484 results about "Graph structured data" patented technology

The invention relates to the technical field of red tide anomaly detection, in particular to an ocean red tide anomaly detection method and system fusing multi-source remote sensing and a graph neural network. The method comprises the following steps: acquiring remote sensing image data, unmanned aerial vehicle image data and monitoring data of a monitoring point; performing data preprocessing on the acquired remote sensing image data and unmanned aerial vehicle image data; performing feature extraction and feature fusion on the remote sensing image and the unmanned aerial vehicle image to obtain remote sensing feature data; constructing a space-time diagram structure based on the monitoring data of the monitoring points to obtain diagram structure data; based on a cross-modal comparison self-supervised learning mechanism, carrying out consistency representation learning on a remote sensing feature mode and a graph structure feature mode; by introducing multi-source heterogeneous data and fusing a graph neural network modeling means, the limitation of a single data driving method in the aspects of coarse red tide recognition granularity, low space-time precision and the like is effectively broken through, and the meticulous property and global perception ability of red tide feature modeling are remarkably improved.

The invention discloses a slope deformation monitoring and dynamic early warning method and system based on multi-sensor data, and relates to the technical field of slope monitoring, and the method comprises the steps: collecting multi-source sensor data by using pre-deployed multi-class sensors, constructing graph structure data according to the sensor distribution and the pre-processed multi-source sensor data, and carrying out the graph structure data; a graph convolutional network is used for modeling, and a slope deformation monitoring model is constructed; introducing a clustering federation learning strategy to carry out joint training on the slope deformation monitoring models of the plurality of sites, and carrying out risk grade division by using the trained slope deformation monitoring models; key influence factors of landslide disasters are extracted, an improved firefly algorithm is introduced to dynamically optimize an early warning threshold value, the optimized early warning threshold value and the current risk level are used for judgment, and early warning information is generated. According to the invention, the reliability of monitoring and the timeliness of early warning are improved through multi-source data fusion and intelligent analysis, and the crossing of slope deformation monitoring from single-point static state to networked intelligence is realized.

The invention relates to the technical field of fault detection, and particularly discloses a prediction reconstruction framework causal perception space-time network for explaining anomaly monitoring in a complex industrial process, comprising the following steps: S01, constructing graph data E (V) and a causal graph; automatically adjusting the fusion proportion of the time-frequency characteristics according to the data characteristics so as to ensure that the model can comprehensively capture the information of the data in the time domain and the frequency domain; secondly, introducing a residual image attention network (RGAT), and converting the image data E (V) into image structure data G (S (V), E (V)); and S03, reconstructing a prediction error by adopting a variational automatic encoder (VAE), learning an error mode of normal data, providing an anomaly judgment AD (V) for anomaly detection, analyzing a causal relationship between data in combination with a causal graph, and positioning an anomaly reason according to an anomaly score, so as to form a prediction result. The network solves the problem that a traditional monitoring network is high in false alarm rate.

The invention discloses an intelligent management system and method for knowledge graph quality evaluation and self-repairing, belongs to the technical field of knowledge graphs, and aims to solve the problems that in traditional knowledge graph management, manual auditing efficiency is low, an effective automatic repairing means is lacked, and data complexity and real-time changes are difficult to deal with. The system firstly collects multi-source heterogeneous data in a target field, cleans the data through a deep learning noise recognition model, extracts entities and relationships by using a natural language processing technology, and adds metadata to convert the entities and relationships into graph structure data; then, a graph framework is defined based on the ontology, entity semantic alignment is achieved in combination with a graph neural network, and a knowledge graph is constructed by complementing implicit relations with the help of a pre-training language model. Then, the quality of the atlas is quantitatively evaluated through a four-layer quality evaluation system, meanwhile, a repair scheme is generated based on vulnerability feature extraction, knowledge base matching and decision fusion, and intelligent self-repair is achieved; the map can be monitored in real time and evaluated regularly, a repair strategy and a knowledge base are optimized through reinforcement learning, it is ensured that the map is kept accurate and time-efficient for a long time, and the practical value is improved.

The invention provides a network traffic data security assessment method and system based on deep learning. The method comprises the following steps: converting original network traffic data into a graph structure data set comprising a topological structure, node attributes and time sequence behaviors; in the process, the time-space fusion input tensor is formed through the association strength between adjacent matrix and Laplacian matrix coding network entities and the fusion of time sequence characteristics extracted by time window slices. Compared with traditional flow analysis which only pays attention to a single protocol or a rate threshold value, the method achieves global relevance expression of network behaviors through graph structure modeling. Through graph structure modeling, multi-dimensional feature fusion and probabilistic risk assessment, the method can adapt to dynamic change of network topology and continuous evolution of an attack mode, so that a final assessment result is more accurate.

The invention relates to the technical field of unmanned aerial vehicle anti-collision path planning, in particular to an unmanned aerial vehicle ad hoc network anti-collision path planning method based on a graph neural network, and the method comprises the steps: obtaining the topographic data of a task airspace, recording the initial positions, target positions and motion constraint conditions of all unmanned aerial vehicles, and constructing unmanned aerial vehicle graph structure data; according to the unmanned aerial vehicle graph structure data, taking each unmanned aerial vehicle as a unit node, and representing a potential collision relationship between two adjacent unmanned aerial vehicles by a side between the nodes; initializing features of the nodes and the edges, and completing training of an unmanned aerial vehicle graph neural network model based on the input features of the nodes and the edges as input of the preset unmanned aerial vehicle graph neural network model; and performing collision risk prediction based on the unmanned aerial vehicle graph neural network model, and generating and outputting an optimal flight path. According to the invention, through the graph neural network and multi-objective optimization, intelligent cooperative flight of the unmanned aerial vehicle group is realized, the collision risk and energy consumption are significantly reduced, and the network connectivity and task efficiency are ensured at the same time.

The invention discloses an unmanned aerial vehicle charging path optimization method based on deep learning, and the method comprises the following steps: S1, collecting the flight state data of an unmanned aerial vehicle, and constructing a state input sequence; s2, normalizing the state input sequence to form modeling input data; s3, inputting the modeling input data into the improved closed continuous time state evolution model to generate a state trajectory prediction result; s4, generating a path adjustment instruction according to a prediction result, and constructing path diagram structure data; s5, inputting the path graph structure data into the improved graph pointer network to generate an optimal path instruction; s6, fusing the optimal path instruction and modeling input data, iteratively updating and re-predicting a state trajectory; s7, generating a charging path execution scheme; and S8, forming an unmanned aerial vehicle path optimization control instruction sequence. According to the method, the path planning and charging scheduling efficiency of the unmanned aerial vehicle in a complex environment is improved, and the method has relatively high intellectualization and adaptability.

The invention discloses a motor fault diagnosis method and system based on color image fusion symmetric point mode, and the method comprises the steps: converting a vibration signal and an electromagnetic signal of a motor into symmetric point mode images, and generating a color signal image fusing feature information; respectively abstracting the color signal images fused with the feature information into nodes and edges in a high-dimensional semantic space so as to construct graph structure data; and performing diagnosis classification on the graph structure data of the vibration signals and the electromagnetic signals by using respective capsule graph network models, and fusing diagnosis classification results of the vibration signals and the electromagnetic signals through a voting mechanism to obtain a final diagnosis classification result. According to the method, multi-channel time domain signals are converted into image expressions with dense information and consistent geometry, and unified feature modeling is carried out on the images based on a depth map structure network with topology perception capability, so that motor fault diagnosis with high diagnosis precision and strong robustness is realized.

The invention provides a traffic flow prediction method, system and device and a medium, and belongs to the technical field of intelligent traffic. The method comprises the steps of obtaining multi-source heterogeneous original traffic flow time sequence data, performing preprocessing to generate time sequence tensor data, and constructing graph structure data based on road network topology and traffic flow correlation; inputting the time sequence tensor data and the graph structure data into a pre-constructed time-space diagram neural network model, processing the graph structure data through a graph convolution module of the time-space diagram neural network model to extract spatial features, and processing the time sequence tensor data through a time sequence modeling module of the time-space diagram neural network model to extract time features, fusing the spatial features and the time features to form joint spatial-temporal features; inputting the joint spatial-temporal characteristics into a prediction layer of a spatial-temporal diagram neural network model for processing, and outputting predicted value data of the traffic flow in a future period as prediction result data; and feeding back prediction result data to the traffic control and management system to drive the traffic control and management system to execute control operation.

The invention relates to the technical field of graph neural network and causal representation learning, in particular to a graph neural network method for separating time-varying and time-invariant causal features, which comprises the following steps: acquiring graph structure data at a plurality of moments, and establishing a dynamic graph sequence; coding the dynamic graph sequence by adopting a time sequence graph neural network to obtain a node representation matrix; respectively adopting a time-varying attention head, a time-invariant attention head and a shortcut attention head to carry out attention score calculation, mask construction and sub-graph decomposition processing on the node representation matrix to obtain a time-varying cause sub-graph, a time-invariant cause sub-graph and a shortcut feature sub-graph; performing independent graph neural network coding and dynamic gating fusion to obtain fusion features; determining a current predicted value according to the fusion feature; according to the method, the causal discovery accuracy, robustness and interpretability can be improved.

The invention provides a brain age estimation method based on a dynamic fuzzy learnable brain network, and belongs to the technical field of medical image processing and artificial intelligence. According to the technical scheme, the method comprises the following steps that S1, brain nuclear magnetic resonance imaging of a subject is collected, and preprocessing and data division are carried out; s2, constructing graph structure data, and performing feature extraction and position information embedding on the data; s3, constructing a dynamic fuzzy learnable brain network model comprising a main branch and a local branch, and respectively extracting global and local connection features; s4, introducing a dynamic fuzzy multi-head self-attention module into the main branch to realize effective modeling of global features; s5, a local branch dynamically models a dependency relationship between channels through a convolution filter and a learnable graph attention module; s6, after the features of the main branches and the local branches are fused, brain age prediction is carried out through a multi-layer perceptron. According to the method, the modeling capability of the brain function connection mode is improved, and the brain age prediction task can be more effectively completed.

The invention belongs to the technical field of engineering simulation data processing, and particularly discloses a finite element grid graph structure construction method and system, a terminal and a medium. Comprising the following steps: analyzing original full-amount grid data exported by finite element simulation, segmenting unstructured grid data into a grid vertex coordinate set and a grid unit mark number set, and constructing a graph edge topological structure corresponding to a grid based on a unit mark number relationship; on the basis, loading physical field data and adopting a tolerance-based coordinate matching algorithm to realize accurate mapping of physical field labels and material attributes with grid nodes; and generating standardized finite element grid graph data which can be directly used for graph neural network processing. By means of the method, high-consistency and high-physical-reliability conversion from the finite element simulation data to the graph structure data is achieved, and the physical field modeling and simulation acceleration capacity based on graph learning is improved.

The invention discloses a building electrical safety protection method, which comprises the following steps of performing real-time data acquisition on a building electrical system to obtain multi-modal time sequence data; constructing the multi-modal time sequence data into graph structure data with a node-edge topological relation, and encoding the multi-modal original observation data corresponding to each node into a multi-modal initial feature vector of the node; graph neural network feature extraction is carried out on the graph structure data, and a global feature vector sequence used for representing the operation state of the electrical system is obtained in combination with an attention mechanism; inputting the global feature vector sequence into a pre-constructed time sequence prediction model to perform operation state prediction, and judging whether potential abnormality exists or not; and when the residual error exceeds a preset threshold value, fault backtracking positioning is carried out on the key node according to the attention weight, and an electrical fault point is generated in combination with node characteristics. According to the invention, the fault response speed and the emergency disposal efficiency can be effectively improved, and the safety accident rate is reduced.

The invention discloses an urban territorial space multi-element coupling intelligent optimization method and system, and the method comprises the steps: quantifying the functional partition transition probability of an ecological protection region, a basic farmland and an urban development region based on a Markov model, and simulating the spatial distribution of functional partitions through a future land utilization simulation model, constructing a first multi-target optimization model in combination with resistance surface analysis of the minimum cumulative resistance model; collecting urban POI data, screening residential, commercial and public service types as optimization objects, dividing the optimization objects by utilizing clustering analysis, extracting core points, and constructing a second multi-target optimization model; and constructing graph structure data based on the road network, defining node and edge features, training the graph structure of the road network by using a graph neural network, extracting the features, and constructing a third multi-target optimization model to make a multi-target optimization decision of the road network.

The invention relates to a wind power cluster short-term power prediction method and device of a space-time diagram neural network fused with physical information and computer equipment, and the method comprises the steps: obtaining related information data of each wind power plant in a wind power cluster, and carrying out the preprocessing; forming a physical prior data set through an engineering analysis model fusing the wake flow analysis model and the blocking effect model; taking each wind power plant as a node of the graph, constructing graph structure data for predicting the power of the wind power plant, and forming a dynamic adjacent matrix; constructing a space-time diagram neural network WB-STGNN model architecture comprising a diagram convolutional neural network module, a gating time convolutional network and a multi-layer perceptron; the method comprises the following steps: pre-training by using a physical prior data set, and then performing formal training based on historical power data and a dynamic adjacency matrix to obtain a space-time diagram neural network WB-STGNN model; inputting the wind speed of the prediction day, and predicting the active power of the whole wind power cluster in 24 hours of the prediction day. By adopting the method, the precision and efficiency of wind power cluster power prediction can be effectively improved.

The invention discloses a drainage basin real-time flood control scheduling risk dynamic early warning and emergency scheme making method, which comprises the steps of obtaining a real-time total element information tensor and an engineering flood control design parameter set, and calculating to obtain a real-time hierarchical risk entropy time sequence front section based on safety margin distribution, and inputting real-time data and pre-constructed entropy flux Markov graph structure data into the trained entropy perception double-flow prediction model for reasoning to obtain a future risk prediction set. And a dynamic risk early warning result set is generated in combination with a preset entropy level threshold and a future failure chain probability, and an emergency scheduling scheme set is generated on the basis of meeting safety margin constraints and entropy reduction targets. According to the invention, dynamic early warning and decision making of strong physical-risk coupling are realized.

The invention discloses a real-time monitoring and closed-loop regulation and control method and device for construction quality of an asphalt pavement, and relates to the technical field of intelligent monitoring of road construction. The method comprises the steps of collecting multi-modal data in a construction process and performing space-time alignment to generate a space-time aligned multi-modal data set; constructing graph structure data of the construction area based on the set; extracting a node embedding vector by using a graph neural network; synchronously outputting the compactness, the temperature uniformity index and the flatness index through a multi-task prediction model based on the vector; identifying a construction track abnormal area based on the road roller track data; and generating a construction quality digital twinborn model by integrating the information, and performing feedback regulation on an automatic control system of the road roller based on the model. Through multi-modal data fusion, graph structure modeling and multi-task collaborative prediction, real-time monitoring and dynamic regulation and control of the construction quality of the asphalt pavement are realized, the prediction precision can be effectively improved, and the monitoring efficiency and accuracy are remarkably improved.

The invention discloses a low-altitude target recognition method based on an attention mechanism, and the method comprises the steps: collecting low-altitude image data, and carrying out the preprocessing, and obtaining standardized low-altitude image data; executing a maximum flow minimum cut algorithm to generate a foreground mask; constructing a space attention mechanism, and generating a space attention weight and a space weighted feature vector; performing channel statistics on the spatial weighted feature vector to generate a channel weight and a target feature vector; constructing a target association matrix and graph structure data to obtain an association enhanced target feature vector; and inputting the associated enhanced target feature vector into an improved Kohonen neural network model, determining a low-altitude target category, and outputting an identification result. According to the invention, by introducing the maximum flow minimum cut algorithm and improving the Kohonen neural network model, stable and accurate identification and classification of the flight target in the complex low-altitude environment are realized.

The embodiment of the invention discloses an assembly type building construction management system based on BIM and GNN models, the system comprises a data collection module, a data processing module, a data application module and a data visualization module, the data collection module is used for collecting multi-source heterogeneous data of the whole period of building construction, the multi-source heterogeneous data comprises a building information model, a data processing module, a data application module and a data visualization module, and the data processing module is used for processing the multi-source heterogeneous data in the whole period of building construction. BIM (Building Information Modeling) data, sensor data, video stream data and external data; the data processing module is used for processing the multi-source heterogeneous data to obtain a data processing result which comprises a fused data set, standardized graph structure data and structured decision suggestions; the data application module is used for adjusting resource allocation according to the data processing result and generating an optimization instruction which comprises a structured instruction set and a structured scheme set; and the data visualization module is used for converting the optimization instruction and the structured decision suggestion and displaying a conversion result in a visual interface. According to the invention, the integration level, the intelligence and the cost-benefit balance degree of fabricated building construction management can be improved.

The invention provides a new energy station high-voltage voltage transformer error evaluation method and system, and the method comprises the steps: collecting the voltage phasors of all measurement nodes in a preset time period at a transformer substation and a new energy station at the same time, obtaining the equipment parameters of a voltage transformer and the line parameters between the measurement nodes, and constructing a measurement data set; the method comprises the following steps: constructing a corresponding adjacency matrix and a node characteristic matrix by extracting line impedance and node working condition characteristics based on a power system line topological structure and data in a measurement data set, and obtaining a time sequence diagram structure data set according to the adjacency matrix and the node characteristic matrix; constructing a graph neural network, and training the graph neural network based on the time sequence graph structure data set; and inputting the voltage phasor of each measurement node, and based on the output result of the trained graph neural network, judging the state of the voltage transformer through a preset threshold value. According to the scheme, online evaluation of the error state of the voltage transformer can be realized, and the accuracy and interpretability of error evaluation are improved.

The invention discloses an unmanned aerial vehicle wind power blade defect dynamic detection method based on AI vision, and relates to the technical field of JSLYMC, and the method comprises the following steps: S1, collecting an original dynamic image sequence; s2, generating an aligned image sequence; s3, generating a standardized blade surface image sequence; s4, constructing a blade topological structure diagram; s5, inputting the graph structure data into the improved TransGAT model, and outputting a defect candidate set; s6, inputting the defect candidate set into the FairMOT model, and outputting a defect space-time trajectory; and S7, generating a structured detection report. The method overcomes the limitations of dependence on manual inspection, poor dynamic adaptability and insufficient identification precision in a traditional wind power blade defect detection method, and provides an efficient and accurate solution for unmanned aerial vehicle wind power blade automatic inspection and intelligent maintenance decision.

The invention discloses a distributed photovoltaic power prediction method and device based on dynamic spatial correlation, and relates to the field of distributed photovoltaic technology, and the method can reflect the real influence relation between photovoltaic power stations at different moments in real time through constructing dynamic graph structure data which can reflect the spatial relation between the photovoltaic power stations more comprehensively and accurately. And the prediction result of the distributed photovoltaic power is improved. The method comprises the following steps: acquiring an influence factor feature set of distributed photovoltaic power prediction; by taking the distributed photovoltaic power station as a node, constructing dynamic graph structure data of the photovoltaic power station according to the influence factor feature set and the spatial weight matrix fused with the meteorological condition information; performing spatial correlation extraction on the dynamic graph structure data by using a pre-trained first network to obtain spatial correlation characteristics of the distributed photovoltaic power station; and performing time domain correlation extraction on the spatial correlation characteristics by using a pre-trained second network to obtain a point prediction result of the distributed photovoltaic power.

The invention relates to the crossing field of computers and geology, in particular to a three-dimensional metallogenic prediction method based on a 3DGCN-CNN model, and the method comprises the steps: S1, constructing a three-dimensional geological database; s2, constructing a three-dimensional geologic model and a three-dimensional ore body model; s3, constructing a three-dimensional metallogenic prediction information set; s4, a 3DGCN-CNN model is constructed; s5, constructing a data set of the 3DCNN and graph structure data of the 3DGCN; s6, inputting the training set into the model for training; s7, inputting the data of the region to be predicted into the trained model for prediction, and obtaining a prediction result; according to the method, the spatial and local features are effectively fused through the 3DGCN-CNN model, quantitative analysis on the three-dimensional geologic model is avoided, and the prediction capability on the metallogenic potential is improved.

The invention provides an abnormal user detection method and system based on topology awareness and hub node guidance. The method comprises the steps of obtaining graph structure data; fusing the topological relation in the graph structure data and the initial features of the nodes by using a topological adaptive label evolution mechanism, and predicting and optimizing labels of unlabeled nodes to generate an optimized label distribution matrix; based on the node initial features, taking the optimized label distribution matrix as a supervision signal, and generating semantic hub node features through a double-layer multi-scale contrast learning mechanism; dynamically screening neighbors of each target node by adopting a strategy gradient driven neighbor aggregation mechanism based on semantic hub node features; performing message passing through a multilayer graph neural network based on the screened neighbor set, and fusing node representations of different layers by using an attention mechanism to obtain a final node representation; and inputting the final node representation into a classifier, and outputting an abnormal user detection result.

The invention discloses a graph neural network performance optimization method and device, a storage medium and computer equipment, and the method comprises the steps: carrying out the linear interpolation of graph node features and labels in original graph structure data, and generating enhanced graph structure data; constructing a teacher graph neural network as a teacher model, and obtaining a soft label predicted by the teacher model for the enhanced graph structure data; and constructing a student graph neural network as a student model, migrating a soft label into the student model by using a knowledge distillation technology, training the student model based on enhanced graph structure data, and simultaneously guiding the student model by using knowledge distillation loss driven by cross entropy loss and the knowledge distillation technology until an optimal student model is trained. By performing linear interpolation on the node features and the labels, while an original graph topological structure is reserved, diversified training samples are generated, and the prediction performance of the student graph neural network can be improved.

The invention relates to a power distribution network safety analysis method, device and equipment based on a graph convolutional neural network, a storage medium and a program product, and relates to the technical field of artificial intelligence. The accuracy and reliability of the analysis result can be improved. The method comprises the following steps: constructing an adjacent matrix of the power distribution network according to structural feature information of the power distribution network, and constructing a node feature matrix of the power distribution network according to multi-source state information of the power distribution network; based on the adjacency matrix and the node feature matrix, generating a graph structured data set of the power distribution network in different operation states, and obtaining a training set, a verification set and a test set according to the graph structured data set; constructing a to-be-trained initial network model according to the target loss function and the target hyper-parameter; and training the initial network model by using the training set and the verification set through a deep learning algorithm and updating the model parameters of the initial network model until the performance index of the updated network model on the test set meets a threshold condition, thereby obtaining a trained graph convolutional neural network model.

The disclosure provides a graph neural network training method, a training system and an abnormal account identification method. The graph neural network training method comprises: obtaining initial graph structure data corresponding to a terminal device; the initial graph structure data obtained by a plurality of distributed training terminals respectively is derived from the same sample graph structure data; the following graph structure data processing stage and graph neural network training stage are executed cyclically until a target neural network meeting the training requirement is obtained: determining a processing time of the current execution graph structure data processing stage according to historical execution data of the historical execution graph structure data processing stage and the historical execution graph neural network training stage; performing graph structure data processing on the initial graph structure data in the graph structure data processing stage according to the processing time to generate target graph structure data; the graph structure data processing comprises data sampling processing and feature extraction processing; and training the target neural network based on the target graph structure data in the graph neural network training stage.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and graph neural network combined earth surface deformation space-time prediction method, which is suitable for space-time prediction of earth surface deformation. Based on DS-InSAR, obtaining radar sight line-to-time sequence earth surface deformation of the high-coherence measuring points; calculating the geographic distance between the measuring points and the mutual information between the deformation sequences corresponding to the points; the method comprises the following steps: judging connectivity between high-coherence measuring points according to a geographic distance between the measuring points and mutual information to generate an adjacent matrix, and organizing an original InSAR deformation monitoring result into graph structure data; and inputting the graph structure data into the LSTM-GCN model to predict and obtain the surface deformation of all the high-coherence measuring points in the whole area. The method is high in prediction precision and wide in application range, and can be effectively applied to the fields of space-time prediction of deformation of earth surfaces and buildings (structures) caused by mine closure, underground resource development, natural disasters and the like.

The invention discloses an impact force time history response prediction method and system based on AEGCN-Dynamiformer, a storage medium and computer equipment, and the prediction method comprises the following steps: S1, constructing a database with component parameters, the component parameters comprising geometric parameters, material parameters, boundary load conditions and impact parameters; s2, converting the component parameters in the database into graph structure data and performing normalization processing to form a data set for training; s3, a network neural network is adopted to extract structure topological features, and a feature fusion module is adopted to fuse the extracted structure topological features and the time sequence coding features so as to construct an AEGCN-Dynamiformer deep neural network; s4, carrying out training optimization on the AEGCN-Dynamiformer deep neural network by adopting the data set, and carrying out training optimization on the AEGCN-Dynamiformer deep neural network; and S5, inputting parameters of a to-be-predicted structure into the trained and optimized AEGCN-Dynamiformer deep neural network, so as to generate an impact force time history curve under a corresponding impact working condition. According to the method, the reliability analysis and parameter optimization research efficiency can be remarkably improved.

The invention discloses an adaptive graph diagnosis method and system fusing structure perception and dynamic propagation. The method comprises the following steps: collecting a vibration acceleration signal in a typical fault state; dividing the vibration acceleration signal data into a plurality of time windows, taking sampling data of each time window as node features of a graph, and constructing graph structure data; based on the graph structure data, utilizing a structure perception similarity modeling mechanism to measure the structure similarity between the nodes, and dynamically adjusting a feature aggregation strategy; performing multi-layer feature propagation and fusion on the feature aggregation strategy by using a local adaptive residual feature propagation mechanism; and inputting the fused node representation into a classification module, and outputting a corresponding fault type to realize intelligent fault diagnosis. While the calculation efficiency is maintained, the recognition capability of weak fault features in complex industrial signals is remarkably improved. Effective representation, feature enhancement and fault type high-precision classification of the multi-source time sequence signals are realized.