✨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《基于大数据的全球航空航班数据可视化分析》主要围绕全球航空航班运行数据展开利用Hadoop和Spark完成海量航班数据的存储、清洗、统计与分析结合HDFS做数据落地通过Spark SQL、Pandas和NumPy完成延误、中断、枢纽机场、航司运营、风险分群等指标的加工计算再把结果交给Django或Spring Boot后端接口前端用Vue、ElementUI、Echarts、HTML、CSS、JavaScript和jQuery做可视化展示。系统功能包含系统首页、数据大屏、用户管理、航班中断信息、枢纽机场分析、延误原因分析、全球中断分析、航司运营分析、航司存续分析、风险分群分析、个人信息和修改密码。用户登录后可以在数据大屏查看全球航班整体态势在航班中断信息里筛选和查看中断记录在枢纽机场分析里观察主要机场的航班承载与中转情况在延误原因分析里按天气、航司、机场等维度定位延误来源在全球中断分析里比较不同地区和航线的中断差异在航司运营分析里查看航司航班量、延误水平和运营表现在航司存续分析里观察航司存续状态与变化在风险分群分析里把航班或航司按风险等级归类方便快速发现高风险对象。整个系统以大数据分析为主线后端提供数据接口前端负责图表呈现MySQL用于保存用户、权限和部分业务结果数据。它适合作为计算机专业毕业设计既能体现Hadoop、Spark和Spark SQL的使用也能把复杂航班数据用比较直观的图表展示出来。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全球航空航班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frompyspark.sqlimportSparkSession sparkSparkSession.builder.appName(GlobalFlightBigDataAnalysis).master(local[*]).config(spark.sql.shuffle.partitions,4).getOrCreate()defflight_interruption_analysis(request):dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/aviation_bigdata).option(dbtable,flight_interruption).option(user,root).option(password,123456).load()df.createOrReplaceTempView(flight_interruption)totalspark.sql(select count(*) as total from flight_interruption).collect()[0][total]interruptedspark.sql(select count(*) as cnt from flight_interruption where interruption_flag1).collect()[0][cnt]rateround(interrupted/total*100,2)iftotalelse0by_airportspark.sql(select airport_name, count(*) as cnt from flight_interruption where interruption_flag1 group by airport_name order by cnt desc limit 10).toPandas()by_airlinespark.sql(select airline_name, count(*) as cnt from flight_interruption where interruption_flag1 group by airline_name order by cnt desc limit 10).toPandas()by_reasonspark.sql(select interruption_reason, count(*) as cnt from flight_interruption where interruption_flag1 group by interruption_reason order by cnt desc).toPandas()importnumpyasnp arrnp.array(by_reason[cnt].tolist())reason_indexint(np.argmax(arr))ifarr.sizeelse-1top_reasonby_reason.iloc[reason_index][interruption_reason]ifreason_index0else暂无result{total:total,interrupted:interrupted,rate:rate,airport_top10:by_airport.to_dict(records),airline_top10:by_airline.to_dict(records),reason_dist:by_reason.to_dict(records),top_reason:top_reason}returnJsonResponse({code:200,data:result})defdelay_reason_analysis(request):dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/aviation_bigdata).option(dbtable,flight_delay).option(user,root).option(password,123456).load()df.createOrReplaceTempView(flight_delay)total_delayspark.sql(select count(*) as cnt from flight_delay where delay_minutes 0).collect()[0][cnt]avg_delayspark.sql(select avg(delay_minutes) as avg_delay from flight_delay where delay_minutes 0).collect()[0][avg_delay]reason_dfspark.sql(select delay_reason, count(*) as cnt, avg(delay_minutes) as avg_minutes from flight_delay where delay_minutes 0 group by delay_reason order by cnt desc).toPandas()weatherspark.sql(select count(*) as cnt from flight_delay where delay_reason like %天气%).collect()[0][cnt]airlinespark.sql(select count(*) as cnt from flight_delay where delay_reason like %航司%).collect()[0][cnt]airportspark.sql(select count(*) as cnt from flight_delay where delay_reason like %机场%).collect()[0][cnt]securityspark.sql(select count(*) as cnt from flight_delay where delay_reason like %安检%).collect()[0][cnt]total_reasonweatherairlineairportsecurity weather_rateround(weather/total_reason*100,2)iftotal_reasonelse0airline_rateround(airline/total_reason*100,2)iftotal_reasonelse0airport_rateround(airport/total_reason*100,2)iftotal_reasonelse0security_rateround(security/total_reason*100,2)iftotal_reasonelse0month_dfspark.sql(select month, count(*) as cnt from flight_delay where delay_minutes 0 group by month order by month).toPandas()result{total_delay:total_delay,avg_delay:round(avg_delay,2)ifavg_delayelse0,reason_list:reason_df.to_dict(records),weather_rate:weather_rate,airline_rate:airline_rate,airport_rate:airport_rate,security_rate:security_rate,month_trend:month_df.to_dict(records)}returnJsonResponse({code:200,data:result})defrisk_group_analysis(request):dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/aviation_bigdata).option(dbtable,flight_risk).option(user,root).option(password,123456).load()df.createOrReplaceTempView(flight_risk)highspark.sql(select count(*) as cnt from flight_risk where delay_minutes 120 or interruption_count 3).collect()[0][cnt]midspark.sql(select count(*) as cnt from flight_risk where (delay_minutes between 60 and 120) or (interruption_count between 1 and 2)).collect()[0][cnt]lowspark.sql(select count(*) as cnt from flight_risk where delay_minutes 60 and interruption_count 0).collect()[0][cnt]totalhighmidlow high_rateround(high/total*100,2)iftotalelse0mid_rateround(mid/total*100,2)iftotalelse0low_rateround(low/total*100,2)iftotalelse0airline_riskspark.sql(select airline_name, count(*) as risk_cnt from flight_risk where delay_minutes 120 or interruption_count 3 group by airline_name order by risk_cnt desc limit 10).toPandas()airport_riskspark.sql(select airport_name, count(*) as risk_cnt from flight_risk where delay_minutes 120 or interruption_count 3 group by airport_name order by risk_cnt desc limit 10).toPandas()route_riskspark.sql(select route, count(*) as risk_cnt from flight_risk where delay_minutes 120 or interruption_count 3 group by route order by risk_cnt desc limit 10).toPandas()risk_dfspark.sql(select flight_id, airline_name, airport_name, route, delay_minutes, interruption_count, case when delay_minutes 120 or interruption_count 3 then 高风险 when delay_minutes between 60 and 120 or interruption_count between 1 and 2 then 中风险 else 低风险 end as risk_level from flight_risk order by delay_minutes desc).toPandas()result{total:total,high:high,mid:mid,low:low,high_rate:high_rate,mid_rate:mid_rate,low_rate:low_rate,airline_risk:airline_risk.to_dict(records),airport_risk:airport_risk.to_dict(records),route_risk:route_risk.to_dict(records),risk_list:risk_df.head(50).to_dict(records)}returnJsonResponse({code:200,data:result})五、论文参考计算机毕业设计选题推荐-基于大数据的全球航空航班数据可视化分析系统-论文参考六、系统视频基于大数据的全球航空航班数据可视化分析系统-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:基于大数据的全球航空航班数据可视化分析|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