基于日心坐标系的三维立体剖分模型及编码

胡雅斯; 宋君君; 时蓬; 段然

doi:10.11728/cjss2016.01.106

基于日心坐标系的三维立体剖分模型及编码

doi: 10.11728/cjss2016.01.106

胡雅斯^1, ,,
宋君君²,
时蓬¹,
段然¹

1.
中国科学院国家空间科学中心北京 100190
2.
北京航天飞行控制中心北京 100094

详细信息

通讯作者:
胡雅斯,E-mail:hyssmily@163.com

中图分类号: P353;TP391.9
计量
- 文章访问数: 864
- HTML全文浏览量: 52
- PDF下载量: 1065
- 被引次数: 0
出版历程
- 收稿日期: 2014-07-26
- 修回日期: 2015-05-17
- 刊出日期: 2016-01-15

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

HU Yasi^{1
, ,},
SONG Junjun²,
SHI Peng¹,
DUAN Ran¹

1.
National Space Science Center, Chinese Academy of Sciences, Beijing 100190
2.
Beijing Aerospace Control Center, Beijing 100094

摘要

摘要: 随着空间数据的大量增长, 对数据可视化和数据存取效率提出了更高要求, 迫切需要对数据进行有效的组织和管理. 对庞大的日地空间, 采用SDOG-R方法将日地空间剖分为不同分辨率等级的格网, 并针对该网格提出相应的编码方案. 以太阳风模型数据为例, 给出了具体的组织实例, 经实验验证, 该剖分模型不仅解决了球心处网格过密问题, 还满足了径向分辨率大于经纬球面分辨率的需求. 基于三维立体剖分的太阳风LOD空间数据模型, 不但能提供多分辨率数据, 而且显著提高了大规模数据检索和存取速度, 有效地支持海量空间数据的组织管理.
- 空间剖分 /
- SDOG-R /
- 编码 /
- 多分辨率
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.
- Space subdivision /
- SDOG-R /
- Encoding /
- Multi-resolution

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