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项目结构：

本文展示了一个珠宝供应链物流规划的Python实现，采用领域驱动设计(DDD)架构，包含Prim和Kruskal两种最小生成树算法。系统主要包含：

1. 领域模型：LogisticsNode(实体)、LogisticsEdge(值对象)、LogisticsMST(聚合根)
2. 核心算法：PrimAlgorithm(稠密图)、KruskalAlgorithm(稀疏图)
3. 应用服务层协调领域对象和算法
4. 示例演示了从缅甸矿区到各地门店的最低成本运输路线规划

系统特点：

• 严格遵循DDD分层架构
• 算法服务封装在领域层
• 支持邻接矩阵(Prim)和边列表(Kruskal)两种输入
• 输出格式化路线详情和总成本

适用于珠宝等贵重物品的高效物流网络规划。

# encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:14 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : AggregateRoot.py class AggregateRoot: """ 聚合根父类：DDD聚合根顶层抽象 """ def __init__(self): self._domain_events = [] def get_domain_events(self): """ :return: """ return self._domain_events.copy() def clear_domain_events(self): """ :return: """ self._domain_events.clear() # encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:36 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : Entity.py class Entity: """ 实体父类：拥有唯一业务ID """ def __init__(self, node_id: int): self._id = node_id @property def id(self) -> int: return self._id def __eq__(self, other): if not isinstance(other, Entity): return False return self.id == other.id # encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:36 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : ValueObject.py class ValueObject: """ 值对象父类：不可变，基于属性判等 """ def __eq__(self, other): if not isinstance(other, ValueObject): return False return self.__dict__ == other.__dict__ def __hash__(self): return hash(tuple(sorted(self.__dict__.items()))) # encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:38 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : DomainException.py class DomainException(Exception): """ 领域统一业务异常 """ def __init__(self, message: str): self.message = message super().__init__(self.message) # encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:38 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : UnionFind.py class UnionFind: """ 并查集基础设施：Kruskal算法专用，路径压缩+普通合并 """ def __init__(self, size: int): self.parent = list(range(size)) def find(self, x: int) -> int: """ 查找根节点+路径压缩 :param x: :return: """ if self.parent[x] != x: self.parent[x] = self.find(self.parent[x]) return self.parent[x] def union(self, x: int, y: int) -> bool: """ 合并两个集合 :return: True=合并成功无环，False=同集合成环 """ root_x = self.find(x) root_y = self.find(y) if root_x == root_y: return False self.parent[root_y] = root_x return True # encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:40 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : LogisticsNode.py from PrimKruskal.Common.Entity import Entity class LogisticsNode(Entity): """ 物流网点【实体】 代表珠宝供应链节点：矿区、加工厂、仓库、线下门店 """ def __init__(self, node_id: int, node_name: str, node_category: str): super().__init__(node_id) self._node_name = node_name self._node_category = node_category @property def node_name(self) -> str: return self._node_name @property def node_category(self) -> str: return self._node_category def __repr__(self): return f"<Node id={self.id}, name={self.node_name}, type={self.node_category}>" # encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:41 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : LogisticsEdge.py from PrimKruskal.Common.ValueObject import ValueObject class LogisticsEdge(ValueObject): """ 物流运输线路【值对象】 两个网点之间运输链路，权重=运输综合成本（押运、损耗、路费、保险） 不可变，排序、判等基于起点、终点、成本 """ def __init__(self, start_id: int, end_id: int, cost: float): self.start_id = start_id self.end_id = end_id self.cost = cost def __repr__(self): return f"<Edge {self.start_id}->{self.end_id}, cost={self.cost}>" # encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:41 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : LogisticsMST.py from PrimKruskal.Common.AggregateRoot import AggregateRoot from PrimKruskal.Domain.Model.LogisticsEdge import LogisticsEdge from PrimKruskal.Domain.Model.LogisticsNode import LogisticsNode from typing import List class LogisticsMST(AggregateRoot): """ 最小生成树【聚合根】 聚合包含：全部网点、MST选中线路、总成本 封装领域结果，统一对外输出结构化数据 """ def __init__(self): super().__init__() self.all_nodes: List[LogisticsNode] = [] self.mst_edges: List[LogisticsEdge] = [] self.total_cost: float = 0.0 def set_nodes(self, nodes: List[LogisticsNode]): self.all_nodes = nodes def set_mst_result(self, edges: List[LogisticsEdge], total_cost: float): self.mst_edges = edges self.total_cost = total_cost def get_edge_detail(self) -> List[tuple]: """格式化线路详情，用于打印展示""" node_map = {node.id: node.node_name for node in self.all_nodes} detail_list = [] for edge in self.mst_edges: s_name = node_map[edge.start_id] e_name = node_map[edge.end_id] detail_list.append((s_name, e_name, edge.cost)) return detail_list # encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:42 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : PrimAlgorithm.py from PrimKruskal.Common.DomainException import DomainException from PrimKruskal.Domain.Model.LogisticsEdge import LogisticsEdge from PrimKruskal.Domain.Model.LogisticsNode import LogisticsNode from typing import List class PrimAlgorithm: """ 领域算法服务：Prim最小生成树 适用场景：珠宝密集网点（加工厂、门店扎堆，稠密图） 入参：邻接矩阵、节点集合；出参：MST线路列表、总成本 """ @staticmethod def calculate(adj_matrix: List[List[float]], nodes: List[LogisticsNode]) -> (List[LogisticsEdge], float): node_count = len(nodes) if node_count == 0: raise DomainException("网点集合不能为空，无法生成物流路网") INF = float("inf") in_mst = [False] * node_count min_dist = [INF] * node_count pre_node = [-1] * node_count min_dist[0] = 0 total_cost = 0.0 mst_edge_list = [] for _ in range(node_count): # 选取距离生成树最近节点 select_idx = -1 min_val = INF for i in range(node_count): if not in_mst[i] and min_dist[i] < min_val: min_val = min_dist[i] select_idx = i if select_idx == -1: raise DomainException("当前网点图不连通，无法构建完整物流最小生成树") in_mst[select_idx] = True total_cost += min_val # 记录前驱边 pre_idx = pre_node[select_idx] if pre_idx != -1: edge = LogisticsEdge(pre_idx, select_idx, adj_matrix[pre_idx][select_idx]) mst_edge_list.append(edge) # 松弛更新邻接点距离 for j in range(node_count): weight = adj_matrix[select_idx][j] if not in_mst[j] and weight > 0 and weight < min_dist[j]: min_dist[j] = weight pre_node[j] = select_idx return mst_edge_list, total_cost # encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:43 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : KruskalAlgorithm.py from PrimKruskal.Common.UnionFind import UnionFind from PrimKruskal.Common.DomainException import DomainException from PrimKruskal.Domain.Model.LogisticsEdge import LogisticsEdge from PrimKruskal.Domain.Model.LogisticsNode import LogisticsNode from typing import List class KruskalAlgorithm: """ 领域算法服务：Kruskal最小生成树 适用场景：珠宝跨城分散门店、矿区（稀疏图，节点多直达线路少） """ @staticmethod def calculate(edge_list: List[LogisticsEdge], nodes: List[LogisticsNode]) -> (List[LogisticsEdge], float): node_count = len(nodes) if node_count == 0: raise DomainException("网点集合不能为空，无法生成物流路网") # 边按成本升序排序 sorted_edges = sorted(edge_list, key=lambda e: e.cost) uf = UnionFind(node_count) mst_edge_list = [] total_cost = 0.0 for edge in sorted_edges: if uf.union(edge.start_id, edge.end_id): mst_edge_list.append(edge) total_cost += edge.cost if len(mst_edge_list) == node_count - 1: break if len(mst_edge_list) != node_count - 1: raise DomainException("当前网点图不连通，无法构建完整物流最小生成树") return mst_edge_list, total_cost # encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:44 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : LogisticsRouteService.py from PrimKruskal.Domain.Algorithm.PrimAlgorithm import PrimAlgorithm from PrimKruskal.Domain.Algorithm.KruskalAlgorithm import KruskalAlgorithm from PrimKruskal.Domain.Model.LogisticsMST import LogisticsMST from PrimKruskal.Domain.Model.LogisticsNode import LogisticsNode from PrimKruskal.Domain.Model.LogisticsEdge import LogisticsEdge from typing import List class LogisticsRouteApplicationService: """ 应用服务：珠宝物流路线规划应用用例 职责：组装领域数据、调用领域算法、组装聚合根、对外提供统一业务接口 不写业务逻辑，只做协调编排 """ @staticmethod def build_mst_by_prim(adj_matrix: List[List[float]], nodes: List[LogisticsNode]) -> LogisticsMST: """使用Prim算法生成物流最小生成树""" edges, cost = PrimAlgorithm.calculate(adj_matrix, nodes) mst = LogisticsMST() mst.set_nodes(nodes) mst.set_mst_result(edges, cost) return mst @staticmethod def build_mst_by_kruskal(edge_list: List[LogisticsEdge], nodes: List[LogisticsNode]) -> LogisticsMST: """使用Kruskal算法生成物流最小生成树""" edges, cost = KruskalAlgorithm.calculate(edge_list, nodes) mst = LogisticsMST() mst.set_nodes(nodes) mst.set_mst_result(edges, cost) return mst

调用：

# encoding: utf-8 # 版权所有 2026 ©涂聚文有限公司™ ® # 许可信息查看：言語成了邀功盡責的功臣，還需要行爲每日來值班嗎 # 描述： Prim Algorithms and Kruskal Algorithms # Author : geovindu,Geovin Du 涂聚文. # IDE : PyCharm 2024.3.6 python 3.11 # os : windows 10 # database : mysql 9.0 sql server 2019, postgreSQL 17.0 Oracle 21c Neo4j # Datetime : 2026/8/7 22:44 # User : geovindu # Product : PyCharm # Project : PyAlgorithms # File : PrimKruskalBll.py from PrimKruskal.Application.LogisticsRouteService import LogisticsRouteApplicationService from PrimKruskal.Domain.Model.LogisticsNode import LogisticsNode from PrimKruskal.Domain.Model.LogisticsEdge import LogisticsEdge class PrimKruskalBll(object): """ """ def demo(self): """ :return: """ # 1. 构建珠宝供应链网点实体 node_list = [ LogisticsNode(0, "缅甸翡翠矿区A", "原料矿区"), LogisticsNode(1, "云南分拣加工厂", "加工中心"), LogisticsNode(2, "深圳总仓储中心", "仓储中心"), LogisticsNode(3, "广州旗舰门店", "线下门店"), LogisticsNode(4, "上海门店", "线下门店"), LogisticsNode(5, "北京门店", "线下门店"), ] # 2. Prim使用：邻接矩阵 单位：千元，0代表无直达线路 adj_matrix = [ [0, 12, 28, 0, 0, 0], [12, 0, 8, 15, 0, 0], [28, 8, 0, 6, 18, 22], [0, 15, 6, 0, 25, 0], [0, 0, 18, 25, 0, 14], [0, 0, 22, 0, 14, 0] ] # 3. Kruskal使用：原始边列表 raw_edges = [ LogisticsEdge(0, 1, 12), LogisticsEdge(0, 2, 28), LogisticsEdge(1, 2, 8), LogisticsEdge(1, 3, 15), LogisticsEdge(2, 3, 6), LogisticsEdge(2, 4, 18), LogisticsEdge(2, 5, 22), LogisticsEdge(3, 4, 25), LogisticsEdge(4, 5, 14), ] # 4. 应用服务调用 print("========== Prim算法-稠密网点物流规划 ==========") prim_mst = LogisticsRouteApplicationService.build_mst_by_prim(adj_matrix, node_list) prim_detail = prim_mst.get_edge_detail() for start, end, cost in prim_detail: print(f"{start} <--> {end} 运输成本：{cost}千元") print(f"全网最低总成本：{prim_mst.total_cost} 千元\n") print("========== Kruskal算法-稀疏跨城网点规划 ==========") krus_mst = LogisticsRouteApplicationService.build_mst_by_kruskal(raw_edges, node_list) krus_detail = krus_mst.get_edge_detail() for start, end, cost in krus_detail: print(f"{start} <--> {end} 运输成本：{cost}千元") print(f"全网最低总成本：{krus_mst.total_cost} 千元")

输出：