79 results about "Graph sampling" patented technology

The embodiment of the invention provides a power distribution network dynamic reconstruction method based on knowledge-guided large model enhanced fine tuning and related equipment, and belongs to the technical field of smart power grids and artificial intelligence. The method comprises the following steps: constructing a power distribution network dynamic knowledge graph, and providing structured knowledge guidance for model training; subgraph sampling is carried out based on the timestamp and converted into a fine tuning sample, and a training data set is generated; utilizing the data set to supervise, finely adjust and preheat the large language model; designing a multi-dimensional reward function of fusion format accuracy, economy and security based on mechanism knowledge in the knowledge graph and expert experience; a group relative strategy optimization mechanism is adopted to carry out reinforced fine tuning on the large language model, and the large language model is guided to output a safe, reliable and economical dynamic reconstruction strategy in interaction with the environment. According to the method, the problems of lack of training data, lack of physical knowledge guidance and insufficient decision reliability of a large language model in the power grid field are solved, and the intelligent level and decision quality of dynamic reconstruction of the power distribution network are remarkably improved.

The invention relates to the technical field of government affair entrepreneurship guidance service intelligent dialogue systems, in particular to a dialogue intention recognition and correction method based on a knowledge graph, and the method comprises the steps: obtaining a voice signal and text information inputted by a user, and carrying out the cross-modal fusion processing to generate a semantic vector; carrying out evolution modeling on the dialogue state by adopting a graph neural network, and outputting a dynamic dialogue state graph containing a time sequence relationship between a user entity and a business object; taking the semantic vector and the focus information as query conditions, executing dynamic subgraph sampling from the government affair knowledge graph, and generating an intention correction strategy vector; and finally, utilizing reinforcement learning to generate personalized guide statements and outputting the guide statements to the user. According to the method, through deep coupling of semantic understanding, state tracking, knowledge reasoning and decision optimization, real-time perception and active completion of dynamic updating of hidden nodes and policies of the government affair process are achieved, and the intention recognition accuracy and systematic adaptive capacity are effectively improved.

The invention provides a workpiece surface target merging and partitioning method, system and device, and relates to the technical field of image processing, and the workpiece surface target merging and partitioning method comprises the steps: obtaining a surface image of a target region of a target maintenance workpiece; performing graph sampling and visual processing on the surface image to generate a binary image of the target area, and analyzing the binary image to obtain a plurality of connected domains of the target area; performing convex hull generation according to the connected domains to obtain a convex hull corresponding to each connected domain; generating a rotation enclosing rectangle set according to the leading edge vector of each convex hull; and through a variant ant colony optimization algorithm, carrying out global optimization on the rotating enclosing rectangle set, and generating a combined partition of the target area. According to the invention, through accurate capturing of the original image and subsequent layer-by-layer optimization, the redundancy of the laser maintenance path is reduced, and the operation cost is reduced; through construction and global optimization of a high-adaptation form carrier, precise coverage of a complex form target is realized, and the maintenance precision is improved.

The invention particularly relates to a CAD part feature recognition method based on a graph neural network, and the method comprises the steps: extracting geometric entity information and topological relation information based on C # and NX Open API, and generating standardized AAGJSON format data; converting the geometric data into a graph structure which comprises a topological adjacent matrix and a multi-dimensional node feature vector comprising geometric, topological and shape features; a graph neural network model comprising a graph convolution layer, a graph attention layer and a graph sampling aggregation layer is adopted for training, and feature categories and confidence degrees of holes, grooves, bosses and the like are output; and the prediction result is corrected based on geometric consistency, topological connectivity and context rationality constraint. The method has the characteristics of high recognition precision, strong adaptability and good robustness of complex parts, can be integrated in UG NX to realize real-time recognition and visualization, provides important technical support for design automation and manufacturing industry digitization, and solves the problems of low recognition precision, poor adaptability and insufficient topological information utilization in the existing CAD part feature recognition method.

The invention relates to the technical field of brain anomaly detection and artificial intelligence auxiliary diagnosis, in particular to a graph convolutional network brain disease diagnosis method based on sub-graph sampling and multi-feature fusion, and aims to improve the accuracy of brain disease diagnosis. The method comprises the following steps: obtaining resting state functional magnetic resonance imaging data, preprocessing the data, and constructing a brain function connection diagram; and the brain function connection graph represents a brain interval collaborative activation relationship in a graph structure. Subgraph sampling is carried out based on function module division and node degree sorting, and an initial subgraph set is generated; and performing optimization selection on the initial sub-graph set by utilizing reinforcement learning to obtain an optimal sub-graph, introducing a node attention mechanism into the optimal sub-graph, screening key nodes based on attention scores, and generating a discriminant sub-graph. And extracting and fusing position features, neighborhood features and structural features of the discriminant subgraphs, and performing brain disease diagnosis based on the fused features.

The invention discloses a federated learning-based functional magnetic resonance imaging data classification method. The method comprises the following steps of obtaining multi-site resting state functional magnetic resonance imaging data and performing preprocessing; dividing a brain region and extracting a time sequence; constructing a brain function connection network by adopting a Pearson correlation coefficient; a graph sampling aggregation neural network fusing a residual connection structure and a multi-head attention mechanism is trained at each site, and shallow brain region features are reserved; adopting a linear kernel maximum mean value difference loss function to align the brain region node feature distribution of each site, and minimizing the data distribution difference between the sites; and carrying out classification training by adopting cross validation, aggregating parameters of each station through federal weighting, and evaluating classification performance indexes to obtain a final classification prediction result. According to the method, the graph sampling aggregation neural network and the cross-network layer feature alignment method are combined, the heterogeneity problem of multi-site functional magnetic resonance imaging data can be solved, and therefore generalization and classification performance of a global model are improved.

The invention provides a global optimization method and device for a refinery enterprise supply chain, and the method comprises the steps: obtaining a supplier set, a sales company set, a refinery plant set, a transportation set, a product set and a spatial position set of a refinery enterprise, and constructing a supply chain model, represented by a network graph, of the refinery enterprise; simplifying the supply chain model represented by the network graph by using a graph sampling algorithm to obtain a sampled supply chain model, constructing an effective lower bound function of a supply chain optimization problem, and solving a first optimal solution according to the effective lower bound function and the sampled supply chain model; reducing the supply chain model represented by the network graph by using a graph reduction algorithm to obtain a reduced supply chain model, constructing an effective upper bound function of a supply chain optimization problem, and solving a second optimal solution according to the effective upper bound function and the reduced supply chain model; and generating a global optimal solution of the supply chain model according to the first optimal solution and the second optimal solution.

The application relates to a kind of smart grid dynamic graph data generation methods based on deep adversarial training, comprising the following steps: obtaining smart grid time series graph data, inputting the smart grid dynamic graph data generation model based on deep adversarial training, and generating smart grid dynamic data graph;The smart grid dynamic graph data generation model based on deep adversarial training includes sampling module, generative adversarial network and reconstruction module, wherein the sampling module is used to generate the center graph using the center graph sampling method;The generative adversarial network includes generator and discriminator, the generator includes decoder and encoder, the encoder is used to encode the center graph through the time series graph self-attention network constructed based on the multi-head self-attention mechanism, and obtain the hidden variable of the center graph;The reconstruction module is used to generate the classification distribution of each time series edge according to the center graph score matrix, and then generate the smart grid dynamic data graph.Compared with the prior art, the application can efficiently and reliably generate smart grid dynamic data graph.

The invention relates to an unsupervised graph anomaly detection model for government affairs, finance and public data governance. The invention discloses an unsupervised graph anomaly detection model for government affairs, finance and public data governance. The unsupervised graph anomaly detection model comprises a graph sampling module, an attribute graph anomaly detection module and a graph anomaly detection module, the neighborhood subtraction identification module is used for representing positive and negative examples and comprises a GNN encoder sub-module, a neighborhood subtraction sub-module and a low-frequency reconstruction sub-module; and the abnormal value scoring module is used for calculating a final abnormal value score. According to the unsupervised graph anomaly detection model for government affairs, finance and public data governance, high-frequency components are amplified without changing data distribution through neighborhood subtraction operation, and the identifiability of abnormal nodes is further improved; low-frequency information of the graph is effectively reserved through low-frequency reconstruction. And finally, determining an abnormal state of the node by an abnormal scoring mechanism.

The invention provides a large-scale supply chain solving method and system based on graph sampling and graph coarsening, and the method comprises the steps: building a target supply chain model with the maximization of total welfare as a target according to the basic element parameters of an electric truck in a supply chain network; constructing a first supply chain model based on an active transportation path in the target supply chain model and a graph sampling technology, and solving the first supply chain model to obtain an optimal lower bound and an optimal feasible candidate solution; constructing a second supply chain model based on active supply chain nodes in the target supply chain model and a graph coarsening technology, and solving the second supply chain model to obtain an optimal upper bound; and according to the optimal lower bound and the optimal upper bound, solving to obtain an optimal gap, and based on the optimal gap and the optimal feasible candidate solution, determining a target large-scale supply chain solving result. According to the invention, the solving efficiency and accuracy are improved.

The present invention relates to a method for generating static graph data of electric power data with structural feature preservation, comprising the following steps: obtaining electric power data, inputting a static graph data generation model into the model, and generating corresponding static graph data; the static graph data generation model comprises a sampling module, a generative adversarial network, and a reconstruction module connected in sequence; wherein the sampling module is used to sample the electric power data using a central graph sampling method to generate a central graph; the generative adversarial network comprises a generator and a discriminator, the generator comprises a decoder and an encoder, the encoder is used to encode the central graph using a graph self-attention network constructed based on a multi-head self-attention mechanism; the decoder is used to decode the output of the encoder to obtain a central graph score matrix; and the reconstruction module is used to generate static graph data based on the central graph score matrix. Compared with the prior art, the present invention can preserve the structural features of the original electric power data as much as possible while improving the efficiency of generating static graph data.

A heterogeneous optimization method and device based on subgraph link prediction, the method comprising the steps of: based on the behavior or relationship between users, commodities and shop entities, abstractly constructing a no-weight and no-direction graph required by a model with node labels; implementing closed subgraph sampling extraction of target node pairs on the no-weight and no-direction graph; simply controlling the upper limit of the size of the subgraph and reasonably filtering the samples; calculating the node heterogeneity index of each closed subgraph; generating a new heterogeneous feature vector according to the calculated index data; replacing the obtained heterogeneous feature vector with the original node feature to become a new node feature; extracting subgraph features and performing model training, and finally realizing the prediction of whether an edge exists between the target node pairs; obtaining a recommended candidate list of each user according to the obtained prediction result of the existing edge; and according to the prediction score, completing the sorting of the recommended candidate list obtained by each user to obtain the final recommended result displayed to the user.

The invention discloses a power system load flow analysis training data enhancement method and related equipment, and the method comprises the following steps: taking all buses in a power grid as nodes in a topological graph, taking all connection equipment as middle edges of the topological graph, and obtaining a structure graph of the power grid; the sample enhancement method is recorded as a sample enhancement method based on subgraph sampling and / or a sample enhancement method based on subgraph grafting based on the structure graph, and samples are generated to enhance the training data. According to the power system load flow analysis training data enhancement method based on subgraph sampling and subgraph grafting, node scale change is carried out on the actual operation scene of a power grid, the number of training samples is increased, the number of nodes of the samples is increased, and the richness of a topological wiring mode is improved. The invention provides a sample expansion method based on sub-graph sampling and sub-graph grafting, and the adaptability of the model to a node number reduction scene caused by starting and stopping of a generator and a node number increase scene caused by power grid expansion can be improved respectively.

The invention discloses a paper classification method based on graph matching and self-supervised graph learning, and relates to the technical field of document classification based on deep learning. According to the method, literature data is represented by adopting a literature relation graph, a graph learning model ConGM based on the literature relation graph is constructed, and reference and theme association between literatures are mined through sub-graph sampling and data enhancement, linear node matching, secondary edge alignment and double-layer negative sample selection, so that precise classification of fields to which papers belong is realized.

The invention provides a method and a system for constructing a knowledge representation learning model based on a periodic perception contrast graph attention network. The method comprises the steps of constructing a sub-graph sampler, constructing a graph attention network and performing time sequence perception contrast learning. According to the method, firstly, adjacent neighbor entities are screened and queried through a period time sensing sub-graph sampler in a period dynamic weighting mode, consumption of computing resources is reduced, and key time sequence context and period information are reserved; then, the graph attention network with the time perception function generates dynamic representation of time perception by using graph attention in the constructed query related sub-graph, and entity embedding representation fused with neighbor information is obtained; then, time sequence perception contrast learning improves robustness in time knowledge graph reasoning through dynamic and static representation contrast learning of an entity, and entity prediction is carried out through embedded representation obtained through learning. Finally, the entity representation is decoded using a decoder.

This application discloses a method and system for predicting consumer behavior driven by spatiotemporal data, which relates to the field of digital and intelligent analysis technology for cultural tourism. The method includes: defining user nodes, business district nodes, and event nodes based on user attribute data, business district attribute data, and historical external events, respectively; analyzing historical user consumption data and business district POI data to determine corresponding user-business district consumption edges, business district-event influence edges, and event-user trigger edges, thereby constructing a heterogeneous ternary graph; performing multi-scale Motif subgraph sampling on the heterogeneous ternary graph to extract multi-granularity subgraphs, and extracting multi-scale spatial features of corresponding user nodes from the multi-granularity subgraphs; and then organically fusing the two into a highly expressive spatiotemporal fusion feature. Based on this spatiotemporal fusion feature, the user's business district consumption behavior in different business districts is predicted. This significantly improves the accuracy and robustness of predicting the user's business district consumption behavior.

The invention relates to a random subgraph sampling method and system for machine or deep learning training and reasoning. The method comprises the following steps: step 1, estimating a budget; 2, scale solving is carried out; 3, at least one sampling rule is selected from a preset sampling rule base to generate sub-image batches meeting constraints; step 4, obtaining sub-graph labels; 5, implementing a boundary retention mechanism and / or virtual neighbor convergence on the cut edges of the cross-sub-graph to compensate cross-sub-graph information; 6, in the training stage, importance weighting and / or reweighting loss are / is carried out on estimation deviation introduced by non-uniform sampling; step 7, training and / or reasoning the machine / deep learning model based on the subgraph batch, and performing budget adaptive adjustment on the subgraph scale and the sampling rule according to actual occupation and time delay during operation; and executing a corresponding task through the machine / deep learning model. According to the method, data which cannot be trained / inferred originally runs on limited equipment in a sub-graph batch form, and the problem of memory overflow is avoided.

The invention belongs to the technical field of artificial intelligence and high-performance computing, and discloses a dynamic sampling and parallelization acceleration method for pulse graph neural network computing. An innovative dynamic graph sampling mechanism is provided, the instantaneous importance of nodes is evaluated according to historical pulse activities of the nodes, limited computing resources are dynamically allocated to the nodes and areas with the most abundant information, and a sampling strategy is adaptively adjusted. Secondly, the invention further provides a state decoupling parallelization method, pre-calculation and caching are carried out on a stateless layer of the SNN, part of serial dependence between time steps is broken, and remarkable acceleration of the calculation process is achieved. According to the method, a better effect is achieved under different data sets, and compared with other methods, the method has competitiveness in performance. On a MINIST data set and a DvsGesture data set, on the premise that the accuracy rate is basically kept unchanged, a remarkable acceleration effect is achieved, and meanwhile memory consumption of the method is obviously reduced.

The application discloses a digital quantum computing-based computing power network DDoS detection method and system, and relates to the technical field of network space safety management and control.The application comprises the following steps: collecting real-time network flow data in a jurisdictional area through a dispersed detection point, constructing a graph neural network DDoS detection algorithm based on quantum random walk sampling, and performing quantum random walk to obtain quantum probability distribution of each node; performing quantum probability weighted non-replacement sampling on neighbor nodes of each target node to obtain a sampled neighbor set; performing message passing on the sampled neighbor set through a graph sampling aggregation network; uploading local model parameters to a safety management center through a federated learning architecture, and outputting a DDoS attack detection result.The application realizes DDoS flow feature extraction based on a graph structure, and utilizes a neighbor sampling mechanism guided by quantum random walk to complete real-time identification and detection of DDoS attacks.

The invention provides a data analysis method and device, electronic equipment, a storage medium and a program product. The method comprises the steps of obtaining input data; performing data matching in a knowledge graph by utilizing a graph sampling and aggregation model according to the input data, performing vector representation conversion and text representation conversion according to a matching result, and splicing the vector representation and the text representation to obtain intermediate data; determining whether the intermediate data accords with a set rule or not; and in response to the intermediate data conforming to a set rule, determining a calculation rule of an analysis result in the knowledge graph according to the intermediate data, determining the analysis result according to the calculation rule and the intermediate data, and outputting the analysis result.

The present invention provides an APT attack detection method, system, storage medium, and electronic device. The method includes: collecting attack logs of multiple known attacks and constructing an attack subgraph based on the attack logs; performing feature enhancement on nodes in the attack subgraph based on an attention mechanism to obtain feature vectors corresponding to the nodes, and training an initial detection model based on the feature vectors to obtain a final detection model; obtaining host logs to be detected, and constructing a traceability graph based on the host logs to be detected, performing subgraph sampling on process nodes in the traceability graph to obtain at least one target subgraph; performing feature enhancement on at least one target subgraph to obtain enhanced target feature vectors, and sequentially inputting the target feature vectors into the final detection model to obtain detection results corresponding to each target subgraph. The present invention can improve the accuracy and flexibility of detection and reduce false positives or missed positives.

The invention discloses a dynamic risk assessment method for an information physical system of a chemical industry park, and relates to the technical field of risk assessment of the information physical system of the chemical industry park, and the method comprises the following steps: S1, building and quantifying a comprehensive risk index system of the information physical system of the chemical industry park; s2, calculating a chemical industry park information physical system risk index weight and constructing a risk coupling network structure; and S3, constructing an improved GraphSAGE dynamic risk assessment model which learns a complex relationship between risk indexes and predicts a risk value by using graph sampling and aggregation so as to realize dynamic risk assessment. The invention provides an effective and feasible method for risk assessment of the information physical system of the chemical industry park, and is helpful for enhancing safety management and risk control of the chemical industry park.

The application relates to a communication-efficient decentralized federated learning method based on subgraph division, comprising the following steps: constructing a physical network topology graph of a decentralized federated learning environment; dividing the physical network topology graph into non-conflict subgraphs, and setting a mixing matrix for each non-conflict subgraph; constructing a problem constraint model with the minimum total iteration time as the target; optimizing a bandwidth allocation matrix with the delay value of the link with the maximum communication delay in the current non-conflict subgraph as the optimization target; optimizing the mixing matrix with the minimum expected communication delay as the optimization target; optimizing the non-conflict subgraph sampling probability based on the optimized mixing matrix with the minimum expected communication delay as the optimization target; and performing global updating of the decentralized federated learning model based on the optimized non-conflict subgraph sampling probability. The application can significantly reduce the global model convergence delay.

This invention relates to a heterogeneous ontology matching method and system based on BERT and graph contrastive learning, belonging to the field of semantic web and deep learning integration. The invention constructs a corpus by extracting triple information from the ontology using a semantic feature extraction module. This corpus is then input into a BERT model for parameter fine-tuning, generating semantic feature vectors for entities. A graph contrastive learning module performs graph sampling based on the topological structure of the ontology graph, generating two views. Training is performed by selecting positive and negative samples and setting a loss function to generate a comprehensive entity feature vector. Finally, a similarity calculation module calculates the final similarity score for different entity pairs. This invention effectively utilizes the topological structure of the ontology graph to extract structural features, overcoming the problem that traditional structural features cannot fully express the complex structural relationships between entities, thus improving matching accuracy.

The embodiment of the invention provides a graph data processing method and system and a model training method and system. Wherein the graph data processing system obtains a sampling sub-graph of a target node from original graph data, and the sampling sub-graph comprises a plurality of target neighbor nodes corresponding to the target node; a node text corresponding to each sub-graph node in the sampling sub-graph is obtained, the node text is input into the large language model to obtain a discriminative text corresponding to each sub-graph node, and the discriminative text comprises discriminative information used for classifying the sub-graph nodes; and inputting the sampling sub-graph and the discriminative text corresponding to each sub-graph node into a graph neural network to obtain a category discrimination result corresponding to the target node.

The present application discloses a method and device for identifying the spatial organizational structure of spatial transcriptome data. In this solution, the gene expression matrix and adjacency matrix corresponding to the spatial transcriptome data of the sample tissue to be identified are obtained; a sample graph is constructed based on the gene expression matrix and the adjacency matrix; the sample graph is input into a graph sampling aggregation network model, and the gene feature data of the neighboring nodes in the neighborhood of each node in the sample graph are aggregated with the gene feature data of the node through the graph sampling aggregation network model to obtain a feature representation of the sample graph; and the identification result of the spatial organizational structure corresponding to the sample tissue is obtained based on the feature representation. The technical solution of the present application can fully utilize the similarity information of the gene expression data between two adjacent nodes in spatial position during the feature aggregation process of the node, thereby improving the accuracy of identifying the spatial organizational structure of the tissue sample.

The application discloses a zero sample graph learning method based on a large language model, comprising: neighborhood subgraph sampling on to-be-processed graph structure data to generate graph query text; based on the graph query text, a structured prompt template containing multi-stage reasoning instructions is constructed, and the prompt template instructs the large language model to sequentially perform topological structure analysis, semantic attribute interpretation, multi-candidate answer enumeration and deep reevaluation before generating a final answer; a graph reasoning training data set is constructed; the basic large language model is fine-tuned by full parameters by using the graph reasoning training data set; the large language model is optimized by using a reinforcement learning algorithm based on group relative advantage; for a new graph task to be reasoned, the new graph task is input into the optimized large language model, and a final prediction answer containing a reasoning process is directly output. The application discards the dependence of a graph neural network, and only uses the text reasoning capability of the large language model to complete a graph task, so that the deployment complexity and the calculation cost are greatly reduced.

The invention relates to the technical field of big data analysis, and discloses a spatio-temporal big data intelligent tracing system and method for new infectious diseases, and the method comprises the steps: converting original multi-source data into a standard spatio-temporal format, and carrying out the entity recognition matching and quality evaluation; constructing a multi-layer dynamic graph structure comprising an individual layer, a region layer and a global layer, and extracting spatio-temporal features to obtain node embedding vectors; calculating the propagation probability between nodes through a graph sampling mechanism and a time sensing mechanism; establishing a multi-dimensional risk system, and designing an adaptive risk scoring function; carrying out cross-mechanism analysis by adopting federated learning and differential privacy protection; constructing a Markov decision model, and generating an intelligent decision scheme through deep reinforcement learning; according to the method, efficient fusion of multi-source heterogeneous data is realized by establishing a unified space-time reference coordinate system and a self-adaptive fusion mechanism.

The present application belongs to the technical field of big data mining, and specifically relates to a social robot detection algorithm based on user semantics, attributes and neighborhood information. The algorithm comprises: encoding the text content through a BERT model, modeling the semantic representation of the user, and combining the user attributes and neighborhood features to build a user relationship network with the user as a node and the forwarding relationship as an edge. The constructed graph data is subjected to self-supervised training using an improved graph attention network model to learn the social user representation. The model is subjected to parallelized calculation in the form of subgraph sampling, and auxiliary tasks are set using multi-task learning. In order to solve the data imbalance problem, a conditional adversarial generative network is used for data augmentation, and the final user vector representation is subjected to density-based clustering to obtain the discrimination result of whether it is a social robot. The algorithm adopts a self-supervised technology and introduces an adversarial idea, and is strong in generalizability and in line with the future technology development trend.