579 results about "Series data" patented technology

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.

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 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.

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.

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.

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.

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 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.

The invention discloses an electric power system data anomaly prediction method based on deep learning, and relates to the field of data anomaly prediction.The method comprises the steps that firstly, a graph convolutional network and LSTM are used for deeply mining time series data of an electric power system and spatial-temporal characteristics of a topological structure, and high-precision anomaly detection and positioning are achieved; and when an exception is detected, an exception attribution mechanism is introduced, and exception signal features (such as exception fragments and positioning information) at a mathematical level are converted into a structured query object at a semantic level. And then, a barrier between numerical data and the unstructured operation and maintenance knowledge base is broken through a vector retrieval technology, and related maintenance regulations and historical cases are accurately recalled. And finally, an executable recommended disposal scheme is automatically generated based on recall knowledge in combination with the generation capability of a large language model, so that an intelligent closed loop from anomaly perception and knowledge matching to decision assistance is constructed, and the accuracy and efficiency of power system fault handling are remarkably improved.

The invention relates to the technical field of biodiversity monitoring, in particular to a biodiversity monitoring method and system and a storage medium. Biodiversity multi-source data of a target area are collected in real time through a distributed sensor network and a mobile terminal device, the multi-source data comprise species image data, environmental factor time sequence data, voiceprint feature data and geographic space trajectory data, and encryption transmission and format standardization preprocessing are carried out on the data. Through a dynamic weight distribution mechanism driven by an environmental factor influence degree, a self-adaptive mapping relation between the sensor data entropy and the species ecological niche width is constructed, the problem of feature space mismatching of a traditional method in a non-stationary environment is solved, and the ecological interpretation of a species-environment joint feature vector is improved.

The invention discloses a charging pile data transmission supervision method and system based on data analysis, and the method comprises the steps: segmenting multi-dimensional time series data collected by a charging pile into subsequences, constructing a feature density map and a feature association map, and forming the representation data of a heterogeneous density-association space; the representation data are input into an improved Transform model, a local manifold curvature is calculated to update a self-attention weight, and a heterogeneous space curvature anomaly score is obtained; estimating a generalized Pareto distribution parameter in real time based on the abnormal score, and dynamically calculating a dynamic threshold value of the abnormal score; and comparing the abnormal score with a dynamic threshold value in real time to determine an abnormal level, and automatically triggering a supervision response rule through a Kafka message queue to realize a real-time supervision closed loop of abnormal data. According to the method, the real-time performance and accuracy of abnormal data identification are remarkably improved.

The invention provides a build-up rate intelligent prediction method and device, computer equipment and a storage medium, and the method comprises the steps: collecting the drilling and measurement data of an actual measurement point, and carrying out the data processing of the drilling and measurement data, and obtaining time series data and non-time series data; inputting the time series data and the non-time series data into a preset double-input GRU model, and calculating the time series data and the non-time series data by using the preset double-input GRU model to obtain an azimuth angle of the actual measurement point and a hole drift angle of the actual measurement point; and extracting well depth data of the actual measurement point from the drilling measurement data, and calculating the build-up rate of the actual measurement point according to the well depth data, the azimuth angle and the hole drift angle. According to the method, the skew rate is calculated by utilizing the preset double-input GRU model, so that the problems that multiple assumed conditions of a mechanism model are difficult to meet at the same time, underground mechanical behaviors are complicated and difficult to describe accurately are solved to a certain extent, and the problems that an intelligent model cannot focus key features and the generalization performance is insufficient are solved, and thus the accuracy of calculating the skew rate is improved.

The invention discloses a text travel scene flow prediction method, device and equipment, a storage medium and a program product, and the method comprises the steps: generating a data set based on a normalized text travel time sequence which comprises tourist flow time sequence data, tourism consumption time sequence data, weather time sequence data and traffic time sequence data; through channel attention feature analysis, key time sequence features are obtained, multi-head attention analysis and time sequence dependency analysis are carried out on the key time sequence features respectively, global dependency features and time sequence dependency features are obtained, feature fusion is carried out on the global dependency features and the time sequence dependency features, and fusion features are input into a multi-layer perceptron for flow prediction. Key time sequence features in a data set are extracted through channel attention feature analysis, and the dependency relationship between different time scales and features is accurately captured in a dynamic complex travel scene by fusing global dependency features and time sequence dependency features, so that the accuracy and robustness of traffic prediction are improved.

The present invention relates to determination of a state of a vehicle on a road portion. The vehicle includes an Automated Driving System (ADS) feature. At first, map data associated with the road portion, positioning data indicating a pose of the vehicle on the road, and sensor data of the vehicle are obtained. Then, a plurality of filters for the road portion are initialized. Further, one or more sensor data point(s) in the obtained sensor data is associated to a corresponding map-element of the obtained map data to determine one or more normalized similarity score(s). Now, based on the determined one or more normalized similarity score(s), one or more multivariate time-series data are also determined and provided as input to a trained machine-learning algorithm. Then, one of the initialized filters is selected by the machine learning algorithm to indicate a current state of the vehicle on the road portion.

The invention discloses a weighing sensor life degradation modeling method based on a bimodal LSTM network, and belongs to the technical field of industrial automation control systems, and the method comprises the steps: obtaining the data of a weighing sensor, generating a bimodal original data set, and carrying out the denoising processing to obtain a bimodal denoised data set; the method comprises the following steps: constructing a dual-channel LSTM-DC-CNN network, fusing stress time sequence data and environment parameter data, performing time sequence feature extraction to obtain a stress degradation feature vector and an environment coupling coefficient vector to generate a fusion degradation feature vector, performing feature enhancement processing on the fusion degradation feature vector, and performing path optimization to generate a life degradation stage judgment result. And a preset historical degradation database is combined to generate a remaining service life index of the weighing sensor. According to the method, two-channel time sequence feature extraction, a dynamic fusion mechanism and an empirical mode compensation technology are adopted, the prediction precision and reliability can be remarkably improved, and efficient and intelligent evaluation of the remaining service life is achieved.

The invention discloses a lightweight time sequence information fusion perception control method and system, and relates to the technical field of Internet of Things weak terminal data acquisition, and the method comprises the steps: obtaining key perception data, and tracking the local task load and communication load of a node in real time; based on a feasible space window of various non-linear fitting functions, carrying out adaptive segmentation on input sensing data and channel states; on the basis of curve features of the time series data, preprocessing the time series data, then performing feature extraction on a curve, and selecting important features; analyzing and predicting the compressed curve characteristic data, classifying the time sequence data into curves with different characteristics, selecting a corresponding empirical function model for fitting according to a classification result, and finally dynamically changing the running state of the node by a self-adaptive adjustment algorithm; and performing dynamic control based on the future trend of the data. According to the method, the future trend of the task and the communication load is coupled with the running state of the task and the communication load, and the change rate of node tracking data is combined with the historical condition of the node tracking data, so that the self-adaptive adjustment of the running state of the node is realized.

The invention discloses a multi-scale power load prediction method and system, and the method comprises the steps: obtaining time series data related to power generation, and decomposing the time series data into a plurality of sub-series data, so as to capture key periodic features in the data; the time sequence data and the subsequences are sent into an encoder and a decoder for feature extraction, in the encoder, local important information is extracted through an expansion causal convolutional network, correlation features between the subsequences are fused into a subsequent attention module, in the encoder, an attention mechanism is sampled through a probabilistic fragment, and the time sequence data and the subsequences are extracted; randomly calculating the attention of a plurality of local blocks, performing dynamic pruning, capturing global dependency on coarse granularity through a multi-scale sparse attention mechanism, identifying a macroscopic mode of a sequence, and calculating a relationship between time steps on fine granularity to capture local dependency; and constructing and training a prediction model based on the encoder and the decoder, outputting a prediction result of the power load data, and performing result evaluation through dimensionality reduction and a loss function.

To provide a data collection system capable of appropriately collecting data related to exercise.SOLUTION: A system 1 comprises a camera 300, estimation means for estimating activity amounts of users based on image data, identification means for identifying users captured by the camera, and presentation means for presenting exercise data indicating the activity amounts to the users. The identification means extracts a person region including a person from the image data, and when the person regions of two or more users overlap, calculates an ID vector indicating a probability of each ID of target users for the overlapping person regions as a branching scene with the two or more users as the target users. The identification means estimates joint coordinates of users during exercise based on the image data. The identification means extracts characteristic data indicating characteristics of exercise patterns of the users from time-series data to identify the users.SELECTED DRAWING: Figure 1