36results about How to "Improve clustering effect" patented technology

The invention provides a virtual power plant resource partitioning method based on a multi-layer perceptron. The method comprises the following steps: acquiring geographical location information of each resource in a virtual power plant; adjusting a density-based clustering algorithm according to the geographical location information of each resource in the virtual power plant, and performing clustering analysis on the geographical location information of each resource by using the adjusted density-based clustering algorithm to obtain a plurality of first clustering results; counting the number of geographical location information contained in each first clustering result, adjusting the first clustering results according to the number to obtain a plurality of second clustering results, and taking a range formed by each second clustering result as a partition; and inputting the geographical location information of each resource into a pre-trained multi-layer perceptron to obtain an importance degree score of each resource. According to the method provided by the invention, each energy resource can be partitioned and effectively regulated and controlled.

The invention discloses a flight path analysis method and system fusing frequency domain features and multi-dimensional evaluation indexes. The method comprises the following steps: performing abnormal flight number filtering on flight path data acquired by a flight path data acquisition device; second track data are obtained through fast Fourier transform, and high-frequency energy features and peak frequency features are extracted; through a unified standardization processing method, target track data needing to be analyzed is screened out; adaptively determining an interval in which the optimal clustering cluster number is located; traversing each clustering cluster number in the interval, determining an optimal clustering cluster number K according to the contour coefficient and the Devens-Pall index, and performing clustering analysis through a K-Means + + algorithm according to the optimal clustering cluster number K; and carrying out visual display on a clustering analysis result of the target track data. The method can provide a theoretical reference for the clustering analysis of the arrival and departure tracks of the aircrafts in the terminal area through the analysis method fusing the frequency domain features and the multi-dimensional evaluation indexes, enables the optimization result to fit the reality, and has the practical application potential.

The human cost data storage method for the power grid engineering system comprises the steps that human cost data related to cost data in the power grid engineering system are acquired, and the human cost data comprise work type data, man-hour data and salary data of constructors; determining a DBSCAN clustering radius range based on the average man-hour salary of each type of work; based on a DBSCAN clustering radius range, performing clustering analysis in a man-hour-salary two-dimensional plane formed by the human cost data to obtain a preliminary classification result; when it is determined that the isolated data points exist based on the preliminary classification result, secondary classification is carried out on the isolated data points based on the distance between the isolated data points and the known clustering cluster, and a final classification result is obtained; and realizing partition storage of the cost data based on a final classification result. By implementing the method, optimized storage of the cost data of the power grid engineering system can be realized.

The invention relates to the technical field of multi-view clustering analysis, and discloses an incomplete multi-view clustering method and system, a storage medium and equipment. In order to solve the problems that a small amount of easy-to-obtain supervision information is not utilized in an existing method, and the clustering performance is deteriorated due to low view interpolation quality under high missing degree, the invention provides a technical scheme of combining paired constraint weighted interpolation and double-level feature fusion. The method comprises the following steps: firstly, screening similar samples by using pairwise constraint information, and reconstructing a missing view through similarity weighting; then extracting feature mean values and standard deviations of all views based on an encoder, aligning features through view hierarchy self-adaptive comparison learning and reserving private information, and optimizing feature distribution in combination with sample hierarchy semi-supervised loss; and finally, completing clustering through a Gaussian mixture model. According to the method, the pairwise constraint information is effectively utilized, the view recovery quality and the feature complementarity under the high-missing scene are improved, the clustering performance is remarkably improved, and the method is suitable for scenes such as data analysis of multi-view data missing and the like.

The embodiment of the invention provides a clustering method and device of multiple view data, equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring view data corresponding to a plurality of views, projecting the corresponding view data to a low-dimensional space based on a projection matrix of the view for each view to obtain a subspace representation matrix and an error matrix, and calculating to obtain local total information according to the subspace representation matrix and a weight matrix, and constructing an optimization target item according to the nuclear norm of the subspace representation matrix, the preset norm of the error matrix and the local total information, solving an optimization target function to obtain a representation optimization matrix corresponding to each view, and performing data clustering according to all the representation optimization matrixes to obtain at least one clustering result. In the dimension reduction process, subspace representation is synchronously carried out, in the process of optimizing the target function, mutual promotion and co-evolution of the projection matrix and the subspace representation matrix are achieved, the accuracy of subspace representation is improved, and the clustering quality is improved.

The invention discloses a disease control big data analysis method and system, and the method comprises the following steps: obtaining a data set X comprising a plurality of class attributes, and carrying out the preprocessing; performing independent optimal sequence search on each category attribute in the data set to obtain an optimal sequence, determining a distance structure suitable for disease control big data analysis, calculating distance measurement between a sample and a cluster according to the obtained optimal sequence, and constructing a clustering model; and by minimizing a target function, updating a sample attribution matrix and sequence, and dynamically adjusting a clustering result. Compared with the prior art, the method has the advantages that the system can adaptively adjust the clustering result by dynamically learning the optimal value sequence of the data and the corresponding distance structure, so that the clustering accuracy is remarkably improved, and visual interpretation is provided for understanding of the data.

The invention relates to the technical field of signal processing, data characterization and classification, and discloses a radar signal spectral clustering sorting method based on SOM anchor point extraction and graph fusion, which comprises the following steps of: configuring a radar signal sorting cluster number and radar pulse parameters, obtaining a normalized radar pulse data set, and storing the normalized radar pulse data set; taking each normalized radar pulse in the radar pulse data set as a node, and constructing a KNN graph of the radar pulses; extracting anchor points of the normalized radar pulse data set based on SOM, calculating the similarity between the extracted SOM anchor points and all nodes to obtain a similarity matrix, and constructing an anchor map adjacent matrix based on the similarity matrix, namely a self-adaptive anchor map; performing weighted fusion on the KNN graph and the self-adaptive anchor graph to obtain a fusion graph; and performing spectral clustering sorting based on the radar signal sorting cluster number and the fusion image to obtain a sorting result. According to the method, the limitation that only distance information and density information can be utilized in a classic radar sorting clustering algorithm is broken through, and the sorting performance under the condition of complex radar pulse distribution is improved.

This application provides an image processing method, apparatus, computer-readable medium, and computer device. The image processing method includes: acquiring multiple original images and descriptive text corresponding to each of the multiple original images; converting the descriptive text corresponding to each of the multiple original images into text vectors to obtain text vectors corresponding to each of the multiple original images; performing clustering processing based on the text vectors corresponding to each of the multiple original images to obtain multiple original image clusters, and generating text embedding vectors corresponding to each original image cluster; using the text embedding vectors corresponding to each original image cluster as text-guided features, performing semantic editing processing on the original images in each original image cluster to obtain processed images, wherein the processed images are used as sample images for a machine learning model. The technical solution of this application embodiment can enrich the diversity of training datasets, thereby improving the generalization ability and robustness of the model.

This disclosure provides a matching model training method, device, and medium, relating to the field of computer technology. The method includes: clustering the original data in an unlabeled target dataset using a clustering algorithm to obtain multiple data clusters. The target dataset is obtained by combining two unlabeled candidate datasets. Each pair of original data from the multiple data clusters is concatenated to obtain N concatenated data. Then, each pair of concatenated data from the N concatenated data is combined to obtain K data groups. The label value of each data group is determined based on the clustering of the original data within each data group. An untrained matching model is trained based on the K data groups and the label values ​​of each data group until a trained matching model is obtained. Labels are added to the unlabeled data through clustering and data sources, and the effect is optimized through an iterative training process, ultimately resulting in a more accurate trained matching model.

The invention relates to the technical field of information security, in particular to an unknown protocol clustering method and system based on frequent item extraction and a double-layer auto-encoder, and the method comprises the steps: converting original data from a bit form into a byte form through a preprocessing process, intercepting the first 32 bytes, carrying out the frequent item statistics of the preprocessed data byte by byte, and obtaining a frequent item clustering result; respectively obtaining the frequent item frequency and the frequent item number of each byte; utilizing a frequency auto-encoder and a number auto-encoder to respectively carry out feature extraction on bytes with larger frequent item frequency and larger frequent item number; and performing coarse clustering on the features extracted by the frequency auto-encoder to obtain coarse clustering tags, and merging the coarse clustering tags and the features extracted by the number auto-encoder and performing clustering to obtain final fine clustering tags. According to the method, not only can protocol-level clustering be reserved, but also category-level clustering can be realized, the calculation amount is small, the clustering instantaneity is ensured, meanwhile, the under-division problem of unknown protocol clustering can be effectively solved, and the clustering performance is improved.

This invention provides a deep multi-view clustering method based on multi-granularity contrastive learning, belonging to the field of computer vision technology. It solves the technical problem that single-granularity feature representations are insufficient to comprehensively capture the multi-level semantic information of data and the quality differences between different views. The method includes the following steps: S10, designing an independent encoder for each view to extract latent embedding features; S20, mapping the attention-enhanced features of each view to the same semantic space; S30, dynamically assigning weights based on the similarity between the features of each view and the global representation; S40, granular-level contrastive learning can enhance the compactness of the cluster structure within the granular sphere and align the granular sphere structure between cross-view granular spheres; S50, performing K-means clustering on the final global features to obtain the final prediction result. This invention enhances the weight of key features by adding a feature attention enhancement module to the view features.

This invention discloses a multimodal brain imaging data classification method and system based on a dual-graph autoencoder. The method includes the following steps: Step S01. Acquire multimodal brain medical imaging data of different target individuals and extract standard uptake ratio data and similarity matrix data; Step S02. Construct individual brain region maps and population maps; Step S03. Extract individual embedding features using an individual graph autoencoder and extract population embedding features using a population graph autoencoder; Step S04. Use the standard uptake ratio, brain region similarity matrix, individual embedding features, and population embedding features as views for enhanced multi-view subspace clustering; Step S05. Select key brain regions and discriminative brain region connections to participate in the next round of clustering as enhanced views until a preset convergence condition is met. This invention can achieve high-precision, highly interpretable data classification and simultaneously output biologically interpretable discriminative brain regions and connections.

A multi-dimensional feature fusion clustering method for visual angle missing face recognition belongs to the field of face image data clustering processing in computer vision, and comprises the following steps: firstly, constructing a kernel matrix for incomplete multi-visual angle face image data, and performing multi-kernel completion; secondly, mapping the kernel matrix under each view angle to different dimension spaces through feature decomposition to obtain feature matrixes of different dimensions, and fusing the feature matrixes into a consistent representation matrix of a fixed dimension through a rotation matrix; and performing tensor subspace learning by taking the consistent representation as substitution of original data, calculating an affinity matrix according to a learned tensor subspace matrix, and performing spectral clustering on the affinity matrix to obtain a final clustering result. Compared with other methods, the method is higher in accuracy and more stable in performance.

The application discloses a radar signal processing method based on TOA sequence correlation degree, which comprises the following steps: parameter initialization; obtaining a radar signal pulse description word; dividing the radar signal pulse description word by using a grid method; extracting grids with a pulse number higher than a threshold value, and traversing all the extracted grids to extract the features of all the pulses contained in the grids in the TOA dimension, and the features are described by using TOA sequence feature vectors to obtain the TOA sequence feature vectors corresponding to each grid; quantifying the correlation degree between the TOA sequence feature vectors extracted by different grids to obtain corresponding correlation coefficients; performing grid merging and clustering, and outputting a radar signal clustering result. The method has high accuracy when clustering a multi-parameter agile periodic scanning radar, and is not affected by the pulse interval modulation mode of the radar. In addition, when facing a multi-parameter agile radar, the method can effectively prevent the problem that a traditional clustering algorithm is prone to produce an increased batch result.

The application discloses a spatial transcriptome data clustering method, comprising the following steps: firstly, preprocessing the original data, and constructing a spot space neighbor network; secondly, further learning spatial information and low-dimensional potential representation of gene expression through a graph attention automatic encoder, learning self-expression coefficient matrixes of different layers in the encoder through a multi-scale self-expression module and fusing the same together, adopting spectral clustering in a deep subspace clustering module, and feeding back the clustering label to a self-supervision module; the self-supervision module guides the learning of the potential representation by constructing a self-supervision path returning to the encoder; finally, biological analysis is carried out; the application learns low-dimensional potential embedding by integrating spatial information and gene expression profiles, and improves the clustering performance of spatial resolution transcriptome by using STMSGAL.

The invention discloses an energy storage station system of a home substation. A household transformer station energy storage station system comprises a wind power module used for generating power by using wind power; the wind power collection module is used for collecting historical wind power data of the wind power module; the photoelectric module is used for generating power by utilizing solar energy; the photoelectric collection module is used for collecting historical photoelectric data of the photoelectric module; the weather module is used for collecting weather information of the location; the energy storage module is used for storing electric energy; the wind-light-electricity prediction device is used for predicting the future wind power generation amount based on the historical wind power data and the weather information and predicting the future photoelectric power generation amount based on the historical photoelectric data and the weather information; and the grid-connected control module is used for grid-connected power transmission based on future photoelectric generating capacity and residual energy storage of the energy storage module. According to the scheme provided by the invention, the prediction accuracy is higher, so that when the household energy storage station is connected with the power grid, the input or output electric energy is high in regularity, and the utilization of clean energy is facilitated.

The invention relates to the technical field of data mining, and discloses a method for improving clustering performance, which comprehensively applies technical means such as subspace learning, nonlinear processing, tensor nuclear norm constraint and consensus similarity learning. Through subspace learning, the method can reveal hidden structures and features in high-dimensional data, and effectively deals with dimension disasters. The nonlinear processing part optimizes nonlinear information in the data and reveals a deep nonlinear structure of the data. By using the tensor nuclear norm constraint, the method not only improves the performance of a clustering algorithm, but also shows a remarkable effect in practical application, for example, the recognition accuracy is improved in face recognition, analysts are helped to more accurately distinguish different consumer groups in the market analysis field and insight into consumer demands, and the market analysis efficiency is improved. Therefore, the accuracy and effectiveness of market analysis are remarkably improved.

The present invention belongs to the field of image classification technology, and discloses a clustering method, device and medium of graph regular non-negative matrix decomposition, the method comprising: obtaining matrix information data and the number of to-be-clustered; defining four initialization sparse matrices, corresponding to the construction matrix E, weight matrix U, coefficient matrix V, and auxiliary matrix Z, respectively, and setting the initial hyperparameter value μ; determining the target equation and the maximum number of iterations, and iteratively solving the construction matrix E, weight matrix U, coefficient matrix V, auxiliary matrix Z, Lagrangian operators Y1 and Y2 according to the update formula; when the number of iterations is the maximum or the iterations converge, outputting the final coefficient matrix V to obtain the clustering result. The present invention improves the objective function construction method, adds better constraints to capture deep-level information features, thereby improving the robustness and accuracy of image classification and obtaining better clustering effects.

The invention relates to a privacy protection robust federated learning method and system based on secret sharing, and belongs to the technical field of privacy protection and robust aggregation. The specific process of the method is as follows: initial model distribution: a server distributes initialized global model parameters to each client; local model training: the client updates a local model by using the global model parameters, trains the local model by using a local data set, and sends the local model to the server through secret sharing after training and updating; and model aggregation distribution: the server receives local model update parameters from the client, performs clustering operation, and distributes global model parameters obtained by clustering to the client, and the client performs local model update according to a received result.

A millimeter wave radar-IMU pose parameter calibration method, comprising: collecting multiple sets of IMU and radar point cloud data on a grid plate, and accumulating multiple frames of point clouds in front and back; for point clouds with unchanged IMU attitude or position, point cloud registration is performed relative to the origin, the relative pose of the radar is calculated, the corresponding IMU data is obtained, and the relative pose of the IMU is calculated; based on the degenerated hand-eye calibration algorithm, a least squares loss function is constructed for the two types of relative poses in turn, and the initial values of the rotation and translation parameters are optimized; the point clouds with changing IMU poses are projected to multiple centers, the center point clouds are characterized by four-dimensional normal distribution, a re-projection error loss function of points and distribution characteristics is constructed, and the final parameter values are optimized. It solves the problem of missing IMU odometry or difficulty in fully rotating the body during calibration, and also proposes a millimeter wave radar point cloud registration method, which improves the adaptability of the millimeter wave radar-IMU system in sensor parameter calibration.

This invention designs an image classification method based on machine learning and spectral clustering of grains, comprising: acquiring an image dataset; extracting the feature vector of each image in the image dataset using a feature extraction model; creating an initial grain as a whole by treating the feature vectors of all images in the image dataset, and calculating the variable sparsity measure (VSM) value of the initial grain; if the VSM value is lower than a preset threshold, dividing the grain into two smaller grains using a 2-means clustering algorithm; continuing until the VSM values ​​of all grains meet the condition; calculating the similarity between the generated grains based on the center feature vector and the radius of the grains; dividing the generated grains into multiple clusters using spectral clustering based on the similarity between the grains, where each cluster represents a category; calculating the similarity between the feature vector of the image sample to be tested and the center feature vector of each grain, and taking the category corresponding to the cluster of the most similar grain as the classification result of the image sample to be tested. This invention provides more reliable and efficient technical support for the field of image classification.

The invention discloses a confidence, centrality and sharpening smoothing-based graph clustering method, which is based on a centrality and confidence enhancement module, adopts a high confidence edge correction strategy, divides a same cluster relationship between nodes according to the confidence and centrality, and enhances a graph structure in combination with a homogeneous relationship between the nodes. Therefore, the effectiveness of the graph structure is enhanced, and the clustering performance of the model can still be improved by fully utilizing the effective structure information when the graph information is sparse or unreasonable.

The invention provides a carbon emission monitoring user classification method, which is characterized by comprising the following steps of: acquiring energy consumption data of carbon emission monitoring users, and checking vacant data of the energy consumption data; performing normalization processing on the checked and complemented energy consumption data; constructing an FCM clustering model based on a quantum flower pollination optimization algorithm; organizing the normalized energy consumption data by using a date-based method, and clustering the normalized energy consumption data by using the FCM clustering model; and carrying out dimensionality reduction on the clustered energy consumption data by using a t-SNE dimensionality reduction method, and visualizing a clustering result. According to the method, clustering processing is firstly carried out on the data, then a t-SNE big data dimension reduction method is adopted to reduce the complexity of energy consumption data, the analysis efficiency is improved, meanwhile, key information is reserved, the clustering result is more accurate and reliable and can be visualized, the relation between the data is fully analyzed, and errors are reduced.

The application provides a kmeans log classification method and device based on fusion of cut-off distance, and relates to the field of big data.The method comprises the following steps: selecting log samples with cosine distance within a first distance range from a center from a log sample set to form a first sample set, and selecting a first log sample from the first sample set according to sample density and adding the first log sample to a center set; selecting log samples with cosine distance within a Kth distance range from the first log sample from the log sample set to form a Kth sample set according to the value of K, and selecting a Kth log sample from the Kth sample set according to sample density and adding the Kth log sample to the center set, wherein the value of K is [1, K] in turn; and fusing the log samples in the center set until the number of the log samples in the center set is K, so as to serve as initial centroids of kmeans clustering.The scheme improves the clustering effect of the Kmeans algorithm.

The present invention relates to the field of data mining technology, and in particular to an incomplete multi-view clustering method and system based on deep learning. An incomplete multi-view clustering method based on deep learning includes S1. obtaining a cross-view training set, including a complete data set and an incomplete data set; S2. building a clustering model, including an encoder network and a decoder network; S3. using the clustering model to extract and reconstruct features of the cross-view training set, and calculating the reconstruction loss, and optimizing the reconstruction loss; S4. based on the reconstruction loss, respectively using the complete data set and the incomplete data set to train the clustering model; S5. using the trained clustering model to make predictions. The present invention solves the problem of improving clustering accuracy through effective double-layer operations on complete and incomplete data.

The invention relates to the technical field of power distribution, in particular to a mountain area power distribution network grid division method considering disaster scene characteristics and a related device, and the method comprises the steps: obtaining map information of a target area; performing preliminary division on the map information of the target area by adopting a U-Net neural network to obtain a plurality of land parcels; obtaining road lattice characteristics of each land parcel, and quantifying the road lattice characteristics; according to the quantized road lattice features, an MST is generated by applying a graph theory; clustering the MST by using a Chameleon clustering method to obtain a refined grid, and completing the grid division of the power distribution network in the mountainous area; according to the method, efficient management of the mountain area power distribution network can be realized, the grid division condition under the extreme weather condition is clear, and disaster risks and power demands of different land parcels can be evaluated more accurately. The problems that in the prior art, due to the fact that influences of power equipment operation information, disaster scene characteristics and the like are not considered in power distribution grid division, grid division is not clear, and a reliable basis cannot be provided for operators are solved.

The present invention discloses a method for designing a mask monitoring and two-dimensional code recognition system based on a MaixduinoAIK210 development board. The present invention utilizes the support of the MaixduinoAIK210 development board for functions such as artificial intelligence, visual target recognition, and deep learning to design an effective, convenient, and practical mask monitoring and two-dimensional code recognition system. The algorithm used in the present invention is the K-Means algorithm, which is an unsupervised clustering algorithm with simple implementation and good clustering effect. The system designed in this way also has the characteristics of simplicity and efficiency, and the actual installation and use cost is low, which is very suitable for the current requirements for large coverage, reliability, and high real-time requirements in the normalization of public epidemic prevention. At the same time, the present invention has higher readability and wider compatibility, and can improve the system and add the required basic functions according to different application scenarios and actual needs, which is very suitable for on-demand expansion and use in various complex real-life scenarios.

The application relates to the computer technical field and provides a data clustering method, a data clustering system, electronic equipment and a storage medium, the method comprises the following steps: initializing an original corpus based on a clustering center point and user features to obtain a first target corpus; similarity matrixes suitable for the term frequency inverse document matrix are calculated according to segmentation adopted in term frequency statistics in the process of constructing the term frequency inverse document matrix; the similarity matrixes are input into the first target corpus, the vector cosine similarity of each query term and all non-query terms is calculated and arranged in descending order to obtain a recommended result of an extended word; a sorting result is obtained by combining the recommended result and user information; the first target corpus is updated based on the sorting result to obtain a second target corpus. The application realizes quick clustering of new data of the same type, improves the clustering efficiency and precision, solves the problem of too many iteration numbers caused by randomly selecting a clustering center, and improves the clustering effect of data clustering.

The invention discloses a multi-view clustering method based on anchor point-to-anchor graph structure collaborative regularization, and belongs to the technical field of multi-view clustering, and the method comprises the following steps: S1, building a model objective function; s2, optimizing an objective function; s3, analyzing algorithm complexity; according to the method, anchor point construction and anchor graph learning are combined into a unified framework: firstly, an implicit anchor point adjacency relation is constructed from an anchor graph, and a local smooth item is introduced to anchor points to guide anchor point learning; secondly, column sparsity is forced to be executed on the anchor map, each sample is encouraged to be only connected to several anchor points, and therefore redundant connection is reduced; and finally, Laplace rank constraint is applied to the anchor map so as to improve the clustering structure of the anchor map. The AGSCR-MVC uses the anchor point diagram as bridge representation and guides local structure learning and structure regularization of the anchor points at the same time, so that a two-stage collaborative modeling framework between an anchor point space and a diagram structure is realized, the clustering structure consistency between the anchor points and samples is enhanced, and the clustering performance is remarkably improved.

The invention relates to the technical field of supply chain management, and discloses an intelligent purchase system of a supply chain, which comprises a control center, a data classification storage module, a new purchase project evaluation module, a supplier evaluation recommendation module and a post-evaluation recording module. The data classification storage module is used for storing past historical purchase data and information data of a plurality of suppliers; the new purchase project evaluation module is used for calculating a new purchase project bid evaluation / comparison score value; the supplier evaluation recommendation module is used for screening a plurality of suppliers by adopting an improved k-means clustering analysis algorithm; and the post-evaluation recording module is used for jointly evaluating the product / service score of the bid-winning supplier by the purchase implementation department and the purchase demand department, and the product / service score is used as a post-evaluation score. According to the method, the improved k-means algorithm is adopted, comprehensive evaluation recommendation is carried out on numerous suppliers, meanwhile, post-evaluation scores after purchasing are evaluated and recorded, multi-aspect and full-process evaluation on the suppliers is achieved, and the purchasing efficiency is improved.