网格计算环境下GML空间分析关键技术研究

发布时间：2019-01-09 12:59

【摘要】：GML作为空间数据编码、传输、存储和发布的国际标准,GML的出现为GIS空间数据建模、集成与共享提供了统一的标准和框架。伴随着GML格式的空间数据大量涌现,单机环境下已不能适应这些海量、半结构化特点的GML数据组织与管理的需要,针对GML数据进行有效组织和管理的技术研究成为当务之急。网格计算环境具有的强大计算和数据管理能力为海量数据的组织与管理提供了一套有效、智能的解决方案。为此如何在网格计算环境下高性能并行计算处理GML数据是研究的初衷。本文以单机环境下GML空间分析相关理论为基础,研究网格计算环境下GML空间分析关键问题,主要包括以下几个方面： (1)GML空间数据拓扑表达与编辑。根据GML半结构化的数据特点,提出了基本基元的拓扑表达方法,并从点、线、面等的表达来具体说明GML拓扑编辑,最后在AE (ArcGIS Engine)中实现GML拓扑编辑。 (2)网格计算环境下GML并行空间分析任务调度研究。考虑GML数据特点,提出了基于Hilbert空间填充曲线的GML数据划分策略对GML数据进行划分,并实现在网格环境下对GML数据的并行查询；结合网格计算调度模型与算法以及负载均衡问题对GML并行空间分析设计了任务调度策略 (3)网格计算环境下GML空间分析的并行计算研究。为了提高并行计算效率,研究基于网格环境下的分布式并行计算,提出基于网格的并行计算模型、算法、策略；并针对空间分析的不同问题,提出了GML空间分析并行算法。 (4)网格计算环境下GML空间分析性能验证。依托构建的网格计算平台Globus Toolkit,设计GML空间分析并行计算流程来进行实验,试验网格计算环境下GML空间分析并行计算效率,得出海量GML数据的空间分析在网格计算环境下的分析效率要优于单机环境。最终通过对网格计算环境下GML空间分析的深入研究,实现了网格计算环境下GML的互操作性,进一步丰富和完善网格计算环境下的GIS应用体系。
[Abstract]:GML is an international standard for spatial data coding, transmission, storage and distribution. The emergence of GML provides a unified standard and framework for GIS spatial data modeling, integration and sharing. With the emergence of large numbers of spatial data in GML format, the single machine environment has been unable to adapt to the needs of these massive, semi-structured GML data organization and management. It is urgent to study the technology of effective organization and management of GML data. The powerful computing and data management ability of grid computing environment provides an effective and intelligent solution for the organization and management of massive data. Therefore, how to process GML data in high performance parallel computing in grid computing environment is the original intention. Based on the theory of GML spatial analysis in a single computer, this paper studies the key problems of GML spatial analysis in grid computing environment, including the following aspects: (1) topological representation and editing of GML spatial data. According to the characteristics of GML semi-structured data, the topological representation method of basic primitive is proposed, and the topological editing of GML is explained from the expression of points, lines and surfaces. Finally, the topology editing of GML is realized in AE (ArcGIS Engine). (2) Research on GML parallel spatial analysis task scheduling in grid computing environment. Considering the characteristics of GML data, a GML data partition strategy based on Hilbert space filling curve is proposed to partition GML data, and the parallel query of GML data is realized in grid environment. Based on grid computing scheduling model and algorithm and load balancing problem, a task scheduling strategy for GML parallel space analysis is designed. (3) parallel computing of GML space analysis in grid computing environment. In order to improve the efficiency of parallel computing, the distributed parallel computing based on grid environment is studied, and the parallel computing model, algorithm and strategy based on grid are proposed, and the parallel algorithm of GML spatial analysis is proposed for different problems of spatial analysis. (4) performance verification of GML spatial analysis in grid computing environment. Based on the grid computing platform Globus Toolkit, the parallel computing flow of GML space analysis is designed to test the efficiency of GML spatial analysis parallel computing in grid computing environment. It is concluded that the efficiency of spatial analysis of massive GML data in grid computing environment is better than that in single computer environment. Finally, through the in-depth study of GML spatial analysis in grid computing environment, the interoperability of GML in grid computing environment is realized, and the GIS application system in grid computing environment is further enriched and improved.
【学位授予单位】：江西理工大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：P208;TP338.6

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