博客 城市访问量统计并且通过Echarts展示
本篇讲解一下 如何 通过QQWry 解析IP 统计每个城市的访问量 并且 在Vue 中使用 Echarts + 百度地图 展示 博客访问量。
效果图如下：
image-20200817111337208
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
<!-- qqwary 是一个 ip 数据库-->
<dependency>
<groupId>com.github.jarod</groupId>
<artifactId>qqwry-java</artifactId>
<version>0.7.0</version>
</dependency>
引入后可以看到 该jar 包其实内部已经引入了 qqwry.dat 库了
image-20200817101056841
使用教程：
QQWry qqwry = new QQWry(); // load qqwry.dat from classpath
QQWry qqwry = new QQWry(Paths.get("path/to/qqwry.dat")); // load qqwry.dat from java.nio.file.Path
byte[] data = Files.readAllBytes(Paths.get("path/to/qqwry.dat"));
QQWry qqwry = new QQWry(data); // create QQWry with provided data
String 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
@Slf4j
public 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)
@Slf4j
public 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 'BMap
require('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<String,AtomicLong> 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
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