1443 results about "Temporal information" patented technology

The invention relates to an automatic driving vehicle trajectory planning method, which comprises the following steps of: extracting comprehensive spatio-temporal information representation of a driving scene from automatic driving multi-modal data by adopting an end-to-end branch, and predicting and generating a trajectory sequence predicted by a vehicle in a future set time period; a visual language model branch is adopted to embed visual features related to the text instruction into a large language model, and a plurality of different track sequences in a future set time period are obtained through prediction; and fusing the trajectory sequences output by the two branches according to the loss value and the danger situation of the current driving scene to obtain a final planning trajectory. Compared with the prior art, the method has the advantages of high safety and reliability.

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

The invention discloses an autoregressive visual target tracking algorithm based on token fusion, and the algorithm specifically comprises the following steps: 1, obtaining a public visual target tracking data set, and converting the data set into a central coordinate format; 2, preprocessing an initial template of a given video sequence, an online template at the current moment and a search image to obtain visual embedding of the initial template, a dynamic template and the search image; the method comprises the following steps: preprocessing an angular coordinate sequence of a target frame in previous historical N frames of a current search image of an input video to obtain historical track word embedding of the current search image; 3, constructing an autoregressive visual target tracking model based on token fusion, and designing a loss function; step 4, obtaining a final autoregressive visual target tracking model based on token fusion; the method solves the problem of insufficient model feature extraction capability caused by insufficient utilization of spatio-temporal information and redundant background information of a search area in an existing method.

The invention provides a predictive maintenance management method and system for an industrial robot, and relates to the technical field of equipment maintenance management, and the method comprises the steps: obtaining multi-modal heterogeneous data of the industrial robot, carrying out the intelligent preprocessing, obtaining first preprocessing data, carrying out the cross-modal correlation fusion of the first preprocessing data, generating fusion data, and carrying out the prediction of the industrial robot; extracting complementary redundant information and constructing a semantic feature map; constructing a health state prediction model, adding the semantic feature map into the health state prediction model, mapping the semantic feature map to an embedding space through linear transformation, extracting time sequence information, carrying out feature interaction to obtain interaction features, and carrying out transformation and feature enhancement through a feedforward neural network layer to obtain health state probability distribution; comprehensive health state distribution is generated through Bayesian reasoning fusion, an optimal maintenance strategy is generated in combination with a detection model, an abnormal mode and a key turning point, collaborative maintenance is conducted on the industrial robot, a maintenance result is uploaded to a server, and the current state is stored.

The invention discloses a power load prediction method and system based on a meteorological prediction model, and relates to the technical field of load prediction, and the method comprises the following steps: obtaining historical power load data and corresponding multi-dimensional meteorological data, constructing a meteorological coupling effect analysis model, calculating a dynamic coupling weight matrix among the meteorological parameters through an attention mechanism network; performing feature fusion on the coupling weight matrix and the multi-dimensional meteorological data through a gating circulation unit to generate a space-time coupling feature vector; establishing a power load prediction model, and extracting a short-term fluctuation feature and a long-term trend feature of the power load from the space-time coupling feature vector based on a deep residual network architecture and a parallel convolution channel; the dynamic coupling weight matrix is updated based on prediction error back propagation, and adaptive adjustment of the meteorological parameter coupling relation is achieved. According to the method, dynamic fusion of the spatial-temporal characteristics is realized through the cross gating unit, so that the model can perform power load prediction by using spatial-temporal information, and the prediction precision is improved.

The invention discloses a full-period traffic flow prediction method based on spatial-temporal feature deep fusion, and aims to deeply integrate complex spatial-temporal dependence of traffic flow, form composite traffic situation perception and realize traffic multi-period comprehensive flow prediction. The method comprises the following steps: firstly, through spatio-temporal information comprehensive representation, extracting time sequence characteristics of multiple time modals and constructing a concrete real spatial relationship; in order to further construct a digital urban traffic flow prediction method fusing multi-scale and multi-granularity comprehensive elements, a comprehensive traffic flow prediction model deeply fusing short-term space-time dependence is provided for short-term traffic flow prediction, so that the sensitivity to instantaneous flow change is improved; the short-term dynamic change of the traffic flow in the multifunctional airspace node is fully modeled; in addition, a deep airspace deconstruction and time sequence feature fusion model is designed for medium and long term traffic prediction and deep extraction of complex traffic modes. Simulation results show that the method provided by the invention is superior to the existing traffic flow prediction technology.

A generative adversarial multi-headed attention neural network self-learning method for aero-engine data reconstruction belongs to the field of end-to-end self-learning of aero-engine missing data. First, the samples are pre-processed, and the machine learning algorithm is used to pre-fill the normalized data first, and the pre-filled information is involved in the network training as part of the training information. Second, a generative adversarial multi-headed attention network model is constructed and the trained sample set is used to train the generative adversarial multi-headed attention network model. Finally, the samples are generated using the trained sample generator G. The method uses the generative adversarial network to better learn the distribution information of the data, and uses parallel convolution and multi-headed attention mechanism to fully exploit the spatial and temporal information among the aero-engine data.

The invention discloses an aviation converter fault detection method based on an information fusion algorithm, and the method comprises the steps: collecting the operation data of an aviation converter, and constructing a data set; performing time information extraction and space information extraction on the data in the data set, and performing feature fusion on time features and space features based on a variational auto-encoder to obtain spatio-temporal data; constructing a Bayesian deep learning model, and training and optimizing the Bayesian deep learning model; inputting the spatio-temporal data into the trained and optimized Bayesian deep learning model for processing, outputting a numerical value representing a fault occurrence probability, and taking a variance based on the probability numerical value as uncertainty estimation; presetting a probability threshold, and when the output probability is greater than the threshold, determining that the converter has a fault; otherwise, judging that the operation state is normal; a more accurate and reliable diagnosis result is provided for fault detection, and the method has high adaptability and robustness in practical application.

The invention provides an unmanned cluster brain-like navigation map fusion construction and cooperative positioning method. The method comprises the following steps: acquiring multi-modal perception data, respectively acquiring vision, sound wave and pose information through a binocular camera, a radar and an inertial sensor, simulating a human brain nerve coding mechanism, and generating a pulse sequence and a feature vector in combination with spatio-temporal information; constructing a multi-modal coding unit into a hypergraph node, and based on a dynamic hyperedge connection topological relation, aggregating spatial-temporal characteristics through a heterogeneous hypergraph convolutional network to generate a high-order brain-like semantic map of a single agent; sharing a local brain-like map by multiple agents through distributed communication, detecting geometric and semantic conflicts of an overlapped region, performing space-time alignment based on an anchor point reference, eliminating feature contradictions by utilizing probability distribution matching, and generating a global consistent high-confidence brain-like map; and outputting the optimal position estimation. Through bionic neural coding, heterogeneous hypergraph modeling and multi-agent collaborative optimization, establishment and positioning of a high-order brain-like map in a dynamic unknown environment are realized.

Provided is a method for driving behavior modeling based on spatio-temporal information fusion, relating to the field of driving behavior simulations. The method includes: constructing a driving behavior model, where the driving behavior model includes a spatial information encoding network, a temporal information encoding network, a feature fusion network, and a feature decoding network, with the feature fusion network being connected to both the spatial information encoding network and the temporal information encoding network, and the feature decoding network being connected to the feature fusion network; determining a future trajectory sequence of a target main vehicle at future time points based on the trained driving behavior model according to spatial information and temporal information of the target main vehicle, where the target main vehicle is controlled to travel according to the future trajectory sequence at the future time points.

The invention provides a time-space dynamic graph construction method and system for an engineering construction project, and relates to the field of railway tunnel engineering management.The method comprises the steps that construction data entities are extracted, and an entity set is generated; taking entities in the entity set as nodes to be added into the graph, and configuring attributes for each node; adding a relation edge for each node according to a logic structure between the construction data; adding a time label and a space label to each node so as to form a data graph; associating each entity in the data atlas with a time chain and a space chain to form a data atlas attached to the time-space chain; combining a node merging strategy and a graph neural network to optimize a graph structure; and dynamically modeling dimensional changes of entities and relationships in time by means of a time sequence diagram neural network. According to the method, the dynamic change graph of the relationship between the construction data entities is constructed, so that the spatial-temporal information and the entity relationship of each process are accurately expressed and efficiently associated, and the retrieval efficiency and the management level are favorably improved.

The invention discloses an old people chronic disease risk prediction method and system based on a time sequence knowledge graph, and belongs to the technical field of medical health information, and the method comprises the following steps: S1, obtaining a health data set of an old people group; s2, on the basis of an ESOA-XGBoost algorithm, performing feature selection and sorting on each piece of health data in the health data set so as to determine correlation among variables, and generating a health data feature set; s3, constructing and training a time sequence knowledge graph prediction model including an STKGR-PR model and a T i PNN model, and predicting a relationship among the target entity, other entities and time sequence information through the time sequence knowledge graph prediction model; s4, predicting the old chronic disease risk of the old people group according to the trained time sequence knowledge graph prediction model; through the method, an elderly chronic disease risk prediction result with a good prediction effect and high accuracy can be obtained.

The invention discloses an insurance claim settlement-oriented multi-modal image video evidence analysis method and system. The method comprises the following steps of: acquiring video / image and multi-source data such as metadata, audio, IMU (Inertial Measurement Unit), GPS (Global Positioning System), OBD (On-Board Diagnostic) and the like; calculating content Hash of the video and the audio according to frames, connecting the content Hash with time information in series to form chained Hash, and adding a verification digital signature and a credible timestamp; realizing cross-modal time sequence alignment based on self-adaptive time anchor-attitude coupling; tampering detection is carried out in combination with PRNU fingerprints, noise field consistency, dual compression, copy-movement and the like; multi-view geometry and monocular depth are fused, IMU scale constraint and micro rendering are introduced, three-dimensional reconstruction and re-projection optimization are completed, and collision dynamics verification is carried out; and constructing an event cause and effect graph, judging responsibility in combination with traffic rules, outputting a confidence coefficient vector and a structured report, and generating a verifiable evidence packet. The scheme has the advantages of high efficiency and traceability in the aspects of space-time restoration and interpretable responsibility judgment.

The invention provides a personalized recommendation system and method for an intelligent terminal, and belongs to the technical field of artificial intelligence and big data, and the system comprises a five-element multi-mode dynamic perception module, a space-time attention feature fusion unit, a hierarchical federal transfer learning framework, a context perception enhanced recommendation engine and an edge-cloud co-evolution mechanism. The five-element multi-mode dynamic sensing module is used for synchronously collecting physiological features, environmental parameters, behavior data, spatio-temporal information and a social relation graph; and the space-time attention feature fusion unit is used for fusing the multi-modal data by adopting an ST-Transform model. According to the system, on the premise of ensuring the privacy of the user, the recommendation accuracy and real-time performance in a complex scene are remarkably improved, and a new technical normal form is provided for the personalized service of the intelligent terminal; according to the personalized recommendation method, cross-device transfer learning enables the time consumption of new user feature mapping to be greatly shortened, environment-driven brightness adjustment effectively reduces the visual fatigue of the user, and incremental learning obviously reduces the data volume updating demand of the model.

A method for predicting remote sensing image time series based on reservoir computing utilizes temporal spectral information of remote sensing images to study prediction methods for high-dimensional short-term sequences. Based on the theoretical model of a nonlinear dynamical system and using the spatiotemporal transformation equation and its conjugate equation, a multi-layer reservoir computing model is constructed to achieve multi-step prediction on a time dimension. Only a small number of short-term samples are required to predict dynamic temporal information. Compared with the traditional neural network training process, the method is low-cost training. Different from traditional reservoir computing, the method converts the observed high-dimensional data into reservoir computing and uses intrinsic dynamics of the observation / target system to analyze remote sensing time series data. With only a few samples, by learning the spatiotemporal transformation equation, effective prediction results can be generated and applied to feature processing of remote sensing images with small samples.

The invention discloses a reliability analysis method and system for a power system, and relates to the technical field of intelligent operation and maintenance of the power system, and the method comprises the steps: collecting data, and constructing a dynamic space-time atlas network of the power system, which comprises time dimension information and space dimension information; feature extraction is carried out based on a spatial-temporal feature learning framework constructed by a dual-channel graph attention network and a gated causal convolution network, and spatial-temporal fusion features containing complete spatial-temporal information are generated by using gated causal convolution and a time attention capture time sequence evolution mode; and converting the space-time fusion features into a local risk state and a global system state through a state feature mapping network, calculating a local component risk index and a system overall reliability index, generating a collaborative decision strategy, and realizing reliability analysis of the power system. According to the method, the intelligent operation and maintenance level of the power system is remarkably improved, the shutdown time and the maintenance cost of the system are reduced, and meanwhile, the system performance is continuously improved through preventive maintenance and an active optimization strategy.

A computing system is described herein that implements a diffusion-based framework for animating reference images. The computing system includes a video diffusion model that is utilized to encode temporal information. The computing system further includes a novel appearance encoder that is utilized to retain the intricate details of the reference image and maintain appearance coherence across frames. The computing system further employs a video fusion technique to smooth transitions between animated segments in long video animation. Potential benefits of the computing system include enhanced temporal consistency, faithful preservation of reference images, and improved animation fidelity in the generated animation sequences.

The embodiment of the invention discloses a community governance decision generation method and device based on a knowledge graph. The method comprises the following steps: collecting community governance data; performing information extraction and text processing on the community governance data by adopting a data extraction model to generate structured data; constructing a causal analysis atlas, a decision treatment atlas and a space-time correlation atlas by using the structured data; fusing the causal analysis graph, the decision governance graph and the space-time association graph to obtain a community governance knowledge graph, wherein the community governance knowledge graph supports semantic-based query and reasoning; acquiring resident appeal information, and performing risk reasoning based on a preset reasoning rule to obtain a risk reasoning result; and based on a risk reasoning result, in combination with spatio-temporal information defined by a spatio-temporal association graph, querying in the community governance knowledge graph, and generating a community governance scheme according to a query result. According to the invention, the dynamism, the accuracy and the intelligent level of community management can be improved.

A method and system for detecting a leakage target by integrating temporal information. The method comprises: acquiring multiple frames of images of a target position, and arranging the multiple frames of images based on temporal information corresponding to the multiple frames of images to obtain a target image sequence; merging, in a channel dimension, the multiple frames of images in the target image sequence to obtain a multi-dimensional tensor; performing convolution processing on the multi-dimensional tensor to obtain a first output result, wherein the first output result is used for representing a frame difference between every two adjacent ones of the multiple frames of images; calculating an accumulated frame difference corresponding to the multiple frames of images based on the first output result; and inputting the accumulated frame difference to a trained target detection model as input data to obtain a second output result integrated with the temporal information.

The invention relates to the technical field of permission adaptation, in particular to a CRM-GIS-oriented multi-protection permission dynamic adaptation method and system. The method comprises the following steps: firstly, defining in a CRM-GIS platform and acquiring a multi-dimensional authority attribute related to an access request in real time; based on a preset permission policy rule in the platform, processing an access request by using a policy decision engine; then, based on the current spatio-temporal information and behavior pattern information of the user, an artificial intelligence risk assessment module is utilized to analyze the real-time risk level of the current operation of the user; thirdly, dynamically judging and adjusting a final permission adaptation decision aiming at the access request by integrating the multi-dimensional permission attribute, the permission strategy rule and the risk level; according to the final permission adaptation decision, the CRM-GIS platform compulsorily executes corresponding access control operation, and limits or allows access to data and functions in the platform; according to the invention, the reliability of multi-dimensional right dynamic adaptation can be improved.

The invention relates to the technical field of crop growth monitoring, in particular to a crop growth monitoring method based on unmanned aerial vehicle remote sensing, which comprises the following steps of: acquiring a time sequence remote sensing image through an unmanned aerial vehicle and performing time phase processing to solve the problem of data inconsistency of a traditional method; a crop segmentation network based on wavelet transformation and edge guidance is constructed, high-frequency details and low-frequency semantic features are captured through wavelet decomposition, multi-scale dynamic interaction is achieved through cross-resolution feature fusion, and the problems of high-frequency detail loss and fuzzy segmentation are solved; based on a twin network, extracting dual-temporal global semantic features, and combining a difference compensation module to enhance the significance of the growth change, suppress noise interference and improve the weak change detection capability; and finally, fusing the segmentation mask and the difference characteristics through a multi-task framework, synchronously generating a pixel-level spatial distribution diagram and a time sequence thermodynamic diagram, realizing spatio-temporal conjoint analysis of the crop growth state, solving the problem of spatio-temporal information segmentation in a traditional method, and providing high-robustness monitoring decision support for precision agriculture.

The invention provides an industrial fault feature adaptive extraction and multi-mode detection system and method, and belongs to the technical field of industrial fault detection. Comprising the steps of collecting multi-source data of industrial equipment, performing timestamp alignment and processing on the multi-source data to obtain a standardized feature sequence, inputting the standardized feature sequence into a dynamic convolutional neural network, extracting signal local features through a deformable convolution kernel, calculating feature weights in combination with a self-attention mechanism, and screening feature channels to obtain feature vectors of all modes; a graph structure with modals as nodes and correlation as edges is constructed, cross-modal features are aggregated through a graph attention network, and global state descriptors fused with spatio-temporal information are generated; and performing time sequence modeling on the global state descriptor through a bidirectional LSTM network, outputting fault type probability distribution, and completing industrial equipment fault detection. According to the method, the problems of insufficient single-modal information, fixed feature extraction, low efficiency of multi-modal correlation modeling and lagging model updating in traditional industrial fault detection are solved.

The invention discloses a nondestructive testing method for infrared thermal imaging spatio-temporal information fusion, which relates to the technical field of nondestructive testing and comprises the following steps: establishing a linear laser heat source scanning infrared thermal imaging nondestructive testing system; collecting a dynamic infrared thermal image sequence; and calculating and analyzing dynamic infrared thermal image sequence data, and carrying out data processing on the acquired dynamic infrared thermal image sequence by using an infrared thermal imaging spatio-temporal information fusion post-processing method to obtain a full-field thermal response image with surface crack defects. According to the method, spatio-temporal information can be fused to obtain data with higher resolution, more comprehensive and accurate full-field thermal response characteristics are obtained while the calculation efficiency is improved, the method has better flexibility and wider applicability, and the purpose of nondestructive detection of metal surface defects is achieved.

The invention discloses a short video network public opinion information identification method based on an image processing technology, and relates to the technical field of artificial intelligence and image processing, and the method comprises the following steps: S001, through obtaining image frames, audio tracks and time sequence information of a short video, constructing a multi-modal fusion model, extracting continuous image frames with suspicious identity features, and carrying out the recognition of the short video network public opinion information; generating a forgery risk area distribution map; and S002, performing semantic consistency verification according to the counterfeit risk region distribution map, and extracting space and time anomaly features existing among facial micro-expressions, pronunciation actions and background semantics in the image frame. According to the method, a multi-modal model is constructed by fusing image, audio and time information, fine abnormal features, traceability forgery starting points and propagation paths in a deep forgery video are identified, and an identification strategy and a public opinion response mechanism are dynamically adjusted, so that accurate identification, adaptive processing and closed-loop control of short video public opinion risks are realized; and the identification accuracy and the treatment efficiency are improved.

The invention discloses a project spatio-temporal information management and control method based on a BIM technology, and the method specifically comprises the following steps: 1, building a BIM collaborative public data environment, and enabling an engineering project decomposition structure coding standard to be analyzed and built in the BIM collaborative public data environment; step 2, establishing a plan information model, inputting budget quota data and engineering quantity list data into the BIM collaborative public data environment established in the step 1, establishing a mapping relationship among quota list items, engineering quantity list items and an engineering project decomposition structure, and determining unique codes of the data; step 3, establishing an execution information model, inputting spatio-temporal information data of the real world and model information data of the virtual world into a BIM collaborative public data environment, establishing a mapping relationship with a plan information model by relying on an engineering project decomposition structure code, and multiplexing the unique code determined in the step 2; 4, establishing a collaborative linkage relationship between the information model and information data, and realizing plan and execution difference comparative analysis based on model definition by means of data fusion and visualization service of a graphic engine; and step five, based on a three-dimensional visualization analysis result in the step four, project cost, progress, quality and safety control is carried out. The method is suitable for traffic infrastructure project management and control.

The invention relates to the technical field of image recognition, and provides a video target recognition method and device based on artificial intelligence and a storage medium, and the method comprises the steps: obtaining a video frame data sequence of a target video, carrying out the multi-dimensional analysis of the video frame data sequence, obtaining global video frame information and local region-of-interest information, and generating a target feature map based on the global video frame information and the local region-of-interest information, then carrying out time sequence mode analysis to obtain time sequence evolution features, combining the generated semantic representation vector, inputting the semantic representation vector into a preset adaptive Transform model to carry out target recognition, and obtaining a target recognition result. A semantic representation vector is generated through feature fusion, and a self-adaptive Transform model is used for target recognition, so that the target recognition precision in a complex scene is improved, and the problems of low detection precision and insufficient time sequence information utilization during complex scene processing, dynamic change and long-time sequence analysis are solved.

The invention discloses an unmanned aerial vehicle dynamic environment 3D target detection method and device based on multi-modal fusion, and the method comprises the steps: calibrating a 4D millimeter-wave radar and a camera, and obtaining a pose relation between the millimeter-wave radar and the camera; the method comprises the following steps: preprocessing 4D millimeter wave radar point cloud data to obtain a denoised point cloud image, and performing feature extraction on the denoised point cloud image; using hidden space normal estimation to obtain a pose matrix between adjacent frames, and then through global discretization analysis, realizing multi-frame point cloud pose correction and fusion in a motion scene, and enhancing point cloud data; processing the radar point cloud and the camera image, and obtaining a BEV feature map through a feature extraction network; using a double-end channel attention mechanism and a cross attention mechanism to fuse BEV features; a detection head module based on an anchor frame is designed, and differentiation setting is carried out for different detection targets; and verifying by adopting different data sets and different test scenes to obtain a 3D target detection result. According to the invention, the hidden space normal estimation method is provided to correlate spatio-temporal information, multi-frame splicing is carried out, the density of point clouds is increased, and the performance of target features is improved.

The invention provides an RGBT target tracking method based on domain adaptation and spatio-temporal information fusion. The problem that an existing target tracking scheme is poor in tracking effect under complex scenes such as target appearance change and shielding is mainly solved. Comprising the following steps: 1) constructing an RGBT data sample set; 2) constructing a dual-branch RGBT target tracking model based on a pre-trained RGB tracker and training the dual-branch RGBT target tracking model; 3) designing an online module updating strategy for dynamically updating the initial template image; 4) taking the initial template image and the search image as the input of the trained model, and taking the dynamic template image as the additional input of the model; and 5) loading the optimal parameter, predicting the motion state of the target in a subsequent frame by using the trained model, and obtaining and outputting a target tracking result. According to the method, the calculation cost is greatly reduced, and the time information of the target can be fully utilized, so that the performance of the feature extraction network is effectively improved, and the accuracy and robustness of target tracking are improved.

Disclosed in the present application are a question answering method and apparatus based on a temporal knowledge graph. The method comprises: acquiring a target question; determining a plurality of first entities in the target question, and determining first semantic role information in the target question, wherein the first semantic role information at least comprises a subject, a predicate, an object and time information; determining from a target temporal knowledge graph a plurality of pieces of first knowledge associated with the plurality of first entities, and determining a first entity relationship between second entities in each piece of first knowledge; determining a piece of first knowledge among the plurality of pieces of first knowledge as target knowledge, which piece of first knowledge corresponds to the first entity relationship matching the first semantic role information; and performing temporal logic reasoning on the basis of the time information and the target knowledge, so as to obtain an answer to the target question.

Vectorization requests are received via active connections for a hybrid time-series vector database that include input including information associated with an event and temporal information. The inputs are processed with a machine-learned vector embedding model to generate vector representations. The vector representations are mapped to corresponding locations within an embedding portion of the hybrid time-series vector database. A query is received for the hybrid time-series vector database via an active connection of the plurality of active connections. Responsive to the query, a first vector representation is retrieved based at least in part on the location to which the first vector representation is mapped.