Vehicle point cloud wind resistance coefficient prediction method and system based on multi-scale learning and convolution

Through multi-scale learning and convolution methods, combined with the farthest point sampling and physical guidance attention mechanism, the problem of insufficient prediction accuracy of stroke resistance coefficient in the existing technology is solved, high-precision and fast prediction of wind resistance coefficients is achieved, and the efficiency and economicality of automobile design and manufacturing are improved.

CN120409269AActive Publication Date: 2025-08-01WUHAN UNIV OF TECH
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Patent Information

Application Number
CN202510577382.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-06
Publication Date
2025-08-01
Estimated Expiration
2045-05-06

AI Technical Summary

Technical Problem

The existing wind resistance coefficient prediction methods fail to fully tap the potential information of point cloud data at the multi-scale level, ignore the synergy between local and global features, resulting in insufficient prediction accuracy and difficult to meet the automotive industry's demand for high-precision and rapid prediction.

Method used

The multi-scale learning and convolution method is adopted to extract the local detailed characteristics and global structural characteristics of point cloud data through farthest point sampling, multi-scale convolution and physical guidance attention mechanism, and establish the mapping relationship of the resistance coefficient to output the resistance coefficient prediction results.

Benefits of technology

It improves the accuracy and reliability of wind resistance coefficient prediction, enhances the diversity and robustness of feature representations, realizes end-to-end prediction, and reduces hardware costs and optimization cycles.

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Abstract

The invention provides a whole vehicle point cloud windage coefficient prediction method and system based on multi-scale learning and convolution, and relates to the technical field of windage coefficient prediction, and the method specifically comprises the steps: obtaining point cloud data of a whole vehicle model, and carrying out the preprocessing of the point cloud data; sampling the preprocessed point cloud data through a farthest point sampling method so as to reserve geometric key points; constructing a convolutional neural network model, inputting the point cloud data into the network, sequentially carrying out two times of multi-scale convolution operations, extracting local details and global structure features through convolution kernels of different scales, mapping the features from low dimensions to 512 dimensions and expanding the features to 1024 dimensions, and completing feature aggregation; the feature expression capability is further enhanced through two-layer convolution; utilizing a maximum pooling layer to aggregate global features, and performing dimension reduction on the features through a multi-layer perceptron; introducing a physical guidance attention mechanism to physically constrain the spatial weight of the features; and establishing a mapping relation between the point cloud features and the wind resistance coefficient through a full connection layer, and outputting a wind resistance coefficient prediction result. According to the invention, the accuracy and efficiency of wind resistance coefficient prediction can be improved.
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