✨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《基于大数据的企业污染源废水监测数据可视化与分析》面向企业污染源废水监测场景把分散的废水监测信息、排口类型、区域分布、水质浓度和企业排况等数据集中起来借助Hadoop和Spark完成大数据存储与批处理使用HDFS保存原始与清洗后的监测数据通过Spark SQL、Pandas和NumPy做统计汇总和指标计算结果写入MySQL供后端查询。系统提供PythonDjango版和JavaSpring Boot版前端采用Vue、ElementUI、Echarts、HTML、CSS、JavaScript和jQuery构建页面与图表。功能包括系统首页、大屏可视化、用户管理、废水监测信息管理、排口类型分析、时序变化分析、区域分布分析、水质浓度分析、企业排况分析、风险洞察分析、个人信息和修改密码。用户可以在大屏上查看废水监测整体态势在废水监测信息模块维护和检索数据在排口类型、区域分布、水质浓度等模块观察不同维度的变化通过时序变化分析判断污染物浓度走势通过企业排况和风险洞察分析定位异常企业与潜在风险点为毕设展示和后续扩展提供一套完整的大数据可视化分析示例。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企业污染源废水监测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sparkSparkSession.builder.appName(EnterprisePollutionWaterMonitor).master(local[*]).config(spark.sql.shuffle.partitions,4).getOrCreate()defsave_water_monitor(request):datajson.loads(request.body)enterprise_iddata.get(enterprise_id)outfall_iddata.get(outfall_id)monitor_timedata.get(monitor_time)codfloat(data.get(cod,0))ammoniafloat(data.get(ammonia,0))phfloat(data.get(ph,7))ifnotenterprise_idornotoutfall_idornotmonitor_time:returnJsonResponse({code:400,msg:企业、排口和监测时间不能为空})ifcod0orammonia0orph0orph14:returnJsonResponse({code:400,msg:水质浓度或pH值不在合理范围})withconnection.cursor()ascursor:cursor.execute(select id from enterprise where id%s,[enterprise_id])ifcursor.fetchone()isNone:returnJsonResponse({code:404,msg:企业不存在})cursor.execute(select id from outfall where id%s and enterprise_id%s,[outfall_id,enterprise_id])ifcursor.fetchone()isNone:returnJsonResponse({code:404,msg:排口不属于该企业})cursor.execute(insert into water_monitor(enterprise_id,outfall_id,monitor_time,cod,ammonia,ph) values(%s,%s,%s,%s,%s,%s),[enterprise_id,outfall_id,monitor_time,cod,ammonia,ph])monitor_idcursor.lastrowid dfspark.createDataFrame([(monitor_id,enterprise_id,outfall_id,monitor_time,cod,ammonia,ph)],[id,enterprise_id,outfall_id,monitor_time,cod,ammonia,ph])df.write.mode(append).format(jdbc).option(url,jdbc:mysql://localhost:3306/water_monitor).option(dbtable,water_monitor_spark).option(user,root).option(password,123456).save()returnJsonResponse({code:200,msg:废水监测信息保存成功,id:monitor_id})deftime_series_analysis(request):enterprise_idrequest.GET.get(enterprise_id)outfall_idrequest.GET.get(outfall_id)start_timerequest.GET.get(start_time)end_timerequest.GET.get(end_time)ifnotstart_timeornotend_time:returnJsonResponse({code:400,msg:请选择时间范围})sqlselect monitor_time,cod,ammonia,ph from water_monitor where 11params[]ifenterprise_id:sql and enterprise_id%sparams.append(enterprise_id)ifoutfall_id:sql and outfall_id%sparams.append(outfall_id)sql and monitor_time between %s and %s order by monitor_timeparams.extend([start_time,end_time])withconnection.cursor()ascursor:cursor.execute(sql,params)rowscursor.fetchall()columns[monitor_time,cod,ammonia,ph]dfspark.createDataFrame(rows,columns)ifdf.count()0:returnJsonResponse({code:200,data:[],msg:当前时间范围没有监测数据})df.createOrReplaceTempView(water_time)resultspark.sql(select date_format(monitor_time,yyyy-MM-dd HH:00) as hour_time, avg(cod) as avg_cod, avg(ammonia) as avg_ammonia, avg(ph) as avg_ph, max(cod) as max_cod from water_time group by date_format(monitor_time,yyyy-MM-dd HH:00) order by hour_time)data[row.asDict()forrowinresult.collect()]trend平稳iflen(data)2:first_coddata[0][avg_cod]or0last_coddata[-1][avg_cod]or0iflast_codfirst_cod*1.2:trend上升eliflast_codfirst_cod*0.8:trend下降returnJsonResponse({code:200,trend:trend,data:data})defrisk_insight_analysis(request):arearequest.GET.get(area)start_timerequest.GET.get(start_time)end_timerequest.GET.get(end_time)sqlselect enterprise_id,outfall_id,monitor_time,cod,ammonia,ph from water_monitor where monitor_time between %s and %sparams[start_time,end_time]ifarea:sql and area%sparams.append(area)withconnection.cursor()ascursor:cursor.execute(sql,params)rowscursor.fetchall()columns[enterprise_id,outfall_id,monitor_time,cod,ammonia,ph]dfspark.createDataFrame(rows,columns)ifdf.count()0:returnJsonResponse({code:200,data:[],msg:暂无风险数据})df.createOrReplaceTempView(water_risk)risk_dfspark.sql(select enterprise_id, outfall_id, count(*) as monitor_count, avg(cod) as avg_cod, avg(ammonia) as avg_ammonia, max(cod) as max_cod, max(ammonia) as max_ammonia, sum(case when cod 80 or ammonia 15 or ph 6 or ph 9 then 1 else 0 end) as abnormal_count from water_risk group by enterprise_id, outfall_id)risk_df.createOrReplaceTempView(risk_group)resultspark.sql(select enterprise_id, outfall_id, monitor_count, avg_cod, avg_ammonia, max_cod, max_ammonia, abnormal_count, case when abnormal_count 5 then 高风险 when abnormal_count 2 then 中风险 else 低风险 end as risk_level from risk_group order by abnormal_count desc, max_cod desc)data[row.asDict()forrowinresult.collect()]high_countsum(1foritemindataifitem[risk_level]高风险)returnJsonResponse({code:200,high_count:high_count,data:data})五、论文参考计算机毕业设计选题推荐-基于大数据的企业污染源废水监测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企业污染源废水监测数据可视化与分析系统-项目视频项目演示视频结语计算机毕业设计选题推荐:基于大数据的企业污染源废水监测数据可视化与分析|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