摘要
随着旅客出行总次数的增加,历史航行数据和气象数据也呈指数及增长,空管系统中的数据容量性能的更新远远不及高速增长的数据,这些数据具有高度零散化、文件化、非结构化的特征。旨在通过Pandas+Python的数据处理框架,提供一种灵活的、高效的气象报文(METAR、TAF、SPECI)和航行报文数据(DEP、ARR)的聚合方式,通过该聚合方式可以灵活有效地提取每日的进出场流量信息和对应时段的具体气象信息,为下一步统计流量预测和极端气象条件分析提供一种可靠的平台支撑。
With the increase of the total number of passenger trips,historical navigation data and meteorological data have also shown an exponential growth.The data capacity performance of the air traffic control system is far less than the rapid growth of data.These data are highly frag mented,documented,structured features.This article aims to provide a flexible and efficient aggregation of meteorological messages(METAR,TAF,SPECI)and navigation message data(DEP,ARR)through the data processing framework of Pandas+Python.This aggrega tion method can flexibly and effectively extract daily arrival and departure flow information and specific weather information for the corre sponding period,and provide a reliable platform support for the next statistical flow prediction and analysis of extreme weather conditions.
作者
李卿
LI Qing(State Key Laboratory of Fundamental Science on Synthetic Vision,Sichuan University,Chengdu 610065)
出处
《现代计算机》
2020年第9期29-32,共4页
Modern Computer
