9 results about "Cluster tree" patented technology

The invention relates to the technical field of digital data processing, in particular to a circuit breaker fault detection method based on electrical parameter analysis, and the method comprises the steps: obtaining monitoring data, and constructing a parameter matrix; screening suspicious elements based on prediction deviation of elements in the parameter matrix and a mutation point detection result, and determining a first characteristic value according to a starting moment determined by the suspicious elements and a rule degree of data fluctuation in a fluctuation duration of the suspicious elements, and determining a fault interval by combining a consistency analysis result of irregular data fluctuation in parameter matrixes of different types of monitoring data, screening target data from residual terms, obtaining a plurality of clustering trees based on the target data by using an agglomerated hierarchical clustering algorithm, and determining a fault detection result of the circuit breaker based on differences among different clustering trees. According to the method, the residual component is used for replacing the initial data, so that the clustering effect of the clustering number on the monitoring data with different fault sensitivity degrees is improved while the clustering data size is reduced.

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 discloses a self-adaptive text classification method in the field of scientific information, which comprises the following steps: training a classifier and establishing an original clustering forest; selecting a suitable classifier and establishing an optimal clustering forest, wherein the classifier is a clustering tree algorithm; and testing the latest sample to obtain an integrated final result. The application can improve the classification effect and generalization ability of the text classification in the field of scientific information, thereby improving the accuracy of the text classification model and the accuracy of the text classification.

The invention discloses a tree point cloud branch and leaf separation method and system based on a multi-dimensional super voxel random forest, terminal equipment and a medium, and relates to the technical field of tree point cloud branch and leaf separation, and the method comprises the steps: carrying out the clustering of tree point clouds, and obtaining a tree super voxel set; performing feature extraction on the tree super voxel set to obtain multi-dimensional features of the tree super voxel set; inputting the multi-dimensional features of the tree super-voxel set into a pre-trained random forest classifier model to obtain an initial branch super-voxel set and an initial leaf super-voxel set; and performing category distance constraint processing on the initial branch super-voxel set and the initial leaf super-voxel set to obtain a branch super-voxel set and a leaf super-voxel set. According to the method, point cloud missing and noise influence are reduced through the super voxel unit, branches and leaves are accurately separated in combination with multi-dimensional features and random forest robustness, support is provided for tree parameter extraction, three-dimensional modeling and forestry management, and the method is suitable for a complex tree structure scene.

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 relates to the technical field of digital data processing, specifically to a circuit breaker fault detection method based on electrical parameter analysis. The method includes: obtaining monitoring data and constructing a parameter matrix; screening suspicious elements based on the prediction deviation of elements in the parameter matrix and the mutation point detection result, determining a first eigenvalue according to the starting moment determined by the suspicious elements and the degree of regularity of data fluctuations within the fluctuation duration of the suspicious elements, and then combining the consistency analysis result of irregular data fluctuations in the parameter matrices of different types of monitoring data to determine a fault interval. Furthermore, target data is screened from the residual term, and a plurality of clustering trees are obtained based on the target data using the agglomerative hierarchical clustering algorithm. The fault detection result of the circuit breaker is determined based on the differences between different clustering trees. In this application, residual components are used to replace the initial data, reducing the amount of clustering data while improving the clustering effect of the clustering number on monitoring data that is sensitive to different faults.

The invention provides a satellite communication system user grouping method based on a clustering tree. The method comprises the following steps: setting a clustering detection condition; setting clustering features of the clusters, wherein the clustering features of the clusters are used as the clustering features of the user groups and are used for obtaining the clustering features of the nodes; initializing a user group and a clustering tree; for a current user to be grouped, searching a cluster with the strongest correlation with the feature attributes of the user, and detecting whether the user can join a user group corresponding to the cluster according to a cluster detection condition; correspondingly updating the user group and the clustering tree according to whether the user joins the user group or not; returning to the step of searching the cluster with the strongest correlation; and obtaining a final grouping result until all users are grouped. The method provided by the invention is designed for a satellite network, and can combine the users which meet the clustering detection condition and have similar attributes into the user group through the set clustering features of the clustering tree and the clustering detection condition under the condition that the clustering number does not need to be preset in advance.

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.