88 results about "Graph Edge" patented technology

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 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 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 regional public opinion propagation mode mining method based on a heterogeneous graph neural network. The method comprises the following specific steps: S1, constructing a heterogeneous graph; s2, weight definition of edge semantics; s3, node feature propagation and updating are carried out based on the graph neural network, and node embedding representation is obtained; and S4, node embedding is clustered, and regional public opinion propagation mode recognition is carried out in an embedding space. According to the method, joint modeling of users, media and regional nodes in a multi-relation heterogeneous graph structure is realized, and cross-semantic-edge information aggregation is completed by using a multi-relation attention mechanism, so that node embedding representation capable of representing multi-level propagation characteristics is obtained; the expressions provide basic support for subsequent public opinion propagation link identification, diffusion range prediction and opinion leader mining.

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 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 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 embodiment of the invention provides a cross-modal space-time perception tensor generation method and device, equipment, a medium and a product, and relates to the field of artificial intelligence. According to the method, through the synergistic effect of an endogenous space-time diagram neural network and a cross-modal attention mechanism, the cross-modal space-time perception tensor is generated by means of the structured modeling capability of the diagram neural network; according to the method, heterogeneous modal data are mapped to a unified representation space, modal interaction strength is dynamically regulated and controlled based on a cross-modal attention mechanism, causal consistency of cross-modal information on a spatial-temporal scale is ensured, in addition, an endogenous spatial-temporal diagram neural network can model an interaction relationship between modals through diagram edges, and the spatial-temporal diagram neural network is more accurate. The adaptability to dynamic environment changes is effectively enhanced, the robustness and generalization ability of multi-modal fusion are further improved, and finally the problem that in the prior art, the sensing tensor generation efficiency is low is solved.

The invention discloses a translation memory bank retrieval and matching method based on semantic similarity, which relates to the technical field of computer-aided translation, and comprises the following steps: generating an internal edge and a cross-graph edge, calculating a one-time jump success probability, calculating edge and cross-graph edge cost based on cleanliness, correcting by using the one-time jump success probability to obtain an edge weight; generating a weighted graph, constructing an initial priority queue, searching by using a shortest path to obtain a distance matrix, calculating density based on a fuzzy similarity matrix, screening the density to obtain a genealogy center, calculating proximity, and generating candidate translations in combination with the distance matrix; static and dynamic fragment sets are constructed through a dependency syntax and BERT-NER, semantic matching precision and noise robustness are improved, a weighted graph is constructed in combination with cleanliness and transfer factors, shortest path matching is carried out, and the retrieval recall rate and quality of technical translation are improved.

The invention discloses an unmanned aerial vehicle complex airspace cooperative passage method based on a graph neural network, and the method comprises the steps: collecting the airspace data of an unmanned aerial vehicle complex airspace, and generating standardized airspace data; constructing a mixed queuing network, and forming arrival, queue length and service capability state of each service node and service link; mapping to obtain graph nodes, graph edges and graph features, and constructing an airspace graph; inputting the airspace graph into the graph neural network, and carrying out constraint updating on the mixed queuing network; integrating the service capability state, the queue length state and the congestion propagation parameters to form a network passing cost relationship; and executing a Frank-Wolfe algorithm, and generating a cooperative passing instruction for each unmanned aerial vehicle. According to the invention, by introducing the mixed queuing network and the Frank-Wolfe algorithm, efficient and stable cooperative passage scheduling of multiple unmanned aerial vehicles in a complex airspace environment is realized.

The invention discloses a query expansion method and system for power grid knowledge search, and the method comprises the steps: obtaining an initial query word of a user, and retrieving an initial document set from a power grid document set; segmenting the document into text fragments by using a preset power grid entity boundary dictionary, and calculating a context comprehensive weight of each fragment; calculating the semantic similarity between the initial query word and each fragment through the word vector, and screening out candidate expansion words in high-similarity fragments; a weighted co-occurrence graph is constructed based on the co-occurrence relation of the candidate expansion words, and edge weights are determined by accumulating context comprehensive weights of co-occurrence fragments; and combining the word vector similarity with the initial query word to carry out comprehensive sorting, selecting a word ranked in the top as an expansion word, and combining the expansion word with the initial query word to form an expansion query.

The application relates to the field of power prediction, and discloses a physical-data double-driven power prediction method of a power generation unit level graph neural network, which comprises the following steps: collecting and preprocessing power generation control unit data; grading and fusing meteorological data to generate multi-scale meteorological features; taking a single power generation unit as a node to construct a graph neural network, and defining physical parameters in a graph edge weight as a learnable tensor; generating preliminary power through graph convolution; adaptively fusing the preliminary power and theoretical physical power through a differentiable physical projection layer to obtain corrected power; constructing an augmented Lagrange compliance constraint, taking a power grid standard as a hard constraint; constructing a three-target collaborative loss function containing a prediction error, a physical deviation and a compliance constraint, and calculating and back propagating through a primal-dual alternating strategy gradient. The application realizes deep fusion of physics and data, and significantly improves prediction accuracy, physical consistency and grid compliance.

This invention relates to the field of product traceability technology, and more particularly to a product traceability method for PCB manufacturing. The method includes: acquiring multi-dimensional physical data; determining a global instability factor for the lamination process based on environmental thermal fluctuation characteristics, normalized resin flow rate, and normalized exposure time, wherein the global instability factor is positively correlated with these factors; determining a hole wall risk index by combining the global instability factor and the normalized standard deviation of hole wall copper thickness; determining the confidence weight of the graph edge transfer from one process node to another using the hole wall risk index and the normalized defect rate in the optical inspection area; constructing a transfer probability matrix based on the graph edge transfer confidence weight, and performing iterative calculations to output the traceability path of the printed circuit board. This invention improves the accuracy and precision of traceability reasoning for broken circuit board data.

The present invention relates to a computer implemented method of natively storing a set of nodes and / or edges in a graph database, comprising generating a bitset representing a set of graph nodes or graph edges, and setting the value of a bit in the bitset in dependence on whether a corresponding node and / or edge is part of the set. The present invention also relates to a computer implemented method of querying data in a graph database, the graph database comprising graph nodes connected by graph edges, the method comprising: receiving a query, the query comprising a first query node, a first query edge, and a second query node arranged in a pattern; for the first query node, identifying a first set of graph nodes or graph edges associated with the graph nodes which satisfy criteria of the first query node; for the first query edge, identifying a second set of graph nodes or graph edges associated with the graph edges which satisfy criteria of the first query edge; for the second query node, identifying a third set of graph nodes or graph edges associated with the graph nodes which satisfy criteria of the second query node; and computing the intersection of the first, second and third sets of graph nodes or graph edges to obtain one or more first output sets of graph nodes or graph edges.

The invention provides a rock mass fracture network medium reconstruction method and system based on image tracking, and belongs to the technical field of fracture network reconstruction, and the method comprises the steps: obtaining a to-be-reconstructed fracture network, and carrying out the preprocessing of the to-be-reconstructed fracture network, and obtaining a binary image; skeletonizing the binary image to obtain a single-pixel wide fissure main ridge; identifying end points and intersections of the fracture skeleton based on a neighborhood connection relation, and performing constrained graph tracking by taking the end points and the intersections as graph nodes, taking a connection skeleton sequence between the end points and the intersections as graph edges and taking the end points as starting points to obtain an ordered vertex coordinate sequence of each fracture broken line; and performing multi-point coordinate calibration on the ordered vertex coordinate sequence, mapping the ordered vertex coordinate sequence to a target two-dimensional coordinate system, generating a polyline command text file based on the mapped coordinates, and reconstructing the fracture network medium by using the polyline command text file. The method is oriented to data flow of heterogeneous software, a special interface is not needed, batch processing is supported, and the efficiency and accuracy of cross-platform modeling are improved.

The invention discloses a response strategy intelligent recommendation method based on multi-dimensional power grid operation data driving. The method comprises the following steps: S1, collecting multi-dimensional power grid operation data and sorting the multi-dimensional power grid operation data according to a time sequence; s2, constructing a space-time diagram structure by taking power grid nodes corresponding to the time-sequential power grid data as graph nodes and taking a connection relationship between the power grid nodes as graph edges; s3, extracting spatial features among graph nodes through graph convolution operation of a spatial dimension, and extracting time sequence features through one-dimensional convolution of a time dimension; s4, processing a real label by adopting an improved label smoothing algorithm; s5, training the space-time diagram convolutional network structure by adopting an end-to-end joint optimization mode; and S6, outputting a response strategy recommendation result by using the optimized space-time diagram convolutional network. According to the method, a space-time diagram convolutional network, time series data modeling and an improved label smoothing algorithm are combined, and response strategy intelligent recommendation based on multi-dimensional power grid operation data driving is realized.

The invention relates to the technical field of trajectory data mining and identity recognition, in particular to a trajectory user linking method and device based on graph edge weight optimization and a storage medium. The model is composed of a local graph representation learning module fused with grid semantics, a global relation graph representation learning module of adaptive edge weight, a layered space-time attention network and a track user link module. The method comprises the following steps: carrying out gridding processing on an anonymous track, extracting hierarchical semantic embedding of POI categories, and constructing a local space graph and a global relation graph; introducing a semantic consistency coefficient into the local graph to re-calibrate an edge weight, and dynamically modeling an interaction relationship between tracks and between users and tracks in the global graph through a self-adaptive edge weight learning mechanism; fusing local space-time features and global interaction representation through a layered space-time attention network; and finally, the features are projected to the user space for matching, so that the accuracy and robustness of the track user link are remarkably improved.

This invention provides an intelligent method and system for confirming, tracing, and preventing counterfeiting of digital copyrights, relating to the field of digital copyright technology. It includes acquiring the atomic operation sequence during the creation process of a digital copyright object and constructing a directed acyclic creation graph with atomic operation records as nodes and causal dependencies between operations as edges, where edge weights represent content inheritance. During the creation process, local root hash values ​​are periodically extracted and submitted to the blockchain network, forming a progressive timestamp chain containing multiple time-series anchored credentials. Simultaneously, cross-modal creative elements are extracted from the digital copyright object and organized into a creative semantic graph, containing multi-level nodes and forming a creative inheritance topology through mapping relationships. When a traceability request is received, the directed acyclic creation graph and creative semantic graph of the object to be verified are extracted, and the operation sequence heritability, semantic node overlap, and creation order are calculated respectively with the corresponding data of historical copyright objects. The copyright ownership relationship is determined based on the combination results.

Systems and methods for processing natural language text using a graph obtain a natural language text and a query text, and parse that the natural language text into the plurality of text units, associating each with a graph node, and removing information leak text units from the plurality of text units. Connecting relationship between at least two of the remaining set of the plurality of text units are determined and associated with a graph edge between graph nodes. Based on the probabilistic relations between each graph node and the query text, graph node restart probabilities are determined for one or more of the graph nodes. The graph nodes that can be ranked.

This invention discloses a data center energy-saving scheduling method based on energy consumption correlation graphs, belonging to the field of data center energy-saving technology. It addresses the problems of lacking a quantitative model of inter-rack thermal interference during the cooling release window of a cold storage device and the inability to predict cooling costs during task scheduling. By reading the remaining cooling capacity of the cold storage device and combining it with the rack inlet air temperature and airflow velocity to generate an overcooling risk index, risk racks are marked. The response sensitivity is calculated by retrieving the fan speed baseline. A thermal interference diffusion graph is constructed with the overcooled risk racks as source nodes. The cooling cost of each rack is calculated to generate a task landing point avoidance set. The heat dissipation tasks are migrated to the rack with the minimum cooling cost to complete the landing point pre-adjustment. After the window ends, the actual cooling cost is collected and compared with the predicted value to generate a weighted deviation rate and correct the graph edge weights, realizing continuous adaptive updating of the graph and reducing the overall energy consumption of the data center.

This invention discloses a method and apparatus for predicting the popularity of short videos based on learnable graph augmentation, belonging to the field of artificial intelligence technology. The method includes: collecting keyframe sets, text data sets, and behavioral data sets from short videos; constructing a graph edge to obtain a heterogeneous social graph; inputting this graph into a heterogeneous attribute graph neural network; extracting content features from the target video; performing odd-hop propagation and even-hop propagation; employing feature fusion operations and learnable sparse masks; and inputting this into a trained popularity prediction model to obtain the predicted future popularity of the target video. This invention, by integrating learnable graph augmentation and social propagation relationship modeling, can effectively utilize unlabeled data even when labeled samples are insufficient, enhancing the model's robustness and generalization ability, thereby more accurately predicting the popularity of short videos.

In various examples, a technique for generating a route plan is disclosed that includes receiving a semantic map that represents a physical environment. The technique further includes generating a route graph based at least on the semantic map, where the route graph includes one or more route graph edges, wherein each route graph edge has an associated location in one or more map regions of the semantic map and an associated cost. The technique also includes determining a cost of a particular graph edge based at least on a region type, where the region type is determined based at least on a map region in which the particular route graph edge is located. The technique further includes generating, using the route graph, a route plan for a mobile robot to move from a given start location on the semantic map to a given destination location on the semantic map.

This invention provides an APT attack detection method, apparatus, and electronic device based on graph attention learning, comprising: acquiring the operation log of the system to be monitored, and forming a time series graph of the system to be monitored based on the operation log, the time series graph including multiple nodes and time series graph edges connecting the nodes; inputting the time series graph into a pre-trained edge type prediction model to obtain the time series graph edge types output by the edge type prediction model, wherein the edge type prediction model includes an encoder based on a time series graph neural network and a decoder based on a graph attention network; based on the time series graph edge types and the true edge types, obtaining the reconstruction error of the actual time series graph edges corresponding to the time series graph edge types through a cross-entropy loss function, wherein the true edge type is the edge type of the observed actual time series graph edges; and determining whether the system to be monitored is subjected to an APT attack based on the reconstruction error. This achieves accurate and efficient APT attack detection without relying on known attack features.

This invention relates to the field of curtain wall performance simulation and optimization technology, specifically disclosing a curtain wall performance simulation and optimization analysis system, including: a drainage path topology construction module, a construction interference parameter extraction module, a pseudo-effective drainage path identification module, a drainage correction and water retention backtracking module, and a drainage structure optimization verification module. This invention constructs a directed graph of drainage path topology and extracts the set of construction interference parameters for each graph edge. It calculates the path effectiveness coefficient to determine the drainage effectiveness of each graph edge, marks pseudo-effective drainage paths, corrects the nominal cross-sectional parameters, re-simulates and evaluates the drainage capacity, and backtracks to locate water retention risk areas. Finally, it generates an optimization scheme and performs closed-loop verification until all pseudo-effective drainage paths are eliminated. This invention solves the problem of existing methods that use geometric connectivity to equip drainage effectiveness but cannot identify pseudo-effective drainage paths, thus improving the accuracy of curtain wall drainage performance simulation and the reliability of optimization.

This invention relates to the field of structural explosion dynamic response calculation, specifically disclosing a heterogeneous graph data representation method for structural explosion dynamic response analysis. The method includes: establishing and validating a high-fidelity finite element baseline model of the structural explosion response, and obtaining a baseline data source for the structural explosion response; establishing and implementing an adaptive sampling algorithm based on physical field gradients to adaptively select key nodes representing the structural dynamic characteristics from the finite element mesh nodes; establishing a multi-attribute graph edge linking and weight quantization method to construct a multi-attribute weighted graph; and establishing an automated construction and storage method for spatiotemporal graph datasets to generate a spatiotemporal sequence graph dataset for training a graph neural network. This invention overcomes the limitations of traditional neural networks on the serialization and meshing of training data, achieving automated, high-fidelity conversion from continuous, heterogeneous finite element simulation data to sparse, discrete graph structure data with well-defined topological relationships, as well as data size simplification and increased physical information density.

The invention provides a coal mining machine cutting curve generation method and system, electronic equipment and a storage medium, and relates to the technical field of coal mining machines. The method comprises the following steps: acquiring operation data of at least two nearest coal mining machine operations, generating a corresponding behavior map node according to the operation data of each coal mining machine operation, and acquiring working condition migration data between adjacent coal mining machine operations in the at least two nearest coal mining machine operations, according to the working condition migration data, generating a corresponding behavior map edge, based on the behavior map node and the behavior map edge, obtaining a behavior map of at least two nearest coal mining machine operations, inputting the behavior map into a cutting curve generation model, and obtaining a cutting curve of the next coal mining machine operation output by the cutting curve generation model; the cutting curve generation model is obtained based on the sample behavior map of the operation data of the at least two sample coal cutter operations and the sample cutting curve of the next coal cutter operation, and the accuracy of the generated coal cutter cutting curve can be improved.

The application discloses a kind of mine rock burst monitoring method, prevention and control method, storage medium and electronic equipment, comprising: the deformation and failure control equation of coal rock mass and energy balance equation are converted into penalty term of loss function, construct multi-scale hierarchical coupling PINN model;With the Euclidean distance between sensor, geological structure correlation and observation signal correlation as the multi-dimensional attribute of graph edge, construct space heterogenous graph, generate virtual node in the monitoring blind area of not laying sensor, the feature of each virtual node is spacially interpolated and spliced with space-time coordinates, form encoding input feature, and input to multi-scale hierarchical coupling PINN model, output the prediction result of stress field and energy field;By variational inference, introduce probability distribution to network weight, so that multi-scale hierarchical coupling PINN model outputs probability distribution prediction result containing confidence interval, and generates cognitive uncertainty graph.The application can monitor and prevent and control mine rock burst in real time, improve safety.

The invention discloses a source network load intelligent enhanced storage optimization operation method and a related device. Comprising the following steps: inputting topological structure information of a graph model of the source network load storage system, feature information of graph nodes in the graph model of the source network load storage system and feature information of graph edges in the graph model of the source network load storage system into a trained physical information graph neural network model, the physical information graph neural network model is trained based on a physical constraint equation set of a graph model of the source network load storage system to obtain an optimized operation strategy of the source network load storage system, the source network load storage system is controlled according to the optimized operation strategy of the source network load storage system, and the physical information graph neural network model is trained based on a physical constraint equation set of the graph model of the source network load storage system. According to the method and the related device, the physical consistency and reliability of source network load storage optimization can be improved.

This invention discloses a method and system for intelligent identification and fault diagnosis of disconnector switch status in substations based on an attention mechanism. The method includes: acquiring current signals, vibration signals, and infrared thermal images of the disconnector switch to construct a multimodal time-series data stream; extracting temporal and spatial features using a Transformer-CNN hybrid network, and performing feature interaction through cross-modal cross-attention; constructing a graph neural network to encode physical coupling relationships as graph edges for message passing, and outputting a fault feature map; outputting diagnostic results and thermal fault segmentation masks through multi-task output using a classification head and a segmentation head; predicting current features and fault trend residuals using a multi-granularity self-attention network; and finally performing reliable decision fusion through a Venn-Abers module to output a comprehensive fault type with a confidence interval. This invention significantly improves the accuracy and robustness of disconnector switch fault diagnosis through multimodal feature fusion, graph neural network physical relationship modeling, and reliable decision fusion.