Dynamic index multi-target self-adaptive construction method for product reverse engineering data

Abstract

The invention provides a dynamic index multi-target self-adaptive construction method for product reverse engineering data, and the method is characterized by comprising the following steps of firstly reading a product reverse engineering data file, constructing an axial bounding box of each space target, constructing a data node corresponding to each space target according to the center and a circumscribed radius of the axial bounding box, storing the data node into a data node sequence, inserting each data node in the sequence into an index structure through steps such as selection of an insert position, forced re-insertion, fracturing of nodes, adjustment of node axial bonding box and the like, re-inserting the data node with large size of the axial bounding box into the index structure to further optimize the index structure, and realizing the construction of the dynamic index structure of the product reverse engineering data. Due to the adoption of the method, the space index structure of different complicated product reverse engineering data can be constructed, and characteristics of low parameter dependence, strong stability and high inquiring efficiency can be realized.

Description

technical field [0001] The invention provides a multi-object self-adaptive construction method for product reverse engineering data dynamic index, which belongs to the technical field of product reverse engineering. Background technique [0002] In the field of product reverse engineering technology, the raw data processed are usually data formats such as scattered point clouds and polygonal mesh models obtained by sampling the surface of the physical object. Surface reconstruction based on such raw data to generate piecewise continuous surfaces is a product reverse engineering. core technology. Since data formats such as scattered point clouds, polygonal meshes, and sliced ​​continuous surfaces all represent complex structures of large-scale or even massive spatial geometric objects, building a general and efficient indexing technology for these data types is crucial for improving product reverse engineering data. Processing efficiency is of great importance. [0003] Exi...

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Problems solved by technology

Sun Dianzhu and others improved the R*-tree in their academic paper "R*-tree node splitting algorithm based on four-dimensional clustering" (Journal of Mechanical Engineering, 2009, 45 (10): 180-184), making it It can uniformly index data types such as scattered point clouds and polygonal grids, and then the improved R* -The tree is used as the index structure of the sliced ​​continuous surface to improve the query efficiency of intersecting triangular Bézier surface slices, but since the improved R-tree uses the k-means clustering algorithm in the process of splitting the index nodes, the user needs to interact to set the clustering The number of clusters and the number of clusters will lead to very different index node split results, resulting in unstable index structure and performance. In addition, the k-means clustering algorithm is a local search algorithm that is too sensitive to the initial value. The hill-climbing method iteratively searches for the optimal index node splitting result, which is easy to fall into local extremum, and it is difficult to obtain the globally optimal index node splitting result, resulting in failure to give full play to the advantages of the R*-tree

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