122 results about "Time dependency" patented technology

The invention belongs to the technical field of intelligent traffic, and particularly relates to a Mama-based traffic flow prediction method, which comprises the following steps of: collecting and preprocessing a data set; static graph construction and spatial feature extraction; performing space-time dual-token decoupling embedding; carrying out dual-branch Mama modeling; performing space-time-variable feature fusion; and carrying out multi-step prediction, outputting a prediction result, and finally, outputting a prediction value of a multi-step sequence through projection mapping. The static graph convolutional network extracts spatial correlation from the inherent topology of the road network, and the double-branch Mama architecture captures the time dependence and variable correlation of the traffic flow. According to the space-time double-token mechanism, the independent influence and the synergistic effect between variables are explicitly distinguished by decoupling the action boundaries of multi-source factors such as traffic flow, environment variables and event interference, and the modeling robustness and the interpretability in a multivariable coupling scene are remarkably improved.

The invention discloses a multi-device cooperative control method applied to deployment of an embedded real-time operating system, and relates to the field of embedded real-time operating systems, which comprises the following steps of: S1, constructing a real-time dependency relationship graph of a task link, a multi-device cooperative task is disassembled into sub-task link units including an execution path, a real-time constraint threshold and a cross-device communication dependency relationship. According to the multi-device cooperative control method applied to the deployment of the embedded real-time operating system, the problems of task priority conflict and resource utilization imbalance in a multi-device heterogeneous environment are effectively solved through a dynamic priority transmission mechanism and a distributed resource elastic perception model. The system can sense equipment load, communication link stability and task real-time requirements in real time, dynamically adjust sub-task weights and execution paths, ensure conflict-free transmission of end-to-end delay constraints, optimize the global resource utilization rate and avoid the problem of equipment overload or idle caused by traditional static scheduling.

The invention relates to a long-range task-oriented VLA model method for a humanoid robot, which comprises the following steps of: S01, analyzing a natural language instruction through a space-time semantic analyzer to generate an atomic operation sequence with a space-time dependency relationship; s02, maintaining a task state machine by using a dynamic memory network, and tracking the task execution progress in real time; s03, integrating vision, language and sensor data through a multi-modal perception fusion engine; s04, calling a predefined action primitive based on an adaptive execution system and optimizing a motion track; and S05, performing online updating and optimization on the model through a continuous learning mechanism. According to the method, a natural language instruction is analyzed into a structured task sequence with space-time dependence through a space-time semantic analyzer, an execution sequence and preconditions are defined, the semantic understanding ability and the structuring degree of task planning are improved, and task decomposition and replanning in a dynamic environment are supported; according to the method, the LSTM and the knowledge graph are combined, and the task state is maintained in real time.

The invention discloses a multi-modal abnormal data detection and restoration method and system oriented to a power business scene. The method comprises the following steps: collecting multi-source heterogeneous power data, abstracting a power system into a weighted undirected graph, uniformly mapping the multi-source heterogeneous data into a graph signal, and preprocessing the collected data; extracting spatial features of nodes in a topological structure by adopting a graph convolutional network, and capturing a time dependency relationship in combination with a time sequence encoder; identifying various types of data abnormal points through an abnormal scoring function fusing the time sequence prediction error and the neighborhood consistency; a prediction-reconstruction combined repair strategy is adopted, time sequence prediction and neighborhood diffusion estimation are fused, and a preliminary repair value is generated; a lightweight parameter adapter is introduced, a scene feature vector is used as input, a repair weight and a regularization coefficient are dynamically generated, and a repair strategy is automatically adjusted; and performing physical consistency verification on a data result, wherein the physical consistency verification comprises power injection conservation constraint, voltage amplitude range constraint and time sequence continuity constraint.

The invention relates to an offshore wind power prediction method, and aims to improve the accuracy and reliability of prediction. The method comprises the following steps: (1) a data preprocessing stage: detecting an abnormal value in data by using a DBSCAN clustering algorithm, reconstructing the abnormal value by using a KNN interpolation method, detecting time sequence abnormity through an LSTM automatic encoder, and performing regression reconstruction by using LSTM to ensure the retention of time sequence features; (2) a feature engineering stage: screening out key features through correlation analysis, generating a label column through K-means clustering and wind direction classification, and extracting features such as wind direction change rate, time periodicity and wind speed interaction; and (3) a model construction stage: constructing a composite model in combination with multi-scale convolution (Inception), a bidirectional long short-term memory neural network (BiLSTM) and a multi-head self-attention mechanism, extracting local features through a convolution layer, capturing a time dependency relationship through a bidirectional LSTM layer, enhancing the attention of key features by using the multi-head self-attention mechanism, and finally realizing high-precision wind power prediction.

The invention discloses a universal multi-modal target tracking method based on space-time propagation and modal cooperation, and belongs to the technical field of computer vision. The method comprises the following steps: converting RGB and X modal images into a token form, and constructing initial features in combination with modal specific time tokens; the method comprises the following steps of: extracting a multi-level enhanced feature through a Transform encoder and a Mama collaborative prompt block; generating discriminative fusion features by using a gating fusion and context sensing module; a time-guided attention mechanism is adopted to strengthen search area features, and a result is output through a tracking prediction head; and transmitting the fusion time token as historical information to the next frame, and dynamically updating the template by combining a long-short time template updating strategy. According to the method, complementarity and space-time dependence between modes are effectively mined, tracking robustness and generalization ability in a complex scene are improved, and the method is suitable for various mode combination tasks such as RGB-D, RGB-T and RGB-E.

The invention relates to a spatio-temporal data prediction method based on space grid coding and a large language model, and belongs to the field of artificial intelligence and spatio-temporal data analysis. The method comprises the following steps: according to a spatial range of a geographic area, dividing the area into grid units, processing the grid units by adopting a coding method, and identifying each unit through a unique identifier; constructing a space-time dependency model based on the stay record of the user, representing each stay as a tuple, and predicting the next position of the user by analyzing a historical stay sequence and a context stay sequence; and designing a space-time instruction optimization prompt template in combination with a large language model, guiding the model to analyze historical data and context data by introducing grid coding and context perception reasoning, generating next position prediction, and providing explanation for each prediction. According to the invention, through grid coding and hierarchical modeling, the generalization ability of the model is enhanced, and accurate spatio-temporal behavior prediction is realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

The invention relates to the technical field of multi-agent systems, in particular to a task matching method and system based on a multi-agent system, a master agent is used for analyzing a request of a user, identifying a plurality of corresponding sub-target tasks according to the request, and establishing task mapping with other slave agents according to the identified sub-target tasks; and the slave agent performs corresponding processing according to the relationship between the sub-target tasks, and stores a corresponding processing result to a local knowledge base. According to the method, the newly added task requests are collected to expand and supplement the primary knowledge graph, the expanded knowledge graph is subjected to element analysis, so that the incidence relation between the elements is recognized, then whether the task requests needing to be processed conform to the incidence relation or not is judged, the time dependence relation of the task requests is further judged, and the task requests are processed according to the time dependence relation. And the master agent allocates different slave agents to perform task processing according to different results which are possibly obtained, so that the corresponding slave agents can be allocated more accurately.

The invention discloses a VAGCN-based complex industrial process operation state evaluation method. The method comprises the following steps: collecting process data and carrying out standardization processing; performing data division on the data by using a sliding window to obtain a time sequence; the method comprises the following steps: converting time sequence data into cross-time-space variable interaction graph structure data through a variable interaction graph structure, and then capturing variable interaction characteristics and local space-time dependency in each time window by using a moving window; time pooling is used, advanced features of variable interaction are extracted and then input into the operation state evaluation model, posterior probabilities of the online data belonging to different operation state evaluation grades are obtained, and a final evaluation result is a state grade corresponding to the maximum posterior probability at the current moment; and performing similarity analysis on the variable interaction graph structure in the non-optimal state and the optimality graph structure in the optimal state so as to evaluate the variable dynamic offset degree in the non-optimal state. According to the method, the interaction among the variables at different time points can be captured, and more accurate process operation state evaluation can be realized.

The invention discloses a hybrid deep learning sea outlet water quality prediction method based on particle swarm optimization, and the method comprises the steps: firstly obtaining time series data through data preprocessing, carrying out the correlation analysis, screening a water quality index, and reducing the data noise; then constructing a water quality prediction model by applying a mixed deep learning mode and combining the advantages of TCN, LSTM and CBAM networks; then, time sequence data is used for training a model type, TCN is used for effectively capturing a long-time dependency relationship in the sequence data, LSTM is used for enhancing the processing capacity of long-term memory, a complex time dependency mode in water quality data is captured, CBAM is used for introducing an attention mechanism to adaptively select important features, and the accuracy of water quality prediction is optimized; and the prediction precision and stability are improved. The method has higher accuracy in daily monitoring and prediction of conventional water quality indexes and early warning and prediction of water quality sudden change under special events, and can meet diversified practical application requirements.

The invention provides a time sequence scene-oriented chip architecture level power consumption evaluation method, which comprises the following steps of: S100, generating an original data set by utilizing micro-architecture design parameters of a chip to be designed and adding simulation excitation; removing redundant data in the original data set, merging to obtain a preprocessed data set, and training a preset LSTM-based power consumption prediction model; and adjusting the micro-architecture design parameters and the ESL model, regenerating input features, and inputting the input features into the trained LSTM-based power consumption prediction model to obtain a power consumption prediction result. According to the method, the multi-dimensional features are introduced, the influence of different workloads and architecture configurations on the power consumption is comprehensively considered, and the time sequence processing capability of the LSTM model is utilized, so that the time dependency relationship in power consumption fluctuation can be effectively captured, and the power consumption prediction precision is remarkably improved, especially in a dynamically changing time sequence scene.

The invention relates to the technical field of action recognition, in particular to a skeleton action recognition method based on a space-time dependency enhanced network, which comprises the following steps: acquiring a graph containing skeleton action articulation points; constructing an AMGC-IBM-CNN network: establishing a space-time diagram by taking joints as nodes and skeletons as edges, performing global feature vector and local feature vector extraction on the space-time diagram by using the AMGC network, inputting the global feature vector into an IBM module, then inputting the global feature vector into the CNN network, performing addition operation on the output feature vector of the CNN network and the local feature vector, and finally obtaining the AMGC-IBM-CNN network. And performing global average pooling, and outputting an action category through a classifier after FC. According to the method, the problems that time and space convolution layers are separated in a traditional model, cross-space-time information exchange is obstructed, and the cross-space-time dependency relationship of the joint points is difficult to obtain are solved.

The invention discloses a soil moisture content prediction method and device based on deep learning, and the method comprises the steps: carrying out the seasonal trend decomposition of historical soil moisture content time sequence data, and obtaining a trend term, a seasonal term and a residual term; secondly, analyzing a causal relationship between each decomposition item and a meteorological variable in combination with a plurality of causal test methods, and constructing a dynamic causal adjacency matrix to obtain a causal tree structure; for the trend item, the season item and the residual item, designing a causal LSTM sub-model according to the fruit tree structure, inputting a historical decomposition sequence and the screened causal meteorological variables, and carrying out independent prediction; and finally, fusing prediction results of the sub-models through a full connection layer, and outputting a final prediction value of the soil moisture content in a future period. The innovation point of the method is that causal information and time dependence characteristics of decomposition items are fused, and the interpretability of the model and the response capability of the model to long-term trend and short-term mutation are enhanced.

The inventive depth of the method presented here lies in the active modification of a time-dependent signal, enabling its use for particle characterization. The so-called oscillating signal offers advantages in signal analysis because the frequency of the high-frequency component can be specified. This improves signal selection within a continuous signal flow. The low-frequency component of the oscillating signal is derived from its limited edges and can be used to determine the particle property vector by analyzing the amplitude and signal smearing. Furthermore, the combinatorial complexity of the oscillating signals offers a possibility for precise particle classification using artificial intelligence.

Methods and systems use an additional determination to generate predictions of future data processing load predictions. Specifically, the methods and systems generate additional predictions based on other data sets and data calculation methodologies (e.g., data with non-homogeneous time dependencies). The methods and systems then use these additional predictions to determine whether or not the prediction of the real-time average of data processing loads is an outlier. Thus, the methods and systems generate a time-specific data processing prediction.

The invention discloses a dam safety monitoring and crack prediction method and system based on vibration sensing data, relates to the technical field of data processing, and solves the problems of incomplete data acquisition, data processing lag and insufficient prediction capability in existing dam safety monitoring. Adaptive normalization processing based on a dynamic window is carried out on the dam vibration data; a prediction model is constructed, an LSTM network captures time dependence of dam vibration data, an attention mechanism network fuses LSTM network output and external environment variables to extract key time steps, and an output layer predicts the probability of crack generation according to the key time steps; inputting the preprocessed dam vibration data into a prediction model to obtain a crack generation probability; calculating a dynamic threshold according to the external environment factor, and comparing the crack probability to obtain a crack prediction result; by collecting dam vibration data at multiple positions, the crack risk is predicted in advance based on the prediction model.

The invention discloses a rainfall prediction method based on LightGBM and a variable attention mechanism, and belongs to the technical field of weather prediction. The objective of the invention is to solve the problem of poor prediction effect of an existing model when noise or missing exists in data. According to the method, the LightGBM and the variable attention mechanism are combined, the variable attention mechanism is used for accurately capturing cross-variable dynamic association and multi-scale time dependency in meteorological data, the model can flexibly adapt to the relative dependency between different time steps through relative position coding, and the modeling capacity for long-time-sequence data is improved; by adopting a split weighting mechanism based on LightGBM, different types of exogenous variable data can be weighted, large-scale data can be effectively processed, and feature selection can be carried out, so that the model is helped to pay more attention to variables having great influence on meteorological prediction in the training process, and the robustness of the model to noise data and missing data is enhanced. The method can be applied to rainfall prediction.

The invention relates to a load prediction method and system based on an attention mechanism, and the system comprises the steps: 1, carrying out the deep analysis of power utilization data, calculating the importance of each feature, selecting a key feature according to the importance, learning the dependency relationship of long sequence input based on a multi-head probability sparse self-attention model, and carrying out the calculation of the importance of each feature; a relative position coding method is adopted to enhance the internal relation between the data, and a data feature set is obtained; and step 2, training a multi-scale time sequence prediction model based on the data feature set obtained in the step 1 as an input feature set, and predicting a load sequence and peak valleys. According to the method, different parts in the time sequence are dynamically focused through the attention mechanism, the long-time dependency relationship in the time sequence is captured, important features can be extracted more effectively, complex modes in data can be captured more effectively, the accuracy of power load prediction is improved, and decision support is provided for defining the adjustable range of the load and optimizing energy distribution.

The invention provides an ROP prediction method based on an LSTM and an ensemble learning algorithm, and the method comprises the steps: carrying out the clustering and denoising: carrying out the denoising of data through employing a DBSCAN clustering algorithm, and removing outliers; data interpolation: applying a Hermite interpolation method to missing data, filling missing values, and ensuring the smoothness of the data; time sequence generation: converting the data into a time sequence format suitable for LSTM, and segmenting the data by using a sliding window; lSTM training: using an LSTM model to train data, learning a time dependency relationship and performing ROP prediction; on the basis of data preprocessing and an LSTM model, regression prediction is carried out by using LightGBM and XGBoost, so that the accuracy of ROP prediction is improved; evaluating a prediction result: evaluating prediction precision on training and measurement data through mean square errors and R2 evaluation indexes; and model optimization: carrying out model optimization according to an evaluation result. According to the method, more accurate prediction can be provided for the drilling operation, and then the efficiency and safety of the drilling operation are optimized.

The invention relates to the technical field of mechanical equipment intelligent operation and maintenance and state monitoring, and discloses a health monitoring method for bearing state evaluation and residual life and degradation trend prediction, which comprises the following steps of: firstly, extracting time domain, frequency domain and time-frequency domain characteristics from original vibration signals of a bearing under different working conditions; according to comprehensive evaluation indexes and known bearing degradation characteristics, features having good characterization capability and trend consistency for bearing degradation performance are screened out, a novel backbone network model is constructed, deep features are mined, effective features are enhanced, meanwhile, a time dependency relationship in a long sequence is captured, and then a multi-task learning mechanism is introduced, so that the bearing degradation performance is evaluated. Through parameter sharing and joint optimization, bearing state identification, residual life prediction and performance degradation trend prediction can be synchronously completed only by training and deploying a single model. According to the method, multi-task collaborative prediction under complex working conditions is realized, and the accuracy and robustness of bearing state recognition and service life prediction are improved.

The invention discloses a time sequence data truth value discovery method based on kernel density estimation, which comprises the following steps of: initializing necessary parameters such as bandwidth and credibility weight for each object and time point; a kernel density estimation stage: performing kernel density estimation on data of each data source at each time point by using a Gaussian kernel function, updating kernel density estimation by using an exponentially weighted recursive formula so as to reflect time dependence of time sequence data, calculating a joint probability of given known data for a missing value, and calculating a kernel density estimation result; kernel density estimation is used to assess the likelihood of missing data. Kernel estimation of multi-source data is carried out, and the credibility of different source data sets is considered; in the truth value estimation stage, an expectation maximization algorithm and a kernel Kalman filtering technology are applied, parameters are optimized, and the most probable truth value is estimated. According to the method, the kernel function and the Kalman filtering algorithm are used, the problem of time sequence data truth value discovery is solved, the truth value discovery effect is optimized, and the method has wide application value and use prospects in the field of big data.

The invention relates to the technical field of task scheduling, in particular to an intelligent routing inspection scheduling method and system for a construction site environment, and the method comprises the following steps: extracting construction layout equipment distribution and task types based on construction site environment information, identifying task space distribution and marking priorities, analyzing time constraint, evaluating conflicts, and adjusting task distribution. Extracting core nodes, analyzing connection strength, screening and classifying, calculating path coverage efficiency ranking priorities, screening efficient paths, and obtaining a construction site intelligent inspection scheduling table. According to the invention, by analyzing the spatial distribution, priority and time dependency of inspection tasks, optimizing task identification and organization, optimizing an execution sequence based on accessibility and spatial layout, dynamically adjusting an allocation scheme, improving path flexibility and cooperation efficiency, extracting key nodes to associate and optimize path logic, and screening efficient paths for scheduling; the resource utilization rate is improved, and the problems of insufficient coverage of regular inspection and fixed paths, scheduling stiffness and the like are solved.

The invention belongs to the technical field of computer networks, and discloses a resource-constrained network equipment-oriented double-layer GRU lightweight real-time network anomaly detection method, which comprises the following steps of: constructing an anomaly detection model and training, deploying the trained model to resource-constrained network equipment, the method comprises the following steps: acquiring a system log generated when network flow generated by each application server node flows through in real time, performing data preprocessing on the acquired system log to obtain log semantic vectors, and forming a window sequence by the continuous log semantic vectors to obtain an abnormal probability value; and comparing the abnormal probability value with a set threshold value, and directly outputting an output detection result at an equipment end. According to the method, complexity of entity information is eliminated, time dependence in a time sequence is enhanced, simplification is carried out, a high anomaly detection recall rate is achieved under the condition that low calculation overhead is kept, the risk of missing detection is reduced, and cascade faults caused by the fact that anomalies are not found in time are avoided.

The embodiment of the invention provides a gas leakage detection method and device, electronic equipment and a readable storage medium, and the method comprises the steps: carrying out the feature extraction and analysis of target time series data collected by a plurality of detection nodes in a target time interval, and combining the dynamic space relation of the detection nodes, node spatial feature representation fused with neighborhood information and spatial relevance is obtained, and spatial-temporal feature representation is further constructed through a target time attention model according to time dependency and spatial-temporal relevance of the pipeline state. In combination with the target space attention model and the target time attention model, key space features and key time features related to a gas leakage event can be effectively focused, so that a damaged pipeline section in the gas pipe network is accurately identified, and an accurate safety detection result of the gas pipe network is obtained. The occurrence of safety accidents is effectively avoided, and the detection precision and the detection accuracy of gas safety are improved.

The invention discloses a multi-dimensional time sequence anomaly detection method based on correlation characteristics and multi-scale integrated decoding. The method comprises a coding stage, a decoding stage and an anomaly detection stage. In the encoding stage, by investigating the correlation between dimensions and the dependency of time, calculating a correlation feature matrix between the dimensions, and mining effective implicit features by using a convolution encoder; in the decoding stage, decoders with different scales are used for decoding the output of the coding layer, the outputs of the decoders are fused, and finally a reconstructed feature matrix is obtained; in the anomaly detection stage, the anomaly is identified by using errors of the reconstruction matrix and the original feature matrix; the method effectively solves the problems that in the prior art, correlation between the sequences is not considered, and a decoder is prone to error accumulation in the decoding process, and has the advantage that the correlation between the sequences and the time dependence of data can be effectively modeled.

The invention discloses an ROV trajectory prediction method based on Transform-LSTM, and the method comprises the steps: generating ROV and USV data sets, carrying out the preprocessing of the data of the data sets, and designing a mixed deep learning model which comprises an input layer, a position coding module, a Transform encoder module, an LSTM module, and a full-connection output layer; training the model, setting start and end time steps of USV observation failure, performing trajectory prediction by using the trained model, and in the prediction process, performing trajectory inference and drawing a three-dimensional trajectory comparison diagram according to a USV observation failure interval and a trajectory prediction model depending on historical observation and a historical prediction result to obtain a prediction result. According to the ROV trajectory prediction method, the precision and the stability of ROV trajectory prediction are remarkably improved by combining the advantage of the Transform model in the aspect of capturing the long-time dependency relationship and the capability of the LSTM in the aspect of processing the dynamic change of the time sequence.

The embodiment of the invention discloses a traffic flow prediction and model training method based on federal space-time diagram learning. The specific implementation mode of the method comprises the following steps: generating a node feature matrix of a space-time diagram based on historical traffic flow stored by a local client; determining a space-time dependency relationship based on the node feature matrix, a pre-trained node embedding matrix and a pre-trained time embedding matrix; the time-space dependency relationship is sent to the server, and an aggregation time-space dependency relationship returned by the server is received, and the aggregation time-space dependency relationship is obtained by aggregating the time-space dependency relationships from different clients by the server; and determining future traffic flow through a pre-trained prediction model based on the aggregation space-time dependency relationship. According to the embodiment, the space-time dependency relationship between the clients and inside the clients can be effectively captured, so that the accuracy of traffic flow prediction is improved.

The invention discloses a micro-service fault diagnosis method and system based on multi-modal data spatio-temporal dependency perception, and the method comprises the steps: fusing various modal data, such as logs, indexes and calls, through a gating index linear unit; and extracting time dependence and space dependence characteristics among multi-modal data through a UGFormer algorithm fusing a Transform attention mechanism and GNN graph structure perception capability, and realizing nonlinear transformation mapping from a characteristic vector to target output by using MLP so as to complete complex fault detection and root cause positioning tasks. Experimental evaluation results prove that in a representative micro-service fault data set, the accuracy rate of the method based on the multi-mode space-time dependency perception in a fault detection task reaches 98.9%, and the accuracy rate of the method based on the multi-mode space-time dependency perception in a root cause positioning task HRat1 (Top-1 hit rate) reaches 82.6%.

A method of analysing a motional activity of particles with a motion detector including the steps of bringing at least one particle into contact with a flexible support, detecting at least one time-dependent signal that is indicative of deflections of the flexible support due to motions of the at least one particle, and evaluating the detected time-dependent signal. The evaluation includes analysing a time-dependency of the signal to derive a plurality of signal parameters from the signal that characterise a variation of the signal as a function of time, and executing a linking algorithm that has, as input variables, an input vector having at least a selection of the signal parameters, and has, as an output variable, at least one activity indicator that is indicative of a motional activity of the particle.