2525 results about "Adjacency matrix" patented technology

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

A method for locating a high-impedance ground fault of a smart distribution network with topology change adaptation includes: acquiring a fault traveling wave sample within a specified time window after a fault occurs, and performing continuous wavelet transform on the fault traveling wave sample to obtain traveling wave full waveform feature information; establishing a graph structure of a power distribution network, obtaining a corresponding adjacency matrix, and obtaining node position and structure encoding information in the graph structure through graph random walk and graph Laplace transform; concatenating the node position, the structure encoding information and the traveling wave full waveform feature information to obtain a node feature, and inputting the node feature and an edge feature into the graph structure to establish a graph sample data set; constructing and training a Graph Transformer model; and calling the trained Graph Transformer model to locate a fault in to-be-detected sample data.

The invention discloses a water supply pipe network water hammer control method and system based on multifunctional module fusion, and relates to the technical field of data identification. A time sequence diagram neural network prediction and traceability module which realizes water hammer risk prediction and propagation path traceability based on a causal constraint graph neural network model; the reinforcement learning intervention decision module is used for generating an active intervention strategy through a reinforcement learning agent, forming closed-loop control, abstracting a water supply pipe network into a graph structure, and modeling in combination with a time sequence, so that a propagation path of pressure waves can be comprehensively reflected, a blind area of traditional local modeling is overcome, and the comprehensiveness and accuracy of water hammer event detection are improved; and a causal analysis result is input as an adjacent matrix, so that the interference of irrelevant edges on prediction in topology is effectively eliminated, and the learning efficiency and causal traceability of the model are improved.

The invention discloses a traffic flow prediction method based on graph diffusion and dynamic graph fusion. The method comprises the following steps: S1, acquiring historical traffic flow time sequence data of each traffic node in a target road network; s2, preprocessing historical traffic flow time series data to obtain a road network node adjacency matrix; taking the historical traffic flow time sequence data and the road network node adjacency matrix as sample data, and dividing a training set, a verification set and a test set according to a preset proportion; s3, constructing a traffic flow prediction model based on graph diffusion and dynamic graph fusion; and S4, performing model training and verification on the traffic flow prediction model through the training set and the verification set to obtain an optimal traffic flow prediction model, and realizing traffic flow prediction of the test set through the optimal traffic flow prediction model. The problems that an existing method does not have the dynamic topology modeling capacity, the high heterogeneous feature fusion capacity and the self-adaptive space-time modeling capacity, and consequently the bottleneck problem of a current model in the aspects of prediction precision, stability and practicability cannot be effectively solved.

The invention relates to the technical field of power system topology identification, in particular to a power distribution network frame topology identification method based on an improved graph neural network, and the method comprises the steps: collecting the real-time electric quantity data of nodes and edges of a power distribution network, and carrying out the modeling of a power distribution network graph structure; the method comprises the following steps: constructing a node, edge and hyperedge feature matrix by using real-time collected data, inputting an improved graph neural network topology identification model, dynamically weighting and adjusting an edge weight through a graph attention network, splicing and fusing local topological features and global topological features, and generating a prediction adjacency matrix; when the power distribution network is dynamically changed, a change area is positioned through adjacency matrix difference, and a sub-graph is extracted for incremental updating; and constructing a topological structure of the power distribution network based on the final adjacent matrix, and outputting a connection relationship between the physical positions of the nodes and the edges. Compared with the prior art, the real-time performance, generalization ability and applicability of the model are remarkably improved, and the problems of accuracy and efficiency of power distribution network topology identification in the dynamic environment are solved.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention provides a computer document intelligent compliance detection system based on deep learning, and relates to the technical field of data processing, and the system comprises the steps: carrying out the topological structure analysis of an adjacent matrix through a graph neural network, so as to detect an abnormality, obtaining a structure deviation degree, and analyzing a decision path node sequence through a pre-training language model to generate semantic conflict features; fusing the structure deviation degree with the semantic conflict feature to generate a risk feature vector; generating an augmented rule set according to the risk feature vector, and updating the formalized rule base; updating parameters of the feature coding component through gradient back propagation; optimizing a differentiable logic layer judgment threshold value and the weight of an analysis module; and obtaining the updated feature coding component, the rule base version and the risk quantification parameter. According to the invention, the efficiency of document compliance detection is effectively improved.

The invention relates to a hydrological flow prediction method and system based on multi-site time-space association. The prediction method comprises the following steps: carrying out dimension reduction and feature reconstruction on original multi-site hydrological data through an auto-encoder; space-time correlation modeling and adjacency matrix dynamic construction are carried out, a multi-dimensional Euclidean distance matrix between stations is calculated based on a multivariable dynamic time warping (MDTW) algorithm, a similarity matrix is generated in combination with dynamic programming, and a dynamic adjacency matrix is constructed by fusing a geographic space adjacency relation; extracting spatial features of a GCN (Graphics Convolutional Network); carrying out adaptive time sequence decomposition and trend-period modeling; and carrying out multi-stage fusion prediction and result output, and generating a final prediction result through a decoder in combination with the decomposed trend item and periodic item. According to the method, accurate extraction and dynamic correlation modeling of spatial-temporal characteristics of multi-site hydrological data are realized, the accuracy and robustness of single-site flow prediction are improved, and the problems that multi-site spatial-temporal correlation modeling is insufficient, non-linear time sequence alignment is difficult, and single-site prediction precision is limited are solved.

The invention discloses a cross-modal image fusion hyperspectral point cloud waste plastic bottle intelligent sorting method, and relates to the technical field of neural network-based data processing, and the method comprises the steps: obtaining hyperspectral image data and point cloud data of a target object through a multi-modal collection system; preprocessing the collected hyperspectral image data and point cloud data, respectively extracting features and constructing a hyperspectral image and a point cloud image; constructing an adjacent matrix through nodes and edges by taking the constructed hyperspectral image and the constructed point cloud image as a reference, and performing normalization; carrying out single-mode feature extraction on the normalized adjacent matrix; carrying out cross-modal fusion on the extracted single-modal features; performing fine-grained modeling on a cross-modal fusion result through a multi-head attention mechanism to generate a final fusion feature; and mapping the final fusion feature to an output space of a regression task, and training network parameters to obtain a cross-modal fusion model to realize identification of a target object.

The invention discloses a traffic flow prediction method based on adaptive dynamic multi-scale space-time hypergraph convolution. The method comprises the following steps: S1, collecting traffic data; s2, forming space-time enhancement data from the traffic data; s3, a multi-scale adaptive causal convolution module extracts multi-scale traffic flow change features; s4, constructing a dynamic hypergraph structure, defining a hypergraph incidence matrix, and generating a self-adaptive hypergraph by adopting an attention enhancement matrix decomposition method; s5, fusing the time features in the S3 and the hypergraph structure in the S4, designing a multi-scale fusion homogeneous convolution module, and realizing fusion of multiple different-scale spatio-temporal features; s6, extracting topological node features through a graph convolutional network and a hypergraph convolutional network by using the multi-scale spatial-temporal features, and performing adaptive weighted fusion through a gating feature fusion unit; s7, performing multi-scale feature integration by a residual feature aggregation module; s8, generating traffic flow prediction results of a plurality of time steps in the future; the method has the advantages of extracting the traffic space-time dependency relationship, improving the prediction precision and generalization ability, and being more suitable for dynamic traffic.

The invention provides a dam safety perception fusion association method based on a multi-modal space-time diagram neural network. The method comprises the following steps: dividing a dam into a plurality of structural units, and mapping various data into a three-dimensional coordinate system; a heterogeneous graph structure is defined, and a dynamic adjacency matrix is calculated based on the real-time stress gradient so as to reflect physical connection, mechanical conduction and geological association relationships among nodes; carrying out fusion modeling on multi-source data in the heterogeneous graph structure by utilizing a multi-modal space-time diagram neural network, constructing a causal inference engine based on an output result of the multi-modal space-time diagram neural network, and updating a three-level modeling system through structural equation modeling, anti-factual inference and dynamic weight to obtain the heterogeneous graph structure. According to the method, the dynamic coupling rule among the dam structure, geology and material states is excavated, cross-modal space-time fusion of manual inspection and sensor monitoring data can be realized, the early recognition capability and early warning accuracy of dam potential safety hazards are improved, and the problems of data islands and insufficient relevance in a traditional monitoring method are effectively solved.

The invention provides a multi-level heterogeneous computing power network task scheduling and resource allocation method and system. In a computing power network, each hierarchical network is connected with an intelligent agent for task scheduling and resource allocation. In the training process, the intelligent agent obtains local observation information of the computing power node in the hierarchical network. And the intelligent agent aggregates and converts local observation information according to the relation feature map to generate a feature matrix and an adjacent matrix. Local feature embedding is calculated by using a graph convolutional network, and feature aggregation is carried out in combination with information of a cooperative agent to obtain a local embedding state; and the intelligent agent executes a joint action according to the local embedding state, wherein the joint action comprises task scheduling and resource allocation. And according to an execution result, the agent obtains a reward value and state information and stores the reward value and the state information into an experience playback buffer area. And sampling data from a buffer area, and updating a value function and strategy parameters by taking optimization of calculation task completion time and resource use efficiency as a target. And after a preset training round, obtaining a trained model. The resource overhead of information interaction can be reduced, and the scheduling efficiency is improved.

The invention discloses an industrial equipment fault detection method fusing a complex relation and space-time dependence, and belongs to the technical field of industrial anomaly detection, and the method comprises the steps: constructing a plurality of adjacent matrixes, carrying out the weighted fusion to form an enhanced adjacent matrix, and comprehensively and accurately describing the complex multi-dimensional relation between industrial equipment; designing a spatial-temporal feature extraction module, extracting spatial features in parallel by using a graph convolutional neural network and a random graph attention network, extracting time features through time convolution and a multi-head attention mechanism, and dynamically fusing the spatial-temporal features by means of a gating mechanism to generate graph-level features; a state judgment layer composed of a plurality of node-level binary classifiers and a voting mechanism are adopted to comprehensively judge classification results of all nodes, so that the stability and reliability of judgment of the overall state of the industrial control system are enhanced, and the risk of misjudgment is reduced; the problems of equipment relation modeling and multi-dimensional information fusion are effectively solved, features are extracted and fused more accurately, and the accuracy and adaptability of anomaly detection are improved.

The invention relates to the technical field of respiratory system risk prediction, and provides a respiratory system risk prediction method and system based on a graph neural network, and the method comprises the steps: collecting the multi-modal medical data of a patient, and constructing a multilayer heterogeneous graph based on the multi-modal medical data; constructing a weighted adjacency matrix and a node feature vector through the multi-layer heterogeneous graph; matrix product operation and convolution operation are carried out based on the weighted adjacent matrix and the node feature vector, splicing combination with historical moment state information is carried out, graph state representation is obtained, weighted aggregation of time dimensions is carried out, and time sequence attention features are obtained; performing coding processing based on the clinical examination data to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a risk classifier for classification calculation to obtain a respiratory system risk level prediction result, generating a risk assessment report, and outputting respiratory risk early warning information. The accuracy and clinical practicability of respiratory system risk prediction are improved.

The invention discloses an urban traffic flow prediction method and system based on time sequence deep learning, and relates to the technical field of intelligent traffic, and the method comprises the steps: 1, collecting traffic flow data and road network topology, and carrying out the preprocessing; 2, constructing a static adjacency matrix and a dynamic adjacency matrix according to the preprocessed traffic flow data and road network topology; 3, calculating a space-time attention weight by adopting a space-time attention mechanism according to the preprocessed traffic flow data; and 4, according to the static adjacency matrix, the dynamic adjacency matrix and the space-time attention weight, using the improved space-time diagram convolutional network to predict and output future traffic flow. The urban traffic flow prediction method combines the space-time diagram convolutional network, the space-time attention mechanism and the adaptive diagram generation mechanism to realize accurate urban traffic flow prediction.

The invention relates to the field of new energy, and discloses a wind power generation power prediction method based on space-time diagram convolution and gating attention, and the method comprises the steps: obtaining the geographic position information, meteorological information and historical wind power generation power data of each fan in a wind power plant, and obtaining the normalized data; constructing a dynamic adjacency matrix based on the maximum information coefficient among the historical power data of each fan in the wind power plant, and generating a graph structure; a node set of the graph structure corresponds to each station in the wind power cluster, and an edge set is dynamically determined by a maximum information coefficient of historical power data between the stations; spatial feature extraction is carried out by using a graph convolutional network, and a graph structure learning module is introduced; and inputting the sequence output by the graph structure learning module into a gating circulation unit, introducing an Informer encoder based on a sparse attention mechanism, and generating a wind power prediction result of a future time step. According to the invention, high-precision prediction of the wind power generation power in a multi-fan scene is realized.

The invention provides a region-level aviation flow prediction method based on Mamb-GCN, and belongs to the technical field of air traffic flow prediction, and the method comprises the steps: constructing a Mamb-GNC collaborative network model; historical flight path data of a target airspace is collected, the target airspace is divided into space grids, the number of aircrafts in each space grid is counted, and the time feature and the space feature of each aircraft are coded to construct a space-time tensor; constructing a dynamic adjacency matrix and a dynamic weight map based on the 8-neighborhood topology of the space grid; inputting the space-time tensor and the dynamic weight graph into a Mamba-GCN collaborative network model for training, and optimizing model parameters; and preprocessing the aviation trajectory data of the target airspace acquired in real time, and inputting the preprocessed aviation trajectory data into the trained Mamba-GCN collaborative network model to obtain an aviation flow prediction result. According to the method, the long-time dependence of the aviation flow in the time dimension and the grid correlation in the space dimension can be captured, and the prediction efficiency is high.

The invention relates to the technical field of electroencephalogram signal processing, and discloses an electroencephalogram emotion recognition method and system based on a deep neural network. The method comprises the following steps: collecting and preprocessing a multi-channel EEG signal; constructing a graph data structure, extracting multi-domain features by taking electroencephalogram channels as nodes, and constructing a self-adaptive dynamic adjacency matrix; constructing a graph convolution long and short-term memory network, learning spatial features by GNN, and extracting time dependence by LSTM; enhancing emotion capture by using a multi-scale time-frequency feature fusion method in combination with STF and CWT; constructing global topological information of an FCN brain extraction region in combination with brain network features; and outputting alertness and other emotion indexes by means of the classification model. According to the method, graph structure learning and time sequence modeling are combined, EEG signal emotion recognition is optimized, and personalized adaptation and emotion recognition accuracy is improved.

The invention discloses a fire prejudgment detection adjustment optimization method and system, and relates to the technical field of adjustment prediction, and the method comprises the following steps: obtaining fire feature data of a to-be-detected region, carrying out the feature extraction and splicing, and constructing a fire feature vector; performing initial node division on a to-be-detected area, dynamically adjusting node division density according to fire features and geographic positions of nodes, extracting spatio-temporal features, and constructing a multi-scale adjacent matrix; obtaining an included angle and a propagation time difference of propagation directions between nodes, establishing a mapping model, obtaining direction sensitivity, and correcting an adjacent matrix; inputting the corrected adjacency matrix and the fire feature vector into a space-time diagram convolutional network model, and outputting a fire risk level; according to the method, the number and density of the nodes are dynamically adjusted, and the multi-scale adjacent matrix and the space-time diagram convolutional network model are combined, so that the accuracy and the real-time performance of fire risk prediction are improved, and the problems of inaccurate node division and insufficient space-time feature processing in fire pre-judgment are solved.

The invention discloses an end-to-end fault diagnosis and identification method based on multi-modal fusion, and the method comprises the steps: 1), collecting a vibration signal and an acoustic signal, carrying out the preprocessing, and constructing a training sample set; 2) performing feature extraction to obtain a high-dimensional modal feature vector; 3) generating a sparse adjacency matrix through an end-to-end deep learning graph generation module, and establishing a graph generation structure relation; 4) constructing a multi-receptive field Chebyshev graph convolutional network, and extracting node-level features in a graph generation structure; 5) inputting the structure sensing features into a full-connection layer for mapping, and completing prediction and discrimination of a fault category to which an input sample belongs; performing model supervision training, and optimizing model parameters in an end-to-end mode; and 6) carrying out prediction output on the fault identification model on the test set, and carrying out quantitative evaluation on the fault identification result to obtain the fault identification device.The method belongs to the technical field of equipment operation state monitoring and fault diagnosis, and realizes accurate fault diagnosis of the rotating equipment.

The invention discloses an earth and rockfill dam seepage-deformation early warning method and system based on time-space combined anomaly, and belongs to the field of dam body safety data research. The method comprises the following steps: constructing a spatio-temporal topological graph based on an engineering coordinate system, integrating multi-dimensional data by nodes, and constructing a dynamic adjacency matrix according to spatial distance and seepage relevance; extracting features by using a space-time diagram convolutional network, a self-loop mechanism and cross-layer attention; and executing dual-drive early warning through standard threshold preliminary screening, multi-scale LSTM prediction and a time decay evidence theory. The system comprises a sensor network and an intelligent computing module, and the intelligent computing module has adaptive modeling and visualization functions. According to the scheme, seepage-deformation space-time correlation quantitative analysis is achieved, the hysteresis effect is captured, the threshold value is dynamically corrected, multi-source evidences are fused, the early warning timeliness and accuracy are improved, and the risk of false alarm and missing alarm is reduced.

The invention discloses a bearing cross-domain fault diagnosis system and method based on a meta-learning domain adversarial graph convolutional network, and particularly relates to the technical field of mechanical fault diagnosis. Multi-source bearing vibration signals are integrated, and a cross-domain graph structure data set including node features and an adjacent matrix is constructed; performing adversarial training through a feature extractor and a domain classifier of the domain adversarial graph convolutional network, and combining a gradient inversion layer to extract domain invariant features; carrying out internal circulation task adaptation and external circulation element parameter updating by utilizing a element learning framework, and optimizing network parameters; and finally carrying out fault diagnosis on the target domain signal. And the total loss function of the system fuses task classification loss, domain adversarial loss and a graph structure regularization item, so that the cross-domain diagnosis precision is improved. The method effectively solves the problem of model generalization caused by domain difference, is suitable for bearing fault diagnosis scenes with few samples and multiple working conditions, and has the advantages of high robustness and high diagnosis precision.

The invention relates to an arterial traffic coordination control real-time optimization method. The method comprises the following steps: S1, collecting traffic data and converting the traffic data into input data suitable for a graph neural network; s2, constructing a graph data structure, setting intersections as nodes, setting road sections as edges, and adjusting the weight of the weighted adjacent matrix at a fixed frequency according to the traffic data of the road and the historical traffic flow; s3, taking the graph data structure in the S2 as input, constructing a graph neural network model, and capturing spatial-temporal characteristics among nodes; and S4, formulating or adjusting an objective function according to the spatial-temporal characteristics, the actual traffic demands and the constraint conditions, solving the objective function by a genetic algorithm, carrying out continuous iteration on crossover and variation, and evaluating individuals according to the objective function and the constraint conditions in each iteration to obtain an optimal signal timing scheme. According to the method, spatial-temporal characteristics are effectively captured by adopting a graph neural network technology, signal timing optimization is carried out in combination with a genetic algorithm and reinforcement learning, and a signal timing scheme can be dynamically adjusted according to real-time traffic demands.

The invention relates to a construction site environment dynamic regulation and control method and system fused with AIoT. According to the method, a multi-source environment sensor network is deployed to collect original data of a construction site environment in real time, a data set with aligned timestamps is generated, an abnormal event is detected by using an isolated forest algorithm after standardized denoising processing, and a specific parameter type and a time window are marked; a causal intensity matrix between parameters is constructed through Granger causal test on the basis of environment data intercepted in an abnormal time period, a directed weighted network adjacency matrix representing a pollution propagation path is generated accordingly, and an abnormal source is accurately positioned through node influence propagation calculation and timestamp verification; and finally, a regulation and control instruction sequence is dynamically generated according to the shortest influence path of the source node in the propagation network, so that autonomous analysis of implicit association among construction site environment parameters, accurate positioning of a pollution source and dynamic distribution and instant deviation correction of a multi-stage regulation and control strategy are realized.

The invention provides an equipment fault diagnosis method based on multi-scale image convolution, and relates to the technical field of industrial equipment intelligent fault diagnosis, and the method comprises the steps: extracting the time domain, frequency spectrum domain and time-frequency features of a vibration signal through a multi-scale input layer, and generating a 32-dimensional feature vector through the fusion of a cross-scale feature coupling module; and constructing an inter-equipment relation perception graph convolution model, generating a dynamic adjacency matrix in combination with a physical distance and a real-time working condition, and extracting space-time fusion features through space-time convolution. Transient and periodic features are enhanced through an accidental fault sensitive time sequence module, and time sequence features are output in combination with dual-channel fusion and a self-attention mechanism. And finally, fault identification and positioning are realized by adopting a double-threshold detection and equipment comparison enhancement strategy, an interpretable diagnosis evidence chain containing multi-scale feature contribution is generated, and the weak fault detection rate and the diagnosis credibility are improved.

The invention belongs to the technical field of geographic information data processing, and discloses a graph attention-based road slope surface displacement time sequence prediction method, which comprises the following steps of: obtaining surface displacement time sequence data of a plurality of monitoring stations and associated environmental influence data, and constructing a weighted adjacency matrix based on geographic positions of the monitoring stations, defining an initial spatial topological relation; carrying out feature fusion on the displacement data and the environment data, and constructing an attribute-enhanced feature matrix; respectively inputting the weighted adjacency matrix and the feature matrix into a graph convolutional network and a graph attention network for parallel processing; extracting structured spatial features by the GCN through a fixed topological structure, and generating a first feature representation; the GAT adaptively allocates a dynamic weight by using an attention mechanism, extracts a non-uniform spatial dependency feature, and generates a second feature representation; and fusing the two feature representations, inputting a time sequence modeling module to analyze time dependence, and finally outputting a surface displacement prediction result at a future moment. According to the method, the accuracy of surface displacement prediction is remarkably improved.

The invention provides a traffic accident prediction method fusing multi-source features and a self-adaptive structure, and the method comprises the steps: extracting spatial features such as a geographic position, traffic flow and interest point distribution, combining the time features such as traffic flow change trend, periodicity and anomaly detection, and the external features such as weather and signal lamp density, and carrying out the prediction of a traffic accident. Node multi-dimensional feature representation is comprehensively constructed, a static adjacency matrix and a dynamic adjacency matrix are respectively constructed, geographic distance and node feature similarity information are fused, a self-adaptive adjacency matrix is generated by utilizing learnable parameters, and road network structure changes are dynamically described. Finally, traffic accidents are modeled and predicted based on a graph convolutional neural network, and accurate identification and early warning of accident risks in a complex traffic environment are realized. According to the method, the modeling capability of the prediction model for nonlinear and strong space-time correlation characteristics of traffic data is effectively improved, the accuracy and robustness of traffic accident prediction are remarkably improved, and the method has wide engineering application prospects and popularization value.

The invention relates to the technical field of traffic prediction, in particular to a multi-layer space-time structure correlation perception traffic flow prediction model, which comprises a space-time structure decomposition layer for decomposing original traffic flow data into a road hierarchical structure feature matrix, a dynamic time period feature matrix and a hidden space topology feature matrix; the dynamic adjacency matrix generation layer is used for constructing a self-adaptive adjacency weight matrix based on the hidden space topological feature matrix; the cross-level correlation perception layer performs bidirectional feature modulation on the road hierarchical structure feature matrix and the dynamic time period feature matrix to generate a space-time coupling feature tensor; and the prediction layer inputs the space-time coupling feature tensor into a space-time diagram convolution prediction network and outputs a traffic flow prediction value in a future time period. According to the method, the expression ability of the model on potential heterogeneous association between the nodes is improved, and the generalization ability across regions and time periods is effectively improved.

The invention discloses a micro-grid dynamic scheduling method based on deep learning, and the method comprises the steps: fusing industrial Internet of Things collection and GIS positioning, and constructing a multivariable original spatio-temporal data set covering multiple nodes; extracting multi-scale features through multi-resolution wavelets and Fourier transform, combining the multi-scale features with a dynamic adjacency matrix, and realizing feature adaptive distribution and nonlinear dynamic modeling by using multi-scale attention gating, graph convolution and a time sequence neural network model; the micro-grid load and state prediction accuracy, the system generalization ability and the abnormal response level can be effectively improved, and powerful support is provided for intelligent scheduling and abnormal analysis.

The invention provides a generative adversarial-driven intelligent security defense method and system, and solves the problem of dynamic network security defense through three-layer architecture innovation: 1, data fusion layer reconstruction: employing a multi-modal feature extraction engine driven by an MoE architecture, dynamically allocating computing power resources to a plurality of expert models, and improving the heterogeneous data distillation efficiency; an LLM for fine adjustment in the security field is introduced, a cross-modal semantic similarity matrix is constructed, and the accuracy of unstructured threat intelligence analysis is improved; a second dynamic attack and defense layer is constructed, a GPT-4 architecture attack generator is deployed, and generation of a multi-stage APT attack chain is simulated; a double-agent reinforcement learning framework is designed, and the confrontation training efficiency is improved; upgrading a three-cognitive decision-making layer, constructing a dynamic threat map based on a time sequence diagram neural network, and updating an adjacent matrix in real time; a plurality of agent clusters are deployed, the capabilities of encrypted traffic analysis and attack blocking are improved, and the problems of data layer defects, attack and defense confrontation limitation and decision-making layer bottleneck in the prior art are solved.