業務場景概述

目標是實作一個公司的申請審批流程，整個業務流程涉及到兩種角色，分別為商務角色與管理員角色。整個流程如下圖所示：

核心流程總結為一句話： 商務角色申請添加公司後由管理員進行審批。

商務在添加公司時，可能為了方便，直接填寫公司簡稱，而公司全稱可能之前已經被添加過了， 為了防止添加重復的公司 ，所以管理員在針對公司資訊審批之前，需要檢視以往添加的公司資訊裏有無同一個公司。

實作思路

以上是一個業務場景的大概介紹。從技術層面需要考慮實作的功能點：

分詞功能有現成的分詞器，所以整個需求的核心重點在於 如何與資料庫中的數據匹配並按照匹配度排序。

模糊匹配技術選型

MySQL提供了以下三種模糊搜尋的方式：

針對於上述業務場景，對相關技術進行優劣分析：

針對於這個場景，記錄數目相對來說沒有那麽多，所以對於效率稍低的結果可以接受，因此技術選型方面采用RegExp正則匹配來實作模糊匹配的需求。

實作效果展示

核心程式碼

整個邏輯基於 提取公司名稱關鍵資訊 -->分詞 --> 匹配 三個核心步驟 。

提取公司關鍵資訊

對輸入的公司名稱去除廢料，保留關鍵資訊。這裏的廢料指的是地名，圓括弧，以及集團，股份，有限等。

匹配前處理公司名稱
/**
* 匹配前去除公司名稱的無意義資訊
* @paramtargetCompanyName
* @return
*/
privateStringformatCompanyName(String targetCompanyName){
String regex ="(?<province>[^省]+自治區|.*?省|.*?行政區|.*?市)"+
"?(?<city>[^市]+自治州|.*?地區|.*?行政單位|.+盟|市轄區|.*?市|.*?縣)"+
"?(?<county>[^(區|市|縣|旗|島)]+區|.*?市|.*?縣|.*?旗|.*?島)"+
"?(?<village>.*)";
Matcher matcher =Pattern.compile(regex).matcher(targetCompanyName);
while(matcher.find()){
String province = matcher.group("province");
log.info("province:{}",province);
if(StringUtils.isNotBlank(province)&& targetCompanyName.contains(province)){
targetCompanyName = targetCompanyName.replace(province,"");
}
log.info("處理完省份的公司名稱:{}",targetCompanyName);
String city = matcher.group("city");
log.info("city:{}",city);
if(StringUtils.isNotBlank(city)&& targetCompanyName.contains(city)){
targetCompanyName = targetCompanyName.replace(city,"");
}
log.info("處理完城市的公司名稱:{}",targetCompanyName);
String county = matcher.group("county");
log.info("county:{}",county);
if(StringUtils.isNotBlank(county)&& targetCompanyName.contains(county)){
targetCompanyName = targetCompanyName.replace(county,"");
}
log.info("處理完區縣級的公司名稱:{}",targetCompanyName);
}
String[][] address =AddressUtil.ADDRESS;
for(String[] city: address){
for(Stringb: city ){
if(targetCompanyName.contains(b)){
targetCompanyName = targetCompanyName.replace(b,"");
}
}
}
log.info("處理後的公司名稱:{}",targetCompanyName);
return targetCompanyName;
}


地名工具類
public classAddressUtil{
publicstatic final String[][]ADDRESS={
{"北京"},
{"天津"},
{"安徽","安慶","蚌埠","亳州","巢湖","池州","滁州","阜陽","合肥","淮北","淮南","黃山","六安","馬鞍山","宿州","銅陵","蕪湖","宣城"},
{"澳門"},
{"香港"},
{"福建","福州","龍巖","南平","寧德","莆田","泉州","廈門","漳州"},
{"甘肅","白銀","定西","甘南藏族自治州","嘉峪關","金昌","酒泉","蘭州","臨夏回族自治州","隴南","平涼","慶陽","天水","武威","張掖"},
{"廣東","潮州","東莞","佛山","廣州","河源","惠州","江門","揭陽","茂名","梅州","清遠","汕頭","汕尾","韶關","深圳","陽江","雲浮","湛江","肇慶","中山","珠海"},
{"廣西","百色","北海","崇左","防城港","貴港","桂林","河池","賀州","來賓","柳州","南寧","欽州","梧州","玉林"},
{"貴州","安順","畢節地區","貴陽","六盤水","黔東南苗族侗族自治州","黔南布依族苗族自治州","黔西南布依族苗族自治州","銅仁地區","遵義"},
{"海南","海口","三亞","直轄縣級行政區劃"},
{"河北","保定","滄州","承德","邯鄲","衡水","廊坊","秦皇島","石家莊","唐山","邢台","張家口"},
{"河南","安陽","鶴壁","焦作","開封","洛陽","漯河","南陽","平頂山","濮陽","三門峽","商丘","新鄉","信陽","許昌","鄭州","周口","駐馬店"},
{"黑龍江","大慶","大興安嶺地區","哈爾濱","鶴崗","黑河","雞西","佳木斯","牡丹江","七台河","齊齊哈爾","雙鴨山","綏化","伊春"},
{"湖北","鄂州","恩施土家族苗族自治州","黃岡","黃石","荊門","荊州","十堰","隨州","武漢","鹹寧","襄樊","孝感","宜昌"},
{"湖南","長沙","常德","郴州","衡陽","懷化","婁底","邵陽","湘潭","湘西土家族苗族自治州","益陽","永州","嶽陽","張家界","株洲"},
{"吉林","白城","白山","長春","吉林","遼源","四平","松原","通化","延邊北韓族自治州"},
{"江蘇","常州","淮安","連雲港","南京","南通","蘇州","宿遷","泰州","無錫","徐州","鹽城","揚州","鎮江"},
{"江西","撫州","贛州","吉安","景德鎮","九江","南昌","萍鄉","上饒","新余","宜春","鷹潭"},
{"遼寧","鞍山","本溪","朝陽","大連","丹東","撫順","阜新","葫蘆島","錦州","遼陽","盤錦","沈陽","鐵嶺","營口"},
{"內蒙古","阿拉善盟","巴彥淖爾","包頭","赤峰","鄂爾多斯","呼和浩特","呼倫貝爾","通遼","烏海","烏蘭察布","錫林郭勒盟","興安盟"},
{"寧夏回族","固原","石嘴山","吳忠","銀川","中衛"},
{"青海","果洛藏族自治州","海北藏族自治州","海東地區","海南藏族自治州","海西蒙古族藏族自治州","黃南藏族自治州","西寧","玉樹藏族自治州"},
{"山東","濱州","德州","東營","菏澤","濟南","濟寧","萊蕪","聊城","臨沂","青島","日照","泰安","威海","濰坊","煙台","棗莊","淄博"},
{"山西","長治","大同","晉城","晉中","臨汾","呂梁","朔州","太原","忻州","陽泉","運城"},
{"陜西","安康","寶雞","漢中","商洛","銅川","渭南","西安","鹹陽","延安","榆林"},
{"上海"},
{"四川","阿壩藏族羌族自治州","巴中","成都","達州","德陽","甘孜藏族自治州","廣安","廣元","樂山","涼山彜族自治州","瀘州","眉山","綿陽","內江","南充","攀枝花","遂寧","雅安","宜賓","資陽","自貢"},
{"西藏","阿裏地區","昌都地區","拉薩","林芝地區","那曲地區","日喀則地區","山南地區"},
{"新疆維吾爾","阿克蘇地區","阿勒泰地區","巴音郭楞蒙古自治州","博爾塔拉蒙古自治州","昌吉回族自治州","哈密地區","和田地區","喀什地區","克拉瑪依","克孜勒蘇柯爾克孜自治州","塔城地區","吐魯番地區","烏魯木齊","伊犁哈薩克自治州","直轄縣級行政區劃"},
{"雲南","保山","楚雄彜族自治州","大理白族自治州","德宏傣族景頗族自治州","迪慶藏族自治州","紅河哈尼族彜族自治州","昆明","麗江","臨滄","怒江僳僳族自治州","普洱","曲靖","文山壯族苗族自治州","西雙版納傣族自治州","玉溪","昭通"},
{"浙江","杭州","湖州","嘉興","金華","麗水","寧波","衢州","紹興","台州","溫州","舟山"},
{"重慶"},
{"台灣","台北","高雄","基隆","台中","台南","新竹","嘉義"},
};
}


分詞相關程式碼

pom檔：引入IK分詞器相關依賴

<!-- ikAnalyzer 中文分詞器 -->
<dependency>
<groupId>com.janeluo</groupId>
<artifactId>ikanalyzer</artifactId>
<version>2012_u6</version>
<exclusions>
<exclusion>
<groupId>org.apache.lucene</groupId>
<artifactId>lucene-core</artifactId>
</exclusion>
<exclusion>
<groupId>org.apache.lucene</groupId>
<artifactId>lucene-queryparser</artifactId>
</exclusion>
<exclusion>
<groupId>org.apache.lucene</groupId>
<artifactId>lucene-analyzers-common</artifactId>
</exclusion>
</exclusions>
</dependency>
<!-- lucene-queryParser 查詢分析器模組 -->
<dependency>
<groupId>org.apache.lucene</groupId>
<artifactId>lucene-queryparser</artifactId>
<version>7.3.0</version>
</dependency>


IKAnalyzerSupport類：用於配置分詞器

@Slf4j
public classIKAnalyzerSupport{
/**
* IK分詞
* @paramtarget
* @return
*/
publicstaticList<String>iKSegmenterToList(String target) throws Exception{
if(StringUtils.isEmpty(target)){
returnnewArrayList();
}
List<String> result =newArrayList<>();
StringReader sr =newStringReader(target);
// false:關閉智慧分詞 (對分詞的精度影響較大)
IKSegmenter ik =newIKSegmenter(sr,true);
Lexeme lex;
while((lex=ik.next())!=null){
String lexemeText = lex.getLexemeText();
result.add(lexemeText);
}
return result;
}
}
ServiceImpl類：進行分詞處理
/**
* 對目標公司名稱進行分詞
* @paramtargetCompanyName
* @return
*/
privateStringsplitWord(String targetCompanyName){
log.info("對處理後端公司名稱進行分詞");
List<String> splitWord =newArrayList<>();
String result = targetCompanyName;
try{
splitWord =iKSegmenterToList(targetCompanyName);
result = splitWord.stream().map(String::valueOf).distinct().collect(Collectors.joining("|"));
log.info("分詞結果:{}",result);
}catch(Exception e){
log.error("分詞報錯:{}",e.getMessage());
}
return result;
}


匹配

ServiceImpl類：匹配核心程式碼

publicJsonResultmatchCompanyName(CompanyDTO companyDTO,String accessToken,String localIp){
// 對公司名稱進行處理
String sourceCompanyName = companyDTO.getCompanyName();
String targetCompanyName = sourceCompanyName;
log.info("處理前公司名稱:{}",targetCompanyName);
// 處理圓括弧
targetCompanyName = targetCompanyName.replaceAll("[（]|[）]|[(]|[)]","");
// 處理公司相關關鍵詞
targetCompanyName = targetCompanyName.replaceAll("[(集團|股份|有限|責任|分公司)]","");
if(!targetCompanyName.contains("銀行")){
// 去除行政區域
targetCompanyName =formatCompanyName(targetCompanyName);
}
// 分詞
String splitCompanyName =splitWord(targetCompanyName);
// 匹配
List<Company> matchedCompany = companyRepository.queryMatchCompanyName(splitCompanyName,targetCompanyName);
List<String> result =newArrayList();
for(Company companyInfo : matchedCompany){
result.add(companyInfo.getCompanyName());
if(companyDTO.getCompanyId().equals(companyInfo.getCompanyId())){
result.remove(companyInfo.getCompanyName());
}
}
returnJsonResult.successResult(result);
}


Repository類：編寫SQL語句

/**
* 模糊匹配公司名稱
* @paramcompanyNameRegex 分詞後的公司名稱
* @paramcompanyName 分詞前的公司名稱
* @return
*/
@Query(value =
"SELECT*FROM company WHERE isDeleted ='0' and companyName REGEXP?1
ORDERBYlength(REPLACE(companyName,?2,''))/length(companyName) ",
nativeQuery =true)
List<Company>queryMatchCompanyName(String companyNameRegex,String companyName);


按照匹配度排序這個功能點，LENGTH(companyName)返回companyName的長度，LENGTH(REPLACE(companyName, ?2, ''))計算出companyName中關鍵詞出現的次數。透過這種方式，我們可以根據匹配程度進行排序，匹配次數越多的公司名稱排序越靠前。

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作者：離開地球表面_99

來源：https://juejin.cn/post/7340574992256466953

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