基于云计算的地理信息服务技术

摘要/Abstract

摘要： 文中基于现有地理信息服务技术研究仅限于已有GIS软件在云计算环境下的新的软件部署和单个领域应用模式的局限,提出了地理信息服务技术的研究需要在云计算环境下开展应用模式的深入研究,深化以对地观测信息为特征的地理时空大数据的融合应用;进而分析了云GIS服务平台在数据管理、地理计算、地理信息制图与终端服务、专题应用系统构建和网络应用与服务模式5个方面的技术特征。参考并分类辩证分析国内外该领域内经典的理论与实证文献资料,顺应云GIS研究的最新趋势,设计了“存储-计算-服务”一体化的云GIS平台技术架构,并针对目前云GIS平台的应用现状,提出了基于云计算的地理信息服务的5种模式。采用MongoDB作为元信息库、业务数据等的载体,应用GridFS文件系统作为底层异构存储的一种,采用Redis作为数据引擎的数据交换缓存以确保处理的效率,采用ZeroMQ作为传输中间件,基于Node.js开发了“管家-工作者”模式的数据引擎和资源服务。针对海量、高吞吐、空间结构化的遥感影像数据及其基础土地信息产品的存储与管理问题,基于MongoDB数据库开发了原型系统并使用PB量级数据进行实验,验证了本文研究成果的可行性和先进性。

关键词: 地理时空大数据, 服务模式, 云计算

Abstract: Based on the limitations of existing research on geographic information service technology,which is only limited to the new software deployment of existing GIS software in the cloud computing environment and the application mode of a single field,this paper proposed that the research on geographic information service technology needs to carry out in-depth research on the application mode in the cloud computing environment and deepen the fusion application of geospatial and temporal big data characterized by earth observation information.Furthermore,it analyzed the technical characteristics of cloud GIS service platform in five aspects:data management,geographic computing,geographic information mapping and terminal service,thematic application system construction,network application and service mode.By referring to and classifying and dialectically analyzing the classic theoretical and empirical literatures in this field at home and abroad,in line with the latest trend of cloud GIS research,the technical framework of cloud GIS platform integrating “store-computing-service” was designed,and five models of geographic information service based on cloud computing were proposed according to the current application status of cloud GIS platform.MongoDB was adopted as the carrier of information and business data and GridFS file system was applied as a kind of underlying heterogeneous storage.At the same time,Redis was utilized as the data exchange cache of the data engine to ensure the processing efficiency,and ZeroMQ was leveraged as the transmission middleware to develop the data engine and resource service of the “Mannger-worker” mode based on Node.js.Aiming at the problem of storage and management of massive,high-throughput,spatially-structured remote sensing image data and its basic land information products,a prototype system was developed based on MongoDB database and tested with PB data,which verified the feasibility and advancement of the research results in this paper.

Key words: Cloud computing, Geographical spatitemporal data, Service mode

中图分类号:

TP391

张新, 胡晓东, 魏嘉伟. 基于云计算的地理信息服务技术[J]. 计算机科学, 2019, 46(6A): 532-536. https://doi.org/

ZHANG Xin, HU Xiao-dong, WEI Jia-wei. Cloud Computing Based Geographical Information Service Technologies[J]. Computer Science, 2019, 46(6A): 532-536. https://doi.org/

参考文献

[1]周成虎.全空间地理信息系统展望[J].地理科学进展,2015,34(2):129-131.
[2]方雷.基于云计算的土地资源服务高效处理平台关键技术探索与研究[D].杭州:浙江大学,2011.
[3]汪品先.穿凿地球系统的时间隧道[J].中国科学(D辑:地球科学),2009,39(10):1313-1338.
[4]王钦敏.经济社会发展中的大数据应用[J].地理学报,2015,70(5):691-695.
[5]李丰丹.基于云GIS架构的地质信息服务关键技术研究[D].北京:中国地质大学(北京),2015.
[6]陆锋,张恒才.大数据与广义GIS[J].武汉大学学报(信息科学版),2014,39(6):645-654.
[7]李清泉,李德仁.大数据GIS[J].武汉大学学报(信息科学版),2014,39(6):641-644,666.
[8]倪永,陈荣国.主流云GIS平台软件应用分析[J].测绘科学技术学报,2013,30(2):177-181.
[9]GOLDBERG D,OLIVARESM,LI Z X,et al.Maps & GIS Data Libraries in the Era of Big Data and Cloud Computing[J].Journal of Map & Geography Libraries,2014,10(1):100-122.
[10]YANG C W,HUANG Q Y,LI Z,et al.Big Data and cloud computing:innovation opportunities and challenges[J].International Journal of Digital Earth,2017,10(1):13-53.
[11]TSAI W F,CHANG J Y,YAN S Y,et al.Cloud Based Web 3D GIS Taiwan Platform[J].ISPRS-International Archives of the Photogrammetry,Remote Sensing and Spatial Information Scien-ces,2012,XXXVIII-6/(1):19-22.
[12]FUSTES D,CANTORNA D,DAFONTE C,et al.Acloud-inte-grated web platform for marine monitoring using GIS and remote sensing.Application to oil spill detection through SARima-ges[J].Future Generation Computer Systems,2014,34(5):155-160.
[13]周成虎,欧阳,李增元.我国遥感数据的集成与共享研究[J].中国工程科学,2008(6):51-55,75.
[14]左尧,王少华,钟耳顺,等.高性能GIS研究进展及评述[J].地球信息科学学报,2017,19(4):437-446.
[15]谭娟,白鹤峰,陈勇,等.开放式遥感数据服务系统架构技术研究[J].武汉大学学报(信息科学版),2015,40(7):950-956.
[16]刘义,陈荦,景宁,等.利用MapReduce进行批量遥感影像瓦片金字塔构建[J].武汉大学学报(信息科学版),2013,38(3):278-282.
[17]李国庆,张红月,张连翀,等.地球观测数据共享的发展和趋势[J].遥感学报,2016,20(5):979-990.
[18]李国庆,黄震春.遥感大数据的基础设施:集成、管理与按需服务[J].计算机研究与发展,2017,54(2):267-283.
[19]任伏虎,王晋年.遥感云服务平台技术研究与实验[J].遥感学报,2012,16(6):1331-1346.
[20]徐冠华.创新驱动—中国GIS软件发展的必由之路[J].地理信息世界,2017,24(5):1-7.

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