217 results about "Hypergraph" patented technology

The invention provides a tunnel construction safety monitoring and early warning method and system based on multi-dimensional data fusion, and relates to the technical field of construction safety early warning, and the method comprises the steps: obtaining first information which comprises tunnel construction parameters and image information; performing space-time fusion processing and redundancy reduction processing on the first information to generate a four-dimensional hypergraph tensor space of tunnel construction; performing cross-scale analysis and topology extraction on the four-dimensional hypergraph tensor space, and constructing a dynamic heterogeneous graph network; performing space-time diagram convolution processing on the dynamic heterogeneous graph network, and generating a composite risk manifold based on a result obtained by processing and a multi-head attention mechanism; coupling processing is carried out according to the composite risk manifold, stability analysis and critical state judgment are carried out based on the risk phase change hypersurface obtained through processing, and real-time early warning is carried out based on a dynamic early warning boundary of tunnel construction obtained through judgment. According to the invention, the risk identification accuracy and the early warning response timeliness in the tunnel construction environment are improved.

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 relates to the technical field of data monitoring, and discloses an intelligent data monitoring method and a monitoring platform, and the intelligent data monitoring method comprises the steps: constructing a dynamic heterogeneous hypergraph model, and representing the complex relation and time sequence evolution characteristics among multi-source data nodes; based on a dynamic heterogeneous hypergraph model, realizing high-order relationship representation and calculation, and obtaining embedded representation of nodes and relationships; performing heterogeneous graph information transmission and aggregation by utilizing embedded representation of nodes and relationships, and capturing complex interaction characteristics among multiple nodes; according to the complex interaction characteristics, an abnormal propagation rate model is established, and accurate prediction of an abnormal diffusion path is realized; based on the abnormal propagation rate model and the abnormal diffusion path, multi-level collaborative anomaly detection is implemented, and collaborative faults across subsystems are identified; according to the invention, the abnormal propagation path can be predicted in advance, the abnormal prediction accuracy is improved, and the system fault response time is advanced.

The invention discloses an adaptive learning path recommendation method based on a hypergraph neural network and knowledge tracking, and relates to the field of learning path recommendation, and the method comprises the steps: determining an incidence relation between learning resources, and enabling the incidence relation to serve as an edge of a learning resource undirected graph; the features of the learning resources serve as embedded feature vectors of all nodes of the learning resource undirected graph; updating the embedded feature vector by using a graph neural network to obtain a resource embedded vector, and taking the resource embedded vector as a node feature of a learner hypergraph structure; performing iterative aggregation on the learner hypergraph structure by using a hypergraph neural network to obtain a dynamic resource embedding and learner behavior sequence, generating an initial recommendation list, further generating a candidate learning path set, and generating a Pareto frontier solution set by using a non-dominated sorting genetic algorithm II; and calculating a comprehensive score of each path in the solution set based on a dynamic weight distribution strategy and a comprehensive utility function, and determining an optimal learning path. According to the invention, the accuracy and effectiveness of learning path recommendation are improved.

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 relates to the technical field of computer vision, in particular to a human body behavior recognition method and system. According to the method, a multi-head space hypergraph convolution module is arranged before each time graph convolution of an ST-GCN model, and a human body behavior recognition model is constructed; the multi-head space hypergraph convolution module constructs a non-uniform hypergraph to represent the topological relation of the human skeleton through the maximum number, which can be contained by hyperedges, of each node, and generates the output of the module in combination with a physical adjacency matrix reflecting the relation of each joint; virtual connection is also added, virtual features which are the same as the input channel dimension and the time dimension of the multi-head space hypergraph convolution module are constructed, and the virtual features and modal data features are spliced along the joint dimension and then serve as module input again, so that global semantic information of real nodes is enriched, generalization information of human body behavior modes is supplemented, and the real-time performance of the multi-head space hypergraph convolution module is improved. And stage dense connection is introduced between different layers to smooth the change degree of the features, so that the human behavior recognition precision is improved.

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 a multi-modal recommendation method based on hypergraph edge diffusion, which comprises the following steps: firstly, collecting user-project interaction and multi-modal features, constructing a multi-source relation graph and generating a modal perception hyperedge; then, optimizing a hypergraph structure through forward diffusion and reverse denoising; performing feature learning in combination with local graph convolution and a global hypergraph attention network; then, cross-modal contrast enhancement is introduced to improve feature consistency; and finally, sorting recommendation is realized through joint optimization of recommendation loss, diffusion loss and comparison loss. According to the multi-modal recommendation method based on hypergraph edge diffusion, a modal perception hypergraph structure and a diffusion generation mechanism are introduced, so that the problem of recommendation performance reduction caused by sparse user-project interaction can be effectively relieved, and modal information and a high-order user-project relationship can be fully mined; and the recommendation accuracy and the model robustness in a data sparse environment are improved.

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 relates to a hypergraph neural network inference method and system. The method comprises the following steps: extracting topological information with a vertex with the highest degree from compressed hypergraph topological representation; identifying the common vertex with the highest degree and the hyperedge connected with the common vertex and sending the common vertex and the hyperedge to a task queue; triggering hyperedge aggregation operation on each hyperedge in parallel based on common vertexes from the common vertex table in the task queue; hyperedge aggregation operation is executed, generated hyperedge aggregation intermediate results are temporarily stored in an intermediate result buffer area of an on-chip cache unit, and in the process of executing hyperedge aggregation operation of hyperedges in sequence based on the hyperedge storage sequence in the hypergraph topology, the hyperedge aggregation intermediate results from common vertexes of a common vertex table are reused, and the hyperedge aggregation intermediate results from the common vertexes of the common vertex table are reused. And thus, a final hyperedge aggregation result of each hyperedge is obtained. According to the method, calculation tasks are executed more compactly, meanwhile, a large amount of unnecessary memory access and communication are avoided through cache reuse, and the calculation efficiency of hypergraph neural network reasoning is improved.

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 field of deep learning, and discloses a multi-agent collaborative task state embedding method based on a multi-scale hypergraph and multi-dimensional aggregation. In order to solve the problems that an existing graph neural network is difficult to capture a high-order interaction relation, poor in dynamic environment adaptability and limited in communication, the method comprises the steps of constructing a state observation graph; calculating an adjacent matrix according to explicit states such as position and speed; generating a latent layer feature adjacency matrix through nonlinear conversion and similarity calculation of a graph convolutional network, and fusing the latent layer feature adjacency matrix with the Hadamard product of the interactive graph; constructing a multi-scale hypergraph (containing S scales) according to the latent layer matrix, and searching a high-density sub-matrix to form hyperedges; building a two-stage information aggregation model: integrating multi-dimensional features and calculating association degree and interaction types in a hyperedge aggregation stage, and updating node features by using a graph attention network GAT in a node aggregation stage; a multi-agent soft behavior-commentator algorithm MASAC is fused, a behavior and reward function is designed, and an MHGNN-MASAC model is formed; the method is applied to cooperative control task decision.

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 relates to the technical field of financial science and technology, in particular to an enterprise credit risk quantification method, which comprises the following steps of: writing finance, Internet of Things, bill chains and remote sensing data characteristics into a data lake in a homomorphic encryption manner to generate a federated event grid; a causal topology hypergraph is constructed, topology and multi-scale frequency domain features are extracted to form node representation, and node states and weights are updated through fractional order graph differential; the mapping hypergraph is a silicon light Mach-Zehnder phase, and node state superposition impact pulse is injected into a photon interference network to obtain optical readout; the spiking neural network-reinforcement learning agent reads out the generation management action to form a reserve tensor; and solving a discrete Schrodinger equation in combination with the tensor and graph Laplacian, and outputting a risk entropy potential and a prediction period default probability confidence interval through fractional order path integral correction. According to the invention, privacy protection, high-order structure identification and long-tail sensitivity are realized, and real-time and auditable credit risk assessment is realized.

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 relates to a power violation work detection method and device based on a dynamic time sequence hypergraph network, and the method comprises the steps: S1, obtaining n continuous frames of power operation videos, and extracting a power operation image; s2, according to the step S1, obtaining human body behavior postures through a human body posture detection algorithm, wherein the human body behavior postures comprise key points and confidence coefficients; s3, constructing a heterogeneous hypergraph model, and introducing hypergraph convolution and a self-attention mechanism; s4, processing through a time sequence three-dimensional hypergraph convolution module, and capturing a vertex dynamic interaction relationship; s5, constructing a dual-channel model, and optimizing feature learning by using a hypergraph auto-encoder and a graph convolutional network; and S6, inputting the power operation video into the trained violation operation model, and detecting violation behaviors in real time. According to the scheme, the dynamic time sequence hypergraph network is utilized to enhance the violation behavior recognition capability, and the method is suitable for real-time analysis of electric power operation videos.

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 invention provides a traffic prediction method based on a double-dynamic graph attention network, and belongs to the technical field of traffic prediction methods. The method comprises the following steps: adding spatio-temporal information to an input traffic signal, generating a dynamic graph adjacency matrix through linear transformation, and performing dual transformation on a dynamic graph to generate a dual dynamic hypergraph adjacency matrix; inputting a traffic signal to be predicted and the double-dynamic graph adjacency matrix into a spatial feature extraction module, and capturing and integrating spatial correlation features; a time feature extraction module is used to capture time-related features on different time scales through a plurality of stacked gating attention linear units, and time-space related features captured by the current time-space feature extraction module are obtained; and the output module carries out linear processing and residual decomposition on the extracted spatio-temporal correlation features to obtain a prediction result of the current module and signal input of the next block, and integrates output of all spatio-temporal feature extraction modules to obtain a final prediction value. According to the method, a space-time convolutional network architecture is adopted to learn dynamic characteristics in traffic signals, and the traffic prediction precision is improved.

The invention relates to a multi-dimensional root cause positioning method and device based on multi-modal learning. The method comprises the following steps: acquiring historical multi-modal data generated in a fault interval during operation of a cloud native system; respectively carrying out serialization processing on Metric data, Log data and Trace data in the historical multi-modal data; selecting a service instance and an API as nodes of the hypergraph, and obtaining node features according to the Metric serialization data, the Log serialization data and the Trace serialization data; according to the service instance, the API, the k8s node and the service, constructing hyperedges of the hypergraph from the angles of physics, logic and interaction, and forming a multi-modal hypergraph by the nodes and node features of the hypergraph and the hyperedges of the hypergraph; and inputting the multi-modal hypergraph into a multi-dimensional root cause positioning model based on a hypergraph neural network and a full-connection neural network, and training the multi-dimensional root cause positioning model to obtain a trained multi-dimensional root cause positioning model. According to the method, the problems of insufficient single-mode data modeling, difficulty in complex relation capture, dynamic property, isomerism and the like are solved.

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.