上传文件至「/」

house_operation.py

@@ -1,6 +1,7 @@
 import json
 import os
 
+#v1
 HOUSE_FILE = "./data/houses.json"
 
 def init_db():
@@ -21,6 +22,7 @@ def write_houses(houses):
     with open(HOUSE_FILE, 'w', encoding='utf-8') as f:
         json.dump(houses, f, ensure_ascii=False, indent=4)
 
+#v2
 STACK_FILE = "./data/stack_data.json"
 
 def init_stack_db():
@@ -47,14 +49,11 @@ def write_stack(stack_type, new_stack):
     with open(STACK_FILE, 'w', encoding='utf-8') as f:
         json.dump(data, f, ensure_ascii=False, indent=4)
 
-# V3.0 字符串+数组：邻接矩阵数据读写
+# v3
 MATRIX_FILE = "./data/matrix.json"
 
 def init_matrix_db():
-    """
+    """初始化小区邻接矩阵文件"""
-    新增：初始化小区邻接矩阵文件
-    存储所有小区名称列表 和 二维距离数组（邻接矩阵）
-    """
     os.makedirs("./data", exist_ok=True)
     if not os.path.exists(MATRIX_FILE):
         init_data = {
@@ -65,13 +64,14 @@ def init_matrix_db():
             json.dump(init_data, f, ensure_ascii=False, indent=4)
 
 def read_matrix_data():
-    """V3.0新增读取小区列表和距离邻接矩阵"""
+    """读取小区列表和距离邻接矩阵"""
     init_matrix_db()
     with open(MATRIX_FILE, 'r', encoding='utf-8') as f:
         return json.load(f)
 
 def write_matrix_data(matrix_dict):
-    """V3.0新增保存小区列表和距离邻接矩阵"""
+    """保存小区列表和距离邻接矩阵"""
     init_matrix_db()
     with open(MATRIX_FILE, 'w', encoding='utf-8') as f:
         json.dump(matrix_dict, f, ensure_ascii=False, indent=4)
+

house_service.py

@@ -1,6 +1,8 @@
 from house_operation import *
 from datetime import datetime
 
+
+# v1
 def get_next_house_id():
     houses = read_houses()
     if not houses:
@@ -8,6 +10,7 @@ def get_next_house_id():
     max_id = max(house["id"] for house in houses)
     return max_id + 1
 
+
 def add_house(address, price, area, house_type):
     if not address or not house_type:
         print("错误：地址和户型不能为空！")
@@ -32,6 +35,7 @@ def add_house(address, price, area, house_type):
     print(f"房源新增成功！房源ID：{new_house['id']}")
     return True
 
+
 def delete_house(house_id):
     houses = read_houses()
     for index, house in enumerate(houses):
@@ -43,6 +47,7 @@ def delete_house(house_id):
     print(f"错误：未找到房源ID {house_id}！")
     return False
 
+
 def update_house(house_id, new_info):
     houses = read_houses()
     for house in houses:
@@ -60,6 +65,7 @@ def update_house(house_id, new_info):
     print(f"错误：未找到房源ID {house_id}！")
     return False
 
+
 def query_house(condition_type, condition_value):
     houses = read_houses()
     result = []
@@ -81,8 +87,11 @@ def query_house(condition_type, condition_value):
             return []
     return result
 
+
+# v2
 MAX_STACK_LENGTH = 100
 
+
 def push_operation_stack(operation_info):
     time_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
     full_info = f"[{time_str}] {operation_info}"
@@ -92,6 +101,7 @@ def push_operation_stack(operation_info):
         stack.pop(0)
     write_stack("operation", stack)
 
+
 def push_browse_stack(house_id):
     stack = read_stack("browse")
     if stack and stack[-1] == house_id:
@@ -101,6 +111,7 @@ def push_browse_stack(house_id):
         stack.pop(0)
     write_stack("browse", stack)
 
+
 def pop_operation_stack():
     stack = read_stack("operation")
     if not stack:
@@ -109,6 +120,7 @@ def pop_operation_stack():
     write_stack("operation", stack)
     return latest_op
 
+
 def pop_browse_stack():
     stack = read_stack("browse")
     if not stack:
@@ -117,17 +129,20 @@ def pop_browse_stack():
     write_stack("browse", stack)
     return latest_hid
 
+
 def get_recent_records(stack_type, limit=10):
     stack = read_stack(stack_type)
     return stack[-limit:][::-1]
 
+
 def clear_stack(stack_type):
     write_stack(stack_type, [])
     print(f"{stack_type}记录已清空！")
 
-def parse_address_string(full_address):
 
-    # 字符串分割（串操作核心）
+#v3
+def parse_address_string(full_address):
+    """解析完整地址字符串：拆分省、市、区、小区"""
     parts = full_address.split("市")
     province_city = parts[0] + "市" if "市" in full_address else parts[0]
     if "省" in province_city:
@@ -138,7 +153,6 @@ def parse_address_string(full_address):
         province = "未知省份"
         city = province_city
 
-    # 继续拆分区县
     if len(parts) > 1:
         district_part = parts[1]
         if "区" in district_part or "县" in district_part:
@@ -158,32 +172,26 @@ def parse_address_string(full_address):
         "community": community
     }
 
-def fuzzy_query_by_district(keyword):
 
+def fuzzy_query_by_district(keyword):
+    """根据区域关键词模糊匹配所有房源"""
     houses = read_houses()
     result = []
     for house in houses:
         addr_info = parse_address_string(house["address"])
-        # 字符串包含匹配
         if keyword in addr_info["district"] or keyword in addr_info["community"]:
             result.append(house)
     return result
 
+
 def init_community_matrix(community_name_list):
-    """
+    """初始化小区距离邻接矩阵"""
-    【V3.0新增 二维数组知识点：邻接矩阵初始化】
-    根据小区列表生成距离邻接矩阵，初始化距离为无穷大，自己到自己为0
-    时间复杂度：O(N²)，N为小区数量
-    空间复杂度：O(N²)
-    """
     INF = float("inf")
     n = len(community_name_list)
-    # 二维数组初始化（邻接矩阵）
     distance_matrix = [[INF] * n for _ in range(n)]
     for i in range(n):
-        distance_matrix[i][i] = 0   # 自己到自己距离为0
+        distance_matrix[i][i] = 0
 
-    # 保存到数据文件
     matrix_data = {
         "community_list": community_name_list,
         "distance_matrix": distance_matrix
@@ -191,8 +199,9 @@ def init_community_matrix(community_name_list):
     write_matrix_data(matrix_data)
     print("邻接矩阵初始化完成！")
 
-def update_matrix_distance(community_a, community_b, distance):
 
+def update_matrix_distance(community_a, community_b, distance):
+    """更新两个小区之间的距离"""
     matrix_data = read_matrix_data()
     community_list = matrix_data["community_list"]
     distance_matrix = matrix_data["distance_matrix"]
@@ -206,3 +215,252 @@ def update_matrix_distance(community_a, community_b, distance):
         print(f"{community_a} <-> {community_b} 距离更新成功！")
     except ValueError:
         print("小区名称不存在！")
+#v4
+class HouseBSTNode:
+    """
+    二叉查找树节点：按指定字段（租金/面积）作为键值
+    """
+
+    def __init__(self, house_data, key_field="price"):
+        self.key = house_data[key_field]  # 排序键（租金/面积）
+        self.house = house_data  # 房源完整数据
+        self.left = None  # 左子节点（键值更小）
+        self.right = None  # 右子节点（键值更大）
+
+
+#  V4.0 新增：二叉查找树核心操作
+def build_house_bst(houses, key_field="price"):
+    """
+    构建房源二叉查找树（按租金/面积）
+    时间复杂度：O(n log n)（最优/平均），O(n²)（最坏，有序数据）
+    """
+    if not houses:
+        return None
+    # 选第一个元素作为根节点
+    root = HouseBSTNode(houses[0], key_field)
+    # 逐个插入剩余节点
+    for house in houses[1:]:
+        insert_bst_node(root, house, key_field)
+    return root
+
+
+def insert_bst_node(root, house_data, key_field="price"):
+    """
+    向二叉查找树插入节点
+    时间复杂度：O(log n)（平均），O(n)（最坏）
+    """
+    current = root
+    while True:
+        # 键值小于当前节点，走左子树
+        if house_data[key_field] < current.key:
+            if current.left is None:
+                current.left = HouseBSTNode(house_data, key_field)
+                break
+            else:
+                current = current.left
+        # 键值大于等于当前节点，走右子树
+        else:
+            if current.right is None:
+                current.right = HouseBSTNode(house_data, key_field)
+                break
+            else:
+                current = current.right
+
+
+def inorder_bst_traversal(root, result_list):
+    """
+    二叉查找树中序遍历（输出有序列表）
+    时间复杂度：O(n)（遍历所有节点）
+    """
+    if root is not None:
+        inorder_bst_traversal(root.left, result_list)
+        result_list.append(root.house)
+        inorder_bst_traversal(root.right, result_list)
+
+
+def search_bst_range(root, min_val, max_val, key_field="price", result_list=None):
+    """
+    二叉查找树范围查询（如：租金5000-8000）
+    时间复杂度：O(log n + k)（k为符合条件的节点数）
+    """
+    if result_list is None:
+        result_list = []
+    if root is None:
+        return result_list
+
+    # 若当前键值大于最小值，遍历左子树
+    if root.key > min_val:
+        search_bst_range(root.left, min_val, max_val, key_field, result_list)
+    # 若当前键值在范围内，加入结果
+    if min_val <= root.key <= max_val:
+        result_list.append(root.house)
+    # 若当前键值小于最大值，遍历右子树
+    if root.key < max_val:
+        search_bst_range(root.right, min_val, max_val, key_field, result_list)
+    return result_list
+
+def bubble_sort_houses(houses, sort_field="price", reverse=False):
+    """
+    V4.0新增 排序知识点
+    冒泡排序：按指定字段（租金/面积）排序
+    时间复杂度：O(n²)（稳定排序）
+    """
+    n = len(houses)
+    # 深拷贝避免修改原列表
+    sorted_houses = [h.copy() for h in houses]
+    for i in range(n):
+        swapped = False
+        for j in range(0, n - i - 1):
+            if sorted_houses[j][sort_field] > sorted_houses[j + 1][sort_field]:
+                # 交换元素
+                sorted_houses[j], sorted_houses[j + 1] = sorted_houses[j + 1], sorted_houses[j]
+                swapped = True
+        # 无交换则提前退出
+        if not swapped:
+            break
+    # 降序反转
+    if reverse:
+        sorted_houses = sorted_houses[::-1]
+    return sorted_houses
+
+
+def quick_sort_houses(houses, sort_field="price", reverse=False):
+    """
+    V4新增 排序知识点
+    快速排序：按指定字段（租金/面积）排序
+    时间复杂度：O(n log n)（平均），O(n²)（最坏）
+    """
+    if len(houses) <= 1:
+        return houses
+    # 深拷贝避免修改原列表
+    sorted_houses = [h.copy() for h in houses]
+    # 选第一个元素作为基准
+    pivot = sorted_houses[0][sort_field]
+    left = [h for h in sorted_houses[1:] if h[sort_field] <= pivot]
+    right = [h for h in sorted_houses[1:] if h[sort_field] > pivot]
+    # 递归排序
+    result = quick_sort_houses(left, sort_field) + [sorted_houses[0]] + quick_sort_houses(right, sort_field)
+    # 降序反转
+    if reverse:
+        result = result[::-1]
+    return result
+
+#V5
+def print_community_graph():
+    """
+    文本形式可视化小区图（邻接矩阵）
+    时间复杂度：O(n²)，n为小区数量
+    """
+    matrix_data = read_matrix_data()
+    community_list = matrix_data["community_list"]
+    distance_matrix = matrix_data["distance_matrix"]
+
+    if not community_list:
+        print("暂无小区图数据！请先初始化邻接矩阵。")
+        return
+
+    # 打印表头
+    print("\n===== 小区距离图（邻接矩阵） =====")
+    print("    " + "    ".join([c[:4].ljust(4) for c in community_list]))
+    # 打印每行数据
+    for i, community in enumerate(community_list):
+        row_str = community[:4].ljust(4)
+        for j in range(len(community_list)):
+            val = distance_matrix[i][j]
+            if val == float("inf"):
+                row_str += "  ∞  "
+            else:
+                row_str += f"{val:5.1f}"
+        print(row_str)
+
+
+def dijkstra_shortest_path(start_community, end_community):
+    """
+    Dijkstra算法：计算两个小区间的最短路径
+    时间复杂度：O(n²)（邻接矩阵实现），n为小区数量
+    return: (最短距离, 路径列表)
+    """
+    matrix_data = read_matrix_data()
+    community_list = matrix_data["community_list"]
+    distance_matrix = matrix_data["distance_matrix"]
+
+    # 校验小区是否存在
+    if start_community not in community_list or end_community not in community_list:
+        print("起始/目标小区不存在！")
+        return (None, None)
+
+    n = len(community_list)
+    start_idx = community_list.index(start_community)
+    end_idx = community_list.index(end_community)
+
+    # 初始化距离数组和前驱节点数组
+    INF = float("inf")
+    dist = [INF] * n  # 各节点到起点的距离
+    visited = [False] * n  # 节点是否已访问
+    prev = [-1] * n  # 前驱节点索引
+
+    dist[start_idx] = 0  # 起点到自己的距离为0
+
+    # 核心Dijkstra循环
+    for _ in range(n):
+        # 找到未访问的距离最小节点
+        min_dist = INF
+        u = -1
+        for i in range(n):
+            if not visited[i] and dist[i] < min_dist:
+                min_dist = dist[i]
+                u = i
+
+        if u == -1 or dist[u] == INF:
+            break  # 无可达路径
+        visited[u] = True
+
+        # 松弛操作：更新邻接节点的距离
+        for v in range(n):
+            if not visited[v] and distance_matrix[u][v] != INF:
+                if dist[v] > dist[u] + distance_matrix[u][v]:
+                    dist[v] = dist[u] + distance_matrix[u][v]
+                    prev[v] = u
+
+    # 回溯路径
+    if dist[end_idx] == INF:
+        print(f"{start_community} 到 {end_community} 无可达路径！")
+        return (None, None)
+
+    # 从终点回溯到起点
+    path = []
+    current = end_idx
+    while current != -1:
+        path.append(community_list[current])
+        current = prev[current]
+    path.reverse()  # 反转得到正序路径
+
+    return (dist[end_idx], path)
+
+
+def recommend_houses_by_path(target_community, max_distance):
+    """
+    推荐目标小区最短距离≤max_distance的周边房源
+    """
+    matrix_data = read_matrix_data()
+    community_list = matrix_data["community_list"]
+    if target_community not in community_list:
+        print("目标小区不存在！")
+        return []
+
+    # 计算目标小区到所有小区的最短路径
+    recommend_communities = []
+    for comm in community_list:
+        dist, _ = dijkstra_shortest_path(target_community, comm)
+        if dist is not None and dist <= max_distance:
+            recommend_communities.append(comm)
+
+    # 查询这些小区的房源
+    all_houses = read_houses()
+    recommend_houses = []
+    for house in all_houses:
+        addr_info = parse_address_string(house["address"])
+        if addr_info["community"] in recommend_communities:
+            recommend_houses.append(house)
+
+    return recommend_houses

main.py

@@ -1,21 +1,29 @@
 from house_service import *
 
 def print_menu():
-    print("\n===== 房屋出租系统 V3.0 =====")
+    print("\n===== 房屋出租系统 V5.0 =====")
     print("1. 新增房源")
     print("2. 删除房源")
     print("3. 修改房源")
     print("4. 查询房源")
     print("5. 查看/管理操作记录")
     print("6. 查看/管理浏览历史")
-    # V3.0 新增菜单
     print("7. 地址字符串解析测试")
     print("8. 按区域关键词模糊查询")
     print("9. 初始化小区邻接矩阵")
     print("10. 更新小区间距离")
-    print("11. 退出系统")
+    print("11. 房源二叉树范围查询（租金/面积）")
+    print("12. 房源冒泡排序（租金/面积）")
+    print("13. 房源快速排序（租金/面积）")
+    #V5.0 新增菜单
+    print("14. 可视化小区距离图（邻接矩阵）")
+    print("15. 计算小区间最短路径（Dijkstra）")
+    print("16. 基于最短路径推荐房源")
+    print("17. 退出系统")
     print("============================")
 
+
+# V2原有操作记录子菜单
 def op_record_menu():
     while True:
         print("\n----- 操作记录管理 -----")
@@ -37,6 +45,7 @@ def op_record_menu():
             print("输入有误")
 
 
+# V2浏览历史子菜单
 def browse_history_menu():
     while True:
         print("\n----- 浏览历史管理 -----")
@@ -71,12 +80,12 @@ def browse_history_menu():
         else:
             print("输入有误")
 
-
 def main():
     while True:
         print_menu()
         choice = input("请输入功能编号：")
 
+        # ========== V1 原有功能==========
         if choice == "1":
             addr = input("输入房源地址：")
             try:
@@ -137,20 +146,19 @@ def main():
                 if map_dic[sel] == "id":
                     push_browse_stack(h["id"])
 
+        # ========== V2 原有功能 ==========
         elif choice == "5":
             op_record_menu()
         elif choice == "6":
             browse_history_menu()
 
-        #3.0
+        # ========== V3 原有功能==========
         elif choice == "7":
-            # 测试地址字符串解析
             full_addr = input("输入完整地址（如：四川省德阳市什邡市XX小区）：")
             res = parse_address_string(full_addr)
             print("解析结果：", res)
 
         elif choice == "8":
-            # 按区域关键词模糊查询
             keyword = input("输入区域关键词（如：什邡、德阳）：")
             res_list = fuzzy_query_by_district(keyword)
             if not res_list:
@@ -160,13 +168,11 @@ def main():
                     print(f"ID:{h['id']} 地址:{h['address']}")
 
         elif choice == "9":
-            # 初始化小区邻接矩阵
             community_input = input("输入所有小区名称，用英文逗号分隔：")
             community_list = community_input.split(",")
             init_community_matrix(community_list)
 
         elif choice == "10":
-            # 更新两个小区距离
             c1 = input("输入小区A名称：")
             c2 = input("输入小区B名称：")
             try:
@@ -175,11 +181,107 @@ def main():
             except:
                 print("距离必须是数字")
 
+        # ========== V4 原有功能==========
         elif choice == "11":
+            print("请选择查询维度：1-租金  2-面积")
+            dim_choice = input("输入维度编号：")
+            key_field = "price" if dim_choice == "1" else "area"
+            field_name = "租金" if dim_choice == "1" else "面积"
+
+            try:
+                min_val = float(input(f"输入{field_name}最小值："))
+                max_val = float(input(f"输入{field_name}最大值："))
+            except:
+                print("数值必须是数字！")
+                continue
+
+            all_houses = read_houses()
+            if not all_houses:
+                print("暂无房源数据！")
+                continue
+            bst_root = build_house_bst(all_houses, key_field)
+            result = search_bst_range(bst_root, min_val, max_val, key_field)
+            if not result:
+                print(f"未找到{field_name}在{min_val}-{max_val}之间的房源")
+            else:
+                print(f"\n{field_name}在{min_val}-{max_val}之间的房源：")
+                for h in result:
+                    print(f"ID:{h['id']} 地址:{h['address']} {field_name}:{h[key_field]}")
+
+        elif choice == "12":
+            print("请选择排序维度：1-租金  2-面积")
+            dim_choice = input("输入维度编号：")
+            sort_field = "price" if dim_choice == "1" else "area"
+            field_name = "租金" if dim_choice == "1" else "面积"
+
+            print("请选择排序方式：1-升序  2-降序")
+            sort_way = input("输入方式编号：")
+            reverse = (sort_way == "2")
+
+            all_houses = read_houses()
+            if not all_houses:
+                print("暂无房源数据！")
+                continue
+            sorted_houses = bubble_sort_houses(all_houses, sort_field, reverse)
+            print(f"\n按{field_name}{'降序' if reverse else '升序'}排序结果（冒泡排序）：")
+            for h in sorted_houses:
+                print(f"ID:{h['id']} 地址:{h['address']} {field_name}:{h[sort_field]}")
+
+        elif choice == "13":
+            print("请选择排序维度：1-租金  2-面积")
+            dim_choice = input("输入维度编号：")
+            sort_field = "price" if dim_choice == "1" else "area"
+            field_name = "租金" if dim_choice == "1" else "面积"
+
+            print("请选择排序方式：1-升序  2-降序")
+            sort_way = input("输入方式编号：")
+            reverse = (sort_way == "2")
+
+            all_houses = read_houses()
+            if not all_houses:
+                print("暂无房源数据！")
+                continue
+            sorted_houses = quick_sort_houses(all_houses, sort_field, reverse)
+            print(f"\n按{field_name}{'降序' if reverse else '升序'}排序结果（快速排序）：")
+            for h in sorted_houses:
+                print(f"ID:{h['id']} 地址:{h['address']} {field_name}:{h[sort_field]}")
+
+        # ========== V5 新增功能分支 ==========
+        elif choice == "14":
+            # 可视化小区图
+            print_community_graph()
+
+        elif choice == "15":
+            # 计算最短路径
+            start_comm = input("输入起始小区名称：")
+            end_comm = input("输入目标小区名称：")
+            shortest_dist, shortest_path = dijkstra_shortest_path(start_comm, end_comm)
+            if shortest_dist is not None:
+                print(f"\n{start_comm} 到 {end_comm} 的最短距离：{shortest_dist:.1f}")
+                print(f"最短路径：{' -> '.join(shortest_path)}")
+
+        elif choice == "16":
+            # 基于路径推荐房源
+            target_comm = input("输入目标小区名称：")
+            try:
+                max_dist = float(input("输入最大推荐距离："))
+            except:
+                print("距离必须是数字！")
+                continue
+            recommend_list = recommend_houses_by_path(target_comm, max_dist)
+            if not recommend_list:
+                print(f"未找到{target_comm}周边{max_dist}范围内的房源")
+            else:
+                print(f"\n{target_comm}周边{max_dist}范围内的推荐房源：")
+                for h in recommend_list:
+                    print(f"ID:{h['id']} 地址:{h['address']} 租金:{h['price']} 面积:{h['area']}")
+
+        elif choice == "17":
             print("系统退出成功！")
             break
         else:
             print("请输入有效编号")
 
+
 if __name__ == "__main__":
     main()