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个人简介：曾从事计算机专业培训教学，擅长Java、Python、PHP、.NET、Node.js、GO、微信小程序、安卓Android等项目实战。接项目定制开发、代码讲解、答辩教学、文档编写、降重等。
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文章目录

一、前言
二、开发环境
三、系统界面展示
四、部分代码设计
五、论文参考
六、系统视频
结语

一、前言

本系统名为《基于大数据的婚姻幸福指数数据可视化与分析》，主要围绕婚姻幸福相关信息展开大数据处理与可视化展示。系统采用Hadoop与Spark作为大数据框架，使用HDFS保存原始数据，借助Spark SQL、Pandas和NumPy完成数据清洗、缺失值处理、统计汇总和幸福指数相关指标计算；后端提供Python+Django与Java+Spring Boot两个可选版本，数据库使用MySQL保存用户信息、婚姻幸福信息和分析结果；前端使用Vue、ElementUI、Echarts、HTML、CSS、JavaScript和jQuery完成页面交互与图表呈现。功能上包含系统首页、大屏可视化、用户、婚姻幸福信息、幸福等级分析、家庭保障分析、乡村数字分析、县域跃迁分析、存款格局分析、自模式洞察分析、个人信息和修改密码。用户可以通过大屏和图表查看不同地区、不同幸福等级下的婚姻幸福指数分布，也能从家庭保障、乡村数字、县域跃迁、存款格局和自模式洞察等角度观察数据差异与关联，从而对婚姻幸福相关数据形成更直观的认识。系统重点放在大数据统计分析和可视化表达上，适合作为计算机专业毕业设计中的大数据应用实践。

二、开发环境

大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
开发语言：Python+Java（两个版本都支持）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL

三、系统界面展示

基于大数据的婚姻幸福指数数据可视化与分析系统界面展示：

四、部分代码设计

项目实战-代码参考：

spark=SparkSession.builder.appName("MarriageHappinessBigData").master("local[*]").config("spark.sql.shuffle.partitions","4").getOrCreate()defmarriage_info_clean_view(request):raw_df=spark.read.format("jdbc").option("url","jdbc:mysql://localhost:3306/marriage_db").option("dbtable","marriage_happiness_info").option("user","root").option("password","123456").load()raw_df.createOrReplaceTempView("marriage_happiness_raw")clean_df=spark.sql("SELECT id,user_id,province,county,village,family_income,deposit_amount,family_security_score,marriage_satisfaction,communication_score,child_education_score,elder_care_score,happiness_score FROM marriage_happiness_raw WHERE happiness_score IS NOT NULL")clean_df=clean_df.fillna({"family_income":0,"deposit_amount":0,"family_security_score":0,"marriage_satisfaction":0,"communication_score":0,"child_education_score":0,"elder_care_score":0})clean_df.createOrReplaceTempView("marriage_happiness_clean")result_df=spark.sql("SELECT province,county,COUNT(*) AS sample_count,ROUND(AVG(happiness_score),2) AS avg_happiness,ROUND(AVG(family_security_score),2) AS avg_security,ROUND(AVG(deposit_amount),2) AS avg_deposit FROM marriage_happiness_clean GROUP BY province,county ORDER BY avg_happiness DESC")rows=result_df.collect()data=[]forrowinrows:item=row.asDict()item["happiness_level"]="高"ifitem["avg_happiness"]>=80else"中"ifitem["avg_happiness"]>=60else"低"item["deposit_wan"]=round(item["avg_deposit"]/10000,2)data.append(item)returnJsonResponse({"code":200,"message":"婚姻幸福信息处理成功","data":data})defhappiness_level_analysis(request):clean_df=spark.sql("SELECT id,province,county,happiness_score,family_security_score,deposit_amount,family_income FROM marriage_happiness_clean")clean_df.createOrReplaceTempView("happiness_level_source")level_df=spark.sql("SELECT id,province,county,happiness_score,family_security_score,deposit_amount,family_income,CASE WHEN happiness_score>=90 THEN '非常幸福' WHEN happiness_score>=75 THEN '比较幸福' WHEN happiness_score>=60 THEN '一般幸福' ELSE '需要关注' END AS happiness_level FROM happiness_level_source")level_df.createOrReplaceTempView("happiness_level_detail")result_df=spark.sql("SELECT happiness_level,COUNT(*) AS level_count,ROUND(AVG(happiness_score),2) AS avg_score,ROUND(AVG(family_security_score),2) AS avg_security,ROUND(AVG(deposit_amount),2) AS avg_deposit,ROUND(AVG(family_income),2) AS avg_income FROM happiness_level_detail GROUP BY happiness_level ORDER BY avg_score DESC")rows=result_df.collect()data=[]total=sum([row["level_count"]forrowinrows])forrowinrows:item=row.asDict()item["level_ratio"]=round(item["level_count"]/total*100,2)iftotalelse0item["suggestion"]="保持沟通与保障"ifitem["happiness_level"]in["非常幸福","比较幸福"]else"关注家庭保障与存款"data.append(item)returnJsonResponse({"code":200,"message":"幸福等级分析成功","data":data})deffamily_security_analysis(request):source_df=spark.sql("SELECT province,county,family_security_score,happiness_score,deposit_amount,family_income FROM marriage_happiness_clean")source_df.createOrReplaceTempView("family_security_source")level_df=spark.sql("SELECT province,county,family_security_score,happiness_score,deposit_amount,family_income,CASE WHEN family_security_score>=80 THEN '高保障' WHEN family_security_score>=60 THEN '中等保障' ELSE '低保障' END AS security_level FROM family_security_source")level_df.createOrReplaceTempView("family_security_level")result_df=spark.sql("SELECT province,county,security_level,COUNT(*) AS family_count,ROUND(AVG(happiness_score),2) AS avg_happiness,ROUND(AVG(deposit_amount),2) AS avg_deposit,ROUND(AVG(family_income),2) AS avg_income FROM family_security_level GROUP BY province,county,security_level ORDER BY avg_happiness DESC")rows=result_df.collect()data=[]forrowinrows:item=row.asDict()item["deposit_wan"]=round(item["avg_deposit"]/10000,2)item["income_wan"]=round(item["avg_income"]/10000,2)item["security_happiness_gap"]=round(item["avg_happiness"]-item["deposit_wan"],2)item["advice"]="保障较好，可继续优化存款结构"ifitem["security_level"]=="高保障"else"建议提升家庭保障与储蓄"data.append(item)returnJsonResponse({"code":200,"message":"家庭保障分析成功","data":data})

五、论文参考

计算机毕业设计选题推荐-基于大数据的婚姻幸福指数数据可视化与分析系统-论文参考：

六、系统视频

基于大数据的婚姻幸福指数数据可视化与分析系统-项目视频：
项目演示视频

结语

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计算机毕业设计选题推荐:基于大数据的婚姻幸福指数数据可视化与分析|毕业设计选题|计算机毕设|选题推荐|毕设指导|项目定制|源码|高质量项目