Argo资料协同管理方法研究

doi:10.3969/j.issn.1001-909X.2017.03.001

海洋学研究 ›› 2017, Vol. 35 ›› Issue (3): 1-8.DOI: 10.3969/j.issn.1001-909X.2017.03.001

• 研究论文 • 下一篇

Argo资料协同管理方法研究

董贵莹^1,2, 曹敏杰^3,4, 张丰^1,2, 杜震洪^*1,2, 刘仁义^1,2, 吴森森^1,2

1.浙江大学浙江省资源与环境信息系统重点实验室,浙江杭州 310028;
2.浙江大学地理信息科学研究所,浙江杭州 310028;
3.卫星海洋环境动力学国家重点实验室,浙江杭州 310012;
4.国家海洋局第二海洋研究所,浙江杭州 310012

收稿日期:2016-12-16 修回日期:2017-03-31 出版日期:2017-09-15 发布日期:2022-11-18
通讯作者: 杜震洪(1981-),男,副教授,主要从事地理信息服务、时空大数据、高效能地学计算、海洋GIS研究。E-mail:duzhenhong@zju.edu.cn
作者简介:董贵莹(1992-),女,天津市人,主要从事海洋GIS相关研究。E-mail:413521577@qq.com
基金资助:
国家自然科学基金项目资助(41101356,41101371,41171321);国家科技基础型工作专项项目资助(2012FY112300);海洋公益性行业科研专项经费项目资助(201305012);测绘地理信息公益性行业科研专项项目资助(201512024)

Research on collaborative management method of Argo data

DONG Gui-ying^1,2, CAO Min-jie^3,4, ZHANG Feng^1,2, DU Zhen-hong^*1,2, LIU Ren-yi^1,2, WU Sen-sen^1,2

1. Zhejiang Provincial Key Lab of GIS, Zhejiang University, Hangzhou 310028, China;
2. Institute of GIS, Zhejiang University, Hangzhou 310028, China;
3. State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou 310012, China;
4. Second Institute of Oceanography, SOA, Hangzhou 310012, China

Received:2016-12-16 Revised:2017-03-31 Online:2017-09-15 Published:2022-11-18

摘要/Abstract

摘要： Argo资料已成为当前海洋和大气科学领域基础研究的重要数据来源。但由于其剖面元数据与观测数据混合存放的特点,现有的共享平台无法实现浮标漂移轨迹与剖面图的实时绘制。因此本文提出一种结构化与半结构化并存的Argo资料协同管理方法,通过分析Argo资料结构组成与特点,将结构化数据与半结构化数据分离提取;然后利用关系型数据库对半结构化类型属性的扩展支持,建立剖面元数据与观测数据间的关联关系;并利用分表存储,降低数据量快速增长对单数据表带来的存储压力。最后通过对近20 a的全球Argo资料解析建库结果进行分析,证明该方法具有良好的可扩展性和高效的轨迹数据获取效率,能够支持浮标漂移轨迹和剖面图的实时绘制。同时,该方法也可为特征类似的剖面观测数据管理提供技术参考。

关键词: Argo资料, 剖面, 结构化与半结构化, 协同管理

Abstract: Argo data have become an important data source in basic researches of ocean and atmosphere sciences. But with the hybrid storage of profile metadata and its observations, it is difficult for existing shared platforms to realize the real-time visualizing of the drift trajectory and profile chart. A method of collaborative management of structured and semi-structured Argo data were proposed. Based on the analysis of Argo data structure and characteristics, Argo data were separated as structured and semi-structured data. By means of extended support of relational database to a semi-structured property, a relationship between profile metadata and observation data was established. Faced of the rapid growth of data, a multi-table storage method was used to reduce storage pressure of single data table. Finally, an Argo database was set up and global Argo data in period of 1997-2016 are parsed and imported. The results indicate that the method has good scalability of both storage and query, as well as high efficiency of float trajectory query, and it is able to support real-time visualization of float trajectory and profile chart. Our result will offer a reference for management of other similar observation profile data.

Key words: Argo data, profile, structured and semi-structured, collaborative management

中图分类号:

P71
TP311.52

董贵莹, 曹敏杰, 张丰, 杜震洪, 刘仁义, 吴森森. Argo资料协同管理方法研究[J]. 海洋学研究, 2017, 35(3): 1-8.

DONG Gui-ying, CAO Min-jie, ZHANG Feng, DU Zhen-hong, LIU Ren-yi, WU Sen-sen. Research on collaborative management method of Argo data[J]. Journal of Marine Sciences, 2017, 35(3): 1-8.

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Argo资料协同管理方法研究

Research on collaborative management method of Argo data

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