5 results about "Sequence graph" patented technology

This invention relates to the field of structural explosion dynamic response calculation, specifically disclosing a heterogeneous graph data representation method for structural explosion dynamic response analysis. The method includes: establishing and validating a high-fidelity finite element baseline model of the structural explosion response, and obtaining a baseline data source for the structural explosion response; establishing and implementing an adaptive sampling algorithm based on physical field gradients to adaptively select key nodes representing the structural dynamic characteristics from the finite element mesh nodes; establishing a multi-attribute graph edge linking and weight quantization method to construct a multi-attribute weighted graph; and establishing an automated construction and storage method for spatiotemporal graph datasets to generate a spatiotemporal sequence graph dataset for training a graph neural network. This invention overcomes the limitations of traditional neural networks on the serialization and meshing of training data, achieving automated, high-fidelity conversion from continuous, heterogeneous finite element simulation data to sparse, discrete graph structure data with well-defined topological relationships, as well as data size simplification and increased physical information density.

The present application relates to the field of graph neural network, deep learning and recommendation system, in particular to a point of interest recommendation method based on time sequence gating graph neural network; the method constructs and trains a user long-term preference model, and uses the trained user long-term preference model to recommend a point of interest for a user; the user long-term preference model comprises a check-in sequence graph construction module, a time sequence gating graph neural network module (TGGNN module), an attention mechanism module and a probability prediction module; the present application represents the check-in activity of a user by constructing a point of interest (POI) check-in sequence graph, realizes the propagation of effective features, designs a time sequence gating graph neural network to dynamically update node vectors by fusing time context information, fully considers the time relationship between nodes in the check-in sequence graph, and can also obtain the complex conversion between different check-in points.

A method and a device for predicting an event to occur in a device are provided. The method includes identifying a plurality of events that occurred in the device; generating a plurality of sequence graphs based on a time-series order of the plurality of events; generating a sequence group graph by combining the plurality of sequence graphs based on relationships between nodes and edges in the plurality of sequence graphs; obtaining a computation graph by sampling the generated sequence group graph based on time-series order information of the plurality of events; and predicting the event to occur in the device using a graph neural network (GNN) based on the obtained computation graph.

An encryption and decryption method and apparatus based on uncertain graphs, relating to the fields of computer and network communication technologies, the encryption method includes: A1. Obtaining a plaintext string of length n, constructing a sequence graph and a sequence matrix using the ASCII code value corresponding to each character as a vertex and the difference between the ASCII code values ​​corresponding to adjacent characters as edges; A2. Generating a sequence of uncertain implication subgraphs and an adjacency matrix of uncertain implication subgraphs based on the number of vertices in the sequence graph, and making the non-invertible uncertain implication subgraphs in the sequence graph invertible to obtain a list of adjacency matrices of invertible uncertain implication subgraphs; A3. Dividing the value of the first vertex of the sequence graph by 2... n The quotient and remainder are obtained. The quotient is added to the order matrix to obtain the encryption matrix. The invertible matrix with the same index and remainder is queried from the adjacency matrix list of the invertible uncertain implication subgraph. Finally, the encryption matrix is ​​multiplied by the invertible matrix to obtain the ciphertext. This solves the quantum risk and structural incompatibility problem of traditional encryption and improves the security of the string.

Embodiments of the present application disclose an attendance recording method and device, electronic equipment and storage medium, the method comprising: obtaining a gait sequence graph of an employee from a collected walking video of the employee; obtaining a gait gradient direction histogram feature and a gait sequence gravity center feature of the employee based on the gait sequence graph; performing feature correlation fusion on the gait gradient direction histogram feature and the gait sequence gravity center feature through kernel canonical correlation analysis, to obtain a gait fusion feature vector of the employee; and recording the attendance of the employee based on an identity label of the employee determined based on the gait fusion feature vector. The technical solution of the present application avoids the loss of effective gait features of the employee when performing feature correlation fusion on the gait gradient direction histogram feature and the gait sequence gravity center feature, solves the problem of incorrect attendance recording caused by the loss of effective gait features of the employee, improves the accuracy of attendance recording, and achieves the purpose of simplifying the attendance process and reducing the workload of the attendance personnel.