4results about How to "Guaranteed classification accuracy" patented technology

The present application belongs to the technical field of power transmission line engineering cableway design and construction, and particularly relates to a power transmission line cableway stockyard point mode recognition method and system based on slope compensation and road width dynamic correction, a storage medium and a computing device. The method comprises the following steps: S1, road feature extraction: including extracting road center line feature data, road final width feature data, and road unit direction feature data; S2, road feature enhancement processing; S3, calculating road curvature; S4, mode recognition classification: using a feature space dynamic weighting segmentation algorithm, through an iterative optimization process in a dynamic weighting road feature space, dynamically adjusting road feature weights, scaling the original road feature space, so that the left and right boundary points in the transformed space can be effectively separated by a hyperplane, realizing accurate two-classification of the stockyard points; S5, space topology consistency enhancement. The method can solve the problem of road width measurement distortion caused by steep slope terrain.

This invention proposes a hierarchical label text classification method based on the MOE model, comprising the following steps: encoding the input text using a shared encoder to obtain a unified vector representation of the text; inputting the text vector representation into a router module to calculate the probability distribution of the first-level labels, and activating the top-k expert modules corresponding to the first-level labels; the activated expert modules performing classification predictions within their respective second-level label subspaces; and weighted fusion of the prediction results from the expert modules to obtain the final hierarchical label classification result. This invention extracts text semantic features uniformly through a shared encoder, avoiding redundant learning across branches; it allows samples to simultaneously activate multiple candidate experts through a Top-k soft routing mechanism, breaking the rigid single-path limitation of traditional top-down methods; and it combines routing weights with expert outputs, using the maximization of joint probability as the decision criterion, significantly reducing the number of model parameters and the risk of error cascading while ensuring classification accuracy.

The application discloses a kind of based on spectral gradient field guide's bayesian adaptive graph convolution hyperspectral remote sensing image classification method, utilize the spectral information of pixel in hyperspectral remote sensing image and spatial neighborhood relationship to construct initial feature representation;Introduce spectral gradient field to describe local spectral variation amplitude and direction, adaptively adjust the connection relationship between pixel nodes;According to local spectral difference, dynamically expand or shrink neighborhood range, optimize the expression of graph structure in complex heterogeneous region;With Beta distribution, the connection probability between nodes is modeled, the robustness and stability of graph topology structure are improved;For large-scale hyperspectral scene, construct block reasoning mechanism, divide the image into several spatial sub-blocks and complete graph construction and classification reasoning independently, reduce the computational complexity and storage overhead;Pixel-level classification prediction is carried out on the whole image, and high-precision classification result map is generated.The application enhances the expression ability of strong spatial heterogeneity region, and improves the hyperspectral image classification precision and robustness.

The application provides a skeleton behavior recognition video frame extraction method and system under multiple time scales, and the technical points are as follows: first, a target detection algorithm is used to frame the person in the video to obtain the position information of the person in the video; then, a human key point estimation algorithm is used to obtain the key point position of the person information in the video; subsequently, the stacked human key point heat map is uniformly sampled, and the uniformly sampled frame is low, medium and high frequency sampled, so that the model can learn the features under different scales of the video from coarse granularity to fine granularity in a hierarchical manner, thereby enhancing the understanding ability of 3D-CNN for long videos; finally, the convolution channels of the frames under different frequencies in multiple scales are obtained in a parallel manner, the corresponding feature information is obtained, the feature information is subjected to judgment and normalization processing, according to the probability result after recognition, and finally the behavior recognition category is output. The application can improve the performance of the model on long videos while ensuring the accuracy of classification.