2results about How to "Improve matching speed" patented technology

The application discloses a virtual power plant user sensitive data identification method and device based on graph learning, and the method comprises the following steps: obtaining to-be-identified user data; generating a to-be-identified graph network according to the relationship of keywords in the to-be-identified user data; inputting the to-be-identified graph network into a pre-trained graph convolutional neural network model to obtain a minimum connected dominating set corresponding to the to-be-identified graph network; generating a simplified graph of the to-be-identified graph network based on the minimum connected dominating set corresponding to the to-be-identified graph network; and performing matching degree calculation on the simplified graph of the to-be-identified graph network and a simplified graph corresponding to standard sensitive level data to obtain a sensitive identification result of the to-be-identified user data. Through the implementation of the application, the minimum connected dominating set is generated by using the graph learning method, the complexity of subsequent graph matching is reduced, the matching speed is improved, and the application has higher efficiency compared with a traditional sensitive identification method.

This invention discloses a robot relocalization method, apparatus, and robot. In the method, laser point cloud data and image data acquired by the robot at its current position are received; keyframe information corresponding to the image data is determined according to a visual dictionary; physical location information corresponding to the keyframe information is obtained from a visual map; feature point information of the image data is matched with feature point information corresponding to the keyframe information; and calculation is performed by combining the physical location information of the feature points in the visual map and the pixel location information of the feature points in the image data. After obtaining the robot's first pose data, a predetermined area is set based on the first pose data, and iterative matching calculations are performed within the predetermined area using the laser map and laser point cloud data to obtain the robot's second pose data. By using the above scheme, the iteration range of laser matching can be reduced by utilizing the initial pose data, thus accelerating the matching speed and quickly achieving robot relocalization.