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.