echarts 地图_博客 城市访问量统计并且通过Echarts+百度地图展示
本篇讲解一下 如何在Vue 中使用 Echarts + 百度地图 统计 博客访问量 并且通过QQWry 解析 ip 地址 利用Echarts 展示出来
效果图如下：
1.纯真Ip地址库 QQWry
这是我在github上找的 java版本的 解析 qqwry的
1.1 maven 引入 qqwry
<dependency> <groupId>com.github.jarodgroupId> <artifactId>qqwry-javaartifactId> <version>0.7.0version> dependency>引入后可以看到 该jar 包其实内部已经引入了 qqwry.dat 库了
使用教程：
QQWry qqwry = new QQWry(); // load qqwry.dat from classpathQQWry qqwry = new QQWry(Paths.get("path/to/qqwry.dat")); // load qqwry.dat from java.nio.file.Pathbyte[] data = Files.readAllBytes(Paths.get("path/to/qqwry.dat"));QQWry qqwry = new QQWry(data); // create QQWry with provided dataString myIP = "127.0.0.1";IPZone ipzone = qqwry.findIP(myIP);System.out.printf("%s, %s", ipzone.getMainInfo(), ipzone.getSubInfo()); // 江苏省无锡市, 电信// IANA, 保留地址用于本地回送1.2 QQWryUtils
用于提供 一个静态的 QQWry 加载 qqwry.dat ，并且提供根据ip 获取 IpZone
public class QQWryUtils { private static QQWry qqWry; static { try { qqWry = new QQWry(); } catch (IOException e) { e.printStackTrace(); } } public static IPZone getIpZoneByIp(String ip) { return qqWry.findIP(ip); }}2.提供拦截器解析Ip并放入队列
给SpringMVC 提供一个拦截器，在拦截器中 用于获取当前的请求ip 并且通过 QQWryUtils 解析该ip ，封装成IpAccessInfo 对象 存入 linkedBlockingQueue 队列中去，这里只是简单处理下
@Slf4jpublic class AccessRecordInterceptor extends HandlerInterceptorAdapter { /** * 目前是 解析 ip 并且生成 IpAccessInfo 放入 linkedBlockingQueue 队列中去 * * @param request * @param response * @param handler * @return * @throws Exception */ @Override public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler) throws Exception { String ip = IpUtil.getIpAddress(request); log.info("【请求者 ip : {} 】", ip); IPZone ipZone = QQWryUtils.getIpZoneByIp(ip); log.info("【解析到 城市: {}】", ipZone.getMainInfo()); IpAccessInfo ipAccessInfo = new IpAccessInfo(); ipAccessInfo.setCity(ipZone.getMainInfo()); ipAccessInfo.setIp(ip); ipAccessInfo.setOperators(ipZone.getSubInfo()); try { IpQueue.linkedBlockingQueue.add(ipAccessInfo); //这里使用 add方法 当添队列满的时候 直接捕获异常 } catch (IllegalStateException e) { log.warn("队列已满 "); } return true; }}IpAccessInfo 我这里入库ip 信息
@Data@Entity@EntityListeners(AuditingEntityListener.class)public class IpAccessInfo { @Id @GeneratedValue(strategy = GenerationType.IDENTITY) private Long id; private String ip; private String city; /** * 运营商 */ private String operators;}3.提供线程消费队列，并且根据城市记录访问量
这里提供 线程消费 队列 并且使用redis的自增 记录每个城市的访问量，并且使用SpringBoot的 CommandLineRunner 接口，在项目启动的时候 加载初始数据
/** * @author johnny * @create 2020-08-15 下午1:56 **/@Component@Order(value = 1)@Slf4jpublic class IpQueue implements CommandLineRunner { public static final LinkedBlockingQueue<IpAccessInfo> linkedBlockingQueue = new LinkedBlockingQueue(10000); @Autowired private ThreadPoolTaskExecutor threadPoolTaskExecutor; @Autowired private IpAccessInfoRepository ipAccessInfoRepository; @Autowired private IpAccessCountRepository ipAccessCountRepository; @Autowired private StringRedisTemplate redisTemplate; @Override public void run(String... args) throws Exception { //SpringBoot CommandLineRunner 接口启动后 该方法会被调用， 进行初始化数据，并且启动线程监听队列 ipAccessCountRepository.findAll().forEach(ipAccessCount -> { if (!redisTemplate.hasKey(ipAccessCount.getCity())) { redisTemplate.opsForValue().set(ipAccessCount.getCity(), String.valueOf(ipAccessCount.getCount())); } else { log.info("【Redis 存在：{} 】", ipAccessCount.getCity()); } }); log.info("【服务启动 -------------- 监听 队列 IpQueue 】"); IpAccessThread ipAccessThread = new IpAccessThread(linkedBlockingQueue, ipAccessInfoRepository, redisTemplate); threadPoolTaskExecutor.submit(ipAccessThread); //使用线程池 提交任务 } static class IpAccessThread implements Runnable { private LinkedBlockingQueue<IpAccessInfo> linkedBlockingQueue; private IpAccessInfoRepository ipAccessInfoRepository; private RedisTemplate redisTemplate; public IpAccessThread(LinkedBlockingQueue<IpAccessInfo> linkedBlockingQueue, IpAccessInfoRepository ipAccessInfoRepository, RedisTemplate redisTemplate) { this.linkedBlockingQueue = linkedBlockingQueue; this.ipAccessInfoRepository = ipAccessInfoRepository; this.redisTemplate = redisTemplate; } @Override public void run() { while (true) { try { System.out.println("开始获取 : "); IpAccessInfo accessInfo = linkedBlockingQueue.take(); System.out.println("监听到 : " + accessInfo); //江苏省无锡市 if (accessInfo.getCity().contains("省") && accessInfo.getCity().contains("市")) { String city = accessInfo.getCity(); city = city.substring(city.indexOf("省") + 1, city.indexOf("市")); if (redisTemplate.hasKey(city)) { redisTemplate.opsForValue().increment(city); //根据城市 key 进行自增 } } else { log.error("【异常 地理位置 {} 】", accessInfo.getCity()); } ipAccessInfoRepository.save(accessInfo); } catch (InterruptedException e) { e.printStackTrace(); } } } }}4. Echarts + 百度地图
由于本博客前端是用Vue 编写的，所以下面的引入就是在Vue下引入的方式
4.1 在public/index.html中添加以下代码
ak密钥：就是百度地图AK密钥，需要自己去百度地图申请，或者网上找可用的ak
53oVIOgmSIejwV7EfphPgTynOZbIiVYu 网上找的可用的密钥
<script type="text/javascript" src="http://api.map.baidu.com/api?v=2.0&ak=你的密钥"></script>4.2 在vue.config.js中添加配置
主要是 externals 部分
module.exports = {publicPath: './',outputDir: 'dist',assetsDir: 'static',indexPath: 'index.html',productionSourceMap: false, configureWebpack: { // provide the app's title in webpack's name field, so that // it can be accessed in index.html to inject the correct title. name: name, resolve: { alias: { '@': resolve('src') } }, externals: { 'BMap': 'BMap', 'BMap_Symbol_SHAPE_POINT':'BMap_Symbol_SHAPE_POINT' } },}4.3 最后在vue main.js 文件中引入
import BMap from 'BMaprequire('echarts/extension/bmap/bmap')
4.4 编写展示Echarts+百度地图的组件
可以参考echarts 网站https://echarts.apache.org/zh/index.html
需要注意 我这里是从后台拿的数据 ，是通过上面拦截器解析ip 后 记录每个城市对应的访问量存入 redis中的。
注意如果需要扩展其他城市，可以找到城市的经纬度，然后扩展 geoCoordMap 就行了。。
<template> <div> <div id="myChart" style="width:100%;height: 1050px">div> div>template><script>import { ipAccess } from "@/api/charts/ip_access_api";export default { name: "IpContent", components: {}, data() { return { chartData: [ { name: "海门", value: 9 }, { name: "鄂尔多斯", value: 12 }, { name: "招远", value: 12 }, { name: "舟山", value: 12 }, { name: "齐齐哈尔", value: 14 }, { name: "盐城", value: 15 }, { name: "赤峰", value: 16 }, { name: "青岛", value: 18 }, { name: "乳山", value: 18 }, { name: "金昌", value: 19 }, { name: "泉州", value: 21 }, { name: "莱西", value: 21 }, { name: "日照", value: 21 }, { name: "胶南", value: 22 }, { name: "南通", value: 23 }, { name: "拉萨", value: 24 }, { name: "云浮", value: 24 }, { name: "梅州", value: 25 }, { name: "文登", value: 25 }, { name: "上海", value: 25 }, { name: "攀枝花", value: 25 }, { name: "威海", value: 25 }, { name: "承德", value: 25 }, { name: "厦门", value: 26 }, { name: "汕尾", value: 26 }, { name: "潮州", value: 26 }, { name: "丹东", value: 27 }, { name: "太仓", value: 27 }, { name: "曲靖", value: 27 }, { name: "烟台", value: 28 }, { name: "福州", value: 29 }, { name: "瓦房店", value: 30 }, { name: "即墨", value: 30 }, { name: "抚顺", value: 31 }, { name: "玉溪", value: 31 }, { name: "张家口", value: 31 }, { name: "阳泉", value: 31 }, { name: "莱州", value: 32 }, { name: "湖州", value: 32 }, { name: "汕头", value: 32 }, { name: "昆山", value: 33 }, { name: "宁波", value: 33 }, { name: "湛江", value: 33 }, { name: "揭阳", value: 34 }, { name: "荣成", value: 34 }, { name: "连云港", value: 35 }, { name: "葫芦岛", value: 35 }, { name: "常熟", value: 36 }, { name: "东莞", value: 36 }, { name: "河源", value: 36 }, { name: "淮安", value: 36 }, { name: "泰州", value: 36 }, { name: "南宁", value: 37 }, { name: "营口", value: 37 }, { name: "惠州", value: 37 }, { name: "江阴", value: 37 }, { name: "蓬莱", value: 37 }, { name: "韶关", value: 38 }, { name: "嘉峪关", value: 38 }, { name: "广州", value: 38 }, { name: "延安", value: 38 }, { name: "太原", value: 39 }, { name: "清远", value: 39 }, { name: "中山", value: 39 }, { name: "昆明", value: 39 }, { name: "寿光", value: 40 }, { name: "盘锦", value: 40 }, { name: "长治", value: 41 }, { name: "深圳", value: 41 }, { name: "珠海", value: 42 }, { name: "宿迁", value: 43 }, { name: "咸阳", value: 43 }, { name: "铜川", value: 44 }, { name: "平度", value: 44 }, { name: "佛山", value: 44 }, { name: "海口", value: 44 }, { name: "江门", value: 45 }, { name: "章丘", value: 45 }, { name: "肇庆", value: 46 }, { name: "大连", value: 47 }, { name: "临汾", value: 47 }, { name: "吴江", value: 47 }, { name: "石嘴山", value: 49 }, { name: "沈阳", value: 50 }, { name: "苏州", value: 50 }, { name: "茂名", value: 50 }, { name: "嘉兴", value: 51 }, { name: "长春", value: 51 }, { name: "胶州", value: 52 }, { name: "银川", value: 52 }, { name: "张家港", value: 52 }, { name: "三门峡", value: 53 }, { name: "锦州", value: 54 }, { name: "南昌", value: 54 }, { name: "柳州", value: 54 }, { name: "三亚", value: 54 }, { name: "自贡", value: 56 }, { name: "吉林", value: 56 }, { name: "阳江", value: 57 }, { name: "泸州", value: 57 }, { name: "西宁", value: 57 }, { name: "宜宾", value: 58 }, { name: "呼和浩特", value: 58 }, { name: "成都", value: 58 }, { name: "大同", value: 58 }, { name: "镇江", value: 59 }, { name: "桂林", value: 59 }, { name: "张家界", value: 59 }, { name: "宜兴", value: 59 }, { name: "北海", value: 60 }, { name: "西安", value: 61 }, { name: "金坛", value: 62 }, { name: "东营", value: 62 }, { name: "牡丹江", value: 63 }, { name: "遵义", value: 63 }, { name: "绍兴", value: 63 }, { name: "扬州", value: 64 }, { name: "常州", value: 64 }, { name: "潍坊", value: 65 }, { name: "重庆", value: 66 }, { name: "台州", value: 67 }, { name: "南京", value: 67 }, { name: "滨州", value: 70 }, { name: "贵阳", value: 71 }, { name: "无锡", value: 71 }, { name: "本溪", value: 71 }, { name: "克拉玛依", value: 72 }, { name: "渭南", value: 72 }, { name: "马鞍山", value: 72 }, { name: "宝鸡", value: 72 }, { name: "焦作", value: 75 }, { name: "句容", value: 75 }, { name: "北京", value: 79 }, { name: "徐州", value: 79 }, { name: "衡水", value: 80 }, { name: "包头", value: 80 }, { name: "绵阳", value: 80 }, { name: "乌鲁木齐", value: 84 }, { name: "枣庄", value: 84 }, { name: "杭州", value: 84 }, { name: "淄博", value: 85 }, { name: "鞍山", value: 86 }, { name: "溧阳", value: 86 }, { name: "库尔勒", value: 86 }, { name: "安阳", value: 90 }, { name: "开封", value: 90 }, { name: "济南", value: 92 }, { name: "德阳", value: 93 }, { name: "温州", value: 95 }, { name: "九江", value: 96 }, { name: "邯郸", value: 98 }, { name: "临安", value: 99 }, { name: "兰州", value: 99 }, { name: "沧州", value: 100 }, { name: "临沂", value: 103 }, { name: "南充", value: 104 }, { name: "天津", value: 105 }, { name: "富阳", value: 106 }, { name: "泰安", value: 112 }, { name: "诸暨", value: 112 }, { name: "郑州", value: 113 }, { name: "哈尔滨", value: 114 }, { name: "聊城", value: 116 }, { name: "芜湖", value: 117 }, { name: "唐山", value: 119 }, { name: "平顶山", value: 119 }, { name: "邢台", value: 119 }, { name: "德州", value: 120 }, { name: "济宁", value: 120 }, { name: "荆州", value: 127 }, { name: "宜昌", value: 130 }, { name: "义乌", value: 132 }, { name: "丽水", value: 133 }, { name: "洛阳", value: 134 }, { name: "秦皇岛", value: 136 }, { name: "株洲", value: 143 }, { name: "石家庄", value: 147 }, { name: "莱芜", value: 148 }, { name: "常德", value: 152 }, { name: "保定", value: 153 }, { name: "湘潭", value: 154 }, { name: "金华", value: 157 }, { name: "岳阳", value: 169 }, { name: "长沙", value: 175 }, { name: "衢州", value: 177 }, { name: "廊坊", value: 193 }, { name: "菏泽", value: 1941 }, { name: "合肥", value: 2291 }, { name: "武汉", value: 2731 }, { name: "大庆", value: 2791 }, ], geoCoordMap: { 海门: [121.15, 31.89], 鄂尔多斯: [109.781327, 39.608266], 招远: [120.38, 37.35], 舟山: [122.207216, 29.985295], 齐齐哈尔: [123.97, 47.33], 盐城: [120.13, 33.38], 赤峰: [118.87, 42.28], 青岛: [120.33, 36.07], 乳山: [121.52, 36.89], 金昌: [102.188043, 38.520089], 泉州: [118.58, 24.93], 莱西: [120.53, 36.86], 日照: [119.46, 35.42], 胶南: [119.97, 35.88], 南通: [121.05, 32.08], 拉萨: [91.11, 29.97], 云浮: [112.02, 22.93], 梅州: [116.1, 24.55], 文登: [122.05, 37.2], 上海: [121.48, 31.22], 攀枝花: [101.718637, 26.582347], 威海: [122.1, 37.5], 承德: [117.93, 40.97], 厦门: [118.1, 24.46], 汕尾: [115.375279, 22.786211], 潮州: [116.63, 23.68], 丹东: [124.37, 40.13], 太仓: [121.1, 31.45], 曲靖: [103.79, 25.51], 烟台: [121.39, 37.52], 福州: [119.3, 26.08], 瓦房店: [121.979603, 39.627114], 即墨: [120.45, 36.38], 抚顺: [123.97, 41.97], 玉溪: [102.52, 24.35], 张家口: [114.87, 40.82], 阳泉: [113.57, 37.85], 莱州: [119.942327, 37.177017], 湖州: [120.1, 30.86], 汕头: [116.69, 23.39], 昆山: [120.95, 31.39], 宁波: [121.56, 29.86], 湛江: [110.359377, 21.270708], 揭阳: [116.35, 23.55], 荣成: [122.41, 37.16], 连云港: [119.16, 34.59], 葫芦岛: [120.836932, 40.711052], 常熟: [120.74, 31.64], 东莞: [113.75, 23.04], 河源: [114.68, 23.73], 淮安: [119.15, 33.5], 泰州: [119.9, 32.49], 南宁: [108.33, 22.84], 营口: [122.18, 40.65], 惠州: [114.4, 23.09], 江阴: [120.26, 31.91], 蓬莱: [120.75, 37.8], 韶关: [113.62, 24.84], 嘉峪关: [98.289152, 39.77313], 广州: [113.23, 23.16], 延安: [109.47, 36.6], 太原: [112.53, 37.87], 清远: [113.01, 23.7], 中山: [113.38, 22.52], 昆明: [102.73, 25.04], 寿光: [118.73, 36.86], 盘锦: [122.070714, 41.119997], 长治: [113.08, 36.18], 深圳: [114.07, 22.62], 珠海: [113.52, 22.3], 宿迁: [118.3, 33.96], 咸阳: [108.72, 34.36], 铜川: [109.11, 35.09], 平度: [119.97, 36.77], 佛山: [113.11, 23.05], 海口: [110.35, 20.02], 江门: [113.06, 22.61], 章丘: [117.53, 36.72], 肇庆: [112.44, 23.05], 大连: [121.62, 38.92], 临汾: [111.5, 36.08], 吴江: [120.63, 31.16], 石嘴山: [106.39, 39.04], 沈阳: [123.38, 41.8], 苏州: [120.62, 31.32], 茂名: [110.88, 21.68], 嘉兴: [120.76, 30.77], 长春: [125.35, 43.88], 胶州: [120.03336, 36.264622], 银川: [106.27, 38.47], 张家港: [120.555821, 31.875428], 三门峡: [111.19, 34.76], 锦州: [121.15, 41.13], 南昌: [115.89, 28.68], 柳州: [109.4, 24.33], 三亚: [109.511909, 18.252847], 自贡: [104.778442, 29.33903], 吉林: [126.57, 43.87], 阳江: [111.95, 21.85], 泸州: [105.39, 28.91], 西宁: [101.74, 36.56], 宜宾: [104.56, 29.77], 呼和浩特: [111.65, 40.82], 成都: [104.06, 30.67], 大同: [113.3, 40.12], 镇江: [119.44, 32.2], 桂林: [110.28, 25.29], 张家界: [110.479191, 29.117096], 宜兴: [119.82, 31.36], 北海: [109.12, 21.49], 西安: [108.95, 34.27], 金坛: [119.56, 31.74], 东营: [118.49, 37.46], 牡丹江: [129.58, 44.6], 遵义: [106.9, 27.7], 绍兴: [120.58, 30.01], 扬州: [119.42, 32.39], 常州: [119.95, 31.79], 潍坊: [119.1, 36.62], 重庆: [106.54, 29.59], 台州: [121.420757, 28.656386], 南京: [118.78, 32.04], 滨州: [118.03, 37.36], 贵阳: [106.71, 26.57], 无锡: [120.29, 31.59], 本溪: [123.73, 41.3], 克拉玛依: [84.77, 45.59], 渭南: [109.5, 34.52], 马鞍山: [118.48, 31.56], 宝鸡: [107.15, 34.38], 焦作: [113.21, 35.24], 句容: [119.16, 31.95], 北京: [116.46, 39.92], 徐州: [117.2, 34.26], 衡水: [115.72, 37.72], 包头: [110, 40.58], 绵阳: [104.73, 31.48], 乌鲁木齐: [87.68, 43.77], 枣庄: [117.57, 34.86], 杭州: [120.19, 30.26], 淄博: [118.05, 36.78], 鞍山: [122.85, 41.12], 溧阳: [119.48, 31.43], 库尔勒: [86.06, 41.68], 安阳: [114.35, 36.1], 开封: [114.35, 34.79], 济南: [117, 36.65], 德阳: [104.37, 31.13], 温州: [120.65, 28.01], 九江: [115.97, 29.71], 邯郸: [114.47, 36.6], 临安: [119.72, 30.23], 兰州: [103.73, 36.03], 沧州: [116.83, 38.33], 临沂: [118.35, 35.05], 南充: [106.110698, 30.837793], 天津: [117.2, 39.13], 富阳: [119.95, 30.07], 泰安: [117.13, 36.18], 诸暨: [120.23, 29.71], 郑州: [113.65, 34.76], 哈尔滨: [126.63, 45.75], 聊城: [115.97, 36.45], 芜湖: [118.38, 31.33], 唐山: [118.02, 39.63], 平顶山: [113.29, 33.75], 邢台: [114.48, 37.05], 德州: [116.29, 37.45], 济宁: [116.59, 35.38], 荆州: [112.239741, 30.335165], 宜昌: [111.3, 30.7], 义乌: [120.06, 29.32], 丽水: [119.92, 28.45], 洛阳: [112.44, 34.7], 秦皇岛: [119.57, 39.95], 株洲: [113.16, 27.83], 石家庄: [114.48, 38.03], 莱芜: [117.67, 36.19], 常德: [111.69, 29.05], 保定: [115.48, 38.85], 湘潭: [112.91, 27.87], 金华: [119.64, 29.12], 岳阳: [113.09, 29.37], 长沙: [113, 28.21], 衢州: [118.88, 28.97], 廊坊: [116.7, 39.53], 菏泽: [115.480656, 35.23375], 合肥: [117.27, 31.86], 武汉: [114.31, 30.52], 大庆: [125.03, 46.58], }, }; }, methods: { drawLine() { var data = this.chartData; var geoCoordMap = this.geoCoordMap; // 基于准备好的dom，初始化echarts实例 let myChart = this.$echarts.init(document.getElementById("myChart")); var convertData = function (data) { var res = []; for (var i = 0; i < data.length; i++) { var geoCoord = geoCoordMap[data[i].name]; if (geoCoord) { res.push({ name: data[i].name, value: geoCoord.concat(data[i].value), }); } } return res; }; function renderItem(params, api) { var coords = [ // [116.7,39.53], // [103.73,36.03], // [112.91,27.87], // [120.65,28.01], // [119.57,39.95] ]; var points = []; for (var i = 0; i < coords.length; i++) { points.push(api.coord(coords[i])); } var color = api.visual("color"); return { // type: "polygon", // shape: { // points: myChart.graphic.clipPointsByRect(points, { // x: params.coordSys.x, // y: params.coordSys.y, // width: params.coordSys.width, // height: params.coordSys.height, // }), // }, // style: api.style({ // fill: color, // stroke: myChart.color.lift(color), // }), }; } // 绘制图表 myChart.setOption({ backgroundColor: "transparent", title: { text: "全国主要城市访问量", subtext: "访问统计", sublink: "http://www.pm25.in", left: "center", textStyle: { color: "#fff", }, }, tooltip: { trigger: "item", }, bmap: { center: [107.114129, 37.550339], zoom: 5, roam: true, mapStyle: { styleJson: [ { featureType: "water", elementType: "all", stylers: { color: "#044161", }, }, { featureType: "land", elementType: "all", stylers: { color: "#004981", }, }, { featureType: "boundary", elementType: "geometry", stylers: { color: "#064f85", }, }, { featureType: "railway", elementType: "all", stylers: { visibility: "off", }, }, { featureType: "highway", elementType: "geometry", stylers: { color: "#004981", }, }, { featureType: "highway", elementType: "geometry.fill", stylers: { color: "#005b96", lightness: 1, }, }, { featureType: "highway", elementType: "labels", stylers: { visibility: "off", }, }, { featureType: "arterial", elementType: "geometry", stylers: { color: "#004981", }, }, { featureType: "arterial", elementType: "geometry.fill", stylers: { color: "#00508b", }, }, { featureType: "poi", elementType: "all", stylers: { visibility: "off", }, }, { featureType: "green", elementType: "all", stylers: { color: "#056197", visibility: "off", }, }, { featureType: "subway", elementType: "all", stylers: { visibility: "off", }, }, { featureType: "manmade", elementType: "all", stylers: { visibility: "off", }, }, { featureType: "local", elementType: "all", stylers: { visibility: "off", }, }, { featureType: "arterial", elementType: "labels", stylers: { visibility: "off", }, }, { featureType: "boundary", elementType: "geometry.fill", stylers: { color: "#029fd4", }, }, { featureType: "building", elementType: "all", stylers: { color: "#1a5787", }, }, { featureType: "label", elementType: "all", stylers: { visibility: "off", }, }, ], }, }, series: [ { name: "访问统计", type: "scatter", coordinateSystem: "bmap", data: convertData(data), encode: { value: 2, }, symbolSize: function (val) { // var value = val[2]; // var l = 0; // while (value >= 1) { // value = value / 10; // l++; // } // var j = Math.pow(10, l - 2); // console.log(l); // console.log(j); return val[2] / 100; // return val[2]; }, label: { formatter: "{b}", position: "right", }, itemStyle: { color: "#ddb926", }, emphasis: { label: { show: true, }, }, }, { name: "Top 5", type: "effectScatter", coordinateSystem: "bmap", data: convertData( data .sort(function (a, b) { return b.value - a.value; }) .slice(0, 6) ), encode: { value: 2, }, symbolSize: function (val) { return val[2] / 100; }, showEffectOn: "emphasis", rippleEffect: { brushType: "stroke", }, hoverAnimation: true, label: { formatter: "{b}", position: "right", show: true, }, itemStyle: { color: "#f4e925", shadowBlur: 10, shadowColor: "#333", }, zlevel: 1, }, { type: "custom", coordinateSystem: "bmap", // renderItem: renderItem, itemStyle: { opacity: 0.5, }, animation: false, silent: true, data: [0], z: -10, }, ], }); //设置 echarts 缩放比例，让其无法缩放 myChart.on("finished", () => { // 从echarts对象中获取bmap对象 var bmap = myChart.getModel().getComponent("bmap").getBMap(); console.log(20180925104046, bmap.getZoom()); // 设置最小缩放值 bmap.setMinZoom(5); // // 设置最大缩放值 bmap.setMaxZoom(5); // 缩放结束后的事件 bmap.addEventListener("zoomend", function () { // 打印出当前缩放值 console.log(20180925104046, bmap.getZoom()); }); }); }, //初始化从后台拿数据 数据的结构和 chartData 一致 init() { ipAccess().then((response) => { console.log(response); this.chartData = []; this.chartData = response.data; this.drawLine(); }); }, }, mounted() { //1.查询后台数据 this.init(); },};script><style scoped>style>总结
本篇主要记录一下 关于如何统计网站访问量，并且利用Echarts + 百度地图 友好的展示出来
1.提供拦截器 拦截Ip 请求，获取到 对应的 城市 我这里使用 纯真qqwry，网上也有其他方法。。
2.解析后 可以根据 key = 城市 存入redis中，利用redis 的 自增操作来 统计城市的 访问量，或者也可以通过Map 等去统计，然后刷入到存储中 。。方式很多
3.利用echarts+百度地图案例，暂时统计的数据，可以参考echarts官网