Multi-level mixed three-dimensional space index method

Abstract

The invention relates to a multi-level mixed three-dimensional space index method which comprises the following steps: 1, building a united three-dimensional space index framework; 2, dividing a large-scale city space into a plurality of small regions, and building a first level grid index; 3, in the first level grid index region, building a second level multi-type mixing index for three-dimensional space data with different types; and 4, based on the multi-level mixed three-dimensional space index, carrying out multi-level query search on a three-dimensional space database. The three-dimensional space index method improves the searching efficiency of the large-scale three-dimensional space database through multi-level filter and search.

Description

technical field [0001] The invention relates to the technical field of geographic space systems, in particular to a multi-level mixed three-dimensional space indexing method. Background technique [0002] In 3D GIS, tens of thousands of 3D space objects such as buildings, pipelines, and geological bodies above and below ground are distributed unevenly in 3D space. How to efficiently query 3D space objects that meet specified spatial conditions from 3D space is a traditional The two-dimensional spatial index technology is difficult to deal with, and the effective support of the three-dimensional spatial index is urgently needed. Since the R tree spatial index method proposed by Guttma in 1984, R tree and its variants have been widely studied and applied, such as R+ tree, R* tree and HilbertR tree, and combined index methods such as QR* and LOD-OR. The current three-dimensional spatial indexing methods mainly include: object segmentation method, which is mainly realized by hi...

AI Technical Summary

Problems solved by technology

[0002] In 3D GIS, tens of thousands of 3D space objects such as buildings, pipelines, and geological bodies above and below ground are distributed unevenly in 3D space. How to efficiently query 3D space objects that meet specified spatial conditions from 3D space is a traditional The two-dimensional spatial index technology is difficult to handle, and the effective support of the three-dimensional spatial index is urgently needed

Therefore, in response to the demand for integrated retrieval of various 3D spatial entities above and below the ground in large-scale 3D spatial databases, various thematic models such as architectural models, geological body models, and pipeline models have different functions in geometric shape and texture characteristics, spatial distribution characteristics, and semantic topological relationships. For example, the architectural model has the characteristics of discrete distribution, different shapes and sizes of objects, and complex indoor semantic topological relations; the geological body model has the characteristics of continuous entity description and complex spatial relationship; the pipeline model has the characteristics of linear continuous distribution , but the semantic topological relationship of pipe points and pipeline segments is clear. Since each 3D spatial index method has its applicability and limitations, it is difficult to use a general 3D spatial index method to meet the efficient indexing of all types of 3D model data.

Images