以下是使用 Python 实现校园导游咨询系统的示例代码： ```python import heapq class Graph: def __init__(self): self.graph = {} # 用字典存储图的邻接表 def add_edge(self, u, v, weight): """ 向图中添加边 :param u: 起点 :param v: 终点 :param weight: 边的权重 """ if u not in self.graph: self.graph[u] = [] if v not in self.graph: self.graph[v] = [] self.graph[u].append((v, weight)) self.graph[v].append((u, weight)) def dijkstra(self, start): """ 迪杰斯特拉算法计算最短路径 :param start: 起始顶点 :return: 距离字典和前驱顶点字典 """ distances = {vertex: float('inf') for vertex in self.graph} distances[start] = 0 predecessors = {vertex: None for vertex in self.graph} priority_queue = [(0, start)] while priority_queue: current_distance, current_vertex = heapq.heappop(priority_queue) if current_distance > distances[current_vertex]: continue for neighbor, weight in self.graph[current_vertex]: distance = current_distance + weight if distance < distances[neighbor]: distances[neighbor] = distance predecessors[neighbor] = current_vertex heapq.heappush(priority_queue, (distance, neighbor)) return distances, predecessors def get_path(self, start, end, predecessors): """ 根据前驱顶点字典获取最短路径 :param start: 起始顶点 :param end: 结束顶点 :param predecessors: 前驱顶点字典 :return: 最短路径列表 """ path = [] current = end while current!= start: path.append(current) current = predecessors[current] path.append(start) path.reverse() return path # 构建校园平面图（示例） graph = Graph() graph.add_edge('A', 'B', 5) graph.add_edge('A', 'C', 3) graph.add_edge('B', 'D', 2) graph.add_edge('C', 'D', 4) # 此处添加更多的边 # 进行路径查询（示例） start_vertex = 'A' end_vertex = 'D' distances, predecessors = graph.dijkstra(start_vertex) path = graph.get_path(start_vertex, end_vertex, predecessors) # 输出最短路径 print(f"从 {start_vertex} 到 {end_vertex} 的最短路径为: {path}") ``` 额外注意的逻辑点： 1. 构建校园平面图时，要确保景点的代号、名称、简介等信息的准确性和完整性。 2. 迪杰斯特拉算法的实现要保证正确性，特别是在处理优先级队列和更新距离时。 3. 对于路径的输出，要考虑是否需要更友好的展示方式，比如将景点代号转换为实际的景点名称。 [2024-12-19 09:50:02 | AI写代码神器 | 731点数解答]