28 results about "Time dynamics" patented technology

The invention discloses a continuous time dynamics prediction method, system, terminal and medium fusing a diffusion model and a graph frequent differential equation, and relates to the technical field of dynamics prediction.The method comprises the steps that a multi-node time sequence is obtained, network structure inference is conducted on the multi-node time sequence through the diffusion model, and a potential graph structure between nodes is obtained; carrying out continuous time dynamic modeling by adopting a Scheng ordinary differential equation, and predicting a node state at any time point; diffusion reconstruction loss, dynamic prediction errors and structure sparsity constraints are constructed, total loss is established, network structure inference based on a diffusion model and dynamic modeling based on a Shenzheng differential equation are coupled based on the total loss, and collaborative training optimization is achieved. According to the method, the potential graph structure of the system can be stably recovered in a complex noise environment, high-precision and continuous prediction can be carried out on dynamic evolution of the potential graph structure, and the limitation that structure inference and continuous time modeling cannot be considered in the prior art is overcome.

The invention discloses a time enhanced knowledge tracking method based on dual-channel deentanglement, and belongs to the technical field of education data mining and cognitive modeling. According to the technical scheme, the method comprises the steps that time dynamic features and behavior reaction features in a learning interaction sequence are extracted and coded through a time domain encoder and a behavior domain encoder respectively; separating long-term trends and short-term fluctuations in the input features by using a multi-scale decoupling layer based on causal convolution; a time perception dual-channel attention module is adopted to independently decouple time and behavior characteristics after decoupling, and a nonlinear attenuation item based on a real interval is introduced to simulate memory forgetting; and finally, integrating dual-channel information through a gating fusion mechanism and predicting future answering performance of the learner. According to the method, optimization conflicts are effectively relieved, the robustness to a complex learning mode is enhanced, and knowledge state modeling which better accords with a cognitive law is realized.

The invention provides a power grid state prediction method based on a graph neural network and a time convolutional network, and the method comprises the steps: firstly carrying out the modeling of a power grid into a graph structure (nodes are power equipment, and edges are physical connection), and constructing a space-time input tensor of continuous time window operation data; generating a dynamic adjacency matrix to capture topological dynamic changes; node space topology dependence is learned through a multi-layer GCN, and space features are output; extracting a time evolution trend through multi-layer TCN (expansion convolution), and outputting time features; multi-scale convolution and an attention mechanism are adopted to fuse spatio-temporal features, key nodes and time steps are weighted, and finally future node-level states, regional-level loads or system-level abnormity early warning is predicted. According to the method, power grid space topology and time dynamic change are fused, and prediction precision, stability and real-time performance can be improved.

The invention relates to the technical field of flux modeling, in particular to an intertidal zone greenhouse gas flux modeling method fusing spatial variation and time series, which comprises the following steps: S1, acquiring spatial variation data of an intertidal zone target area, and outputting a spatial variation feature set; s2, acquiring time sequence data of an intertidal zone target area, and outputting a time sequence feature set; s3, calculating a spatial weight factor based on the spatial variation feature set, and outputting spatial weight distribution; s4, calculating a time dynamic factor based on the time sequence feature set, and outputting a time dynamic sequence; s5, constructing a greenhouse gas flux model, and outputting a flux estimation value; and S6, outputting a greenhouse gas flux distribution diagram of the target area of the intertidal zone according to the greenhouse gas flux model. According to the method, a coupling mechanism of a spatial variation factor weight field and a time dynamic factor sequence is introduced, the flux estimation model of space-time joint regulation is constructed, and the estimation precision of the model in different regions and different meteorological conditions is improved.

The invention discloses a landslide early warning method, device and equipment and a medium, and the method comprises the steps: obtaining historical landslide three-dimensional displacement time sequence data of a historical landslide event; constructing a landslide state evolution model based on a Gaussian mixture model, a hidden Markov model and historical landslide three-dimensional displacement time series data; determining a current displacement state of the target landslide body based on the real-time three-dimensional displacement time sequence data of the target landslide body and a landslide state evolution model; obtaining the remaining time of the target landslide body from the current displacement state to the landslide occurrence state; and carrying out landslide early warning on the target landslide mass based on the current displacement state and the remaining time. By analyzing the displacement time sequence data of the historical landslide event and optimizing the prediction performance of the HMM model by using the GMM model, a scientific and reliable geological disaster monitoring and early warning system is constructed, real-time dynamic monitoring and accurate early warning of the landslide geological disaster are realized, and the timeliness and accuracy of early warning of the landslide geological disaster are remarkably improved.

The invention discloses a high-order multi-agent system control method with unknown input time lag and quantization, which comprises the following steps: S1) under the condition of unknown time-varying time lag and input quantization, carrying out system description on an uncertain high-order multi-agent system and introducing a lag uniform quantizer; s2) converting an uncertain high-order multi-agent system tracking control problem with unknown time-varying time-lag and input quantization into a bounded problem of a differential equation solution with time-lag; s3) constructing a dominant expression of the system and feasibility conditions for realizing bounded tracking according to the bounded problem of the time delay differential equation; and S4) performing control design according to a dominant expression of the system and bounded tracking feasibility conditions. The invention designs an adaptive finite time dynamic surface control scheme for a high-order multi-agent system with unknown input delay. Meanwhile, the finite time stability theorem is utilized to prove that all signals in the closed-loop system are semi-globally practical and stable in finite time.

The application discloses a kind of equipment dynamic health degree fusion evaluation methods based on multi-source time series data, comprising: collecting and aligning working condition monitoring data and environmental monitoring data in equipment running cycle, and constructing multi-source time series training sample set with health label and environmental label;Introduce the causal representation model including environment encoding subnetwork and degradation encoding subnetwork, use the time window as contrast sample with the same health label but different environmental label, obtain the degradation representation with lower sensitivity to environmental change;Then input the degradation representation sequence into health degree regression network to generate dynamic health degree sequence, and set uniform health degree threshold on multiple devices and multiple working condition distribution, to give stable degradation degree evaluation results from real-time dynamic health degree sequence.

The invention discloses a time sequence prediction method based on multi-dimensional feature fusion and a computer program product. The prediction method is constructed based on a lightweight architecture, and complex module stacking is avoided. The method comprises the following steps: firstly, designing a multi-dimensional convolution extraction module, respectively capturing short-term fluctuation, long-term trend and timestamp association by utilizing three-dimensional convolution operations, and realizing comprehensive coverage of local details, global trend and time dynamic; secondly, a mask mechanism is designed, and masks are used on the dimension based on periodic modeling so as to improve the prediction effect and the model stability. And finally, the features of the three dimensions are sent to a prediction module and weighted fusion is carried out to obtain a final result. Therefore, the features of different time dimensions can be effectively captured, the prediction efficiency is improved, and the computing power demand is reduced. In the face of different service data in the time sequence field, the model can actively capture the core law and potential association of different time sequence data without complex adjustment, so that accurate prediction is realized.

The invention discloses a bus bunching prediction method and system based on a time-aware network. The method comprises the following steps: preprocessing bus arrival data, fusing multi-source spatial-temporal characteristics, introducing time period codes, and processing abnormal and missing values; targeted denoising is performed on data with different characteristics based on Fourier analysis, so that the data quality is improved; a time perception neural network is established, local features and long-term dependence are captured through a CNN-LSTM architecture, a time decay factor mechanism is introduced, dynamic modeling of a time sequence information importance decay process is realized, and a long-term rule of data and short-term disturbance after time calibration are effectively integrated; and a dual attention mechanism of time dimension and feature dimension is fused, key features are extracted and integrated, and multi-step prediction of bus bunching is realized. According to the method, the non-linear relation and the space-time dynamic rule of the bus data can be fully captured, and the problem of unequal intervals in a bunching time sequence is effectively solved.

The invention provides a social situation factor modeling and intelligent fusion method and system based on time dynamics to solve the problems that in existing network information demand prediction, social situation factors lack time dynamics modeling, news recognition precision is low, a fusion mechanism is static, and multi-source factors do not have a unified framework. According to the method, three types of situation factors are collected in a sampling period of one hour, and classification completion is carried out according to an Ingwersen framework; traversing [24, 24] hours through the CCF to determine the optimal lag time, and extracting the time dynamic characteristics in combination with Granger causality test (plt; using a BiGRU + CRF classifier to identify sensitive news and quantify emotion intensity; and according to factor types, a differential attenuation function is matched to establish a dynamic weight mechanism, and feature vectors are fused and output. The method can be used as an independent module for a time sequence prediction model; in the BAI data set emergency scene, the MSE is reduced by 47.8%, the news identification accuracy is 91.2%, the community scene prediction precision is improved by 32.1%, the operation and maintenance response time is shortened by 57%, and the prediction precision and robustness are effectively improved.

The invention discloses a power system space-time hybrid mode uncertainty modeling method and system based on aggregation-decomposition, and aims to solve the problem of mode uncertainty caused by multiple factors such as new energy output and load change. The method comprises the following steps: firstly, collecting time sequence operation data of a power system, and performing stationarity test on the time sequence operation data to obtain a stationary time sequence; then, disassembling a time dynamic dependency and space correlation dependency relationship, and extracting multi-dimensional spatio-temporal data characteristics to obtain a data basis suitable for spatio-temporal modeling; then, on the basis of an aggregation-decomposition framework, performing aggregation processing on the original data, performing modeling on time sequence correlation characteristics of the aggregated data, and calculating spatial correlation of the objects by taking the time sequence data of the objects as characteristics of the objects; and finally, establishing a space-time hybrid mode uncertainty modeling evaluation index, and evaluating a space-time hybrid modeling effect. According to the method, the uncertainty of the space-time hybrid mode can be efficiently represented, and a reliable basis is provided for stability analysis, risk assessment and decision making of a power system.

The disclosure relates to a method (600) of providing a representation of temporal dynamics of a first system (200) comprising sensors (212) by utilizing a middleware system (300), the middleware system (300) comprising two or more network nodes (355), wherein a first set of the two or more network nodes (355) are connectable to the sensors (212), the method comprising: receiving (620) activity information from the sensors (212) indicative of the temporal dynamics of the first system (200), wherein the activity information evolves over time; applying (630) a set of unsupervised learning rules to each of the one or more network nodes (355); learning (640) a representation of the temporal dynamics of the first system (200) by organizing (645) the middleware system (300) in accordance with the received activity information and in accordance with the applied sets of unsupervised learning rules; and providing (650) the representation of the temporal dynamics of the first system (200).The disclosure further relates to a middleware system, a controller system, computer program products and non-transitory computer-readable storage media.

The invention discloses an adaptive continuous learning time-varying distribution parameter system space-time modeling method, device, equipment and medium, an initial space-time prediction model is constructed through off-line data, after entering an online stage, the space-time non-stability degree of a system is quantified in real time, and the time-varying distribution parameter system space-time modeling method is realized. A space-time forgetting factor is adaptively generated by using a multi-criterion inference mechanism, so that the updating rhythm of the model is dynamically matched with the variable-scale time-varying rhythm of system dynamics, and the problem that a fixed learning rate is difficult to adapt to multi-scale time-varying characteristics is solved; through a space-time collaborative replay learning mechanism, a disastrous forgetting phenomenon in space-time continuous learning is overcome by recall consolidation of a historical core space-time dynamic mode, and the steps of online distributed parameter system data acquisition and model dynamic updating are repeated until an online stage is ended. Accurate tracking and long-term knowledge maintenance of time-varying space-time dynamics are realized, and prediction precision and robustness of a distributed parameter system in a complex non-stationary environment are remarkably improved.

The invention provides a Bi-GRU and ARIMAX-based hybrid prediction method for solving the problems that in network information demand prediction, a single model is difficult to give consideration to both a linear period and nonlinear burst, social situation factors lack time dynamics modeling, and a cross-scene cold start period is long. Network scene characteristics are quantized through a scene heterogeneity index (SHI), a double-branch parallel architecture is constructed, Bi-GRU is used for modeling nonlinear time sequence dependence, ARIMAX is used for depicting linear trends and exogenous variables, a noise adaptive fusion mechanism is introduced to dynamically adjust model weights, and 7-14-day rapid deployment is realized in combination with a transfer learning strategy of parameter freezing. On a BAI data set, the model MSE is 0.078, the MAPE is 6.26%, and the MSE and the MAPE are respectively reduced by 69.4% and 58.7% compared with those of ARIMAX and LSTM; and the error amplification is only 56% under the condition of 20% noise. In cross-scene application, the cold start period is shortened from 30-60 days to 7-14 days. After the method is applied to a university library scene, the network operation and maintenance cost is reduced by 35%, the service response time is shortened by 57%, and the resource utilization rate is increased from 47% to 83%.

The invention discloses a time sequence prediction method and system based on endogenous-exogenous cooperation. The method comprises the following steps: acquiring endogenous input and exogenous input of a prediction target; integrating the endogenous input into an endogenous embedded vector; integrating the exogenous input into an exogenous embedded vector; splicing the endogenous embedded vector and the exogenous embedded vector to obtain a joint feature; endogenous characterization is optimized through a multi-stage time attention mechanism; endogenous embedding and exogenous embedding are fused through a dynamic interaction attention mechanism, and cross-modal interaction features are obtained; fusing the optimized endogenous representation with the cross-modal interaction feature to obtain an intermediate dynamic representation; and performing time series prediction based on the intermediate dynamic representation. Unified characterization of multi-source heterogeneous features is achieved, local short-term correlation and global long-term dependence of endogenous variables are integrated through self-adaptive weight distribution, and collaborative correlation, dynamically evolved along with time, between input of the internal source and input of the external source can be accurately described.

The invention discloses an industrial equipment cooperative task anomaly detection method and system, and belongs to the technical field of industrial equipment detection. The method comprises the steps of preprocessing time sequence data of multiple mechanical arms, determining an initial window based on a task complexity index, dynamically determining an optimal time window through Bayesian optimization, and adopting the window to train a VAE-LSTM model to extract time dynamic features; a mechanical arm is modeled into graph nodes, a collaborative relation topological graph is constructed, local interaction features and global topological features are extracted through a multi-scale attention mechanism, and collaborative spatial features are extracted through graph convolution after self-adaptive fusion is conducted through a gating mechanism; and finally fusing the spatio-temporal features and carrying out anomaly detection. According to the method, through a dynamic window and a multi-scale attention mechanism, the space-time interaction relation in the mechanical arm cooperative task is captured in a self-adaptive mode, the accuracy, the real-time performance and the adaptability of anomaly detection are remarkably improved, and the defects of an existing method in the aspects of dynamic task adaptation and multi-modal data fusion are effectively overcome.

The application discloses a kind of multi-working condition industrial control method and system based on integrated space-time model prediction, method includes: the typical working condition feature extraction method based on orthogonal test, constructs working condition identifier;The time dynamic model and spatial distribution model under each working condition are constructed, and the optimal parameters of the space-time model corresponding to each working condition are obtained using data-driven and integrated training method;The current working condition of multi-working condition industrial system is identified in real time using working condition identifier, and the space-time model under the current working condition is used as prediction model, and the current optimal control input of system is obtained by rolling optimization method.The application learns the space-time correlation of observable point and unobservable point, establishes the prediction model of unobservable point, and integrates it into the prediction control framework, to realize the accurate control of unobservable point.

The application discloses a shallow seawater temperature space-time prediction method, which is realized through the following steps: 1, collecting shallow seawater temperature environment data; 2, establishing a seawater surface temperature time sequence model; 3, establishing a shallow seawater temperature vertical structure model; 4, establishing a shallow seawater temperature vertical structure time-varying model; and 5, establishing a shallow seawater temperature space-time prediction model. Through the above steps, the time dynamic of the shallow seawater temperature is mapped to the parameters of the time-varying function for time scale prediction, and the effect of the shallow seawater temperature space-time prediction is further realized through the Kriging interpolation method. The practical problem that the existing method cannot accurately describe the time dynamic and regional difference of the shallow ocean temperature is solved, and the function of the space-time description of the large-range synchronous ocean temperature data is realized.

The invention discloses a calculation method and device for time sequence operation and maintenance early warning based on a pipe network spatial topological structure, belongs to the technical field of sewage pipe networks, and solves the problems that an existing pipe network early warning method is low in prediction precision and is difficult to capture time-space dynamics at the same time. According to the method, directed hydraulic association between monitoring nodes is dynamically deduced by using a time-space diagram neural network, and time-space convolution calculation is synchronously performed, so that high-precision time sequence prediction of the global liquid level in a period of time in the future is realized, a potential time-space dynamic relationship in historical monitoring data is fully excavated, and the accuracy of time sequence prediction is improved. And by fusing physical prior information and a federal learning data complementation mechanism, the reliability and the spatial learning ability of the prediction model are remarkably improved. And finally, an operation and maintenance report containing over-limit early warning and a topological correlation-based key attention node list is generated, and a clear basis is provided for accurate troubleshooting and efficient decision making of operation and maintenance personnel, so that the maintenance and decision making cost of a pipe network system is remarkably reduced, and the management level and efficiency of water resources are improved.

The invention discloses an AI video source affiliation generation method and system based on time sequence dynamic modeling, and provides a novel TPDNet network architecture. The method comprises the following steps of: firstly, processing an input video frame sequence by adopting a visual encoder, decomposing the video frame sequence into a plurality of image blocks in space, and extracting a depth feature of each image block; through unique dimension rearrangement, an independent time sequence is constructed for an image block at each spatial position, and a module is utilized to perform deep dynamic modeling on the sequences so as to capture tiny time sequence inconsistency of a generator; and finally, fusing the features of all the image blocks subjected to time sequence modeling with the global features of the video, and sending the fused image blocks to a classifier to determine the specific AI generation source of the video. According to the method, through fine analysis of local space-time dynamic, the accuracy and robustness of source affiliation are remarkably improved, and an efficient and reliable technical scheme is provided for authenticity verification of video content in the composite media era.

In various embodiments, systems and methods for distributed simulation of time dynamics of quantum systems with an optimized tensor mode redistribution schedule are provided. A scheduling optimizer may create a schedule of tensor redistributions based on a hypergraph constructed from a quantum many-body operator. The scheduling optimizer iteratively distributes a quantum state tensor into state tensor slices based on a clustering process that partitions the hypergraph into clusters, and redistributes the quantum state tensor across processing units based on identified sliced or non-sliced tensor modes according to the obtained clusters. Operators that can be applied to non-sliced modes of the redistributed quantum state tensor are applied, and the iterative process repeats until each of the operators of the full quantum many-body operator have been applied. Because these operators act on non-sliced modes, inter-processing unit communication is minimized while executing these operations, and tensor redistributions across all processing units is minimized.

The invention relates to a port distributing and setting method based on a port arranging time dynamic prediction model, and the method comprises the steps: obtaining the daily average total processing capability of a port in a situation that all types of berths are compatible with corresponding ship types, constructing a predicted queuing time model according to the daily average total processing capability of the port, and introducing a buffer fluctuation factor, the optimization module is used for optimizing a predicted queuing time model; and by using the optimized predicted queuing time model, obtaining the predicted queuing time of port diffusion and port determination. According to the method, a dynamic evolutionary model is taken as a core, data is taken as a drive, the arrival time of the ship and the port operation capability are analyzed in real time for each candidate port before the port is expanded and fixed, the predicted queuing time after the ship arrives at each candidate port is scientifically and dynamically estimated, and the optimal port is selected for port expansion and fixed. Therefore, the port queuing congestion risk is avoided as much as possible, the operation efficiency is improved, and the cost is saved.

The invention discloses a continuous emotion prediction method and system based on electroencephalogram signal space-time modeling, and the method comprises the steps: carrying out the preprocessing of EEG data, constructing graph structure data fusing function connection and spatial distance, and extracting multi-scale spatial features through a parallel multi-branch expansion graph convolution network in combination with a feature-level linear modulation (FiLM) mechanism; time sequence dynamic modeling and feature fusion are realized through a BiLSTM (Bidirectional Long Short-Term Memory) network, multi-head self-attention and adaptive multi-strategy pooling; and finally, continuous scores of four emotion dimensions including titer, wakeup degree, dominance degree and preference degree are output through a multi-layer perceptron (MLP) regression head. According to the method, the complex space-time dynamic state of the electroencephalogram signals is effectively captured, the generalization ability and emotion quantification precision of the model are improved, and the method is obviously superior to various mainstream baseline models in the continuous emotion regression task.

The invention relates to the technical field of ecological change evaluation, and discloses a land ecological quality interannual rating and change detection method to solve the technical problems of complex modeling, strong parameter dependence, tedious operation and the like in a conventional change extraction method, and a land ecological quality rating scheme is constructed by using the first two principal components of tasseled cap transformation, ALBEDO and LST. The ecological quality level of the land surface is quantified, and periodic monitoring of the ecological quality condition is achieved; the method comprises the following steps: introducing a multi-time sequence dynamic rate of ecological change, quantifying a land surface ecological quality change (improvement / degradation) level, combining a semi-variance function theory, constructing a function relationship between the ecological quality dynamic rate and a corresponding observation time interval according to different time intervals (from small to large) in sequence, and obtaining a dynamic rate mean value at the same time interval; and carrying out extreme value detection on the maximum / minimum value of the dynamic rate under different time intervals in the interannual observation period to obtain a multi-time-sequence dynamic rate product data set representing the change of the land ecosystem.

The invention relates to an urban water supply pipe network observation generation method based on pipe network space-time dynamics, belongs to the technical field of urban water supply pipe network data processing, and solves the problems that in the prior art, due to the fact that the proportion of normal working condition samples in historical data of a water supply pipe network is too high, the generation result is likely to be excessively smooth, and the efficiency is poor. The method comprises the following steps: S1, acquiring initial data of the urban water supply network, and carrying out preprocessing to obtain a processed space-time observation sequence set; s2, performing causal mode decomposition based on a Koopman operator to obtain a stable causal component; s3, based on the stable causal component, establishing a causal network dominated by the stable causal component to obtain a generative model; and S4, obtaining a generation result by using the trained generation model.

The application discloses an industrial process remaining time prediction method and device and a storage medium, and belongs to the field of data processing. The method comprises the following steps: acquiring a track prefix sequence and inputting the track prefix sequence into an activity completion model to output a complete track prefix sequence after activity completion; extracting a semantic feature vector and a time interval feature vector of each activity in the complete track prefix sequence, splicing the semantic feature vector and the time interval feature vector to generate a fusion feature vector, and arranging the fusion feature vector in an activity order to form a feature sequence; inputting the feature sequence into a remaining time prediction model to output a predicted remaining time of an industrial process instance; wherein the activity completion model is obtained based on a BERT architecture and combined with contrast learning training, and the remaining time prediction model is based on a Transformer architecture and introduces a time perception attention mechanism. The effective repair of missing industrial process activities, the deep fusion of multi-dimensional features and the accurate modeling of time dynamic characteristics are realized, and the accuracy of the predicted remaining time is improved.

The invention discloses a zero-sequence inverse time limit dynamic setting method and system based on time domain fault positioning. The method comprises the following steps: acquiring voltage and current at a protection installation position; constructing a fault distance measurement equation according to the collected time domain information, and solving to obtain fault distance measurement; obtaining a zero-sequence inverse time limit time adjustment coefficient according to fault distance measurement; standard action time is calculated by using the zero-sequence inverse time limit over-current protection action time function, and the standard action time is corrected by using the time adjustment coefficient to obtain corrected zero-sequence inverse time limit over-current protection action time; and determining a final time constant value of the zero-sequence inverse time limit overcurrent protection action according to the corrected zero-sequence inverse time limit overcurrent protection action time, the zero-sequence inverse time limit matching time and the zero-sequence inverse time limit minimum action time. On the premise of maintaining protection selectivity, the high-resistance fault response speed is remarkably increased, and the zero-sequence inverse time limit overcurrent protection performance is improved.

The application provides a power grid state prediction method based on a graph neural network and a time convolution network. Firstly, the power grid is modeled as a graph structure (nodes are power equipment, and edges are physical connections), and a space-time input tensor of continuous time window operation data is constructed. A dynamic adjacency matrix is generated to capture the dynamic changes in topology. The spatial topological dependence of nodes is learned through a multilayer GCN, and spatial features are output. The time evolution trend is extracted through a multilayer TCN (dilated convolution), and time features are output. Multiscale convolution and attention mechanism are used to fuse space-time features, weight key nodes and time steps, and finally predict future node-level state, regional-level load or system-level abnormal early warning. The application combines the spatial topology and time dynamic changes of the power grid, and can improve the prediction accuracy, stability and real-time performance.