397 results about "Spectral clustering" patented technology

The invention provides a virtual DPU power plant simulation fault restoration method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the preprocessing of collected DPU power plant operation data, including noise reduction, time sequence alignment and abnormal point elimination, carrying out the data quality evaluation, training a fault feature mapping model based on the processed data, and carrying out the fault restoration of the DPU power plant. The model is used for recognizing abnormal clusters in real time, a fault evolution path is searched and determined in combination with a conditional random field and a Monte Carlo tree, an optimal path is determined through a particle filtering algorithm, fault root causes are determined in combination with causal analysis, spectral clustering and a Bayesian network, and a fault diagnosis report is generated.

The invention discloses a hierarchical contrast anti-fact learning method and system for a visual question and answer model. The method comprises the steps of obtaining original image data and question text data, constructing multi-modal joint tensor data, calculating probability distribution based on a projection matrix, screening samples and dynamically adjusting weights. Constructing a multi-modal graph structure, performing modal alignment based on the feature enhancement matrix, and executing dynamic feature fusion; establishing a multi-level probability knowledge structure, generating an anti-fact sample, and executing knowledge fusion operation; constructing a multi-level distance measurement system, executing adaptive weight optimization, and performing spectral clustering optimization and boundary adjustment; and constructing a multi-dimensional evaluation index system, executing stability analysis and dynamic performance tracking, and obtaining final performance evaluation index data. According to the method, the performance of the visual question and answer model is improved through a hierarchical comparison anti-fact learning framework, and optimization of feature representation, dynamic adjustment of a knowledge structure and accurate evaluation of the model performance are realized.

The invention provides an urban functional area gridding identification and classification method based on multi-source spatio-temporal data, and relates to the technical field of area identification, and the method comprises the steps: carrying out the dynamic grid division of an urban area through employing a self-adaptive grid division algorithm, obtaining the multi-source spatio-temporal data of a gridding area, carrying out the space-time alignment, and generating a multi-dimensional time sequence feature set; extracting multi-dimensional feature vectors of the grid units through a multi-modal feature fusion algorithm, acquiring long-range time sequence features by using a bidirectional gating recursion unit, calculating spatial correlation features based on a multi-head attention mechanism, and inputting the two types of features into a feature fusion network with jump connection to generate enhanced spatial-temporal features; and an optimization strategy is adopted, a functional area clustering center point is determined through density peak value clustering in sequence to obtain an initial division result, the initial result is optimized by combining spectral clustering of spatial adjacency constraints, and the boundary of the functional area is optimized by using a simulated annealing algorithm, so that accurate identification and classification of the urban functional area are realized.

The present application discloses an underground water real-time monitoring method and system, and a medium. The method comprises: acquiring historical environment data and historical monitoring data in an underground water monitoring area for spectral clustering data partitioning, and importing related data into a pollution monitoring model for predictive analysis and model training; acquiring pollution monitoring data in monitoring sub-areas in real time, importing the pollution monitoring data into the pollution monitoring model for pollution prediction, and obtaining pollution diffusion directions and pollution source prediction areas; and on the basis of the pollution source prediction areas and environment data of the monitoring sub-areas, calculating a monitoring demand degree of each monitoring sub-area, and generating a monitoring scheme on the basis of the monitoring demand degree. By means of the data spectral clustering method in the present application, accurate grouping and acquisition of training data can be effectively implemented, and model prediction precision is improved. In addition, the present application can realize dynamic adjustment of monitoring areas, effectively conserve human and material resources in underground water monitoring areas, and realize the cost reduction and efficiency improvement of underground water pollution monitoring.

The invention relates to the technical field of database retrieval, in particular to a knowledge database retrieval method based on feature extraction. The method comprises the following steps of obtaining a document to be retrieved, performing local hash de-duplication processing and extracting multi-modal features, performing feature spectrum clustering processing and associated entity analysis on the extracted features, generating a feature associated entity structure, performing document topic modeling and semantic direction positioning, and extracting a semantic topic of the document. The method comprises the following steps: performing word vector retrieval on a to-be-retrieved document and a knowledge database by utilizing a document semantic topic, generating word vector retrieval data, performing sentence vector retrieval, generating a final document retrieval result, integrating word vectors and the document retrieval result, and executing feature retrieval of the knowledge database through an intelligent retrieval engine, thereby realizing efficient knowledge query. The knowledge database retrieval method is more intelligent and more accurate.

The invention provides a private domain social relation mining method and system based on a graph neural network, and relates to the technical field of social relation mining, and the method comprises the steps: carrying out the entity alignment and relation mapping of historical behavior data of a private domain platform user and an external knowledge base through employing a knowledge graph technology, generating user behavior feature data, and constructing a user relation graph; processing the user data by using a federal map neural network model containing a space-time attention layer to obtain a user node representation vector; and according to the user node representation vector, calculating a social influence index and a relationship strength index between the users, determining a final social circle layer structure by adopting a spectral clustering method and causal intervention analysis, and generating a social relationship analysis report. According to the method, complex user social relations in a private domain platform can be effectively mined, the accuracy and stability of social circle layer identification are improved, and technical support is provided for precise marketing and personalized recommendation.

The invention relates to the technical field of data protection, and discloses a privacy protection type data joint modeling method based on federated learning, which comprises the following steps: acquiring local data to perform meta-feature extraction, calculating key statistics to characterize data characteristics, collecting meta-features, grouping the meta-features into similar feature clusters through spectral clusters, and carrying out feature clustering on the similar feature clusters; dynamically allocating and calculating resource weights according to the similar characteristic cluster scale and the equipment computing power; distributing a basic privacy budget according to the client type, calculating a local model accuracy rate and an intra-cluster level difference, dynamically adjusting the privacy budget, adding adaptive Gaussian noise based on the privacy budget, and adjusting gradient sensitivity of gradient calculation; verifying gradient compliance through zero knowledge, carrying out safe aggregation on gradients passing verification, optimizing a meta-model through a knowledge distillation loss function, and generating confrontation sample analysis to obtain a leakage risk value to identify knowledge leakage risks; sensitive neurons in the neuron sensitivity positioning element model are analyzed and calculated, directional noise is injected, and initial parameters are adjusted for initialization training.

The invention discloses a power grid dispatching anti-error method and system based on topology analysis, and relates to the technical field of power grid dispatching anti-error, and the method comprises the following steps: building a remote signaling credibility evaluation model based on remote signaling evaluation data, and obtaining high-credibility remote signaling data; performing sequential logic verification based on high-credibility remote signaling data and multi-source data fusion to generate a power grid topology model; carrying out partition division on the power grid topology model based on spectral clustering and a state-driven edge weight method, and carrying out boundary equivalence processing to obtain a plurality of topology modules; and performing simulation scheduling operation on the plurality of topology modules based on a scheduling instruction dispatching order objectification analysis result, performing path traversal based on a depth-first search method, and performing anti-error analysis on the traversed path in combination with a reinforcement learning method. According to the power grid dispatching anti-error method combining remote signaling credibility evaluation, dynamic topology partition and reinforcement learning algorithm, the anti-error verification capability and the intelligent level of a power grid dispatching system are comprehensively improved.

The invention discloses a road user track and behavior prediction method in a dense heterogeneous traffic environment, and the method comprises the steps: carrying out the target detection and recognition of each road user in a video image sequence frame based on a deep convolutional neural network, and taking a recognition result as a road user type constraint; taking the detected and identified track coordinates and timestamp information of the road user as historical track information of the road user; according to the method, on one hand, a traffic map sequence of a target road user per second within a tf time period after a timestamp t is obtained through prediction, the traffic map sequence is subjected to spectral clustering and then serves as a map regularization constraint in a regularization loss function form to be used for correcting future trajectory coordinates of the road target user output by semi-supervised pipeline prediction; and on the other hand, entering a logistic regression function network after passing through a full-connection network, and outputting the behavior prediction type of the road user. According to the method, the track and behavior of the road user can be accurately predicted in a dense heterogeneous traffic environment.

The invention discloses an interest point recommendation method based on a knowledge graph, and relates to the technical field of computers, and the method comprises the following steps: obtaining user sign-in data to obtain user information, respectively dividing according to interest points and time to obtain interest areas and a plurality of time slots, calculating user similarity, and performing spectral clustering according to the user similarity to obtain user groups, and according to the user group, the time slot and the region of interest, constructing a multi-level point-of-interest knowledge graph fused with context information. Preliminarily learning an interest point knowledge graph based on a graph translation model to obtain an embedded vector, and using a gating graph neural network introduced with geographic information for a plurality of sign-in sequences to obtain interest point representation with spatio-temporal characteristics; through the graph attention mechanism model, the sign-in relation with user group features and interest area features is fused, user preferences are spread, possible interest point preferences of a single user are mined and predicted, user requirements can be more effectively met, and a more highly personalized recommendation service is provided.

The invention discloses a voiceprint clustering method guided by a speaker recognition pre-training model based on short voices, and the method comprises the following steps: obtaining an original voice signal, and randomly combining a plurality of enhancement strategies to achieve data enhancement; performing voice segmentation on the voice signal after data enhancement based on a uniform segmentation mode; extracting voiceprint features of the segmented voice based on an attention mechanism of global time-frequency domain context modeling; and obtaining a clustering result based on K-means clustering and spectral clustering, matching the clustering result with a real speaker tag, performing reverse transmission based on angle-dependent AAM-Softmax loss, and outputting a voiceprint clustering result. According to the method, the influence of environmental interference on feature extraction can be overcome, feature dimensions which are more effective for identity identification can be screened, a clustering output effect which is superior to that of a mainstream algorithm can be obtained with a relatively low parameter quantity, and the robustness under a noise interference condition is improved.

The invention discloses an electricity larceny identification method based on graph calculation, and particularly relates to the technical field of electricity utilization anomaly detection of an electric power system. Historical power consumption data and a power supply topological relation of power consumers are collected, and a multi-dimensional behavior graph model fusing behavior characteristics and structural information is constructed; performing structure disturbance analysis on each node in the graph, calculating information entropy change before and after node removal, performing attention fusion on a time sequence behavior feature of the node and a structure disturbance vector, constructing a joint feature vector, and enhancing feature expression through spectral clustering and linear reconstruction; a behavior propagation field and a disturbance adjustment mechanism are introduced into the graph to form a disturbance response graph, and an abnormal gathering area is identified through path energy analysis and focusing area fitting; calculating confidence scores of the nodes and outputting a suspicious user list; the method can realize efficient identification of electricity stealing behaviors with strong concealment and complex transmissibility, and has the advantages of high precision, strong interpretability and wide application scene adaptability.

The invention discloses a multi-level track disease recognition system based on vehicle body vibration data, and particularly relates to the field of blasting auxiliary devices. Comprising the steps that a data acquisition module acquires heterogeneity data of a track environment; the environment adaptive module analyzes the correlation between the vehicle body vibration data and the heterogeneity data, constructs a dual-view similarity matrix, and realizes adaptive classification of the track environment through a spectral clustering method; the label enhancement module generates high-quality pseudo labels and forms a completely labeled sample set; the disease recognition module adopts an attention-guided multi-task cascade CNN architecture, and outputs a multi-level recognition result containing disease positions and severity. By the adoption of the technical scheme, the problem that an existing track state evaluation method cannot evaluate the real state of track diseases in a complex environment is solved, and good performance is achieved in the aspect of geometric and structural disease recognition.

The invention discloses a networking charging data cross-platform fusion and transmission method based on an intelligent scheduling algorithm. The method comprises the following steps: S1, collecting and preprocessing networking platform charging data; s2, feature similarity is calculated, and an undirected graph with samples as nodes and similarity as edge weight is constructed; s3, calculating a standardized Laplacian matrix, extracting feature vectors, and clustering to generate feature cluster tags; s4, constructing a modal mapping matrix, mapping the feature vectors, and inputting the mapped feature vectors into a first convolution restricted Boltzmann machine to extract fusion features; s5, inputting the fusion features into a second convolution restricted Boltzmann machine, and combining graph structure sorting and constructing a weighted directed graph; s6, initializing a virtual scheduling factor in the graph, and searching an optimal transmission path by an ant colony algorithm; and S7, distributing fusion features according to paths, and realizing cross-platform efficient transmission of charging data. According to the method, spectral clustering, modal mapping, convolution extraction and intelligent scheduling are fused, and the multi-platform charging data fusion accuracy and transmission efficiency are improved.

The invention relates to the technical field of computer security, in particular to an embedded software reinforcement method based on a national cryptographic algorithm. The method comprises the following steps: generating a key pair by using SM2, constructing a code dependency graph in combination with spectral clustering, and optimizing key distribution; carrying out segmented encryption by adopting SM4 symmetric encryption, and identifying sensitive code snippets; the integrity of the embedded software is monitored and responded in real time through SM3 hash check and anomaly detection; analyzing vulnerability distribution based on random forest regression by using an SM4 encryption update package and dynamically adjusting an update frequency; and encrypting an operation log through SM3, and identifying a risk trend and generating an operation report by using a variational auto-encoder. According to the invention, through a plurality of technical means such as hierarchical encryption, real-time monitoring, dynamic key management and risk prediction, an efficient, dynamic and intelligent security reinforcement scheme is provided for embedded software, and the overall security and stability of the system are improved.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention provides an artificial intelligence advertisement pushing method based on multi-dimensional historical data analysis, and the method comprises the steps: constructing second-level, minute-level, hour-level and other behavior feature views of different time granularities for extracted original features, and forming multi-time scale feature representation; analyzing feature views under different time scales through a graph mining algorithm, mining an association rule between feature nodes, and constructing a cross-scale feature association graph; in the feature correlation map, according to the spectral clustering result of the feature nodes and the gravitation direction of the feature edges, the temporal correlation degree of different feature combinations is judged; if the time correlation degree of the feature combination exceeds an empirical threshold value, endowing the time weight of the feature combination with a high weight coefficient of power law distribution; and dynamically adjusting the network depth and the connection density of the association atlas according to the vertical dimension and the semantic hierarchy of the features, and optimizing the feature weight distribution in the atlas.

The invention discloses a high and cold arid region slope soil stability safety risk evaluation system and method, and relates to the technical field of slope engineering. The state of each side slope under multi-dimensional indexes such as freeze-thaw cycle frequency, dry-wet alternation index, wind erosion strength, shear strength, water content, porosity and fracture density is quantified, the feature similarity between any two side slopes is calculated, and then a side slope feature similarity matrix is formed. Based on the matrix, an unsupervised clustering algorithm (such as spectral clustering, similarity propagation and the like) can be adopted to divide a plurality of side slopes into similar subsets with structural characteristics similar to environmental response, and category attribution of risks is achieved. On the basis, structural variation analysis, historical instability statistics and central risk difference extraction are performed on similar slope samples, so that the internal instability tendency of the slope can be identified, and a risk prediction model suitable for the type of slope can be constructed through feature training.

The invention relates to the technical field of cloud platform computer control, and discloses a computer control system and method based on a cloud platform. The system comprises a multi-source data acquisition module which acquires heterogeneous data in real time and divides the heterogeneous data into time sequence segments; the data fusion preprocessing module is used for removing noise and screening key features to generate a standardized data stream; the dynamic decision control module outputs a control instruction set based on a deep reinforcement learning algorithm; the abnormal mode recognition module is used for recognizing anomalies by adopting spectral clustering and an isolated forest model; the resource flexible scheduling module is used for optimizing resource scheduling by using an improved genetic algorithm; and a safety audit module is also arranged to guarantee instruction safety. According to the system and the method, heterogeneous data of the cloud platform can be effectively acquired and processed, accurate decision control is realized, abnormity is accurately identified, resources are reasonably scheduled, system security, stability and resource utilization rate are improved, and complex service requirements of the cloud platform are met.

The invention relates to the technical field of tunnel defect identification, in particular to a tunnel defect identification method based on a dielectric distribution diagram. According to the method, on the basis of AI rock-soil perspective radar field monitoring, gprMax simulation and laboratory electromagnetic data, a two-dimensional dielectric distribution diagram is generated through variational Bayesian inversion fusion. Through multi-scale spectral clustering and expert knowledge, a potential abnormal region is automatically identified, and then accurate classification and identification of a defect region are realized by adopting graph form constraint propagation and integrating a Transform-graph neural network model. And finally, projecting an identification result to an original image, and generating a visual defect labeling layer and a structured report. According to the invention, high-precision, automatic, visual and structured detection of tunnel structure defects is realized, the intelligent level and risk early warning capability of tunnel safety operation and maintenance are significantly improved, and the digitization and intelligent process of tunnel operation and maintenance management is promoted.

The invention discloses a virtual power plant capacity optimization configuration method and system considering carbon tax, and belongs to the field of power markets. The invention discloses a virtual power plant capacity optimization configuration method considering carbon tax. The method comprises the following steps: analyzing virtual power plant capacity cost and operation income considering carbon tax and market transaction; constructing a virtual power plant internal resource capacity optimization configuration double-layer model based on the analysis of the virtual power plant capacity cost and the operation benefit; generating a spectral clustering typical scene based on an information gap decision theory considering source load uncertainty; and solving the virtual power plant internal resource capacity optimization configuration double-layer model by adopting a differential evolution algorithm to obtain a capacity optimization configuration scheme.

The invention discloses a joint optimization clustering distributed photovoltaic power prediction method and system, and relates to the related field of distributed energy management, and the method comprises the steps: carrying out the local calling of data, carrying out the time sequence alignment, and constructing a multi-dimensional feature association number table; extracting geographic position information and historical output data to perform similarity analysis, performing spectral clustering, and outputting a photovoltaic cluster; collecting meteorological time series data according to the power prediction scale, inputting the meteorological time series data into the cluster power prediction model, and performing cluster power prediction; grid-connected space-time joint optimization is carried out according to the cluster-level power prediction value, and an energy storage charging and discharging scheduling strategy is output; after single-station fitting conversion is carried out and a distributed power prediction value is output, output deviation identification is carried out, and a fault node is positioned; and dynamically updating the energy storage charging and discharging scheduling strategy. The technical problems that existing power prediction is not high in precision, cross-cluster power optimization scheduling is difficult to achieve, and the power grid operation stability is poor are solved, and the technical effect of improving prediction accuracy and the power grid operation stability is achieved.

The invention belongs to the field of machine learning and graph data mining, and relates to a consumption financial commodity recommendation method based on graph clustering of granular ball calculation. The method comprises the steps of obtaining a financial transaction network graph data set and performing information extraction on the financial transaction network graph data set to obtain graph data G; carrying out granular ball calculation on the graph data G through a self-adaptive splitting mechanism, and finally generating a plurality of granular balls; edges are added between the pellets according to the adjacency relation between the pellets, and a granulation graph is generated; constructing an objective function of a particle-sphere similarity matrix; solving an objective function of the granular-spherical similarity matrix to obtain the granular-spherical similarity matrix; performing symmetric non-negative on the particle-ball similarity matrix, and performing spectral clustering on the symmetric non-negative particle-ball similarity matrix to obtain a commodity clustering result; according to the method, transaction graph data are preliminarily covered and represented by high-quality granular balls generated through an adaptive splitting mechanism, fine-grained graph nodes are replaced by coarse-grained granular balls to perform a clustering task, global and local features are captured through multi-granularity representation, and thus rapid and accurate node clustering is realized.

The invention provides a hybrid wireless eye movement tracking method and system based on multi-sensor fusion, electronic equipment and a storage medium, and relates to the technical field of eye movement tracking. The method comprises the following steps: firstly, acquiring various data by using various different types of sensors such as a Q-Var sensor, an inertial measurement unit and a high-speed panoramic camera; secondly, preprocessing the various sensor data by means of graph Fourier transform, a Kalman filter, a spectral clustering technology and the like; fusing the preprocessed sensor data based on a graph theory method; and finally, performing fixation track prediction based on a convolutional neural network and a gating circulation unit. According to the technical scheme, the performance of eye movement tracking under different environment conditions is improved, the eye movement tracking precision is higher, and the stability is better. In addition, the technology of the application also uses a 5G millimeter wave technology or an IEEE 802.11 ac protocol to transmit gaze tracking data, thereby achieving the purpose of bandwidth enhancement and low-delay data transmission.

The invention discloses a method for constructing an evaluation model for the influence of climate change on biodiversity, and particularly relates to the technical field of ecological modeling and climate response analysis. The method comprises the following steps: generating a high-resolution characteristic grid based on microclimate disturbance data and a remote sensing image, extracting ecological plaques and establishing a heterogeneity spatial index model, constructing a connectivity map and a coupling response path library, extracting a typical species response mode by using nonlinear dimension reduction and spectral clustering, and performing dynamic fitting in combination with biological survey data. And finally, generating a diversity attenuation trend prediction curve and a risk thermodynamic diagram, identifying a biodiversity collapse critical point and outputting an intervention priority, and the method can be widely applied to regional ecological early warning and protection planning.

The invention discloses a winter wheat LAI inversion method based on scenarized parameter calibration and ensemble learning. The method comprises the following steps: fusing a remote sensing image with auxiliary geographic information such as terrain and soil, and performing typical planting scene division on a research area by adopting a spectral clustering method; then scene optimization is carried out on highly sensitive parameters (such as LAI, Cab, Cm and ALA) in the radiation transfer model PROSAIL based on a Markov chain Monte Carlo (MCMC) algorithm, and parameter posterior distribution more consistent with actual observation is obtained; further utilizing optimization parameters to generate a high-quality and homogenized simulation sample set, and mapping the simulation sample set to a Sentinel-2 waveband to construct a training data set; and finally, performing training by adopting various machine learning models (such as LightGBM, XGBoost, RF and KNN), and constructing a fusion model through a stacking integration strategy to realize high-precision inversion of LAI. Experimental results show that the method has higher fitting precision and stronger generalization ability in a plurality of typical planting scenes.

The invention discloses a decision scheme generation method and device based on complex multi-modal management data, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: receiving enterprise management data of texts, numerical values and images, extracting a unified feature vector, carrying out the clustering analysis through the dynamic updating of torque clustering and spectral clustering, and obtaining a clustering result; and a causal knowledge graph is constructed according to the result, and a decision scheme is generated by using a fourth-order inference engine, so that efficient processing and real-time response of an enterprise to dynamic multi-modal management data are realized, the cross-modal data analysis capability is enhanced, and the decision accuracy is improved.

The invention belongs to the technical field of electric power information, and discloses a spectral clustering-based power distribution network medium-low voltage topology model generation method and related device, and the method comprises the steps: taking a transformer and a low-voltage meter box as nodes, and taking switch equipment as edges to construct a low-voltage topological graph; determining a connection weight coefficient of edges between the nodes according to the type and number of switches between the nodes, constructing an adjacent weight coefficient matrix of the low-voltage topological graph according to the connection weight coefficient, and constructing a degree matrix of the low-voltage topological graph according to the number of the edges connected with the nodes; and according to the adjacency weight coefficient matrix and the degree matrix of the low-voltage topological graph, clustering each node of the low-voltage topological graph by adopting a spectral clustering method to obtain a clustering result, and based on the low-voltage topological graph, dividing the nodes with the same category identifier into the same sub-graph to obtain a plurality of low-voltage topological sub-graphs and taking the low-voltage topological sub-graphs as low-voltage topological models. Not only is effective segmentation and simplification of the low-voltage topology in the power distribution network realized, but also powerful technical support is provided for intelligent management, rapid fault positioning and resource optimization configuration of the power grid.

The invention discloses a wireless communication resource allocation method based on dynamic spectrum sensing. The method comprises the following steps: acquiring equipment node data and network environment parameters, and performing preprocessing to generate feature data; based on the preprocessed feature data and the time window, generating a spatio-temporal feature map by adopting dynamic spatio-temporal feature coding so as to capture spatio-temporal association between the devices; a hyperedge set is constructed through a hyperedge dynamic generation mechanism by using equipment position information, and network topology changes are reflected; based on the hyperedge set, the transmitting power and the channel gain, a high-order interference tensor is constructed and decomposed, and the interference relation is accurately quantified; according to the interference tensor, the number of resource blocks and the service quality requirement, generating an optimal resource allocation strategy by adopting spectral clustering and an optimization algorithm; according to the method, through the space-time attention mechanism and the high-order interference tensor modeling, the dynamic adaptability and optimization performance of resource allocation can be improved, and an efficient solution is provided for a modern wireless communication network.

The invention provides a multi-view text clustering method based on a convolutional neural network. The method comprises the following steps: preprocessing original multi-view text data; calculating a TF-IDF value of the preprocessed original multi-view text data, and then combining the TF-IDF value with a Word2Vec word vector to generate a text word embedding weighted vector; generating an LDA text topic vector for the preprocessed original multi-view text data; respectively carrying out spectral clustering on vector groups formed by the text word embedding weighting vector and the LDA text topic vector to obtain clustering results; the same classification data in the clustering result serve as marked data to be input into a convolutional neural network for training, different classification data in the clustering result serve as unmarked samples, the trained convolutional neural network is used for conducting feature extraction on the unmarked samples, spectral clustering is conducted on obtained feature vectors, and a final clustering result is obtained.