diff --git a/main.py b/main.py
index b31186d..a9c5c85 100644
--- a/main.py
+++ b/main.py
@@ -8,6 +8,7 @@ from collections import defaultdict
 import networkx as nx
 import math
 import argparse
+import os
 
 # 设置matplotlib支持中文显示
 plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Arial']
@@ -66,6 +67,7 @@ def generate_wind_farm_data(n_turbines=30, seed=42, layout='random', spacing=800
         'x': x_coords,
         'y': y_coords,
         'power': power_ratings,
+        'platform_height': np.zeros(n_turbines),
         'cumulative_power': np.zeros(n_turbines)  # 用于后续计算
     })
     
@@ -410,12 +412,22 @@ def get_max_cable_capacity_mw(cable_specs=None):
     异常:
         Exception: 当未提供 cable_specs 时抛出，提示截面不满足。
     """
-    if cable_specs:
-        # 从所有电缆规格中找到最大的额定电流容量
-        max_current_capacity = max(spec[1] for spec in cable_specs)
-    else:
-        # 如果没有传入电缆规格，通常意味着已尝试过所有规格但仍不满足需求
-        raise Exception("没有提供电缆参数")
+    if cable_specs is None:
+        # Default cable specs if not provided (same as in evaluate_design)
+        cable_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)
+        ]
+
+    # 从所有电缆规格中找到最大的额定电流容量
+    max_current_capacity = max(spec[1] for spec in cable_specs)
         
     # 计算最大功率：P = √3 * U * I * cosφ
     # 这里假设降额系数为 1 (不降额)
@@ -593,15 +605,32 @@ def export_to_dxf(turbines, substation, connections_details, filename):
     # 1. 建立图层
     doc.layers.add('Substation', color=1) # Red
     doc.layers.add('Turbines', color=5)   # Blue
-    doc.layers.add('Cable_35mm', color=3) # Green
-    doc.layers.add('Cable_70mm', color=130) 
-    doc.layers.add('Cable_95mm', color=150)
-    doc.layers.add('Cable_120mm', color=4) # Cyan
-    doc.layers.add('Cable_150mm', color=6) # Magenta
-    doc.layers.add('Cable_185mm', color=30) # Orange
-    doc.layers.add('Cable_240mm', color=1) # Red
-    doc.layers.add('Cable_300mm', color=250) 
-    doc.layers.add('Cable_400mm', color=7) # White/Black
+    
+    # 动态确定电缆颜色
+    # 提取所有使用到的电缆截面
+    used_sections = sorted(list(set(conn['cable']['cross_section'] for conn in connections_details)))
+    
+    # 定义颜色映射规则 (AutoCAD Color Index)
+    # 1=Red, 2=Yellow, 3=Green, 4=Cyan, 5=Blue, 6=Magenta
+    color_rank = [
+        2, # 1st (Smallest): Yellow
+        3, # 2nd: Green
+        1, # 3rd: Red
+        5, # 4th: Blue
+        6  # 5th: Magenta
+    ]
+    default_color = 3 # Others: Green
+    
+    # 创建电缆图层
+    for i, section in enumerate(used_sections):
+        if i < len(color_rank):
+            c = color_rank[i]
+        else:
+            c = default_color
+            
+        layer_name = f'Cable_{section}mm'
+        if layer_name not in doc.layers:
+            doc.layers.add(layer_name, color=c)
     
     # 2. 绘制升压站
     sx, sy = substation[0, 0], substation[0, 1]
@@ -856,16 +885,24 @@ def compare_design_methods(excel_path=None, n_clusters_override=None):
                     ax=axes[1])
     
     plt.tight_layout()
-    output_filename = 'wind_farm_design_imported.png' if excel_path else 'offshore_wind_farm_design.png'
+    output_filename = 'wind_farm_design_comparison.png'
     plt.savefig(output_filename, dpi=300)
     
-    # 导出DXF
-    dxf_filename = 'wind_farm_design.dxf'
-    # 默认导出更优的方案（通常K-means扇区聚类在海上更合理，或者成本更低者）
-    # 这里我们导出Sector Clustering的结果
+    # 导出最佳方案 DXF
+    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，或者使用 os.path.join('', ...) 也是安全的
+        dxf_filename = os.path.join(dir_name, f"{base_name}.dxf")
+    else:
+        dxf_filename = 'wind_farm_design.dxf'
+        
+    # Use the connection details from the best evaluation
+    # Note: We need the turbine DataFrame corresponding to the best method to get cluster IDs if possible, 
+    # but export_to_dxf mainly needs coordinates which are constant.
     export_to_dxf(clustered_turbines, substation, kmeans_evaluation['details'], dxf_filename)
     
-    # plt.show() 
+    # plt.show()
     
     # 打印详细结果
     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'}) =====")
@@ -921,14 +958,10 @@ def compare_design_methods(excel_path=None, n_clusters_override=None):
 if __name__ == "__main__":
     # 解析命令行参数
     parser = argparse.ArgumentParser(description='Wind Farm Collector System Design')
+    parser.add_argument('--excel', help='Path to the Excel coordinates file', default=None)
     parser.add_argument('--clusters', type=int, help='Specify the number of clusters (circuits) manually', default=None)
     args = parser.parse_args()
 
-    # 2. 读取 Excel 坐标数据 (如果存在)
-    excel_path = 'coordinates2.xlsx'
-    # 尝试从命令行参数获取文件路径 (可选扩展)
-    # if len(sys.argv) > 1: excel_path = sys.argv[1]
-    
     # 3. 运行比较
-    # 如果本地没有excel文件，将自动回退到生成数据模式
-    compare_design_methods(excel_path, n_clusters_override=args.clusters)
\ No newline at end of file
+    # 如果没有提供excel文件，将自动回退到生成数据模式
+    compare_design_methods(args.excel, n_clusters_override=args.clusters)
\ No newline at end of file