4 results about "Cluster tree" patented technology

The invention discloses a crowd test report sorting method based on big language model multi-modal multi-layer clustering tree analysis. A clustering module, a structuring module and a sorting module are included. And the clustering module comprises feature extraction and clustering, performs semantic understanding on the image and the text by utilizing LLM, constructs a clustering prompt according to a preset prompt template, and inputs the LLM to generate a clustering result. The structuring module analyzes the clustering result output by the LLM into a multi-level clustering tree so as to represent the hierarchical relation and category between the defect reports, and metadata such as access time and accessibility are recorded on nodes. And the sorting module generates a report sorting sequence which covers different categories and is not repeated by adopting a recursive selection strategy based on the clustering tree and the node metadata so as to improve the examination efficiency and the defect discovery capability of the test report.

The application relates to the field of artificial intelligence and discloses a content generation method and device based on retrieval and electronic equipment. The method comprises the following steps: determining a first candidate text block from a plurality of preset text blocks based on a user input problem to be solved; determining the first contribution degree of each first candidate text block based on the first candidate text block and the problem to be solved; obtaining the clustering label information corresponding to the first candidate text block by querying a text block clustering tree based on the first contribution degree and the first candidate text block; determining a second candidate text block from the plurality of preset text blocks based on the first contribution degree and the clustering label information; determining target content based on the first candidate text block and / or the second candidate text block, and instructing a preset model to generate an answer. The method introduces a text block clustering tree structure, utilizes a retrieval mechanism based on contribution degree, realizes dynamic focusing and semantic diffusion in the retrieval process, and improves retrieval precision and content generation quality under complex queries.

This application belongs to the technical field of spectral analysis and discloses a method, device, electronic device, and storage medium for identifying spectral transition times. The method includes: periodically acquiring real-time spectral data of the sample to be tested; employing an agglomerative hierarchical clustering method, combined with Mahalanobis distance and minimum variance method, merging the real-time spectral data into corresponding cluster tree structure data according to the time series; determining the transition point data that meets preset clustering distance abrupt change conditions and preset frequency domain feature jump conditions based on the merging distance in the cluster tree structure data using a sliding window algorithm and a Fourier transform algorithm; verifying the transition point data according to the isolated forest algorithm to determine the time point corresponding to the transition point data of non-abnormal data as the transition time point; the above method improves the identification efficiency of spectral transition times.

The application relates to the technical field of automatic driving, and specifically provides a clustering method, a target contour and type acquisition method, equipment, a medium and a vehicle, and aims to solve the problem of improving point cloud clustering accuracy. The method provided by the application comprises the following steps: voxelizing a three-dimensional point cloud to form a plurality of voxels; establishing an index relationship between each voxel and a first neighborhood voxel with the smallest spatial position in the neighborhood of each voxel; establishing a plurality of clustering trees according to the index relationship of each voxel; and respectively merging a second neighborhood voxel in each voxel and the neighborhood of each voxel into the same clustering tree, wherein the second neighborhood voxel is a neighborhood voxel similar to the multi-source information between the voxels, and the multi-source information comprises result information corresponding to the voxels in the results of each target perception task after the three-dimensional point cloud is subjected to a plurality of target perception tasks. Through the above method, the efficiency and accuracy of point cloud clustering can be improved simultaneously, and the accuracy of target classification according to the clustering result is improved.