365 results about "Message passing" patented technology

The invention discloses a machine vision-based resistor disc defect online detection system and a grading method, relates to the technical field of industrial machine vision detection, and solves the defect problems in the aspects of multi-scale defect dynamic perception, cross-level feature interaction and process adaptive optimization in the prior art. According to the scheme, metal reflection interference is inhibited through Retinex illumination correction and a combined denoising model; adopting a deformable convolution kernel and cavity space pyramid pooling to realize gradient entropy driving dynamic sensing of the multi-scale defect; constructing a bidirectional cross-layer attention network to realize early fusion of high-resolution details and high-level semantics; modeling local-global feature physical association based on a graph attention network and a self-supervised message passing mechanism; integrating reinforcement learning and a memristor random calculation unit to form a closed-loop parameter optimization system; according to the method, the multi-scale defect detection precision, the cross-modal feature fusion efficiency and the system adaptive capacity under complex working conditions are remarkably improved.

The invention discloses a fault detection method and device for secondary equipment of a transformer substation. The method comprises the following steps: acquiring real-time operation parameters of the secondary equipment of the transformer substation, inputting the real-time operation parameters into a pre-trained graph neural network model to obtain equipment operation characteristics, and determining an equipment state vector according to a plurality of equipment operation characteristics in a continuous time window and a message passing mechanism; determining an equipment health degree score according to the equipment state vector and a knowledge graph, and triggering the intelligent inspection robot to execute a deep detection task under the condition that the health degree is lower than a preset health degree threshold value so as to collect target test data; determining a target fault type according to the target test data, the knowledge graph and the inference rule base, obtaining a fault processing scheme corresponding to a historical fault case, and generating corresponding maintenance guide information according to the fault processing scheme, thereby achieving the real-time detection, precise fault diagnosis and automatic maintenance of the secondary equipment of the transformer substation. And the operation safety and the maintenance efficiency of the power system are improved.

The invention belongs to the field of intelligent traffic systems, and relates to a multi-mode space-time traffic flow modeling method supporting large-scale road network real-time prediction, and the method comprises the steps: firstly designing a space-time prediction framework facing a dynamic traffic network, and then carrying out the training to obtain a final prediction model; the space-time prediction framework comprises a data embedding layer, a space-time coding module and a deep modeling and output module based on an expert hybrid mechanism; the data embedding layer comprises two parallel channels of time embedding and spectral domain space embedding and a time-space data fusion module; the space-time coding module comprises a block-level sparse time attention module, a space attention-message passing module and a weighted fusion layer which are parallel; the deep modeling and output module based on the expert hybrid mechanism comprises an MoE dynamic expert modeling module, a full-connection mapping module, a jump connection layer and an output layer; according to the design, the response speed, the prediction precision and the cross-regional adaptive capacity of the model in a high-heterogeneity scene are improved.

The invention discloses a network protocol reverse analysis method based on deep learning and a graph neural network, and the method comprises the steps: basic field detection: carrying out the byte-level feature extraction of a binary data stream through sliding window embedding, bidirectional LSTM coding and knowledge enhanced CRF decoding, and outputting a structured field labeling sequence; performing protocol format clustering based on the sequence: calculating a multi-dimensional similarity through an improved Needleman-Wunsch algorithm, realizing automatic classification of unknown protocols in combination with a dynamic density clustering algorithm optimized by LSH, and outputting a protocol cluster; and performing composite structure analysis based on the protocol cluster: constructing a protocol syntax tree based on a graph neural network, performing multiple rounds of message passing through a graph attention network GAT attention mechanism, identifying a nested structure and performing recursive analysis, and generating a multi-level protocol syntax tree. According to the method, the automation degree and accuracy of complex protocol analysis can be remarkably improved.

The invention discloses a relation extraction method and system based on a graph neural network, and belongs to the technical field of natural language processing. A target text is obtained, word segmentation, part-of-speech tagging and named entity recognition are carried out, and an entity set is extracted; constructing a text graph structure containing multiple edge types based on the entity set; performing feature coding on nodes in the graph to generate an initial feature vector fusing semantic, part-of-speech and position information; inputting the graph into the graph neural network model, and obtaining high-order node representation through multi-layer message passing and aggregation; modeling the entity pair in combination with the structure path and the context information, and inputting a multi-channel classification network to predict the relationship type of the multi-channel classification network; and finally, outputting an entity relationship triple according to a prediction result. The method has stronger semantic modeling ability and structure expression ability in a relation extraction task, and is suitable for scenes such as knowledge graph construction and information extraction systems.

The invention discloses a construction environment sudden change risk field prediction method fusing geological and meteorological data, and belongs to the technical field of construction engineering risk prediction, and the method comprises the steps: obtaining geological data, meteorological data and construction progress data of a construction area, carrying out the structural processing of the multi-modal heterogeneous data, and carrying out the construction engineering risk prediction. And generating graph data containing the multi-modal features. According to the method, a space-time adaptive graph neural network prediction model based on an attention mechanism is adopted, weights of different features can be dynamically calculated and allocated according to real-time environment and construction data, and a space-time propagation relationship of risks among geographic nodes is captured by using a message passing mechanism of a graph. According to the method, data of different modals can be deeply fused, accurate prediction of the construction environment sudden change risk is realized, corresponding early warning is triggered according to the risk level, the accuracy and timeliness of risk prediction are improved, and scientific support is provided for intelligent decision making of constructional engineering.

The invention provides a network node classification method based on a multilayer graph neural network model, and the method comprises the steps: constructing a graph according to network nodes, carrying out the preprocessing of initial node features in the graph, and obtaining an initial embedded vector; according to the initial embedding vectors of the node and the neighbor node thereof, updating the node representation by adopting a layered message passing mechanism; according to the initial embedded vectors of the nodes and the neighbor nodes thereof, the graph attention network uses an attention mechanism to calculate weights for the neighbor nodes of each node and then dynamically updates feature vectors of the nodes; a multi-head attention mechanism is adopted to splice and merge the feature vectors of the nodes output by the graph attention network and the updated node representation, and the comprehensive feature vector of each node is obtained; the comprehensive feature vectors are further optimized through graph contrast learning and a node embedding module, and optimized features are obtained; and obtaining predicted node classification according to the optimization features. According to the method, complex features of heterogeneous graph data can be effectively captured, the method has good robustness for an over-smoothing problem, more accurate heterogeneous network representation learning is realized, and the method has advanced performance and robustness in a node classification task.

The embodiment of the invention provides a low-altitude digital management method and device based on an urban space-time grid, and the method and device achieve the precise division of an airspace through a plurality of layers of grid units by creatively building a unified urban space-time reference grid system. And designing a space-time grid data assignment model, processing multi-source heterogeneous data by using a deep neural network and a space-time convolutional layer, and realizing intelligent mapping of grid features through an attention mechanism. And constructing an airspace situation awareness model based on a graph neural network, and capturing a space-time dependency relationship among grid units in combination with a message passing mechanism to realize accurate prediction and hierarchical management and control of an airspace state. According to the method, the defects of the traditional technology in the aspects of airspace division, data processing, situation awareness and the like are effectively overcome, and the accuracy and practicability of urban low-altitude digital management are remarkably improved.

The invention provides a miRNA-disease association prediction method based on a graph neural network, and relates to the technical field of graph neural networks. The method comprises the following steps: acquiring miRNA sequence, a disease description text, miRNA family information, a disease father-son relationship and miRNA-disease association relationship data, and constructing a heterograph after performing data alignment; based on the miRNA sequence and the disease description text, using a pre-training language model to obtain miRNA representation and disease representation as node features, and updating the heterogeneous graph; and constructing a heterograph MDA association prediction model, and inputting the updated heterograph and the to-be-predicted miRNA-disease pair to obtain an association prediction score of the to-be-predicted miRNA-disease pair. According to the method, the biological entity can be fully expressed in entity expression; according to the method, information of different types of nodes can be effectively aggregated on model construction, multi-depth message passing is executed on different types of edges, integration of heterogeneous information of the nodes and the edges is achieved, and improvement of miRNA-disease association prediction performance is facilitated.

The embodiment of the invention relates to the technical field of data storage, and discloses a cross-K8S cluster configuration change storage method and device based on a block chain, and the method comprises the steps: obtaining a transaction request message passing the compliance verification of an intelligent contract engine, and the transaction request message comprises a configuration change request; obtaining log information of the configuration change request in the transaction request message executed by the target K8S cluster, wherein the log information further comprises configuration change cluster difference information; the log information is stored in a fragmented mode based on the IPFS network, and content hash codes of the log information are obtained; and taking the transaction request message and the content hash code as complete transaction information, and storing the transaction request message and the content hash code in a transaction pool. Decentralized storage is achieved based on an IPFS network fragmentation storage mode, the problem that a centralized database or a log system is maliciously modified or historical records are deleted easily in centralized storage is solved, and auditing integrity and data storage safety are guaranteed.

The invention relates to the technical field of drug interaction prediction, and discloses a drug interaction prediction method based on multilayer attention and elastic message passing, which comprises the following steps: firstly, extracting graph structure information of drug molecules from a 3D structure of the drug molecules, including associated features of atoms and chemical bonds; on the atomic feature level, learning updating and denoising of different atom and chemical bond features in drug molecules are achieved by using an elastic graph neural network and fusing the attention mechanism of Transform and combining a 3D position coding method based on the molecular mass center; on the molecular feature level, the attention mechanism is further utilized, and through supervised learning, comparative learning and structured learning, the potential interaction relationship of different drug molecules is mined, and potential network attacks are detected and defended; and interaction prediction of any two drug molecules i and j is realized. The results of four indexes of ACC, AUC, AP and F1 all reach 98% or above, and the method exceeds the most advanced method at present.

The invention relates to an unmanned aerial vehicle path planning method and system based on GNN and high-order security constraints. The method comprises the steps that a navigation scene where an unmanned aerial vehicle is located is represented as a heterogeneous directed graph, message passing and feature updating are conducted through the GNN, a risk-aware attention mechanism is introduced, and an interpretable decision result is output; a differentiable HoCBF-QP optimization layer is introduced, an original control command output by a strategy network is used as input, quadratic programming with high-order control barrier function constraints is solved online, minimum-amplitude safety correction is carried out on the control command, and an actuator control command meeting safety constraints is output; starting a HoCBF safety shield during operation so as to strictly ensure that all safety constraints are met before execution; a calculation task of the whole control cycle is modeled into a directed acyclic graph form, and parallel execution is carried out on heterogeneous multiple cores by utilizing a real-time scheduling strategy. The problem that unmanned aerial vehicle navigation control is not effective and unified in three aspects of structure, safety and scheduling is solved.

The present disclosure describes approaches to camera re-localization using a graph neural network (GNN). A re-localization model includes encoding an input image into a feature map. The model retrieves reference images from an image database of a previously scanned environment based on the feature map of the image. The model builds a graph based on the image and the reference images, wherein nodes represent the image and the reference images, and edges are defined between the nodes. The model may iteratively refine the graph through auto-aggressive edge-updating and message passing between nodes. With the graph built, the model predicts a pose of the image based on the edges of the graph. The pose may be a relative pose in relation to the reference images, or an absolute pose.

The invention discloses an electric power engineering cost simulation analysis method and system based on digital twinning, and relates to the technical field of simulation analysis, and the method comprises the steps: obtaining multiple engineering task multi-modal data and task dependency relationships for electric power engineering; processing the multi-modal data of the plurality of engineering tasks and the task dependency relationship based on a digital twinning technology so as to construct an electric power engineering virtual model; inputting the electric power engineering virtual model into a graph neural network so as to extract task node features corresponding to each engineering task virtual module through a message passing mechanism; and defining an input state of a reinforcement learning model according to each extracted task node feature so as to generate a task resource allocation action for each engineering task through reinforcement learning. Therefore, the digital twinborn technology, the graph theory feature extraction technology and the reinforcement learning technology are comprehensively applied, real-time monitoring and simulation analysis are carried out on cost and resource consumption of different links in the electric power engineering, and resource allocation is intelligently optimized.

The invention provides a drug response prediction method based on a gene relation network and a drug substructure. The drug response prediction method comprises the following steps: performing feature extraction on three multi-modal features of gene expression, copy number variation and mutation of a cell line by adopting an enhanced graph attention network; acquiring a substructure of the medicine by using a gating message passing neural network, and extracting features by enhancing a graph attention network; constructing two complementary graph structures, namely an atom-bond graph and a bond-angle graph, and performing feature extraction and message passing on the two graphs by using a graph convolutional neural network so as to extract 3D features of the medicine; the characteristics of the drug and the cell line are integrated through concat operation, and drug response prediction is carried out through mlp. According to the drug response prediction method provided by the invention, cell line features are extracted by establishing and using three kinds of omics data with different emphasis, and drug feature extraction is performed by using a drug molecular map, a drug 3D structure, a drug molecular fingerprint and a drug substructure relationship.

The invention discloses a transformer fault diagnosis and root cause positioning method based on a space-time diagram neural network, and relates to the field of transformer fault diagnosis, and the method comprises the steps: S1, carrying out the preprocessing of the structure information and DGA time series data of a transformer, and obtaining a topological network structure diagram and a DGA time series; s2, obtaining a spatial vector based on a message passing mechanism of a graph convolutional network; s3, a time sequence vector is obtained in combination with a Transform encoder and multi-head self-attention; s4, obtaining space-time fusion features; s5, constructing a multi-task prediction head based on the space-time fusion feature, the fault type historical data and the fault root cause; and S6, carrying out fault detection and outputting a corresponding fault type and root cause positioning result. According to the application, the accuracy of fault type identification can be remarkably improved, and accurate positioning of the fault root cause is realized.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to predicting an optimized result for a graph neural network (GNN). A system can comprise a memory configured to store computer executable components; and a processor configured to execute the computer executable components stored in the memory, wherein the computer executable components comprise: a fusion component that that models non-linear modality correlations within and across entities through Hirschfeld-Gebelein-Re'nyi maximal correlation (MaxCorr) embeddings that generates a multi-graph that preserves identities of modalities and entities; and a multi-graph neural network (MGNN) component for task-informed reasoning in multi-graphs, that learns parameters defining entity-modality graph connectivity and message passing in an end-to-end fashion.

The invention discloses an industrial chain integrity assessment model, system and method, and relates to the technical field of industrial chain assessment, the model comprises a node feature coding module, a candidate node set selection module, a pairwise code generation module and an assessment module; the node feature coding module is used for learning feature representation of nodes in the industry chain recessive heterogeneous graph based on a message passing neural network; the candidate node set selection module is used for screening nodes which have obvious influence on the target node pair from the industry chain recessive heterogeneous graph based on the PPR score; the pairwise code generation module is used for calculating pairwise codes of a target node pair based on a graph attention mechanism and capturing a complex relationship between nodes; and the evaluation module is used for predicting the existence probability of the target node pair through the multi-layer perceptron and carrying out quantitative evaluation on the predicted existence probability through the integrity evaluation index. According to the method, the complex relation between the nodes in the industry chain recessive heterogeneous graph is accurately captured, and the influence relation between the nodes can be comprehensively quantified.

The disclosure relates to methods and systems of partitioning-based scalable weighted aggregation composition for embeddings learned from knowledge graphs for training neural networks to perform downstream machine-learning tasks. For example, a system may access a knowledge graph comprising a plurality of nodes and partition the knowledge graph into a plurality of partitions based on edge densities between nodes of the knowledge graph. The system may perform partition-wise encoding using compositional message passing between nodes that enables learning from neighboring nodes. The system may generate an embedding for each node and each relation type in each partition based on the partition-wise encoding using compositional message passing. The system may concatenate the generated embeddings from the plurality of partitions. The system may train a global neural network for a downstream prediction task based on the concatenated embeddings using one or more weight matrices.

The invention relates to the technical field of infrared light machine system thermal deformation prediction, in particular to a multi-material structure thermally induced stress deformation prediction method based on a graph neural network. The method comprises the steps of data set establishment, graph structure establishment, graph neural network model establishment and training and model and parameter optimization. Finite element nodes correspond to graph nodes, finite element edges correspond to graph edges, an encoder-message passing-decoder architecture model is established, and node states are updated through a three-layer physical symmetry message passing mechanism. Physical constraint loss including minimum displacement smoothness constraint and stress continuity constraint is innovatively added into a loss function. Compared with traditional finite element calculation, the method has the advantages that the speed is increased by more than 100 times, high hardware adaptability is achieved, the black box limitation of a data-driven neural network model is broken through, thermally induced stress deformation analysis caused by different material coefficients can be processed, the adaptability to geometric changes is high, and good engineering application value is achieved.

The invention discloses an integrated circuit layout design teaching assistance method, and relates to the field of integrated circuits, and the method comprises the steps: receiving a circuit diagram creation instruction, and generating a circuit diagram drawing interface according to the circuit diagram creation instruction; identifying types and connection relations of circuit devices in the circuit diagram drawing interface; according to the identified circuit device type and the connection relation, generating a corresponding circuit schematic diagram data structure by adopting a graph neural network GNN algorithm based on message passing; converting the schematic circuit diagram data structure into a layout data structure; the layout data structure comprises geometric parameters and layout position data of layout elements; and according to the layout data structure, generating a circuit layout by using a reinforcement learning algorithm based on a graph neural network. In view of low integrated circuit layout design efficiency in the prior art, the design efficiency is improved by constructing a multi-objective optimized Markov decision process, utilizing deep learning and reinforcement learning algorithms and the like.

The invention relates to the technical field of building engineering construction management, and discloses a house steel building construction prediction progress and deployment system based on deep learning. According to the system, periodic stable paragraphs and non-periodic fluctuation paragraphs are recognized by analyzing the time sequence form of construction flow data, and construction links are stripped accordingly. And performing cross mapping on the hoisting event sequence in the link and the environmental monitoring reading of the associated link to generate a link toughness spectrogram representing the construction robustness. And determining a resource demand based on the spectrum graph, and forming a multi-dimensional resource demand vector, so as to drive a graph convolutional network to construct a dynamic construction deduction graph. And carrying out message passing and neighborhood aggregation on the graph, analyzing a key bottleneck path, and iteratively generating a progress prediction and resource allocation scheme. According to the method, the construction dynamic toughness can be evaluated, and accurate positioning and dynamic optimization of resource bottlenecks are realized.

The invention discloses a mining equipment state prediction method based on digital twinning and multi-modal fusion, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: inputting a multi-modal alignment working condition window sequence into a digital twinning mechanism link, carrying out the same-window ideal response deduction, outputting an ideal multi-modal response, and constructing a twinning dynamic residual error; meanwhile, segmenting a residual structure event to generate a residual event token set; positioning a multi-modal data fragment based on the residual event token set, executing cross-modal attention alignment, and performing topology propagation aggregation in combination with a mechanical topology observation mapping table to generate a topology constraint fusion table set; and inputting the topological constraint fusion table set into the graph neural network, carrying out message passing in combination with a mechanical topological graph, outputting a component state and a complete machine state, and packaging the component state and the complete machine state into a mining equipment state prediction set. According to the method, structured analysis of the twinborn dynamic residual error is realized, and the method is used for accurately positioning an abnormal event and improving the sensitivity and timeliness of state prediction.

The invention discloses a three-dimensional geographic environment real-time intelligent deduction method based on a multi-modal large model, and relates to the technical field of computer vision. The method is used for solving the technical problem of unified modeling and real-time deduction of a dynamic object and a static environment in a three-dimensional geographical environment. The method comprises the following steps: firstly, resolving a camera pose through a motion recovery structure algorithm to generate a sparse point cloud, and separating a dynamic foreground object in a monitoring video to extract motion features; thirdly, initializing a three-dimensional Gaussian distribution set based on the sparse point cloud, extracting semantic features through a visual encoder, and mapping the semantic features to corresponding Gaussian distribution; thirdly, a topological graph structure of Gaussian distribution is constructed, motion features are used as initial excitation, and coordinate offset and appearance variation of each distribution are iteratively updated through message passing calculation; finally, Gaussian distribution attributes are dynamically updated, a continuous deduction image sequence is synthesized through micro-rasterization rendering, and high-reality real-time simulation of dynamic evolution of the three-dimensional geographical environment is achieved.

The invention relates to the technical field of market research data analysis, in particular to a market research data analysis method and system.The method comprises the steps that multi-source data is collected and cleaned through a multi-channel self-adaptive data integration technology, and a comprehensive data set is generated; constructing a graph network structure based on a graph neural network, updating node features through a message passing mechanism, generating market prediction data, and optimizing an investigation path based on a reinforcement learning algorithm; comparing the market feedback data with the prediction data through a feedback self-learning mechanism, and adjusting an investigation strategy to obtain an optimized investigation path; the performance of the market under extreme conditions is simulated through Monte Carlo simulation, the abnormal behaviors of the market are identified through dynamic outlier detection, a coping strategy is generated, and an investigation path is adjusted. The method effectively improves the accuracy of investigation data and the allocation efficiency of investigation resources, and adapts to a complex market environment.

A system and method for generating thermostable variants of a protein is disclosed. The system receives a three-dimensional structure of a target protein and identifies mutable regions, including solvent-exposed residues and loop regions. Conserved and active site residues are excluded from mutation through a fixed-position mask. A message-passing neural network (MPNN) generates mutant sequences at unmasked positions, executed under multiple temperature parameters. Design scores based on Shannon entropy and log probability are computed, and high-confidence variants are selected. Predicted structures for selected variants are evaluated using structural and sequence-based features to compute stability scores. A ranked list of thermostable variants is generated. Top candidates undergo molecular dynamics simulations to compute dynamic metrics such as RMSD, radius of gyration, SASA, and ddG, and are re-ranked accordingly. The system enables accurate, constraint-driven protein design with high structural and functional fidelity, suitable for industrial and therapeutic applications.

The invention provides a power grid fault propagation path identification method and system based on a graph neural network, and relates to the technical field of power grid fault identification, and the method comprises the steps: obtaining a power grid topological structure and fault observation data, constructing a direction perception graph neural network, achieving the one-way message passing based on potential energy difference, and obtaining a node potential energy value and embedded representation; edges are screened to generate a directed graph, and candidate paths are generated in the potential energy gradient direction; and verifying the satisfaction degree of the graph neural network evaluation path to the physical constraint through an invariant, and finally selecting the path with the minimum topological entropy. According to the invention, the accuracy and reliability of fault propagation path identification are improved.

The invention discloses an oil reservoir water content prediction method based on a graph neural network and Transform, and belongs to the technical field of oil and gas field development, and the method comprises the following steps: S1, converting an oil reservoir injection-production model at each time point into a graph structure, and constructing a connection network from a water injection well to a production well; and S2, constructing a GNT proxy model in combination with the graph neural network and Transform, training the GNT proxy model, and predicting the water content of each production well. According to the constructed GNT proxy model, an oil reservoir injection-production structure is converted into graph data through a GCN module, the information transmission strength between nodes is dynamically adjusted through an edge feature enhanced message transmission mechanism, and inter-well communication features are accurately captured; and the multi-head self-attention mechanism of the Transform encoder module is combined to process the time sequence dependence, so that the long-distance time dynamic relationship is effectively modeled, and an efficient and reliable agent model is provided for the dynamic analysis of the oil reservoir.

The invention discloses an industrial product quality risk association method based on a graph neural network, and relates to the field of industrial manufacturing, and the method comprises the steps: collecting multi-source industrial data, carrying out the preprocessing of the multi-source industrial data, obtaining a standardized data table, and constructing an industrial element heterogeneous graph based on the standardized data table; inputting the industrial element heterogeneous graph into a graph attention network basic model for node representation learning, and setting a supervised learning task taking a product batch node in the industrial element heterogeneous graph as a prediction target and taking a historical quality detection result as a supervised label; and introducing a meta-path self-learning layer in a message passing process of the graph attention network basic model. According to the method, deterministic prediction is upgraded into probabilistic inference, internal confidence degree measurement is provided for each risk prediction value, and automatic, accurate and interpretable correlation analysis and traceability positioning of the industrial quality risk are realized.

The invention discloses a discrete dynamic graph link prediction method based on a self-adaptive product space, and the method comprises the steps: obtaining a dynamic relation network, and dividing the dynamic relation network into a plurality of discrete graph snapshots; combining the Euclidean space, the hyperbolic space and the hyperspherical space to construct a product space, and determining parameters of each space; node features are projected to a product space through exponential mapping, and initial node embedding is generated; executing message passing in the product space to obtain node embedding containing geometric structure information; aggregation node embedding is carried out by using an attention mechanism, and historical node embedding is generated; the current snapshot embedding and historical information is dynamically updated through the HyperGRU; calculating a link prediction probability of any two nodes by using a Fermi-Dirac decoder, and optimizing parameters through cross entropy loss; dynamically adjusting each space parameter by using a reinforcement learning mechanism; and calculating the link prediction probability at the next moment based on the optimized product space and parameters. According to the method, the complex geometric characteristics and the time evolution law of the dynamic graph can be efficiently captured, and accurate prediction is realized.