4results about How to "Improve algorithm efficiency" patented technology

The present application belongs to the technical field of ground laser radar forestry data processing, and discloses a broad-leaved forest single tree segmentation method and system based on branch information guidance, taking ground-based laser radar broad-leaved forest point cloud as a processing object, using RANSAC cylindrical fitting to combine the growth characteristics of the tree trunk to detect the tree trunk; starting from the top of the tree trunk in the low vegetation area, the tree branches are extracted by segmenting and growing the branches and combining the thickness changes of the branch segments; starting from the end of the branch, the tree crown leaf point cloud is segmented by layer-by-layer growth. The present application has stronger trunk detection capability under the conditions of lush low vegetation and complex terrain; on the other hand, the present application can accurately segment the tree crown when the large and small crowns are intertwined and the multiple crowns are closely surrounded. The algorithm is efficient, simple and easy to use, and has great practical significance for improving the semantic understanding ability of forest scenes, assisting forest resource investigation, vegetation ecological research, and satellite remote sensing product calibration and verification.

The disclosure provides a social network data analysis method and system based on limited k-means, and relates to the technical field of social network data processing. In the method, the initialization center selection stage mainly considers the connected constraint. After the first center is randomly selected, the remaining clustering centers are selected by cyclically calculating the weight probability affected by the connected constraint. Due to the influence of the connected constraint, the centroid of each connected set is used to represent the data points in the connected set to solve the limited k-means problem. Then, in the assignment step of the algorithm iteration stage, for the two types of data constraints, the strategy of preferentially processing the disjoint non-connected set and preferentially considering the intersection of the disjoint non-connected set and the connected set is adopted, and the constraint points are classified and processed, so that higher algorithm efficiency is achieved. The disclosure solves the problem of inaccurate data processing of the connected constraint and the disjoint non-connected constraint in the clustering process.

This invention relates to the field of robot skill learning technology, specifically to an efficient robot skill learning method, system, device, program product, and medium. The method includes: obtaining the robot's task policy equation in Cartesian space; obtaining the expected trajectory at each time step of the robot's task policy; constructing the cost function of the robot's task policy; and obtaining the robot's final expected trajectory. This invention utilizes interactive reinforcement learning to integrate human and machine intelligence, improving algorithm efficiency and overcoming the limitations of existing reinforcement learning methods in robot operation applications. It enables real-world robot skill learning, allowing robots to be applied in a wider range of scenarios.

The present application relates to the technical field of wireless communication, and particularly relates to a kind of super-diagonal RIS enhanced multi-target sensing integration optimization method and system. Including: establishing super-diagonal RIS enhanced multi-target sensing integration system model;Under the constraints such as meeting user minimum rate constraint and base station transmitting power constraint, with base station transmitting beamforming matrix and super-diagonal RIS scattering matrix as optimization variable, the sensing estimation Cramer-Rao bound minimization is constructed Sensing integration joint optimization problem;The joint optimization problem is solved by alternating optimization algorithm, on the one hand, the Shur complement convex conversion technology and semi-positive definite relaxation method are used to optimize base station transmitting beamforming matrix, on the other hand, the adaptive Riemann steepest descent method based on geodesic is used to optimize super-diagonal RIS scattering matrix under unitary constraint, and the common promotion of sensing performance of super-diagonal RIS enhanced multi-target sensing integration system is realized by the alternating optimization of two parts.