46 results about "Time-series segmentation" patented technology

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle battery endurance flight capability prediction system, which comprises a multi-dimensional data acquisition module, a feature mapping module, a prediction module, an optimization module and a feedback optimization module, and can be additionally provided with an early warning module. The multi-dimensional data acquisition module acquires battery data and cleans the battery data to generate standardized data; the feature mapping module maps the data to a feature space, and generates a feature sequence cluster containing a multi-dimensional association relationship by using a time sequence segmentation algorithm; the prediction module divides prediction intervals based on a support vector machine algorithm and extracts prediction indexes; the optimization module generates an endurance prediction strategy by predicting and optimizing the network model; and the feedback optimization module performs multi-source data fusion optimization and outputs a prediction instruction. The early warning module can associate the prediction instruction with the battery health degree, output a grading early warning signal and trigger a response mechanism.

An adaptive calibration method and system for an electric energy meter, relating to the technical field of electric energy meter calibration. The method comprises: acquiring basic data and additional data of an electric energy meter (S1); performing time series data alignment, and performing period division on an environment data set to generate a time series segmentation result (S2); acquiring device information of the electric energy meter, performing key feature analysis within a period, and establishing an interference weight factor for each feature (S3); building a performance degradation model (S4); acquiring usage environment data of the electric energy meter by means of a monitoring sensor (S5); then generating an adaptive calibration result on the basis of the performance degradation model (S6); and finally performing electric energy meter calibration management (S7). The present invention solves the technical problem in the prior art where the accuracy of electric energy metering is affected due to untimely calibration caused by an inability to monitor performance degradation of an electric energy meter in real time, thereby implementing effective calibration management for the electric energy meter, ensuring the long-term accuracy and reliability of the electric energy meter, and improving the efficiency of calibration work.

The invention discloses a multi-scale time-frequency network sleep stage classification method based on single-channel electroencephalogram, and belongs to the technical field of medical signal processing. Aiming at the problems of capturing multi-scale time-frequency characteristics, processing variability among subjects and modeling long-range time dependence of a sleep staging method, the method comprises the following steps: firstly, acquiring electroencephalogram signal data, and performing time sequence segmentation, sleep stage category label determination and standardized preprocessing by taking a single-channel electroencephalogram signal as an analysis object; performing data set division by adopting nested cross validation, and constructing a multi-scale time-frequency network model; the model comprises a feature extraction module and a sequence learning module, wherein the feature extraction module comprises a time domain branch and a frequency domain branch; a subject adaptive feature calibration module is proposed to dynamically compensate the influence brought by individual difference and signal quality fluctuation; respectively training a feature extraction module and a sequence learning module by adopting a component type training strategy; and inputting to-be-classified electroencephalogram signal data into the trained model, and outputting a corresponding sleep stage classification result.

The invention provides a multi-parameter adaptive dynamic multilevel decision converter transformer fault diagnosis method and system, and the method comprises the steps: constructing a multi-parameter fuzzy feature space based on temperature, voiceprint, current and oil soluble gas parameter data, carrying out the statistics of fuzzy features through sliding time sequence segmentation, generating a multi-parameter fuzzy time sequence feature vector, and carrying out the fault diagnosis of a converter transformer. Obtaining multi-parameter internal cross characteristics and multi-parameter external cross characteristics, and performing splicing fusion to form multi-parameter correlation characteristics; constructing a multi-stage aggregation classifier, dynamically distributing a weight for each classifier based on verification set performance, fusing probability output fault types of each layer, and adjusting the matching degree of a model prediction probability and a real fault occurrence probability; and dynamically adjusting the depth of the hierarchy according to the matching degree adjustment condition, if the improvement of the fault diagnosis precision of continuous multiple layers is smaller than a set threshold value, terminating the expansion and retaining the current hierarchy, and executing multi-level processing on a prediction result. According to the invention, various parameter information is effectively fused, and the prediction accuracy and reliability are improved.

The invention relates to the technical field of fault diagnosis, in particular to a circuit breaker mechanical fault pre-diagnosis method based on time sequence analysis, which comprises the following steps of: firstly, acquiring mechanical operation data of a circuit breaker in real time by installing a sensor, and preprocessing the data; then, on the basis of standard operation data, a time sequence segmentation strategy is adopted, the data is segmented into independent time sequence sequences, time domain features and frequency domain features are extracted for each independent time sequence sequence, and a time sequence dependence mode is extracted in combination with an autocorrelation function and a cross-correlation function; fusing the time domain feature, the frequency domain feature and the time sequence dependency mode through a time frequency-time sequence collaborative modeling framework; and finally, on the basis of the time sequence features, a federated Bayesian unit is constructed for fault diagnosis, local units provide fault type and confidence prediction, and a global unit integrates models of a plurality of local units through a federated learning framework. According to the invention, circuit breaker mechanical fault pre-diagnosis is realized through time sequence analysis.

The invention discloses a time sequence prediction method and system based on adaptive period segmentation and parallel decoding, and the method comprises the following steps: judging the period length of time sequence data, segmenting the time sequence data according to the period length, and obtaining time sequence segmentation data; adaptively adjusting the hiding dimension of the embedded layer according to the period length, and mapping the time sequence segmentation data into the hiding dimension to obtain a time sequence representation; the obtained time sequence representation is input into a Transform encoder to carry out feature extraction; the decoder input is initialized according to the Transform encoder output, the initialized decoder input and the encoder output are subjected to feature interaction in the decoder based on a cross attention mechanism, and finally prediction output is obtained. According to the method, a more flexible period segmentation scheme and a decoding mechanism are adopted, the reasoning efficiency is improved, accumulative errors are reduced, and the time sequence basic model can achieve a better prediction effect with fewer parameters.

The present invention relates to the field of video recognition technology, specifically a method and system for video content structured decomposition and analysis based on time segmentation. First, the video and metadata are acquired, and a first artificial intelligence model integrating a spatiotemporal attention mechanism and a sliding window mechanism is used to perform time segmentation to generate time segments. The video is cut into several video segments using the FFmpeg tool, and a second artificial intelligence model integrating multimodal fusion and timestamp embedding is used to extract text descriptions of each segment to achieve semantic alignment of actions and texts. At the same time, FFmpeg is used to extract key frames to generate picture groups, and a third artificial intelligence model combining target detection and cross-frame consistency constraints performs semantic analysis to generate action labels. Finally, the time segments, key frames, text descriptions, and action labels are integrated to generate a complete structured output. The present invention can improve the recognition accuracy of video content.

The invention provides a real-time customer portrait dynamic update prediction method and system for a retail scene, and relates to the technical field of data analysis, and the method comprises the steps: determining a customer portrait feature set, calculating the information gain values of features under different time windows, and generating a weighted feature vector; performing time sequence division by adopting a sliding time window method; calculating a dynamic correlation coefficient, and generating customer behavior fusion features with a time sequence dependency relationship; calculating a feature relevancy matrix, and predicting customer consumption behaviors; and the customer portrait features are dynamically updated, and personalized recommendation is realized. According to the method, the real-time performance and the accuracy of the customer portrait are improved, and the effectiveness of personalized recommendation is enhanced.

The invention discloses a rock climbing action analysis system and method based on multi-dimensional quantitative evaluation, and relates to the technical field of computer vision, and the method comprises the steps: carrying out the spatial registration and skeleton node positioning of a multi-mode rock climbing data set through employing an ICP algorithm in combination with an OpenPose algorithm, forming a skeleton motion state matrix, and carrying out the calculation of the skeleton motion state matrix; inputting the skeleton motion state matrix into a skeleton motion multi-dimensional quantitative model, performing time sequence segmentation and dynamic behavior classification by a time sequence analysis layer, performing multi-dimensional index calculation and weight distribution by an action evaluation layer, outputting a rock climbing action comprehensive score, performing differential operation on the rock climbing action comprehensive score and a standard action score, and obtaining a quantitative offset tensor. According to the invention, through the ICP algorithm, the OpenPose algorithm and the skeleton motion multi-dimensional quantitative model, the spatial registration precision of the multi-modal rock climbing data is improved, and fine dynamic classification and multi-dimensional index evaluation of rock climbing actions are realized.

The application provides an intelligent operation and maintenance learning method and system related to monitoring a baseline, and relates to the technical field of computer operation and maintenance. The method comprises: collecting a plurality of core index monitoring data of a target server cluster in a set historical period to obtain a historical monitoring data set; performing time series segmentation processing on the historical monitoring data set to obtain a time series data segment set; performing trend fitting on the time series data segment set by a linear regression algorithm to obtain a corresponding trend fitting straight line; determining a center data point based on the trend fitting straight line, and determining a bidirectional data fluctuation interval according to a residual error analysis result at the center data point. The application realizes dynamic matching of the baseline with the business, full-process automation, precision improvement, and reduction of false and missed reports.

The application relates to the technical field of fault diagnosis, in particular to a circuit breaker mechanical fault pre-diagnosis method based on time series analysis. First, the operation data of the circuit breaker mechanical are collected in real time through the installation of sensors, and the data are preprocessed; then, the data are divided into independent time series sequences based on standard operation data by adopting a time series segmentation strategy, time domain features and frequency domain features are extracted for each independent time series sequence, and time series dependent patterns are extracted in combination with autocorrelation functions and cross-correlation functions; the time domain features, the frequency domain features and the time series dependent patterns are fused through a time-frequency-time series collaborative modeling framework; finally, based on the time series features, a federal Bayesian unit is constructed for fault diagnosis, wherein a local unit provides fault type and confidence prediction, and a global unit integrates the models of multiple local units through a federal learning framework. The application realizes circuit breaker mechanical fault pre-diagnosis through time series analysis.

This invention belongs to the field of artificial intelligence technology and discloses a traffic flow prediction method, system, and device based on intra-block convolution and axis remapping. The invention divides the input traffic flow time series into multiple data blocks; uses a temporal convolutional network as the backbone to independently extract local temporal features within each data block; transforms the dimension representing the data block index into a channel dimension through axis remapping, thereby reorganizing the discrete block features into a structured multi-channel feature tensor; maps the tensor to the target dimension through a projection head network to obtain an encoded representation; and inputs the encoded representation into a prediction decoder to generate a prediction result for future traffic flow. This invention integrates the local temporal capture capability of TCN with the strong representation capability of modern sequence models, achieving both high computational efficiency and high accuracy.

The invention relates to a method for extracting the power consumption of an individual device within a set of devices (E1, E2, E3,..., En), the method comprising transforming a stream of measurements of an overall power consumption into a time series, and dividing (S1) the time series into a set of sequences, and then, for each sequence, the steps of: - generating (S4) a positional encoding matrix (MEP); - extracting (S5) features from the sequence to form a feature matrix (MF); - projecting (S6) statistical metrics into a statistics vector (MS); - concatenating (S7) these various elements (MEP, MF, MS) to form an input matrix (ME) provided to a transformation neural network designed to infer (S8) a time sequence corresponding to the individual device.

The invention discloses a zero sample fault detection method based on weighted semantic consistency embedding, and belongs to the field of industrial fault diagnosis. The method comprises the following steps that firstly, industrial equipment is used for collecting multi-mode sensor data, and standardized data are obtained through normalization, wavelet denoising and time sequence segmentation preprocessing; determining a fault attribute dimension based on domain expert knowledge, assigning each fault class to form a semantic attribute vector, and constructing a semantic attribute weight matrix through mutual information gain between attributes and fault tags; embedding data and semantic attributes into a shared space by using a data encoder and a semantic attribute encoder, decoding and calculating reconstruction loss by combining a data decoder and a semantic attribute decoder, and introducing weight to construct weighted semantic consistency loss; combining the two types of loss to train an encoder and a decoder, and extracting training data features by using the trained encoder and training a multi-attribute classifier; and finally, in a zero sample scene, extracting embedded features without fault data, inputting the embedded features into an attribute classifier, and matching fault categories through a maximum posterior probability. According to the method, the equal-weight hypothesis defect of an existing semantic consistency embedding method is broken through, key semantics are strengthened through mutual information gain weights, consistency loss is weighted to resist noise interference, and the precision, robustness and generalization ability of zero sample fault diagnosis are remarkably improved.

A method for extracting the power consumption of an individual piece of equipment within a set of equipment (E1, E2, E3, …, En), comprising transforming a stream of measurements of overall power consumption into a time series, and dividing this series (S1) into a set of sequences. Then, for each sequence, the following steps are performed: generation (S4) of a positional encoding matrix (PEM); extraction (S5) of features from the sequence to form a feature matrix (FM); projection (S6) of statistical metrics into a statistics vector (SM); and concatenation (S7) of these different elements (PEM, FM, SM) to form an input matrix (IM) provided to a transformation neural network designed to infer (S8) a time sequence corresponding to the individual piece of equipment. Figure for the abstract: Fig. 1

The present invention relates to the technical field of playback quality monitoring, specifically to a method and system for evaluating the playback quality of an in-vehicle display, comprising the following steps: acquiring an in-vehicle display signal, extracting the signal's amplitude, frequency, energy distribution, color channel spectral characteristics, and inter-frame timestamps, performing time series segmentation, extracting a set of characteristic parameters within each time window, and obtaining a display signal feature data set. In the present invention, a systematic evaluation of playback quality is achieved by extracting and analyzing the multidimensional features of the in-vehicle display signal. The calculation of feature offset trends enhances the ability to identify abnormal signal fluctuations. Combined with the analysis of the time axis offset rate, the accuracy of detecting playback signal synchronization is improved, enabling the identification of short-term jitter and sudden offsets. By comparing frame sequence time curves, abnormal playback frame rate fluctuations are accurately determined. Combined with the measurement and evaluation of color offsets, the comprehensiveness of playback quality evaluation is optimized.

A large-model-based wind turbine equipment fault diagnosis method and system includes the following steps: initializing a feature set based on normal data; selecting multiple candidate features based on the mixed score of each candidate feature and saving them into the feature set; using a time series segmentation algorithm to perform sample division on the normal data and construct a multi-dimensional spatiotemporal feature representation; constructing a fault diagnosis model, including: loading a large language model as the basic architecture, injecting the multi-dimensional spatiotemporal feature representation into the embedding input layer of the large language model; connecting an adaptive pooling layer after the output of the large language model to construct a two-layer MLP classifier for realizing the recognition and classification of different fault types; the first layer of the two-layer MLP classifier compresses the input features to half of the original feature dimension and applies GELU activation, and the second layer of the two-layer MLP classifier maps to the corresponding fault category space; constructing a knowledge mechanism library to provide the model with prior knowledge of wind turbines and design a loss function driven by wind turbine knowledge for model training.

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based claim settlement processing method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a medical document image file and insurance policy information inputted by a user; performing character recognition and extraction processing on the document image file to obtain a character information list; performing entity recognition on the text information list based on an entity recognition model to obtain document information; performing time sequence segmentation processing on the document information based on the segmentation model to obtain a document information sequence; processing the document information sequence based on an archive construction model to obtain medical archive data; based on the first prompt text, performing statistics on the medical archive data and the insurance policy information by using a statistical model to obtain medical claim amount data; and performing output processing on the medical claim amount data. The method can be applied to claim settlement processing scenes in the fields of financial science and technology and medical health insurance, and improves the efficiency and accuracy of claim settlement processing by combining the use of a plurality of models.

The application provides a kind of tunnel boring machine's measured data outlier detection and correction method, belongs to the field of outlier data detection, and the measured data of tunnel boring machine under different working conditions is detected by the method of sliding window, and the outlier point is corrected and filled up.The method first divides the original time series into multiple sub-time series by sliding window, and extracts the confidence interval radius of sub-time series slope by fast calculation and identifies abnormal sub-time series, then further determines the outliers using local outlier factor algorithm, and finally uses regression technique to reasonably fill the outliers removed.The application can effectively identify outliers in tunnel boring machine measured data, and reasonably correct and fill the outliers, ensuring the engineering usability of tunnel boring machine measured data and providing good conditions for further data analysis.

The application provides a time series segmented ship model resistance test data evaluation method, which is a method for evaluating single ship model resistance test data by using a segmented analysis method of time series segmentation considering the periodicity of the ship model in the running process. The method can be applied to single test of any ship type, eliminates the influence of artificial factors in the ship model resistance test, and makes the determination result of the single ship model resistance test more reliable.

The application provides a shield tail sealing system pressure abnormal state detection method, and belongs to the technical field of shield machines. Specifically, the following steps are included: S1, collecting pressure data recorded in a shield tail sealing system during shield machine construction; S2, preprocessing data recorded by a sensor; S3, using a time sequence segmentation method to segment and extract the pressure data and establishing a data set; S4, performing offline anomaly detection by using a density clustering DBSCAN method in unsupervised learning; S5, expanding abnormal sample points by using a Smote algorithm; and S6, training an online anomaly detection model by using the expanded data set by using a RUSBoost algorithm. The application provides a shield tail sealing pressure data processing method, so that shield tail sealing rear cavity pressure data recorded during construction can be fully utilized, so that whether the shield tail sealing rear cavity pressure state is abnormal can be quickly found during shield machine tunneling, and a certain guarantee is provided for the safe construction of the shield machine.

The invention relates to the related technical field of application performance monitoring, in particular to a low-Token consumption time sequence analysis method based on time sequence data visualization and a multi-modal large model, which comprises the following steps: step S1, cleaning, normalizing and feature extraction are performed on original time sequence data, and features comprise trend features, periodic features and abnormal features; s2, a customized time sequence trend chart is generated according to the features extracted in the step S1, the trend chart comprises a customized coordinate axis, a multivariable visual distinguishing identifier, a feature highlight identifier and a long time sequence segmentation identifier, and the method relates to a long time sequence data efficient analysis technology under the scenes of industrial equipment operation and maintenance, financial risk control, urban traffic scheduling and the like. By customizing the time sequence visual coding compression data Token amount and combining with the time sequence enhancement fine tuning optimization multi-modal model, low-cost and high-precision analysis of long time sequence data is realized, and technical support is provided for time sequence abnormity early warning, trend prediction and correlation diagnosis.

A kind of oil pipeline abnormal early warning monitoring method and system based on multi-source data fusion attention mechanism, oil pipeline abnormal early warning technology, to realize the early small leakage anomaly detection of oil pipeline by multi-source data fusion technology and deep learning technology.Technical points: collect pipeline operation state historical data from acoustic wave, temperature, negative pressure wave, vibration sensor, construct data set;Get time series segmentation code for algorithm training;Build fusion attention module for algorithm training;Based on the obtained multi-source data fusion attention Transformer model, adopt the training method of adversarial learning to predict the future operation state of oil pipeline;Use the Gaussian distribution with learnable scale parameter to train the oil pipeline early warning model;Use the trained multi-source data fusion attention Transformer model as the oil pipeline abnormal early warning model to identify the abnormal behavior of future operation state and obtain the failure probability.The present application effectively improves the accuracy and reliability of oil pipeline abnormal early warning by combining multi-source data analysis technology and deep learning technology, and using innovative Gaussian kernel scaling parameter and failure probability calculation method, which has important application value in small leakage detection.

Provided are an information processing method, a computer program, and an information processing device which can be expected to accurately detect similar objects from a plurality of non-time series image data items relating to substrate processing.　An information processing method according to the present embodiment involves an information processing device that comprises a time-series segmentation model that sequentially receives time-series image data items as input and sequentially outputs, for each image item, detection results for a region in which an object is captured. The information processing device acquires a plurality of non-time-series image items obtained by imaging an object relating to substrate processing. An acquired image data item is input to the time-series segmentation model to acquire detection results, and the acquired detection results are output.

The application discloses a cloud server performance degradation prediction method based on time sequence segmentation, first extracts performance resource time sequence data on a cloud server, decomposes the obtained time sequence data by adopting a DTW-BU time sequence segmentation algorithm, respectively constructs LSTM models for segmented subsequences, and predicts cloud server resource time sequence data, verifies model precision by using root mean square error and average absolute percentage error, predicts system performance degradation trend according to time sequence prediction data of the LSTM model, tests data fitting degree, and determines a software regeneration time node according to prediction data threshold value; the application can improve the precision of cloud server performance degradation prediction results, avoid overfitting phenomenon in the prediction process, and solves the problem of how to perform software regeneration at an optimal time point for cloud server performance degradation.

The invention discloses a new energy power generation power prediction method based on multiple meteorological conditions, and the method comprises the following steps: 1, selecting historical multi-source meteorological data and power data in the same period, and carrying out the format standardization and temporal-spatial resolution unification of the data; the method comprises the steps of 1, extracting basic features and cross features of multi-source meteorological data and carrying out feature screening and dimension reduction processing, 4, carrying out weighted fusion processing on the extracted meteorological features, 5, constructing a power generation power prediction model based on fused meteorological features and preprocessed power data, 6, training a data set based on time sequence segmentation and verifying the prediction model, and 7, carrying out prediction. 7, applying the prediction model, namely inputting fused meteorological characteristics in a prediction time range, and outputting generated power data in the prediction time range, and 8, performing closed-loop optimization management on the prediction model; the method has the advantages of being high in data reliability, high in prediction precision and high in project landing performance, and effective data support can be provided for planning of a power grid dispatching strategy.

The invention provides an oil leakage fault detection method based on data change of an oil sensor, and the method comprises the steps: collecting oil parameters and working condition factor data, building a normal fluctuation rule model through a time sequence segmentation and clustering algorithm, and generating a dynamic alarm boundary in combination with regression prediction; in the real-time monitoring process, real leakage events are accurately recognized by means of sliding window analysis and historical data fusion, meanwhile, model parameters are dynamically updated to optimize the detection precision, a reasonable alarm boundary can be automatically adjusted along with the real-time change of the equipment operation state, and the detection precision is improved.

The invention discloses a quasi-periodic time sequence segmentation method and system, a storage medium and equipment, and belongs to the technical field of time sequence processing, and the method comprises the following steps: S1, carrying out the self-feedback directional compression of an input original quasi-periodic time sequence, and outputting a key point index; s2, executing multi-view collaborative clustering based on the key point index and the original quasi-periodic time sequence, identifying periodic segmentation points in the original quasi-periodic time sequence, and outputting segmented quasi-periodic fragments according to the periodic segmentation points; and S3, evaluating the segmentation result in the step S2 by using the segmentation integrity factor, considering the period accuracy rate and the coverage rate, and iteratively optimizing the step S1 and / or the step S2 according to the evaluation result. The method is remarkably superior to the prior art in the aspects of cross-domain adaptability, large-scale data processing efficiency and segmentation precision, and is suitable for automatic segmentation of various QTS data such as electrocardiosignals, motion data and industrial vibration signals.

The invention discloses a time series data prediction method based on adaptive patch selection and dynamic recombination, which comprises the following steps of: segmenting a time series of input data into adjacent patches by adopting an adjacent patch division strategy; performing filling alignment on the time sequence according to a fixed step length and a patch length determined by an adjacent patch division strategy; extracting potential patches in the aligned time sequence under the conditions of patch length and step length 1; and scoring the potential patches by using a first scoring device based on the MLP to obtain n potential patches with the highest score, and taking the n potential patches as the selected potential patches. Generating a sequence score for the selected potential patches by using a second scoring device based on the MLP, and sorting to obtain an optimal arrangement sequence of the potential patches; respectively coding the optimal arrangement sequences of the adjacent patches and the potential patches, and carrying out weighted combination to obtain fusion embedded coding representation; and finally, predicting according to the fused embedded coding representation. According to the invention, the accuracy of time series data prediction is improved.

The application discloses a kind of data center refrigeration system prediction and shutdown temperature setting optimization method, comprising: obtaining the time series data when refrigeration system runs;Time series segmentation clustering model is used to identify and classify the time series data obtained in stage using manual labeling method, and sample data set is constructed;The start-stop process of refrigeration system under given heat load and the refrigeration power consumption of refrigeration system are predicted, and the refrigeration system prediction model is constructed, and the refrigeration system prediction model constructed is trained using the sample data set;The operation of refrigeration system is simulated using the trained refrigeration system prediction model, to determine the shutdown temperature setting value that makes the cumulative energy consumption lowest, to realize the optimization of shutdown temperature setting.The application can predict the start-stop process and power consumption of refrigeration system in a period of time in the future, and reduce the overall energy consumption of refrigeration system by optimizing the shutdown temperature configuration of refrigeration system.