667 results about "Series data" patented technology

The invention discloses a time sequence data modeling method for an agent with a body based on frequency domain learning, and the method comprises the steps: carrying out the self-adaptive partitioning of an original time sequence data sequence, extracting features through multi-scale convolution, and generating a position code; fourier transform is carried out on the feature block to extract frequency domain information, and nonlinear transform and residual compensation are carried out; calculating an attention relationship between the features and carrying out feature fusion; constructing a time sequence dependency relationship graph, and extracting time sequence aggregation features and dynamic system features; and constructing a probability prediction model, and calculating the uncertainty of a prediction result. According to the method, the modeling precision is improved through self-adaptive blocking and frequency domain feature extraction, the feature expression ability is enhanced through an attention mechanism and graph structure modeling, the prediction reliability is improved through uncertainty quantification, and the method is suitable for time series data modeling of an agent with a body in a complex environment.

The invention belongs to the technical field of equipment health monitoring and anomaly detection, and discloses an industrial time series data learning fusion and anomaly detection method, which comprises the steps of constructing an IP-PLC mapping relation table, collecting multi-modal data, constructing a physical constraint parameter list, and generating structured data and a storage index. Time domain features and frequency domain features are extracted, a spatial topological graph is constructed, node spatial feature vectors and edge association strength are extracted, spatial association feature vectors are generated, a constraint rule base is constructed, and an enhanced feature set is formed; aggregating the enhanced feature set and the constraint rule base, generating a multi-dimensional feature matrix and a global reference parameter table, further constructing a global reference system, obtaining an equipment-level anomaly probability matrix, and generating a working condition-level anomaly probability matrix; hierarchical optimization is carried out through hierarchical modeling, and an optimization parameter set is generated; constructing an alarm response mechanism, and performing reverse updating to form closed-loop iteration; and an interpretable and extensible solution is provided for equipment health management in a complex industrial scene.

An approach to time-series data point anomaly detection may be presented. Data point anomalies in time-series data can cause a cascade of incorrect predictions in a time-series data prediction model. Presented herein may be an approach to decompose a time-series training data set into elementary components, such as seasonal, trend and residual. The approach may determine one or more confidence intervals for elementary components of data points including level shift, variance, and outlier. From these confidence intervals, new data points can be analyzed and identified as anomaly data points. The approach may also prevent anomaly data points from being incorporated into a time series data prediction model, reducing prediction error in the prediction model.

The invention relates to the technical field of mining data processing, and particularly discloses a coal yard production operation real-time monitoring management system. Aiming at the problems of difficulty in dynamic matching of time series data caused by insufficient cache capacity of edge nodes and fault diagnosis delay caused by data flow breakpoints or redundancy, the system adopts a multi-stage cache architecture, and physical mapping and quick positioning of data are realized through dynamic resource allocation of a real-time processing layer and a batch buffer layer in combination with three-dimensional grid spatio-temporal indexing. A breakpoint compensation mechanism is utilized to trigger target area resampling and historical data prefetching, and data stream continuity is guaranteed; and dynamically screening the data based on the confidence coefficient weight, and inhibiting redundancy accumulation. The collaborative optimization engine establishes a parameter linkage rule of the collection frequency, the cache period and the fusion threshold value, and the resource priority is inclined during high-risk early warning. And through closed-loop feedback and edge-cloud collaborative learning, a data processing strategy is continuously optimized. The cache resource utilization rate and the diagnosis timeliness are improved, and the method is suitable for real-time safety monitoring of complex industrial scenes.

A system and methods for upsampling compressed data using a jointly trained Vector Quantized Variational Autoencoder (VQ-VAE) and neural upsampler. The system compresses input data into a discrete latent space using a VQ-VAE encoder, reconstructs the data using a VQ-VAE decoder, and enhances the reconstructed data using a neural upsampler. The VQ-VAE and neural upsampler are jointly trained using a combined loss function, enabling end-to-end optimization. The system allows for efficient compression and high-quality reconstruction of various data types, including financial time-series, images, audio, video, sensor data, and text. The learned discrete latent space can be explored and manipulated using techniques such as interpolation, extrapolation, and vector arithmetic to generate new or modified data samples. The system finds applications in data storage, transmission, analysis, and generation across multiple domains.

Systems and methods for time-series forecasting via multi-modal augmentation and fusion. Time-series data and modality data can be decomposed into seasonal and trend representations with trend-seasonal decomposition. Using an encoder transformer model, time-series data embeddings and modality data embeddings can be concatenated from the seasonal representations and the trend representations to obtain crossed representations. Using the encoder transformer model, the modality data embeddings and the time-series data embeddings can be processed separately to obtain singular representations. The crossed representations and the singular representations can be augmented through joint trend-seasonal decomposition to obtain augmented seasonal data and augmented trend data. Using a decoder, augmented seasonal data and augmented trend data can be fused to obtain fused augmented data. Corrective action can be performed to correct predicted future events using a system with a prediction model trained with the fused augmented data.

The invention relates to a vehicle suspension control method and device, computer equipment and a readable storage medium. The method comprises the following steps: combining obtained vehicle body dynamics time series data, vehicle control time series data and environment perception time series data in a vehicle driving process into time series data of multiple channels, determining a self-adaptive modal number of the time series data under each channel according to the time series data under each channel, and determining the self-adaptive modal number of the time series data under each channel according to the self-adaptive modal number of the time series data under each channel; and performing modal decomposition on the time sequence data based on the adaptive modal number, extracting frequency domain characteristics of the time sequence data, integrating the frequency domain characteristics of the time sequence data of the multiple channels, inputting the frequency domain characteristics into a trained suspension control parameter prediction model to obtain target time sequence characteristics for predicting suspension control parameters, and performing prediction on the suspension control parameters based on the target time sequence characteristics. Vehicle suspension control parameters are obtained through prediction, and a suspension system of the vehicle is controlled based on the vehicle suspension control parameters. By adopting the method, the risk of rollover of the vehicle can be obviously reduced.

The invention discloses an intelligent data management system and method based on multi-source data collection, and relates to the technical field of data processing. Multi-source data from multiple data sources are obtained, an original data set with credibility marks is generated, data records in the original data set are classified according to data types, and for time series data, data records in the original data set are classified according to credibility marks. Extracting trend features by applying a segmented multi-model time sequence feature extraction framework; for the data of the unstructured data, extraction of unstructured features is carried out through a feature extraction model based on a multi-layer perceptron; for structured data, a knowledge graph technology is adopted to establish an adaptive association relationship, association relationship data is generated, trend features, unstructured features and the association relationship data are used as input of a data value evaluation model, and data asset performance evaluation of dynamic weight is generated; and intelligent management and value evaluation of the multi-source heterogeneous data are realized.

The invention discloses an equipment abnormal data detection method and device, equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: carrying out the information extraction of historical time series data of target equipment within a preset duration before a current moment according to different time scales, and obtaining a plurality of time scale series data; calculating an abnormal score of each time scale based on a preset sliding window size and the time scale sequence data to obtain a plurality of first abnormal scores; calculating the sensitivity of each time scale based on the first abnormal score and a preset score threshold, and calculating an abnormal data detection threshold based on the maximum value in the sensitivity; calculating an abnormal score of the to-be-detected data of the target equipment at the current moment based on the size of a preset sliding window to obtain a current abnormal score; and determining whether the to-be-detected data is abnormal data or not according to a comparison result between the current abnormal score and an abnormal data detection threshold. The abnormal data detection efficiency can be improved, and the requirement for processing a large amount of data in real time is met.

The invention discloses a grid coding-based remote sensing image general basic model construction and analysis method, which comprises the following steps of: pre-processing multi-modal data, eliminating cloud layer influence, aligning space coordinates and unifying data scales; generating a unified identification code with geographical meaning by using efficient space-time grid integrated coding; performing multi-scale feature extraction by means of a visual Transform and a convolutional neural network, and optimizing multi-source data fusion in combination with an anchoring feature extraction strategy; and finally, cross-task downstream application adaptation is realized on the basis of a multi-task Transform architecture. According to the method, the problems of high manual annotation cost, difficulty in multi-source data integration, low mass data processing efficiency and the like in traditional remote sensing image analysis are effectively solved. Experiments show that the multi-source data integration efficiency is improved, the model generalization ability is enhanced, the classification precision in high-resolution series data tests reaches 90% or above, the segmentation IoU exceeds 80%, and an efficient solution is provided for remote sensing image analysis.

Provided in the embodiments of the present application are a training method and apparatus for a human-factor intelligence state monitoring model, and a human-factor intelligence state monitoring method and apparatus. The training method comprises: collecting physiological signals and video signals of persons to construct a training sample set, wherein an initial neural network model comprises a data preprocessing module, a common feature extraction module, a multi-head feature extraction module, etc.; the data preprocessing module preprocessing the physiological signals and the video signals to generate two-dimensional spatial data, and fusing the two-dimensional spatial data; the common feature extraction module performing feature extraction and fusion on a fused signal to generate feature maps on multiple scales, and summarizing the feature maps on multiple scales; the multi-head feature extraction module fusing the feature maps on multiple scales to obtain a high-dimensional feature map, and generating a prediction result on the basis of the high-dimensional feature map; and using the training sample set to train the initial model, so as to finally obtain a human-factor intelligence state monitoring model. In the present application, multivariate time-series data is fused with two-dimensional image data to construct and train a human-factor intelligence state monitoring model, so that faster and more accurate monitoring is implemented.

The invention discloses a mold life prediction and maintenance strategy making method based on big data analysis, and particularly relates to the field of mold life prediction and maintenance, and the method comprises the following steps: collecting and associating multi-source heterogeneous data in a mold production process; comprising process parameter time sequence data, on-line monitoring time sequence data and structured and image data of off-line inspection; performing time domain alignment and collaborative feature extraction on the data, and constructing a structured training data set taking a production cycle as a unit; inputting the data set into a dynamic evolution prediction model, outputting a mold health state score, and generating a dynamic maintenance strategy through a multi-objective optimization model in combination with production context information; and finally, visually displaying the strategy, converting the strategy into a control instruction, issuing the control instruction to a production management system, and automatically triggering maintenance execution. The method can realize high-precision life prediction, dynamically optimize the maintenance decision, significantly improve the service life and production efficiency of the mold, and reduce the maintenance cost and production risk.

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.

Computerized methodologies are disclosed that are directed to detecting anomalies within a time-series data set. An aspect of the anomaly detection process includes determining one or more seasonality patterns that correspond to a specific time-series data set by evaluating a set of candidate seasonality patterns (e.g., hourly, daily, weekly, day-start off-sets, etc.). The evaluation of a candidate seasonality pattern may include dividing the time-series data set into a collection of subsequences based on the particular candidate seasonality pattern. Further, the collection of subsequences may be divided into clusters and a silhouette score may be computed to measure the clustering quality of the candidate seasonality pattern. In some instances, the candidate seasonality pattern having the highest silhouette score is selected and utilized in anomaly detection process. In other instances, a plurality of seasonality patterns may be combined forming a time policy, where the time policy is utilized in anomaly detection process.

The invention relates to the technical field of nuclear power equipment quality management and control, in particular to a nuclear power unit quality defect source analysis and total value chain tracing method, which comprises the following steps: S01, collecting total value chain multi-modal data including structured data, logs, texts and sensor time sequence data, and carrying out BIO labeling, BERT coding, BiGRU-Attention feature extraction and CRF sequence labeling; constructing a mixed storage data resource pool; s02, mining association rules by adopting a dynamic pruning improved Apriori algorithm, processing sample imbalance in combination with chi-square test and an NSGAII algorithm, and analyzing defect causes; s03, a dynamic knowledge graph containing multiple types of nodes, relations and threshold attributes is constructed, and rapid defect tracing is achieved through entity linking and probability path searching; the objective of the invention is to solve the problems of difficulty in defect source calibration, low tracing efficiency and the like in nuclear power equipment quality control.

A system and method include tuning hyperparameters for an ARIMA model using a derivative free approach by determining a set of initial hyperparameter values, fitting an ARIMA model to the set of initial hyperparameter values, selecting a tuning method for the set of hyperparameters, responsive to selecting a single-objective method, computing a first objective function value from time-series data applied to the ARIMA model based on the set of initial hyperparameter values, or responsive to selecting a multi-objective method, computing at least a second objective function value and a third objective function value from the time-series data applied to the ARIMA model based on the set of initial hyperparameter values, determining whether a stopping criterion for tuning the set of hyperparameters has reached, responsive to determining that the stopping criteria has reached, outputting a set of tuned hyperparameter values.

The invention discloses a multi-data-source rapid classification screening system and method for security protection, and relates to the technical field of intelligent security protection, the classification screening system comprises a multi-source heterogeneous data fusion module, a classification screening module and a calculation acceleration module, and the classification screening system has the advantages that a protocol conversion layer realizes unified coding and standardization of multiple protocols; meanwhile, a space-time alignment engine is combined with a dynamic feedback mechanism, feature weight distribution of multi-modal data is optimized in real time through a reinforcement learning algorithm, high-precision time synchronization of video streams, sensor time sequence data and audio streams is ensured, instantaneous abnormal events are effectively captured, and the real-time performance of the system is improved. Compared with a traditional static strategy, according to the scheme, through a cross-modal attention mechanism and online weight updating, collaborative improvement of data fusion efficiency and real-time response capability is achieved, and reliable guarantee is provided for accurate alarm in a complex environment.

The invention relates to a method for constructing a cross-modal time sequence diagram model of a complex common disease network, and belongs to the technical field of common disease networks, and the method comprises the following steps: S1, dividing the diagnosis and treatment data of a patient into text data, image data and time sequence data classification; s2, processing text information by adopting a word embedding model, processing image information by adopting a convolutional neural network, and processing time sequence data by adopting a time sequence modeling module; then, a cross-modal multi-head attention CM-MHA module is adopted to carry out cross-modal fusion on the three features, and a feature matrix Ffuse with a time sequence is formed; s3, according to the current diagnosis and treatment information of the patient, establishing a complex common disease network by adopting an advantage ratio RR method; and S4, embedding the feature matrix Ffuse with the time sequence into the complex common disease network by adopting a graph embedding technology, and according to a time sequence data updating rule, forming a cross-modal time sequence common disease network.

Computerized methodologies are disclosed that are directed to detecting anomalies within a time-series data set. A first aspect of the anomaly detection process includes analyzing the regularity of the data points of the time-series data set and determining whether a data aggregation process is to be performed based on the regularity of the data points, which results in a time-series data set having data points occurring at regular intervals. A seasonality pattern may be determined for the time-series data set, where a silhouette score is computed to measure the quality of the fit of the seasonality pattern to the time-series data. The silhouette score may be compared to a threshold and based on the comparison, the seasonality pattern or a set of heuristics may be utilized in an anomaly detection process. When the seasonality pattern is utilized, the seasonality pattern may be utilized to generate thresholds indicating anomalous behavior.

The invention provides a credit evaluation system and evaluation method, and relates to the technical field of data processing. The system comprises a feature extraction module, a feature fusion module and a multi-path reasoning module, the feature extraction module is used for performing feature extraction on to-be-evaluated credit data to obtain to-be-evaluated features; the feature fusion module is used for performing adaptive fusion on the to-be-evaluated features through a gating cross-modal attention mechanism to obtain fused features; and the multi-path reasoning module is used for performing multi-path credit risk assessment based on the fused features and outputting a credit assessment result. According to the system, adaptive fusion of at least two kinds of data in structured data, text data, time series data and graph data is realized, so that information provided by each modal data is fully captured for credit evaluation, and the evaluation accuracy of the credit evaluation system is improved.

The invention discloses an intelligent cleaning processing method and system for multi-source heterogeneous data, and belongs to the technical field of data cleaning processing methods, industrial sensor time sequence data, long text log data and other high-dimensional industrial data are obtained, the obtained data are subjected to cleaning, missing value processing, standardization, text processing and timestamp alignment, and the obtained data are subjected to data cleaning, missing value processing, standardization, text processing and timestamp alignment. The method comprises the following steps: preprocessing time sequence data of an industrial sensor, inputting the preprocessed time sequence data of the industrial sensor into a Transform auto-encoder, and in a low-dimensional feature space output by the Transform, applying a density-sensitive clustering method, setting a neighborhood radius and a minimum point number, and identifying a data cluster formed in a data dense region and noise points in a sparse region.

The invention belongs to the technical field of artificial intelligence and satellite remote sensing crossing, and particularly relates to a cross-scale space-time fusion ground feature classification method based on a double-branch architecture, and the method comprises the steps: preprocessing a remote sensing image: obtaining a high-resolution optical image and a multi-stage medium-resolution time sequence image, carrying out the processing, generating time sequence data, carrying out the marking, and segmenting a data set; constructing a double-branch network, wherein the network comprises space and time feature extraction branches and fusion and decoding modules; training an optimization model by using a weight optimizer and a loss function for dynamically adjusting category weights; and post-processing the classification result, and outputting vector data. According to the method, heterogeneous information fusion is realized, the problems of same object and different spectrums and the like are solved, the feature utilization rate and minority class recognition precision are improved, end-to-end design is convenient, the generalization ability is high, and reasoning is fast.

An information processing system predicts a condition of a device from time-series data acquired from the device, by using a trained model. When predicting, the information processing system allows the trained model to extract features that depend on the sequence from pieces of partial time-series data obtained by dividing the time-series data along the time axis, generate first vectors in which the extracted features are embedded, each of the first vectors corresponding to each of the pieces of the partial time-series data one to one, generate a second vector in which the first vectors are embedded, extract features that depend on the sequence from the first vectors, generate a third vector in which the extracted features are embedded, generate a fourth vector in which the second vector and the third vector are embedded, and transform the fourth vector into a first value that represents a condition of the device.

The invention relates to the technical field of data recognition, and discloses an abnormal data recognition method, device and equipment and a storage medium, and the method comprises the steps: extracting data distribution characteristics in each time window in a high-dimensional time series data stream, and carrying out the recognition of abnormal data based on the data distribution characteristics and an adjusted random projection matrix of the high-dimensional time series data stream; the method comprises the steps of generating dimension-reduced low-dimensional data, configuring local sensitive hash resolution parameters according to local volatility of the low-dimensional data, constructing a multi-resolution hash table, and identifying abnormal data points according to in-bucket data density of the multi-resolution hash table. According to the method, the dimension reduction process is adjusted by extracting the data distribution characteristics, the calculation complexity is reduced while key information is reserved, the multi-resolution hash table is constructed based on the local volatility of the dimension reduction data, adaptive analysis of different data areas is achieved, abnormal data points are identified according to the density of the data in the barrel, and the accuracy of the data in the barrel is improved. The dimension problem and the distribution change problem can be effectively solved, and the processing efficiency is improved while the detection precision is ensured.

The invention discloses a water conservancy data analysis method and device based on a self-attention mechanism and a medium, and relates to the technical field of water conservancy. The method comprises the following steps: constructing a water conservancy time sequence data warehouse, and establishing a water conservancy source data access mechanism to access to-be-processed water conservancy time sequence data; performing standardized preprocessing on the to-be-processed water conservancy time sequence data to obtain standard time sequence data; inputting the standard time sequence data into a pre-trained water conservancy data analysis model, and calculating attention weight distribution between different time steps and different variables through a self-attention mechanism unit in the water conservancy data analysis model; according to the attention weight distribution, performing weighted summation on the standard time sequence data, generating a spatial-temporal feature representation vector, inputting the spatial-temporal feature representation vector into a gating circulation unit in the water conservancy data analysis model, and executing spatial-temporal information fusion by updating a gate and resetting a gate; and performing water conservancy data trend prediction based on the fused features, and outputting flood prevention scheduling decision support data.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a wind power prediction method based on multi-objective optimization, and the method comprises the steps: wind power data collection and training data set construction, adaptive sliding window and dynamic fluctuation decomposition of wind power time series data, construction of a short-term power prediction model, and short-term power prediction. According to the method, the historical window length and the decomposition scale can be autonomously adjusted according to the inherent fluctuation characteristics of the data, and multi-scale accurate characterization of the non-stationary power sequence is realized; according to the method, a dynamic space-time diagram fusing geographic distance and instantaneous power correlation is constructed, and a diagram attention network combined with trend similarity gating is designed, so that dynamic refined modeling of a space incidence relation is realized; according to fluctuation intensity self-adaptive loss function dynamic balance point prediction precision and interval prediction reliability, synchronously outputting deterministic and probabilistic prediction results; the rated power limit and the ramp rate constraint are embedded into the model in a soft mode, and it is ensured that the prediction result conforms to the actual operation rule of the wind turbine generator.

A time series data prediction method and apparatus, and a storage medium are provided. The method includes: obtaining current time series data collected in a current time window that is adjacent to and precedes a prediction time window in a current time period, and obtaining a plurality of groups of historical time series data separately collected in a same target time window of a plurality of historical time periods; encoding the plurality of groups of historical time series data by using a plurality of encoders respectively, to obtain a plurality of historical time series features, where each historical time series feature represents relative location information and change trend information of each group of historical time series data in the target time window; and determining, predicted time series data corresponding to a target object in the prediction time window.

The invention provides an additional recording method and device for water affair monitoring data, electronic equipment and a medium. The method comprises the steps of obtaining time sequence data of water affair monitoring; segmenting the time sequence data into a series of data windows according to a preset time window size and a sliding step length; counting data points in each data window to judge whether missing data points exist in the window or not; and when missing data points are detected, marking the corresponding windows as abnormal windows, and taking corresponding additional recording measures based on the number of the missing data points. The missing or damaged data points in the water affair monitoring data are effectively identified and repaired, and the integrity and accuracy of the data are ensured. According to the method, a small number of missing value windows and a batch of missing value windows are distinguished through the preset threshold value, and a corresponding additional recording strategy is adopted, so that the problem of data missing under different conditions can be adaptively solved, and the robustness under the situation of water affair monitoring data loss is remarkably improved.

The systems and methods for improved precision in detecting false-positive changepoints in time-series data. The system may receive a first dataset of time-series datapoints. The system may determine a first changepoint in the first dataset. The system may determine a first category of known values for the first dataset. The system may, based on the first category of the known values, select a first model from a plurality of models for determining whether the first changepoint corresponds to a first false-positive changepoint. The system may, in response to selecting the first model, process, using the first model, the first changepoint and a first value of the known values to determine a first output. The system may generate for display, in a user interface, a first recommendation based on the first output, wherein the first recommendation indicates whether the first changepoint corresponds to the first false-positive changepoint.

A system for processing multivariate, time-series data to classify portions of the data corresponding to an identified event or condition. Time periods of interest may be identified as search criteria for a variety of purposes. As an example, an event or condition may occur, whereby, precursors to the event or condition may be present in the time-series data, and thereby be a time period of interest as a predictor of future occurrences. As another example, the state of the system during normal operation may be identified. Based on the provided data, the processing system may output a prediction or a likelihood of a re-occurrence of the condition. One or more actions may be taken in response to the output of the system to prevent the occurrence of the condition or otherwise respond to the possibility of the occurrence of the condition.