87 results about "Spectral clustering algorithm" patented technology

The invention provides a sewing thread defect classification method based on multi-scale fusion and adaptive learning, which solves the technical problems of extremely unbalanced data and low expert labeling efficiency in industrial quality inspection, and can be extensively applied to surface defect detection tasks in multiple manufacturing fields such as spinning, packaging and electronics. According to the method, four core modules of multi-scale depth feature extraction, dynamic loss prediction, adaptive spectral clustering and intelligent pre-classification are creatively fused, and multi-level semantic features are extracted through a multi-scale feature pyramid network; designing a dynamic weight loss prediction network, and accurately evaluating the sample information amount; constructing an adaptive spectral clustering algorithm, and adaptively adjusting clustering parameters; a multi-feature fusion pre-classification algorithm is combined to assist experts in efficient labeling. According to the method, the defect classification precision can be remarkably improved in a few-sample scene.

The invention relates to the technical field of indoor layout design, and discloses an indoor layout design system based on user behavior data analysis. The system comprises a user behavior data acquisition module which captures and transmits a user movement track coordinate sequence, residence time distribution data and equipment interaction event records in real time through a multi-sensor network; the layout analysis module constructs a dynamic layout model by using a decision tree integration algorithm, and outputs theoretical layout parameters including a space occupancy rate and functional region division; the layout difference detection module performs multi-dimensional comparison of space overlapping degree, path conflict index and regional utilization efficiency difference on theoretical and actual layout parameters to generate a difference index vector; the problem region positioning module identifies a layout abnormal region through a spectral clustering algorithm based on the difference index vector and predefined spatial topology information; and the adaptive design strategy generation module automatically generates a layout adjustment strategy instruction according to the abnormal region position and the attribute data.

The invention discloses a semi-supervised image clustering method based on adaptive graph learning, and the method comprises the steps: iteratively updating a sparse representation matrix and a paired constraint matrix until the value of a target function is unchanged or reaches a maximum iteration number; taking the updated pairwise constraint matrix as an input similar matrix, calling a spectral clustering algorithm to divide image sample data into a plurality of sample groups to finish clustering output; wherein the objective function is a weighted sum of a propagation consistency error based on a sparse representation matrix and a paired constraint matrix, an image sample data reconstruction error based on the sparse representation matrix, an L1 norm of the sparse representation matrix and a matrix correlation error based on the sparse representation matrix and the paired constraint matrix. The invention further discloses a system, a computer storage medium and a program for implementing the method. According to the method, the problem of separation of similar graph learning and constraint propagation can be solved, and the image clustering performance is improved.

The invention provides a seismic fault identification method and system based on multi-attribute collaboration and spectral domain graph enhancement, and relates to the technical field of seismic exploration, and the method comprises the steps: carrying out the all-directional dip angle and azimuth angle scanning of a seismic data volume, extracting multi-scale guide field information through combining structure tensor decomposition, and executing anisotropic diffusion filtering; calculating characteristic value distribution through a characteristic value coherence algorithm based on the filtering data volume, and determining the structural consistency difference of adjacent seismic traces to obtain a fracture coherence attribute data volume; carrying out azimuth gather sorting and pre-stack time migration processing on the seismic data volume, extracting seismic wave dynamic response characteristics, carrying out Fourier series expansion on the azimuth change rate to obtain a crack indication information data volume, splicing the data volume and executing multi-scale three-dimensional convolution solution, and constructing a spatial dependency graph through a spectral clustering algorithm; spectral domain enhancement features are obtained through spectral domain graph transformation and frequency selective filtering reconstruction, and morphological connectivity analysis is executed to obtain a crack prediction result.

The invention discloses a degradation dominant characteristic factor identification method for a river ecosystem, and particularly relates to the technical field of ecological environment monitoring and system modeling. A space-time tensor model of multi-source ecological observation data is constructed, multi-dimensional dynamic principal component analysis and a spectral clustering algorithm are fused, a high-contribution-degree feature sequence is extracted, and a multi-scale ecological feature association map is constructed; on the basis, interaction weights among ecological variables are defined, an ecological degradation path network is constructed, and an intermediary variable and a response variable are identified. The explanatory force of each feature is further evaluated by adopting a Shapley value method, a dominant feature importance degree matrix is formed, and a driving response model is established in combination with historical intervention measure data, so that scene optimization of a repair strategy is realized.

The invention discloses a health perception adaptive control method and system for cooperation of multiple energy storage converters, and relates to the technical field of adaptive adjustment. The method comprises the steps of performing feature extraction based on operation state data and operation environment data of N energy storage converters at M time points to obtain a corresponding comprehensive feature set; inputting the comprehensive feature set into a state diagnosis model to obtain an energy storage converter state label; the method comprises the following steps: constructing an undirected graph by taking an energy storage converter as a node and an electrical connection line as an edge, and dividing the undirected graph into G sub-graphs based on topological connection closeness and state similarity by adopting a spectral clustering algorithm; extracting a topological characteristic parameter set from each sub-graph; traversing each sub-graph, judging whether adjustment is ended or not according to a state balance degree threshold value, if adjustment is not ended, identifying an unbalanced scene according to topological characteristic parameters, and executing adaptive control for different scenes; according to the invention, health perception self-adaptive power distribution of cooperation of multiple energy storage converters is realized, overload and idle coexistence are avoided, and circulation oscillation is suppressed.

The application discloses a kind of city area photovoltaic cluster dynamic grid division methods based on space-time coupling tensor, comprising: synchronously collecting the active power time series and geographic coordinates of all photovoltaic nodes in city area;Build the heterogeneous space adjacency matrix considering electrical topology constraint, calculate the static space correlation degree between any two photovoltaic nodes;Build the time-shift cross-correlation matrix that captures the moving characteristics of weather system, quantify the dynamic time correlation degree between nodes;Perform space-time feature tensor fusion, obtain global space-time affinity matrix;Photovoltaic nodes are divided using improved spectral clustering algorithm, and the optimal grid number is adaptively determined by contour coefficient, finally generate photovoltaic cluster grid division scheme at this moment;According to grid drift threshold, trigger grid reconstruction.The application can effectively consider physical topology constraint and meteorological time delay characteristics, realize the accurate aggregation of photovoltaic cluster, significantly improve the accommodation capacity and regulation flexibility of distribution network to distributed energy.

The application discloses a kind of wind turbine power intelligent prediction method and device of multi-model synergic fusion, belong to wind turbine operation control and new energy prediction technical field.The method includes obtaining wind turbine data, wind turbine data is divided into several wind processes according to fixed sample length, each wind process index is calculated, and effective wind process set is obtained according to each wind process index;Obtain the feature vector of each effective wind process, obtain the similarity matrix using the feature vector, and divide each effective wind process into several working condition clusters based on spectral clustering algorithm;Each working condition cluster sub-model is constructed to each working condition cluster, and working condition cluster sub-model stack is formed;Global prediction model is constructed based on all effective wind processes, and global prediction model stack is formed;For the target wind process to be predicted, working condition cluster sub-model prediction value and global prediction model prediction value are obtained using working condition cluster sub-model and global prediction model, and power prediction value is obtained using dynamic weighting mode.

The invention discloses a machine learning spectral clustering analysis method and system for fatty liver auxiliary screening. Comprising the following steps: acquiring medical data and a fatty liver diagnosis result, and preprocessing and standardizing the medical data to obtain a training data set; reversely searching an optimal feature combination from a plurality of data indexes through an enumeration method; training the fatty liver auxiliary analysis model based on the spectral clustering algorithm by using the training data only containing the optimal feature combination; and obtaining a fatty liver auxiliary analysis result by using the trained fatty liver auxiliary analysis model. The method is used for early-stage rapid screening of the fatty liver diseases, the classification accuracy and auxiliary diagnosis efficiency of the fatty liver diseases are improved, and high calculation cost is not needed.

The invention provides a historical building group temporary support system collaborative optimization method and system, and relates to the technical field of building protection, and the method comprises the steps: obtaining point cloud data through three-dimensional laser scanning, carrying out the component recognition and force transmission sequence analysis, carrying out the support partitioning through a spectral clustering algorithm, and carrying out the building group temporary support system collaborative optimization. And a group action strength matrix is constructed based on the geometric similarity and the structural coupling degree, and support parameters are optimized by using a genetic algorithm. The overall cooperative arrangement of the historical building group supporting system is achieved, the supporting efficiency and safety are improved, and the supporting cost is reduced.

The invention provides a multi-view subspace clustering cancer subtype identification method based on self-reinforcement learning, and the method comprises the steps: firstly, extracting the potential feature representation of each view from multi-omics data through a potential feature learning module; then, clustering similar samples by using a self-expression learning module, and introducing initial graph information as a supervision signal to construct a self-expression coefficient matrix; secondly, inputting the matrix into a view image fusion unit, and fusing multi-view information to generate a consensus image; in addition, in order to further suppress noise interference in multi-omics data, a self-strengthening back propagation unit is introduced, a confidence matrix is generated by optimizing a self-expression coefficient, fusion loss back propagation is guided, the quality of the self-expression coefficient is iteratively improved, and a consensus graph is optimized; and finally, based on the optimized consensus graph, realizing cancer subtype identification by applying a spectral clustering algorithm. According to the method, the self-reinforcement learning strategy is introduced, the interference of noise on sample relation capture is effectively relieved, and the clustering performance is remarkably improved.

The present application relates to the technical field of power system metering, in particular to a method and system for operation error diagnosis and analysis of electric energy meter based on electric energy meter operation error, wherein the method comprises: obtaining electric energy meter measurement value curve data of a target area in a set period from a metering automation system, and generating standardized electric energy meter operation data sequence through preprocessing; using an improved spectral clustering algorithm to carry out curve shape clustering on the data sequence, combining elbow rule to determine the optimal classification number of electric energy meter operation state, and completing the classification of electric energy meters in different operation states; relying on the principle of energy conservation to build a state evaluation model, screening effective data points through a data optimization strategy, reducing the model ill-conditioning by means of an improved Tikhonov regularization method, solving the error estimation equation to obtain the electric energy meter operation error estimation value and complete the state diagnosis. This method is suitable for processing massive electric energy meter time series data, smoothly weakening model operation interference, regularizing the electric energy meter operation error solving process, and optimizing the working condition diagnosis adaptability.

The present application relates to a kind of multi-center medical image lesion typing methods based on federal spectrum clustering, belong to medical image analysis technical field, including the following steps: S1: multiple medical institutions are regarded as federal learning client, and the medical image data and supporting clinical data of each client local are obtained and stored in each client local;S2: feature extraction and multimodal feature fusion are carried out in each client local, and multimodal lesion feature set is obtained;S3: center server and each client pass through iteration communication, and the multimodal lesion feature set is jointly clustered using federal spectrum clustering algorithm, until global clustering center converges;Only transmission encrypted statistical parameters in communication process;S4: according to clustering result, output lesion typing information.The present application solves the pain points of difficult data sharing and high privacy leakage risk in traditional centralized lesion analysis, and improves the lesion typing accuracy in heterogeneous data scenario.

The invention relates to the field of ideological and political course classification teaching, in particular to a subspace clustering method and device for ideological and political course teaching and a storage medium. The method comprises the following steps: extracting numerical feature vectors of ideological and political course data, performing normalization processing to obtain an input matrix, performing clustering analysis on the input matrix through a subspace clustering method to obtain a low-rank coefficient representation matrix, constructing an affinity graph, and based on the affinity graph, obtaining a clustering result by applying a spectral clustering algorithm. Dividing the original data set of the ideological and political courses into different clusters; according to the method, a non-convex truncation norm is used to constrain low-rank representation and a feature matrix at the same time, a truncation singular value square operator with soft rank constraint is used to enable a low-rank representation matrix to meet the size of a target rank, and a globally optimal solution is obtained in a closed form. The complexity and diversity of ideological and political course data are effectively dealt with, the accuracy and practicability of ideological and political course classification are improved, and implementation of ideological and political course personalized teaching and ideological and political course optimization is supported.

The application discloses a wireless map-based link switching method and system for a vehicle-mounted access terminal, and the method comprises the following steps: acquiring LTE-M wireless characteristic information and position information of the vehicle-mounted access terminal, and constructing historical wireless link big data; constructing a wireless link map based on the historical wireless link big data of the vehicle-mounted access terminal by using a spectral clustering algorithm, and acquiring an output result of the spectral clustering algorithm; comparing and analyzing the output result of the spectral clustering algorithm with LTE-M network indexes, and performing link switching of the vehicle-mounted access terminal according to a comparison and analysis result. The system comprises an acquisition module, a clustering module and a switching module. By using the application, intelligent link switching of a rail transit vehicle-mounted access terminal can be performed according to a comparison result of real-time wireless link data and a wireless link map. The application can be widely applied to the technical field of wireless link switching.

A method for GNN-based automatic bay zoning includes the following steps: S1, using a modified data packet to convert an output file of the hydrodynamic model into a .nc file that can be read by Python software; S2, reading the converted .nc file and performing data preprocessing; S3, using a tsfresh data packet to extract time series feature values of different features; S4, using a Delaunay triangulation algorithm and a four-way matrix or an eight-way matrix to determine spatial connectivity, and constructing an adjacency matrix; S5, constructing a convolutional GNN to learn bay characteristics and spatial connectivity; S6, using a Louvain algorithm or a Spectral Clustering algorithm to perform unsupervised classification of bay; S7, post-processing broken edges of bay zoning; and S8, outputting zoning results in the form of a .shp file to realize the automatic bay zoning.

The application relates to the technical field of unmanned aerial vehicle control, and proposes a multi-unmanned aerial vehicle task allocation method, device, equipment and medium, the method comprising the following steps: constructing a task similarity matrix coupled with space-time and load based on geographical position information, respective time window information and task load requirements of a plurality of task points to be allocated; clustering the plurality of task points by using a spectral clustering algorithm to obtain a plurality of task clusters; generating an initial task allocation solution of an unmanned aerial vehicle task allocation scheme by using an integer coding mode to form an initial grey wolf population; optimizing the initial grey wolf population by using an evolutionary population dynamics strategy, dynamically adjusting a convergence factor by using a nonlinear adjustment strategy in a position updating stage of grey wolf optimization iteration; constructing a weighted fitness function fusing economic cost, time cost and task delay penalty; and based on the optimized grey wolf population, performing global optimization under the condition of meeting preset unmanned aerial vehicle constraints to obtain and output an optimal unmanned aerial vehicle task allocation scheme.

The invention provides a fracture reconstruction method, system and equipment for a fault control carbonate rock stratum based on a spectral clustering algorithm, and a medium. The method comprises the following steps: step 1, obtaining seismic volume data of a target area; step 2, processing the obtained seismic volume data by using a spectral clustering algorithm to obtain a cluster division result; step 3, connecting each cluster point set in the cluster division result by taking a seismic exploration fracture azimuth angle in the seismic volume data as a constraint, and reconstructing to obtain the associated fracture of the strike-slip fault zone of the fault control type carbonate rock stratum; according to the method, the reconstruction precision of the associated fracture of the strike-slip fault zone of the fault control type carbonate rock stratum is improved, and the high fidelity of seismic fracture attribute extraction is ensured.

The application relates to a single-cell Hi-C clustering method and system based on contact number weight smoothing and feature fusion, which comprises the following steps: (1) data preprocessing; (2) contact number weight-based smoothing; using a weight matrix to quantify the influence of different neighbor fragments on a target chromosome fragment; (3) restart random walk smoothing; using a restart random walk algorithm to perform smoothing processing on the chromosome contact matrix after the contact number weight-based smoothing processing; (4) contact binarization; (5) principal component extraction; using KPCA to perform dimension reduction and generate cell embedding; (6) feature fusion; (7) spectral clustering; using a spectral clustering algorithm to realize cell clustering. The single-cell Hi-C clustering method provided by the application is obviously superior to existing clustering methods, greatly improves the clustering effect when a large-scale single-cell Hi-C data set is processed, and can identify cell types with a small number of cells in the data set.

The invention discloses a city area photovoltaic cluster dynamic grid division method based on a space-time coupling tensor. The method comprises the steps that active power time sequences and geographic coordinates of all photovoltaic nodes in a city area are synchronously collected; constructing a heterogeneous space adjacency matrix considering the electrical topology constraint, and calculating the static space correlation degree between any two photovoltaic nodes; constructing a time-shifting cross-correlation matrix for capturing the moving characteristics of the weather system, and quantifying the dynamic time correlation degree between nodes; executing spatio-temporal feature tensor fusion to obtain a global spatio-temporal affinity matrix; dividing photovoltaic nodes by using an improved spectral clustering algorithm, adaptively determining an optimal grid number through a contour coefficient, and finally generating a photovoltaic cluster grid division scheme at the moment; and triggering grid reconstruction according to the grid drift threshold. According to the method, physical topology constraints and meteorological time delay characteristics can be effectively considered, accurate aggregation of photovoltaic clusters is realized, and the acceptance capability and regulation and control flexibility of a power distribution network to distributed energy sources are remarkably improved.

The invention discloses a customer group data processing method and device, electronic equipment and a storage medium, and relates to the technical field of big data or other related fields, and the method comprises the steps: obtaining transaction data of N financial customers from a financial database, and constructing a financial customer group network according to the transaction data, N being a positive integer; based on a preset threshold value formula and a spectral clustering algorithm, the financial customer group network is classified, the financial customer group network is disassembled according to a classification result, M sub-graphs are obtained, and M is a positive integer; solving a maximum independent subset for each sub-graph by adopting a quantum computing optimization strategy, and determining customer associated information corresponding to the maximum independent subset; and updating the current customer group information in the financial database by using the customer association information corresponding to all the sub-graphs. Through the method and the device, the technical problem that customer group information cannot be updated in time due to low processing efficiency of financial customer relationship data in related technologies is solved.

The invention provides a ganoderma lucidum traceability identification and positioning method, which comprises the following steps of: measuring morphological parameters such as pileus diameter and stipe length of a ganoderma lucidum sample, and constructing a space-time multi-dimensional environment factor data set in combination with geographical coordinate information, climate condition data and soil physicochemical properties of a sampling point; taking the ganoderma lucidum morphological parameters as dependent variables and the environmental factors as independent variables, and carrying out feature selection by adopting Lasso regression to obtain a key environmental factor combination which has obvious influence on the growth of the ganoderma lucidum; performing morphological measurement on newly collected ganoderma lucidum sample data in the target area, judging the availability of the samples according to attribute information such as ganoderma lucidum varieties, sampling time and sampling sites, and removing abnormal samples which do not meet the standard; the method comprises the following steps: extracting chemical component fingerprints of ganoderma lucidum samples, grouping the samples by adopting a spectral clustering algorithm, determining classification results of ganoderma lucidum from different producing areas according to similarity of chemical components, and constructing a ganoderma lucidum producing area discrimination model.

The application provides a multimedia data security measurement method based on multi-dimension correlation. For multimedia data, a plurality of features of a single image in the multimedia data are divided into a plurality of clustering groups based on spatial similarity by a spectral clustering algorithm. Whether the multimedia data is safe is judged by combining content security score, privacy information security score, semantic security score and formal security score, so as to realize security evaluation and optimization of calculation efficiency.

The invention provides a machine room equipment fault prediction and health management method, which comprises the following steps that: a multi-dimensional sensor acquires multiple physical quantity signals such as temperature, vibration, current harmonic and load, and constructs a standardized equipment health sequence through time sequence alignment, noise suppression and normalization processing; an evolution path of a unified structure is extracted through a sliding window and integrity evaluation, and a typical degradation mode is identified by applying similarity measurement based on dynamic time warping and a spectral clustering algorithm; an LSTM encoder is trained offline for each type of degradation mode, and a standardized threshold template is generated; and during online operation, the current track is compared with various degradation mode templates in real time, an equipment self-adaptive early warning threshold value is dynamically generated, trend anomaly detection and self-fine adjustment of a threshold value model are achieved, and the intelligence, the dynamics and the accuracy of health state monitoring of large-scale machine room equipment are improved.

The invention relates to the technical field of power distribution networks, and discloses a mountainous area power distribution network new energy installed capacity planning method, system, device and medium, and the method comprises the steps: firstly collecting the network structure, climate and new energy and load historical operation data of a mountainous area power distribution network; performing region division on the nodes based on a principal component analysis method, and extracting time sequence characteristics of new energy output of each region by using an improved spectral clustering algorithm fusing graph structure information; a multi-scene new energy output model covering normal and extreme meteorological conditions is constructed; on the basis, establishing a distribution network operation constraint system comprising power flow, voltage, current and equipment output constraints, and constructing a comprehensive objective function taking full life cycle economy as an objective; and finally, solving a new energy installed capacity configuration scheme which meets the constraint and is optimal in economical efficiency through iterative optimization. According to the method, the planning robustness, the power supply reliability and the new energy consumption capability of the mountain area power distribution network under extreme conditions are effectively improved.

The invention relates to the technical field of ecological system monitoring and evaluation, and discloses an ecological system health evaluation method based on machine learning. The method comprises the following steps: collecting multi-source continuous monitoring data of an ecological system, and carrying out integrity check and noise filtering to generate a standardized data set; performing multi-scale feature mining on the data set, and extracting ecological feature sequences of a time domain and a frequency domain; performing community discovery on the feature sequence by adopting a spectral clustering algorithm, and outputting a clustering result of the ecological health state; constructing a multi-class classification model according to the clustering result, and adjusting the weight of the model through iterative learning; and inputting the real-time ecological data flow into the trained multi-class classification model, performing probability prediction of the health state, and automatically generating a state evaluation chart. According to the method, the health state can be adaptively defined from the data, and objective and dynamic evaluation of ecological system health is realized.

The application provides a historical building group temporary support system collaborative optimization method and system, relates to the building protection technical field, and comprises the following steps: obtaining point cloud data through three-dimensional laser scanning, performing component identification and force transmission sequence analysis, adopting a spectral clustering algorithm to perform support partitioning, constructing a group action strength matrix based on geometric similarity and structure coupling degree, and optimizing support parameters by using a genetic algorithm. The application realizes the overall collaborative arrangement of the historical building group support system, improves support efficiency and safety, and reduces support cost.

This invention provides a financial data management system based on a large language model, belonging to the field of financial data management technology. It performs semantic analysis and access feature extraction on financial data access requests based on a large language model, combines the fluctuation trend of data read popularity curves, and uses a spectral clustering algorithm to dynamically divide data access levels. Furthermore, it employs a dynamic statistical periodic model for time-segmented preloading. This improves the accuracy and efficiency of data access, optimizes server resource scheduling, facilitates intelligent management of financial data in high-concurrency scenarios, reduces server load, and enhances the overall performance and responsiveness of the system.

The embodiment of the invention provides a multi-computing-unit efficient clustering interconnection method for an SAR imaging processing chip, and the method comprises the steps: obtaining overall correlation features among multi-computing-unit engines of the SAR imaging processing chip based on a standard spectral clustering algorithm, and regulating and controlling the cluster scale through combining with a normalized cut target, so as to generate a feature vector; wherein a balance factor regulation and control mechanism is introduced into the normalized cut target; executing a Kmeans clustering step on the feature vector and outputting a clustering result; wherein the capacity constraint is introduced into the Kmeans clustering; a balance factor is introduced into the normalized cut target to regulate and control the cluster scale, the node degree weight is balanced and enhanced, and the problem of cluster scale imbalance is solved; clustering under physical limitation is realized by adopting capacity-constrained K-means, the number of single-cluster engines is strictly limited not to exceed the upper limit of physical bus mounting, and the scheme can be physically realized.

This invention relates to the field of brain network analysis technology, specifically to a multimodal brain network fusion method based on tensor spectral clustering. The method includes: acquiring multimodal brain network data, including structural magnetic resonance imaging (SMRI) data, functional magnetic resonance imaging (fMRI) data, and diffusion tensor imaging (DTI) data; preprocessing the multimodal brain network data; constructing individual morphological brain networks, functional brain networks, and structural brain networks based on the preprocessed multimodal brain network data, and performing normalization processing; fusing the normalized multimodal brain networks using a tensor spectral clustering algorithm to obtain a fused brain network; acquiring the subject's behavioral data, inputting the behavioral data into the fused brain network, and calculating the subject's behavioral prediction values. This invention not only advances research in neuroscience, cognitive science, and psychology but also provides strong support for related clinical applications and personalized medicine.