14 results about "Adjacency relation" patented technology

Disclosed is a network and method for creating and searching a graph in a massively parallel manner. The network includes a plurality of graph storage instances that collectively store a graph formed from a plurality of entities and comprising a vertex for each entity, edges connecting pairs of vertices, and adjacency relations. Each graph storage instance stores a subgraph of the graph and comprises a partition with a non-overlapping vertex set of all the vertices in the respective subgraph, an edge set of all the edges between vertices in the respective vertex set, and the adjacency relations for the edge set and to any edges outside the partition, to which the edge set connects. Each graph storage instance also includes an executor for executing one or more of the actions over the respective subgraph, and an action monitor for supplying actions to the respective executor.

The invention relates to a building layout generation method and system based on a generative adversarial network. According to the method, original node features and adjacency relations of a building are collected, and a bubble graph topological structure is constructed to serve as a constraint basis; a message aggregation mechanism of the graph neural network is utilized, weight self-adaptive regulation and control are carried out in combination with the initial probability distribution of the nodes, and an initial dynamic layout graph is generated; for the soft constraint node probability distribution, probability reweighting processing is carried out based on a conflict detection result, and updated spatial constraints are formed; and finally, through multiple rounds of adversarial training of the adversarial generative network, realizing iterative optimization and correction of the dynamic layout diagram. Therefore, the adaptability, the coordination and the conflict resolution capability of the layout scheme are remarkably improved while the topological compliance of the building function space is ensured.

Disclosed is a network and method for creating and searching a graph in a massively parallel manner. The network includes a plurality of graph storage instances that collectively store a graph formed from a plurality of entities and comprising a vertex for each entity, edges connecting pairs of vertices, and adjacency relations. Each graph storage instance stores a subgraph of the graph and comprises a partition with a non-overlapping vertex set of all the vertices in the respective subgraph, an edge set of all the edges between vertices in the respective vertex set, and the adjacency relations for the edge set and to any edges outside the partition, to which the edge set connects. Each graph storage instance also includes an executor for executing one or more of the actions over the respective subgraph, and an action monitor for supplying actions to the respective executor.

The present invention provides a method for converting an irregular table structure into a JSON file, comprising obtaining an irregularly divided original table file, extracting each sub-cell in the original table file as an independent node to form a node set; based on the spatial position information of the node, traversing the neighbor nodes that satisfy a predetermined adjacency relationship of each node to construct an adjacency graph structure; calculating the text cosine similarity, text type matching score and context semantic score between two adjacent nodes according to the text content and corresponding type of the node, and deleting the connection edges without semantic association in the adjacency graph structure; deleting and updating the direction with the smallest number of connection edges in the adjacency graph structure; parsing and organizing the table semantic structure according to the connection relationship retained between nodes in the updated adjacency graph structure to generate a nested JSON file. Implementation of the present invention can efficiently process complex table structures, improve the accuracy of structured extraction and the semantic consistency of JSON expression.

The invention discloses an adjacent relation-based dense graph quick sorting method, which comprises the following steps of: extracting each independent connected region from an image through a connected region algorithm, calculating an average row spacing and an average column spacing, obtaining a position approximate to the center of the image and searching a nearest point location of the whole image; searching a nearest point location in the negative direction of the X axis from the current point, taking the point location as the current point, performing loop iteration again until no appropriate point is found in the negative direction, searching a nearest point location in the positive direction of the X axis from the current point in a manner similar to that in the negative direction until no appropriate point is found in the positive direction, and taking the point location as the current point; then, a nearest point is searched upwards from the position where a middle point of a whole line is found, the found nearest point needs to conform to the range of the line spacing, the operation of the previous steps is executed after an appropriate point position is found, and iteration is conducted upwards and downwards till all point sets are found out. The method overcomes the defect that image correction is not carried out or a small number of connected regions are lost and can also carry out correct arrangement.

The application provides a three-dimensional model retrieval method and system based on high-order adjacency relation constraint, and belongs to the technical field of three-dimensional model retrieval, and comprises the following steps: obtaining a three-dimensional model to be queried, rendering an illumination view and a depth view of the three-dimensional model to be queried, and splicing the illumination view and the depth view into multi-channel information; based on the multi-channel information, an initial feature is extracted by using a feature extraction model to obtain initial feature descriptors of each channel; the initial feature descriptors between channels are used for initial grouping of multi-views based on cosine similarity; a high-order adjacency relation matrix is used for representing the structural correlation degree of each view, the initial grouping is optimized based on the high-order adjacency relation matrix to obtain a final grouping result, and final feature descriptors are obtained based on the final grouping result; and similarity between the final feature descriptors and model features in a feature database is measured to obtain a three-dimensional model retrieval result. The application improves the stability and accuracy of three-dimensional model retrieval.

The invention relates to the field of graph calculation, in particular to a data use method and device in a graph calculation hybrid calculation mode. The method mainly comprises the following steps: partitioning vertexes according to weights of the vertexes and edges, and dividing the vertexes in each partition to corresponding computational nodes; each computing node stores the divided vertexes, a row and column sparse compression matrix is used for storing the adjacency relation of the vertexes and the edges, and the vertexes and the edges stored in each computing node are segmented and reconstructed according to the adjacency relation, so that the reconstructed vertexes and the reconstructed edges can adapt to all computing modes; and obtaining a current calculation mode according to the number of the currently active vertexes and the number of the currently active edges, and performing data processing in the current calculation mode by using the reconstructed vertexes and the reconstructed edges. According to the method, the storage representation mode of the graph data can be flexibly and efficiently converted in a mixed graph calculation mode system, and the calculation preposition requirements of different graph calculation modes are met.

The invention provides a method for converting an irregular table structure into a JSON (JavaScript Object Notation) file, which comprises the following steps of: acquiring an irregularly divided original table file, and extracting each sub-cell in the original table file as an independent node to form a node set; on the basis of the spatial position information of the nodes, traversing neighbor nodes meeting a predetermined adjacency relation in each node to construct an adjacency graph structure; according to the text content and the corresponding type of the node, calculating the text cosine similarity, the text type matching degree score and the context semantic score between two adjacent nodes, and deleting the connection edge without semantic association in the adjacent graph structure; deleting and updating the direction with the minimum number of connecting edges in the adjacent graph structure; and analyzing and organizing a table semantic structure to generate a nested JSON file according to the connection relationship reserved among the nodes in the updated adjacent graph structure. By implementing the method, a complex table structure can be efficiently processed, and the structured extraction accuracy and the semantic consistency of JSON expression are improved.

The invention provides a communication method and a communication device. The method comprises: obtaining a first adjacency relationship, the first adjacency relationship being used for indicating an adjacency relationship between at least two positions in N positions, the first adjacency relationship being determined according to first information, the first information comprising transmission time information of reference signals transmitted on the at least two positions; and training a first model according to the first adjacency relation, wherein the first model is used for positioning. According to the method and the device, the positioning precision of the first model can be improved under the condition that the first model is trained by using less label data.

The application discloses an implementation method, system and device of a distributed original graph and a storage medium. Access to vertices / edges of the whole distributed system is directly based on global array subscripts, is fast, avoids calculation, and automatically maintains adjacency relations. When N-hop query is performed, access is directly based on global array subscripts instead of keys, various comparison operations required in various tree structures are avoided, and the performance of array subscript access is improved by up to 190 times compared with key-based access.

The invention discloses a realization method and a query method for a materialized adjacency index of a relational database, and the method comprises the steps: introducing the materialized adjacency index in the relational database to identify the relation between different records in two tables, storing the connection relation between the two tables in the materialized adjacency index, and identifying a specific relation through a RowID partner pair; the materialized adjacency index is created according to the following steps: creating an adjacency index, and performing structured organization on data through the adjacency index; performing adjacency relation query by utilizing the adjacency index to obtain an adjacency data set; materializing the adjacent data set into an adjacent index to realize that the record in the materialized adjacent index is an edge in the graph model, and each edge passes through RowID-gt; and performing RowID marking.

The invention provides a three-dimensional model retrieval method and system based on high-order adjacency relation constraint, and belongs to the technical field of three-dimensional model retrieval, and the method comprises the steps: obtaining a to-be-queried three-dimensional model, rendering an illumination view and a depth view of the to-be-queried three-dimensional model, and carrying out the channel splicing of the illumination view and the depth view to form multi-channel information; based on multi-channel information, adopting a feature extraction model to extract initial features to obtain initial feature descriptors of all channels; performing initial grouping on the multiple views based on the cosine similarity of the initial feature descriptors among the channels; representing the structural correlation degree of each view by adopting a high-order adjacency relation matrix, optimizing the initial grouping based on the high-order adjacency relation matrix to obtain a final grouping result, and obtaining a final feature descriptor based on the final grouping result; and performing similarity measurement on the final feature descriptors and model features in a feature database to obtain a three-dimensional model retrieval result. According to the method, the retrieval stability and accuracy of the three-dimensional model are improved.

A vertical stacking topology reconstruction method based on adjacency data and graph traversal, belonging to the field of measurement, is presented. The method includes acquiring adjacency data representing confirmed vertical relationships between containers; constructing a graph structure where each container is represented as a node, and each confirmed adjacency relationship is represented as a directed edge from a lower container to an upper container; identifying the bottom container, defined as a node with no incoming edges; assigning a vertical hierarchy index to each container through bottom-up graph traversal; and generating a stacking map representing the vertical arrangement of containers. By reconstructing the stacking topology using locally acquired adjacency data and graph modeling, discrete adjacency events are transformed into a directed graph, and vertical hierarchy is assigned through structured traversal. This eliminates the need for centralized coordination or predefined stacking patterns, enabling infrastructure-independent deployment. It can reconstruct the stacking topology using locally acquired adjacency data, exhibits determinism, supports decentralized deployment, and adapts to dynamic container arrangements, without relying on fragile or expensive sensing systems.

This invention discloses a method for evaluating the response delay of an electromagnetic clutch based on edge computing, comprising: acquiring multi-source time-series data and preprocessing it to generate a multi-channel sample sequence; calculating the local similarity between channels to establish a time-varying adjacency relation matrix sequence; aggregating the time-varying adjacency relation matrix sequence and weightedly fusing it with a physical topology matrix to form a topological constraint matrix; performing Laplace eigenmaps to generate low-dimensional manifold eigenvectors; mapping the low-dimensional manifold eigenvectors and inputting them into an improved quantum heuristic neural network model to generate a quantum hidden space representation; measuring the quantum hidden space representation to generate a response delay estimate and confidence level; and performing adaptive correction to generate the final delay result. This invention achieves accurate evaluation and dynamic correction of the response delay of an electromagnetic clutch by fusing Laplace eigenmaps and an improved quantum heuristic neural network model.