Research on the quality evaluation method for digital twin laser scanning of mine coal flow
Laser scanning technology is the basic method of mine digital twin modeling,and the quality of the model point cloud directly affects the accuracy of twin modeling and intelligent analysis.The principle of digital twin modeling for coal flow laser scanning was introduced.The analysis of information entropy of the geometric parameters of the coal flow point cloud clarified the relationship between the point cloud information entropy and the noise intensity,which can be used as an experimental basis for establishing the quality evaluation method of the laser scanning coal flow point cloud model.The influencing factors of information entropy were ascertained,and a multi-factor variance experiment was carried out to test the significant effect,and the experiment results showed that the change of information entropy was mainly related to coal flow and noise intensity.Combining the quadric surface equation and the Gauss Newton iteration method,the prediction model of noise intensity was optimally trained.By constructing a fuzzy relationship matrix,the fuzzy inference of point cloud filter parameters was achieved,resulting in a high-fidelity,high-reliability and high-accuracy coal flow point cloud model.Based on the experiment with coal flow point cloud samples collected on the coal mine fully mechanized caving face,the results indicated that the point cloud quality evaluation algorithm had better consistency,stability and monotonicity,and was more suitable for complex working conditions in underground coal mines.
digital twinintelligent coal flowpoint cloud datamodel qualityinformation entropy
NETL
NSTL
万方数据
