Rock mass structural surface intelligent identification method based on three-dimensional point cloud

By using gridded spherical accumulator and rotary point cloud technology in the intelligent recognition method of rock mass structure surfaces to improve the accuracy of normal vectors, combined with improved regional growth algorithms and K-means++ clustering method, the distortion and density anisotropy of normal vectors on the high surfaces is solved, and high-precision rock mass structure surface recognition and cluster segmentation are achieved.

CN119693931APending Publication Date: 2025-03-25CHINA AVIATION OIL PENGZHOU PIPELINE TRANSPORTATION CO LTD +1
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Patent Information

Application Number
CN202411481364.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-23
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

In the prior art, when calculating the normal vector of rock mass structure surfaces, the normal vector is distorted under the condition of a highly curved surface, which cannot effectively solve the density anisotropy and point cloud structural surface marginalization effects, and there are problems of iterative non-standard and inaccurate selection of optimal cluster counts.

Method used

A rock mass structural surface intelligent recognition method based on three-dimensional point clouds is adopted. By filtering out noise and outliers, the initial point cloud cluster is constructed and the initial normal vector is calculated. The gridded spherical accumulator and rotating point cloud technology are used to improve the accuracy of the normal vector. Then, using improved region growth algorithms and improved K-means++ clustering method, structural surfaces are segmented and clustered to determine the division of structural surfaces of dominant groups.

Benefits of technology

It improves the accuracy and efficiency of rock mass structural surface recognition, can accurately obtain normal vectors in complex and changeable environments, and achieves a better extraction effect of single discontinuous structural surfaces.

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Abstract

The invention provides a rock mass structural plane intelligent identification method based on three-dimensional point clouds, and belongs to the technical field of rock mass structural plane intelligent identification, and the method comprises the steps: selecting a target collection point, constructing an initial point cloud set, calculating an initial normal vector, setting a spherical accumulator, and rotating the point clouds in unstructured point clouds formed by a rock mass structural plane; obtaining an accurate normal vector; according to an improved region growing algorithm, obtaining a better number of structural surfaces, and obtaining a single structural surface of a plurality of rock mass point clouds; and traversing a single structural surface of each rock mass point cloud, replacing a distance function with an angle error quadratic sum according to an improved K-means + + clustering method, determining an optimal clustering number, and completing the intelligent identification method for the rock mass structural surface. According to the method, the high precision of the normal vector at the edge junction of the structural plane is realized under the normal vector smoothing phenomenon, the high standard of K-means clustering iteration and the high accuracy of the optimal cluster number of selected clustering are realized, and the problems of density anisotropy and the marginalization effect of the point cloud structural plane are solved.
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