Volume 36 Issue 1
Jan.  2016
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Article Navigation >  Chinese Journal of Space Science  > 2016  >  36(1): 106-116
HU Yasi, SONG Junjun, SHI Peng, DUAN Ran. Study on 3D Subdivision Mode and Encoding in Heliocentric Coordination Systemormalsize[J]. Chinese Journal of Space Science, 2016, 36(1): 106-116. doi: 10.11728/cjss2016.01.106
Citation: HU Yasi, SONG Junjun, SHI Peng, DUAN Ran. Study on 3D Subdivision Mode and Encoding in Heliocentric Coordination Systemormalsize[J]. Chinese Journal of Space Science, 2016, 36(1): 106-116. doi: 10.11728/cjss2016.01.106
shu

Study on 3D Subdivision Mode and Encoding in Heliocentric Coordination Systemormalsize

doi: 10.11728/cjss2016.01.106 cstr: 32142.14.cjss2016.01.106
  • 1.

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 2.

    Beijing Aerospace Control Center, Beijing 100094

  • Received Date: 2014-07-26
  • Rev Recd Date: 2015-05-17
  • Publish Date: 2016-01-15
    Abstract
  • With the ever-increasing of space data, a reasonable data-arrangement is strongly required for high efficiency in data accessing and visualization. In this paper, basing on the characteristics of solar-wind data, a SDOG-R grid model is employed, which is radial independent division in the adaptive SDOG grid. For huge Sun-Earth space, it is divided into multi-resolution grids. The original data, which is output from a SIP-CESE solar wind model, are re-sampled, and put into the subdivided grids. The grid model is applied to both regular and irregular sampling data, and fully maintains the characteristics of original data to the best and encodes grids with an improved CDZ curve. Specific examples of data organization are given. The experiments prove that the grid model not only resolves the problem that grids are too dense at two poles and the spherical center, but also meets the need for higher resolution in radius than in latitude and longitude. Besides, 3D LOD spatial data model can not only provide multi-resolution data, but also significantly improve the large-scale mass data retrieval and access efficiency, and can support the organization and management of massive spatial data effectively.

     

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