17 results about "Temporal similarity" patented technology

The invention discloses an office building energy consumption prediction method and system, and relates to the technical field of energy consumption prediction, and the method comprises the steps: collecting the current office building energy consumption data; decomposing the load sequence into a plurality of modal components, calculating the sample entropy of each modal component, and recombining the modal components by using a K-means clustering algorithm according to the calculation result of the sample entropy; taking each mode component after recombination as a node, taking the time sequence similarity between the mode components as an edge between the nodes, and constructing a graph structure; inputting the graph structure into a GCN-Transform model, extracting spatial features of nodes in the graph structure, and introducing a self-attention mechanism to extract time sequence features; inputting the spatial-temporal characteristics into a full-connection layer to obtain an energy consumption predicted value in a future period of time; according to the method, space cooperation and time dependence can be considered at the same time in a complex and changeable energy consumption scene, so that a more accurate prediction result is provided.

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 present disclosure proposes an image processing method, apparatus, imaging device, electronic device and storage medium, wherein the apparatus first obtains an original frame and at least one reference frame in a video, wherein the at least one reference frame includes a previous frame of the original frame, and then performs spatial denoising on at least one pixel group in the original frame to obtain a corresponding spatial denoising pixel group, and then determines a corresponding fusion coefficient based on the temporal similarity between the spatial denoising pixel group and the corresponding pixel group in at least one reference frame, and fuses the spatial denoising pixel group with the corresponding pixel group in the previous frame according to the corresponding fusion coefficient to obtain a corresponding target pixel group, thereby generating a target frame after denoising the original frame. This achieves spatiotemporal denoising using different denoising intensities for different pixel groups in the original frame, and the denoising intensities are also different for different original frames in the video, thereby avoiding the use of a fixed denoising intensity for denoising and improving the denoising effect of the video.

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.

The present invention discloses a method for intelligent prediction and analysis of tripping of low-voltage shunts in distribution networks based on dual similarity. The method judges whether the operating status of low-voltage shunts in distribution networks is normal based on the similarity of operating modes and the similarity of time. First, a typical operating mode set is obtained based on k-means, and the similarity of operating modes is measured using Euclidean distance; then, the temporal similarity is modeled based on LSTM, and intelligent prediction and analysis of tripping is performed based on the time continuity of high-resolution continuous measurement. Through the dual similarity method, the proposed solution can effectively identify typical abnormal phenomena in the distribution network with high accuracy and recall rate. The present invention has been extensively evaluated by verifying a series of actual tripping events of 75 distribution transformers, confirming that the proposed solution is superior to traditional fault detection solutions.

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.

The invention discloses an intelligent agricultural information management platform based on cloud computing, and relates to the technical field of agricultural information management.The intelligent agricultural information management platform is characterized in that space and time similarity parameters are preset, an exponential decay weighting algorithm is adopted, and missing and original data are efficiently integrated; capturing a spatial dependency relationship by using a graph neural network, and mining a data time sequence dynamic state in combination with an LSTM model to realize an accurate decision; the edge real-time response unit monitors field abnormity through an automatic encoder, and once deviation is detected, the deviation is quickly fed back and adjusted through a cloud edge coordination mechanism, so that the operation error of the automatic agricultural machine is reduced.

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 invention provides a commercial building energy consumption anomaly detection method based on a graph method, and relates to the technical field of intelligent constructions.The method comprises the steps that a graph structure is constructed based on similarity, nodes are mapped into sub-item and sub-partition sub-tables, and edge weights represent time sequence similarity between the corresponding nodes; generating spectral distribution representing topological characteristics of the graph structures by calculating a Laplacian matrix eigenvalue set of the graph structures, and performing hierarchical clustering on the plurality of graph structures based on a Batta-just distance between the spectral distribution to obtain energy consumption mode clustering results in different time periods; and carrying out contour coefficient analysis on the clustering result, screening a class cluster number corresponding to a maximum contour coefficient value as a corresponding partition, and defining a class cluster set containing historical normal data as a baseline class cluster. According to the invention, the accuracy of building energy consumption anomaly detection can be improved.

The invention discloses a travel trajectory data generation method and device, a medium and equipment, and relates to the technical field of information security. The method comprises the following steps: constructing a data set based on collected travel track data containing geographic coordinates, time and travel track category attributes; constructing a generative adversarial network, and training the generative adversarial network by minimizing the space similarity, the time similarity and the category similarity between the generated travel trajectory data output by a generator in the generative adversarial network and the real travel trajectory data in the data set; and generating new travel trajectory data by using a generator of the trained generative adversarial network. The training mode forces the generator to learn spatial features and time features of real travel trajectory data and category features of different categories of real travel trajectory data, and the multi-dimensional feature learning enables the trained generator to generate travel trajectory data with more authenticity in multiple dimensions. And the generated travel track data is more deceptive.

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.

The invention discloses a programmable network traffic analysis model training method and device, equipment and a medium, and the method comprises the steps: determining the same network address of each network traffic data set of each network node, and determining target network traffic data in the corresponding network traffic data set according to the same network address; determining space-time similarity characteristics of the network nodes based on the space similarity and the time similarity between the target network flow data of the different network nodes; determining topological direction characteristics of each network node based on the protocol distribution vectors of different network nodes and the Warisstein distance between the network flow data sets; and determining position features of the corresponding network nodes according to the space-time similarity features and the topological direction features, and performing unsupervised training on the target model based on the position features of the network nodes and the network traffic data set to obtain a programmable network traffic analysis model. According to the invention, by considering the position features among the network nodes, the training precision of the model training method is improved.

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.