Add MIP module for collector layout optimization

gui.py

@@ -91,6 +91,7 @@ def index():
         "current_file_container": None,  # 替换 label 为 container
         "info_container": None,  # 新增信息展示容器
         "ga_switch": None,  # 遗传算法开关
+        "mip_switch": None,  # MIP开关
     }
 
     def update_info_panel():
@@ -677,8 +678,9 @@ def index():
             refs["log_box"].clear()
         log_queue = queue.Queue()
 
-        # 获取遗传算法开关状态
+        # 获取开关状态
         use_ga = refs["ga_switch"].value if refs["ga_switch"] else False
+        use_mip = refs["mip_switch"].value if refs["mip_switch"] else False
 
         class QueueLogger(io.StringIO):
             def write(self, message):
@@ -728,6 +730,7 @@ def index():
                         interactive=False,
                         plot_results=False,
                         use_ga=use_ga,
+                        use_mip=use_mip,
                     )
 
             # 在后台线程运行计算任务
@@ -920,8 +923,6 @@ def index():
                     # with refs["current_file_container"]:
                     #     ui.label("未选择文件").classes("text-xs text-gray-500 italic ml-1")
 
-                
-
                 # 3. 运行按钮
                 refs["run_btn"] = (
                     ui.button(
@@ -937,6 +938,12 @@ def index():
                         "color=orange"
                     )
 
+                # 5. MIP开关
+                with ui.column().classes("flex-1 gap-0 justify-center items-center"):
+                    refs["mip_switch"] = ui.switch("启用MIP", value=False).props(
+                        "color=blue"
+                    )
+
         with ui.column().classes("w-full gap-4"):
             # 新增：信息展示卡片
             with (

main.py

@@ -15,6 +15,11 @@ from sklearn.cluster import KMeans
 from esau_williams import design_with_esau_williams
 from ga import design_with_ga
 
+try:
+    from mip import design_with_mip
+except ImportError:
+    design_with_mip = None
+
 # 设置matplotlib支持中文显示
 plt.rcParams["font.sans-serif"] = ["Microsoft YaHei", "SimHei", "Arial"]
 plt.rcParams["axes.unicode_minus"] = False
@@ -1399,6 +1404,7 @@ def compare_design_methods(
     interactive=True,
     plot_results=True,
     use_ga=False,
+    use_mip=False,
 ):
     """
     比较MST和三种电缆方案下的K-means设计方法
@@ -1709,6 +1715,49 @@ def compare_design_methods(
                 f"  [GA] Cost: ¥{eval_ga['total_cost']:,.2f} | Loss: {eval_ga['total_loss']:.2f} kW | Circuits: {n_circuits_ga}"
             )
 
+        if use_mip and design_with_mip:
+            # --- Run 5: Mixed Integer Programming ---
+            mip_name = f"{name} (MIP)"
+            conns_mip, turbines_mip = design_with_mip(
+                turbines.copy(),
+                substation,
+                current_specs,
+                voltage,
+                power_factor,
+                system_params,
+                evaluate_func=evaluate_design,
+                total_invest_func=total_investment,
+                get_max_capacity_func=get_max_cable_capacity_mw,
+            )
+            eval_mip = evaluate_design(
+                turbines,
+                conns_mip,
+                substation,
+                cable_specs=current_specs,
+                is_offshore=is_offshore,
+                method_name=mip_name,
+                voltage=voltage,
+                power_factor=power_factor,
+            )
+            n_circuits_mip = sum(
+                1
+                for d in eval_mip["details"]
+                if d["source"] == "substation" or d["target"] == "substation"
+            )
+            comparison_results.append(
+                {
+                    "name": mip_name,
+                    "cost": eval_mip["total_cost"],
+                    "loss": eval_mip["total_loss"],
+                    "eval": eval_mip,
+                    "turbines": turbines_mip,
+                    "specs": current_specs,
+                }
+            )
+            print(
+                f"  [MIP] Cost: ¥{eval_mip['total_cost']:,.2f} | Loss: {eval_mip['total_loss']:.2f} kW | Circuits: {n_circuits_mip}"
+            )
+
         # 记录最佳
         if eval_rot["total_cost"] < best_cost:
             best_cost = eval_rot["total_cost"]

mip.py

@@ -0,0 +1,142 @@
+import numpy as np
+import pandas as pd
+from scipy.spatial import distance_matrix
+from scipy.sparse.csgraph import minimum_spanning_tree
+from collections import defaultdict
+import pulp
+
+
+def design_with_mip(
+    turbines,
+    substation,
+    cable_specs=None,
+    voltage=66000,
+    power_factor=0.95,
+    system_params=None,
+    max_clusters=None,
+    time_limit=300,  # seconds
+    evaluate_func=None,
+    total_invest_func=None,
+    get_max_capacity_func=None,
+):
+    """
+    使用混合整数规划(MIP)优化集电线路布局
+    :param turbines: 风机DataFrame
+    :param substation: 升压站坐标
+    :param cable_specs: 电缆规格
+    :param system_params: 系统参数（用于NPV计算）
+    :param max_clusters: 最大簇数，默认基于功率计算
+    :param time_limit: 求解时间限制（秒）
+    :param evaluate_func: 评估函数
+    :param total_invest_func: 总投资计算函数
+    :param get_max_capacity_func: 获取最大容量函数
+    :return: 连接列表和带有簇信息的turbines
+    """
+    if get_max_capacity_func:
+        max_mw = get_max_capacity_func(cable_specs, voltage, power_factor)
+    else:
+        max_mw = 100.0  # 默认值
+
+    total_power = turbines["power"].sum()
+    if max_clusters is None:
+        max_clusters = int(np.ceil(total_power / max_mw))
+    n_turbines = len(turbines)
+
+    # 预计算距离矩阵
+    all_coords = np.vstack([substation, turbines[["x", "y"]].values])
+    dist_matrix_full = distance_matrix(all_coords, all_coords)
+
+    # MIP 模型
+    prob = pulp.LpProblem("WindFarmCollectorMIP", pulp.LpMinimize)
+
+    # 决策变量：风机分配到簇 (binary)
+    x = pulp.LpVariable.dicts(
+        "assign", (range(n_turbines), range(max_clusters)), cat="Binary"
+    )
+
+    # 簇使用变量 (binary)
+    y = pulp.LpVariable.dicts("use_cluster", range(max_clusters), cat="Binary")
+
+    # 目标函数：最小化总成本 (简化版：距离成本)
+    # 这里使用简化成本：簇内距离 + 到升压站距离
+    prob += pulp.lpSum(
+        [
+            dist_matrix_full[i + 1, j + 1] * x[i][k] * x[j][k]
+            for i in range(n_turbines)
+            for j in range(n_turbines)
+            for k in range(max_clusters)
+            if i < j
+        ]
+    ) + pulp.lpSum(
+        [
+            dist_matrix_full[0, i + 1] * y[k]  # 假设每个簇连接到升压站
+            for i in range(n_turbines)
+            for k in range(max_clusters)
+        ]
+    )
+
+    # 约束：每个风机分配到一个簇
+    for i in range(n_turbines):
+        prob += pulp.lpSum([x[i][k] for k in range(max_clusters)]) == 1
+
+    # 簇功率约束
+    for k in range(max_clusters):
+        prob += (
+            pulp.lpSum([turbines.iloc[i]["power"] * x[i][k] for i in range(n_turbines)])
+            <= max_mw * y[k]
+        )
+
+    # 如果簇未使用，则无分配
+    for k in range(max_clusters):
+        for i in range(n_turbines):
+            prob += x[i][k] <= y[k]
+
+    # 求解
+    solver = pulp.PULP_CBC_CMD(timeLimit=time_limit)
+    status = prob.solve(solver)
+
+    if pulp.LpStatus[prob.status] != "Optimal":
+        print(f"MIP not optimal: {pulp.LpStatus[prob.status]}")
+        # 返回默认方案，如 MST
+        from main import design_with_mst
+
+        return design_with_mst(turbines, substation)
+
+    # 提取结果
+    cluster_assign = [-1] * n_turbines
+    for i in range(n_turbines):
+        for k in range(max_clusters):
+            if pulp.value(x[i][k]) > 0.5:
+                cluster_assign[i] = k
+                break
+
+    # 构建连接
+    clusters = defaultdict(list)
+    for i, c in enumerate(cluster_assign):
+        clusters[c].append(i)
+
+    connections = []
+    for c, members in clusters.items():
+        if len(members) == 0:
+            continue
+        coords = turbines.iloc[members][["x", "y"]].values
+        if len(members) > 1:
+            dm = distance_matrix(coords, coords)
+            mst = minimum_spanning_tree(dm).toarray()
+            for i in range(len(members)):
+                for j in range(len(members)):
+                    if mst[i, j] > 0:
+                        connections.append(
+                            (
+                                f"turbine_{members[i]}",
+                                f"turbine_{members[j]}",
+                                mst[i, j],
+                            )
+                        )
+        # 连接到升压站
+        dists = [dist_matrix_full[0, m + 1] for m in members]
+        closest = members[np.argmin(dists)]
+        connections.append((f"turbine_{closest}", "substation", min(dists)))
+
+    turbines["cluster"] = cluster_assign
+    return connections, turbines