摘要
本文旨在研究一种面向格网化立方体元数据的抽象树模型,以支持多源异构对地观测元数据的管理,减少对异构元数据的解析、适配和存储结构设计。首先,基于对对地观测元数据基本特征的分析,本文抽象出元数据的一般构成要素,定义统一的抽象树模型及其操作方法;然后,基于关系型数据库系统实现抽象树模型的存储与操作映射,结合主流地理信息互操作相关国际标准设计格网化立方体元数据模型,实现异构元数据的统一集成与存储;最后,构建格网化立方体元数据管理原型系统,以北大西洋环境变量的数据管理为应用案例,展示基于抽象树模型的格网化立方体元数据操作。应用表明,本文提出的元数据模型易于操作,拓展性强,能有效支持对地观测元数据的集成与存储。
This paper aimed to investigate an abstract tree model for gridded cube metadata,designed to facilitate the management of heterogeneous multisource earth observation metadata,thereby reducing the need for parsing,adaptation,and storage structure design for heterogeneous metadata.Firstly,based on the analysis of the fundamental characteristics of earth observation metadata,this study abstracted the general components of metadata and defined a unified abstract tree model along with its operational methods.Subsequently,the storage and operational mapping of abstract tree models were implemented based on a relational database system.This endeavor was complemented by the design of a gridded cube metadata model aligned with prevalent international standards for geographic information interoperation.The overarching objective was to achieve unified integration and storage of heterogeneous metadata.Finally,a prototype system for managing gridded cube metadata was constructed.The management of North Atlantic environmental variables data was presented as an application case to demonstrate the operations of gridded cube metadata based on the abstract tree model.The application indicates that the proposed metadata model is user-friendly,highly extensible,and effectively supports the integration and storage of earth observation metadata.
作者
李德友
余劲松弟
魏丹丹
罗源
佟瑞菊
LI Deyou;YU Jinsongdi;WEI Dandan;LUO Yuan;TONG Ruiju(Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education,Fuzhou University,Fuzhou 350108,China;Academy of Digital China,Fuzhou University,Fuzhou 350108,China;School of Transportation,Fujian University of Technology,Fuzhou 350118,China)
出处
《计算机与现代化》
2024年第11期1-6,共6页
Computer and Modernization
基金
国家重点研发计划项目(2019YFE0127100)。