流量，跨膜压差，温度
+2. UF1_init_cycle.csv 示例数据，实际路径可更改，水厂当前周期初始数据，建议在水厂当前周期进水稳定后传入并调用，程序计算该csv数据均值作为初始数据，eg：传入前10min数据，取周期前十分钟均值为初始值
+职责：
+0. 根据水厂csv数据构造状态
+1. 构造物理世界（physics）
+2. 实例化决策器（UFDQNDecider）
+3. 构造当前工厂状态（observation）
+4. 调用模型给出策略建议
+5. 生成 PLC 下发指令（限幅 / 限速）
+6. 评估该指令在物理模型下的效果（只评估，不下发）
+"""
+
+from pathlib import Path
+import numpy as np
+
+# ========== 参数 / 物理 ==========
+from uf_train.env.uf_resistance_models_load import load_resistance_models
+from uf_train.env.uf_physics import UFPhysicsModel
+from uf_train.env.env_params import UFState, UFPhysicsParams, UFStateBounds, UFRewardParams, UFActionSpec
+
+
+# ========== 决策器 ==========
+from uf_train.rl_model.DQN.dqn_decider import UFDQNDecider
+# ========== 决策状态构建器 ==========
+from uf_train.rl_model.DQN.dqn_statebuilder import DQNStateBuilder
+
+
+
+def build_physics():
+    """
+    构造与训练一致的物理模型（只做一次）
+    """
+    phys_params = UFPhysicsParams()
+    res_fp, res_bw = load_resistance_models(phys_params)
+
+    physics = UFPhysicsModel(
+        phys_params=phys_params,
+        resistance_model_fp=res_fp,
+        resistance_model_bw=res_bw,
+    )
+    return physics
+
+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。
+    """
+
+    action_spec = UFActionSpec()
+    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 (action_spec.L_min_s <= current_L_s <= action_spec.L_max_s):
+        print(f"警告: 当前过滤时长 {current_L_s} 秒不在允许范围内 [{action_spec.L_min_s}, {action_spec.L_max_s}]")
+    if current_t_bw_s is not None and not (action_spec.t_bw_min_s <= current_t_bw_s <= action_spec.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 [{action_spec.t_bw_min_s}, {action_spec.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 (action_spec.L_min_s <= model_prev_L_s <= action_spec.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 [{action_spec.L_min_s}, {action_spec.L_max_s}]")
+    if model_prev_t_bw_s is not None and not (action_spec.t_bw_min_s <= model_prev_t_bw_s <= action_spec.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 [{action_spec.t_bw_min_s}, {action_spec.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 (action_spec.L_min_s <= model_L_s <= action_spec.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 [{action_spec.L_min_s}, {action_spec.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 (action_spec.t_bw_min_s <= model_t_bw_s <= action_spec.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 [{action_spec.t_bw_min_s}, {action_spec.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 * action_spec.L_step_s:
+        if L_diff >= 0:
+            L_adjustment = action_spec.L_step_s
+        else:
+            L_adjustment = -action_spec.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 * action_spec.t_bw_step_s:
+        if t_bw_diff >= 0:
+            t_bw_adjustment = action_spec.t_bw_step_s
+        else:
+            t_bw_adjustment = -action_spec.t_bw_step_s
+    next_t_bw_s = effective_current_t_bw + t_bw_adjustment
+
+    return next_L_s, next_t_bw_s
+
+
+
+def calc_uf_cycle_metrics(current_state, 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:
+
+        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)
+        }
+    """
+
+    # 模拟该参数下的超级周期
+    info, next_state = physics.simulate_one_supercycle(current_state, L_s=L_s, t_bw_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"]
+    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 * current_state.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,
+        "daily_prod_time_h": daily_prod_time_h,
+        "max_permeability": max_permeability
+    }
+
+def run_dqn_decide(
+    model_path: Path,
+    physics,
+    # -------- 工厂当前值 --------
+    current_state: UFState
+):
+    """
+    单轮 DQN 决策流程
+    """
+
+    # 构造决策器
+    decider = UFDQNDecider(
+        physics=physics,
+        model_path=model_path,
+        seed=0,
+    )
+    # 模型决策（不推进真实环境）
+
+    decision = decider.decide(current_state)
+    action_id = decision["action_id"]
+    model_L_s = decision["L_s"]
+    model_t_bw_s = decision["t_bw_s"]
+
+    return action_id, model_L_s, model_t_bw_s
+
+
+# ==============================
+# 示例调用
+# ==============================
+if __name__ == "__main__":
+
+    THIS_FILE = Path(__file__).resolve()
+    UF_RL_ROOT = THIS_FILE.parents[3]
+    CONFIG_PATH = UF_RL_ROOT / "config" / "uf_analyze_config.yaml"
+
+    MODEL_PATH = "model/dqn_model.zip"
+    prev_cycle_csv = "online_datasets/UF1_prev_cycle.csv"
+    init_cycle_csv = "online_datasets/UF1_init_cycle.csv"
+
+    # 构建强化学习状态
+    state_builder = DQNStateBuilder(config_path=CONFIG_PATH)
+    current_state: UFState = state_builder.build_from_csv_pair(
+        prev_cycle_csv=prev_cycle_csv,
+        init_cycle_csv=init_cycle_csv,
+    )
+
+    physics = build_physics()
+
+    action_id, model_L_s, model_t_bw_s = run_dqn_decide(
+        model_path=MODEL_PATH,
+        physics=physics,
+        current_state=current_state,
+    ) # 环境实例化，模型加载等功能放在UFDQNDecider类中
+
+    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(current_state, 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: {action_id}")
+    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']}")
+