✨作者主页IT毕设梦工厂✨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。☑文末获取源码☑精彩专栏推荐⬇⬇⬇Java项目Python项目安卓项目微信小程序项目文章目录一、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《基于大数据的网格化城市管理问题分析与可视化》这套系统主要围绕网格化城市管理中的问题数据展开把网格员上报、部门处置、区域分布、问题类别、时间变化这些信息集中起来做分析。技术上使用Hadoop和Spark作为大数据处理核心配合HDFS、Spark SQL、Pandas、NumPy完成数据存储、清洗、统计和挖掘后端用Django或Spring Boot提供接口前端用Vue、ElementUI、ECharts、HTML、CSS、JavaScript、jQuery做页面展示。系统里有系统首页、数据大屏、用户管理、网格化城市管理问题、问题结构分析、处置效能分析、部门负荷分析、时序态势分析、类别热点分析、区域处置分析、问题画像分析、个人信息和修改密码这些功能。用户可以从区域、时间、类别、部门等角度查看问题数量、处置效率、热点分布和问题画像结果通过图表和可视化大屏更直观地发现管理中的薄弱点。整个系统不算复杂但能把大数据分析流程和可视化展示串起来适合作为计算机专业毕业设计来理解和实现。二、开发环境大数据框架HadoopSpark本次没用Hive支持定制开发语言PythonJava两个版本都支持后端框架DjangoSpring Boot(SpringSpringMVCMybatis)两个版本都支持前端VueElementUIEchartsHTMLCSSJavaScriptjQuery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网格化城市管理问题分析与可视化系统界面展示四、部分代码设计项目实战-代码参考frompyspark.sqlimportSparkSessiondefdashboard_data(request):sparkSparkSession.builder.appName(GridCityDashboard).master(local[*]).config(spark.sql.shuffle.partitions,4).getOrCreate()dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/grid_city).option(dbtable,city_problem).option(user,root).option(password,123456).load()df.createOrReplaceTempView(city_problem)totalspark.sql(select count(*) as total from city_problem).collect()[0][total]donespark.sql(select count(*) as done from city_problem where status已处置).collect()[0][done]pendingtotal-done rateround(done/total*100,2)iftotalelse0today_numspark.sql(select count(*) as num from city_problem where to_date(report_time)current_date()).collect()[0][num]week_numspark.sql(select count(*) as num from city_problem where report_timedate_sub(current_date(),7)).collect()[0][num]month_numspark.sql(select count(*) as num from city_problem where date_format(report_time,yyyy-MM)date_format(current_date(),yyyy-MM)).collect()[0][num]area_topspark.sql(select grid_area,count(*) as num from city_problem group by grid_area order by num desc limit 5).collect()type_topspark.sql(select problem_type,count(*) as num from city_problem group by problem_type order by num desc limit 5).collect()trendspark.sql(select date_format(report_time,MM-dd) as day,count(*) as num from city_problem group by date_format(report_time,MM-dd) order by day).collect()dept_loadspark.sql(select dept_name,count(*) as num,sum(case when status已处置 then 1 else 0 end) as done from city_problem group by dept_name order by num desc).collect()return{total:total,done:done,pending:pending,disposal_rate:rate,today_num:today_num,week_num:week_num,month_num:month_num,area_top:area_top,type_top:type_top,trend:trend,dept_load:dept_load}defarea_disposal_analysis(request):sparkSparkSession.builder.appName(AreaDisposalAnalysis).master(local[*]).getOrCreate()dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/grid_city).option(dbtable,city_problem).option(user,root).option(password,123456).load()df.createOrReplaceTempView(city_problem)area_dfspark.sql(select grid_area,count(*) as total,sum(case when status已处置 then 1 else 0 end) as done,sum(case when status!已处置 then 1 else 0 end) as undone from city_problem group by grid_area)area_df.createOrReplaceTempView(area_stat)resultspark.sql(select grid_area,total,done,undone,round(done/total*100,2) as done_rate,round(undone/total*100,2) as undone_rate from area_stat order by undone_rate desc)rowsresult.collect()area_list[]forrowinrows:area_list.append({grid_area:row[grid_area],total:row[total],done:row[done],undone:row[undone],done_rate:row[done_rate],undone_rate:row[undone_rate]})slow_areaspark.sql(select grid_area,round(avg(datediff(deal_time,report_time)),2) as avg_days from city_problem where status已处置 group by grid_area order by avg_days desc limit 5).collect()fast_areaspark.sql(select grid_area,round(avg(datediff(deal_time,report_time)),2) as avg_days from city_problem where status已处置 group by grid_area order by avg_days asc limit 5).collect()type_areaspark.sql(select grid_area,problem_type,count(*) as num from city_problem group by grid_area,problem_type order by num desc limit 10).collect()month_areaspark.sql(select date_format(report_time,yyyy-MM) as month,grid_area,count(*) as num from city_problem group by date_format(report_time,yyyy-MM),grid_area order by month, num desc).collect()return{area_list:area_list,slow_area:slow_area,fast_area:fast_area,type_area:type_area,month_area:month_area}defproblem_profile_analysis(request):sparkSparkSession.builder.appName(ProblemProfileAnalysis).master(local[*]).getOrCreate()dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/grid_city).option(dbtable,city_problem).option(user,root).option(password,123456).load()df.createOrReplaceTempView(city_problem)profilespark.sql(select problem_type,grid_area,dept_name,count(*) as num,round(avg(datediff(deal_time,report_time)),2) as avg_days,sum(case when status已处置 then 1 else 0 end) as done from city_problem group by problem_type,grid_area,dept_name)profile.createOrReplaceTempView(problem_profile)hot_profilespark.sql(select problem_type,grid_area,dept_name,num,avg_days,done,round(done/num*100,2) as done_rate from problem_profile order by num desc limit 20).collect()tag_dfspark.sql(select problem_type,case when count(*)100 then 高频 when count(*)50 then 中频 else 低频 end as freq_tag,round(avg(datediff(deal_time,report_time)),2) as avg_days from city_problem group by problem_type)tag_df.createOrReplaceTempView(type_tag)tag_resultspark.sql(select problem_type,freq_tag,avg_days from type_tag order by avg_days desc).collect()area_profilespark.sql(select grid_area,count(distinct problem_type) as type_num,count(*) as total,round(avg(datediff(deal_time,report_time)),2) as avg_days from city_problem group by grid_area order by type_num desc).collect()dept_profilespark.sql(select dept_name,count(distinct problem_type) as type_num,count(*) as total,round(sum(case when status已处置 then 1 else 0 end)/count(*)*100,2) as done_rate from city_problem group by dept_name order by total desc).collect()hot_typespark.sql(select problem_type,count(*) as num from city_problem group by problem_type order by num desc limit 10).collect()slow_typespark.sql(select problem_type,round(avg(datediff(deal_time,report_time)),2) as avg_days from city_problem where status已处置 group by problem_type order by avg_days desc limit 10).collect()total_profileprofile.count()return{hot_profile:hot_profile,tag_result:tag_result,area_profile:area_profile,dept_profile:dept_profile,hot_type:hot_type,slow_type:slow_type,total_profile:total_profile}五、论文参考计算机毕业设计选题推荐-基于大数据的网格化城市管理问题分析与可视化系统-论文参考六、系统视频基于大数据的网格化城市管理问题分析与可视化系统-项目视频点击观看项目演示视频结语计算机毕业设计选题推荐:基于大数据的网格化城市管理问题分析与可视化|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|高质量项目大家可以帮忙点赞、收藏、关注、评论啦源码获取⬇⬇⬇精彩专栏推荐⬇⬇⬇Java项目Python项目安卓项目微信小程序项目
