Dynamic index self-adaptive construction method of product reverse engineering data based on mean value shift

Abstract

The invention provides a dynamic index self-adaptive construction method of product reverse engineering data based on mean value shift. The method is characterized by comprising the following steps of: firstly reading a product reverse engineering data file, establishing axial bounding boxes of spatial spaces, establishing data nodes corresponding to the spatial spaces according to the centers of the axial bounding boxes and the radiuses of external spheres, storing the data nodes into a data node sequence, inserting the data nodes in the sequence into an index structure through steps of insertion position selection, forced reinsertion, node splitting, axial bounding box node adjustment and the like, reinserting data nodes with larger axial bounding box sizes into the index structure, and further optimizing the index structure so as to realize the establishment of product reverse engineering data based on mean value shift. With the adoption of the method, spatial index structures of various complex product reverse engineering data can be established, and the method has the characteristics of being low in parameter dependence, strong in stability and high in checking efficiency.

Description

technical field [0001] The invention provides a method for self-adaptively constructing a dynamic index of product reverse engineering data based on mean drift, and 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. ...

AI Technical Summary

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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