444 results about "Series data" patented technology

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 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 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 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 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 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 digital cloud platform multi-dimensional data analysis system construction method, and belongs to the technical field of data analysis system.The method comprises the steps that image data, text data, time sequence data and space data are collected, a knowledge graph containing materials, processes, scenes and feedback nodes is constructed based on the data collected in the first step, the incidence relation between the nodes is defined, and the knowledge graph is constructed; a cross-modal alignment model is trained through multi-modal data, semantic association of images, texts and time sequence features is achieved, model parameters are dynamically adjusted through a metadata system, the generalization ability of the model is improved in combination with meta-learning and data enhancement technologies, and demand prediction and quality score multi-task output are conducted based on the trained model.

The invention discloses a main beam formwork erection elevation adjustment method based on machine learning, and belongs to the technical field of elevation adjustment. The method comprises the steps that a space-time database is constructed by collecting data of the construction period of a cable-free section; processing the time sequence data by adopting an LSTM-Transform hybrid model, extracting long-period characteristics, and outputting an initial prediction value of the elevation; analyzing nonlinear variables such as a cable force change rate, a sunlight gradient and a material age by using an XG-Boost algorithm, and outputting a compensation factor; fusing and generating a joint prediction value; constructing a reinforcement learning agent, and outputting an adjustment instruction by taking a construction stage as a state space, taking an elevation adjustment amount as an action space, minimizing deviation between a predicted value and a measured value and taking construction stability as a reward function; the formwork erecting elevation is adjusted according to the driving hydraulic system, and the database is updated in real time. According to the invention, through multi-dimensional data fusion and intelligent optimization, the elevation adjustment precision and stability are improved.

The invention discloses a passenger flow multi-source data statistical method for a bus doorless signal, which relates to the technical field of data statistics, and comprises the following steps: based on multi-source time sequence data of a target vehicle in an operation plan time window, identifying a plurality of candidate boundary judgment events, and endowing each event with a quantitative confidence evaluation value; aiming at the current shift, independently generating an ordered candidate event list for the first station operation boundary and the last station operation boundary respectively based on the confidence evaluation values, and arranging events in the list according to the high-low sequence of the confidence evaluation values of the events; constructing an optimized objective function by taking elimination of passenger flow data string between the first station and the last station in a non-operation period as a target; according to the method, the boundary event is identified in a multi-source data fusion mode, and the operation boundary can be accurately positioned even if no door signal exists, so that the problem of data cross in a non-operation period is finally eliminated.

The disclosure is directed to methods to design and revise hydraulic fracturing (HF) job plans. The methods can utilize one or more data sources from public, proprietary, confidential, and historical sources. The methods can build mathematical, statistical, machine learning, neural network, and deep learning models to predict production outcomes based on the data source inputs. In some aspects, the data sources are processed, quality checked, and combined into composite data sources. In some aspects, ensemble modeling techniques can be applied to combine multiple data sources and multiple models. In some aspects, response features can be utilized as data inputs into the modeling process. In some aspects, time-series extracted features can be utilized as data inputs into the modeling process. In some aspects, the methods can be used to build a HF job plan prior to the start of work at a well site. In other aspects, the methods can be used to revise an existing HF job plan in real-time, such as after a treatment cycle, a pumping stage, or a time interval.

The invention provides a vehicle collision detection method, a medium and a vehicle, and relates to the technical field of vehicle collision detection. The method comprises the following steps: firstly, based on various state data of a target vehicle, obtaining standard time sequence data corresponding to each type of state data, and carrying out Grubby angle field conversion on the standard time sequence data to obtain a two-dimensional image corresponding to each type of state data, so as to convert one-dimensional vehicle time sequence data into the two-dimensional image, thereby retaining a time sequence relationship of original data; and the data integrity is improved. And then based on the two-dimensional images corresponding to the various state data, a multi-dimensional feature matrix corresponding to the target vehicle is generated so as to capture correlation between different time sequence data, and the state of the vehicle is reflected more comprehensively. And finally, inputting the multi-dimensional feature matrix into a preset collision detection model for collision prediction to obtain a collision detection result of the target vehicle so as to improve the accuracy of vehicle collision identification.

The invention provides a power load prediction method and device, equipment and a medium. Relates to the technical field of power load prediction. The method comprises the following steps: respectively mapping historical time sequence data from different data sources of a to-be-predicted power system into single-source time sequence characteristics representing overall time sequence change characteristics of the corresponding data sources, and modeling a dependency relationship of the single-source time sequence characteristics corresponding to the data sources, obtaining time sequence embedding representing overall dynamic evolution of the to-be-predicted power system; historical time sequence data from different data sources are converted into natural language texts expressing data source attributes, time sequence characteristics and power load associated information, the natural language texts corresponding to the data sources are coded, and prompt embedding including semantic information of the data sources is obtained; fusing the time sequence embedding and the prompt embedding to obtain cross-modal enhanced embedding; and decoding the cross-modal enhanced embedding to obtain a power load prediction result of the to-be-predicted power system.

The invention discloses an open caisson sinking posture prediction method, which belongs to the technical field of open caisson sinking prediction, and comprises the following steps: constructing a primary data matrix according to on-site actual measurement time sequence data; smoothing the first-level data matrix by using a Savitz-Gauge filtering algorithm to obtain a second-level data matrix; constructing a third-level data matrix in combination with the second-level data matrix and the construction excavation information representation matrix; performing correlation analysis on the three-level data matrix by using three analysis methods to obtain a correlation characterization matrix; screening key variables by using the correlation characterization matrix to construct a four-level data matrix; performing dimension reduction processing on the four-level data matrix by using a principal component analysis method to obtain a principal component matrix; and training a long-short-term memory network by using the principal component matrix to obtain an open caisson sinking posture prediction model to perform open caisson sinking posture prediction so as to obtain an open caisson sinking posture prediction result. According to the method, the accuracy and stability of deep water open caisson sinking posture prediction can be improved.

The invention discloses a vehicle lane changing intention prediction method based on comparative learning under a data missing condition, and the method comprises the steps: building a self-supervised comparative learning framework, employing a twin network structure for an upstream task, and employing a graph attention network module for a downstream task; by constructing positive and negative sample pairs, data representation is learned, so that the positive sample pairs are closer in a feature space, and the negative sample pairs are farther; and respectively processing a complete data sample and a missing data sample by adopting a dual-channel trend attention mechanism. The mechanism can effectively capture the global trend and local change in the time sequence data, and improves the accuracy and reliability of feature extraction. According to the method, the prediction robustness is remarkably improved, the complex operation of traditional data filling is avoided, time sequence dynamic and space interaction can be fused, efficient space-time feature modeling is achieved, and the problems of data missing and space-time interaction modeling confronted by vehicle lane changing intention prediction in an intelligent driving scene are effectively solved.

The invention discloses a financial flow situation awareness system and method based on a multi-modal large model, and relates to the technical field of supply chain financial risk control. Aiming at the problems of data isolation, high processing delay and risk perception lagging in the existing storage pledge financing scene, the method comprises the following steps: obtaining a storage digital planar graph, dividing logic sub-regions, collecting and processing multi-source data of each sub-region in parallel, and extracting pledge stock time sequence data and warehouse receipt information by utilizing a visual and text model respectively; constructing a sub-region association map by taking the cargo identifier as a node and fusing the fund flow; and fusing all the sub-maps to construct a global association map, performing cross-regional consistency verification and anomaly recognition, generating a dynamic risk judgment result, and outputting hierarchical situation awareness information. According to the invention, accurate association and panoramic risk perception of the "object-bill-money" state are realized, and the real-time performance and accuracy of risk identification and the expandability of the system are improved.

The invention relates to the technical field of industrial automation and energy management, in particular to a smoke waste heat utilization monitoring system based on big data. Comprising a multi-dimensional time sequence data acquisition and fusion module used for generating a unified data set after cleaning and time alignment processing; the heat source-load prediction module based on time sequence deep learning is used for generating a predicted supply and demand curve in a future scheduling period; the multi-target dynamic optimization and scheduling decision module is used for generating an optimal heat energy distribution scheduling scheme; and the real-time feedback and self-adaptive control module takes the optimal heat energy distribution scheduling scheme as a dynamic set value of the control system, and returns the actual distribution power measured and calculated in real time to the multi-dimensional time sequence data acquisition and fusion module for periodically retraining the attention mechanism long and short term memory network model. According to the system, the precision of supply and demand matching is remarkably improved, and the problem of supply and demand mismatching caused by untimely response in a traditional mode is effectively solved.

The invention relates to an automatic scheduling method based on load and distributed energy fluctuation, and the method comprises the following steps: data acquisition: obtaining load prediction time sequence data and distributed energy power generation prediction time sequence data, and generating an initial scheduling plan based on the load prediction time sequence data and the distributed energy power generation prediction time sequence data, the initial scheduling plan comprises a conventional unit output plan and an energy storage charging and discharging plan; data acquisition: acquiring load actual power time sequence data and distributed energy actual power time sequence data in real time; fluctuation decomposition: performing adaptive variational mode decomposition on the actual power time sequence data of the load and the actual power time sequence data of the distributed energy, and extracting a load fluctuation component and a distributed energy fluctuation component; calculating an index according to a load fluctuation component and a distributed energy fluctuation component; according to the invention, fluctuation dynamic scheduling can be accurately quantified to improve the stability and economy of the power grid.

The invention discloses a fault diagnosis and health management prediction method based on a laser gyroscope, and belongs to the technical field of inertial navigation equipment health management. The method comprises the following steps: acquiring and preprocessing a time sequence data set of operation core parameters of the laser gyroscope; performing wavelet packet decomposition on the time sequence data to extract high-frequency fault features, extracting time sequence statistical features by a sliding window method, and combining principal component analysis to perform dimensionality reduction and fusion to obtain a multi-dimensional feature vector; inputting the feature vectors into an improved kernel extreme learning machine model, optimizing parameters through a particle swarm optimization algorithm, and then outputting fault types and grades; on the basis of a fault diagnosis result, an analytic hierarchy process is adopted to endow parameter weights, and a health factor calculation model is constructed to quantify a health state; taking the health factor sequential sequence and the key parameter degradation trend as input, and combining a bidirectional long-short-term memory neural network with health factor sequence constraint to predict residual life and a confidence interval; new data are regularly brought in, and model parameters are updated through transfer learning to realize dynamic iteration. The method solves the problems of early fault recognition lag and low life prediction precision of a traditional method, is suitable for the fields of aerospace, precision navigation and the like, and has remarkable engineering application value.

This invention provides a training method, monitoring method, and device for a human-centric intelligent state monitoring model. It involves collecting physiological and video signals from individuals to construct a training sample set. The initial neural network model includes a data preprocessing module, a common feature extraction module, and a multi-head feature extraction module. The data preprocessing module preprocesses the physiological and video signals to generate two-dimensional spatial data and then fuses them. The common feature extraction module extracts and fuses features from the fused signals to generate multi-scale feature maps, which are then summarized. The multi-head feature extraction module fuses the multi-scale feature maps to obtain a high-dimensional feature map, and generates prediction results based on the high-dimensional feature map. The initial model is trained using the training sample set to ultimately obtain the human-centric intelligent state monitoring model. This invention fuses multivariate time-series data with two-dimensional image data to construct and train a human-centric intelligent state monitoring model, resulting in faster and more accurate monitoring.

This application relates to a real-time power supply and demand forecasting method and system based on a cloud-native architecture. The method includes: deploying a data access service in a cloud-native cluster using containerized microservices to receive real-time supply and demand data streams and historical time-series data from a power trading system; writing the data streams to distributed storage and pushing them to the forecasting pipeline via a message queue; performing timestamp alignment, missing data handling, normalization, and smoothing / denoising on the supply and demand data by a preprocessing service to obtain a low-noise supply and demand sequence; updating model parameters in a rolling window by an ARIMA forecasting service and outputting linear forecast values ​​as the first forecast result; calculating the forecast residuals based on the first forecast result and the actual observations, constructing residual time-series samples, and outputting residual forecast values ​​by an LSTM forecasting service; and superimposing the first forecast result and the residual forecast values ​​by a fusion service to obtain the real-time supply and demand forecast result and publishing it to the real-time trading business interface.

The invention discloses an optical scanning radiometer in-orbit dark background long time series data extraction method and system, and the method comprises the steps: obtaining cold air observation data and solar zenith angle data from an optical scanning radiometer, and generating a channel-level cold air observation data set and a channel-level solar zenith angle data set. According to the channel-level cold air observation data set and the channel-level solar zenith angle data set, various screening methods are adopted to generate to-be-selected schemes and screening data sets of the to-be-selected schemes. For each to-be-selected scheme, calculating a statistical mean value and data volatility based on a screening data set of the to-be-selected scheme, calculating a dark background representative value of the to-be-selected scheme, and determining a mean value sorting sequence number and a volatility sorting sequence number; and determining an optimal dark background value scheme by adopting a weighted assignment method according to the mean sorting sequence number and the volatility sorting sequence number. Therefore, according to the in-orbit dark background long-time-sequence data extraction method of the optical scanning radiometer, long-time-sequence stable extraction is achieved, and the remote sensing data radiometric calibration precision is remarkably improved.

The invention provides a supplier grading and classifying method based on multi-dimensional feature fusion, and relates to the technical field of supply chain management. The method comprises the steps of obtaining structured indexes and unstructured text data of suppliers; structural features are extracted through a multi-layer perceptron, and semantic features are extracted through Transform; a cross-modal attention mechanism is adopted, and deep fusion is carried out with the structured features as queries and the semantic features as key values to obtain interaction features; performing dynamic modeling on the historical time sequence data through bidirectional LSTM to generate time sequence comprehensive features; inputting a multi-task hierarchical classifier, combining ordinal regression and contrast learning loss optimization, and outputting categories and grades; according to the management priority, the weight is dynamically adjusted through attention bias, and grading flexible adaptation is achieved; and incremental learning fine tuning is carried out by using actual service data through closed-loop feedback. The method solves the problems that multi-source heterogeneous features are difficult to fuse, and feature space semantic distinguishing and grading standards lack dynamic adaptation, and is applied to intelligent evaluation of industrial internet suppliers.

Systems and methods for trending patterns within a set of time-series data are described. In one or more embodiments, a set of one or more groups of data points that are associated with a particular seasonal pattern are generated within volatile and / or non-volatile storage. A set of pairwise slopes is determined for data point pairs within the set of one or more groups of data points. Based, at least in part on the plurality of pairwise slopes, a representative trend rate for the particular seasonal pattern is determined. A set of forecasted values is then generated within volatile or non-volatile storage based, at least in part, on the representative trend rate for the particular seasonal pattern.

The invention provides a self-supervised multi-scale cloud workload prediction method and system in the technical field of cloud computing resource management. The method comprises the steps of S1, collecting time sequence data of workloads of physical machines in a cloud environment; s2, performing data enhancement operation on each time sequence data to obtain enhanced sequence data; s3, constructing a multi-scale encoder based on the multi-scale patch embedding layer and the scale sensing layer; s4, performing self-supervised training on the multi-scale encoder based on the enhanced sequence data; step S5, adding a prediction head in the multi-scale encoder, introducing an adapter with a specific scale, obtaining a preset number of annotation data, performing fine adjustment on the prediction head and adapter parameters of the adapter based on each annotation data, and then constructing a cloud workload prediction model; and S6, performing workload prediction based on the cloud workload prediction model. The method has the advantage that the workload prediction precision is greatly improved under the condition of scarcity of labeled data.

This invention discloses a real-time processing and database entry method for tunnel boring machine (TBM) construction time-series data based on integrated batch processing. The method includes: acquiring the original time-series data of the TBM and its associated original ring numbers; performing standardization processing based on a pre-built semantic rule base to obtain standardized records; identifying the TBM's operating status and determining the target ring number based on the standardized records and a judgment threshold; performing differentiated data cleaning strategies on the standardized records according to the operating status and evaluating the cleaning results to obtain a comprehensive quality score; and routing the standardized records to matching processing channels based on dynamic diversion conditions including the comprehensive quality score and arrival delay, and writing them into the time-series database using the target ring number as a reference. This invention overcomes the defects of missed physical anomaly detection and reference misalignment, improving the operational condition reproduction accuracy of the entered data.

According to various embodiments, there is provided an operation method of an electronic device including: acquiring a plurality of images of a user, captured by an RGB camera of the electronic device, acquiring a specific area on a specific body part of the user from the plurality of images, generating a plurality of first data associated with an RGB color model on the specific area associated with the plurality of images, generating a plurality of second data associated with a YCrCb color model based on the plurality of first data, generating first time-series data associated with a green channel based on the plurality of first data; generating third time-series data based on combining the first time-series data and the second time-series data; and estimating a pulse of the user based on conversion of the third time-series data into a frequency domain.