From 34b0d703090e0228631c44b2ed28a4c57190e874 Mon Sep 17 00:00:00 2001
From: dmy <dmy@dmy.com>
Date: Thu, 1 Jan 2026 16:25:55 +0800
Subject: [PATCH] =?UTF-8?q?feat:=20=E6=96=B0=E5=A2=9EExcel=E6=8A=A5?=
 =?UTF-8?q?=E8=A1=A8=E5=AF=BC=E5=87=BA=E5=8A=9F=E8=83=BD?=
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---
 main.py | 48 +++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 45 insertions(+), 3 deletions(-)

diff --git a/main.py b/main.py
index a9c5c85..4a90baf 100644
--- a/main.py
+++ b/main.py
@@ -687,6 +687,45 @@ def export_to_dxf(turbines, substation, connections_details, filename):
     except Exception as e:
         print(f"导出DXF失败: {e}")
 
+# 6.6 导出Excel报表
+def export_to_excel(connections_details, filename):
+    """
+    将设计方案详情导出为Excel文件
+    :param connections_details: evaluate_design返回的'details'列表
+    """
+    data = []
+    for conn in connections_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)
+    
+    # 汇总统计
+    summary = {
+        'Total Cost (¥)': df['Cost (¥)'].sum(),
+        'Total Effective Length (m)': df['Effective Length (m)'].sum(),
+        'Total Vertical Length (m)': df['Vertical Length (m)'].sum()
+    }
+    summary_df = pd.DataFrame([summary])
+    
+    try:
+        with pd.ExcelWriter(filename) as writer:
+            df.to_excel(writer, sheet_name='Cable Schedule', index=False)
+            summary_df.to_excel(writer, sheet_name='Summary', index=False)
+        print(f"成功导出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"""
@@ -888,19 +927,22 @@ def compare_design_methods(excel_path=None, n_clusters_override=None):
     output_filename = 'wind_farm_design_comparison.png'
     plt.savefig(output_filename, dpi=300)
     
-    # 导出最佳方案 DXF
+    # 导出最佳方案 DXF 和 Excel
     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")
+        # 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
     # 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)
+    export_to_excel(kmeans_evaluation['details'], excel_out_filename)
     
     # plt.show()