112 results about "Connectivity" patented technology

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to predicting an optimized result for a graph neural network (GNN). A system can comprise a memory configured to store computer executable components; and a processor configured to execute the computer executable components stored in the memory, wherein the computer executable components comprise: a fusion component that that models non-linear modality correlations within and across entities through Hirschfeld-Gebelein-Re'nyi maximal correlation (MaxCorr) embeddings that generates a multi-graph that preserves identities of modalities and entities; and a multi-graph neural network (MGNN) component for task-informed reasoning in multi-graphs, that learns parameters defining entity-modality graph connectivity and message passing in an end-to-end fashion.

The invention discloses a visual process automatic layout method based on dependency graph optimization, and relates to the technical field of visual layout optimization, and the method comprises the following steps: S1, carrying out the global scanning of an overall dependency graph, employing a self-adaptive structure entropy algorithm, extracting the connectivity and path span information of each node, and constructing a structure bit order vector of the connection strength between the nodes; according to the method, low coupling dependence is identified through structure bit sequence vectors and semantic confidence scores, spatial distribution of weak connection subgraphs is optimized by combining a field model and repulsive force, path smoothness and layout stability are ensured through dynamic path adjustment and local simulation optimization, and visual hierarchy definition is improved through hierarchy consistency verification and global correction. According to the scheme, the readability and interactivity of the flow chart and the stability and adaptability in dynamic updating and industrial application are remarkably enhanced.

The invention particularly relates to a CAD part feature recognition method based on a graph neural network, and the method comprises the steps: extracting geometric entity information and topological relation information based on C # and NX Open API, and generating standardized AAGJSON format data; converting the geometric data into a graph structure which comprises a topological adjacent matrix and a multi-dimensional node feature vector comprising geometric, topological and shape features; a graph neural network model comprising a graph convolution layer, a graph attention layer and a graph sampling aggregation layer is adopted for training, and feature categories and confidence degrees of holes, grooves, bosses and the like are output; and the prediction result is corrected based on geometric consistency, topological connectivity and context rationality constraint. The method has the characteristics of high recognition precision, strong adaptability and good robustness of complex parts, can be integrated in UG NX to realize real-time recognition and visualization, provides important technical support for design automation and manufacturing industry digitization, and solves the problems of low recognition precision, poor adaptability and insufficient topological information utilization in the existing CAD part feature recognition method.

The invention provides a three-dimensional steel structure full-coverage path planning inspection method based on topology reconstruction, and relates to the technical field of industrial robots, and the method comprises the steps: obtaining a model file of a to-be-inspected three-dimensional steel structure; constructing an original vertex set and an original edge set according to the model file; performing adaptive clustering on each original vertex in the original vertex set through a density-based spatial clustering algorithm to obtain a cluster; taking the geometric center of the original vertex in each cluster as a topological node, and establishing a mapping relation between the original vertex and the topological node; carrying out topology reconstruction on the original edge set, and constructing an undirected weighted graph; stitching non-connected sub-graphs in the undirected weighted graph through a K-dimensional tree and a minimum spanning tree algorithm to obtain a fully connected graph; generating a full-coverage continuous path sequence through a Chinese postman algorithm; the full-coverage continuous path sequence is converted into a three-dimensional space coordinate sequence, and the three-dimensional steel structure to be inspected is inspected according to the three-dimensional space coordinate sequence.

The invention discloses a narrow space automatic driving path planning method based on artificial intelligence, and the method comprises the following steps: S1, collecting laser radar point cloud and image data, executing semantic segmentation and clustering, and building a topological graph; s2, mapping topological graph nodes into pulse coupling neural network neurons, and constructing a double-domain coupling mechanism comprising a structure coupling item and a state coupling item; s3, generating a path direction vector, constructing a path intention gating factor, and dynamically adjusting a coupling input value; s4, periodically updating the neuron membrane potential, and adaptively adjusting the discharge threshold; s5, recording a first discharge node number and time, and generating a path activation sequence according to a discharge sequence; s6, performing connectivity check and curvature smoothing on the path activation sequence to generate a continuous track; and S7, converting the trajectory into a control instruction, and driving the vehicle to execute path tracking. According to the invention, high-precision, high-connectivity and high-control-stability output of path planning in a narrow space is realized.

This invention discloses a graph theory-based method and system for analyzing the connection, expansion, and instability of coal and rock fractures. The method includes: acquiring acoustic data through microseismic monitoring, performing location calculations and source mechanism inversion, and extracting core parameters of coal and rock fractures; comprehensively considering the geometric dimensions and directional characteristics of fractures, calculating the spatial geometric relationships between fractures, defining four topological relationships, and calculating the fracture penetration probability index (BCI); constructing a fracture network topological model based on graph theory, with fractures as graph nodes and geometric information allocated to edges, defining node types and edge directions; acquiring spatiotemporal topological attribute parameters of the fracture network, quantitatively analyzing its stage-specific time-varying trends with coal and rock deformation and the dynamic expansion characteristics of fracture network spatial propagation, identifying key modes of abrupt instability and key stages of fracture spatial expansion; and proposing a graph neural network-based fracture surface development morphology reconstruction method, providing a means to visualize the cross-scale evolution and spatial development morphology of fracture connections, expansion, and instability.

The invention discloses an overlying strata fracture evolution characterization method based on topology and graph theory, and belongs to the technical field of mine ground pressure and rock stratum control. The method comprises the steps of firstly obtaining an overlying strata fissure image and extracting a topological skeleton thereof; constructing a multi-scale filtering pure complex sequence, and extracting essential topology invariants such as a zero-dimensional Betti number beta 0 and a one-dimensional Betti number beta 1 by applying a continuous coherence theory; then, fusing fracture geometric attributes to construct a weighted topological graph model, and calculating node betweenness centrality and algebraic connectivity to analyze a network key path and overall robustness; and finally, on the basis of a dynamic evolution rule of topological invariants and graph theory indexes, a critical through criterion is constructed, and a compaction area, a gas migration area and a reservoir area are divided. According to the method, the limitation of a traditional geometric characterization method is broken through, the isolated fracture and the through network can be differentiated from topological essence robustness, the percolation critical state of integral communication of the fracture network is accurately captured, and a quantitative decision basis is provided for accurate prevention and control of gas disasters.

The application discloses a steel bar mesh welding defect identification method based on a graph neural network, generates a standardized reference image, forms a welding area candidate set, extracts a steel bar mesh structure graph, marks a suspicious welding point position, forms a steel bar mesh structure graph, executes a topology keeping graph embedding operation on the steel bar mesh structure graph, constructs a low-dimensional embedding space, outputs a node embedding representation and a connected confidence graph, divides the reference image and the steel bar mesh structure graph into a plurality of image blocks and graph structure blocks, outputs a fusion attention feature graph including local details and cross-cell continuity features, judges whether the connectivity of each candidate welding point is abnormal, locates a welding breakpoint, outputs a defect position and a breakpoint type, and performs consistency verification on a judgment result. The application significantly improves the structural consistency and anti-artifact capability of welding breakpoint detection in a complex scene.

The application relates to a simplification method, device and storage medium of a three-dimensional model, wherein the method comprises the following steps: converting a grid of a three-dimensional model into a plurality of connected graph sets, wherein each connected graph set comprises a plurality of connected graphs; for each connected graph, determining the simplification weight of the triangle edge of all triangle facets in the connected graph according to the vertex attribute of the triangle facet in the connected graph and the topological information of the triangle facet in the connected graph; and determining the to-be-simplified edge in all triangle edges according to the simplification weight of all triangle edges and a basic simplification error, and simplifying the three-dimensional model based on the to-be-simplified edge, so that the simplified three-dimensional model still maintains the original model topology.

The invention provides a method and system for calculating the topological connectivity of a two-dimensional discrete fracture network containing n-type nodes, and the method comprises the steps: carrying out the binarization processing of an original fracture image of a rock surface, and generating a fracture binary image; extracting each crack region from the crack binary image based on connected domain analysis; extracting an outer contour pixel point set and a central axis point set of any crack region; an intersection point cluster is extracted from the central axis point set, and an effective extension point, located on the central axis, of each intersection point is obtained; determining the node type of each intersection point according to the number of the effective extension points of each intersection point; and calculating connectivity parameters of any crack region according to the node type of each intersection point of any crack region. Through the method, the node types in the two-dimensional discrete fracture network can be automatically identified, accurate quantization of connectivity is realized in combination with graph theory parameters, and the analysis precision and efficiency of the complex fracture network are remarkably improved.

This invention belongs to the interdisciplinary field of digital twins and artificial intelligence, specifically disclosing an automatic generation method for manufacturing equipment control logic based on neural-symbolic collaboration and intermediate representation verification. This method utilizes a neural-symbolic collaboration strategy, employing four-element semantic model constraints and thought chain reasoning to transform unstructured natural language process instructions into structured hierarchical state machine (HSM) intermediate representations. Through linear temporal logic (LTL) formal verification and graph theory connectivity analysis, static security verification of the control logic is performed, effectively improving the system's temporal determinism and deadlock resistance. Based on this, the system performs dynamic physical simulation in a digital twin environment, extracting spatial coordinate deviations when mechanical interference occurs, and inversely mapping these deviations into structured error correction prompts, which are then fed back to the large language model for logic reconstruction. This invention constructs a cross-modal "generation-verification-correction" closed-loop mechanism, achieving deep integration of model cognitive intent and underlying physical space constraints, ultimately deterministically compiling the verified intermediate representation into highly reliable industrial standard control code.

The invention provides a method for searching a longest circuit link based on a layer connection file, and the method comprises the steps: constructing a bidirectional connected graph model based on all layers and connection relationships in the layer connection file; traversing the graph model by adopting an improved depth-first search algorithm to search for a longest path; the improvement comprises the following steps: managing via hole layer nodes and physical layer nodes as access states together, and executing complete backtracking containing the states; and after all traversal is completed, outputting the finally obtained longest path. According to the method, the complex topology can be robustly processed and the longest circuit link can be accurately extracted by constructing the bidirectional connected graph model and bringing the via hole layer into a complete backtracking mechanism of state management.

The application discloses a sequence construction method and system. The method constructs a switching matrix based on the number of various types of elements in the sequence, establishes a unified constraint system, and obtains a numerical solution of the switching matrix through optimization. Based on the numerical solution, a graph structure is constructed and connectivity is determined to determine whether a complete sequence can be formed; when the connectivity is satisfied, a target sequence that meets the switching relationship and quantity requirements is generated based on the graph structure. The method supports segment-level structure expression, modeling of the head-tail relationship of the augmented matrix, and sub-sequence combination expression, and is suitable for multiple sequence construction tasks. Accordingly, the application also provides a sequence construction system, which includes a matrix modeling module, a constraint construction module, a solving module, a connectivity determination module, and a sequence generation module.

The invention relates to the technical field of computer software demand analysis and automation, and discloses an automatic writing method and system oriented to demand analysis, and the method comprises the steps: constructing a domain knowledge graph containing a component mutual exclusion and dependency relationship, and deducing and generating a component compatibility matrix; converting the unstructured demand text into structured features by using a natural language processing algorithm; generating a virtual configuration list in combination with the atlas, and performing conflict detection based on a compatibility matrix and a supervision protocol; when violation is detected, the topological connectivity is quantified by calculating the sum of in-degree and out-degree of the component in the map, and the component is retrieved and replaced according to the topological connectivity for automatic correction; monitoring a change event, calculating an influence domain by using a weight conduction algorithm, and triggering local re-verification; and finally, generating a descriptive text, and executing consistency check by reversely extracting numerical values. According to the method, the configuration verification efficiency and the correction stability are improved through matrix operation and topology analysis, and the accuracy of the demand analysis document is ensured.

The embodiment of the invention provides a hierarchical routing data auditing method and device, and the method comprises the steps: constructing a directed graph according to a port directed connection relation in routing data, judging the connectivity of a route and the integrity of a topological structure through employing a graph theory connection component, and when a channel route passes a connectivity test and an auditing result is normal, judging whether the channel route is normal or not. Different integrity auditing rules are selected in combination with service scene characteristics, routing missing judgment is carried out, whether channel routing is complete on a service networking structure or not is judged, and connectivity and integrity of the channel routing are rapidly and accurately detected through auditing rule checking of dividing connected components by using a graph algorithm and adding data dimensions. In the checking process, the checking process does not depend on segment group sequence attributes in the routing data, and it is avoided that checking cannot be carried out due to the data quality of a shallow layer.

This invention discloses a method for generating poses of articulated objects based on physical perception and graph diffusion, relating to the field of computer vision. The method first reconstructs the componentized geometric and physical properties of objects from images using a bi-branch neural implicit network and initializes component connectivity relationships. Subsequently, it refines the component relationship graph through kinematic fitting and temporal consistency checks, and uses a physically enhanced graph diffusion process to infer the prior pose distribution of components on an SE(3) manifold. Finally, using this prior and reconstructed information as conditions, a conditional diffusion model is constructed on the SE(3) manifold, and backsampling is performed through a physically guided two-step backsampling framework to generate a diverse and physically plausible set of pose assumptions for articulated objects. This invention achieves efficient generation of diverse and highly physically plausible poses of articulated objects by tightly coupling physical laws with data-driven generation.

The invention discloses a bipartite graph-oriented maximum balance k-biplex search method. The method comprises the following steps of: 1, setting an MBKBP (Maximum Balanced Kalman Binary Pattern) model corresponding to the maximum balance k-biplex; step 2, based on an MBKBP model, proposing a scale-limited interlaced search framework; step 3, optimizing by using a plurality of pruning methods; the pruning method comprises enumeration pruning, vertex pruning and graph reduction pruning. According to the method, the strict connectivity requirement of MBBC is relaxed, the robustness is higher, and the flexibility and applicability of the model are high. According to the method, a scale-limited staggered search framework and three types of pruning optimization rules are provided, so that the search space can be remarkably reduced.

The application discloses a multi-target path optimization method and device and a medium, and relates to the technical field of path planning. The method comprises the following steps: constructing a weighted graph model of a region to be planned and verifying an adjacency matrix to obtain verified weighted graph data; calculating the shortest path distance between all node pairs to form a full-source shortest path distance matrix and a predecessor record set, performing connectivity detection and feasible region pruning processing on unreachable node pairs; generating an optimized access sequence covering all target nodes based on the processed full-source shortest path metric data and a nearest neighbor greedy strategy, accumulating a composite path cost, performing a feasibility test on a time window, a capacity and a risk constraint to generate an initial path sequence and accumulated cost data; expanding adjacent node pairs in the initial path sequence into specific executable paths on the original graph according to the predecessor record set, and performing neighborhood optimization on the executable paths by using a local search operator, and outputting an optimized final path scheme and an accumulated cost report.

The application relates to the technical field of graph structure prediction, and discloses a directed multivariate relation prediction method based on a hypergraph motif. The method first constructs an associated representation of a directed multivariate relation based on a tail node set and a head node set of a hyperlink in a directed hypergraph; secondly, based on the topology of the directed hypergraph, vertex features are generated from two perspectives of structural equivalence and local connectivity; the structural features capture the role similarity of the vertex in the high-order neighborhood through a hypergraph motif counting vector, and the connectivity features quantify the co-occurrence frequency of the vertex in the directed relation by using a hypergraph head-tail association matrix, thereby solving the feature initialization problem in the attributeless transduction learning scene; then, a global and local two-level attention mechanism is adopted to optimize the feature representation; finally, a candidate directed hyperlink scoring mechanism is constructed to evaluate from three dimensions of local topological attributes, node feature similarity and head-tail direction consistency, and the output candidate directed hyperlink is the prediction result of a real link, and the method does not depend on any external prior annotation information.

The invention discloses a fault positioning method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining key index information of a node, a container group and a service which are involved when a target cluster breaks down; performing knowledge graph construction based on the nodes, the container groups and the key index information of the services to obtain a cluster topological graph; performing fault analysis and positioning on the cluster topological graph by adopting a target analysis algorithm to obtain one or more of a fault node, a fault container group and a fault service in the cluster topological graph and fault causes of one or more of the fault node, the fault container group and the fault service; wherein the target analysis algorithm is used for performing connectivity analysis or shortest path calculation or centrality analysis on the cluster topological graph.

This invention presents a knowledge evolution poisoning attack method for graph-enhanced retrieval and generation systems, belonging to the field of retrieval enhancement and generation. Through knowledge evolution forgery attacks and multi-target cross-subgraph collaborative attacks, it can generate evolutionary corpora that satisfy temporal constraints and have stronger structural connectivity without compromising the overall consistency of the knowledge graph. This allows for a more thorough exposure of GraphRAG's vulnerabilities in knowledge extraction, community partitioning, subgraph retrieval, and evidence aggregation stages in authorized evaluation environments. Compared to direct splicing injection, the samples generated by this method more closely resemble the "real knowledge update" distribution and can be used for pre-deployment red team evaluation, post-deployment regression testing, and effectiveness verification and parameter selection for defense modules such as consistency detection, retrieval filtering, and fact verification.

This invention relates to the field of digital archives management technology, specifically to an artificial intelligence-based system for analyzing and warning of abnormal archive behavior. The invention uses a graph construction module to transform archive access logs into a relational topology graph; an attribute propagation module performs weighted transfer and accumulation of weights based on a graph traversal algorithm to calculate the cumulative relational index value of nodes; a connectivity retrieval module retrieves cross-regional links based on partition isolation rules; and an early warning output module generates early warning signals based on index values ​​and link status. This invention utilizes the flow mechanism of feature values ​​in a graph structure to aggregate minute risky behaviors within discrete long-term windows, accurately identifying complex abnormal patterns where a single operation may not violate regulations but the long-term cumulative risk exceeds the limit. This solves the problem that existing static statistical rules are unable to detect hidden logical violations, effectively reducing false alarms and false negatives.

This invention relates to a smart device connectivity maintenance method for automatic switching of multimodal communication links, aiming to solve the problems of inconsistent state data, context drift, and limited real-time adaptability of end-side models in multi-source heterogeneous links. The technical solution uses a graph neural network model to perform multimodal standardization, dynamic topology mapping, and deep extraction of spatiotemporal features on link state data, including signal quality, service load, device movement trajectory, and environmental interference information, generating a multi-candidate link switching strategy distribution. On the end side, a lightweight temporal graph neural network is used, along with cloud-based knowledge distillation and single-step strategy alignment mechanisms, to calibrate local inference model parameters in real time, improving the accuracy and robustness of switching decisions. This solution significantly improves the real-time performance, stability, and service continuity of communication link switching, and can adaptively adjust the end-side knowledge synchronization frequency to achieve optimal communication experience under end-cloud collaboration.

The application is suitable for the field of communication technology, and provides a global frequency planning method and system based on graph coloring and cost function, the method comprising: obtaining site information of to-be-planned frequencies, outputting a division result about a sub-connected graph according to the site information, and performing frequency point allocation through a graph coloring algorithm according to the principle that the frequencies between directly connected adjacent sites are different; traversing all sites, and counting a site set which has not been allocated frequencies; judging whether the site set is empty or not, when the site set is not empty, calculating an interference cost function when the site which has not been allocated frequencies and its directly connected sites are allocated the same frequency, and performing frequency allocation according to the principle that the minimum cost function is optimal, until the site set is empty, the embodiment of the application can realize automatic and rapid global frequency planning according to site positions and user working probability and other information, and can effectively reduce mutual interference between sites.

This invention provides a modeling method and simulation system for complex mobile communication networks, belonging to the field of communication network simulation technology. The system includes a communication network model initialization module, a scenario data parsing module, a communication network state update module, a communication network model data management module, a communication link connectivity calculation module, a communication link seed edge selection module, a classical graph theory algorithm module, and an information communication performance calculation module. This invention first uses a seed edge algorithm to select the primary link, reducing redundant communication links to backup links. Then, it uses a classical graph theory algorithm to abstract the communication network in operation into a weighted undirected graph, thus simplifying, abstracting, and modeling complex mobile communication networks, reducing the complexity of topology analysis, and simplifying communication network solution. This invention supports communication performance calculation during the simulation process, effectively meeting the needs of communication network models in the simulation field.

In particular, the invention relates to determining a sequence for sequentially processing a group of semi-finished products, the semi-finished products being fixed as end semi-finished products in said sequence. A graph (Gr) represents a set of semi-finished products, each of which is represented by nodes. To determine the processing order, candidate paths are determined on the graph by gradually adding nodes to the paths in construction. During path construction, a predictive feasibility test is performed based on a portion (G) that has not been explored in the graph (Gr). The test is positive in that the portion (G) of the graph supplemented with an auxiliary arc (a), which is a directed arc connecting an end node (e) of the graph (Gr) to the candidate node (cn) considered, is a strongly connected graph.

The invention provides a road network simplification method and device based on a multi-branch graph neural network, equipment and a storage medium, and relates to the technical field of road network simplification. Extracting global topology, local neighborhood and attention weighted features of the road network through multi-branch graph neural network fusion, and generating uniform node embedding through linear fusion; then identifying redundant nodes based on unsupervised node importance evaluation and dynamic threshold optimization; and a multi-criterion collaborative progressive pruning strategy is adopted, key nodes are protected, and dynamic connectivity restoration is implemented. According to the method, the geometric fidelity, the topological consistency and the semantic integrity are ensured, and meanwhile, efficient self-adaptive simplification of the road network is also realized.

The invention discloses a method for realizing bilateral consistency of a multi-agent system under sequence scaling attack, and relates to the technical field of intelligent control. The method comprises the following steps: constructing a leader-follower multi-agent system model, depicting an interaction relationship of multiple agents through a Laplacian matrix based on a connected and structurally balanced undirected signed graph, designing a distributed sequence scaling attack model to input attacks to the multi-agent system model, defining bilateral consistency variables and estimation errors, and constructing a multi-agent system model. Setting a dynamic event triggering mechanism, designing a controller formula on each edge, solving a feedback gain matrix according to an algebraic Riccati equation, substituting the feedback gain matrix into the controller formulas to obtain a result, substituting the result into a multi-agent system model, and judging bilateral consistency according to an obtained initial state; by adopting the method for realizing bilateral consistency of the multi-agent system under the sequence scaling attack, the robustness of the multi-agent system for resisting the mainstream hostile attack is obviously enhanced.

This specification provides an AI-based energy edge platform with a wide range of functions, components, and capabilities aimed at managing and improving legacy infrastructure, coordinating with distributed systems, and supporting critical use cases across various enterprises. The AI-based energy edge platform may comprise a graph neural network including a set of nodes, each representing at least one distributed energy resource (DER), and a set of edges interconnecting these node sets. Each edge represents at least one energy-related characteristic between at least two nodes within the node set. The platform may incorporate emerging technologies to achieve efficiency, agility, engagement, and profitability for the ecosystem and individual energy edge nodes. Examples include forecasting, planning, and managing energy demand and utilization in larger-scale distributed environments. Examples leverage intelligent provisioning, data aggregation, and analytics, and utilize a platform to enable connectivity, communication, and trading in energy markets.