264 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 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 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 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 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 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 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.

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 belongs to the technical field of metabonomics large model construction, and relates to a metabonomics large model construction method based on multi-dimensional database fusion, a global unique ID is allocated for metabolites, and multi-source heterogeneous data is uniformly coded into a multi-modal feature matrix, so that information loss caused by data isomerism in a traditional method is avoided; a dynamic graph generator is adopted, a multi-modal feature matrix and a prior path adjacency matrix are combined, dynamic edge weights are generated through similarity and learnable parameters to construct a metabolic relationship graph, a multi-modal graph attention mechanism is utilized to distribute adaptive weights for different modal features, graph convolution message passing is executed, and the metabolic relationship graph is obtained. Generating a node embedding matrix fused with multi-modal information and an optimized dynamic metabolism relation graph; besides, through a meta-learning method, each database is split into meta-tasks, a dynamic graph is used for training a task exclusive classifier in an inner cycle, a dynamic graph generator and meta-model parameters are jointly optimized through query set loss in an outer cycle, and the cross-database generalization ability of the model is remarkably improved.

The invention discloses a wind power plant data synchronous correction method based on view topology and a mask graph neural network, and belongs to the field of wind power plant data processing and artificial intelligence space-time sequence prediction.The method comprises the steps that firstly, historical space-time observation data of unit nodes are obtained, and a space-time feature tensor is constructed; generating a mask matrix; extracting space and time sequence waveform correlation characteristics of a plurality of unit nodes, and constructing a global multi-view adjacency matrix; inputting the spatio-temporal feature tensor and the global multi-view adjacency matrix into a spatio-temporal diagram neural network, performing blocking and shielding operation in a message passing stage of the network, extracting full-field features, and outputting an initial deduction sequence; and obtaining a boundary physical residual error, reversely compensating the boundary physical residual error to the initial deduction sequence, and generating and outputting final correction data. According to the method, test data high-fidelity error correction under high concurrency missing rate and strong noise interference is realized, and the space-time diagram calculation memory overhead of a bottom-layer deep learning framework is remarkably reduced by designing a static topology cache mechanism.

The invention relates to a UGT enzyme subtype substrate specificity prediction method and model. On the basis of a directional message passing neural network, graph structure characterization of a small molecule compound and features of specific protein binding sites of UGT enzyme are deeply fused, a bimodal prediction normal form of'molecule + protein binding sites' is designed, a deep learning model is constructed, conversion from compound center prediction to molecule-enzyme binding site comprehensive prediction is achieved, and the prediction accuracy is improved. And accurate classification prediction can be carried out on UGT enzyme substrates and non-substrates.

The invention discloses an intelligent supply and demand matching dynamic optimization method and device and a medium, and relates to the technical field. The method comprises the steps that demand side data and supply side data are collected, the demand side data comprise user interaction logs and real-time positions, and the supply side data comprise inventory data, logistics track data and production line load data; fusing demand side data and supply side data through a knowledge graph, inputting the demand side data and the supply side data into a graph neural network, encoding the demand side data and the supply side data into high-dimensional vectors, and generating a supply-demand matching preliminary screening candidate set; expanding a preliminary screening candidate set through a message passing mechanism of the graph neural network; and searching a non-dominated solution set by adopting a multi-objective optimization algorithm, and distributing index weights in real time so as to sort the expanded preliminary screening candidate sets. According to the method, real-time fusion of multi-source data is realized, a multi-target optimal solution is generated by using deep learning and operational research algorithms, and the strategy is continuously adjusted and optimized in combination with reinforcement learning.

The invention discloses a material performance prediction method and system based on deep learning, and relates to the technical field of material performance prediction, and the method comprises the steps: building a cross-level message passing network based on a double-layer graph data structure, extracting atomic-level and functional group-level feature embedding, and generating a molecular global latent variable through bidirectional cross-scale attention interaction; fusing molecular global latent variables with atomic-scale and functional group-scale features, applying physical constraints in combination with a chemical bonding rule base and a group contribution theory, and constructing a physical property prediction model; obtaining candidate molecules through autoregression generation and molecular force field verification based on a molecular global latent variable and a physical property prediction model; and screening candidate molecules by using the physical property prediction model, and outputting a final molecule set through molecular dynamics simulation and synthesis feasibility evaluation verification. Through multi-stage verification of molecular dynamics simulation and synthesis feasibility evaluation, a high-tension ring or a non-synthesized structure is effectively eliminated, so that the generated molecule has high performance and manufacturability.

The invention relates to the technical field of battery thermal safety management, and discloses an energy storage battery thermal runaway intelligent early warning method and system based on machine learning, and the method comprises the steps: obtaining an energy storage system cooling layout parameter and a sensor data flow, generating a data missing mode recognition result containing a missing time period, a missing position and a missing type; probability distribution of temperature and flow physical quantity of the missing sensor is inferred through a message passing mechanism, and a numerical value distribution inference result of the missing sensor is output; posterior state distribution is calculated through Bayesian reasoning, and a virtual-real mixed thermal state estimation result is generated; and analyzing a thermal state estimation result based on a confidence coefficient weighted multi-objective optimization algorithm, constructing a multi-objective function including weighted risk minimization and cooling energy consumption cost minimization, and outputting a cooling control instruction. The technical problem that a traditional digital twinning system is highly dependent on data integrity is solved.

The invention discloses a cascade reservoir optimal scheduling system and method based on a graph neural network and reinforcement learning, and overcomes the limitation of a traditional algorithm by constructing a cascade reservoir optimal scheduling model based on the graph neural network and reinforcement learning. Specifically, a graph neural network architecture is adopted, a message passing neural network MPNN is utilized to extract a local relationship between reservoirs, and a global relationship is captured through a self-attention mechanism of a Transform encoder, so that a complex space-time dependency relationship between cascade reservoirs is comprehensively modeled; in addition, in combination with a PPO reinforcement learning model, a scheduling strategy can be autonomously learned in a dynamic environment, and a globally optimal solution is gradually approached. Compared with a traditional method, the method has remarkable advantages in calculation efficiency, global optimality and capability of adapting to complex scheduling scenes, and an innovative solution is provided for solving the optimal scheduling problem of the cascade reservoirs.

The invention relates to an Internet of Vehicles computing power scheduling method based on a graph neural network and deep reinforcement learning, and the method comprises the steps: S1, collecting the state information of network entities in the Internet of Vehicles and the association information between the entities in real time, and constructing a dynamic time-space attribute graph; s2, inputting the dynamic space-time attribute graph into a pre-trained graph neural network encoder, and outputting a node embedding feature set containing a high-order topological relation and global graph embedding features through multi-layer message passing and feature aggregation; s3, the node embedding feature set and / or the global graph embedding feature are / is used as the state of a deep reinforcement learning agent and input to a strategy network, the computing power scheduling action at the current moment is output, and the computing power scheduling action comprises the steps of assigning an unloading target node for a to-be-processed computing task and distributing corresponding computing and communication resources; s4, dispatching actions are distributed to the corresponding network entities to be executed, environment feedback is collected and used for model updating and next round of dispatching, and the method has the advantages of improving dispatching efficiency, adaptability and system energy efficiency and the like.

The invention discloses a course recommendation method and system based on learner multi-behavior relation mining. The method comprises the following steps: firstly, constructing a learner multi-behavior heterogeneous graph; extracting a plurality of single-behavior sub-graphs from the multi-behavior heterogeneous graph of the learner; based on each single behavior sub-graph, fusing the embedded representation of the behavior into message transmission of GCN, and learning the embedded representation of the learner node and the embedded representation of the course node to obtain the embedded representation of the learner node and the embedded representation of the course node under each behavior; then, performing multi-behavior generality fusion on the embedded representations of the learner node and the course node based on the meta-path, and further performing multi-behavior generality enhancement on the embedded representations of the learner node and the course node to obtain the enhanced embedded representations of the learner node and the course node; and finally, calculating a correlation score of the learner-course pair, and recommending a course to the learner according to the correlation score. The behaviors are integrated into the embedded representation learning of the learner and the course nodes, so that the recommendation accuracy and the user satisfaction are remarkably improved.

The invention relates to a microservice anomaly detection method and system based on a causal graph converter. The microservice anomaly detection method comprises the following steps: constructing graph data based on index data during microservice system architecture and operation; inputting graph data into a graph neural network of an anomaly detection model, performing multilayer message passing and feature aggregation on neighbor node features, and extracting local dependency features of nodes; converting node features extracted by the graph neural network into a sequence form, inputting the sequence form into a converter encoder of an anomaly detection model, and learning long-distance global interaction features between nodes through a self-attention mechanism; respectively calculating a causal attention mask and a non-causal attention mask based on output features of a converter encoder by using a causal attention learning module in the anomaly detection model, and softly dividing the original image into a causal representation image and a non-causal representation image; and reading the graph-level representation corresponding to the causal representation graph through the corresponding readout layer, and obtaining a micro-service anomaly detection classification result through the first classifier. Compared with the prior art, the method has the advantages that the microservice anomaly detection accuracy can be improved, and the like.

The invention provides a sewage plant water-energy-drug collaborative optimization method and system, and the method comprises the steps: obtaining the data of a technological process and a material transfer relationship of a sewage plant, constructing a graph network structure model with a technological unit as a node and material flow as an edge, and carrying out the preprocessing, thereby obtaining dynamic coupling graph structure data; a dynamic coupling graph neural network model containing a node feature coding layer, a time sequence coding module, a space message passing layer and an attention mechanism layer is constructed based on the data, and a prediction model capable of representing the dynamic coupling relation of the process unit is obtained through historical data training; then, a multi-objective optimization function which takes the lowest ton water treatment cost as an objective and covers water quality standard reaching, energy consumption and medicament dosage constraints is constructed; and in combination with the prediction model and the optimization function, the optimal operation parameters are solved through an optimization algorithm, and a whole-plant collaborative optimization decision scheme is generated. According to the invention, the dynamic coupling GNN prediction model and the virtual element multi-domain parallel optimization technology are integrated, and intelligent operation management of the sewage treatment plant is realized.

The invention discloses a single-carrier signal detection method and device based on an expansion factor graph and Gaussian interference, relates to the field of digital signal processing, solves the problem that a traditional single-carrier frequency domain equalization technology and a message passing algorithm are invalid in an SCMA system, and is characterized in that a signal model is established according to a received signal; modeling the interference term as a Gaussian random variable distribution model to obtain Gaussian information; constructing an expansion factor graph; performing function node updating to obtain transmission information; the transmission message is transmitted between the function node and the variable node according to the expansion factor graph; performing variable node updating on the transmitted information to obtain a normalized variable node message; returning the variable node message to the function node for iterative updating until a preset number of times is reached; after the last iteration is completed, calculating and outputting the log-likelihood ratio of the bit stream according to the variable node information obtained after the iteration update; and the effects of remarkably reducing the calculation burden and remarkably improving the detection performance are achieved.

The invention discloses an asynchronous task message passing method, device and equipment based on an MCP protocol and a storage medium, is applied to a calling end registered as an MCP endpoint, and relates to the technical field of computer networks, and the method comprises the following steps: establishing a full duplex communication session with an execution end based on instance information issued by a service registration center, establishing task semantic information based on the initial task request by using an MCP protocol layer; a target task request is sent to the execution end through the session, and the execution end executes a task by using the Nacos service according to the target task request to generate an event flow. And when the network connection is abnormal, the calling end locates the position of the event successfully received last time based on the global unique event identifier of the event stream, generates a reconnection request and issues the reconnection request to the execution end, and the execution end returns the event stream in sequence according to the reconnection request. And the calling end sends the confirmation instruction to the execution end, the execution end generates the event confirmation sequence number and returns the event confirmation sequence number, and the calling end judges that task message transmission is completed after receiving the sequence number, so that the task message transmission efficiency is improved.

The invention provides a fluctuation collaborative distributed intelligent healing power distribution network method and system. The method comprises the steps that power distribution network operation data and distributed energy dynamic characteristic data are collected in real time; calculating distributed energy real-time output power and line voltage deviation based on a distributed energy dynamic output model; if the line voltage deviation exceeds a threshold value, triggering a multi-agent negotiation mechanism; each agent selects an optimal cooperation strategy through message transmission and strategy updating; and executing self-healing control operation. By collecting the dynamic characteristic data of the distributed energy in real time and establishing the accurate dynamic output model, the power output of the intermittent power supply can be accurately predicted, and the problem of insufficient consideration of the volatility of the distributed energy in the prior art is effectively improved.

The invention discloses an intelligent substation state evaluation method and system based on digital twinning, and relates to the technical field of state evaluation, and the method comprises the steps: firstly constructing a heterogeneous knowledge graph capable of precisely mapping a physical entity logic relation based on the ontology definition, asset information and topological data of a substation; then, massive real-time fast-change data streams are introduced into the atlas framework, and node states are dynamically mapped by using a time sequence feature coding technology; and a complex message passing and aggregation mechanism is carried out in the graph structure through the relation perception graph neural network, and node embedding representation with local personality and global generality is generated. And finally, synchronously finishing equipment risk prediction at a micro level and total-station comprehensive evaluation at a macro level based on the high-dimensional representation, and carrying out path backtracking and cause analysis on a high-risk state based on map relevance. Therefore, the problem of equipment health and system operation state separation can be solved, and intelligent substation panoramic state perception and risk early warning with interpretability can be realized.