Research On Key Technologies For Virtual Geographic Environment Based On Distributed Storage

In this paper ,on the basis of learning from domestic and foreign research results, the author do a more in-depth research on some important theories, key technologies and related algorithms of the distributed storage virtual geographic environment, In this article, adaptive spatial data models and metadata-based spatial data engine are demonstrated by a large number of experimental data. On this basis a multi-level and multi-area distributed storage virtual geographic environment is simulated and ultimately the prototype platform is completed practically. By this platform we can achieve the storage, management, sharing, and visualization of geography spatial data. This paper has conducted some research and practice in more detail, the main contents and innovations are as follows:1. Analyse the features and problems of the existing distributed storage VGE by summarizing the system architecture, data resource, supporting technologies and application platform. We construct four-level theoretical system architecture for distributed storage VGE, and give the content structure and supporting technologies for four layers, including base layer, resource layer, service layer and application layer. The four layers provide a theoretical and methodological guidance for the study design and application service of distributed storage VGE. We also explore and design the system architecture of VGE based on grid, cloud patterns, internet of things, which not only provide a good thought for the development of distributed storage VGE, but also demonstrate the expansibility and compatibility of the proposed architecture.2. Establish VGE metadata model. By combining with the application requirements of distributed storage VGE, we build the metadata model for three-dimensional simulation model and vector and raster data integrated expression, and realize eight key technologies for distributed storage VGE metadata, such as dynamic data management, rapid analysis and cache maintenance. All these work provide reference for normalization and standardization of distributed storage VGE metadata.3. Build the adaptive spatial data model. We propose five constrained factors to build the adaptive spatial data model, including data, software, user, computer and network. By improving the data model based on spherical Clipmap and distributed storage VGE data model, we construct the adaptive spatial data model. The data model help us solve the problems of adaptive spatial data organization and management in distributed storage VGE. By using Improved spatial data model based on spherical Clipmap, the spatial data utilization can increase by 2 times.4. We establish the metadata-based distributed spatial data engine to improve the indexing and retrieval speed of spatial data. We make construction and analysis of spatial data storage system, and propose a new data service model by the composition of several Linux cache servers. The service model not only accelerates the data access speed, but also eliminates the bottleneck of Lustre file system. We improve and achieve a number of key spatial data engine technologies, including server quick location, data cache design, data parallelism extraction, socket connection pooling, etc. Finally, we build a spatial data engine for distributed storage VGE.5. We achieve the multi-modal applications for adaptive spatial data visualization, and build a global vector and raster integrated visualization framework and distributed spatial data visualization framework. We also explore and practise some acceleration technologies of distributed spatial data visualization application. Finally, we generate multi-modal applications for spatial data visualization based on adaptive spatial data model, which verify the validity of the data model.6. We design and implement the system named DSVGEP (Distributed Storage Virtual Geographic Environment Platform). We make detailed description to the key modules of DSVGEP, including data storage, metadata management, adaptive spatial data model, spatial data service engine and visual expression. At last, we simulate a hardware deployment and complete the functional practice. By the experimental test, we find a single Linux client can support the concurrent access of 100 clients.