9 results about "Dual graph" patented technology

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

The invention belongs to the technical field of pipeline safety assessment, and discloses a multi-dimensional information interconnection and autonomous evolution collaborative pipeline full-state safety assessment method. And capturing a high-order relationship of data through double hypergraph reasoning of the instance-level hypergraph and the modal-level hypergraph to realize efficient interconnection. According to the method, mode-level and instance-level hypergraph information features are extracted through hypergraph information propagation, high-order correlation is mined through double-graph information aggregation, cross-mode and cross-instance consistency information and exclusive information are output after feature recombination, multi-dimensional data deep fusion is promoted, and high-quality data support is provided for follow-up pipeline full-state safety assessment. A two-stage autonomous evolution mechanism of intra-class progressive calibration and inter-class knowledge migration is respectively adapted to slight fluctuation and significant change scenes of the deep sea environment: precise adaptation of environment perturbation is realized through dual-branch feature extraction and dynamic weight adjustment in a domain; model parameter dynamic optimization is completed between domains through spatial-temporal feature clustering and cross-domain knowledge migration, and dynamic environment self-adaption can be achieved without manual intervention.

The invention relates to a structure-semantic double-graph collaborative SAR image quality evaluation method, which comprises the following steps of separating an SAR image, and constructing a double-channel quality measurement set for independent modeling; indexes are obtained, correlation calculation is carried out, an index map is constructed, and each node corresponds to one index; on the basis of the index map, the dependency relationship between indexes is calculated from the structure dimension and the semantic dimension through self-supervised learning, the dependency relationship of the two dimensions is integrated, and an image level score based on the integration result is output; and constructing a measurement index shared with system analysis and a measurement index special for information retrieval, and carrying out quality measurement evaluation: after extracting a structure sub-graph, integrating an IR special index which is not contained in SAR prior into a global graph. The method is superior to a reference model in the aspects of recognition accuracy, recall rate, precision and efficiency, and a solution with high universality and good interpretability is provided for cross-modal image quality evaluation.

The invention relates to the technical field of data analysis, and provides a dynamic hierarchical rendering and context awareness-based report header management method, which comprises the following steps of: acquiring financial index historical data, identifying a supervision period type, processing time sequence data through a supervision period embedding layer and a three-layer attention mechanism of a hierarchical time sequence encoder, and performing dynamic hierarchical rendering and context awareness; outputting a time sequence weight feature vector; constructing a main graph and a constraint graph by using a dual graph convolutional network, propagating information through a constraint perception message transfer function, adjusting a graph topological structure according to a constraint default degree, and outputting an index relation weight matrix; inputting the time sequence weight feature vector and the index relation weight matrix into a neural symbol fusion network to obtain a final display weight value; and sorting and layering the financial indexes by adopting a dynamic layering strategy based on the weight values, and outputting a layered rendered report header. According to the method, the accuracy and adaptability of business index importance weight prediction are improved, and intelligent header layout optimization is realized through a dynamic layering strategy.

This invention provides a method and system for crack recognition in ground-penetrating radar (GPR) images based on frequency-domain enhanced YOLOv11. The method comprises the following steps: S1, constructing a GPR B-scan image dataset containing road cracks and dividing it into training and validation sets; S2, constructing a PFAE-YOLOv11 feature extraction model, which embeds a pyramid frequency attention extraction module into the YOLOv11 backbone network; S3, training the PFAE-YOLOv11 feature extraction model using the training and validation sets to optimize network parameters; S4, inputting the GPR image to be identified into the trained model and outputting initial detection bounding boxes for the cracks; S5, constructing a dual graph convolutional inference module, using the initial detection bounding boxes output in step S4 as graph nodes, and using a graph convolutional network to infer the topological relationships of the cracks, outputting corrected continuous crack paths. This invention significantly improves the accuracy, completeness, and anti-interference capability of crack detection in GPR images.

The present application relates to the technical field of computer-aided design, in particular to a distribution box thermal fault prediction system based on a mechanism model, which comprises a model voxelization processing module, used for calling a three-dimensional model of a distribution box containing busbars and circuit breaker components, performing spatial discretization on the three-dimensional model of the distribution box, establishing thermal conduction connection edges between adjacent voxel units, and generating a voxelized dual graph.In the present application, the number of non-empty grids is counted and the streamline fractal dimension is calculated by covering different scale space grids, so that the geometric distribution characteristics of the flow field are converted into quantifiable complexity indicators, and then the flow field dimension attenuation coefficient distribution is generated in combination with the reference dimension value under the standard operating condition, thereby realizing quantitative identification of the dust deposition state of the air inlet.The thermal fault prediction is extended from a single temperature criterion to the structure coupling and flow evolution level, the forward-looking of thermal anomaly identification is improved, and the calculation burden caused by the model size is reduced.

The present application belongs to the technical field of pipeline safety assessment, and discloses a pipeline full-state safety assessment method based on multi-dimensional information interconnection and autonomous evolution coordination. The high-order relationship of data is captured through dual hypergraph reasoning of instance-level hypergraph and modal-level hypergraph, and efficient interconnection is realized. The modal-level and instance-level hypergraph information features are extracted through hypergraph information propagation, the high-order correlation is mined through dual graph information aggregation, and the cross-modal and cross-instance consistent information and exclusive information are output after feature reorganization, which promotes the deep fusion of multi-dimensional data and provides high-quality data support for subsequent pipeline full-state safety assessment. The two-level autonomous evolution mechanism of intra-class gradual calibration and inter-class knowledge transfer is respectively adapted to the scenes of slight fluctuation and significant change of deep sea environment: within the domain, the dual-branch feature extraction and dynamic weight adjustment are realized to accurately adapt to the environmental perturbation; between the domains, the spatiotemporal feature clustering and cross-domain knowledge transfer are completed to realize the dynamic optimization of model parameters without manual intervention, and the dynamic environment self-adaptation is realized.

The invention belongs to the technical field of electronic packaging material analysis, and discloses a modeling method for a polycrystalline porous microstructure of a sintered nano-silver material, and the method comprises the steps: firstly carrying out the binarization processing of an SEM image of sintered nano-silver, and separating out a pore region and a solid-phase region; then extracting the contour of the pore region to obtain a discrete point set on the contour; based on the discrete point set, constructing geometric constraint conditions representing pore boundaries; then, by taking the geometric constraint condition as a boundary which cannot be invaded, performing constrained Delaunay triangulation in the solid-phase region, and generating a constrained Delaunay triangulation network; and finally, extracting a geometric dual graph of the constrained Delaunay triangulation network, and generating a Voronoi graph. According to the method, the limitation of simplifying pore morphology or neglecting solid-phase anisotropy in a traditional method is overcome, and a real and reliable geometric model basis is provided for high-precision finite element simulation.

This invention proposes a path search method, electronic device, and storage medium that considers turning delays, belonging to the field of path search technology. It includes: S1. Establishing a mapping relationship based on the features of the original directed graph; S2. Establishing a mapping relationship from the original directed graph to the dual graph and creating the dual graph; S3. Adding edge weights of the dual graph based on the weights of the original directed graph; S4. Performing path search based on the dual graph; S5. Restoring the shortest path in the dual graph to a path in the original directed graph. This invention transforms the original directed graph into a dual graph, converting the edges of the original directed graph into nodes of the dual graph, and vice versa. The network size remains unchanged before and after the transformation, without altering the physical structure of the network. It also considers edge-to-edge turning delays, supporting path search starting with nodes and path search starting with directed edges. This solves the problem of low computational efficiency in existing path search methods that consider delays.