166 results about "Hypergraph" patented technology

The invention discloses a hypergraph feature representation method, system and device based on fuzzy logic and a medium, and the method comprises the steps: obtaining original data, and constructing a hypergraph structure containing vertexes and hyperedges according to the original data; the hypergraph structure is initialized, and an initialized hypergraph structure is obtained; and inputting the initialized hypergraph structure into a preset hypergraph convolutional fuzzy network model for feature representation processing to obtain a hypergraph structure after feature representation processing. According to the hypergraph convolutional fuzzy network model, fuzzy representation and the hypergraph technology are deeply fused, the membership core concept of fuzzy logic is introduced, the hard coding association limitation of an existing hypergraph representation learning method that no black is white is broken through, the technical problems that a traditional hypergraph model cannot capture association gradients and fuzzy semantics are lost are effectively solved, and the learning efficiency is improved. Therefore, the flexibility and accuracy of the model are effectively improved, and stable prediction is provided in an uncertain data environment.

The invention provides a storage resource optimization method and system based on a time sequence dependence hypergraph neural network, and belongs to the field of artificial intelligence computing. Based on medical health big data and computing resources, constructing and fusing data and computing resource dependency matrixes to obtain a static dependency relationship matrix; the method comprises the following steps: collecting a computing resource multi-source operation log, generating dynamic characteristics of each moment according to a fixed interval, introducing time sequence position coding and self-attention mechanism weighting in a sliding time window to obtain attention optimization characteristics, and generating a dynamic dependency weight matrix by combining a modeling historical hidden state and the dynamic characteristics; the static and dynamic dependency weight matrixes are fused to obtain a comprehensive dependency matrix, attention optimization features are used as nodes, hyperedges are constructed in combination with the comprehensive dependency matrix, and hypergraph association and other matrixes are generated; and inputting the matrix into a graph neural network, learning node representation in combination with a time sequence attention network, classifying nodes and mapping the nodes into scheduling actions, and realizing self-adaptive allocation of storage resources in combination with target function optimization of medical scene constraints.

The invention discloses an electricity consumption anomaly detection method based on a graph structure, and relates to the technical field of electricity consumption anomaly detection, and the method comprises the steps: obtaining historical electricity consumption data, environment variable data and power grid physical topology information of a power grid region, and carrying out the standardization processing of the historical electricity consumption data, the environment variable data and the power grid physical topology information, a standardized time sequence-environment data set is obtained; based on the standardized time sequence-environment data set, identifying a causal association relationship between electricity consumption and environment variables through a causal discovery algorithm, and constructing a causal perception heterogeneous graph containing region nodes and environment factor nodes; based on a causal perception heterogeneous graph, introducing a plurality of region nodes of which hyperedge connection is influenced by the same environmental event to form a dynamic hypergraph structure for representing a many-to-many environment-region coupling relationship; based on the dynamic hypergraph structure, a graph comparison learning task is constructed, positive and negative sample pairs are generated by applying disturbance to node features and the graph structure, and unsupervised pre-training is completed;

The invention discloses a multi-modal data drawing logical relationship analysis method, electronic equipment and a medium, and the method comprises the steps: generating a node set based on drawing image data and text data; generating a cross-modal hyperedge set based on the spatial proximity relationship, the visual feature similarity and the semantic correlation between the node sets; generating a hypergraph embedding input representation based on the node set and the cross-modal hyperedge set; the hypergraph is embedded into the input representation input improved hypergraph self-attention network model, and a hyperedge logic relation type and a corresponding hyperedge confidence coefficient are generated; generating a graph structure result based on the hyperedge logic relationship type and the node set, wherein the graph structure result meets the structure legality requirement; and performing hyper-parameter automatic adjustment and convergence control on the atlas structure result based on hyper-edge confidence, and generating an optimal atlas analysis model and a structured output result. According to the method, the reliability and the quality of analysis of component nodes, logic edge relationships and semantic structures in the drawing are improved.

A hypergraph workload manager in a server is configured for failure tolerant and explainable state machine driven hypergraph execution. The hypergraph executor comprises a query optimizer, a hypergraph enlister, a pipeline analyzer, and a state machine generator. The query optimizer translates a user query into a query operator graph. The hypergraph enlister enlists the query operator graph into a hypergraph containing a set of query operator graphs representative of already submitted user queries. The enlistment is configured to join query operator graphs where it makes sense to optimize query executions. Updates to the hypergraph based on the enlistment results in a set of disconnected graphs. The pipeline analyzer performs an analysis of all operators of all queries in the hypergraph to find an optimal sequencing of execution. The state machine generator is configured to generate a hierarchical state machine for all operators of a disconnected graph of the hypergraph.

The invention discloses an e-commerce advertisement accurate recommendation system based on deep learning, and relates to the technical field of e-commerce, and the system comprises the following modules: an entity index module used for constructing a node registry; the hypergraph construction module is used for continuously monitoring an event stream and constructing a dynamic hypergraph; the initial embedding module is used for extracting a node embedding tensor and a hyperedge embedding tensor; the graph representation learning module is used for extracting a final node embedding representation tensor and a final hyperedge embedding representation tensor by improving a GraphTransform model; the click rate estimation module is used for outputting the predicted click rate of each candidate advertisement; and the sorting decision module is used for screening a candidate advertisement list. According to the method, the limitation that a traditional recommendation method depends on simple characteristics and neglects high-order association and dynamic evolution information is overcome, and a powerful solution is provided for realizing efficient, accurate and personalized e-commerce advertisement recommendation.

The invention relates to the technical field of field programmable gate array physical optimization, and discloses a field programmable gate array multi-resource division method, device and equipment, the method comprises the following steps: generating a hypergraph comprising vertexes and hyperedges based on a netlist, the hypergraph comprising one or more types of resources; carrying out first partition division on the hypergraph, and carrying out multi-level vertex combination; partitioning the hypergraph of the maximum merging hierarchy, dividing vertexes in the maximum merging hierarchy to each partition, and carrying out the following processing on the hypergraph of each hierarchy from the maximum merging hierarchy to the minimum merging hierarchy: moving a first vertex between the partitions in the target merging hierarchy, and meeting the multi-resource constraint of the partitions in the moving process; splitting the vertexes in the target merging hierarchy into the vertexes of the previous hierarchy of the target merging hierarchy; until a first hypergraph is obtained; moving a first vertex in the first hypergraph to obtain a first partitioning result; and carrying out partition division again until the position of each functional module is determined.

The invention discloses a system call behavior modeling method based on a graph neural network, and the method comprises the following steps: S1, collecting system call event data, and carrying out the standardization processing of a resource identifier to generate a uniform resource identifier; s2, constructing a double-layer hypergraph model based on a uniform resource identifier, and jointly establishing a calling-resource layer and a constraint layer; s3, executing graph neural network modeling on the double-layer hypergraph model, and fusing two layers of embedding to generate a unified representation; s4, calculating joint loss and optimizing parameters of the double-layer hypergraph model by utilizing unified representation in a training stage; s5, adopting a reversible sliding window in a reasoning stage, outputting an abnormal score and generating a structure reconstruction plan; and S6, generating an anti-fact explanation based on the structure reconstruction plan, and outputting a calling set and a related identification sequence. According to the method, the double-layer hypergraph is constructed, and the graph neural network is combined for modeling, so that accurate detection and interpretable analysis of the system calling behavior are realized.

The invention relates to the technical field of mechanical response monitoring, in particular to a complex pavement mechanical response monitoring device and method, and the method comprises the steps: collecting sensor data of each section of pavement of a road; the method comprises the following steps: constructing a graph structure by utilizing the correlation among mechanics, heat and humidity among sensors, and mapping the graph structure into a graph frequency domain feature vector by utilizing graph Fourier transform to form a hypergraph space; constructing an objective function based on the spatial proximity and the time continuity of the graph frequency domain feature vector in the hypergraph space so as to perform global optimization on the graph frequency domain feature vector by using a particle swarm optimization algorithm; and inversely transforming the optimized graph frequency domain feature vector into a data space, generating a sensor signal coupling curve, and identifying road surface damage according to abnormal change of the curve, thereby realizing real-time monitoring of the road surface health state. The invention aims to improve the accuracy and real-time performance of road surface health state monitoring.

The invention belongs to the field of product top-layer system design and modeling, and discloses an interactive feedback-oriented product top-layer system design method, which comprises the following steps of: constructing a multi-layer model based on a demand R, a function F, a behavior B and a structure S, establishing a knowledge graph for representing a semantic relationship between layers and between nodes on the same layer by using weighted directed edges, forming a layer-by-layer mapping matrix by using edge weights, and constructing a layer-by-layer mapping model; realizing weight decomposition from the demand to the structural unit; a constraint hypergraph is constructed on the node set, and structural unit combination constraints are depicted through mutual exclusion, dependency, collaboration and conflict hyperedges; physical parameters of the structural units are normalized into performance vectors, weighted aggregation is carried out according to weights, legality judgment and performance correction are carried out in combination with a hypergraph, a system-level performance evaluation result is obtained, candidate configuration schemes are compared and optimized according to the result, and a product configuration scheme meeting requirements and constraint conditions is formed. The method provides a model basis for subsequent product structure configuration optimization based on interactive feedback.

The invention discloses a highway node risk calculation method based on a graph attention model. The highway node risk calculation method comprises the following steps: collecting and preprocessing multi-source node feature data of highway nodes; constructing a road network topological graph; generating an input feature representation vector set by improving an input coding module of the HGAT model; obtaining a neighbor node set; a hypergraph attention calculation module of the HGAT model is improved, a topology centrality guiding attention mechanism is introduced, and a road node representation vector is generated; a road node risk representation vector is obtained by improving a deep fusion module of the HGAT model; a road node risk value is obtained through a risk prediction output module of the improved HGAT model; and outputting a highway node risk assessment result, thereby improving the precision and stability of highway node risk calculation.

The invention discloses a hypergraph-based unknown network attack detection method for an electric power Internet of Things terminal, relates to the technical field of network attack detection, and solves the problems of insufficient model expression ability, overfitting and learning set deviation caused by the fact that a model method in the prior art ensures that known classes are fully separated and unknown classes are far away from the centers of the known classes. According to the method, dual modeling capabilities of a graph structure and a time sequence structure are combined, so that the method can effectively adapt to complex distribution characteristics in a dynamic electric power Internet of Things environment, and is particularly suitable for the problems of non-uniformity, burstiness, unknown traffic characteristic change and the like in an electric power Internet of Things terminal data stream; and the adaptability of the model to the diversified data structure of the edge device is improved. A dimension compression mechanism is introduced in the structural design of the model, the parameter quantity of the model is effectively reduced, lightweight deployment and end-side reasoning on an edge node or an industrial terminal are ensured, and therefore the real-time attack detection capacity of the power system is improved.

The invention provides a method and a system for constructing an online rainfall forecasting model based on a space-time dynamic hypergraph neural network. The method comprises the following steps of: establishing a graph model of a hypergraph, an adjacent matrix of an original graph and an incidence matrix of the hypergraph; taking a hypergraph neural network mechanism as a core, and establishing an online rainfall forecasting model based on a space-time dynamic hypergraph neural network; splicing the time sequence and the space sequence of the rainfall data by adopting an encoder, inputting the spliced time sequence and space sequence into a multi-layer perceptron with a full-connection structure, and finally outputting a prediction sequence by adopting the multi-layer perceptron with the full-connection structure by a decoder; wherein the step of establishing the encoder comprises establishing a time embedding module for capturing meteorological elements and establishing a hypergraph attention network; wherein the step of establishing the decoder comprises the step of establishing a multi-layer sensor with a full-connection structure. And establishing an online learning mechanism, a seasonal sample storage pool and an online training mechanism based on mixed domain dynamic playback for correcting the output of the online rainfall forecasting model.

The present disclosure provides a Boolean formula unsatisfiability core prediction method based on hypergraph modeling. A CNF formula generated by a software and hardware formal verification problem coding in the field of electronic design automation is received from the outside, input into a hypergraph representation learning model, and modeling for solving a SAT problem corresponding to the software and hardware formal verification problem is obtained. The model is constructed in the following manner: first, SAT problem representation based on hypergraph is performed. Specifically, the CNF formula is modeled as a clause-literal hypergraph by using a SAT problem modeling method based on hypergraph and a message passing method based on hypergraph, and a clause correlation graph is further constructed to obtain problem representation. Then, a polarity-aware variable decomposition method is applied to the clause correlation graph, and the problem representation is structured and modeled. The hypergraph representation learning model is trained by using a training method based on polarity-aware consistency constraints, and is integrated with a SAT problem solver, so that the model can be used to solve the problems that the existing graph neural network-based SAT learning method has in the aspects of polarity modeling and high-order structure expression.

The invention discloses an event prediction method and system based on a time sequence hypergraph, and belongs to the technical field of event prediction. The method comprises the following steps: acquiring multi-source heterogeneous data of a target region, defining a unified time index, and generating a time sequence feature sequence of each variable of the region through preprocessing; then, identifying a variable causal relationship in the region based on a frequency domain anti-fact condition mutual information algorithm, and fusing the variable causal relationship with a time sequence evolution relationship to construct a time sequence hypergraph structure representing the interior of the region; a dynamic filter is used for filtering the graph, and deep features of the graph are learned by means of a multi-band spectrum gating mechanism, so that internal complex causal and time sequence modes are effectively captured; and finally, performing dichotomy prediction based on the learned graph representation, and outputting the occurrence probability of future events in the region. According to the method, accurate and explainable event prediction is realized, training and prediction do not need to cross regions, data privacy and calculation efficiency are guaranteed, and stronger robustness is shown for specific data distribution change of the regions.

The invention relates to the technical field of multispectral image fusion, and discloses a real-time multispectral image fusion system based on deep learning. The system comprises a multispectral image spectrum module, a process spectrum synthesis module, a personalized process extraction module, a process vertex labeling module and a fusion process verification module. The multispectral image map module stores various image category templates of a map structure, and each template corresponds to a fusion flow chart containing a processing vertex and a connecting edge; the process map synthesis module fuses a plurality of templates to generate a unified hypergraph through vertex clustering and edge redirection; the personalized process extraction module analyzes a user demand text and converts the user demand text into a demand graph, and a unified hypergraph is matched with a sub-graph to serve as a basic fusion process; the flow vertex labeling module identifies adjustable vertexes and adds marks; and the fusion process verification module verifies logic through a deep learning model, detects abnormal vertexes and generates a report. The system adapts to multiple scenes, and the flexibility and reliability of the fusion process are improved.

A trajectory end point prediction method based on double-branch road network pre-training representation comprises the following steps: firstly, processing initial road feature data to obtain features of roads and road connections; and then constructing a road network graph structure, a hypergraph structure and road time dynamic characteristics. Firstly, a multi-hop weighted road network embedded vector is calculated, then a graph node embedded vector is calculated to be adjacent to a hypergraph node embedded vector, and a comparative learning mode is adopted to carry out stochastic gradient descent to optimize parameters; meanwhile, a time dynamic embedding vector is calculated through a Transform structure, and time dynamic prediction and classification loss function optimization model parameters are calculated; and inputting the initial features into the trained graph model, hypergraph model and Transform model to obtain enhanced road network representation, and finally applying the enhanced road network representation to a trajectory end point prediction task. The invention further comprises a system of the track end point prediction method based on the double-branch road network pre-training representation.

The application discloses a kind of bridge monitoring data accurate multi-step prediction method based on space-time hypergraph neural network, the method comprises the following steps: one, collect bridge structure space-time monitoring two-dimensional data as original data set;Two, original data set is carried out missing data filling, trend extraction, data standardization preprocessing operation;Three, space domain data is expressed as different vertex on hypergraph, time domain data is expressed as one-dimensional time series on each vertex of hypergraph, define correlation matrix;Four, design space-time hypergraph neural network model to carry out space-time correlation modeling to bridge monitoring data;Five, use the monitoring data in the initial stage of bridge operation within half a year to train space-time hypergraph neural network model;Six, the trained space-time hypergraph neural network model is applied to monitoring data after half a year.The application solves the shortcoming that data-driven response prediction method is insufficient in the degree of space-time correlation of monitoring data, realizes the multi-step accurate prediction of monitoring data.

The invention discloses an electric power system key line identification method based on a hypergraph model, and the method comprises the steps: 1, defining each power transmission path as a hyperedge through power flow tracking, defining each line as a node, and constructing a hypergraph model of an electric power system; 2, performing K-Shell decomposition on the hypergraph model, and calculating the structural importance of each power transmission line; 3, calculating the magnitude of the load flow of the hyperedge, and calculating the magnitude of the weight of the hyperedge by fusing the importance of the branch structure and the magnitude of the load flow of the hyperedge through an entropy weight method; 4, descending sorting is carried out according to hyperedge weights, and a key line sequence is output; step 5, constructing a node transition probability matrix based on hyperedge weight; step 6, screening unconnected high transition probability node pairs to generate a candidate branch set; and step 7, with maximization of the evaluation index load balancing entropy LBE as a target, adding branches in an accumulated manner until the LBE reaches a peak value, and completing structure optimization. According to the invention, the recognition precision of the key line can be improved, and balanced power flow distribution can be realized.

The invention relates to a time-space dynamic evolution-oriented man-machine cooperation assembly cognitive inference method, which comprises the following steps of: extracting visual features in an assembly scene, generating a scene graph, constructing a time hyperedge, a space hyperedge and a task hyperedge, and fusing the three types of hyperedges to form a hyperedge set; time-varying non-pairwise relationships among human operators, robots, assembly operations and various assembly component nodes are represented, a hyperedge incidence matrix is defined, a man-machine cooperation assembly knowledge space-time hypergraph is constructed, and hyperedge representation among assembly components is realized; designing a stacked graph neural network with a self-excitation characteristic based on a multi-event hokes process, learning high-order task association among assembly nodes, and updating the change of the high-order task association along with time to realize space-time hypergraph representation; and modeling a self-excitation process among different sub-tasks, and capturing individual features and collective association to realize man-machine cooperation assembly. The problem that man-machine cooperation cognition in a time-varying task is difficult to infer is solved, and man-machine cooperation assembly efficiency and initiative are improved.

The application discloses a hypergraph recommendation method based on multiple conversation awareness, uses a hypergraph to obtain high-order relationship representation between items, combines a co-occurrence graph and a local conversation graph to obtain item correlation representation within and between conversations, uses a gating mechanism to mine mutual beneficial information between multiple conversations, adaptively captures the dependency relationship of items in the conversation behavior sequence, and further provides more accurate recommendation results for users. The application uses the natural property of the hypergraph to model the complex multi-element relationship in the conversation, alleviates the hyperedge isomorphism problem by keeping the sequence information. The application establishes a co-occurrence graph structure and a local conversation graph structure, realizes the connection between similar user intentions in different conversations and potential behavior patterns in the same conversation, and can explore the mutual influence of complex cross-conversation and intra-conversation behavior patterns.

The application provides a recommendation method for optimizing multi-user representation based on a hypergraph motif, and relates to the fields of knowledge graphs, recommendation systems and the like. The method constructs a user-item hypergraph based on the historical interactions of the user, maps it to a user adjacency graph, and uses it as the initial representation of the user implicit relationship network. Then, the hypergraph motif is extracted to learn the interaction mode of the multi-user, the implicit relationship network is reconstructed, and the item features in the hypergraph and the user association features in the implicit relationship network are fused to obtain a high-order representation of the user. Finally, the preference information of the user is propagated in the knowledge graph, and the representation of the item is aggregated. The method uses the hypergraph motif to learn the interaction mode of the multi-user, establishes a user implicit relationship network with direction and weight, can learn the interaction information of the multi-user, and then fuses the implicit relationship features of the user and the historical interaction item features to optimize the representation of the user and improve the performance of the recommendation system.

The embodiment of the invention provides a traffic scheduling method in a time-sensitive network and related equipment, and belongs to the technical field of communication networks. According to the method, a TSN scheduling problem is modeled as a maximum independent set solution problem on a dynamic conflict hypergraph, and decoupling of accurate constraint and efficient solution is realized by maintaining a double-graph structure of a reference hypergraph and a sparse working hypergraph. And dynamically maintaining the working hypergraph quality by adopting a semi-static sparse strategy combining global reconstruction based on semi-definite programming and heuristic local updating. The resource occupancy index is utilized to realize nearly linear increment construction of the conflict hypergraph, and approximate optimal scheduling is quickly solved on the working hypergraph through a flow sensing parallel greedy algorithm. And finally, the feasibility of the scheduling scheme under accurate constraint is ensured by verifying a repair mechanism. According to the method, the expandability bottleneck in large-scale TSN network scheduling is solved, the computing efficiency is improved while the scheduling quality is ensured, and the method is suitable for scenes such as the industrial Internet of Things and the Internet of Vehicles which have strict requirements on deterministic communication.

The invention provides a multi-source fusion knowledge point labeling method and device based on hypergraph knowledge enhancement, and relates to the technical field of knowledge point labeling, and the method comprises the steps: inputting a knowledge point text set into a large language model to obtain a knowledge element system matrix and a knowledge point incidence matrix, constructing a hypergraph matrix according to the knowledge point text set, the knowledge element system matrix and the knowledge point incidence matrix, and inputting the hypergraph matrix into a hypergraph convolutional neural network to obtain a hyperedge matrix; inputting the test question text into a large language model to obtain a vocabulary sequence, inputting the vocabulary sequence into a bidirectional semantic encoder to obtain a vocabulary vector matrix, and inputting the vocabulary vector matrix into a bidirectional long-short-term memory network to obtain a vocabulary feature matrix; splicing the hyperedge matrix and the vocabulary feature matrix to obtain a spliced feature matrix, and inputting the spliced feature matrix into a multilayer perception neural network to obtain a knowledge point prediction matrix; and performing knowledge point labeling on the test question text according to the knowledge point prediction matrix to obtain all predicted knowledge point texts of the test question text.

This application discloses a hypergraph representation learning method and apparatus based on heterogeneity adaptive positional encoding, which can be applied to the field of graph neural network technology. This application constructs a heterogeneity adaptive positional encoding module and a heterogeneity-aware distinguishable aggregation module. A triple array is input into the heterogeneity adaptive positional encoding module to capture the first output representation corresponding to long-distance semantically related neighbor relationships in the hypergraph to be analyzed. The obtained triple array is then input into the heterogeneity-aware distinguishable aggregation module to obtain a second output representation that can distinguish the importance of different neighbor nodes in the hypergraph to feature aggregation. The first and second output representations are then fused to obtain the target node embedding representation. Based on the target node embedding representation, a pre-defined hypergraph neural network model is optimized using supervised learning, thereby achieving low-dimensional feature representation learning for heterogeneous hypergraph nodes and effectively improving the representation learning performance of heterogeneous hypergraphs.

Embodiments of the present application provide a point cloud matching method and device, and a storage medium. The method can include determining a consistency matrix between a first point cloud and a second point cloud, wherein an element in the consistency matrix represents a consistency size between a corresponding relationship of two points included in a first group of points and a corresponding relationship of two points included in a second group of points; constructing a hypergraph according to the consistency matrix between the first point cloud and the second point cloud; performing graph convolution processing on the hypergraph to obtain at least two groups of initial transformation matrices; and obtaining a target transformation matrix between the first point cloud and the second point cloud according to the at least two groups of initial transformation matrices. The high-order consistency represented by the hypergraph structure is more accurate, and is more robust to abnormal points and noise. The method can provide more accurate information for identifying and registering inlier points, and is helpful for effectively registering point clouds in a low overlap rate situation.

This invention belongs to the field of conversation recommendation technology, and specifically provides a conversation recommendation method based on multi-intent expression using cross-category heterogeneous hypergraphs. The method includes preprocessed data and the following steps: S1: The preprocessed data is input to a category recognition processing module, which uses a Latent Dirichlet Allocation (LDA) model to mine the latent category distribution of items and outputs category-classified items; S2: The preprocessed data and category-classified items are respectively input to a self-loop star graph module and a cross-category heterogeneous hypergraph module to construct the graph structure, outputting a self-loop star graph and a cross-category heterogeneous hypergraph. This invention combines the conversational intra-conversational structure modeling of self-loop star graphs with the semantic association expression of cross-category heterogeneous hypergraphs to construct a multi-intent modeling framework that can simultaneously capture local behavioral dependencies and global interest transfers. It dynamically identifies latent intent patterns and multi-category preference features even without long-term user history information, thereby significantly improving the accuracy, personalization, and robustness of conversation recommendations.

The invention discloses a knowledge hypergraph-based power system query method and related equipment, and the method comprises the steps: obtaining a description document and a data file of a power system, and generating a power system knowledge graph; analyzing each independent data card in the data file, converting each independent data card into a corresponding hyperedge, connecting the hyperedge with all entity nodes related to the corresponding card to form a power system multivariate relation knowledge hypergraph, and converting the hypergraph into a power system bipartite graph for storage; when a query instruction is received, entity nodes matched with key entities in the query instruction and hyperedge nodes related to semantics are retrieved in the bipartite graph of the power system, and after a context subgraph is constructed through bidirectional expansion, a language model is input to generate a query feedback result. The problems that a traditional mode is poor in expansibility, information is lost, retrieval is low in efficiency and answers are fuzzy are solved, and construction efficiency, association integrity, retrieval performance and query accuracy are greatly improved.

The invention discloses a hypergraph propagation source positioning method based on interaction enhancement. A traditional traceability method mainly depends on a pairwise interaction hypothesis of a simple graph structure, and the positioning precision under a complex topology is limited. The method comprises the following steps: firstly, constructing a hypergraph propagation model and obtaining a complete observation snapshot of a network node; then, extracting the dynamic state and global spectrum features of the nodes, and constructing a basic feature vector; an improved double-flow interaction hypergraph neural network is utilized, hyperedge internal key information is aggregated through local attention flow, and multi-scale topology diffusion flow is utilized in parallel to capture overall long-range dependence; and finally, introducing a gating residual fusion mechanism to adaptively integrate the double-flow features, and accurately outputting the propagation source probability based on a weighted cross entropy loss function. According to the method, the path reconstruction deviation of a traditional method can be effectively overcome and the robustness can be improved in the aspect of processing high-order interaction and long-distance dependence. According to the method, the performance bottleneck based on simple graph traceability is broken through, and powerful technical support is provided for propagation source positioning.

The invention belongs to the technical field of power grid safety risk early warning, and particularly relates to a power grid safety risk early warning method based on data consanguinity map driving. The method comprises the steps of firstly collecting operation data at key nodes of a power grid and preprocessing and storing; then constructing an equipment-data-physical field ternary hypergraph model based on a hypergraph semantic modeling theory, fusing data and a topological relation, and generating a dynamic blood relationship map by means of a Lie group differential geometry algorithm and a quantum graph state storage technology; then identifying an abnormal mode by using a space-time hypergraph convolutional network, a quantum enhanced causal discovery algorithm and a photon pulse neural network, and outputting an interpretable abnormal report; and finally, establishing a risk assessment model according to the abnormal report, determining a risk value and an early warning level, and generating and issuing early warning information. Power failure accidents are reduced through accurate early warning, operation and maintenance management and resource allocation are optimized, blind maintenance is avoided, innovative development of the power grid technology is promoted, and a new way is provided for power grid safety risk early warning.