10 results about "Temporal similarity" patented technology

The invention discloses a main earthquake group intelligent identification method considering a micro-earthquake contour coefficient and spectrogram clustering. The method comprises the following steps: 1, acquiring micro-earthquake monitoring data; 2, acquiring a spatial similarity matrix, an energy similarity matrix and a time similarity matrix, and weighting to form a comprehensive similarity matrix; 3, forming a feature matrix based on the comprehensive similarity matrix, and mapping data points in the feature matrix into two-dimensional space coordinates; 4, clustering data points in the two-dimensional space coordinates by adopting a DBSCAN algorithm, and obtaining a neighborhood radius and a minimum sample number optimization combination based on a micro-seismic contour coefficient; and 5, under the optimization combination of the neighborhood radius and the minimum sample number, clustering data points in the two-dimensional space coordinates by adopting a DBSCAN algorithm to obtain a plurality of micro-seismic event optimization clusters. And 6, screening out a micro-seismic event cluster with the highest comprehensive feature score as a main seismic group. The method is reasonable in design, and the main earthquake group is selected by fusing evaluation indexes of space density, energy characteristics and time characteristics.

The application provides a method, device and equipment for identifying a relationship between trajectories. The method of the application connects the starting points of two target trajectories and the ending points of the two target trajectories to form a closed trajectory line containing the two target trajectories. The total area and total perimeter of the closed area surrounded by the closed trajectory line are determined, the similarity distance between the two target trajectories is determined according to the ratio of the total area to the total perimeter, the average time difference between the two target trajectories is determined according to the time stamps of the trajectory points in the trajectory point sequences of the two target trajectories, and whether the two target trajectories have a relationship is determined according to the similarity distance and the average time difference, the spatial similarity and the time similarity. The method can accurately identify whether the two trajectories have a relationship, has low computational complexity, and improves the efficiency of the relationship analysis.

The invention discloses a reservoir group long series scheduling process multi-objective optimization method based on space-time similarity dimensionality reduction, and belongs to the technical field of reservoir scheduling of hydraulic engineering. Performing space-time multi-scale dimension reduction on the scheduling process to obtain a dimension-reduced scheduling process; according to the scheduling process after dimension reduction, based on a multi-objective optimization scheduling algorithm, similarity judgment is carried out on the offspring individuals; directly reusing the parent scheduling process for the individuals which are determined to be similar to obtain corresponding objective function values, and scheduling the reservoir group scheduling model for the individuals which are determined to be dissimilar to obtain corresponding objective function values; and updating the non-dominated solution set according to the obtained objective function value, and generating an optimized reservoir group length series scheduling process. According to the method, the optimized reservoir group length series scheduling process can be quickly generated on the premise of ensuring the quality of the solution set.

This invention discloses a method and system for spatiotemporal prediction of charging station load based on multi-source matrix fusion. The method includes: collecting historical load data and geographical coordinates of charging stations, performing standardization and sample partitioning; constructing in parallel a prior adjacency matrix based on geographical distance, a temporal similarity adjacency matrix based on dynamic time warping distance, and a dynamic adaptive adjacency matrix based on learnable node embedding; adaptively weighting and fusing the three matrices through trainable scalar parameters to generate a fused adjacency matrix; and constructing and utilizing a spatiotemporal graph convolutional network for load prediction based on the fused adjacency matrix as the spatial relationship basis. This invention comprehensively improves the accuracy and reliability of spatiotemporal prediction of charging station group load by fusing complementary spatial relationships from multiple sources, enhancing the robustness of temporal similarity measurement through dynamic time warping, and combining data-driven adaptive learning.

Ultrasonic image processing method and ultrasonic imaging device The present invention relates to an ultrasonic image processing method comprising at least: - decomposition (204) into singular values ​​of each image of at least one initial sequence of ultrasonic images, calculating spatial eigenvectors and / or temporal eigenvectors of each of the images; - partitioning (206) of said spatial eigenvectors and / or said temporal eigenvectors, distributing said spatial eigenvectors and / or said temporal eigenvectors into several groups of spatial eigenvectors and / or several groups of temporal eigenvectors on the basis respectively of spatial and / or temporal similarities; - calculation (208) of at least one final sequence of images by concatenation of said spatial eigenvectors and / or said temporal eigenvectors of one of the groups of vectors. Figure for the summary: Fig. 2

The application discloses a brain function subregion division method and device, equipment and medium, and relates to the technical field of biomedical imaging signal processing. The method comprises the following steps: after obtaining initial time characteristics and initial space characteristics of target fMRI data, performing feature extraction on the initial time characteristics and the initial space characteristics by using a trained automatic encoder to obtain deep time characteristics and deep space characteristics, considering spatial, time and hidden nonlinear characteristics, and emphasizing the properties of space-time interaction and complementation; performing spectral clustering analysis on a final similarity matrix which is constructed by iteratively fusing a time similarity matrix and a space similarity matrix of a similarity network, to obtain a brain function subregion division result of a user. The application improves the comprehensiveness and accuracy of brain function subregion division.

A temporal sequence of multi-modal signals is generated from a feature probe signal, a relation probe signal, and attribute probe signal, and multi-modal signals are selected from the temporal sequence of multi-modal signals. The selected multi-modal signals are compared to a model multi-modal embedding space cluster to generate the multi-modal temporal similarity measures. The multi-modal temporal similarity measures are compared to a model similarity measure boundary to generate object recognition verification data associated with an object classification.

This invention relates to the field of traffic flow prediction technology, and in particular to a multi-task traffic flow prediction method and system for highway networks based on deep learning. This invention focuses on the correlation and temporal similarity between inbound and outbound traffic flow, using multi-task learning technology to characterize their common features. These common features are then used as input for feature fusion, enabling multi-task collaborative prediction of inbound and outbound traffic flow at target stations on highway networks. By employing a deep learning traffic prediction model, the invention delves into the spatiotemporal correlation characteristics of highway network traffic and the changing patterns of the influence of different external factors, thereby improving the accuracy of traffic flow prediction.

The invention discloses an irregular multivariable time sequence prediction method and device based on patch block pre-alignment, which are applied to an irregular multivariable time sequence prediction model, and the method comprises the following steps: a patch block aligner performs efficient mapping on an irregular time sequence to obtain an aligned regular time sequence; carrying out layer-by-layer aggregation on the fine-grained patch blocks, the medium-grained patch blocks and the coarse-grained patch blocks by a multi-scale time encoder to construct multi-scale time embedding features; the group mixer carries out fusion of time period features and channel features on the multi-scale time embedding features to obtain global representation; and the prediction output module processes the global representation to obtain an irregular time sequence prediction result. According to the method provided by the invention, an efficient patch block type pre-alignment mechanism is introduced, the multi-scale time similarity is reserved in the modeling process, the problem that a traditional prediction algorithm is difficult to adapt to irregular sampling data is solved, the limitations that an existing irregular prediction model is unstable in performance, too large in calculation overhead and the like are overcome, and the prediction precision is remarkably improved.

The application relates to the field of network security and management, and provides a causal reasoning method and system for video multi-modal traffic. The method comprises the following steps: preprocessing original network traffic data to obtain traffic data sequences; setting threshold conditions of video streams based on the traffic data sequences, and identifying the video streams; determining the correlation between the video streams and other related streams by adopting time similarity measurement weights, to obtain a preliminary core traffic set meeting the conditions; based on the preliminary core traffic set, the independence of the video streams and other related streams is tested by adopting conditional independence, to obtain a core traffic set; based on the core traffic set, video traffic features are extracted, the causal relationship between a live broadcast scene and the video traffic features in structure and parameters is analyzed, the live broadcast scene is reasoned, and the label of the live broadcast scene is obtained.