37 results about "Multiple edges" patented technology

The invention relates to the technical field of computer vision and image recognition, in particular to an image recognition method based on edge computing, which comprises the following steps: dynamically capturing an original image through a plurality of edge nodes, rejecting redundant regions through a multi-modal perception triggering mechanism, and establishing a cooperative processing group. Illumination equalization, noise filtering and resolution self-adaptive compression tasks are distributed according to dynamic role election, a standardized preprocessed image is generated, a lightweight convolutional neural network is operated in parallel to extract a dual-channel feature vector, and after entropy coding lossless compression and equipment identity tag and time sequence stamp attachment, the dual-channel feature vector is transmitted to a cloud end by adopting a lightweight encryption protocol. The cloud end analyzes the data packet, reconstructs a feature topological graph based on space-time relevance, loads a depth residual error recognition model to execute feature fusion and classification decision, feeds back and updates the weight of an edge node model, solves the problems of low collaborative efficiency and feature distortion, and improves the efficiency and precision of image recognition.

The invention relates to the technical field of edge computing, in particular to an edge node collaborative optimization method and system of a computing power network. Comprising the steps of collecting resource state information and historical behavior data of a plurality of edge nodes to form a resource state data set; constructing a computing power network topological graph based on the resource state data set, and extracting node feature representation of each node through a graph neural network; calculating the comprehensive credibility of each edge node based on the historical behavior data, and integrating the comprehensive credibility into the node feature representation; collecting energy consumption state data of each edge node, and calculating an energy consumption efficiency index of each edge node; and a coupling linkage mechanism of credibility and energy consumption perception is established. Through a coupling linkage mechanism of credibility and energy consumption perception, high resource utilization rate, low task delay, strong system robustness and green energy conservation of computing power network edge node scheduling are realized.

This article relates to the field of communication technology, and in particular to a device access authentication method, apparatus, and system. The method includes: in response to receiving an access authentication request, determining transmission parameters corresponding to each CPE, wherein the access authentication request is sent by the requesting CPE among multiple CPEs; based on the transmission parameters, performing edge connectivity judgment and processing on a pre-constructed CPE topology graph to obtain a target CPE topology graph, the CPE topology graph including multiple nodes and multiple edges, the edges being used to connect nodes to represent connectivity with two CPEs corresponding to two connected nodes; determining an index value for each edge in the target CPE topology graph based on the transmission parameters and the weights corresponding to the transmission parameters; and based on the index values, determining a target path in the target CPE topology graph, starting from the requesting node corresponding to the requesting CPE and ending at the target node corresponding to the target CPE, as the device access authentication path.

Systems and methods for estimating a graph edit distance (GED) between compounds are provided. A first graph representing a first compound includes a plurality of nodes and a plurality of edges. An atom of the first compound is represented by the node of the first graph, and a bond of the first compound is represented by the edge of the first graph. A second graph representing a second compound also includes a plurality of nodes and a plurality of edges. Atoms of the second compound are represented by the nodes of the second graph, and bonds of the second compound are represented by edges of the second graph. The first graph is input into a model to generate a first potential embedding. The second graph is input into the model to generate a second potential embedding. An estimated value (GED) between the two compounds is determined from a difference between the two potential embeddings.

The invention discloses a multi-dimensional data intelligent processing and analysis method, which relates to the technical field of data processing, and comprises the following steps of: performing static dependency analysis on a parallel task set, and constructing a directed resource dependency graph containing a semaphore occupancy relationship, a calling hierarchy and a recursive path; depth-first loop detection is performed based on the directed resource dependency graph, a potential deadlock closed loop is identified and divided into a plurality of edge loop-free sub-graphs, and a sequenced execution plan is generated for each sub-graph. According to the method, by introducing static dependency analysis, resource dependency graph construction, a token lease mechanism and a multi-level semaphore pool, active avoidance of task deadlock risks and automatic recovery of resource anomalies in the engineering machinery multi-dimensional data processing process are achieved. Through sequential scheduling, adaptive regulation and control and self-healing monitoring strategies, the task scheduling stability and the resource utilization efficiency in a high-concurrency scene are effectively improved, and closed-loop optimization of task scheduling is realized in cooperation with process traceability and evolutionary knowledge base feedback.

The invention discloses an open set identification decision-making method based on an extreme value Copula joint model. The method comprises the following implementation steps: firstly, extracting training sample features through a deep neural network, forming a known category compact cluster and determining a center; calculating the distance from each training sample to the center of each cluster, and fitting generalized extreme value distribution for each known category as edge distribution; secondly, introducing an extreme value Copula function, constructing a multivariate joint extreme value distribution model in combination with a plurality of edge distributions, and depicting an extreme value distance dependent structure between clusters; and finally, calculating a new sample joint extremum probability based on the model, and setting a threshold to judge whether the new sample joint extremum probability is a known or open set unknown category. According to the method, the multi-edge distribution and cluster dependency relationship is unified, the traditional'tail independence 'constraint is broken through, the feature space geometrical relationship is quantified, and the accuracy, interpretability and robustness of open set recognition are remarkably improved.

In some examples, a method of determining a shortest path between two locations includes: a first device receiving a first graph representing a graph network including a plurality of regions, each region including a plurality of vertices and a plurality of edges; identifying each of the plurality of regions and sending the first map to a mobile device; the first device partitions the first map based on the identified plurality of regions, thereby generating a set of partition maps, each region of the identified plurality of regions corresponding to a respective partition map of the set of partition maps; generating, by the first device, a second graph defining information including the set of partitioned graphs and defining connectivity between the set of partitioned graphs, and transmitting the second graph to a mobile device; traversing, by the mobile device, the second graph to determine an advanced path including a plurality of shortcuts representing a side of the plurality of sides of the first graph that connects a first vertex and a second vertex; the mobile device determines a shortest path between the first vertex and the second vertex, the shortest path being determined based on the advanced path, the shortest path comprising an edge connecting the first vertex and the second vertex in the plurality of edges of the first graph; and the mobile device outputs the shortest path.

This application discloses a lightweight preprocessing method for geometric components in 3D scene rendering, relating to the field of 3D rendering. The method includes: when there are multiple edge points in a triangular mesh model, performing component edge shape detection based on the position information of the multiple edge points to determine whether the component to be processed is a cylindrical component; when the component to be processed is determined to be a cylindrical component, performing circular fullness detection on the bottom surface to determine whether the cylindrical component is a cylindrical pipeline; converting the geometric features of each cylindrical pipeline into an instantiated reuse matrix; combining the view frustum parameters of the current viewpoint and the instantiated reuse matrix to perform view frustum culling and occlusion judgment to determine whether each cylindrical pipeline is visible; for cylindrical pipelines, collaborative optimization is achieved in three stages: recognition efficiency, lightweight transmission, and rendering visibility screening. Through the preprocessing mechanism in the early stage of rendering, the overall efficiency of subsequent rendering is effectively improved.

The application discloses a retrieval enhancement generation method and related products, which comprises the following steps: obtaining a query vector; matching the query vector with a multi-dimensional feature graph to determine a target node; the multi-dimensional feature graph comprises multiple nodes and multiple edges; the node represents a node vector; the edge represents a relationship vector; there is an index relationship between the node vector and the relationship vector; the multiple nodes comprise the target node; performing feature extraction on the query vector to obtain a relationship feature corresponding to the query vector; performing dynamic graph walking in the multi-dimensional feature graph based on the target node and the relationship feature to obtain one or more effective paths; and obtaining a final answer based on the one or more effective paths. The application effectively avoids the "illusion" risk of a large model, ensures that the output content is compliant, reliable and auditable, improves the accuracy of the retrieval result in retrieval enhancement generation, and effectively breaks through the inherent limitations in semantic integrity, multi-hop reasoning capability and relationship intention understanding.

This application discloses an image processing method, apparatus, device, and storage medium. The method includes: acquiring input data, which includes data of multiple modalities, and the input data is used to generate a three-dimensional scene; generating a mixed modality graph based on the input data, the mixed modality graph including multiple nodes and multiple edges between the nodes, each node representing an object, and each edge representing the relationship between two nodes corresponding to each edge; generating a three-dimensional scene based on the mixed modality graph, the content of the three-dimensional scene including the geometric shape information of the object corresponding to each node and the spatial position information of the object corresponding to each node in the three-dimensional scene. This allows for flexible processing of data of different modalities, improves the adaptability to input data, significantly enhances the support for complex user-inputted data, and enables the generation of three-dimensional scenes with high spatial consistency and geometric detail.

Generating transportation network solutions using graph neural networks, includes: generating, based on input data describing a product transportation network, a graph neural network representing the product transportation network, wherein the graph neural network comprises multiple edges, multiple source nodes, multiple destination nodes, and multiple intermediary nodes; performing a graph neural network prediction based on the graph neural network, wherein performing the graph neural network prediction comprises predicting inclusion in the transportation network solution for each edge and intermediary node; and generating a transportation network solution by performing a model optimization using an initial state based on the graph neural network prediction.

The application discloses an industrial internet remote monitoring operation and maintenance management system and method, and belongs to the technical field of industrial internet. The method comprises the following steps: in response to access requests of multiple edge nodes, collecting real-time running state parameters of each edge node and performing heterogeneous data graph construction to obtain a dynamic attribute graph structure; performing federated transfer learning on the multiple edge nodes based on the dynamic attribute graph structure to obtain a cross-domain joint inference model; constructing a dynamic reconfigurable digital twin corresponding to each edge node, and performing closed-loop dynamic reconfiguration on the digital twin by using real-time feedback data; performing multi-step simulation deduction based on the reconfigured digital twin to generate an adaptive operation and maintenance strategy; and decomposing the adaptive operation and maintenance strategy into an atomic control instruction sequence, and delivering the atomic control instruction sequence to an execution unit through an asynchronous message queue to realize remote collaborative control.

The invention discloses an abnormal object recognition method and device, electronic equipment, a medium and a program product, and relates to the technical field of computers.The method comprises the steps that a graph model is constructed based on a graph structure corresponding to at least one time dimension, the graph structure comprises a plurality of nodes and a plurality of edges, the plurality of nodes comprise a user node, a device node, a website node and an application APP node; determining a plurality of first target nodes from the graph structure corresponding to the at least one target time dimension, wherein the first target nodes are device nodes in the graph structure corresponding to the target time dimension in the at least one time dimension; inputting the multiple pieces of first feature information into a preset decision tree model to determine at least one second target node; constructing a target graph structure based on the plurality of first target nodes, the at least one second target node and the user node associated with each first target node; and identifying abnormal user nodes in the target graph structure. According to the invention, the accuracy of abnormal object recognition can be effectively improved.

The application discloses a network abnormal traffic detection method and system based on federated learning and drift detection. Through data preprocessing and spatio-temporal feature extraction on multiple edge nodes, feature data is generated. Each edge node independently detects whether the traffic has concept drift. If drift is detected, incremental learning is started and only the changed data is updated. The updated local model parameters are uploaded to the central server, aggregated and a global model is generated. After evaluation, it is distributed to the edge nodes for real-time traffic detection. This method combines federated learning and drift detection technology, not only protects data privacy, but also improves the accuracy and real-time performance of network traffic detection, suitable for traffic monitoring and anomaly detection in large-scale distributed networks. Graph neural networks and bidirectional long short-term memory networks are used to improve feature extraction and model training accuracy, ensuring stability and reliability in complex network environments.

The invention provides a risk score calculation method and device and related equipment, and relates to the technical field of virtualization and communication, and the method comprises the steps: obtaining attack difficulty information among a plurality of network nodes through calculation according to the obtained historical utilization frequency of the plurality of network nodes, a time window needed by an attack, and an environment impact factor; an attack graph is constructed according to the obtained node information and attack difficulty information of the multiple network nodes, the attack graph comprises multiple elements, multiple edges and multiple weights, the weights correspond to the edges, the elements are used for representing the network nodes, the edges are used for connecting any two elements in the multiple elements, and the edges are used for corresponding to attack paths between the network nodes; the weight is used for representing attack difficulty information between two network nodes of corresponding edge connection; and inputting the attack graph into the graph attention network for scoring, and outputting a first risk score of the target node. In this way, the accuracy of the obtained first risk score of the target node can be further improved.

Generating transportation network solutions using graph neural networks, includes: generating, based on input data describing a product transportation network, a graph neural network representing the product transportation network, wherein the graph neural network comprises multiple edges, multiple source nodes, multiple destination nodes, and multiple intermediary nodes; performing a graph neural network prediction based on the graph neural network, wherein performing the graph neural network prediction comprises predicting inclusion in the transportation network solution for each edge and intermediary node; and generating a transportation network solution by performing a model optimization using an initial state based on the graph neural network prediction.

The invention provides a graph data preprocessing method, system and device and a medium, and belongs to the technical field of graph data processing.The method comprises the following steps that a plurality of disturbance edges of graph data to be processed are removed, and a rough graph is obtained; randomly selecting some edges from the plurality of disturbance edges, and adding the selected edges into the rough image to generate a to-be-predicted image; using a depth map mutual information model (DGI) to learn mutual information between the rough image and the representation of the rough image and mutual information between the image to be predicted and the representation of the image to be predicted; whether the difference value of the two pieces of mutual information is smaller than a threshold value or not is judged, if not, multiple edges are selected from the remaining disturbance edges to be added into the to-be-predicted image to generate a new to-be-predicted image, whether the difference value of the new to-be-predicted image and the mutual information represented by the new to-be-predicted image and the mutual information between the rough image and the rough image is smaller than the set threshold value or not is judged again, and if yes, the new to-be-predicted image is generated. And obtaining a clean image. According to the method, tiny disturbance structures caused by adversarial attacks in the graph data can be removed, and clean graph data is obtained and can be conveniently applied to downstream tasks.

The invention discloses a heterogeneous graph contrast learning recommendation method fusing multi-scene information, and the method comprises the following steps: introducing a scene identifier in an interaction relationship between a user and an article, and constructing an interaction heterogeneous graph with various edge types; through a plurality of independent graph neural networks, respectively executing graph convolution for the edge set of the specific scene and the edge set of the multi-scene mixture to obtain scene-specific user article representation and scene-fused user article representation; based on a scene perception gating mechanism, adaptively adjusting a combination weight of the scene specific representation and the scene fusion representation, and generating a final user representation and an article representation with scene perception characteristics; and finally, through a contrast learning mechanism, the scene specific representation and the final representation of the same user or article are regarded as a positive sample pair, and consistency constraint is applied to maximize the correlation between the scene specific representation and the final representation, so that the robustness and the cross-scene generalization ability of the method are enhanced.

A WebGL-based network graph processing method, apparatus, and electronic device are disclosed. The WebGL-based network graph processing method includes: creating an initial object based on WebGL, the initial object including multiple nodes and multiple edges connecting at least a portion of the multiple nodes; acquiring initial input data for the multiple nodes and initial input data for the multiple edges, the initial input data for the multiple nodes including the coordinates of the multiple nodes; iteratively updating the coordinates of each of the multiple nodes; and outputting the processed network graph based on the update result of the coordinates of each node. Thus, by combining WebGL technology with traditional graph layout algorithms, high-performance network graph layout on a web browser can be achieved.

The invention relates to the field of graph processing, and discloses a graph approximate matching method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the smoothing of a to-be-matched non-Manhattan two-dimensional graph, and obtaining a smoothed non-Manhattan two-dimensional graph; breaking a target edge in the smoothed non-Manhattan two-dimensional graph, and segmenting the target edge into a plurality of edges to obtain a processed non-Manhattan two-dimensional graph; determining the number of edges in the processed non-Manhattan two-dimensional graph, and determining the preliminary similarity between the non-Manhattan two-dimensional graph and the reference graph based on the number and the corresponding number in the reference graph; and when the non-Manhattan two-dimensional graph is preliminarily similar to the reference graph, matching the edges of the non-Manhattan two-dimensional graph and the reference graph, and determining the similarity between the non-Manhattan two-dimensional graph and the reference graph. The number of times of matching calculation can be reduced and the approximate matching speed of the non-Manhattan graph can be improved by carrying out smoothing processing on the non-Manhattan two-dimensional graph and carrying out preliminary screening on the number of the edges of the non-Manhattan two-dimensional graph.

The invention provides a task execution method and system. The execution efficiency of a multi-level task can be improved. The task execution method can be applied to the task execution system. Specifically, the task execution system obtains a task scheduling graph, the task scheduling graph is of a multi-layer nested topological structure composed of a plurality of nodes and a plurality of edges, the nodes are used for indicating tasks, the edges are used for indicating the dependency relationship between the tasks, and the multi-layer nested structure of the task scheduling graph comprises a first nested layer and a second nested layer, the first nodes in the first nested layer include a plurality of second nodes in the second nested layer. The task execution system further determines a first thread pool and a second thread pool based on the task scheduling graph, then the first thread pool is used for executing the task indicated by the node in the first nested layer, and when the first thread in the first thread pool is used for executing the task indicated by the first node, the second thread pool is used for executing the task indicated by the node in the second nested layer. And calling a plurality of second threads in the second thread pool to execute tasks indicated by a plurality of second nodes nested by the first node.

This invention provides an AI-based method, system, and platform for automatic multi-document parsing. The method includes: acquiring a candidate set of ghost references and performing cyclical sequential joint inference using a conditional random field to obtain a ghost reference set; acquiring four-dimensional feature vectors based on the ghost reference set and the original document, constructing a Bayesian network using these vectors, and obtaining the inference result through inference via the Bayesian network; creating a document heterogeneous graph based on the ghost reference set and the original document, generating aggregated text node vectors through attention aggregation based on multiple edge types, and obtaining community results using a community adversarial strategy; constructing a document dynamic graph based on the community results and temporal data, obtaining hidden states by processing the temporal and spatial dimensions of the document dynamic graph, and outputting the parsing result. This method uses ghost references as a unified analytical clue and central feature to construct a multi-dimensional joint evaluation intelligent parsing framework to improve the depth of document parsing.

The present application relates to natural language processing and cross-modal semantic understanding technical field, especially relate to a kind of demand semantic analysis method based on cross-modal fusion, including the acquisition text, structured form image and voice instruction etc.cross-modal data;Respectively using ModernBERT, LayoutLMv3 extracts text semantic vector, layout structure vector and audio semantic vector;The heterogeneous graph structure of fusing three kinds of nodes and multiple edge relations is constructed;Adopt HGT graph neural network to carry out type perception semantic propagation, generate unified cross-modal joint representation vector;The present application fuses language, image and multi-source information of voice, realizes to complex user's cross-modal data sample accurate understanding, with stronger semantic consistency, generalization ability and robustness, applicable to task scheduling, service recommendation etc.Intelligent response scene.

This invention provides an image interference method, apparatus, device, storage medium, and program product, relating to the field of data processing technology. The method includes: acquiring an attribute graph of a first image, the attribute graph including multiple nodes, multiple edges, and a node feature matrix, the node feature matrix being used to characterize the features of different nodes among the multiple nodes; generating a first perturbation adjacency matrix based on the node feature matrix, the first perturbation adjacency matrix being used to characterize the structural perturbation of different nodes; generating a first node feature perturbation parameter corresponding to each of the multiple nodes based on the multiple edges; and perturbing each node based on the first node feature perturbation parameter corresponding to each node and the first perturbation adjacency matrix, respectively, to obtain an interference image, the interference image being used to train an image recognition model. This invention can improve the effect of image interference.

Various embodiments include techniques for performing parallel edge decimation on a high resolution mesh by collapsing multiple edges in parallel by blocking only the neighbor edges of the edges selected as collapse candidates. Effectively, the disclosed techniques dynamically partition the mesh into small partitions around the collapse candidates. In this manner, the techniques identify all the edges that may be independently collapsed in a single, now parallel, iteration. Edge decimation may be performed so that certain computational geometry techniques can be efficiently applied to a simpler mesh. In so doing, the disclosed techniques preserve the history of how the edge decimation process displaces the vertices of the original mesh to generate the simplified mesh. As a result, the results of the computational geometry techniques as applied to the simplified mesh can be propagated back to the original mesh.

A system and method for estimating the graph edit distance (GED) between compounds are provided. A first graph representing a first compound contains multiple nodes and multiple edges. The atoms of the first compound are represented by nodes, and the bonds of the first compound are represented by edges in the first graph. A second graph representing a second compound also contains multiple nodes and multiple edges. The atoms of the second compound are represented by nodes, and the bonds of the second compound are represented by edges in the second graph. The first graph is input into the model to generate a first latent embedding. The second graph is input into the model to generate a second latent embedding. The estimate (GED) between the two compounds is determined as the difference between the two latent embeddings.

A method for determining the topology (2) of a heating network is proposed, wherein the heating network has several pipes, branches and / or connected heat consumers. The method according to the invention is characterized by the following steps: - Modeling the heat network using an initial graph (1) with multiple edges (E) and nodes (V), where the edges (E) are associated with the pipes of the heat network and the nodes (V) are associated with the branches and / or heat consumers of the heat network, and each edge (E) has an edge weight and each node (V) has a node weight; - Providing an objective function associated with the topology (2) of the heat network, which depends on the edge weights and / or node weights; and - Determining the topology (2) of the heat network using a growing graph algorithm as graph (2) starting from the initial graph (1), whereby the objective function is extremalized using the growing graph algorithm. Furthermore, the invention relates to a method for the construction or expansion of a heat network and a computer program product.

This application discloses an object representation method, apparatus, device, storage medium, and computer program product. The method includes: acquiring an object relationship topology graph, which includes multiple nodes and multiple edges; each node represents an object, and nodes corresponding to two objects with an association relationship are connected by at least one edge; using multiple object information sources, mining the node similarity between connected nodes in the object relationship topology graph to obtain a similarity matrix; based on the similarity matrix, performing characterization processing on each node in the object relationship topology graph to obtain a node characterization vector for each node; and using the node characterization vectors of each node as the object representation vectors of the corresponding objects. This application embodiment can effectively improve the accuracy of object representation.

The invention provides an inter-component calling relation analysis method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-analyzed byte code, and carrying out the static analysis of the to-be-analyzed byte code, so as to construct an initial calling relation graph; wherein the initial calling relation graph comprises a plurality of nodes and a plurality of edges; the nodes are components, and the edges are calling relations between the two components; configuring a lightweight probe based on the initial calling relation graph, and dynamically tracking the bytecode to be analyzed based on the lightweight probe to obtain a plurality of calling records; wherein the calling record comprises two components and a calling relation and a calling type between the two components; the calling type is a dynamic calling type which is not covered by static analysis; and performing fusion optimization on the initial calling relation graph and each calling record to obtain a final calling relation graph.