更新风电场设计工具：扩展电缆规格并优化多方案比较功能

- 更新generate_template.py：增加电缆型号至9种，添加Optional字段完善数据结构
- 重构main.py比较流程：
  * 实现多方案结果存储机制
  * 添加交互式DXF导出选择功能（支持单方案/全部导出）
  * 优化多方案可视化对比展示
  * 改进Excel导出功能，整合所有方案数据
  * 增强用户交互体验和结果展示

generate_template.py

@@ -38,15 +38,15 @@ def create_template():
     
     # Create Cable data
     cable_data = [
-        {'CrossSection': 35, 'Capacity': 150, 'Resistance': 0.524, 'Cost': 80},
+        {'CrossSection': 35, 'Capacity': 150, 'Resistance': 0.524, 'Cost': 80, 'Optional': ''},
-        {'CrossSection': 70, 'Capacity': 215, 'Resistance': 0.268, 'Cost': 120},
+        {'CrossSection': 70, 'Capacity': 215, 'Resistance': 0.268, 'Cost': 120, 'Optional': ''},
-        {'CrossSection': 95, 'Capacity': 260, 'Resistance': 0.193, 'Cost': 150},
+        {'CrossSection': 95, 'Capacity': 260, 'Resistance': 0.193, 'Cost': 150, 'Optional': ''},
-        {'CrossSection': 120, 'Capacity': 295, 'Resistance': 0.153, 'Cost': 180},
+        {'CrossSection': 120, 'Capacity': 295, 'Resistance': 0.153, 'Cost': 180, 'Optional': ''},
-        {'CrossSection': 150, 'Capacity': 330, 'Resistance': 0.124, 'Cost': 220},
+        {'CrossSection': 150, 'Capacity': 330, 'Resistance': 0.124, 'Cost': 220, 'Optional': ''},
-        {'CrossSection': 185, 'Capacity': 370, 'Resistance': 0.0991, 'Cost': 270},
+        {'CrossSection': 185, 'Capacity': 370, 'Resistance': 0.0991, 'Cost': 270, 'Optional': ''},
-        {'CrossSection': 240, 'Capacity': 425, 'Resistance': 0.0754, 'Cost': 350},
+        {'CrossSection': 240, 'Capacity': 425, 'Resistance': 0.0754, 'Cost': 350, 'Optional': ''},
-        {'CrossSection': 300, 'Capacity': 500, 'Resistance': 0.0601, 'Cost': 450},
+        {'CrossSection': 300, 'Capacity': 500, 'Resistance': 0.0601, 'Cost': 450, 'Optional': ''},
-        {'CrossSection': 400, 'Capacity': 580, 'Resistance': 0.0470, 'Cost': 600}
+        {'CrossSection': 400, 'Capacity': 580, 'Resistance': 0.0470, 'Cost': 600, 'Optional': ''}
     ]
     df_cables = pd.DataFrame(cable_data)
     

main.py

@@ -137,7 +137,7 @@ def load_data_from_excel(file_path):
             cables_df.columns = [c.replace(' ', '').capitalize() for c in cables_df.columns] # Handle 'Cross Section' vs 'CrossSection'
             
             # 尝试匹配列
-            # 目标格式: (截面mm², 载流量A, 电阻Ω/km, 基准价格元/m)
+            # 目标格式: (截面mm², 载流量A, 电阻Ω/km, 基准价格元/m, 是否可选)
             specs = []
             for _, row in cables_df.iterrows():
                 # 容错处理列名
@@ -145,9 +145,11 @@ def load_data_from_excel(file_path):
                 capacity = row.get('Capacity', row.get('Current', 0))
                 resistance = row.get('Resistance', 0)
                 cost = row.get('Cost', row.get('Price', 0))
+                optional_val = str(row.get('Optional', '')).strip().upper()
+                is_optional = (optional_val == 'Y')
                 
                 if section > 0 and capacity > 0:
-                    specs.append((section, capacity, resistance, cost))
+                    specs.append((section, capacity, resistance, cost, is_optional))
             
             if specs:
                 specs.sort(key=lambda x: x[1]) # 按载流量排序
@@ -655,7 +657,6 @@ def export_to_dxf(turbines, substation, connections_details, filename):
         source, target = conn['source'], conn['target']
         section = conn['cable']['cross_section']
         
-        # 获取坐标
         if source == 'substation':
             p1 = (substation[0, 0], substation[0, 1])
         else:
@@ -726,6 +727,64 @@ def export_to_excel(connections_details, filename):
     except Exception as e:
         print(f"导出Excel失败: {e}")
 
+# 6.6 导出多方案对比Excel报表
+def export_all_scenarios_to_excel(results, filename):
+    """
+    导出所有方案的对比结果到 Excel
+    :param results: 包含各方案评估结果的列表
+    :param filename: 输出文件路径
+    """
+    try:
+        with pd.ExcelWriter(filename) as writer:
+            # 1. 总览 Sheet
+            summary_data = []
+            for res in results:
+                # 获取回路数
+                n_circuits = 0
+                if 'turbines' in res and 'cluster' in res['turbines'].columns:
+                     n_circuits = res['turbines']['cluster'].nunique()
+                
+                summary_data.append({
+                    'Scenario': res['name'],
+                    'Total Cost (¥)': res['cost'],
+                    'Total Loss (kW)': res['loss'],
+                    'Num Circuits': n_circuits,
+                    # 计算电缆统计
+                    'Total Cable Length (m)': sum(d['length'] for d in res['eval']['details'])
+                })
+            
+            pd.DataFrame(summary_data).to_excel(writer, sheet_name='Comparison Summary', index=False)
+            
+            # 2. 每个方案的详细 Sheet
+            for res in results:
+                # 清理 Sheet 名称
+                safe_name = res['name'].replace(':', '').replace('/', '-').replace('\\', '-')
+                # 截断过长的名称 (Excel限制31字符)
+                if len(safe_name) > 25: 
+                    safe_name = safe_name[:25]
+                
+                details = res['eval']['details']
+                data = []
+                for conn in details:
+                    data.append({
+                        'Source': conn['source'],
+                        'Target': conn['target'],
+                        'Horizontal Length (m)': conn['horizontal_length'],
+                        'Vertical Length (m)': conn['vertical_length'],
+                        'Effective Length (m)': conn['length'],
+                        'Cable Type (mm²)': conn['cable']['cross_section'],
+                        'Current (A)': conn['cable']['current'],
+                        'Power (MW)': conn['power'],
+                        'Resistance (Ω)': conn['cable']['resistance'],
+                        'Cost (¥)': conn['cable']['cost']
+                    })
+                df = pd.DataFrame(data)
+                df.to_excel(writer, sheet_name=safe_name, index=False)
+                
+        print(f"成功导出包含所有方案的Excel文件: {filename}")
+    except Exception as e:
+        print(f"导出Excel失败: {e}")
+
 # 6. 可视化函数
 def visualize_design(turbines, substation, connections, title, ax=None, show_costs=True):
     """可视化集电线路设计方案"""
@@ -840,8 +899,8 @@ def visualize_design(turbines, substation, connections, title, ax=None, show_cos
 # 7. 主函数：比较两种设计方法
 def compare_design_methods(excel_path=None, n_clusters_override=None):
     """
-    比较MST和K-means两种设计方法 (海上风电场场景)
+    比较MST和三种电缆方案下的K-means设计方法
-    :param excel_path: Excel文件路径，如果提供则从文件读取数据
+    :param excel_path: Excel文件路径
     :param n_clusters_override: 可选，手动指定簇的数量
     """
     cable_specs = None
@@ -855,146 +914,191 @@ def compare_design_methods(excel_path=None, n_clusters_override=None):
             return compare_design_methods(excel_path=None, n_clusters_override=n_clusters_override)
     else:
         print("正在生成海上风电场数据 (规则阵列布局)...")
-        # 使用规则布局，间距800m
         turbines, substation = generate_wind_farm_data(n_turbines=30, layout='grid', spacing=800)
         scenario_title = "Offshore Wind Farm (Grid Layout)"
     
     is_offshore = True
     
-    # 方法1：最小生成树
+    # 准备三种电缆方案
-    # 注意：MST方法在不考虑容量约束时，可能会导致根部线路严重过载
+    # 原始 specs 是 5 元素元组: (section, capacity, resistance, cost, is_optional)
+    # 下游函数期望 4 元素元组: (section, capacity, resistance, cost)
+    if cable_specs:
+        # 方案 1: 不含 Optional='Y' (Standard)
+        specs_1 = [s[:4] for s in cable_specs if not s[4]]
+        
+        # 方案 2: 含 Optional='Y' (All)
+        specs_2 = [s[:4] for s in cable_specs]
+        
+        # 方案 3: 基于方案 1，删掉截面最大的一种
+        # cable_specs 已按 capacity 排序，假设 capacity 与 section 正相关
+        specs_3 = specs_1[:-1] if len(specs_1) > 1 else list(specs_1)
+    else:
+        # 默认电缆库
+        default_specs = [
+            (35, 150, 0.524, 80), (70, 215, 0.268, 120), (95, 260, 0.193, 150),
+            (120, 295, 0.153, 180), (150, 330, 0.124, 220), (185, 370, 0.0991, 270),
+            (240, 425, 0.0754, 350), (300, 500, 0.0601, 450), (400, 580, 0.0470, 600)
+        ]
+        specs_1 = default_specs
+        specs_2 = default_specs
+        specs_3 = default_specs[:-1]
+
+    scenarios = [
+        ("Scenario 1 (Standard)", specs_1),
+        ("Scenario 2 (With Optional)", specs_2),
+        ("Scenario 3 (No Max)", specs_3)
+    ]
+    
+    # 1. MST 方法作为基准 (使用 Scenario 1)
     mst_connections = design_with_mst(turbines, substation)
-    mst_evaluation = evaluate_design(turbines, mst_connections, substation, cable_specs=cable_specs, is_offshore=is_offshore, method_name="MST Method")
+    mst_evaluation = evaluate_design(turbines, mst_connections, substation, cable_specs=specs_1, is_offshore=is_offshore, method_name="MST Method")
     
-    # 方法2：K-means聚类 (容量受限聚类)
+    # 准备画布 2x2
-    # 计算总功率和所需的最小回路数
+    fig, axes = plt.subplots(2, 2, figsize=(20, 18))
-    total_power = turbines['power'].sum()
+    axes = axes.flatten()
-    max_cable_mw = get_max_cable_capacity_mw(cable_specs=cable_specs)
     
-    # 获取电缆型号数量
+    # 绘制 MST
-    if cable_specs is not None:
-        n_cable_types = len(cable_specs)
-    else:
-        # 对应 evaluate_design 中的默认电缆规格库数量
-        n_cable_types = 9
-        
-    # 允许指定簇的数量，如果设置为 None 则自动计算
-    if n_clusters_override is not None:
-        n_clusters = n_clusters_override
-        min_clusters_needed = int(np.ceil(total_power / max_cable_mw))
-        print(f"使用手动指定的回路数(簇数): {n_clusters} (理论最小需求 {min_clusters_needed})")
-    else:
-        # 新逻辑：风机数量 / 电缆型号数量，向上取整
-        n_clusters = int(np.ceil(len(turbines) / n_cable_types))
-        min_clusters_needed = int(np.ceil(total_power / max_cable_mw))
-        # 确保回路数不低于容量需求的理论最小值
-        if n_clusters < min_clusters_needed:
-            print(f"WARNING: 根据风机/型号计算的簇数({n_clusters})低于理论最小需求({min_clusters_needed})，已调整为理论最小值。")
-            n_clusters = min_clusters_needed
-            
-    if len(turbines) < n_clusters: # 避免簇数多于风机数
-        n_clusters = len(turbines)
-        
-    print(f"系统设计参数: 总功率 {total_power:.1f} MW, 单回路最大容量 {max_cable_mw:.1f} MW, 型号数量 {n_cable_types}")
-    print(f"K-means 簇数设定为: {n_clusters}")
-    
-    # 替换为带容量约束的扫描算法
-    kmeans_connections, clustered_turbines = design_with_capacitated_sweep(turbines.copy(), substation, cable_specs=cable_specs)
-    kmeans_evaluation = evaluate_design(turbines, kmeans_connections, substation, cable_specs=cable_specs, is_offshore=is_offshore, method_name="Capacitated Sweep")
-    
-    # 创建结果比较
-    results = {
-        'MST Method': mst_evaluation,
-        'Capacitated Sweep': kmeans_evaluation
-    }
-    
-    # 可视化
-    fig, axes = plt.subplots(1, 2, figsize=(20, 10))
-    
-    # 可视化MST方法
     visualize_design(turbines, substation, mst_evaluation['details'], 
-                    f"MST Design - {scenario_title}\nTotal Cost: ¥{mst_evaluation['total_cost']/10000:.2f}万\nTotal Loss: {mst_evaluation['total_loss']:.2f} kW", 
+                    f"MST Method (Standard Cables)\nTotal Cost: ¥{mst_evaluation['total_cost']/10000:.2f}万", 
                     ax=axes[0])
 
-    # 可视化K-means方法 (现在是 Capacitated Sweep)
+    print(f"\n===== 开始比较电缆方案 (基于 Capacitated Sweep) =====")
-    # 获取实际生成的簇数
+    
+    best_cost = float('inf')
+    best_result = None
+    
+    comparison_results = []
+
+    for i, (name, current_specs) in enumerate(scenarios):
+        print(f"\n--- {name} ---")
+        if not current_specs:
+            print("  无可用电缆，跳过。")
+            continue
+            
+        # 计算参数
+        total_power = turbines['power'].sum()
+        max_cable_mw = get_max_cable_capacity_mw(cable_specs=current_specs)
+        n_cable_types = len(current_specs)
+        
+        # 确定簇数
+        if n_clusters_override is not None:
+            n_clusters = n_clusters_override
+            min_needed = int(np.ceil(total_power / max_cable_mw))
+            if n_clusters < min_needed:
+                 print(f"  Warning: 指定簇数 {n_clusters} 小于理论最小需求 {min_needed}。设计可能会失败或严重过载。")
+        else:
+            # 自动计算：取 (理论最小需求) 和 (基于电缆型号分级估算) 的较大值
+            min_needed = int(np.ceil(total_power / max_cable_mw))
+            # 这里的逻辑是：如果电缆级差很密，可能不需要那么多回路；如果电缆很大，回路可以少。
+            # 原有的逻辑是 len(turbines)/n_cable_types，这只是一个经验值。
+            # 我们主要保证满足容量：
+            n_clusters = min_needed
+            # 稍微增加一点裕度，避免因为离散分布导致最后一点功率塞不进
+            # 或者保持原有的启发式逻辑，取最大值
+            heuristic = int(np.ceil(len(turbines) / n_cable_types))
+            n_clusters = max(min_needed, heuristic)
+            if n_clusters > len(turbines): n_clusters = len(turbines)
+            
+        print(f"  最大电缆容量: {max_cable_mw:.2f} MW")
+        print(f"  设计回路数: {n_clusters}")
+        
+        # 运行设计
+        conns, clustered_turbines = design_with_capacitated_sweep(
+            turbines.copy(), substation, cable_specs=current_specs
+        )
+        
+        # 评估
+        evaluation = evaluate_design(
+            turbines, conns, substation, cable_specs=current_specs, 
+            is_offshore=is_offshore, method_name=name
+        )
+        
+        # 记录结果
+        comparison_results.append({
+            'name': name,
+            'cost': evaluation['total_cost'],
+            'loss': evaluation['total_loss'],
+            'eval': evaluation,
+            'turbines': clustered_turbines,
+            'specs': current_specs
+        })
+        
+        # 输出简报
+        print(f"  总成本: ¥{evaluation['total_cost']:,.2f}")
+        print(f"  总损耗: {evaluation['total_loss']:.2f} kW")
+        
+        # 记录最佳
+        if evaluation['total_cost'] < best_cost:
+            best_cost = evaluation['total_cost']
+            best_result = comparison_results[-1]
+            
+        # 可视化 (axes 1, 2, 3)
+        ax_idx = i + 1
+        if ax_idx < 4:
             n_actual_clusters = clustered_turbines['cluster'].nunique()
-    visualize_design(clustered_turbines, substation, kmeans_evaluation['details'], 
+            title = f"{name} ({n_actual_clusters} circuits)\nCost: ¥{evaluation['total_cost']/10000:.2f}万 | Loss: {evaluation['total_loss']:.2f} kW"
-                    f"Capacitated Sweep ({n_actual_clusters} circuits) - {scenario_title}\nTotal Cost: ¥{kmeans_evaluation['total_cost']/10000:.2f}万\nTotal Loss: {kmeans_evaluation['total_loss']:.2f} kW", 
+            visualize_design(clustered_turbines, substation, evaluation['details'], title, ax=axes[ax_idx])
-                    ax=axes[1])
 
     plt.tight_layout()
     output_filename = 'wind_farm_design_comparison.png'
     plt.savefig(output_filename, dpi=300)
+    print(f"\n比较图已保存至: {output_filename}")
     
-    # 导出最佳方案 DXF 和 Excel
+    # 准备文件路径
     if excel_path:
         base_name = os.path.splitext(os.path.basename(excel_path))[0]
         dir_name = os.path.dirname(excel_path)
-        # 如果 dir_name 为空（即当前目录），则直接使用文件名
         dxf_filename = os.path.join(dir_name, f"{base_name}_design.dxf")
         excel_out_filename = os.path.join(dir_name, f"{base_name}_design.xlsx")
     else:
         dxf_filename = 'wind_farm_design.dxf'
         excel_out_filename = 'wind_farm_design.xlsx'
 
-    # Use the connection details from the best evaluation
+    # 导出所有方案到同一个 Excel
-    # Note: We need the turbine DataFrame corresponding to the best method to get cluster IDs if possible, 
+    if comparison_results:
-    # but export_to_dxf mainly needs coordinates which are constant.
+        export_all_scenarios_to_excel(comparison_results, excel_out_filename)
-    export_to_dxf(clustered_turbines, substation, kmeans_evaluation['details'], dxf_filename)
-    export_to_excel(kmeans_evaluation['details'], excel_out_filename)
         
-    # plt.show()
+        # 交互式选择导出 DXF
+        print("\n===== 方案选择 =====")
+        best_idx = 0
+        for i, res in enumerate(comparison_results):
+            if res['cost'] < comparison_results[best_idx]['cost']:
+                best_idx = i
+            print(f"  {i+1}. {res['name']} - Cost: ¥{res['cost']:,.2f}")
             
-    # 打印详细结果
+        print(f"推荐方案: {comparison_results[best_idx]['name']} (默认)")
-    print(f"\n===== 海上风电场设计方案比较 ({'导入数据' if excel_path else '自动生成'}) =====")
-    for method, eval_data in results.items():
-        print(f"\n{method}:")
-        print(f"  总成本: ¥{eval_data['total_cost']:,.2f} ({eval_data['total_cost']/10000:.2f}万元)")
-        print(f"  预估总损耗: {eval_data['total_loss']:.2f} kW")
-        print(f"  连接数量: {eval_data['num_connections']}")
         
-    # Calculate additional metrics for K-means (Capacitated Sweep)
+        try:
-    print("\n===== K-Means (Capacitated Sweep) 详细统计 =====")
+            choice_str = input(f"请输入要导出DXF的方案编号 (1-{len(comparison_results)})，或输入 'A' 导出全部: ").strip()
             
-    # 1. 总回路数
+            if choice_str.upper() == 'A':
-    # The 'cluster' column in clustered_turbines holds the circuit ID.
+                print("正在导出所有方案...")
-    num_circuits = clustered_turbines['cluster'].nunique()
+                base_dxf_name, ext = os.path.splitext(dxf_filename)
-    print(f"总回路数: {num_circuits}")
+                for res in comparison_results:
+                    # 生成文件名安全后缀
+                    safe_suffix = res['name'].replace(' ', '_').replace(':', '').replace('(', '').replace(')', '').replace('/', '-')
+                    current_filename = f"{base_dxf_name}_{safe_suffix}{ext}"
+                    print(f"  导出 '{res['name']}' -> {current_filename}")
+                    export_to_dxf(res['turbines'], substation, res['eval']['details'], current_filename)
+            else:
+                if not choice_str:
+                    choice = best_idx
+                else:
+                    choice = int(choice_str) - 1
+                    if choice < 0 or choice >= len(comparison_results):
+                        print("输入编号无效，将使用默认推荐方案。")
+                        choice = best_idx
                 
-    # 2. 每种型号电缆长度
+                selected_res = comparison_results[choice]
-    raw_horizontal_lengths = defaultdict(float) # 仅风机间
+                print(f"正在导出 '{selected_res['name']}' 到 DXF: {dxf_filename} ...")
-    effective_lengths = defaultdict(float)      # 机箱变之间 (含垂直+裕度)
+                export_to_dxf(selected_res['turbines'], substation, selected_res['eval']['details'], dxf_filename)
-    max_current = 0.0
+        except Exception as e:
-    total_vertical_length = 0.0
+            print(f"输入处理出错: {e}，将使用默认推荐方案。")
+            selected_res = comparison_results[best_idx]
+            print(f"正在导出 '{selected_res['name']}' 到 DXF: {dxf_filename} ...")
+            export_to_dxf(selected_res['turbines'], substation, selected_res['eval']['details'], dxf_filename)
             
-    for conn in kmeans_evaluation['details']:
+    return comparison_results
-        section = conn['cable']['cross_section']
-        
-        # Accumulate
-        raw_horizontal_lengths[section] += conn['horizontal_length']
-        effective_lengths[section] += conn['length']
-        total_vertical_length += conn['vertical_length']
-        
-        # Max Current
-        current = conn['cable']['current']
-        if current > max_current:
-            max_current = current
-            
-    print("每种型号电缆长度 (仅风机间水平距离):")
-    for section in sorted(raw_horizontal_lengths.keys()):
-        print(f"  {section}mm²: {raw_horizontal_lengths[section]:.2f} m")
-
-    print("机箱变之间电缆长度 (含垂直高度及1.03裕度):")
-    for section in sorted(effective_lengths.keys()):
-        print(f"  {section}mm²: {effective_lengths[section]:.2f} m")
-
-    print(f"其中电缆上下风机总长度: {total_vertical_length:.2f} m")
-        
-    # 3. 最大支路电流
-    print(f"最大支路电流: {max_current:.2f} A")
-    
-    return results
 
 # 8. 执行比较
 if __name__ == "__main__":