676 results about "Graph Node" patented technology

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

The present application discloses a database query method and apparatus, an electronic device, and a non-volatile storage medium. The method comprises: determining a knowledge graph corresponding to a database to be queried, wherein the knowledge graph is used for representing a logical structure and an association relationship of data in said database; determining similarity scores between user question text and graph nodes in the knowledge graph, and determining a target node from among the graph nodes of the knowledge graph on the basis of the similarity scores, wherein the similarity scores are used for representing the degree of association between the graph nodes and the user question text; and on the basis of the target node, generating database schema information corresponding to said database, and using a large language model to generate, on the basis of the database schema information, a structured query language statement corresponding to the user question text.

The invention relates to the technical field of intelligent agriculture, and particularly discloses an orchard intelligent water and fertilizer integration-based edge end fruit tree fertilizer demand prediction method, which comprises the following steps of: constructing each fruit tree as a graph node, and fusing canopy spatial characteristics extracted by unmanned aerial vehicle multispectral remote sensing and nutrient time sequence characteristics monitored by a soil sensor; space influence weights among nodes are calculated according to the terrain, the soil texture and the space distance, and a fruit tree individual relation graph is established; then, through a space-time diagram convolutional network, aggregating space neighborhood features and capturing time dynamic changes, and outputting target state features subjected to multi-scale enhancement; when the canopy conflicts with the soil characteristic indication, combined diagnosis is carried out based on a neighborhood consistency index and an adjacent node state, systematic nutrient stress and local non-nutrient factors are distinguished, and final fertilizer demand prediction is generated after the conflict is eliminated; a digital fertilization prescription map is generated through spatial interpolation, soil characteristics and slope correction, and the water and fertilizer integrated equipment is driven to execute precise operation.

The invention discloses an agricultural product supply chain abnormal event tracing method and system based on a knowledge graph, and relates to the technical field of agricultural product supply chain management, and the method comprises the steps: constructing a supply chain knowledge graph, and enabling graph nodes to cover agricultural product batches, production subjects, processing equipment, transport vehicles, storage warehouses and other entities; when an anomaly is detected, performing reverse traversal from an abnormal node based on a graph neural network to determine a candidate propagation path; calculating an abnormal source probability score by integrating the historical abnormal record, the time window goodness of fit and the association strength, and outputting a responsibility subject sequence; the method supports hypothetical reasoning to predict the downstream influence range, and solves the problems that a traditional traceability scheme is difficult to efficiently associate and analyze cross-link data and lacks abnormal path intelligent reasoning ability.

The invention discloses an electromagnetic field simulation grid adaptive generation method based on a neural network. The method comprises the following steps: performing mesh generation on a semiconductor device simulation model to obtain original generation data, and constructing node features; an undirected graph is constructed, graph nodes of the undirected graph adopt node features of grid points, and connecting edges adopt Euclidean distances between the grid points and adjacent grid points; inputting the undirected graph into a double-branch neural network, and outputting a predicted node position; and correcting each node of the original subdivision data by using the predicted node position obtained by the double-branch neural network to form new subdivision data for electromagnetic field simulation. According to the method, physical field information can be fused to efficiently adjust the node density, and the geometric boundary and topological structure characteristics of the device can be strictly kept.

The invention provides an elevator multi-mode crowd feature perception and intelligent advertisement putting method and system, and relates to the technical field of intelligent advertisement putting, and the method comprises the steps: collecting multi-mode perception data in an elevator through an edge computing terminal; performing feature decoupling on the data, performing cross-modal semantic alignment, establishing a directed association relationship between modals, and constructing a scene feature map; calculating the topology importance degree of map nodes, screening feature nodes, and extracting context information for semantic coding; mapping the scene semantic code and the advertisement audience semantic code to a two-dimensional coordinate system to construct a semantic matching graph, and extracting an optimal matching path to form a candidate set; predicting a scene evolution trend based on the scene characteristic spectrum evolution trajectory, and calculating an advertisement adaptive score to generate a playing sequence; putting and collecting user interaction data feedback according to the sequence to update the graph structure. According to the invention, accurate crowd feature recognition and advertisement dynamic matching are realized, and the advertisement putting efficiency and the user experience are improved.

The invention discloses a small sample remote sensing image classification method based on hierarchical spatial structure learning. The method comprises the following steps: firstly, extracting multi-scale features of a remote sensing image by using a ViT (Visual Transform) model, and capturing rich semantic information and spatial structure relationships in the image; secondly, constructing a graph structure based on spatial adjacency and attention weight to model a structured relationship between samples, and encoding graph node features through a graph convolutional network (GCN) so as to enhance the discrimination ability of the features in a structural semantic space; thirdly, a residual enhancement mechanism is introduced to fuse global semantic information, and the discrimination capability of graph embedding is improved; then, based on the structural similarity between the support set and the query set, performing classification decision, and realizing accurate classification under a small sample condition; and finally, carrying out joint optimization on the whole model by adopting a training strategy of a small sample meta learning task and a supervision loss function.

The invention provides an intelligent data consanguinity tracking and visualization method based on graph calculation, and the method comprises the steps: carrying out the data structure analysis and metadata extraction of original data assets, so as to generate a standardized data asset package; performing graph node attribute definition on the data entities in the standardized data asset package, and performing graph edge attribute definition on the association relationship between the data entities to generate a first data blood relationship model; performing blood relationship path mining on graph nodes and graph edges in the first data blood relationship graph model to obtain a basic blood relationship path set, and performing quantitative calculation and feature labeling on path association strength in the basic blood relationship path set to generate a second data blood relationship graph model; and performing visual rule mapping on graph node attributes and graph edge attributes in the second data blood relationship graph model to construct a standardized visual data set and generate a data blood relationship visual interaction interface according to the standardized visual data set.

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 a three-dimensional physical field real-time prediction method and system based on geometric deep learning and physical constraint, and relates to the technical field of computer-aided engineering and artificial intelligence. The method comprises the following steps: directly extracting native boundary representation data (B-Rep) of a three-dimensional model from a computer aided design system; constructing a heterogeneous dual graph taking a parameterized curved surface as a graph node, skipping finite element grid division, and aggregating local and global topological features by using a graph neural network; in combination with a physical information driving mechanism, a partial differential equation (PDE) residual error is introduced as a loss function for constraint training, and generalization prediction of a novel geometric structure is realized; and finally, the physical field state quantity is predicted through direct regression and is rendered in real time. An incremental reasoning mechanism based on a local topology subgraph is adopted, millisecond-level physical field real-time feedback under design modification is achieved, and the method is suitable for scheme rapid screening and trend prediction in the initial stage of design.

The invention discloses a multi-terminal collaborative modular power operation and maintenance method and system based on digital twinning, and the method comprises the following steps: constructing a global power topological graph of a power energy site, mapping power equipment, sensors and connecting lines into graph nodes and graph edges, and binding the static attributes of the equipment with the topological structure; and acquiring real-time operation data of the power equipment, and distributing a unified time label for the multi-source heterogeneous data. According to the method, a unified twinborn model of cross-voltage class, cross-region and cross-equipment type is established in a digital twinborn platform, and operation data of different equipment such as photovoltaic equipment, battery energy storage equipment, an inverter, a protection device, an electric energy meter and temperature control equipment are expressed in a unified data semantic mode; according to the invention, the system can still automatically establish a consistent device portrait under the conditions of a large number of multi-terminal devices and diversified communication protocols, and the multi-terminal cooperative computing capability is significantly improved.

The invention provides a drainage basin water quality abnormity tracing method and system based on time sequence fluctuation characteristics, and the method comprises the steps: taking each monitoring point in a drainage basin as a graph node, constructing a directed edge connected with the graph node according to the spatial relation between the monitoring points and the water flow direction, and forming a drainage basin graph network; acquiring historical monitoring data, extracting amplitude variation features and fluctuation features from the historical monitoring data, and calculating a time sequence fluctuation feature vector; traversing graph nodes with directed edge connection, constructing index pairs corresponding to the graph nodes, analyzing the correlation of the index pairs under different lag dimensions, and calculating response lag dimension coefficients; according to the time sequence fluctuation feature vector and the response lag dimension coefficient, calculating an abnormal correlation coefficient between graph nodes, and constructing an abnormal transmission path; and according to the abnormal transmission path, dividing a pollution troubleshooting area for a worker to carry out troubleshooting treatment. The water quality abnormal fluctuation excitation source position can be accurately positioned, and the water quality abnormal analysis efficiency and the abnormal source positioning precision are improved.

The invention relates to the field of data retrieval, and particularly provides a dynamic retrieval enhancement generation method and system based on a knowledge graph, a terminal and a medium, and the method comprises the following steps: extracting a structured triple from multi-source heterogeneous data through a large language model, and constructing a global knowledge graph by means of an entity linking technology; integrating a real-time data stream interface, and dynamically updating graph nodes and attributes based on an event-driven mechanism; adopting a RotatE model to respectively encode the entity and the relationship to a complex number space, and fusing to generate a mixed vector to construct an efficient index; after user query is received, topic nodes are positioned through semantic analysis, related entities are retrieved through mixed indexes, and multi-hop reasoning is executed along a relation path to generate reasoning sub-graphs and extended contexts; and finally, generating structured text answers by using a large language model, and adaptively outputting multi-modal results such as texts, charts and the like according to user requirements. According to the method, the knowledge updating timeliness, the complex query reasoning capability and the retrieval precision are effectively improved.

The invention discloses an underground water pollution diffusion model construction method based on multi-modal data fusion, and the method comprises the steps: fusing the dynamic time sequence characteristics of a monitoring well and the static space attributes of a geological field, and generating an initial state vector with rich information for a graph node; based on the instantaneous water level difference between the nodes and the equivalent permeability coefficient, a dynamic graph topological structure which evolves along with time and represents the hydraulic connection relation is constructed; deducing a future state through numerical integration by using a graph neural network model with a node state derivative as output; and constructing a composite loss function containing a data fitting item and a physical law residual item, and training the model through back propagation. According to the method, the dynamic graph of physical driving is constructed and the physical equation constraint is introduced, so that the model prediction has the data driving precision and the reliability of the physical mechanism, and the generalization ability and the prediction precision of the model under the complex hydrogeological condition are improved.

The invention discloses an image semantic segmentation method based on a graph convolutional network, and the method comprises the following steps: obtaining original image data, and carrying out the preprocessing; performing feature extraction to generate a multi-scale feature map; dividing regions based on the multi-scale feature graph, defining region units as graph nodes, and constructing a node feature set; constructing an initial adjacency matrix according to the spatial proximity relation and the feature similarity of the region units; carrying out self-adaptive updating on the initial adjacent matrix, and correcting an edge weight based on semantic similarity and spatial connectivity between nodes; inputting the adaptive adjacency matrix and the node feature set into a graph convolution network, and performing graph convolution operation and feature aggregation; fusing the node feature representation with the multi-scale feature map to obtain a fused feature; and performing up-sampling and pixel-level mapping on the fused features to generate a semantic segmentation result map. According to the method, the image convolutional network and a multi-layer attention mechanism are fused, adaptive modeling of image semantic segmentation is realized, and the method has the advantages of high precision, strong robustness and global perception.

The invention discloses a bridge structure risk safety identification method based on artificial intelligence, and relates to the technical field of bridge structure risk identification, and the method comprises the steps: building a topological graph model which reflects the geometric and mechanical connection relation of a bridge, taking piers, main beam sections and supports as graph nodes, and taking the physical connection between components as graph edges; structure response signals and environment load parameters in the service period of the bridge are collected, and the collected data are synchronized according to time and then mapped to corresponding nodes and edges in the topological graph model; based on the mapped data and a topological graph model, establishing a graph neural network embedded with structural dynamic constraints, and outputting the risk probability of a component corresponding to each node by jointly optimizing the consistency of a monitoring data fitting error and a physical rule; and performing causal relationship analysis on the structural response signal and the environmental load parameter, identifying a causal path between environmental interference and structural abnormality, and separating an abnormal component caused by structural degradation from the original response according to an identification result.

The invention belongs to the technical field of fan arrangement, and discloses a fan point location automatic arrangement method and system based on artificial intelligence, and the method comprises the steps: firstly receiving a plurality of spatial constraint layers and deployment parameters, and unifying the coordinates; a conflict relation graph is constructed based on the processed constraint layers, nodes of the graph are different constraint layers, and edges are conflict strength among constraints; inputting the map into a pre-training map attention network, and reasoning to obtain a conflict slow-release factor which quantifies a constraint relaxation degree; then the factors are fed back to a point location search algorithm, and candidate point locations are evaluated by using a scoring function of the fusion factors; and finally, outputting a final arrangement scheme according to a scoring result. According to the method, through artificial intelligence middleware, such as a graph attention network, an artificial intelligence-based search algorithm and the like, the problem of excessive region deletion of a traditional method is solved, and the area and arrangement reasonability of a deployable region are improved.

The invention relates to the technical field of path planning, in particular to a pesticide spraying robot positioning method and system based on graph neural network multi-source fusion. Performing data preprocessing based on the acquired multi-source sensor information; graph node multi-modal data embedding is carried out on the preprocessed information based on perception information mapping; a multi-modal space-time diagram structure is built based on graph node multi-modal data embedding; performing graph feature propagation and fusion based on space-time attention weighting; and carrying out positioning calculation and path decision on the fused features based on spatial-temporal feature constraints. According to the method, a sensor observation graph structure is constructed, multi-source information such as GPS, IMU, vision, radar and the like is modeled in a node form, and nonlinear feature fusion is realized through graph convolution and an attention mechanism.

The invention discloses a model-training-oriented multi-source heterogeneous data fusion method and device, and relates to the technical field of data processing. The method comprises the following steps: collecting multi-source heterogeneous data in an urban governance scene, and converting the multi-source heterogeneous data into unified standardized structure data; executing space-time dimension association and semantic dimension association to generate an association data set; mapping the entities to graph nodes based on a pre-constructed domain knowledge graph, and reasoning and enriching by utilizing a graph relation path to generate a fusion data block containing context semantics; screening and constructing a target training data set according to a quality rule, and training the urban governance large model; performance indexes of the large model in downstream tasks are monitored in real time, and control parameters of time-space, semantics or quality rules are dynamically adjusted. According to the method, adaptive optimization of the data fusion process is realized through a closed-loop feedback mechanism, and the quality of training data and the task performance of a downstream model are improved.

The invention discloses an oil and gas field inter-well connectivity recognition and injection-production optimization method and device. The method comprises the following steps: acquiring dynamic production data, and performing feature embedding, position coding and time sequence feature extraction processing to obtain a time feature vector; obtaining a sparse graph structure through graph node construction and Granger causality test processing, and obtaining an inter-well connectivity matrix through spatial feature propagation, updating and correction processing; yield prediction is completed, the corrected inter-well connectivity matrix, a yield prediction result, oil reservoir physical property constraints and injection-production range constraints are fused for injection-production parameter optimization processing, and an injection-production optimization strategy is obtained; and finally, performing executable study and judgment on the injection-production optimization strategy to obtain an injection-production optimization scheme. According to the method, coupling modeling of injection and production well spatial-temporal characteristics is achieved, the inter-well connectivity recognition precision is improved, an injection and production optimization scheme has both physical rationality and engineering performability, the method is suitable for injection and production management of various displacement reservoirs, and data support is provided for efficient development of oil and gas fields.

The invention discloses a robot environment identification method based on multi-modal fusion, and the method comprises the following steps: collecting image data, point cloud data and acoustic data in a robot operation environment, and carrying out the preprocessing; inputting the structured multi-modal input sample into a modal perception type quantum encoder of a multi-modal quantum graph neural network; constructing a modal coupling quantum diagram based on the modal quantum state representation set; inputting the modal coupling quantum diagram into an entangled modal propagation unit, and performing cross-modal information propagation of nodes of the modal coupling quantum diagram through a parameterized quantum circuit; quantum measurement operation is executed on the fusion quantum graph embedded representation, and an environment recognition result is output; and inputting an environment identification result into a robot control module, and driving the robot to execute path adjustment, obstacle avoidance or action response. According to the method, the multi-mode quantum graph neural network is adopted, and autonomous recognition of the robot in a complex environment is achieved.

The invention relates to the technical field of image data processing and detection, and discloses an image data processing and detection method of an OLED packaging structure, and the method comprises the steps: obtaining a high-resolution image of an OLED packaging region; extracting texture and edge features of the packaging layer through a multi-scale feature fusion network; the response of the tiny defect area is enhanced in combination with an attention mechanism; and classifying and positioning defects such as bubbles, cracks and foreign matters by using the trained deep learning model. In order to solve the problems of fuzziness and breakage of the bubble edge of the OLED packaging layer caused by gradual change of the refractive index of a material, a multi-scale Gaussian derivative filtering and path integral edge connection technology is innovatively adopted, gradient responses of different scales are dynamically weighted and fused, weak gradient edge signals are effectively enhanced, meanwhile, the broken edge is searched and repaired through a graph node path, and the defect that the bubble edge is broken is overcome. And complete capture of the bubble contour is realized.

The invention provides a level protection-oriented network security configuration checking method, which comprises the following steps of: constructing a knowledge graph associated with a security control item and a technical implementation mode by relying on a standard document and configuration data, and implementing a control item logic dependency and semantic weight reconstruction model; security configuration of assets is automatically collected and semantically mapped to map nodes, configuration deviation is recognized, a risk influence path template is matched in combination with asset importance and service context, semantic propagation path influence intensity is dynamically calculated, and an accumulated risk influence value of configuration deviation is generated in an aggregation mode; according to the method, risk level judgment is carried out by adopting a piecewise nonlinear function, a structured risk assessment record is output, risk identification and reinforcement suggestion closed loop are realized, and the hierarchical management and control and automatic compliance response capability of security configuration deviation in a network environment is improved.

The invention discloses a welding seam defect X-ray image intelligent diagnosis and credible traceability method based on multi-modal heterogeneous information collaboration, which is characterized in that a defect analysis network fusing multi-domain feature modeling and graph structure expression is constructed on the basis of bimodal data formed by a welding seam X-ray image and an industry detection standard text. In the image mode, dividing the weld seam image into a plurality of local area units through superpixel segmentation, taking the areas as image nodes, respectively extracting spatial domain, frequency domain, wavelet domain and edge domain features, and constructing a weighted graph structure by combining the spatial adjacency relation and the feature similarity relation between the areas; realizing overall modeling and correlation analysis of weld defect structure information by using a graph convolutional network; in a text mode, feature coding is carried out on an industry detection standard text, and the feature coding is used as an important prior constraint for defect judgment. Collaborative modeling of an image detection result and standard semantic information is achieved through a gating fusion mechanism, a mapping relation between a detection conclusion and a standard term is established, and interpretable expression and result credible traceability of the weld defect diagnosis process are achieved. And a welding seam X-ray film automatic digital acquisition and observation device is adopted in a matched manner, so that stable transmission, positioning observation and high-resolution digital imaging of the industrial ray film are realized, and reliable and consistent image data input is provided for the intelligent diagnosis method. The method is suitable for intelligent defect detection under complex welding seam structures and multi-working-condition imaging conditions, and has high engineering application value and popularization prospect.

The invention relates to the technical field of railway four-power operation and maintenance, provides a railway four-power operation and maintenance knowledge intelligent question-answering method and system based on a large language model, and solves the problems of low logic accuracy and poor field applicability of an operation and maintenance question-answering result. The method comprises the following steps: acquiring a question and answer request containing a device identifier, position data and a question text; performing syntactic analysis on the problem text to obtain a target equipment entity and an operation intention, positioning graph nodes in the railway four-electric interlocking relation knowledge graph according to the equipment identifier, performing graph traversal, obtaining associated equipment entities, an electrical connection relation and interlocking logic information, and combining the associated equipment entities, the electrical connection relation and the interlocking logic information into a regulation constraint condition set; performing vector retrieval based on the equipment identifier and the position data to obtain an operation and maintenance knowledge fragment; and inputting the regulation constraint condition set and the operation and maintenance knowledge fragment into the large language model, and carrying out logic constraint on the generation process according to the regulation constraint condition set to generate an intelligent question and answer result text. The logic accuracy and field applicability of the operation and maintenance question and answer result are improved.

The invention provides a posture recognition method and system based on a posture recognition neural network, and relates to the technical field of posture recognition, and the method comprises the steps: constructing a standard library containing safety and dangerous posture types, and marking a plurality of key points to generate a soft label sample data set; defining the key points as graph nodes, and constructing a feature matrix and an adjacent matrix; a double-branch fusion network model is designed, key point topological structure features are extracted through image convolution branch, image global context features are extracted through global visual branch, and attitude similarity spectral vectors are output after fusion; and finally, the attitude state is judged by calculating the geometric difference degree between the image to be analyzed and the most similar attitude template on the key point distance and the joint angle and comparing the overall difference between the image to be analyzed and the safety / danger camp. According to the method, the dual advantages of data driving and rule verification are combined, the recognition robustness and decision interpretability of dangerous postures are remarkably improved, and the safety risk is effectively reduced.

The invention discloses a cloud platform access path optimization method based on a graph convolutional network, and the method comprises the following steps: S1, collecting user access behavior data and resource state data in a cloud platform, and constructing an original access data set; s2, constructing an access path graph; s3, graph structure features, access behavior features and resource state features are extracted, and node input vectors are generated; s4, inputting to an improved GraphSAGE model, and outputting a path scoring result; s5, identifying bottleneck nodes and high-risk path segments; s6, constructing an optimized path candidate set, and generating an optimal access path combination; and S7, collecting an actual access result of the user to construct access feedback data, updating graph edge attributes and graph nodes, and adjusting aggregation parameters and scoring weights to realize adaptive updating of the path scoring network. The resource scheduling efficiency of the cloud platform can be effectively improved, the access conflict risk is reduced, and the system response performance is enhanced.

The invention discloses a coastal wind turbine group generation power multi-scale space-time prediction method, which mainly comprises the following steps: carrying out space interpolation and error correction on a target wind turbine position by adopting a numerical weather forecast statistical downscaling technology, generating a high-resolution wind speed prediction sequence covering a short term and a long term, aligning and splicing the predicted wind speed, the field actually-measured wind speed, the environment and the unit operation variables into node dynamic input characteristics; the method comprises the following steps: encoding longitude and latitude and time sequence monitoring data of N fans of a coastal fan group into graph nodes, determining an edge weight according to geographic distance and wake flow coupling, and forming a fan graph network containing static and dynamic characteristics; and inputting the static and dynamic feature sequences into a graph neural network comprising a space attention layer, a time recursion layer and a physical constraint regular term, completing model training, and outputting the generated power of each fan in a plurality of time steps in the future and the total power predicted value of the fan group. The method can provide powerful support for wind power plant operation scheduling, power grid-connected management and new energy consumption.

The invention discloses a target abnormal movement early-warning method based on eye walking along with a hook, and the method comprises the following steps: collecting an eye movement data stream, constructing a fixation behavior data set, and generating a fixation track sequence; constructing the fixation points as graph nodes, generating graph edges according to a time sequence and spatial proximity, and forming a graph structure sequence; inputting to the improved ST-GCN model, and outputting a target prediction vector; identifying offset candidate segments; performing trajectory morphological analysis on the offset candidate segments, and judging whether formation conditions of a trajectory loopback structure are met or not; if not, calculating an access frequency domain; if the access frequency is greater than a preset frequency threshold, outputting a low early warning signal; and screening the high-weight fixation segment based on the offset candidate segment, carrying out similarity matching, and outputting formal early warning information according to a matching result. According to the invention, multi-level accurate early warning of the abnormal motion state of the target is realized, and the method has the advantages of clear structure, strong real-time performance, good adaptability and the like.

The invention discloses a structure safety risk reasoning method and system based on a multi-modal graph, and relates to the technical field of pattern recognition, and the method comprises the steps: obtaining the multi-modal information of a to-be-evaluated city structure group, representing the structure units in the structure group as graph nodes, distributing node attributes for each graph node, and obtaining the multi-modal information of the to-be-evaluated city structure group; defining edges according to the relationship among the structural units, and constructing to obtain a multi-modal heterogeneous graph; on the basis of the multi-modal heterogeneous graph, graph nodes are coded by using a graph neural network, the risk state of each graph node is learned, propagation of risks among the graph nodes is simulated, and risk state prediction of each graph node and a risk propagation path among the graph nodes are obtained; and outputting a risk state prediction result and a risk propagation path of each graph node. Through the technical scheme of the invention, a more real and more representative structure diagram is constructed, the physical process of structure performance degradation is better fitted, the adaptability and expansibility are high, and the visualization and interpretability are good.