154 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.

The invention discloses an injection-production well connectivity modeling and yield prediction method and device, and relates to the technical field of oil reservoir engineering. Comprising the following steps: on the basis of preprocessed time sequence injection-production data, constructing a spatial topological graph structure according to screening conditions; constructing a yield prediction model based on the LSTM-GAT network, taking the spatial topological graph structure as the input of a graph attention mechanism, extracting the time dependence characteristics of the gas injection well and the production well by using the long short-term memory network, and extracting the spatial dependence relationship of the gas injection well and the production well by using the graph attention mechanism so as to output the predicted yield of the production well at the future moment; constructing a fitness function, and introducing a whale optimization algorithm to perform global optimization on the injection-production parameters to obtain an optimal injection-production parameter combination; and outputting the optimal injection-production parameter combination. The problems that when an existing method is used for determining the inter-well communication relation, a large amount of geological data and experiment cost are needed, the analysis process is complex, consumed time is long, and accurate optimization of injection-production parameters is difficult to achieve are solved.

The invention provides a geological reservoir fracture segmentation method fusing a 3DU-Net graph attention mechanism and graph theory connectivity detection and a system thereof, and aims at solving the technical problems of multi-modal data registration, 3D U-Net graph attention feature extraction, graph theory connectivity detection and the like. The problem of insufficient geological reservoir fracture segmentation precision caused by data heterogeneity and connectivity abnormity is effectively solved, high-precision segmentation of a complex geological reservoir fracture network is realized through accurate alignment of multi-modal data and a graph attention feature extraction and graph theory connectivity detection technology based on 3D U-Net, and the method has the advantages of high precision and high efficiency. The problems of local misjudgment and abnormal connectivity caused by the fact that a traditional method only depends on amplitude or texture features are effectively solved, and therefore segmentation precision and robustness are remarkably improved.

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 an unmanned aerial vehicle automatic homeward voyage method based on a global topological map and a graph search algorithm, which comprises a global passable area topological connected graph module, a homeward voyage path search module, a homeward voyage path optimization module and a homeward voyage path error processing module. According to the method, global path searching is carried out in combination with a Dijkstra algorithm, path smoothness, flight time and obstacle avoidance are optimized by using a B spline curve, the precision and speed of path planning are effectively improved, and meanwhile, the system can cope with changes in the environment by updating a topological connected graph and optimizing the path in real time, so that the path planning efficiency is improved. And secondly, the path safety and the flight stability are both considered, and an obstacle distance penalty term is introduced, so that the flight path can avoid the obstacle, and the collision risk is reduced.

The invention discloses a three-dimensional fracture network seepage and solute transport numerical simulation method based on topological pruning, which comprises the following steps of: analyzing a fracture network topological structure from global and local scales by applying a graph theory, establishing a quantitative relation between fracture connectivity and an underground flow field, and accurately describing an influence mechanism of topological characteristics on a seepage field; on the basis of network topology analysis, the key fracture sub-network is extracted through a pruning method, the key fracture sub-network is used for replacing a complete network for high-precision numerical simulation, and the calculation efficiency is remarkably improved while the calculation precision is guaranteed. The invention provides an efficient and reliable solution for the problems of seepage and solute transport in a hard rock fractured medium.

The invention provides a reef limestone crack connectivity prediction method and device in combination with a graph neural network, and the method comprises the following steps: S1, taking the intersection points of reef limestone crack line segments as graph nodes, and recording the three-dimensional space coordinates of each node; taking actual crack line segments between adjacent nodes as edges of the graph, and endowing each edge with an edge feature vector; the edge feature vector at least comprises a crack trend azimuth angle, a crack section length, a crack section curvature and a minimum included angle formed by the crack section and all adjacent crack sections; s2, constructing an adjacent matrix and a node feature matrix, and constructing structured graph data for describing the geometrical morphology and topological connection of the crack network in combination with the edge feature vectors; and S3, inputting the structured graph data into the graph neural network to obtain a connected probability graph. A reef limestone fracture network is converted into a graph structure and a graph neural network, and collaborative breakthrough of precision and efficiency of seepage capacity evaluation and decision reliability is achieved.

The invention discloses an oil reservoir water content prediction method based on a graph neural network and Transform, and belongs to the technical field of oil and gas field development, and the method comprises the following steps: S1, converting an oil reservoir injection-production model at each time point into a graph structure, and constructing a connection network from a water injection well to a production well; and S2, constructing a GNT proxy model in combination with the graph neural network and Transform, training the GNT proxy model, and predicting the water content of each production well. According to the constructed GNT proxy model, an oil reservoir injection-production structure is converted into graph data through a GCN module, the information transmission strength between nodes is dynamically adjusted through an edge feature enhanced message transmission mechanism, and inter-well communication features are accurately captured; and the multi-head self-attention mechanism of the Transform encoder module is combined to process the time sequence dependence, so that the long-distance time dynamic relationship is effectively modeled, and an efficient and reliable agent model is provided for the dynamic analysis of the oil reservoir.

The invention discloses a topological communication coverage path planning method and system of an unmanned ship cluster for wide-area target search and detection, and belongs to the technical field of multi-agent collaborative path planning. According to the method, turning frequency optimization is converted into a minimum rectangular coverage problem, an integer programming model is established, a layered relaxation integer programming acceleration strategy based on maximum extension candidates is proposed, and the bottleneck of model expansion and calculation efficiency caused by candidate solution space explosion in a large-scale scene is broken through; then, constructing a rectangular topological connected graph, designing an improved spectral clustering algorithm fusing load balancing constraint and subgraph connectivity guarantee, realizing efficient and balanced distribution of regions in a complex environment, and ensuring internal connectivity of subregions; and finally, providing a dynamic pruning optimization method for the minimum corner spanning tree based on strong and weak connection characteristics, iteratively evaluating the corner cost and removing redundant edges, and finally generating a coverage path spanning tree with the minimum corner cost, thereby providing an accurate and efficient full-coverage operation path for scenes such as regional exploration and environment monitoring.

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

The invention relates to a distribution network topology panoramic perception method and system based on an AI technology, and a medium. The method comprises the following steps: collecting and fusing a line public information model CIM model file and equipment ledger information in a distribution network homologous system; carrying out structure analysis and field correction on the collected data; a main feeder path is extracted, main line segments are divided, and key control switch positions and segment boundary points are identified; branch level-to-level management and modeling are supported; the graph model can be folded or unfolded for display; closed-loop splicing and overall connectivity analysis of multiple lines are realized; panoramic perception of the distribution network is realized; performing problem identification and deep analysis on the non-standardized structure of the current graph model; according to the method, automatic identification and optimization of the topological structure of the power distribution network are realized, the accuracy, the generation efficiency and the service adaptation capability of the graph model are remarkably improved, and unified support is provided for digital operation and maintenance and intelligent management of the power distribution network.

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.

Aspects of the disclosure includes methods and apparatuses for coding connectivity information of a polygon mesh. A method for decoding the connectivity information includes selecting an active vertex from a plurality of active vertices in a plurality of vertices as a pivot vertex that is to be processed based on a priority of the active vertex. The priority of the active vertex is based on one of (i) a number of processed faces to which the active vertex is incident, (ii) a sum of an angle of each processed incident corner of the active vertex, (iii) a number of unprocessed faces to which the active vertex is incident, and (iv) a number of unprocessed face fans to which the active vertex is incident. The connectivity information of the polygon mesh is reconstructed using the selected active vertex as the pivot vertex.

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.

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

The invention discloses an FMCW radar moving target detection method based on graph connectivity density, which systematically fuses a graph signal processing framework and an FMCW radar system for the first time on the basis of the prior art, and introduces graph features into moving target feature extraction through graph feature extraction of radar echo signals, so that the moving target detection accuracy is improved. A spectrum distribution diagram of a radar echo signal is converted into a connected graph, features of the connected graph are extracted for detection, moving target detection is realized, and actually measured data verify that the method improves the low signal-to-noise ratio target detection rate. According to the method, traditional signal processing and graph theory analysis are combined, and the detection capability of a weak target is enhanced by utilizing topological characteristics of a graph structure while the operation of a traditional signal detection algorithm (such as CFAR detection and a constant threshold value method) is kept.

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 present invention discloses a global optimization defect skeleton extraction system and method based on an energy function, comprising a refinement module, a node and branch extraction module, an energy calculation module, a graph construction and maximum spanning tree pruning module, and a skeleton reconstruction module: the refinement module is used to first perform binary segmentation on the defect image to be processed to obtain binary areas, and apply a morphological refinement algorithm to the binary areas to generate a preliminary skeleton; the node and branch extraction module is used to calculate the 8-neighborhood connectivity of each pixel on the skeleton; the energy calculation module calculates normalized features for each branch; the graph construction and maximum spanning tree pruning module obtains a global optimal skeleton structure based on the above-mentioned normalized features; the skeleton reconstruction module generates a final pruned skeleton according to the edges retained in the global optimal skeleton structure, which can retain the main trunk direction of the defect area, effectively remove pseudo branches, and improve the accuracy and stability of skeleton extraction.

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.