95 results about "Time dependency" patented technology

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 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 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.

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 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 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%.

The invention provides a multivariate time sequence prediction method based on a GLoSyform model, and aims to improve the prediction precision by effectively capturing the time dependence of multivariate data and the correlation among multiple variables. According to the method, firstly, a double-sampling embedding strategy is introduced from the global and local angles, so that the representation of variable token time characteristics is enhanced; secondly, a TimeBlock module is designed, and the module is used for processing the enhanced tokens in a centralized manner by utilizing Cross-Attention so as to capture global and local time dependence; in order to better fuse the captured global and local time features, a self-adaptive gating fusion unit is adopted to distribute proper weight for information of each time step, and flexible and self-adaptive fusion of the global and local time features is realized. Finally, the invention designs a sparse variable attention mechanism, which sparses the attention score by calculating the Pear son correlation coefficient between the variables, thereby reducing the influence of irrelevant variables on the prediction result. According to the method, the global and local time feature representation capability of the time sequence is enhanced, interference of irrelevant variables is reduced, time dependence and multivariate correlation can be modeled more accurately, and therefore the accuracy and reliability of multivariate time sequence prediction are effectively improved.

This invention discloses a rubidium atomic clock frequency adaptive prediction method and system based on LSTM network, belonging to the field of time and frequency technology. The invention includes the following steps: Step 1. Data acquisition; Step 2. Filtering and denoising preprocessing; Step 3. Normalization processing; Step 4. LSTM model training and deployment; Step 5. Frequency prediction; Step 6. Online model adaptation. This invention employs a Long Short-Term Memory (LSTM) network, a special type of recurrent neural network, which can effectively learn the complex time dependencies and noise patterns in rubidium atomic clock time and frequency data. The inherent gating mechanism of the LSTM model makes it adept at capturing long-term dependencies in time series, thus enabling high-precision prediction of short-term frequency fluctuations. Through an online fine-tuning mechanism, this invention allows the LSTM model to continuously adapt to the individual drift and aging characteristics of a single rubidium atomic clock, achieving precise optimization for each clock and model, significantly improving the practicality and long-term stability of the method.

The invention discloses an optical cable damage monitoring method, system and device based on GAT and LSTM algorithms, and a medium, and relates to the technical field of optical fiber communication monitoring, and the method comprises the steps: collecting an original signal, carrying out the noise reduction processing, carrying out the dynamic boundary segmentation of time series data after noise reduction, forming a time segment sequence, calculating the similarity between time segments through an elastic alignment mode, and carrying out the detection of the optical cable damage. The method comprises the following steps: constructing a graph structure, performing spatial feature extraction on the graph structure, dynamically distributing importance weights among nodes, generating feature vectors, inputting the feature vectors into a time sequence modeling unit, capturing time dependence, outputting final time sequence features, and performing disaster type classification and position positioning based on the final time sequence features. According to the method, the spatial-temporal characteristics are modeled cooperatively through the GAT-LSTM mixed architecture, the OTDR data are analyzed by using the graph structure, and the decoupling capability and the detection sensitivity of the optical cable composite disaster spatial-temporal propagation path are improved.

The invention discloses a central heating and refrigerating system power adjusting method, terminal equipment and a storage medium. All available time data are mapped into a hidden space. Data points located on the manifold between the points associated with the available training time data are selected so that new data can be generated that complies with a new time dependency. And the enhanced data is input into the graph structure network, so that the model prediction performance is improved. And lagging relationship representation is obtained by performing lagging relationship calculation on the time series data, future values of the time series data are predicted in an auxiliary manner, and the capturing capability of the model on the large inertia characteristic of the system is enhanced. The improved ST-GNN prediction model and an MPC optimization controller are deeply integrated to form a complete closed loop from perception, prediction, optimization, execution and feedback. A model online updating mechanism is introduced, when the prediction deviation is increased due to the change of system characteristics, the model can be quickly adjusted based on a small amount of new data, and the model performance is prevented from attenuating along with time.

The present application provides an information security level protection evaluation system and method. First, different types of system logs in the information system to be tested are obtained; the time dependency between each system log when the information system to be tested is performing data protection is extracted from all system logs; the element association graph between each log event is determined based on the semantic features of each log event in each system log and the association features of each log event in the forward propagation process of the long short-term memory network; the evaluation constraint value of each system log in the level protection evaluation process is determined through the element association graph and the time dependency between each system log; the evaluation model of the level protection of the information system to be tested is constructed by combining the evaluation constraint value of each system log in the level protection evaluation process with the long short-term memory network. The solution of the present application can realize adaptive evaluation of the security level of the information system under dynamic changes in system configuration.

The invention relates to the crossing field of medical data processing and artificial intelligence technology, in particular to an Alzheimer's disease progress prediction method and system fused with time dependency, and the method comprises the steps: obtaining a standardized data set, each sample in the standardized data set comprising data of at least three time points; constructing a dynamic multi-task time accumulation model, aiming at each sample, enabling data of each time point to correspond to one task, and taking the weighted sum of task prediction results of all time points as a model prediction value; a cross entropy loss function is adopted to calculate an error between a model prediction value and a real diagnosis label, and back propagation is carried out to update parameters; inputting to-be-tested data into the trained dynamic multi-task time accumulation model, and outputting prediction results of different time points; the method can achieve the precise prediction of the progress track of the whole course from normal cognitive impairment and mild cognitive impairment to definite diagnosis of Alzheimer's disease, and provides data support for clinical early intervention and personalized treatment scheme formulation.

The application discloses a kind of telemetry data prediction method, device, medium and product, it is related to spacecraft telemetry data prediction field.The method includes: obtaining the telemetry data corresponding to different variables of target spacecraft in each time in the time sequence to be measured, to obtain the real-time telemetry data sequence corresponding to each variable;Real-time telemetry data sequence corresponding to multiple variables is all input into trained multi-scale time convolution network, to obtain real-time time correlation feature;According to the real-time telemetry data sequence corresponding to multiple variables, based on similarity principle and trained multi-head attention mechanism, determine real-time graph structure feature;Real-time time correlation feature and real-time graph structure feature are all input into trained graph attention network, to obtain the telemetry prediction data corresponding to different variables of next time of current time.The application can simultaneously capture the time dependence of telemetry data and the spatial dependence between each variable, to further improve the prediction accuracy of spacecraft telemetry data.

The invention discloses an electrocorticogram motion trail prediction method based on a deep attention network, and belongs to the field of motor nerve decoding in a brain-computer interface. According to the method, the feature extraction and trajectory prediction performance of the ECoG electroencephalogram signals is improved. For the problem that an ECoG electroencephalogram signal has long-range time dependence, an ABL mechanism is introduced, local features of time dimension are extracted by using CNN in a feature extraction stage, and weight fusion is performed by using loss output by the local features and output loss of a main model, so that long-range features and short-time local features are concerned while long-range features are concerned; meanwhile, a Transform model is used for extracting global features in the feature extraction process; according to the method, the accuracy and robustness of model joint trajectory decoding are improved, and the method has remarkable advantages in the aspects of difficulty in effectively capturing a global time sequence mode, difficulty in capturing a complex mapping relation between an ECoG signal and a joint and the like.

The invention discloses a time action positioning method and system based on time sequence context maximum pooling. The method comprises the following steps: acquiring a to-be-identified action video, and extracting and acquiring a feature coding sequence of the to-be-identified action video; presetting a time action positioning model, inputting the feature coding sequence into the time action positioning model, and obtaining an action classification result; by introducing the time sequence context maximum pooling operation and the multi-scale time feature pyramid structure, the calculation complexity is effectively reduced, and the reasoning speed and efficiency are improved. Meanwhile, by designing a time action positioning model comprising an encoder, a long-term time context module and a decoder, the model can simultaneously capture short-time and long-time dependency, so that the accuracy of time action positioning is improved. Due to the advantages, the method has important application value in the field of time action positioning.

The invention provides an encrypted traffic classification method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining a plurality of classification rule sets, and dividing an encrypted traffic set into first classification difficulty traffic and second classification difficulty traffic; determining a classification result of the first classification difficulty traffic; generating a session image based on the second classification difficulty traffic; decomposing the session image into a plurality of data packets, performing embedding processing, and capturing interaction information among all the data packets to obtain global features; extracting spatial features of the data packet, and extracting time dependence of the spatial features to obtain spatial-temporal features of the data packet; and fusing the global features and the spatial-temporal features to obtain fused features, and generating a classification result of the second classification difficulty traffic. Wherein the classification efficiency is improved by classifying and analyzing the encrypted traffic with different classification difficulties, and the classification precision is improved by classifying the encrypted traffic with higher classification difficulty on the basis of global features and spatio-temporal features of the encrypted traffic.