12results about How to "Improve clustering accuracy" patented technology

The invention discloses a user load curve analysis method and system based on Bayesian nonparametric clustering and a time covariance structure, and relates to the technical field of power system data analysis, and the method comprises the steps: collecting intelligent electric meter data, and carrying out the preprocessing of the original intelligent electric meter data; calculating a covariance matrix of the preprocessed data; calculating a mahalanobis distance between the load curves based on the covariance matrix; and constructing a distance dependent Chinese restaurant process model based on mahalanobis distance, performing link allocation and clustering parameter inference in combination with Gibbs sampling, and performing adaptive clustering to obtain a user load curve analysis result. According to the method, the problem of singularity of a covariance matrix under a high-dimensional small sample is solved by adopting Leidoit-Wolf shrinkage estimation, complete Bayesian inference is realized, the clustering number is adaptively determined, and manual intervention is not needed; while high clustering precision is maintained, calculation complexity is significantly reduced, and the method is suitable for large-scale actual data.

This application relates to an image processing method, apparatus, electronic device, and storage medium, which can be applied to the field of mapping. The method includes: acquiring an image of an object to be clustered and a target image of an object; determining a first object image to be segmented and a second object image to be unsegmented from the image of the object to be clustered and the target image of the object; the size of the first object image is larger than the size of the second object image; segmenting the first object image to obtain at least two sub-images; inputting the second object image and each sub-image into an image matching model for image matching processing to obtain a first matching result; and determining a target image set of the image of the object to be clustered based on the first matching result. According to the technical solution of this application, the accuracy and efficiency of image clustering can be improved.

The application discloses a laser radar point cloud object detection method and system, comprising the following steps: obtaining a three-dimensional point cloud of a laser radar, and after filtering interference point clouds and compensating for missing point clouds, obtaining a to-be-segmented point cloud; generating a two-dimensional distance image from the to-be-segmented point cloud; setting an angle constraint; the angle constraint is that the included angle between the line connecting two adjacent points in the two-dimensional distance image and the line connecting the radar center and the point farther away from the radar center among the two adjacent points is not less than an angle threshold; setting a distance constraint; the distance constraint is that the distance between the two adjacent points in the two-dimensional distance image is not greater than a distance threshold; traversing the points in the two-dimensional distance image, judging whether the two adjacent points satisfy the angle constraint or the distance constraint, if any constraint is satisfied, marking the two adjacent points as the same object, otherwise, marking the two adjacent points as different objects, and thus obtaining an object segmentation result. The object can be quickly detected under sparse point cloud data.

This application provides a mask layout correction method, system, medium, program product, and terminal based on a combination of membrane computing and the DBSCAN algorithm. The method involves obtaining an initial layout, performing preliminary clustering on the initial layout using a preset clustering method to obtain several partitions and their corresponding density and contour features; obtaining initial density and contour parameters for DBSCAN clustering; iteratively optimizing the initial clustering parameters of the DBSCAN clustering using a preset membrane computing algorithm based on the density and contour features of each partition to obtain the optimal clustering parameters for each partition; performing DBSCAN clustering on each partition based on the corresponding optimal clustering parameters to obtain the core region, boundary region, and sparse region of the initial layout; and correcting the core region, boundary region, and sparse region based on a preset correction strategy to obtain the corrected layout of the initial layout. This improves the accuracy of mask layout correction.

This invention relates to the field of e-commerce data processing technology, specifically to a method and apparatus for multi-platform e-commerce order aggregation based on dynamic clustering rules. The technical solution includes: collecting order data from e-commerce platforms and performing standardized processing to obtain platform order data; extracting various clustering feature values ​​from a clustering feature system; matching these values ​​with rules in a dynamic clustering rule set to determine the candidate cluster to which the order belongs; calculating the comprehensive similarity between orders; determining whether the comprehensive similarity is greater than or equal to a preset similarity threshold, and assigning the order to that cluster; performing missed order detection for orders not clustered; and outputting the order results for the clusters. This significantly improves clustering accuracy, effectively reduces missed orders, and minimizes operating and transportation costs. It also significantly improves order processing efficiency, adapting to the needs of large-scale order processing. The system operates stably and supports efficient processing of massive amounts of data.

The application discloses a multi-target heuristic single cell deep clustering method, and the multi-target heuristic single cell deep clustering algorithm is obtained by improving the scDHA algorithm. The multi-target heuristic single cell deep clustering algorithm firstly adopts a stacked Bayesian autoencoder algorithm and an Autoencoder algorithm to jointly perform multiple dimension reductions on single cell transcriptome sequencing data after preprocessing, then adopts a K-means algorithm and an Ultra-Scalable Spectral Clustering algorithm to jointly cluster multiple dimension reduction data to generate multiple clustering results, and finally adopts a dynamic positive and negative sine optimization algorithm to calculate a unique clustering result from the multiple clustering results. The method has the advantages of fast clustering speed, small hardware memory consumption, low hardware cost, high clustering precision and strong robustness.

This invention relates to the field of data processing technology, specifically to an artificial intelligence-based production line scheduling optimization method. The method obtains the adjustment time and corresponding change magnitude based on the data change characteristics of parameter adjustment curves; it obtains the process difference significance based on the distribution characteristics of the adjustment times of parameter adjustment curves for all orders under the same process and the change magnitudes of all adjustment times; it obtains the target process based on the difference significance of all processes; and it obtains the comprehensive difference degree based on the difference characteristics of the parameter adjustment curves of the target process between different orders. This invention clusters all orders based on the comprehensive difference degree and order production time to obtain different order clusters; and it schedules and allocates production lines based on all order clusters, which can reduce equipment wear and tear and improve production efficiency.

The invention discloses a power grid database security protection method and device, equipment and a medium, and relates to the technical field of data security access, the method comprises the following steps: identifying a plurality of groups of historical access logs of a power grid database to obtain a plurality of historical access behaviors, and analyzing and extracting to obtain corresponding access behavior characteristics; performing density-based clustering analysis on all the access behavior characteristics to obtain a plurality of behavior modes; predicting a risk coefficient of each behavior pattern based on a long short-term memory network model, and classifying the plurality of behavior patterns into a plurality of risk levels; matching a first behavior mode corresponding to the plurality of behavior modes according to the current access behavior, determining a protection strategy according to a first risk level determined by the first behavior mode in combination with the behavior intention of the current access behavior, and performing security protection; wherein the behavior intention is obtained based on analysis of the current access behavior. According to the method, the access behavior can be accurately identified, so that the power grid database is reasonably and safely protected.

The application discloses a kind of dam deformation prediction method, equipment, storage medium and product, the prediction method includes preprocessing to dam monitoring data;The environmental data after pretreatment is extracted to depth feature, and environment characteristic is obtained;The reservoir water level after pretreatment and environment characteristic are carried out cluster analysis, and according to the time domain division of time factor and dam survey point displacement according to cluster analysis result, obtain the cluster division result of different time periods;According to each cluster division result, respectively construct sample data set;Each sample data set is used to train and test Transformer-LSTM model respectively, and the dam deformation prediction model of each time period is obtained;Obtain the data to be predicted, and the dam deformation prediction model corresponding to the time period of the data to be predicted is used to predict the prediction data, and the dam deformation prediction result is obtained.The dam deformation prediction precision of the application is improved.

The application discloses a kind of based on multi-stage heuristic's blockchain address entity identification method and equipment, including extracting key field containing UTXO level information from original blockchain data;Original blockchain data is reorganized into standardized table;Directed bipartite graph is constructed;Transaction in graph is divided into simple transaction and complex transaction;All transactions are sorted according to timestamp from small to large, for simple transaction, simple heuristic fast clustering is carried out by applying high credibility rule;Complex heuristic rule is executed to remaining transaction, and fine entity identification is realized;Address set triggered by simple heuristic and complex heuristic rule is merged into unified entity, and complete address-entity mapping table is output.The application significantly improves address clustering accuracy, can identify complex transaction mode, fills the processing blank of privacy-enhanced transaction for existing method, quickly processes explicit transaction by simple heuristic, complex heuristic finely processes difficult transaction, and overall computing efficiency is significantly improved.

The application provides a single-cell rare cell type identification method and system based on a divide-and-conquer strategy, comprising the following steps: S1, using a variational autoencoder to encode single-cell transcriptome data into Gaussian distribution hidden variables; S2, performing spectral clustering on the Gaussian distribution hidden variables to obtain a single-cell coarse-grained clustering result; and S3, optimizing the single-cell coarse-grained clustering result based on intra-class cluster loss and inter-class cluster loss until the intra-class cluster loss and the inter-class cluster loss are minimized to obtain a single-cell clustering result containing rare cell types. The application can overcome the deficiency that previous single-cell clustering and identification methods do not optimize rare cell types, leading to misclassification or difficulty in identifying rare cell types.

The present application relates to the technical field of data processing, in particular to a warehouse heterogeneous data intelligent management method, device, equipment and computer storage medium. The present application obtains unstructured text data and structured time series data locally at each federal node, maps the data quality correction parameters to a unified dimension numerical vector; constructs a distributed clustering objective function based on adaptive weights for local clustering calculation, generates local clustering statistical information and encrypts it; receives the encrypted statistical information, calculates the cross-node cluster similarity based on the intra-cluster data consistency coefficient and the dynamic threshold, and executes merging on the clusters that meet the conditions to generate the global clustering result; updates the standard data dictionary according to the global clustering result and synchronizes it to each node, and simultaneously performs incremental update on the feature mapping parameters and clustering parameters based on the clustering effect feedback using transfer learning. The present application improves the accuracy and consistency of heterogeneous data management while ensuring data privacy.