feat: add case demo module and streamline Model assets

CHANGELOG.md

@@ -5,6 +5,22 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [1.2.0] - 2026-04-07
+
+### Added
+- ✨ 新增“算例演示（Case Demo）”标签页，位于根轨迹与智能问答之间
+- ✨ 新增 `case_demo_functions.py`，支持参数化工况运行完整混动模型
+- ✨ 新增图文输出：转速响应、功率分配、电气状态、关键时刻数据表
+- ✨ 新增算例结果自动解读（最大转速误差、SOC变化、平均功率、平均燃油流量）
+
+### Changed
+- 🔧 `Model/src/series_hybrid_sim.py` 改为按 Model 目录定位 GPR 数据与权重
+- 🔧 `requirements.txt` 固定关键依赖版本并加入 PyTorch CPU 下载源
+- 🔧 文档更新：README 与 GUIDANCE 同步为“五大功能”结构与最新运行方式
+
+### Removed
+- 🧹 精简 `Model` 目录：移除 `scripts/`、`EngineData.xlsx`、`.git/`、`.claude/`、`README.md`、`LICENSE`、`environment.yml`、`figures/`、`.gitignore`
+
 ## [1.0.0] - 2025-10-15
 
 ### Added

GUIDANCE.md

@@ -10,12 +10,13 @@
 
 ## 二、平台功能简介
 
-本平台是《自动控制理论》课程的配套学习工具，提供四大核心功能：
+本平台是《自动控制理论》课程的配套学习工具，提供五大核心功能：
 
 1. **时域分析** - 分析系统的阶跃响应和脉冲响应
 2. **频域分析** - 绘制Bode图和Nyquist图，分析系统稳定性
 3. **根轨迹分析** - 观察增益变化对系统极点的影响
-4. **AI智能问答** - 24小时在线的自动控制理论助教
+4. **算例演示** - 基于完整混动发动机模型的参数化仿真
+5. **AI智能问答** - 24小时在线的自动控制理论助教
 
 ---
 
@@ -107,7 +108,38 @@
 
 ---
 
-### 3.4 AI智能问答
+### 3.4 算例演示
+
+#### 3.4.1 功能说明
+
+通过完整混动模型进行系统级算例仿真，输出图文结果用于教学演示与参数对比。
+
+#### 3.4.2 使用步骤
+
+1. 点击顶部"算例演示"标签页
+2. 设置工况模板与参数：
+   - 仿真时长、仿真步长
+   - 初始SOC、初始发动机功率
+   - 目标转速缩放系数、负载转矩缩放系数
+3. 点击"运行混动算例"
+4. 查看三联图、结果解读与关键时刻数据表
+
+#### 3.4.3 输出结果
+
+- **推进轴转速响应图**：目标与实际转速跟踪
+- **功率分配图**：电机需求、发动机输出、电池功率
+- **电气状态图**：母线电压与SOC
+- **结果解读**：最大转速误差、SOC变化、平均功率、平均燃油流量
+
+#### 3.4.4 结果解释要点
+
+- 仿真时长是模型时间，不等于程序实际等待时间
+- 电池功率正值表示放电，负值表示充电
+- 输入超限时会自动裁剪到安全范围
+
+---
+
+### 3.5 AI智能问答
 
 #### 3.4.1 功能说明
 
@@ -268,12 +300,12 @@
 ### 9.1 平台特色功能
 
 1. **实时在线人数统计** - 页面顶部显示当前在线用户数
-2. **自动保存会话** - AI问答记录自动保存
+2. **图文联动演示** - 算例演示支持图、表、文本同步展示
 3. **LaTeX公式支持** - 完美显示数学公式
 4. **响应式设计** - 适配不同屏幕尺寸
 
 ### 9.2 更新说明
 
-- 版本: v1.1.0
-- 更新日期: 2025年10月19日
-- 主要更新: 新增实时在线人数统计功能
+- 版本: v1.2.0
+- 更新日期: 2026年4月7日
+- 主要更新: 新增算例演示模块与完整混动模型联动

Model/data/Cleaned_Engine_Data_Full.csv

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Model/src/battery_sim.py

@@ -0,0 +1,168 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+class BatterySim:
+    """
+    混电飞机高压电池模型
+    """
+    def __init__(self, capacity_kwh=50.0, nom_voltage=500.0, initial_soc=0.5):
+        """
+        初始化电池参数
+        :param capacity_kwh: 电池总能量 (kWh)，默认 50 kWh
+        :param nom_voltage: 额定电压 (V)，默认 500 V
+        :param initial_soc: 初始荷电状态 (0.0 到 1.0)
+        """
+        # --- 基本物理参数 ---
+        # 单体电池为 3.7V，根据额定电压计算串联数
+        self.cells_in_series = int(nom_voltage / 3.7) 
+        
+        # 电池组电压安全极限 (工程极限)
+        self.V_min = self.cells_in_series * 2.7  # 绝对放电截止电压 (约 364.5V)
+        self.V_max = self.cells_in_series * 4.2  # 绝对充电截止电压 (约 567.0V)
+        
+        # 容量与内阻标定
+        self.capacity_Ah = (capacity_kwh * 1000) / nom_voltage  # 安时容量 (约 100Ah)
+        
+        # 电池内阻为 0.15 欧姆。
+        # 在 SOC=50% 时，此内阻可将最大放电/充电安全功率限制在 300kW 左右。
+        self.R_in = 0.15  
+        
+        # --- 状态变量 ---
+        self.SOC = initial_soc
+        self.V_t = 0.0  # 端电压 (V)
+        self.I = 0.0    # 实际电流 (A)，放电为正，充电为负
+        
+        # --- OCV-SOC 查表曲线 Voc = f(SOC)  ---
+        self.soc_table = np.array([0.0, 0.1, 0.2, 0.5, 0.8, 0.9, 1.0])
+        self.cell_ocv_table = np.array([2.8, 3.3, 3.4, 3.6, 3.9, 4.0, 4.15])
+        self.Voc_table = self.cell_ocv_table * self.cells_in_series
+
+    def _get_ocv(self, soc):
+        """根据当前 SOC 线性插值获取开路电压"""
+        return np.interp(soc, self.soc_table, self.Voc_table)
+
+    def step(self, dt, P_req_kw):
+        """
+        单步执行电池仿真，包含过充/过放保护(功率限幅)
+        :param dt: 仿真步长 (秒)
+        :param P_req_kw: 外部请求功率 (kW)。正数表示放电，负数表示充电
+        :return: (实际输出/吸收功率 kW, 端电压 V, 实际电流 A, 当前 SOC)
+        """
+        # 1. 获取当前开路电压
+        V_oc = self._get_ocv(self.SOC)
+        
+        # 2. 计算工程安全极限 (基于电压边界)
+        # 2.1 最大放电极限 (限制 V_t >= V_min)
+        I_dis_max = (V_oc - self.V_min) / self.R_in
+        P_dis_max_W = self.V_min * I_dis_max  # 瓦特
+        
+        # 2.2 最大充电极限 (限制 V_t <= V_max, 电流和功率为负数)
+        I_cha_max = (V_oc - self.V_max) / self.R_in
+        P_cha_max_W = self.V_max * I_cha_max  # 瓦特
+        
+        # SOC 保护限制 (防止 SOC 突破 0% 和 100%)
+        if self.SOC <= 0.05:
+            P_dis_max_W = 0.0  # 电量极低，禁止放电
+        if self.SOC >= 0.98:
+            P_cha_max_W = 0.0  # 电量极高，禁止充电
+            
+        # 3. 保护逻辑：功率限幅 (Clipping)
+        P_req_W = P_req_kw * 1000.0
+        # 强制将需求功率限制在安全充放电区间内
+        P_actual_W = max(P_cha_max_W, min(P_req_W, P_dis_max_W))
+        
+        # 4. 根据实际功率求解电流 (一元二次方程)
+        # R * I^2 - Voc * I + P_actual = 0
+        discriminant = V_oc**2 - 4 * self.R_in * P_actual_W
+        
+        if discriminant < 0:
+            self.I = V_oc / (2 * self.R_in)
+            P_actual_W = V_oc**2 / (4 * self.R_in)
+        else:
+            self.I = (V_oc - np.sqrt(discriminant)) / (2 * self.R_in)
+            
+        # 5. 计算端电压
+        self.V_t = V_oc - self.I * self.R_in
+        
+        # 6. 安时积分更新 SOC
+        delta_soc = (self.I * (dt / 3600.0)) / self.capacity_Ah
+        self.SOC = self.SOC - delta_soc
+        self.SOC = max(0.0, min(1.0, self.SOC))
+        
+        P_actual_kw = P_actual_W / 1000.0
+        return P_actual_kw, self.V_t, self.I, self.SOC
+
+if __name__ == "__main__":
+    import matplotlib
+    matplotlib.use('Agg')
+    
+    # ==========================================
+    # 【测试示例】展示完整的充放电循环与边界保护
+    # ==========================================
+    plt.rcParams['font.family'] = 'serif'
+    plt.rcParams['font.serif'] = ['DejaVu Serif', 'Times New Roman']
+    plt.rcParams['axes.unicode_minus'] = True
+    
+    battery = BatterySim(initial_soc=0.5)
+    
+    dt = 0.5
+    # 仿真总时长 1 小时，包含一个完整的充放电循环，且在边界处持续请求以验证保护机制
+    time_array = np.arange(0, 3600, dt)
+    
+    req_power_log = []
+    act_power_log = []
+    soc_log = []
+    vt_log = []
+    
+    for t in time_array:
+        if t < 100:
+            P_req = 0.0
+        elif t < 1200:
+            # 持续放电 150kW，将SOC从50%消耗至最低限制(5%)
+            # 在触发限制后继续请求，以验证禁止放电保护生效
+            P_req = 150.0 
+        elif t < 3300:
+            # 持续充电 150kW，将SOC从5%一直充满至最高限制(98%)
+            # 同样在触发满电后继续请求，验证禁止充电保护生效
+            P_req = -150.0 
+        else:
+            P_req = 0.0
+            
+        P_act, V_t, I_act, soc = battery.step(dt, P_req)
+        
+        req_power_log.append(P_req)
+        act_power_log.append(P_act)
+        vt_log.append(V_t)
+        soc_log.append(soc * 100)
+        
+    # 绘图展示
+    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 8), sharex=True)
+    
+    ax1.plot(time_array, req_power_log, 'k--', linewidth=2, label='Requested Power (kW)')
+    ax1.plot(time_array, act_power_log, 'r-', linewidth=2, label='Actual Power (kW)')
+    ax1.set_ylabel('Power [kW]')
+    ax1.set_title('Battery Power: Complete Cycle Protection Demo', fontweight='bold')
+    ax1.legend()
+    ax1.grid(True)
+    
+    ax2.plot(time_array, vt_log, 'b-', linewidth=2)
+    ax2.set_ylabel('Terminal Voltage [V]')
+    ax2.axhline(battery.V_min, color='r', linestyle=':', label='V_min limit')
+    ax2.axhline(battery.V_max, color='g', linestyle=':', label='V_max limit')
+    ax2.set_title('Voltage Dynamics with Physical Limits', fontweight='bold')
+    ax2.legend()
+    ax2.grid(True)
+    
+    ax3.plot(time_array, soc_log, 'g-', linewidth=2)
+    ax3.axhline(5, color='r', linestyle=':', alpha=0.8, label='SOC Lower Limit (5%)')
+    ax3.axhline(98, color='g', linestyle=':', alpha=0.8, label='SOC Upper Limit (98%)')
+    ax3.set_ylabel('SOC [%]')
+    ax3.set_xlabel('Time [s]')
+    ax3.set_title('State of Charge (SOC) Evolution', fontweight='bold')
+    ax3.legend()
+    ax3.grid(True)
+    
+    plt.tight_layout()
+    plt.savefig('figures/battery_sim_output.png')
+    print('Plot saved to figures/battery_sim_output.png')
+    # plt.show()

Model/src/engine_dynamic_sim.py

@@ -0,0 +1,358 @@
+import sys
+import os
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from src.engine_gpr_class import EngineGPRModel
+from src.increPID import IncrementalPIDController
+
+class TurboshaftDynamicSim:
+    """
+    涡轴发动机动态仿真类
+    包含一阶燃油执行机构和转子动力学模型
+    """
+    def __init__(self, gpr_csv_path="data/Cleaned_Engine_Data_Full.csv", gpr_pth_path="data/engine_gpr_model.pth",
+                 tau_fuel=0.15, K_inertia=100.0,
+                 kp=4.652, ki=7.078, kd=0.222, min_fuel=10.0, max_fuel=600.0,
+                 verbose=False):
+        """
+        初始化仿真环境和代理模型
+        :param gpr_csv_path: GPR模型使用的数据集路径
+        :param gpr_pth_path: 预训练的模型参数路径
+        :param tau_fuel: 燃油执行机构时间常数 (s)
+        :param K_inertia: 转子惯性增益 (RPM / (kg/h))
+        :param kp: PID比例系数 (用于功率跟随控制，归一化域)
+        :param ki: PID积分系数 (归一化域)
+        :param kd: PID微分系数 (归一化域)
+        :param min_fuel: 燃油流量下限 (kg/h)
+        :param max_fuel: 燃油流量上限 (kg/h)
+        :param verbose: 是否打印详细信息
+        """
+        import os
+        self.verbose = verbose
+
+        if verbose:
+            print("-> 正在加载 GPR 稳态代理模型...")
+        self.engine_model = EngineGPRModel(gpr_csv_path)
+
+        # 检查模型文件是否存在，不存在则训练
+        if not os.path.exists(gpr_pth_path):
+            if verbose:
+                print(f"-> 模型文件 {gpr_pth_path} 不存在，正在训练模型...")
+            self.engine_model.train(save_path=gpr_pth_path)
+        else:
+            success = self.engine_model.load_model(gpr_pth_path)
+            if not success:
+                if verbose:
+                    print(f"-> 模型加载失败，正在重新训练...")
+                self.engine_model.train(save_path=gpr_pth_path)
+            
+        # 动态参数
+        self.tau_fuel = tau_fuel
+        self.K_inertia = K_inertia
+        
+        # 归一化基准 (用于PID计算)
+        self.max_fuel = max_fuel
+        self.max_power_ref = 300.0 # 300kw为功率归一化基准，根据实际数据调整
+        
+        # 控制器初始化 (使用归一化参数，通过 scaling 自动处理)
+        self.pid = IncrementalPIDController(
+            kp=kp, ki=ki, kd=kd, dt=0.01, 
+            output_min=min_fuel, output_max=max_fuel,
+            input_scale=self.max_power_ref, output_scale=self.max_fuel
+        )
+        
+        # 环境与状态变量
+        self.H_env = 0.0
+        self.Ma_env = 0.0
+        self.N_current = 0.0
+        self.Wf_act_current = 0.0
+        self.power_generated = 0.0
+        
+        # 控制指令
+        self.Wf_cmd = 0.0
+
+    def _solve_steady_rpm(self, target_val, target_type='power'):
+        """
+        内部方法：反解稳态转速 (RPM)
+        :param target_val: 目标值 (Power[kW] 或 Fuel[kg/h])
+        :param target_type: 'power' 或 'fuel'
+        :return: 对应的稳态转速
+        """
+        from scipy.optimize import brentq
+        
+        # 搜索范围 [RPM_min, RPM_max]，根据经验或数据范围设定
+        low_bound, high_bound = 0.0, 60000.0
+        
+        def objective(n):
+            current_input = np.array([[self.H_env, self.Ma_env, n]])
+            pred_mean, _ = self.engine_model.predict(current_input)
+            
+            # index 0: Fuel Flow (kg/h), index 1: Power (kW)
+            val = pred_mean[0, 1] if target_type == 'power' else pred_mean[0, 0]
+            return val - target_val
+
+        try:
+            # 简单的边界检查，防止报错
+            f_low = objective(low_bound)
+            f_high = objective(high_bound)
+            if f_low * f_high > 0:
+                print(f"[Warn] 目标值超出模型范围 [{low_bound}, {high_bound}] RPM 对应的输出。将使用最接近的边界。")
+                return low_bound if abs(f_low) < abs(f_high) else high_bound
+            
+            n_solution = brentq(objective, low_bound, high_bound)
+            return n_solution
+        except Exception as e:
+            print(f"[Error] 稳态求解失败: {e}")
+            return (low_bound + high_bound) / 2.0
+
+    def set_steady_state_by_power(self, H_env, Ma_env, Power_target):
+        """
+        通过目标功率初始化稳态
+        :param Power_target: 目标轴功率 (kW)
+        :return: 对应的稳态转速 (RPM)
+        """
+        self.H_env = H_env
+        self.Ma_env = Ma_env
+        
+        # 反解转速
+        self.N_current = self._solve_steady_rpm(Power_target, target_type='power')
+        
+        # 计算该转速下的稳态燃油
+        current_input = np.array([[self.H_env, self.Ma_env, self.N_current]])
+        pred_mean, _ = self.engine_model.predict(current_input)
+        
+        self.power_generated = pred_mean[0, 1]
+        self.Wf_act_current = pred_mean[0, 0]
+        self.Wf_cmd = self.Wf_act_current
+        
+        print(f"-> 稳态(Power)已配置: H={H_env}, Ma={Ma_env}, Target_P={Power_target:.1f} kW => N={self.N_current:.1f} RPM, Wf={self.Wf_cmd:.2f} kg/h") if self.verbose else None
+        
+        # 重置PID控制器至当前稳态输出 (自动处理归一化)
+        self.pid.reset(initial_output=self.Wf_cmd)
+        
+        return self.N_current
+
+    def set_steady_state_by_fuel(self, H_env, Ma_env, Wf_target):
+        """
+        通过目标燃油流量初始化稳态
+        :param Wf_target: 目标燃油流量 (kg/h)
+        :return: 对应的稳态转速 (RPM)
+        """
+        self.H_env = H_env
+        self.Ma_env = Ma_env
+        
+        # 反解转速
+        self.N_current = self._solve_steady_rpm(Wf_target, target_type='fuel')
+        
+        # 确认该状态下的功率
+        current_input = np.array([[self.H_env, self.Ma_env, self.N_current]])
+        pred_mean, _ = self.engine_model.predict(current_input)
+        
+        self.power_generated = pred_mean[0, 1]
+        self.Wf_act_current = pred_mean[0, 0] # 理论上应该非常接近 Wf_target
+        self.Wf_cmd = self.Wf_act_current
+        
+        print(f"-> 稳态(Fuel)已配置: H={H_env}, Ma={Ma_env}, Target_Wf={Wf_target:.1f} kg/h => N={self.N_current:.1f} RPM, Power={self.power_generated:.1f} kW") if self.verbose else None
+        
+        # 重置PID控制器至当前稳态输出 (自动处理归一化)
+        self.pid.reset(initial_output=self.Wf_cmd)
+        
+        return self.N_current
+
+    def set_fuel_command(self, Wf_cmd):
+        """
+        更改燃油流量指令
+        :param Wf_cmd: 目标燃油流量指令
+        """
+        self.Wf_cmd = Wf_cmd
+
+    def set_flight_condition(self, H_env=None, Ma_env=None):
+        """
+        在运行过程中更改当前的飞行条件 (高度和马赫数)
+        :param H_env: 新的飞行高度 (m)。如果为 None，则保持不变。
+        :param Ma_env: 新的飞行马赫数。如果为 None，则保持不变。
+        """
+        if H_env is not None:
+            self.H_env = H_env
+        if Ma_env is not None:
+            self.Ma_env = Ma_env
+
+    def compute_control_law(self, dt, target_power):
+        """
+        计算控制律 (PID控制: Power -> Wf)
+        :param dt: 控制周期 (s)
+        :param target_power: 期望功率 (kW)
+        :return: 计算出的燃油指令
+        """
+        self.pid.dt = dt
+        # 1. 计算控制增量 (PID内部会自动处理归一化)
+        self.Wf_cmd = self.pid.compute(setpoint=target_power, measurement=self.power_generated)
+        return self.Wf_cmd
+
+    def step(self, dt, target_power=None):
+        """
+        执行单步动态仿真
+        :param dt: 积分步长 (s)
+        :param target_power: 目标轴功率 (kW)，若不为 None 则执行一次PID控制
+        :return: (当前转速, 实际供油量, 当前需要的平衡供油量, 当前功率)
+        """
+        # 0. 闭环控制计算
+        if target_power is not None:
+            self.compute_control_law(dt, target_power)
+
+        # 1. 燃油执行机构动态 (一阶惯性)
+        dWf_act_dt = (self.Wf_cmd - self.Wf_act_current) / self.tau_fuel
+        Wf_act_next = self.Wf_act_current + dWf_act_dt * dt
+        
+        # 2. 调用GPR代理模型计算当前转速下的阻力矩(需求燃油)
+        current_input = np.array([[self.H_env, self.Ma_env, self.N_current]])
+        pred_mean, _ = self.engine_model.predict(current_input)
+        
+        # GPR 输出: [0]: Fuel Flow, [1]: Shaft Power
+        Wf_req_current = pred_mean[0, 0]  #维持当前转速所需的稳态燃油
+        Power_current = pred_mean[0, 1]
+        self.power_generated = Power_current  # 更新当前功率状态
+        
+        # 3. 转子动力学积分 (燃料差额 -> 转速加速度)
+        dN_dt = self.K_inertia * (self.Wf_act_current - Wf_req_current)
+        N_next = self.N_current + dN_dt * dt
+        
+        # 4. 状态更新
+        self.Wf_act_current = Wf_act_next
+        self.N_current = N_next
+        
+        return self.N_current, self.Wf_act_current, Wf_req_current, Power_current
+
+
+if __name__ == "__main__":
+    # ==========================================
+    # 【测试示例】利用类运行功率闭环控制仿真
+    # ==========================================
+    import matplotlib
+    matplotlib.use('Agg')
+    import matplotlib.pyplot as plt
+
+    plt.rcParams['font.family'] = 'serif'
+    plt.rcParams['font.serif'] = ['DejaVu Serif', 'Times New Roman']
+    plt.rcParams['axes.unicode_minus'] = True
+
+    # 初始化仿真 (优化后的PID参数)
+    # kp: 比例系数 - 增大以加快响应速度
+    # ki: 积分系数 - 适中以消除稳态误差
+    # kd: 微分系数 - 设为0避免控制振荡
+    sim = TurboshaftDynamicSim()
+    
+    # 1. 设置初始稳态 (通过目标功率设定)
+    P_initial_target = 100.0  # kW
+    sim.set_steady_state_by_power(H_env=0.0, Ma_env=0.0, Power_target=P_initial_target)
+    
+    # 仿真参数
+    dt = 0.02
+    t_end = 30.0  # 增加仿真时间以展示更多指令变化
+    time_array = np.arange(0, t_end, dt)
+
+    # 数据记录
+    N_log = []
+    Wf_act_log = []
+    Wf_cmd_log = []
+    Power_log = []
+    Power_target_log = []
+
+    # 定义多段功率指令 (时间[s], 目标功率[kW])
+    power_profile = [
+        (0.0, 100.0),    # 初始稳态
+        (3.0, 200.0),    # 阶跃上升
+        (8.0, 150.0),    # 阶跃下降
+        (13.0, 250.0),   # 阶跃上升至高功率
+        (18.0, 100.0),   # 快速下降
+        (23.0, 180.0),   # 再次上升
+    ]
+
+    # 斜坡指令测试：从18kW开始以一定速率上升
+    ramp_start_time = 25.0
+    ramp_rate = 10.0  # kW/s
+
+    # 设定飞行条件改变的时间
+    flight_change_time = 28.0
+
+    def get_target_power(t, profile, ramp_start, ramp_rate, default_power):
+        """根据时间获取当前目标功率"""
+        for i, (time, _) in enumerate(profile):
+            if t < time:
+                return profile[i-1][1] if i > 0 else default_power
+        # 如果在斜坡区间
+        if t >= ramp_start:
+            last_static_power = profile[-1][1]
+            ramp_power = last_static_power + ramp_rate * (t - ramp_start)
+            return min(ramp_power, 300.0)  # 限制最大300kW
+        return profile[-1][1]
+
+    print("-> 开始仿真步进 (闭环控制)...")
+    print("-> 功率指令配置文件:")
+    for time, power in power_profile:
+        print(f"   t={time:.1f}s -> {power:.0f} kW")
+    print(f"   t={ramp_start_time:.1f}s -> 斜坡上升 (速率 {ramp_rate} kW/s)")
+    print()
+
+    for t in time_array:
+        # 确定当前的目标功率
+        current_target_P = get_target_power(t, power_profile, ramp_start_time, ramp_rate, P_initial_target)
+            
+        # 触发飞行条件变化 (确保仅触发一次以免重复打印)
+        if flight_change_time <= t < flight_change_time + dt:
+            print(f"\n[!] 时间 t={t:.2f}s, 模拟飞行条件跃变...")
+            sim.set_flight_condition(H_env=3000.0, Ma_env=0.2)
+        
+        # 执行闭环仿真步进
+        N_cur, Wf_act_cur, Wf_req, Power_cur = sim.step(dt, target_power=current_target_P)
+        
+        # 记录数据
+        N_log.append(N_cur)
+        Wf_act_log.append(Wf_act_cur)
+        Wf_cmd_log.append(sim.Wf_cmd)
+        Power_log.append(Power_cur)
+        Power_target_log.append(current_target_P)
+        
+    # 绘图
+    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 10))
+    fig.suptitle(f'Turboshaft Engine Dynamic Simulation\nPID Parameters: Kp={sim.pid.kp}, Ki={sim.pid.ki}, Kd={sim.pid.kd}',
+                 fontsize=12, fontweight='bold')
+    
+    # Plot 1: Power Tracking
+    ax1.plot(time_array, Power_target_log, 'k--', linewidth=2, label='Target Power')
+    ax1.plot(time_array, Power_log, 'g-', linewidth=2, label='Actual Power')
+    ax1.set_ylabel('Shaft Power [kW]')
+    ax1.set_title('Closed-Loop Control: Power Tracking', fontweight='bold')
+    ax1.grid(True, linestyle=':', alpha=0.7)
+    ax1.legend()
+    
+    # Plot 2: Rotor Speed
+    ax2.plot(time_array, N_log, 'b-', linewidth=2, label='Engine Speed (N)')
+    ax2.set_ylabel('Rotor Speed [RPM]')
+    ax2.set_title('Engine Response: Rotor Speed', fontweight='bold')
+    ax2.grid(True, linestyle=':', alpha=0.7)
+    ax2.legend()
+    
+    # Plot 3: Fuel Flow (Control Input)
+    ax3.plot(time_array, Wf_cmd_log, 'k--', linewidth=1.5, label='Fuel Command')
+    ax3.plot(time_array, Wf_act_log, 'r-', linewidth=2, label='Actual Fuel')
+    ax3.set_xlabel('Time [s]')
+    ax3.set_ylabel('Fuel Flow [kg/h]')
+    ax3.set_title('Control Effort: Fuel Flow', fontweight='bold')
+    ax3.grid(True, linestyle=':', alpha=0.7)
+    ax3.legend()
+    
+    plt.tight_layout(rect=[0, 0, 1, 0.96])
+    plt.savefig('figures/engine_dynamic_sim_plot.png')
+    print('Plot saved to figures/engine_dynamic_sim_plot.png')
+
+    # 保存数据到 .dat 文件
+    dat_file = 'data/pid_tuning_data.dat'
+    with open(dat_file, 'w') as f:
+        f.write('# Time[s]\tTarget_Power[kW]\tActual_Power[kW]\tRotor_Speed[RPM]\tFuel_Command[kg/h]\tActual_Fuel[kg/h]\n')
+        for i in range(len(time_array)):
+            f.write(f'{time_array[i]:.4f}\t{Power_target_log[i]:.2f}\t{Power_log[i]:.2f}\t{N_log[i]:.2f}\t{Wf_cmd_log[i]:.2f}\t{Wf_act_log[i]:.2f}\n')
+    print(f'Data saved to {dat_file}')
+    # plt.show()

Model/src/engine_gpr_class.py

@@ -0,0 +1,283 @@
+import torch
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import LogNorm
+from botorch.models import SingleTaskGP
+from botorch.fit import fit_gpytorch_mll
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from gpytorch.means import LinearMean
+from gpytorch.priors import GammaPrior
+from sklearn.preprocessing import StandardScaler
+import warnings
+import os
+
+warnings.filterwarnings("ignore")
+
+# ==========================================
+# 【全局绘图设置】学术规范
+# ==========================================
+plt.rcParams['font.family'] = 'serif'
+plt.rcParams['font.serif'] = ['DejaVu Serif', 'Times New Roman']
+plt.rcParams['mathtext.fontset'] = 'stix'
+plt.rcParams['axes.unicode_minus'] = True
+
+class EngineGPRModel:
+    """
+    涡轴发动机 GPR 代理模型类
+    支持: 训练、加载已有模型、预测推断、单马赫数切面可视化
+    """
+    def __init__(self, csv_path="data/Cleaned_Engine_Data_Full.csv"):
+        self.csv_path = csv_path
+        self.df = None
+        self.valid_X_cols = []
+        self.scaler_X = StandardScaler()
+        self.scaler_Y = StandardScaler()
+        self.model = None
+
+    def _prepare_data(self):
+        """数据预处理：读取、筛选、Log变换、标准化"""
+        self.df = pd.read_csv(self.csv_path)
+        X_df = self.df[['Altitude_m', 'Mach', 'RPM']]
+        
+        # 剔除无效特征
+        self.valid_X_cols = [col for col in X_df.columns if X_df[col].nunique() > 1]
+        X_numpy = self.df[self.valid_X_cols].values
+        
+        if 'WF_kg_h' not in self.df.columns:
+            raise ValueError("Column 'WF_kg_h' not found.")
+        
+        Y_numpy = self.df[['WF_kg_h', 'Power_kW']].values
+
+        # Log1p 变换：保证非负性，并线性化指数规律
+        Y_numpy = np.log1p(Y_numpy)
+
+        # Z-Score 归一化
+        X_scaled = self.scaler_X.fit_transform(X_numpy)
+        Y_scaled = self.scaler_Y.fit_transform(Y_numpy)
+
+        return torch.tensor(X_scaled, dtype=torch.double), torch.tensor(Y_scaled, dtype=torch.double)
+
+    def _init_model(self, train_X, train_Y):
+        """内部方法：统一初始化模型结构（包含 Mean 和 Prior 设置）"""
+        mean_module = LinearMean(input_size=train_X.shape[-1], batch_shape=torch.Size([train_Y.shape[-1]]))
+        model = SingleTaskGP(train_X, train_Y, mean_module=mean_module)
+        
+        # 统一施加 GammaPrior 防止结构不匹配
+        # 这一步非常关键：加载模型时，模型结构必须与训练时完全一致，包括 Prior
+        if hasattr(model.covar_module, 'base_kernel'):
+            kernel = model.covar_module.base_kernel
+        else:
+            kernel = model.covar_module
+
+        if hasattr(kernel, 'lengthscale'):
+            kernel.lengthscale_prior = GammaPrior(4.0, 1.0)
+        
+        return model
+
+    def train(self, save_path=None):
+        """训练 GPR 模型 (Log-Space + 平滑约束)"""
+        print(f"{'='*30}\n🚀 GP Training (Log-Space)\n{'='*30}")
+        
+        train_X, train_Y = self._prepare_data()
+
+        # 使用统一初始化方法
+        self.model = self._init_model(train_X, train_Y)
+        
+        # 仅在训练开始前设定初始值，引导优化方向
+        if hasattr(self.model.covar_module, 'base_kernel'):
+             kernel = self.model.covar_module.base_kernel
+        else:
+             kernel = self.model.covar_module
+        
+        if hasattr(kernel, 'lengthscale'):
+             kernel.lengthscale = 2.0
+
+        print("-> Optimizing hyperparameters...")
+        mll = ExactMarginalLogLikelihood(self.model.likelihood, self.model)
+        fit_gpytorch_mll(mll)
+        print("-> Training completed.")
+        
+        if save_path:
+            torch.save(self.model.state_dict(), save_path)
+            print(f"-> Model saved to {save_path}")
+
+    def load_model(self, pth_path="data/engine_gpr_model.pth"):
+        """加载已训练的模型"""
+        print(f"Loading model from {pth_path}...")
+        try:
+            train_X, train_Y = self._prepare_data()
+            # 必须使用完全相同的结构初始化，否则 load_state_dict 会报错
+            self.model = self._init_model(train_X, train_Y)
+            
+            # 使用 strict=False 忽略 Prior 缓冲区的差异（例如 _transformed_loc 等内部参数）
+            # 这些参数通常不影响模型预测，只影响后续继续训练时的约束
+            self.model.load_state_dict(torch.load(pth_path), strict=True)
+            self.model.eval()
+            print("-> Model loaded successfully.")
+            return True
+        except Exception as e:
+            print(f"-> Load failed: {e}")
+            return False
+
+    def _predict_log_space(self, test_X_real: np.ndarray):
+        """预测 Log 空间下的均值和标准差"""
+        if self.model is None: raise ValueError("Model not initialized.")
+        
+        self.model.eval()
+        test_X_scaled = torch.tensor(self.scaler_X.transform(test_X_real), dtype=torch.double)
+        
+        with torch.no_grad():
+            posterior = self.model.posterior(test_X_scaled)
+            mu_scaled = posterior.mean.numpy()
+            var_scaled = posterior.variance.numpy()
+        
+        # 反归一化
+        mu_log_real = self.scaler_Y.inverse_transform(mu_scaled)
+        var_log_real = var_scaled * self.scaler_Y.var_
+        return mu_log_real, np.sqrt(var_log_real)
+
+    def predict(self, test_X_real: np.ndarray):
+        """预测物理值 (expm1 还原)"""
+        mu_log, std_log = self._predict_log_space(test_X_real)
+        
+        # 中位数点估计 (Median of LogNormal)
+        pred_mean = np.expm1(mu_log)
+        # 近似方差
+        var_log = std_log ** 2
+        pred_var = (np.expm1(var_log)) * np.exp(2*mu_log + var_log)
+        
+        return pred_mean, pred_var
+
+    def visualize(self, target_mach=0.0):
+        """可视化：生成方差热力图、切面曲线、均值云图"""
+        if self.model is None: raise ValueError("Model not initialized.")
+        print(f"Generating Plots for Mach = {target_mach}")
+        
+        # 确定绘图范围 (训练数据 Range)
+        H_min, H_max = self.df['Altitude_m'].min(), self.df['Altitude_m'].max()
+        RPM_min, RPM_max = self.df['RPM'].min(), self.df['RPM'].max()
+        
+        if H_min == H_max: H_min, H_max = H_min-1, H_max+1
+        if RPM_min == RPM_max: RPM_min, RPM_max = RPM_min-100, RPM_max+100
+        
+        H_grid, RPM_grid = np.meshgrid(np.linspace(H_min, H_max, 60), np.linspace(RPM_min, RPM_max, 60), indexing='ij')
+
+        test_X_dict = {'Altitude_m': H_grid.flatten(), 'RPM': RPM_grid.flatten()}
+        if 'Mach' in self.valid_X_cols:
+            test_X_dict['Mach'] = np.full_like(H_grid.flatten(), target_mach)
+            
+        test_X_real = np.column_stack([test_X_dict[col] for col in self.valid_X_cols])
+        
+        # 预测并重塑 (60x60)
+        pred_mean, pred_var = self.predict(test_X_real)
+        wf_mean, pow_mean = pred_mean[:, 0].reshape(60, 60), pred_mean[:, 1].reshape(60, 60)
+        wf_var, pow_var = pred_var[:, 0].reshape(60, 60), pred_var[:, 1].reshape(60, 60)
+
+        # 筛选绘图用的真实数据点
+        tol = 1e-3
+        plot_df = self.df[(self.df['Mach'] >= target_mach - tol) & (self.df['Mach'] <= target_mach + tol)] if 'Mach' in self.valid_X_cols else self.df
+        
+        # 1. 方差热力图 (Log Scale)
+        fig, axes = plt.subplots(1, 2, figsize=(14, 5.5))
+        for ax, var_data, title in zip(axes, [wf_var, pow_var], ['Fuel Flow Variance', 'Shaft Power Variance']):
+            log_var = np.log10(np.maximum(var_data, 1e-16))
+            cf = ax.contourf(RPM_grid, H_grid, log_var, levels=50, cmap='jet', alpha=0.9)
+            if not plot_df.empty: ax.scatter(plot_df['RPM'], plot_df['Altitude_m'], c='white', edgecolors='black', s=25, label='Data')
+            ax.set_title(f"{title} (Log10)"); ax.set_xlabel('RPM'); ax.set_ylabel('Altitude')
+            plt.colorbar(cf, ax=ax)
+        plt.tight_layout()
+        plt.savefig(f'figures/gpr_mach_{target_mach}_variance.png')
+        print(f"Saved figures/gpr_mach_{target_mach}_variance.png")
+        plt.close()
+
+        # 2. 高度切面图 (95% CI)
+        fig, axes = plt.subplots(1, 2, figsize=(14, 5.5))
+        colors = ['#1f77b4', '#ff7f0e', '#2ca02c']
+        RPM_1D = np.linspace(RPM_min, RPM_max, 100)
+        
+        for idx, alt in enumerate([0.0, 3000.0, 6000.0]):
+            d_1D = {'Altitude_m': np.full_like(RPM_1D, alt), 'RPM': RPM_1D}
+            if 'Mach' in self.valid_X_cols: d_1D['Mach'] = np.full_like(RPM_1D, target_mach)
+            X_1D = np.column_stack([d_1D[c] for c in self.valid_X_cols])
+            
+            # Log空间预测 -> 计算物理置信区间 (保证下界非负)
+            mu_log, std_log = self._predict_log_space(X_1D)
+            mean = np.expm1(mu_log)
+            lower = np.expm1(mu_log - 2*std_log)
+            upper = np.expm1(mu_log + 2*std_log)
+
+            for ax, i in zip(axes, [0, 1]):
+                ax.plot(RPM_1D, mean[:, i], color=colors[idx], label=f'Alt={int(alt)}m')
+                ax.fill_between(RPM_1D, lower[:, i], upper[:, i], color=colors[idx], alpha=0.2)
+
+        for ax, title, ylab in zip(axes, ['Fuel Flow', 'Shaft Power'], ['kg/h', 'kW']):
+            ax.set_title(title); ax.set_ylabel(ylab); ax.set_xlabel('RPM')
+            ax.legend(); ax.grid(True, alpha=0.5)
+        plt.tight_layout()
+        plt.savefig(f'figures/gpr_mach_{target_mach}_section.png')
+        print(f"Saved figures/gpr_mach_{target_mach}_section.png")
+        plt.close()
+
+        # 3. 均值云图
+        fig, axes = plt.subplots(1, 2, figsize=(14, 5.5))
+        for ax, mean_data, title in zip(axes, [wf_mean, pow_mean], ['Fuel Flow Mean', 'Shaft Power Mean']):
+            cf = ax.contourf(RPM_grid, H_grid, mean_data, levels=50, cmap='viridis', alpha=0.9)
+            if not plot_df.empty: ax.scatter(plot_df['RPM'], plot_df['Altitude_m'], c='red', s=20, edgecolors='white', label='Data')
+            ax.set_title(title); ax.set_xlabel('RPM'); ax.set_ylabel('Altitude')
+            plt.colorbar(cf, ax=ax)
+        plt.tight_layout()
+        plt.savefig(f'figures/gpr_mach_{target_mach}_mean.png')
+        print(f"Saved figures/gpr_mach_{target_mach}_mean.png")
+        plt.close()
+
+if __name__ == "__main__":
+    import matplotlib
+    matplotlib.use('Agg')
+    print("Using Agg backend for plotting.")
+
+    engine_model = EngineGPRModel()
+    
+    # 智能选择加载或训练
+    model_path = "data/engine_gpr_model.pth"
+    if os.path.exists(model_path):
+        success = engine_model.load_model(model_path)
+        if not success:
+             # 如果加载失败（结构不匹配），则重新训练
+            engine_model.train(save_path=model_path)
+    else:
+        # 如果模型不存在，则训练并保存
+        engine_model.train(save_path=model_path)
+    
+    # ==========================================
+    # 【使用示例】
+    # ==========================================
+    print("\n" + "="*50)
+    print("💡 预测使用示例 (Prediction Example)")
+    print("="*50)
+    # 假设默认特征组合为: [Altitude_m, Mach, RPM]
+    sample_inputs = np.array([
+        [0.0, 0.0, 15000.0],       # 测试点 1: 海平面静止, 15000 RPM
+        [3000.0, 0.2, 22000.0]     # 测试点 2: 3000米, 0.2马赫, 22000 RPM
+    ])
+    
+        
+    mean_preds, var_preds = engine_model.predict(sample_inputs)
+    
+    for i, (X_in, Y_mean, Y_var) in enumerate(zip(sample_inputs, mean_preds, var_preds)):
+        # 格式化输出
+        x_str = "[" + ", ".join([f"{val:8.2f}" for val in X_in]) + " ]"
+        mean_str = "[" + ", ".join([f"{val:10.4f}" for val in Y_mean]) + " ]"
+        var_str = "[" + ", ".join([f"{val:10.4f}" for val in Y_var]) + " ]"
+
+        print(f"[测试点 {i+1}]")
+        print(f"  -> 输入特征 {engine_model.valid_X_cols}: {x_str}")
+        print(f"  -> 预测均值 [WF (kg/h), Power_kW]: {mean_str}")
+        print(f"  -> 预测方差 [WF (kg/h), Power_kW]: {var_str}\n")
+
+    # ==========================================
+    # 循环调用绘图方法，分别输出所需的马赫数切面图
+    # ==========================================
+    target_machs = [0.0, 0.1, 0.2, 0.3, 0.4]
+    for m in target_machs:
+        engine_model.visualize(target_mach=m)

Model/src/increPID.py

@@ -0,0 +1,88 @@
+class IncrementalPIDController:
+    """
+    增量式 PID 控制器 (支持自动归一化缩放)
+    适用于发动机燃油控制等具有保持特性的执行机构
+    """
+    def __init__(self, kp, ki, kd, dt, output_min, output_max, input_scale=1.0, output_scale=1.0):
+        """
+        初始化 PID 控制器
+        
+        如果提供了 scale 参数，则 kp/ki/kd 被视为归一化域的参数。
+        内部运算逻辑：
+          norm_error = (setpoint - measurement) / input_scale
+          norm_output += PID(norm_error, kp, ki, kd)
+          physical_output = norm_output * output_scale
+        
+        :param kp: 比例系数 (建议使用归一化参数)
+        :param ki: 积分系数
+        :param kd: 微分系数
+        :param dt: 控制周期 (s)
+        :param output_min: 执行机构输出下限 (物理量)
+        :param output_max: 执行机构输出上限 (物理量)
+        :param input_scale: 输入测量值的量程基准 (例如额定功率 1000.0)
+        :param output_scale: 输出控制量的量程基准 (例如最大燃油 600.0)
+        """
+        self.kp = kp
+        self.ki = ki
+        self.kd = kd
+        self.dt = dt
+        
+        # 记录缩放因子
+        self.input_scale = input_scale
+        self.output_scale = output_scale
+        
+        # 将物理限制转换为内部的归一化限制
+        self.out_min_norm = output_min / output_scale
+        self.out_max_norm = output_max / output_scale
+        
+        # 历史误差状态 (归一化误差)
+        self.e_k1 = 0.0  # e(k-1)
+        self.e_k2 = 0.0  # e(k-2)
+        
+        # 当前实际的控制输出 (归一化值 [0~1] 或 [-1~1])
+        self.current_output_norm = 0.0
+
+    def reset(self, initial_output=0.0):
+        """
+        重置控制器状态，用于初始化或开闭环的无扰切换
+        :param initial_output: 当前执行机构的实际位置（物理量，如燃油流量）
+        """
+        self.e_k1 = 0.0
+        self.e_k2 = 0.0
+        # 将物理初始值转换为归一化内部状态
+        self.current_output_norm = initial_output / self.output_scale
+
+    def compute(self, setpoint, measurement):
+        """
+        计算下一拍的控制量
+        :param setpoint: 目标设定值 (物理量)
+        :param measurement: 当前测量值 (物理量)
+        :return: 经过限幅的绝对控制指令 (物理量)
+        """
+        # 1. 计算归一化误差 e(k)
+        # 误差除以输入量程，使得误差在 -1~1 之间 (对于阶跃通常更小)
+        e_k = (setpoint - measurement) / self.input_scale
+        
+        # 2. 计算归一化控制增量 delta_u
+        p_term = self.kp * (e_k - self.e_k1)
+        i_term = self.ki * e_k * self.dt
+        # 微分项除以dt可能会很大，归一化的时间常数有助于平滑
+        d_term = self.kd * (e_k - 2 * self.e_k1 + self.e_k2) / self.dt
+        
+        delta_u_norm = p_term + i_term + d_term
+        
+        # 3. 更新当前归一化输出量
+        self.current_output_norm += delta_u_norm
+        
+        # 4. 绝对位置限幅 (在归一化域进行)
+        if self.current_output_norm > self.out_max_norm:
+            self.current_output_norm = self.out_max_norm
+        elif self.current_output_norm < self.out_min_norm:
+            self.current_output_norm = self.out_min_norm
+            
+        # 5. 更新历史误差
+        self.e_k2 = self.e_k1
+        self.e_k1 = e_k
+        
+        # 6. 返回物理量输出
+        return self.current_output_norm * self.output_scale

Model/src/series_hybrid_sim.py

@@ -0,0 +1,261 @@
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+from engine_dynamic_sim import TurboshaftDynamicSim
+from motor_sim import MotorSim
+from battery_sim import BatterySim
+
+class SeriesHybridSystem:
+    """
+    串联式混合动力系统总成
+    """
+    def __init__(self):
+        # ===== 新增：按Model目录定位GPR数据与权重，避免从主项目调用时路径错误 =====
+        model_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        gpr_csv_path = os.path.join(model_root, "data", "Cleaned_Engine_Data_Full.csv")
+        gpr_pth_path = os.path.join(model_root, "data", "engine_gpr_model.pth")
+        self.genset = TurboshaftDynamicSim(
+            gpr_csv_path=gpr_csv_path,
+            gpr_pth_path=gpr_pth_path,
+            kp=0.5,
+            ki=0.5,
+            kd=0.0
+        )
+        self.drive_motor = MotorSim(
+            P_rate=300e3,
+            w_rate=575.95,
+            k_p_w=5.0,   # 增大比例增益
+            k_i_w=2.0,   # 增大积分增益
+            k_d_w=0.5,   # 适度微分
+            J=1.0,
+        )
+        self.battery = BatterySim(capacity_kwh=50.0, initial_soc=0.6)
+
+        self.bus_voltage = self.battery._get_ocv(self.battery.SOC)
+        self.motor_actual_power_kw = 0.0
+
+        # 滞环控制状态
+        self._genset_low_power_mode = False  # 发动机低功率模式标志
+        self._hysteresis_rpm = 100  # 滞环带宽 RPM
+
+    def energy_management_strategy(self, p_drive_req_kw, soc, speed_error_rpm=0.0):
+        """
+        功率跟随策略 (Power Following Control) - 增强版
+        核心思想：
+        - 使用滞环控制避免频繁切换
+        - 当转速持续高于目标时，降低发动机功率
+        - 当转速持续低于目标时，增加发动机功率
+        """
+        P_eng_max = 300.0
+        P_eng_min = 20.0
+        SOC_TARGET = 0.60
+        K_soc = 30.0  # kW / ΔSOC
+        K_speed = 1.5  # kW / RPM
+        p_dyn_reserve = P_eng_max * 0.3  # 90 kW 基础储备
+
+        # 滞环控制逻辑
+        # 进入低功率模式：转速高于目标 + 电机发电
+        if speed_error_rpm < -self._hysteresis_rpm and p_drive_req_kw < 0:
+            self._genset_low_power_mode = True
+        # 退出低功率模式：转速低于目标 - 滞环带宽
+        elif speed_error_rpm > self._hysteresis_rpm:
+            self._genset_low_power_mode = False
+
+        # 根据模式计算目标功率
+        if self._genset_low_power_mode:
+            # 低功率模式：发动机降到最低
+            target_engine_power = P_eng_min
+        elif speed_error_rpm > 0:
+            # 加速模式
+            p_speed_comp = min(speed_error_rpm * K_speed, 250.0)
+            target_engine_power = p_drive_req_kw + p_dyn_reserve + p_speed_comp
+        else:
+            # 减速/稳态模式
+            p_speed_comp = max(speed_error_rpm * K_speed, -250.0)
+            target_engine_power = p_drive_req_kw + p_dyn_reserve + p_speed_comp
+
+        # SOC补偿
+        soc_error = SOC_TARGET - soc
+        if soc < 0.2 or soc > 0.85:
+            K_soc_effective = 0
+        else:
+            K_soc_effective = K_soc
+        p_charge_req = soc_error * K_soc_effective
+        target_engine_power += p_charge_req
+
+        # 极限状态越界保护
+        if soc < 0.15:
+            target_engine_power = P_eng_max
+        elif soc > 0.95:
+            target_engine_power = P_eng_min
+
+        # 限制输出范围
+        target_engine_power = max(P_eng_min, min(P_eng_max, target_engine_power))
+
+        return target_engine_power
+
+    def step(self, dt, target_prop_speed, prop_load_torque):
+        """
+        全系统单步动力学仿真
+        """
+        # 1. 需求端：驱动电机电功率请求
+        motor_state = self.drive_motor.step(
+            dt=dt,
+            n_setpoint=target_prop_speed,
+            p_bus_actual_kw=self.motor_actual_power_kw,
+            v_bus=self.bus_voltage,
+            t_load=prop_load_torque,
+            t_ext=0.0
+        )
+        p_drive_req = motor_state['p_bus_req_kw']
+        actual_rpm = motor_state['n_rpm']
+
+        # 计算转速误差（用于EMS）
+        speed_error_rpm = target_prop_speed - actual_rpm
+
+        # 2. 决策端：EMS 目标功率计算（加入转速误差反馈）
+        target_engine_pwr = self.energy_management_strategy(p_drive_req, self.battery.SOC, speed_error_rpm)
+
+        # 3. 发电端：涡轴发动机响应并输出电能
+        N_eng, Wf_act, Wf_req, P_eng_out = self.genset.step(dt, target_power=target_engine_pwr)
+        # GPR模型输出的 P_eng_out > 0 表示发动机输出功率（发电）
+        p_gen_elec = P_eng_out  # 正值表示发电功率
+
+        # 4. 汇流端：电池功率补偿与直流母线状态更新
+        # 功率平衡：电机需求 = 发动机发电 + 电池补充
+        # p_batt_req > 0 表示电池放电，p_batt_req < 0 表示电池充电
+        p_batt_req = p_drive_req - p_gen_elec
+        p_batt_actual, v_bus, i_batt, soc = self.battery.step(dt, p_batt_req)
+
+        # 5. 状态记忆：为打破代数环，保留关键变量至下一拍
+        self.bus_voltage = v_bus
+
+        # 电机实际获得的功率 = 发电功率 + 电池放电功率
+        # 注意：允许负值存在，表示电机在发电模式（制动）
+        # 这样电机控制器才能正确产生制动转矩
+        self.motor_actual_power_kw = p_gen_elec + p_batt_actual
+
+        return {
+            'soc': soc * 100.0,
+            'bus_voltage': v_bus,
+            'prop_speed_rpm': motor_state['n_rpm'],
+            'target_engine_pwr': target_engine_pwr,
+            'p_engine_out_kw': P_eng_out,
+            'p_drive_req_kw': p_drive_req,
+            'p_motor_actual_kw': self.motor_actual_power_kw,
+            'p_batt_actual_kw': p_batt_actual,
+            'wf_kg_h': Wf_act,
+            'engine_rpm': N_eng
+        }
+
+
+if __name__ == "__main__":
+    import os
+    import matplotlib
+    matplotlib.use('Agg')  # 非交互后端
+    import matplotlib.pyplot as plt
+
+    plt.rcParams['font.family'] = 'serif'
+    plt.rcParams['axes.unicode_minus'] = True
+
+    print("-> 初始化串联混电系统...")
+    system = SeriesHybridSystem()
+    system.genset.set_steady_state_by_power(H_env=0.0, Ma_env=0.0, Power_target=50.0)
+    
+    dt = 0.02
+    t_end = 180.0
+    time_array = np.arange(0, t_end, dt)
+    
+    log = {k: [] for k in ['soc', 'bus_voltage', 'prop_speed_rpm', 'target_prop_rpm', 'target_engine_pwr', 
+                           'p_engine_out_kw', 'p_drive_req_kw', 'p_motor_actual_kw', 
+                           'p_batt_actual_kw', 'wf_kg_h']}
+    
+    print("-> 开始全系统闭环步进仿真 (总时长 3 分钟)...")
+    for t in time_array:
+        # 3分钟测试剖面
+        if t < 15.0:
+            target_rpm, load_torque = 1500.0, 50.0   # 地面滑行
+        elif t < 60.0:
+            target_rpm, load_torque = 3000.0, 200.0  # 暴力拉升
+        elif t < 120.0:
+            target_rpm, load_torque = 2800.0, 150.0  # 重载巡航
+        else:
+            target_rpm, load_torque = 1800.0, 60.0   # 降落滑行
+            
+        res = system.step(dt, target_rpm, load_torque)
+        res['target_prop_rpm'] = target_rpm
+        for k in log.keys():
+            log[k].append(res[k])
+
+    print("-> 仿真完成，正在绘制系统响应曲线...")
+    
+    # 绘图逻辑
+    fig, axes = plt.subplots(4, 1, figsize=(14, 12), sharex=True)
+    fig.suptitle('Series Hybrid Electric System Dynamics (Load Following EMS)', fontweight='bold', fontsize=14)
+
+    # 1. 转速响应
+    axes[0].plot(time_array, log['target_prop_rpm'], 'k--', lw=1.5, label='Target Speed')
+    axes[0].plot(time_array, log['prop_speed_rpm'], 'b-', lw=1.5, label='Actual Speed')
+    axes[0].set_ylabel('Speed [RPM]')
+    axes[0].set_title('Drive Motor Speed Response')
+    axes[0].grid(True, linestyle=':', alpha=0.7)
+    axes[0].legend()
+
+    # 2. 功率分配流向
+    axes[1].plot(time_array, log['p_drive_req_kw'], 'k--', lw=1.5, label='Drive Motor Request')
+    axes[1].plot(time_array, log['p_engine_out_kw'], 'r-', lw=1.5, label='Engine Output (APU)')
+    axes[1].plot(time_array, log['p_batt_actual_kw'], 'g-', lw=1.5, label='Battery Output')
+    axes[1].set_ylabel('Power [kW]')
+    axes[1].set_title('System Power Flow (Energy Management)')
+    axes[1].axhline(0, color='gray', lw=1)
+    axes[1].grid(True, linestyle=':', alpha=0.7)
+    axes[1].legend()
+
+    # 3. 电池状态
+    axes[2].plot(time_array, log['bus_voltage'], 'm-', lw=1.5, label='DC Bus Voltage')
+    axes[2].set_ylabel('Voltage [V]')
+    axes[2].set_title('DC Bus Electrical State')
+    axes[2].grid(True, linestyle=':', alpha=0.7)
+    axes[2].legend(loc='upper left')
+    
+    ax2_soc = axes[2].twinx()
+    ax2_soc.plot(time_array, log['soc'], 'c--', lw=2, label='Battery SOC')
+    ax2_soc.set_ylabel('SOC [%]')
+    ax2_soc.legend(loc='upper right')
+
+    # 4. 燃油消耗
+    axes[3].plot(time_array, log['wf_kg_h'], 'tab:orange', lw=1.5, label='Engine Fuel Flow')
+    axes[3].set_ylabel('Fuel Flow [kg/h]')
+    axes[3].set_xlabel('Time [s]')
+    axes[3].set_title('Turboshaft Engine Fuel Consumption')
+    axes[3].grid(True, linestyle=':', alpha=0.7)
+    axes[3].legend()
+
+    plt.tight_layout(rect=[0, 0, 1, 0.96])
+    
+    # 保存到 ../figures 目录
+    figures_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'figures')
+    if not os.path.exists(figures_dir):
+        os.makedirs(figures_dir)
+    save_path = os.path.join(figures_dir, 'series_hybrid_system_3min_test.png')
+    
+    plt.savefig(save_path, dpi=300)
+    print(f"-> 绘图已保存: {save_path}")
+
+    # 保存数据到dat文件
+    data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'data')
+    if not os.path.exists(data_dir):
+        os.makedirs(data_dir)
+    dat_path = os.path.join(data_dir, 'series_hybrid_data.dat')
+    
+    with open(dat_path, 'w') as f:
+        f.write('# Time(s)\tTarget_RPM\tActual_RPM\tError_RPM\tSOC\tEngine_Power\tDrive_Req\tBatt_Power\n')
+        for i in range(len(time_array)):
+            err = log['target_prop_rpm'][i] - log['prop_speed_rpm'][i]
+            f.write(f'{time_array[i]:.3f}\t{log["target_prop_rpm"][i]:.1f}\t'
+                    f'{log["prop_speed_rpm"][i]:.1f}\t{err:.1f}\t{log["soc"][i]:.2f}\t'
+                    f'{log["p_engine_out_kw"][i]:.2f}\t{log["p_drive_req_kw"][i]:.2f}\t'
+                    f'{log["p_batt_actual_kw"][i]:.2f}\n')
+    print(f"-> 数据已保存: {dat_path}")
+    # plt.show()

README.md

@@ -1,8 +1,8 @@
 # 🎛️ 自动控制原理AI+数智平台
 
-> 交互式控制系统分析与设计工具 | 时域·频域·根轨迹·AI问答
+> 交互式控制系统分析与设计工具 | 时域·频域·根轨迹·算例演示·AI问答
 
-一个基于 Gradio 构建的现代化自动控制原理学习平台，集成了系统分析工具和 AI 智能问答功能。
+一个基于 Gradio 构建的现代化自动控制原理学习平台，集成了系统分析工具、完整混动模型算例演示与 AI 智能问答功能。
 
 ## ✨ 核心功能
 
@@ -54,7 +54,14 @@
 - s 平面稳定性区域
 - 阻尼比等值线
 
-### 🤖 4. AI 智能问答 (Q&A)
+### 🧪 4. 算例演示 (Case Demo)
+
+- **完整混动模型**：调用 `Model/src/series_hybrid_sim.py` 进行系统级仿真
+- **参数化工况**：支持工况模板、仿真时长、步长、SOC、初始发动机功率与缩放系数
+- **图文结果**：输出三联图（转速响应/功率分配/电气状态）与关键数据表
+- **教学解读**：自动生成指标摘要（最大转速误差、SOC变化、平均功率与燃油流量）
+
+### 🤖 5. AI 智能问答 (Q&A)
 
 - **专业教学助手**：精通自动控制原理的 AI 教授
 - **流式响应**：实时显示 AI 回复过程
@@ -62,14 +69,13 @@
 - **上下文记忆**：支持多轮对话
 
 **支持的 API**：
-- DeepSeek API（推荐，国内网络友好）
-- Google Gemini API
+- DeepSeek API
 
 ## 🚀 快速开始
 
 ### 环境要求
 
-- Python 3.8+
+- Python 3.10+
 - pip 包管理器
 
 ### 安装步骤
@@ -84,20 +90,20 @@ cd AutoControl
 2. **安装依赖**
 
 ```bash
-pip install gradio numpy control matplotlib aiohttp
+pip install -r requirements.txt
 ```
 
 或使用 conda：
 
 ```bash
-conda create -n autocontrol python=3.9
+conda create -n autocontrol python=3.10
 conda activate autocontrol
-pip install gradio numpy control matplotlib aiohttp
+pip install -r requirements.txt
 ```
 
 3. **配置 API 密钥**
 
-编辑 `app.py` 文件开头的配置区域：
+编辑 `config.py` 文件中的配置区域：
 
 ```python
 # ==================== API 配置 ====================
@@ -240,27 +246,28 @@ AI 回复中的数学公式会自动渲染，支持以下格式：
 
 ## 📁 项目结构
 
-```
-AutoControl/
-├── app.py                 # 主应用程序
-├── README.md             # 项目文档
-├── API_CONFIG.md         # API 配置详细说明（可选）
-├── QUICK_START.md        # 快速入门指南（可选）
-└── requirements.txt      # 依赖列表（可选）
+```text
+AutoControlCourse/
+├── app.py                     # Gradio 入口与事件绑定
+├── ui_components.py           # 各标签页 UI 组件
+├── analysis_functions.py      # 时域/频域/根轨迹计算
+├── case_demo_functions.py     # 算例演示模块（调用 Model）
+├── chatbot.py                 # AI 问答
+├── user_stats.py              # 在线人数统计
+├── config.py                  # API 与运行配置
+├── Model/
+│   ├── src/                   # 混动模型核心代码
+│   └── data/                  # 运行所需模型权重与数据
+└── requirements.txt           # 依赖列表
 ```
 
 ## 🔧 高级配置
 
-### 切换到 Gemini API
+### 算例演示说明
 
-如果您想使用 Google Gemini API：
-
-```python
-API_KEY = "your-gemini-api-key"
-API_BASE_URL = "https://generativelanguage.googleapis.com/v1beta"
-API_MODEL = "gemini-1.5-flash"
-API_TYPE = "gemini"
-```
+- 仿真时长是模型时间，不等于程序实际等待时间
+- 电池功率符号定义：正值放电，负值充电
+- 若参数超限，程序会在安全范围内自动裁剪
 
 ### 自定义系统提示词
 
@@ -333,4 +340,4 @@ ax.grid(True, alpha=0.3, linestyle='--')
 
 **⭐ 如果这个项目对您有帮助，请给它一个 Star！**
 
-最后更新：2025年10月15日
+最后更新：2026年4月7日

app.py

@@ -10,6 +10,8 @@ from analysis_functions import (
     frequency_domain_analysis,
     root_locus_analysis
 )
+# ===== 新增：算例演示模块函数导入 =====
+from case_demo_functions import run_case_demo
 from chatbot import chat_with_ai
 from user_stats import get_online_status_html, update_user_activity
 from ui_components import (
@@ -17,6 +19,7 @@ from ui_components import (
     create_time_domain_tab,
     create_frequency_domain_tab,
     create_root_locus_tab,
+    create_case_demo_tab,
     create_chatbot_tab
 )
 
@@ -59,7 +62,10 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
             freq_domain_ui = create_frequency_domain_tab()
         with gr.TabItem("🎯 根轨迹 (Root Locus)", id=2):
             root_locus_ui = create_root_locus_tab()
-        with gr.TabItem("🤖 智能问答 (Q&A)", id=3):
+        # ===== 新增：算例演示 Tab（位于根轨迹与智能问答之间） =====
+        with gr.TabItem("🧪 算例演示 (Case Demo)", id=3):
+            case_demo_ui = create_case_demo_tab()
+        with gr.TabItem("🤖 智能问答 (Q&A)", id=4):
             chatbot_ui = create_chatbot_tab()
 
     # 2. 绑定事件逻辑
@@ -144,6 +150,41 @@ with gr.Blocks(title="自动控制理论学习网站 - AI+数智平台", css=cus
     num_input.change(fn=update_all_on_tf_change, inputs=tf_change_inputs, outputs=tf_change_outputs)
     den_input.change(fn=update_all_on_tf_change, inputs=tf_change_inputs, outputs=tf_change_outputs)
 
+    # ===== 新增：算例演示事件包装器 =====
+    def run_case_demo_wrapper(sim_time, dt, initial_soc, initial_engine_power, profile, rpm_scale, load_scale, sid):
+        update_user_activity(sid)
+        fig, summary, table_data = run_case_demo(
+            sim_time_s=sim_time,
+            dt=dt,
+            initial_soc_pct=initial_soc,
+            initial_engine_power_kw=initial_engine_power,
+            profile_name=profile,
+            rpm_scale=rpm_scale,
+            load_scale=load_scale
+        )
+        return fig, summary, table_data, get_online_status_html()
+
+    # ===== 新增：算例演示按钮事件绑定 =====
+    case_demo_ui["run_button"].click(
+        fn=run_case_demo_wrapper,
+        inputs=[
+            case_demo_ui["sim_time"],
+            case_demo_ui["dt"],
+            case_demo_ui["initial_soc"],
+            case_demo_ui["initial_engine_power"],
+            case_demo_ui["profile"],
+            case_demo_ui["rpm_scale"],
+            case_demo_ui["load_scale"],
+            session_id
+        ],
+        outputs=[
+            case_demo_ui["plot"],
+            case_demo_ui["summary"],
+            case_demo_ui["table"],
+            online_counter
+        ]
+    )
+
     # --- 聊天机器人事件 ---
     async def chat_wrapper(message, history, sid):
         update_user_activity(sid)

case_demo_functions.py

@@ -0,0 +1,145 @@
+import os
+import sys
+# ===== 新增：OpenMP冲突兼容设置，避免PyTorch初始化报错 =====
+os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "TRUE")
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+MODEL_SRC_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Model", "src")
+if MODEL_SRC_PATH not in sys.path:
+    # ===== 新增：将混动模型源码路径加入导入搜索路径 =====
+    sys.path.insert(0, MODEL_SRC_PATH)
+
+
+def _profile_points(profile_name):
+    if profile_name == "高机动阶跃":
+        return [
+            (0.0, 1600.0, 70.0),
+            (8.0, 3200.0, 220.0),
+            (20.0, 2500.0, 130.0),
+            (35.0, 3400.0, 250.0),
+            (50.0, 1800.0, 80.0),
+        ]
+    if profile_name == "经济巡航":
+        return [
+            (0.0, 1500.0, 60.0),
+            (15.0, 2100.0, 95.0),
+            (35.0, 2300.0, 105.0),
+            (55.0, 2000.0, 90.0),
+        ]
+    return [
+        (0.0, 1500.0, 50.0),
+        (10.0, 3000.0, 200.0),
+        (30.0, 2800.0, 150.0),
+        (50.0, 1800.0, 60.0),
+    ]
+
+
+def _target_from_profile(t, points, rpm_scale, load_scale):
+    rpm, torque = points[0][1], points[0][2]
+    for p_t, p_rpm, p_torque in points:
+        if t >= p_t:
+            rpm, torque = p_rpm, p_torque
+        else:
+            break
+    return rpm * rpm_scale, torque * load_scale
+
+
+def run_case_demo(sim_time_s, dt, initial_soc_pct, initial_engine_power_kw, profile_name, rpm_scale, load_scale):
+    try:
+        try:
+            # ===== 新增：懒加载混动系统模型，便于捕获缺失依赖 =====
+            from series_hybrid_sim import SeriesHybridSystem
+        except ModuleNotFoundError as e:
+            if getattr(e, "name", "") == "torch":
+                return None, "算例仿真失败：缺少依赖 torch，请先在当前环境安装 PyTorch。", []
+            return None, f"算例仿真失败：缺少依赖 {e.name}。", []
+
+        sim_time_s = float(np.clip(sim_time_s, 10.0, 240.0))
+        dt = float(np.clip(dt, 0.01, 0.2))
+        initial_soc_pct = float(np.clip(initial_soc_pct, 10.0, 95.0))
+        initial_engine_power_kw = float(np.clip(initial_engine_power_kw, 20.0, 260.0))
+        rpm_scale = float(np.clip(rpm_scale, 0.5, 1.6))
+        load_scale = float(np.clip(load_scale, 0.5, 1.6))
+
+        points = _profile_points(profile_name)
+        system = SeriesHybridSystem()
+        system.battery.SOC = initial_soc_pct / 100.0
+        system.bus_voltage = system.battery._get_ocv(system.battery.SOC)
+        system.genset.set_steady_state_by_power(H_env=0.0, Ma_env=0.0, Power_target=initial_engine_power_kw)
+
+        time_array = np.arange(0.0, sim_time_s, dt)
+        log = {k: [] for k in [
+            "soc", "bus_voltage", "prop_speed_rpm", "target_prop_rpm",
+            "target_engine_pwr", "p_engine_out_kw", "p_drive_req_kw",
+            "p_batt_actual_kw", "wf_kg_h"
+        ]}
+
+        for t in time_array:
+            target_rpm, load_torque = _target_from_profile(t, points, rpm_scale, load_scale)
+            res = system.step(dt, target_rpm, load_torque)
+            res["target_prop_rpm"] = target_rpm
+            for k in log:
+                log[k].append(res[k])
+
+        speed_error = np.array(log["target_prop_rpm"]) - np.array(log["prop_speed_rpm"])
+        soc_arr = np.array(log["soc"])
+        fuel_arr = np.array(log["wf_kg_h"])
+        engine_pwr_arr = np.array(log["p_engine_out_kw"])
+        batt_pwr_arr = np.array(log["p_batt_actual_kw"])
+
+        fig, axes = plt.subplots(3, 1, figsize=(12, 10), sharex=True)
+        axes[0].plot(time_array, log["target_prop_rpm"], "k--", lw=1.5, label="目标转速")
+        axes[0].plot(time_array, log["prop_speed_rpm"], "b-", lw=1.5, label="实际转速")
+        axes[0].set_ylabel("RPM")
+        axes[0].set_title("推进轴转速响应")
+        axes[0].grid(True, linestyle=":")
+        axes[0].legend()
+
+        axes[1].plot(time_array, log["p_drive_req_kw"], "k--", lw=1.2, label="电机需求")
+        axes[1].plot(time_array, log["p_engine_out_kw"], "r-", lw=1.2, label="发动机输出")
+        axes[1].plot(time_array, log["p_batt_actual_kw"], "g-", lw=1.2, label="电池功率")
+        axes[1].axhline(0, color="gray", lw=1)
+        axes[1].set_ylabel("kW")
+        axes[1].set_title("功率分配")
+        axes[1].grid(True, linestyle=":")
+        axes[1].legend()
+
+        axes[2].plot(time_array, log["bus_voltage"], "m-", lw=1.2, label="母线电压")
+        axes[2].set_ylabel("V")
+        axes[2].set_xlabel("时间 (s)")
+        axes[2].set_title("电气状态")
+        axes[2].grid(True, linestyle=":")
+        ax_soc = axes[2].twinx()
+        ax_soc.plot(time_array, log["soc"], "c--", lw=1.6, label="SOC")
+        ax_soc.set_ylabel("SOC (%)")
+
+        fig.tight_layout()
+
+        summary = (
+            f"### 算例结果解读\n"
+            f"- 仿真时长：{sim_time_s:.1f} s，步长：{dt:.3f} s\n"
+            f"- 最大转速误差：{np.max(np.abs(speed_error)):.1f} RPM\n"
+            f"- SOC 变化：{soc_arr[0]:.2f}% → {soc_arr[-1]:.2f}%（最小 {np.min(soc_arr):.2f}%）\n"
+            f"- 平均发动机输出：{np.mean(engine_pwr_arr):.2f} kW\n"
+            f"- 平均电池功率：{np.mean(batt_pwr_arr):.2f} kW\n"
+            f"- 平均燃油流量：{np.mean(fuel_arr):.2f} kg/h"
+        )
+
+        pick_idx = np.linspace(0, len(time_array) - 1, 8, dtype=int)
+        table_data = []
+        for idx in pick_idx:
+            table_data.append([
+                round(float(time_array[idx]), 2),
+                round(float(log["target_prop_rpm"][idx]), 1),
+                round(float(log["prop_speed_rpm"][idx]), 1),
+                round(float(log["p_engine_out_kw"][idx]), 2),
+                round(float(log["p_batt_actual_kw"][idx]), 2),
+                round(float(log["soc"][idx]), 2),
+            ])
+
+        return fig, summary, table_data
+    except Exception as e:
+        return None, f"算例仿真失败：{e}", []

config.py

@@ -16,4 +16,4 @@ API_TYPE = "deepseek"  # 当前支持 "deepseek"
 # ==================== Gradio 应用启动配置 ====================
 SERVER_NAME = "0.0.0.0"  # 监听所有网络接口
 SERVER_PORT = 7860       # 指定一个端口
-SHARE = False            # 是否创建Gradio的公开分享链接
+SHARE = True            # 是否创建Gradio的公开分享链接

data_usage/case_demo_test_results.json

@@ -0,0 +1,70 @@
+[
+  {
+    "name": "常规-起飞巡航",
+    "figure_ok": true,
+    "summary_ok": true,
+    "table_len": 8,
+    "summary_head": "### 算例结果解读",
+    "soc_min": 60.0,
+    "soc_max": 62.05,
+    "first_row": [
+      0.0,
+      1500.0,
+      0.0,
+      50.0,
+      -0.65,
+      60.0
+    ]
+  },
+  {
+    "name": "高机动-高负载",
+    "figure_ok": true,
+    "summary_ok": true,
+    "table_len": 8,
+    "summary_head": "### 算例结果解读",
+    "soc_min": 55.0,
+    "soc_max": 61.41,
+    "first_row": [
+      0.0,
+      1920.0,
+      0.0,
+      80.0,
+      -30.65,
+      55.0
+    ]
+  },
+  {
+    "name": "经济巡航-低负载",
+    "figure_ok": true,
+    "summary_ok": true,
+    "table_len": 8,
+    "summary_head": "### 算例结果解读",
+    "soc_min": 70.0,
+    "soc_max": 76.3,
+    "first_row": [
+      0.0,
+      1275.0,
+      0.0,
+      40.0,
+      9.35,
+      70.0
+    ]
+  },
+  {
+    "name": "边界输入-自动裁剪",
+    "figure_ok": true,
+    "summary_ok": true,
+    "table_len": 8,
+    "summary_head": "### 算例结果解读",
+    "soc_min": 10.0,
+    "soc_max": 26.51,
+    "first_row": [
+      0.0,
+      2400.0,
+      0.0,
+      260.0,
+      -210.65,
+      10.0
+    ]
+  }
+]

data_usage/usage_stats.json

@@ -1,4 +1,4 @@
 {
-  "total_users": 15,
-  "last_saved_at": 1760890996.5595384
+  "total_users": 22,
+  "last_saved_at": 1775496284.1673155
 }

requirements.txt

@@ -1,7 +1,23 @@
 # requirements.txt
 
-gradio
-numpy
-control
-matplotlib
-aiohttp
+# ===== 新增：PyTorch CPU 轮子下载源 =====
+--extra-index-url https://download.pytorch.org/whl/cpu
+
+gradio==4.44.1
+gradio-client==1.3.0
+pydantic==2.10.6
+pydantic-core==2.27.2
+huggingface_hub==0.23.0
+numpy==1.26.4
+control==0.9.4
+matplotlib==3.9.4
+aiohttp==3.13.5
+pillow==10.4.0
+# ===== 新增：混动模型（Model）运行依赖 =====
+torch==2.4.1
+botorch==0.14.0
+gpytorch==1.14
+pyro-ppl==1.9.1
+pandas==2.3.3
+scipy==1.15.3
+scikit-learn==1.7.1

ui_components.py

@@ -200,6 +200,37 @@ def create_root_locus_tab():
             """)
     return ui_dict
 
+def create_case_demo_tab():
+    """创建算例演示选项卡的UI组件"""
+    ui_dict = {}
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Group():
+                gr.HTML("<div class='card-title'>🧪 算例参数设置</div>")
+                ui_dict["profile"] = gr.Dropdown(
+                    choices=["起飞-巡航-降落", "高机动阶跃", "经济巡航"],
+                    value="起飞-巡航-降落",
+                    label="工况模板"
+                )
+                ui_dict["sim_time"] = gr.Slider(minimum=20, maximum=180, value=60, step=5, label="仿真时长 (s)")
+                ui_dict["dt"] = gr.Dropdown(choices=[0.02, 0.05, 0.1], value=0.02, label="仿真步长 (s)")
+                ui_dict["initial_soc"] = gr.Slider(minimum=20, maximum=90, value=60, step=1, label="初始SOC (%)")
+                ui_dict["initial_engine_power"] = gr.Slider(minimum=20, maximum=250, value=50, step=5, label="初始发动机功率 (kW)")
+                ui_dict["rpm_scale"] = gr.Slider(minimum=0.6, maximum=1.4, value=1.0, step=0.05, label="目标转速缩放系数")
+                ui_dict["load_scale"] = gr.Slider(minimum=0.6, maximum=1.4, value=1.0, step=0.05, label="负载转矩缩放系数")
+                ui_dict["run_button"] = gr.Button("🚀 运行混动算例", variant="primary", elem_classes="primary-btn")
+            with gr.Group():
+                gr.HTML("<div class='card-title'>📝 结果解读</div>")
+                ui_dict["summary"] = gr.Markdown()
+        with gr.Column(scale=2):
+            ui_dict["plot"] = gr.Plot(label="混动系统响应图")
+            ui_dict["table"] = gr.Dataframe(
+                headers=["时间(s)", "目标转速", "实际转速", "发动机功率(kW)", "电池功率(kW)", "SOC(%)"],
+                label="关键时刻数据",
+                interactive=False
+            )
+    return ui_dict
+
 def create_chatbot_tab():
     """创建AI问答选项卡的UI组件"""
     ui_dict = {}