✨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、前言本系统《基于大数据的环境空气质量数据可视化分析》主要围绕环境空气质量数据的采集、存储、清洗、统计分析和可视化展示展开核心目标是把分散的空气质量监测数据整理成便于观察和比较的分析结果。系统采用Hadoop和Spark作为大数据处理框架使用HDFS保存原始数据借助Spark SQL、Pandas、NumPy完成数据读取、清洗、聚合、统计和污染物指标计算数据库采用MySQL保存用户、站点、城市、空气质量信息以及分析结果。后端同时提供PythonDjango和JavaSpring Boot两个版本前端使用Vue、ElementUI、Echarts、HTML、CSS、JavaScript和jQuery完成页面交互与图表呈现。功能方面包含系统首页、大屏可视化、用户管理、环境空气质量信息、空气质量分析、污染物谱分析、时空演变分析、城市对比分析、站点分布分析、预报时效分析、污染画像分析、个人信息和修改密码等模块。通过这些模块系统能够从时间、城市、站点和污染物种类等角度展示空气质量变化帮助使用者快速查看AQI、PM2.5、PM10、SO2、NO2、CO、O3等指标的分析结果并用图表和大屏方式呈现适合作为计算机专业毕设中大数据分析与可视化方向的实践项目。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环境空气质量数据可视化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(AirQualityBigDataAnalysis).master(local[*]).getOrCreate()defair_quality_analysis(city_name,start_date,end_date):dfspark.read.option(header,True).option(inferSchema,True).csv(hdfs:///air_quality/air_quality.csv)dfdf.filter((df.citycity_name)(df.datestart_date)(df.dateend_date))dfdf.withColumn(aqi,df[aqi].cast(double)).withColumn(pm25,df[pm25].cast(double)).withColumn(pm10,df[pm10].cast(double)).withColumn(so2,df[so2].cast(double)).withColumn(no2,df[no2].cast(double)).withColumn(co,df[co].cast(double)).withColumn(o3,df[o3].cast(double))df.createOrReplaceTempView(air_quality_view)resultspark.sql(SELECT date, AVG(aqi) AS avg_aqi, AVG(pm25) AS avg_pm25, AVG(pm10) AS avg_pm10, AVG(so2) AS avg_so2, AVG(no2) AS avg_no2, AVG(co) AS avg_co, AVG(o3) AS avg_o3, COUNT(*) AS sample_count FROM air_quality_view GROUP BY date ORDER BY date)result.createOrReplaceTempView(air_quality_day_view)resultspark.sql(SELECT date, avg_aqi, avg_pm25, avg_pm10, avg_so2, avg_no2, avg_co, avg_o3, sample_count, CASE WHEN avg_aqi 50 THEN 优 WHEN avg_aqi 100 THEN 良 ELSE 污染 END AS quality_level FROM air_quality_day_view ORDER BY date)pandas_dfresult.toPandas()pandas_df[avg_aqi]pandas_df[avg_aqi].round(2)pandas_df[avg_pm25]pandas_df[avg_pm25].round(2)pandas_df[avg_pm10]pandas_df[avg_pm10].round(2)pandas_df[avg_so2]pandas_df[avg_so2].round(2)pandas_df[avg_no2]pandas_df[avg_no2].round(2)pandas_df[avg_co]pandas_df[avg_co].round(2)pandas_df[avg_o3]pandas_df[avg_o3].round(2)pandas_df.to_csv(/tmp/air_quality_analysis.csv,indexFalse)returnpandas_dfdefspatiotemporal_evolution(city_name,pollutant):dfspark.read.option(header,True).option(inferSchema,True).csv(hdfs:///air_quality/air_quality.csv)dfdf.filter(df.citycity_name)dfdf.withColumn(month,df[date].substr(1,7))dfdf.withColumn(pollutant,df[pollutant].cast(double))df.createOrReplaceTempView(spatiotemporal_view)resultspark.sql(fSELECT month, station, AVG({pollutant}) AS avg_value, MAX({pollutant}) AS max_value, MIN({pollutant}) AS min_value, COUNT(*) AS sample_count FROM spatiotemporal_view GROUP BY month, station ORDER BY month, station)result.createOrReplaceTempView(month_station_result)trendspark.sql(SELECT month, station, avg_value, max_value, min_value, sample_count, LAG(avg_value) OVER (PARTITION BY station ORDER BY month) AS last_month_value FROM month_station_result)trend.createOrReplaceTempView(trend_view)trend_resultspark.sql(SELECT month, station, avg_value, max_value, min_value, sample_count, last_month_value, CASE WHEN last_month_value IS NULL OR last_month_value 0 THEN 0 ELSE ROUND((avg_value - last_month_value) / last_month_value * 100, 2) END AS change_rate FROM trend_view ORDER BY station, month)pandas_dftrend_result.toPandas()pandas_df[avg_value]pandas_df[avg_value].round(2)pandas_df[max_value]pandas_df[max_value].round(2)pandas_df[min_value]pandas_df[min_value].round(2)pandas_df.to_csv(/tmp/spatiotemporal_evolution.csv,indexFalse)returnpandas_dfdefcity_comparison(pollutant,top_n):dfspark.read.option(header,True).option(inferSchema,True).csv(hdfs:///air_quality/air_quality.csv)dfdf.withColumn(pollutant,df[pollutant].cast(double))df.createOrReplaceTempView(city_air_view)resultspark.sql(fSELECT city, AVG({pollutant}) AS avg_value, MAX({pollutant}) AS max_value, MIN({pollutant}) AS min_value, COUNT(*) AS sample_count FROM city_air_view GROUP BY city ORDER BY avg_value DESC LIMIT{top_n})result.createOrReplaceTempView(city_rank_view)compare_resultspark.sql(SELECT city, avg_value, max_value, min_value, sample_count, CASE WHEN avg_value 35 THEN 低 WHEN avg_value 75 THEN 中 ELSE 高 END AS compare_level FROM city_rank_view ORDER BY avg_value DESC)pandas_dfcompare_result.toPandas()pandas_df[avg_value]pandas_df[avg_value].round(2)pandas_df[max_value]pandas_df[max_value].round(2)pandas_df[min_value]pandas_df[min_value].round(2)pandas_df[rank]range(1,len(pandas_df)1)pandas_df.to_csv(/tmp/city_comparison.csv,indexFalse)returnpandas_df五、论文参考计算机毕业设计选题推荐-基于大数据的环境空气质量数据可视化分析系统-论文参考六、系统视频基于大数据的环境空气质量数据可视化分析系统-项目视频项目演示视频结语计算机毕业设计选题推荐:基于大数据的环境空气质量数据可视化分析|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