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--- highlight: a11y-dark theme: smartblue --- # 0 版本 ``` python == 3.8.0 Django == 4.1.2 py2neo == 2021.2.3 pyecharts == 1.9.1 ``` # 1 目的及功能 **目的：** 由于neo4j自带的可视化界面展示效果有限，重建可视化界面 **功能：** 实现与neo4j数据库实时连接，可动态更新，动态显示 # 2 Neo4j **Neo4j安装之前需要先安装jdk**，这里的neo4j版本是4.3.19 对应jdk11可顺利运行。若为windows系统可在华为仓库中搜索下载安装包 > 华为仓库中的jdk安装包：https://repo.huaweicloud.com/java/jdk/ Ubuntu下可直接安装jdk11，指令如下： ``` sudo apt install openjdk-11-jdk ``` 安装完成后输入三条指令检查是否成功 ``` java javac java -version ``` 如下图即为安装成功 ![image.png](https://p1-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/9b4920df6ad0490fab4bbbb096da7350~tplv-k3u1fbpfcp-watermark.image?) ## 2.1 Neo4安装在Neo4j官网[下载](https://neo4j.com/download-center/#community)社区版 ![image.png](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/8dad52d171334e3e968b2ae5195b6306~tplv-k3u1fbpfcp-watermark.image?) 下载对应版本压缩包，解压即可。我使用的是Ubuntu系统，解压完成后进入bin文件夹下 ![image.png](https://p1-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/8c08657ded9943b4a0fd1662febc38a5~tplv-k3u1fbpfcp-watermark.image?) 在当前界面启动终端，输入启动命令： ``` ./neo4j console ``` 可以看到启动成功后可以在 `localhost:7474`打开neo4j ![image.png](https://p6-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/79cfde67cd72496fb712dda347bbbc6d~tplv-k3u1fbpfcp-watermark.image?) `默认用户名和密码是:neo4j` 登录后会提示你修改，如果想更换用户名和密码可以在system用户下操作。这里不再赘述。 ![image.png](https://p1-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/1fbd0dd000dd4c3f8c45886bd5e61f78~tplv-k3u1fbpfcp-watermark.image?) ## 2.2 使用py2neo导入数据本文使用json格式对数据存储，参考`[1][2]` ![image.png](https://p9-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/71f6818218ac4c0da9be1fb0de69e17c~tplv-k3u1fbpfcp-watermark.image?) 导入到neo4j ``` python #!/usr/bin/env python3 # coding: utf-8 # File: MilitaryGraph.py # Author: zpeng # Date: 22-10-20 import os import json import codecs from py2neo import Graph,Node class MilitaryGraph: def __init__(self): c_dir = '/'.join(os.path.abspath(__file__).split('/')[:-1]) self.datapath = os.path.join(c_dir,'military.json') self.g = Graph('http://localhost:7474/',auth=("neo4j","123456")) def read_nodes(self): # 节点类别 military_sys = [] #实体名称 categorys = [] #类别 performances = [] #性能指标 locations = [] #地点 deploys = [] #部署方式 exps = [] #案例 militarysys_infos = [] #系统信息 # 实体关系 rels_per = [] #实体和性能的关系 rels_subcategory = [] #实体与小类之间的关系 rels_categorys = [] #大类与小类之间的关系 rels_rootcategory =[] #大类和根节点之间的关系 rels_loca = [] #实体与地点的关系 rels_deploy = [] #实体与部署的关系 rels_exp = [] #实体与案例的关系 cont = 0 for data in open(self.datapath): militarysys_dict = {} cont +=1 print(cont) data_json = json.loads(data) militarysys = data_json['name'] militarysys_dict['name'] = militarysys military_sys.append(militarysys) militarysys_dict['desc'] = '' militarysys_dict['category'] = '' militarysys_dict['performance'] = '' militarysys_dict['location'] = '' militarysys_dict['deploy'] = '' militarysys_dict['exp'] = '' if 'performance' in data_json: performances += data_json['performance'] for performance in data_json['performance']: rels_per.append([militarysys,performance]) if 'desc' in data_json: militarysys_dict['desc'] = data_json['desc'] if 'location' in data_json: locations += data_json['location'] for location in data_json['location']: rels_loca.append([militarysys,location]) if 'deploy' in data_json: deploys += data_json['deploy'] for deploy in data_json['deploy']: rels_deploy.append([militarysys,deploy]) if 'exp' in data_json: exps += data_json['exp'] for exp in data_json['exp']: rels_exp.append([militarysys,exp]) if 'category' in data_json: cure_categorys = data_json['category'] if len(cure_categorys) == 2: rels_rootcategory.append([cure_categorys[1],cure_categorys[0]]) rels_subcategory.append([militarysys,cure_categorys[1]]) if len(cure_categorys) == 3: root = cure_categorys[0] big = cure_categorys[1] small = cure_categorys[2] rels_rootcategory.append([big,root]) rels_categorys.append([small,big]) rels_subcategory.append([militarysys,small]) militarysys_dict['category'] = cure_categorys categorys += cure_categorys militarysys_infos.append(militarysys_dict) return militarysys_infos, set(military_sys),set(locations),set(performances),set(categorys),set(exps),set(deploys),\ rels_per,rels_loca,rels_deploy,rels_exp,rels_subcategory,rels_categorys,rels_rootcategory '''创建实体节点''' def create_node(self, label, nodes): count = 0 for node_name in nodes: node = Node(label, name=node_name) self.g.create(node) count += 1 print(count, len(nodes)) return def create_militarysys_nodes(self, militarysys_infos): count = 0 for militarysys_dict in militarysys_infos: node = Node("MSYS", name=militarysys_dict['name'], desc=militarysys_dict['desc'], performance = militarysys_dict['performance'], category=militarysys_dict['category'] ,location=militarysys_dict['location'], deploy=militarysys_dict['deploy'],exp=militarysys_dict['exp']) self.g.create(node) count += 1 print(count) return def create_graphnodes(self): militarysys_infos, military_sys,locations,performances,categorys,exps,deploys,\ rels_per,rels_loca,rels_deploy,rels_exp,rels_subcategory,rels_categorys,rels_rootcategory = self.read_nodes() self.create_militarysys_nodes(militarysys_infos) self.create_node('LOCA',locations) self.create_node('PERF',performances) self.create_node('CATE',categorys) print(len(categorys)) self.create_node('EXPS',exps) self.create_node('DEPLOY',deploys) return '''创建实体关联边''' def create_graphrels(self): militarysys_infos, military_sys,locations,performances,categorys,exps,deploys,\ rels_per,rels_loca,rels_deploy,rels_exp,rels_subcategory,rels_categorys,rels_rootcategory = self.read_nodes() self.create_relationship('MSYS','PERF',rels_per,'performances','性能指标') self.create_relationship('MSYS','LOCA',rels_loca,'locations','位置') self.create_relationship('MSYS','DEPLOY',rels_deploy,'deploys','部署') self.create_relationship('MSYS','CATE',rels_subcategory,'belongsto','所属类别') self.create_relationship('CATE','CATE',rels_categorys,'belongsto','属于') self.create_relationship('CATE','CATE',rels_rootcategory,'belongsto','属于') self.create_relationship('MSYS','EXPS',rels_exp,'examples','案例') def create_relationship(self, start_node, end_node, edges, rel_type, rel_name): count = 0 # 去重处理 set_edges = [] for edge in edges: set_edges.append('###'.join(edge)) all = len(set(set_edges)) for edge in set(set_edges): edge = edge.split('###') p = edge[0] q = edge[1] query = "match(p:%s),(q:%s) where p.name='%s'and q.name='%s' create (p)-[rel:%s{name:'%s'}]->(q)" % ( start_node, end_node, p, q, rel_type, rel_name) try: self.g.run(query) count += 1 print(rel_type, count, all) except Exception as e: print(e) return '''导出数据txt格式''' def export_txtdata(self): militarysys_infos, military_sys,locations,performances,categorys,exps,deploys,\ rels_per,rels_loca,rels_deploy,rels_exp,rels_subcategory,rels_categorys,rels_rootcategory = self.read_nodes() f_military_sys = open('militarysys.txt', 'w+') f_locations = open('locations.txt', 'w+') f_performances = open('performances.txt', 'w+') f_categorys = open('categorys.txt', 'w+') f_exps = open('exps.txt', 'w+') f_deploys = open('deploys.txt', 'w+') f_military_sys.write('\n'.join(list(military_sys))) f_locations.write('\n'.join(list(locations))) f_performances.write('\n'.join(list(performances))) f_categorys.write('\n'.join(list(categorys))) f_exps.write('\n'.join(list(exps))) f_deploys.write('\n'.join(list(deploys))) f_military_sys.close() f_locations.close() f_performances.close() f_categorys.close() f_exps.close() f_deploys.close() return '''导出数据为json格式''' def export_data(self,data,path): if isinstance(data[0],str): data = sorted([d.strip("...") for d in set(data)]) with codecs.open(path, 'w', encoding='utf-8') as f: json.dump(data, f, indent=4, ensure_ascii=False) def export_entitys_relations(self): militarysys_infos, military_sys,locations,performances,categorys,exps,deploys,\ rels_per,rels_loca,rels_deploy,rels_exp,rels_subcategory,rels_categorys,rels_rootcategory = self.read_nodes() #导出实体，属性 self.export_data(list(military_sys),'./graph_data/military_sys.json') self.export_data(list(locations),'./graph_data/locations.json') self.export_data(list(performances),'./graph_data/performances.json') self.export_data(list(categorys),'./graph_data/categorys.json') self.export_data(list(exps),'./graph_data/exps.json') self.export_data(list(deploys),'./graph_data/deploys.json') #导出关系 self.export_data(rels_per,'./graph_data/rels_per.json') self.export_data(rels_loca,'./graph_data/rels_loca.json') self.export_data(rels_deploy,'./graph_data/rels_deploy.json') self.export_data(rels_exp,'./graph_data/rels_exp.json') self.export_data(rels_subcategory,'./graph_data/rels_subcategory.json') self.export_data(rels_categorys,'./graph_data/rels_categorys.json') self.export_data(rels_rootcategory,'./graph_data/rels_rootcategory.json') if __name__ == '__main__': handler = MilitaryGraph() print("step1:导入图谱节点中") handler.create_graphnodes() print("step2:导入图谱边中") handler.create_graphrels() # print("step3:导出数据") # handler.export_entitys_relations() ``` 导入后在neo4j数据库查看 ![image.png](https://p6-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/07001ea8fc954b5693e02f041b7a0cd2~tplv-k3u1fbpfcp-watermark.image?) 包涵136个节点，150组关系。输入查询语句 `MATCH(n) RETURN n` 输出所有节点和关系 # 3 前后端控制使用Django进行后端控制，Echarts进行前端显示。参考`[3]`项目结构 ![image.png](https://p1-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/673defad26c94c44899cbe79c4b4608f~tplv-k3u1fbpfcp-watermark.image?) ## 3.1 Django后端后端使用Django控制，对view进行改写，增加分类 `view.py`文件内容 ``` import json from py2neo import * from django.shortcuts import render graph = Graph('http://localhost:7474/', auth=('zp', '123456')) # 连接数据库 def search_all(): # 定义data数组，存放节点信息 data = [] # 定义关系数组，存放节点间的关系 links = [] # 查询所有节点，并将节点信息取出存放在data数组中 for n in graph.nodes: # 将节点信息转化为json格式，否则中文会不显示 # print(n) nodesStr = json.dumps(graph.nodes[n], ensure_ascii=False) # 取出节点的name node_name = json.loads(nodesStr)['name'] # 构造字典，存储单个节点信息 dict = { # 'id':str(n), # 防止重复节点 'name': node_name, 'symbolSize': 50, 'category': '对象' } # 将单个节点信息存放在data数组中 data.append(dict) # 查询所有关系，并将所有的关系信息存放在links数组中 rps = graph.relationships for r in rps: # 取出开始节点的name source = str(rps[r].start_node['name']) # for i in data: #需要使用ID # if source == i['name']: # source = i['id'] # 取出结束节点的name target = str(rps[r].end_node['name']) # for i in data: #需要使用ID # if target == i['name']: # target = i['id'] # 取出开始节点的结束节点之间的关系 name = str(type(rps[r]).__name__) # 构造字典存储单个关系信息 dict = { 'source': source, 'target': target, 'name': name } # 将单个关系信息存放进links数组中 links.append(dict) # 输出所有节点信息 # for item in data: # print(item) # 输出所有关系信息 # for item in links: # print(item) # 将所有的节点信息和关系信息存放在一个字典中 neo4j_data = { 'data': data, 'links': links } neo4j_data = json.dumps(neo4j_data) return neo4j_data def search_all_category(): data = []# 定义data数组，存放节点信息 links = []# 定义关系数组，存放节点间的关系 # 节点分类 node_DEPLOY = graph.run('MATCH (n:DEPLOY) RETURN n').data() node_CATE = graph.run('MATCH (n:CATE) RETURN n').data() node_EXPS = graph.run('MATCH (n:EXPS) RETURN n').data() node_LOCA = graph.run('MATCH (n:LOCA) RETURN n').data() node_MSYS = graph.run('MATCH (n:MSYS) RETURN n').data() node_PERF = graph.run('MATCH (n:PERF) RETURN n').data() for n in node_DEPLOY: nodesStr = json.dumps(n, ensure_ascii=False)# 将节点信息转化为json格式，否则中文会不显示 node_name = json.loads(nodesStr) node_name = node_name['n']['name'] # 取出节点的name # print(node_name) dict = { # 'id':str(n), # 防止重复节点 'name': node_name, 'symbolSize': 50, 'category': 'DEPLOY' } data.append(dict) # 将单个节点信息存放在data数组中 for n in node_CATE: nodesStr = json.dumps(n, ensure_ascii=False)# 将节点信息转化为json格式，否则中文会不显示 node_name = json.loads(nodesStr) node_name = node_name['n']['name'] # 取出节点的name # print(node_name) dict = { # 'id':str(n), # 防止重复节点 'name': node_name, 'symbolSize': 50, 'category': 'CATE' } data.append(dict) # 将单个节点信息存放在data数组中 for n in node_EXPS: nodesStr = json.dumps(n, ensure_ascii=False)# 将节点信息转化为json格式，否则中文会不显示 node_name = json.loads(nodesStr) node_name = node_name['n']['name'] # 取出节点的name # print(node_name) dict = { # 'id':str(n), # 防止重复节点 'name': node_name, 'symbolSize': 50, 'category': 'EXPS' } data.append(dict) # 将单个节点信息存放在data数组中 for n in node_LOCA: nodesStr = json.dumps(n, ensure_ascii=False)# 将节点信息转化为json格式，否则中文会不显示 node_name = json.loads(nodesStr) node_name = node_name['n']['name'] # 取出节点的name # print(node_name) dict = { # 'id':str(n), # 防止重复节点 'name': node_name, 'symbolSize': 50, 'category': 'LOCA' } data.append(dict) # 将单个节点信息存放在data数组中 for n in node_MSYS: nodesStr = json.dumps(n, ensure_ascii=False)# 将节点信息转化为json格式，否则中文会不显示 node_name = json.loads(nodesStr) node_name = node_name['n']['name'] # 取出节点的name # print(node_name) dict = { # 'id':str(n), # 防止重复节点 'name': node_name, 'symbolSize': 50, 'category': 'MSYS' } data.append(dict) # 将单个节点信息存放在data数组中 for n in node_PERF: nodesStr = json.dumps(n, ensure_ascii=False)# 将节点信息转化为json格式，否则中文会不显示 node_name = json.loads(nodesStr) node_name = node_name['n']['name'] # 取出节点的name # print(node_name) dict = { # 'id':str(n), # 防止重复节点 'name': node_name, 'symbolSize': 50, 'category': 'PERF' } data.append(dict) # 将单个节点信息存放在data数组中 # 查询所有关系，并将所有的关系信息存放在links数组中 rps = graph.relationships for r in rps: source = str(rps[r].start_node['name']) # 取出开始节点的name target = str(rps[r].end_node['name']) name = str(type(rps[r]).__name__)# 取出开始节点的结束节点之间的关系 # 构造字典存储单个关系信息 dict = { 'source': source, 'target': target, 'name': name } links.append(dict)# 将单个关系信息存放进links数组中 neo4j_data = { 'data': data, 'links': links } neo4j_data = json.dumps(neo4j_data) return neo4j_data def search_one(value): # 定义data数组存储节点信息 data = [] # 定义links数组存储关系信息 links = [] # 查询节点是否存在 node = graph.run('MATCH(n:person{name:"' + value + '"}) return n').data() # 如果节点存在len(node)的值为1不存在的话len(node)的值为0 if len(node): # 如果该节点存在将该节点存入data数组中 # 构造字典存放节点信息 dict = { 'name': value, 'symbolSize': 50, 'category': '对象' } data.append(dict) # 查询与该节点有关的节点，无向，步长为1，并返回这些节点 nodes = graph.run('MATCH(n:person{name:"' + value + '"})<-->(m:person) return m').data() # 查询该节点所涉及的所有relationship，无向，步长为1，并返回这些relationship reps = graph.run('MATCH(n:person{name:"' + value + '"})<-[rel]->(m:person) return rel').data() # 处理节点信息 for n in nodes: # 将节点信息的格式转化为json node = json.dumps(n, ensure_ascii=False) node = json.loads(node) # 取出节点信息中person的name name = str(node['m']['name']) # 构造字典存放单个节点信息 dict = { 'name': name, 'symbolSize': 50, 'category': '对象' } # 将单个节点信息存储进data数组中 data.append(dict) # 处理relationship for r in reps: source = str(r['rel'].start_node['name']) target = str(r['rel'].end_node['name']) name = str(type(r['rel']).__name__) dict = { 'source': source, 'target': target, 'name': name } links.append(dict) # 构造字典存储data和links search_neo4j_data = { 'data': data, 'links': links } # 将dict转化为json格式 search_neo4j_data = json.dumps(search_neo4j_data) return search_neo4j_data else: # print("查无此节点") return 0 def index(request): ctx = {} if request.method == 'POST': # 接收前端传过来的查询值 node_name = request.POST.get('node') # 查询结果 search_neo4j_data = search_one(node_name) # 未查询到该节点 if search_neo4j_data == 0: ctx = {'title': '数据库中暂未添加该实体'} neo4j_data = search_all_category() return render(request, 'index.html', {'neo4j_data': neo4j_data, 'ctx': ctx}) # 查询到了该节点 else: neo4j_data = search_all_category() return render(request, 'index.html', {'neo4j_data': neo4j_data, 'search_neo4j_data': search_neo4j_data, 'ctx': ctx}) neo4j_data = search_all_category() return render(request, 'index.html', {'neo4j_data': neo4j_data, 'ctx': ctx}) # if __name__ == '__main__': # neo4j_data = search_all_category() # print(neo4j_data) ``` ## 3.2 Echarts前端 `index.html`文件内容 ``` {% load static %} Title

``` # 4 项目启动进入到主目录文件夹下，运行`manage.py`启动项目，输入命令行: ``` python manage.py runserver ``` `manage.py`中的内容 ```python #!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): """Run administrative tasks.""" os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'neo4jconnect_test.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main() ``` 默认启动地址和端口为：`http://127.0.0.1:8000/` 若希望指定ip地址和端口，可按格式： ``` python manage.py runserver --host 0.0.0.0 --port 9008 ``` `--host`参数是ip地址，`--port`参数是端口号启动成功的命令输出 ![image.png](https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/094696b8fa6545298ec681b86186a882~tplv-k3u1fbpfcp-watermark.image?) 浏览器中输入地址和端口号，显示效果 ![image.png](https://p9-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/aed9683734ff478fbb919ddf2151a407~tplv-k3u1fbpfcp-watermark.image?) # 遇到问题 ## echarts问题 1.加载`echarts.min.js`需要确定好路径 2.初始化要定义好dom，即，`div中的id`定义要和`getElementById`方法中初始化名称一致。否则会报错误 `t is null` ```

... var dom = document.getElementById("chart-panel"); var myChart1 = echarts.init(dom); ``` 3.导入data和link时，要确保数据中**没有重复字段**，否则会造成节点和连线都不显示 4.保证link中的target和source都是**字符串**类型 ## 数据问题 1.导入neo4j中的数据，要避免重复字段。错误示例：实体名称和类名称一致 # 参考 [1] https://github.com/liuhuanyong/QASystemOnMedicalKG [2] https://github.com/wangle1218/KBQA-for-Diagnosis [3] https://github.com/Sjyzheishuai/Neo4j-visualization [4] https://blog.csdn.net/Fimooo/article/details/103069928 [5] https://blog.csdn.net/weixin_44747173/article/details/124835406 [6] https://blog.csdn.net/zjw120/article/details/124194577 [7] https://github.com/zhangxiang0316/echartsDemo [8] https://github.com/pyecharts/pyecharts-gallery [9] https://github.com/ecomfe/awesome-echarts