Please wait a minute...
浙江大学学报（理学版）
浙江大学学报（理学版）  2019, Vol. 46 Issue (1): 92-100    DOI: 10.3785/j.issn.1008-9497.2019.01.011
地理信息系统     
基于大数据技术的水情云数据中心设计与研究
邱超1, 许金涛2, 元晓华1
1.浙江省水文局，浙江 杭州 310009
2.浙江大学 环境与资源学院 农业遥感与信息技术应用研究所，浙江 杭州 310058
Design and research of Zhejiang hydrologic cloud data center based on big data technology
QIU Chao1, XU Jintao2, YUAN Xiaohua1
1.Zhejiang Provincial Hydrology Bureau, Hangzhou 310009, China
2.Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
 全文: PDF(3711 KB)   HTML  
摘要： 随着浙江省水情信息化的不断推进，水情数据种类、结构和数据量不断丰富和发展，传统的单结构化数据存储管理方式已不适合当前信息化社会的应用和服务需求。 本研究采用先进的分布式数据采集、智能数据过滤、大数据存储等技术，构建基于大数据云平台技术的浙江省水情云数据中心，对各类分散的多源异构数据进行全面整合。实现了水情大数据的质量管控、深度挖掘和高效共享，丰富了水情信息的资源样本，拓展了服务深度和广度，提升了数据挖掘的服务效率和能力，为水利业务和事务的现代化发展奠定了坚实的基础。
关键词： 分布式数据采集;  智能数据过滤;  大数据存储    
Abstract: With the rapid advancement of hydrologic informatization in Zhejiang province, the type, structure and volume of hydrological data are continuously enriched and developed. Traditional single-structured data storage and management methods cannot meet the requirements of the information-based society, hence not suitable today. This research adopts advanced technologies such as distributed data acquisition, intelligent data filtering and big data storage to build a hydrologic cloud data center in Zhejiang province. Based on the big data cloud platform technology, the cloud data center integrates various types of distributed multi-source heterogeneous hydrological data. It not only supports quality control, deep mining and efficient sharing of hydrological data, but enriches the resource sample of hydrological information and expands the depth and breadth of hydrological services. With the improved data mining and analysis efficiency, the cloud data center lays a solid foundation for modernization of water conservancy.
Key words: distributed data acquisition    intelligent data filtering    big data storage
收稿日期: 2018-01-01 出版日期: 2019-01-25
CLC:  TP932  
基金资助: 浙江省水利科技计划项目（RB1717）.
作者简介: 邱超（1978—），ORCID：http：// orcid.org/0000-0001-7908-1269，男，硕士，高级工程师，主要从事水文水资源利用研究，E-mail：qc8587@163.com.
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
邱超
许金涛
元晓华

引用本文:

邱超, 许金涛, 元晓华. 基于大数据技术的水情云数据中心设计与研究[J]. 浙江大学学报（理学版）, 2019, 46(1): 92-100.

QIU Chao, XU Jintao, YUAN Xiaohua. Design and research of Zhejiang hydrologic cloud data center based on big data technology. Journal of ZheJIang University(Science Edition), 2019, 46(1): 92-100.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2019.01.011    或    https://www.zjujournals.com/sci/CN/Y2019/V46/I1/92

1 YEY, WANGZ Y, FANB Q.An analysis method for ascertain critical rainfall of mountain flood disaster of small watershed in Zhejiang province [J]. Journal of China Hydrology, 2008, 28(1):56-58. DOI: 10.3969/j.issn.1000-0852.2008.01.013
2 CHENJ F, DENGM H, WANGH M.A review of water resources big data[J]. Advances in Water Science, 2017, 28(4):622-631. DOI:10.14042/j.cnki.32.1309.2017.04.017
3 LIG J, CHENGX Q.Research status and scientific thinking of big data[J]. Bulletin of the Chinese Academy of Sciences, 2012,27(6):647-657. DOI: 10.3969/j.issn.1000-3045.2012.06.001
4 XIAN N.Research for data architecture of Guangdong provincial data center[J]. Science & Technology Vision, 2014(20):76-77. DOI：10.19694/j.cnki.issn2095-2457.2014.20.054
5 YANGF, FENGP, YUEZ X, et al.Analysis of application effects of Guangdong water resources data center[J]. Water Resources Informatization, 2014(2):20-23. DOI：10.19364/j.1674-9405.2014.02.005
6 ZHAOJ D.Design and development of hydrologic database system of Jilin province[J]. Water Resources Informatization, 2014(5):21-25. DOI: 10.3969/j.issn.1674-9405.2014.05.006
7 GAOX P, YUZ B, ZHAOW.Research on the reconstruction of Shandong the Yellow River data center based on virtualization technology[J]. Shandong Chemical Industry, 2016, 45(17):141-143. DOI：10.19319/j.cnki.issn.1008-021x.2016.17.062
8 MA Z S, XUJ B. Planning and construction of low carbon energy efficiency water resources data center [J]. Water Resources Informatization, 2011(s1):48-51. DOI：10.19364/j.1674-9405.2011.s1.015
9 WUH Y, MA M, YUH J. Research on the construction of hydraulic data center based on GIS[J]. Geomatics & Spatial Information Technology, 2017, 40(s1)：193-196. DOI: 10.3969/j.issn.1672-5867.2017.z1.054
10 WANGH F.Application system deployment based on the Internet of things and virtualization in the Ningxia water data center [J]. China Management Informationization, 2014, 18(24):26-26.
11 CHEND Q, WUL F, FENGJ, et al.Research on data integration of water resources data center based grid technology[J]. Hydrology, 2010, 30(1):76-78. DOI:10.3969/j.issn.1000-0852.2010.01.018
12 HUJ L, BAIP, ZHUH B, et al.Technology research of data exchange and application service based on data center[J]. Water Resources Informatization, 2014(2):5-8. DOI：10.19364/j.1674-9405.2014.02.002
13 GONGQ H, LIUW, LIK, et al.Construction of water data center based on big data[C]//China WRI&Hydrotechnics. Beijing: Beijing Water Consulting Co. Ltd. ，2015:6.
14 YANGC H.Design and Implementation on the Management Platform of Water Resources Geographic Information Data Based on SOA[D]. Guangzhou: Sun Yat-sen University, 2015.
15 SHAFIEEM E, BARKERZ, RASEKHA.Enhancing water system models by integrating big data[J]. Sustainable Cities & Society, 2018, 37:485-491. DOI：10.1016/j.scs.2017.11.042
16 HUY, GARCIA-CABREJOO, CAIX, et al.Global sensitivity analysis for large-scale socio-hydrological models using Hadoop[J]. Environmental Modelling & Software, 2015, 73:231-243. DOI：10.1016/j.envsoft.2015.08.015
17 BERGD J, CASSIDYA S, DEBOLEM V, et al. Streaming Programmable Point Mapper and Compute Hardware：US20170155698[P]. 2017-06-01.
18 LIUC, LIB.Research on the application of Aliyun in computing industry[J]. Science and Informatization, 2016(34):52-53.
No related articles found!