Research Based On Cloud Computing Of Massive Terrain Data Processing Method

High realism, immersion and friendly human-computer interaction is the trend of the development of computer applications. Graphics hardware acceleration and graphics technology development related research to promote the rapid development of digital earth concept, establishing a realistic, high resolution, and can roam in the scene through virtual three-dimensional rendering of Digital Earth. However, for the massive terrain data processing, storage and query problem, the traditional method has a high cost of hardware, storage efficiency and low defects.To solve these problems, this paper will analyze and research-based processing, indexing and transmission, the main contents massive terrain data Hadoop cloud platform include:1. Environmental Terrain Data Processing Method on Cloud computingThere are different between treatment and handling of data in a distributed environment and in a stand-alone environment. Stand-alone environment for data block, focusing on upgrading the efficiency of drawing; and the purpose of data processing in a distributed environment is mainly to mass data storage and reasonable, and quickly from the distributed database indexing and scheduling, and reasonable points block size can save bandwidth and improve the transmission efficiency of the network data.Therefore, the current analysis stratified spherical terrain data and chunked encoding and building block approach, as well as their advantages and disadvantages, and implementation. Combined with the current global meshing algorithm research in a distributed environment, how the terrain data for more rational hierarchical, block and coded to quickly adapt to the cloud for data processing, as well as the local terrain fast processing of the data.2. Research massive data storage in a distributed databaseLarge data storage structure and are stored in the database, and the indexing data block has a great influence on the extraction efficiency of data blocks. We analysis and research the mode of operation of distributed database, as well as the current study large data storage and storage policies in a distributed database. And create massive terrain data storage structure and the establishment of an efficient way to speed up the data block index lookup speed, improve the efficiency of the data block to find.3. Hadoop cloud environment designed real-time interactive systemsApplication of the above findings, the use of Hadoop technology design three-dimensional real-time visualization roaming system, and verify the efficiency of the parallel algorithm to build and to compare the performance of distributed database storage.