219 results about "Sequential data" patented technology

The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokens—or fingerprints—that encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation

The invention relates to the technical field of bridge long-term health monitoring, and discloses a mass fatigue damage monitoring data abnormity identification method based on time series data reconstruction and an adaptive threshold, and the method comprises the steps: obtaining normal data from original bridge strain data of a monitoring target, and training a time series data reconstruction model based on a deep learning network; performing high-precision reconstruction on possible abnormal data in the original bridge strain data of the monitoring target by using a time sequence data reconstruction model to obtain a transient energy response decision coefficient of data of each time period in a plurality of time periods on a frequency domain and a strain amplitude response decision coefficient of data of each time period on a time domain; and then clustering the mahalanobis distances between the data, and dynamically and accurately identifying real abnormal data in the possible abnormal data of the monitoring target by combining a time domain-frequency domain two-dimensional adaptive abnormal threshold. According to the method, the abnormal recognition of the mass fatigue damage monitoring data can be efficiently and precisely realized, and the generalization ability is relatively high.

An in-vehicle device is an in-vehicle device connected to an in-vehicle ECU mounted on a vehicle such that communication is available, and includes a control unit performing processing relevant to transmission data transmitted from the in-vehicle ECU, in which the control unit receives transmission data transmitted from the in-vehicle ECU, registers the received transmission data in association with a reception time point of the transmission data in a chronological database, specifies abnormal transmission data from the transmission data registered in the chronological database, and registers information relevant to the specified abnormal transmission data in an abnormality history database.

The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a manufacturing quality prediction method and system based on a multi-mode sequential network and application, and the method comprises the steps: carrying out the preprocessing of the sequential data of a process manufacturing production line, obtaining a sample set, and carrying out the sequential division into a training set, a verification set and a test set; on the basis of the sample set, key features are enhanced through a frequency domain enhanced channel attention mechanism, a multi-period mode of a time sequence dependence and period sensing module is captured in combination with a multi-layer expansion convolutional network structure, and a multi-mode time sequence network model is constructed; and sequentially carrying out training set training, verification set parameter adjustment optimization and test set performance verification on the multi-modal sequential network model, and outputting a prediction result. According to the method, the deep dynamic association among the multivariable time series data can be mined, the accuracy and robustness of manufacturing quality prediction are improved, and an efficient and reliable technical scheme and an implementation path are provided for process industry quality control and intelligent optimization.

The invention relates to a computer-implemented method for determining features of the interior (1) of a vehicle (2) with at least one image capturing unit (3), a data processing unit (4) and a database (5), comprising the capturing, by the image capturing unit (3), of a photo (11) or video of the interior (1) and transmission to the data processing unit (4), the analysis of the photo (11) or video in the data processing unit (4) by a sequential data processing chain (10) of program modules (12, 12′, 12″), comprising the steps of rough analysis for the localisation of persons and objects and generation of body and object images (15, 15′, 15″), detailed analysis of the body and object images (15, 15′, 15″), extraction of body and object data (7), and storage, by the data processing unit (4), of the determined body and object data (7) in a hierarchical data model (9) in the database (5).

The invention discloses a big data real-time processing system and method, and relates to the technical field of big data processing, and the big data real-time processing system comprises a data partitioning module, a data storage module, a data reading module and a data backup module; the data partitioning module is used for partitioning big data entering a block chain in real time by using a partitioning strategy, extracting keywords, and distinguishing the frequency of data blocks according to historical query records; the data storage module stores data blocks through a storage contract, encrypts identifiers of the data blocks and establishes a mapping relation; the data reading module screens data blocks based on the private key set when a user queries, and determines a sending sequence through keyword matching; the data backup module generates a backup after storage, performs different updating strategies for data blocks with different frequencies, and performs adaptive modification on original data by using a hash value; in combination with the block chain technology, multiple advantages of real-time processing, efficient storage, safe reading, flexible backup and the like are integrated, and a comprehensive and efficient solution is provided for big data processing.

The invention discloses a network information security detection method and device, equipment and a storage medium, and the method comprises the steps: obtaining network flow data, network log data and user behavior data, and carrying out the data preprocessing operation, and obtaining multi-dimensional network time sequence data; according to the multi-dimensional network time sequence data, performing multi-scale feature extraction through a pre-training model to obtain multi-scale feature data, and inputting the multi-scale feature data into a multi-scale normalized stream model to obtain multi-scale normalized stream data; inputting the multi-scale normalized stream data into a hyperspherical support vector machine model, and performing multi-dimensional time series data anomaly classification to obtain classified time series data; according to the time sequence data, abnormal score calculation of the time sequence data is carried out, and a real-time abnormal score of each time sequence data is obtained; and when it is judged that the real-time abnormal score is greater than a preset abnormal threshold value, generating an early warning signal, and sending the early warning signal to a user side. According to the method, real-time detection and accurate early warning of network information security can be realized.

The invention is suitable for the technical field of computers, and provides a time sequence prediction method, a time sequence prediction device and electronic equipment. The method comprises the following steps: acquiring historical time sequence data of a target object; k target time sequence data samples matched with the historical time sequence data are determined from at least two time sequence data samples, and K is an integer larger than zero; determining at least one target expert model matched with the historical time sequence data based on expert index sets corresponding to the K target time sequence data samples; the expert index set comprises a corresponding relation between M expert models and performance indexes, the performance indexes represent the performance of the corresponding expert models when the corresponding time series data samples are subjected to time series prediction, and M is an integer greater than 1; and predicting target time sequence data of the target object in a target time period after the current moment based on the historical time sequence data and each target expert model. According to the invention, the time sequence prediction accuracy can be improved.

A method includes obtaining first feature vectors and second feature vectors representing contents of a first and second image frame, respectively, of an input video. The method may also include generating, based on the first feature vectors, first slot vectors, where each slot vector represents attributes of a corresponding entity as represented in the first image frame, and generating, based on the first slot vectors, predicted slot vectors including a corresponding predicted slot vector that represents a transition of the attributes of the corresponding entity from the first to the second image frame. The method may additionally include generating, based on the predicted slot vectors and the second feature vectors, second slot vectors including a corresponding slot vector that represents the attributes of the corresponding entity as represented in the second image frame, and determining an output based on the predicted slot vectors or the second slot vectors.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to TQFMs for kernel-based sequential data prediction. A system can comprise a memory that can store computer-executable components. The system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a computation component that can use a TQFM to compute a kernel element between two sequences of symbols, on a quantum computer, by respectively processing two input sequences as vectors.

The invention discloses a two-stage checking method for the node water demand and the pipe section friction resistance coefficient of a water supply pipe network, and is applied to the technical field of real-time online correction of a water supply pipe network model. Comprising the following steps: grouping node water demand and a pipe section friction resistance coefficient based on prior information, calculating a covariance matrix of pressure and flow measuring points, calculating estimated values and covariances of the node water demand and the pipe section friction resistance coefficient through a first-stage sequential data assimilation algorithm according to monitoring data at the current moment, and calculating the node water demand and the pipe section friction resistance coefficient through a second-stage sequential data assimilation algorithm; and then a calculation result serves as an empirical value to be added into a target function of second-stage iterative calculation, an estimated value serves as an initial value of an iterative algorithm to be subjected to fine tuning optimization, an optimal estimated value of parameters at the current moment and a covariance matrix of the optimal estimated value are obtained, the calculation result is brought into the next moment, and the two-stage algorithm is repeated to check model parameters at the next moment. The advantages of the two-stage algorithm complement each other, the checking precision of the parameters is improved, and a new thought is provided for real-time online correction of the water supply pipe network model parameters.

A computer-implemented method for anomaly detection for a time series data is provided. Aspects include receiving a time series data including a plurality of sequential data points, calculating an expected next value for the time series data based on the plurality of sequential data points, and receiving an actual next value corresponding to the time series data. Aspects also include calculating an anomaly strength estimate based on the expected next value and the actual next value, identifying one of a plurality of anomaly detection pipelines based on the anomaly strength estimate and a portrait associated with each of the plurality of anomaly detection pipelines, and obtaining an anomaly prediction by inputting the time series data and the actual next value into the one of the plurality of anomaly detection pipelines.

This application discloses a vibration monitoring method, system, and device based on an artificial intelligence model, comprising: acquiring first time-series data of a target seismic isolation bearing; obtaining second time-series data of a first seismic isolation bearing based on the association information of the target seismic isolation bearing; obtaining third time-series data of a first device based on the installation information of the target seismic isolation bearing; establishing an initial model; training the initial model to obtain a vibration analysis model; acquiring real-time monitoring data of the target seismic isolation bearing and the first seismic isolation bearing, inputting the real-time monitoring data into the vibration analysis model to obtain predicted operating data; acquiring real-time operating data of the first device in the building where the target seismic isolation bearing is located, and obtaining vibration analysis results based on the real-time operating data and the predicted operating data. This at least solves the problem of a large amount of data assets being wasted in existing systems and the low utilization rate of intelligent bearing monitoring systems.

The invention discloses an abnormal Transform model-based electricity larceny detection method and device and a storage medium, and relates to the field of electric power system electricity larceny detection, and the abnormal Transform model-based electricity larceny detection method comprises the following steps: capturing complex correlation among different time points in sequence data by using a self-attention mechanism of a Transform model; the characteristics of the normal time point and the abnormal time point are effectively distinguished by learning the relevance difference of each time point, the accuracy of electricity stealing user identification is improved, and the misjudgment rate is reduced. The method comprises the following steps: S1, obtaining user multi-dimensional power consumption time series data, preprocessing the data, and constructing a sliding window sample; s2, constructing an electricity larceny detection model based on an Angle-Attention mechanism, wherein the electricity larceny detection model comprises a prior correlation branch and a sequence correlation branch; s3, quantifying the time dependence deviation of the power consumption behavior by taking the relevance difference as an anomaly judgment standard; s4, training the model by using a minimax optimization strategy, and amplifying the relevance difference between normal electricity utilization and electricity stealing electricity utilization; and S5, performing electricity larceny risk prediction and model performance evaluation through the joint score of the relevance difference and the reconstruction error.

A data processing method and apparatus, which are applied to a front-end device in a data processing system. The data processing system further comprises a target device and a database, wherein the target device is configured to collect a plurality of pieces of time series data in an operation process of the target device, and each piece of time series data comprises a collection time for the time series data. The method comprises: a front-end device acquiring first time series data, and determining whether the first time series data is sequential data; further, when determining that the first time series data is sequential data, the front-end device storing the first time series data in a database; alternatively, when determining that the first time series data is out-of-order data, the front-end device storing the first time series data in a sort buffer of the front-end device. In this way, the impact on the performance of the database caused by the front-end device storing the out-of-order data in the database is avoided.

Computerized methods and systems obtain threat data generated from activity data using unsupervised learning. The activity data is collected from enterprises and describes activities performed on the enterprises. The threat data indicates likelihood that sequences of activities performed on the enterprises are indicative of malicious intent. A supervised ML model that processes sequential data is trained by providing a training set of sequential data to the supervised ML model. The training set includes at least some of the obtained threat data, and data derived from activity data collected from at least some of the enterprises. The trained supervised ML receives new data that describes a sequence of activities performed on an enterprise, and processes the received new data to produce a prediction of whether the sequence of activities performed on the enterprise will lead to a malicious action on the enterprise. In some embodiments, multiple supervised ML models are used.

A data storage device includes a bandwidth balancing system operable to reduce or eliminate bandwidth availability fluctuations that occur as a result of the performance of various internal operations and host operations. The bandwidth balancing system reduces or eliminates bandwidth availability fluctuations using a randomness factor. The randomness factor is a value that indicates a probability that an entire memory block will be invalidated by a single operation, which would cause the validity count of the memory block to significantly drop, thereby causing bandwidth availability fluctuations. The bandwidth balancing system ensures the memory blocks have a desired randomness factor by enabling the memory blocks to store both random data and sequential data. Specifically, the bandwidth balancing system intelligently mixes random data and sequential data within a memory block to achieve the desired randomness factor.

A computer-implemented method for anomaly detection for a time series data is provided. Aspects include receiving a time series data including a plurality of sequential data points, calculating an expected next value for the time series data based on the plurality of sequential data points, and receiving an actual next value corresponding to the time series data. Aspects also include calculating an anomaly strength estimate based on the expected next value and the actual next value, identifying one of a plurality of anomaly detection pipelines based on the anomaly strength estimate and a portrait associated with each of the plurality of anomaly detection pipelines, and obtaining an anomaly prediction by inputting the time series data and the actual next value into the one of the plurality of anomaly detection pipelines.

Methods, systems, and devices for write buffer management for a memory system are described. The described techniques provide for a memory system to receive data associated with multiple applications being executed concurrently and store the data to portions of a write buffer according to whether the data is sequential or non-sequential. For example, the memory system may receive sequential data for a first application between receiving non-sequential data for one or more second applications, and may partition a write buffer such that the sequential data is stored (e.g., sequentially) within a portion the write buffer and the non-sequential data is stored within a different portion of the write buffer. The memory system may flush portions of the write buffer to multiple-level memory cells once a portion is full, thereby storing sequential data to sequential physical addresses within the memory system.

A method for computing an organization's credit score, the method comprises collecting sequential data of the organization; processing the sequential data using a Sequential Deep Neural Network (SDNN) computer model such that the SDNN computer model outputs a first credit score value; collecting non-sequential data of the organization; processing the non-sequential data using a Convolutional Neural Network (CNN) computer model such that the CNN computer model outputs a second credit score value; computing a total credit score based on the first credit score value and the second credit score value.

Aspects of a storage device are provided including improved victim zone selection for zone-based GC in ZNS. The storage device includes a NVM having first, second, and third blocks, and a controller. The controller creates a first superblock including the first blocks, a second superblock including the second blocks, and a third superblock including the third blocks. The controller determines whether a quantity of data overwrites associated with a first zone in the first superblock is larger than a quantity of data overwrites associated with a second zone in the first superblock. From the determination, the controller performs GC for the first zone prior to GC for the second zone, in which data overwrites associated with the first zone in the first superblock and sequential data associated with the first zone in the third superblock are relocated to the second superblock. Thus, storage device performance is improved and WAF reduced.

The invention relates to the technical field of computers and remote sensing, and discloses a land cover change monitoring method, a model training method and related devices. The method comprises the following steps: acquiring original remote sensing time sequence data of a region to be monitored, and performing vectorization preprocessing on the original remote sensing time sequence data to obtain processed remote sensing time sequence data; based on a pre-constructed land coverage type prediction model, processing the processed remote sensing time series data to obtain a land coverage type prediction result sequence; the land coverage type prediction model is used for predicting land coverage types of various types of regions; and monitoring the land coverage change of the to-be-monitored region based on the land coverage type prediction result sequence. According to the method, the prediction model capable of predicting the land coverage types of various types of regions is utilized, the land coverage type prediction result sequence of the to-be-monitored region can be rapidly and accurately generated, and accurate monitoring of land coverage changes of the to-be-monitored region is achieved.

A system and method for robotic teleoperation enable a teleoperated robotic element to perform a sequence of actions based on intention estimation, eliminating the need for continuous human control. The system includes a robotic teleoperation input that receives motion inputs and gaze data from a human operator performing a robotic teleoperation task. A robotic teleoperation feature extractor analyzes and processes the motion inputs and the gaze data into sequential input data. A multi-window model assigns hierarchical prediction windows to the input data, generating windowed sequential data. A hierarchical neural network processes the windowed sequential data to determine low-level action intentions and high-level task intentions, and generates an intention estimation based on the low-level action intentions and the high-level task intentions. A hierarchical dependency model incorporates hierarchical dependent loss to refine the intention estimation.

An apparatus in an illustrative embodiment comprises at least one processing device, with the processing device being configured to receive in a storage system from a host device an indication of a data size utilized in a multi-path layer of the host device to select paths for delivery of input-output operations to different storage controllers of the storage system, to determine in the storage system a prefetch data size based at least in part on the data size indication received from the host device, and responsive to detection in the storage system of sequential data reads in input-output operations received from the host device, to prefetch from one or more backend storage devices of the storage system, into a memory associated with a particular one of the storage controllers of the storage system, an amount of data that is determined based at least in part on the prefetch data size.

The invention discloses a multi-source information fused intelligent inspection system, method and equipment with a target guiding function. The system comprises an inspection management platform, a data base station, a wireless routing base station, a quadruped robot, a pose sensor and a multi-mode sensor unit, wherein the inspection management platform, the data base station, the wireless routing base station, the quadruped robot, the pose sensor and the multi-mode sensor unit comprise a laser radar, a visible light camera and an infrared thermal imager; a control processing terminal is arranged on the quadruped robot and is used for acquiring data acquired by the multi-mode sensor unit and the pose sensor, processing the acquired data and outputting a control signal according to external instruction data, so that the quadruped robot moves according to an inspection planning path; the multi-mode sensor unit and the pose sensor are controlled to work; and the inspection management platform is used for analyzing and processing the data acquired and processed by the control processing terminal, and outputting instruction data including an inspection planning path to the control processing terminal. According to the invention, the inspection safety of engineering scenes in dangerous environments such as underground spaces and tailings ponds can be improved.

The invention provides a profile data set construction method and system based on multi-source heterogeneous ocean observation, and the method comprises the steps: obtaining multi-source heterogeneous original ocean observation profile data and description information of the data from a plurality of target ocean data centers / observation mechanisms in a target time period; performing version cleaning on the original ocean observation profile data according to the unique identifier determined by the metadata and the description information of the original ocean observation profile data, and performing high-frequency cleaning on the original ocean observation profile data based on the spatio-temporal joint features to obtain multiple pieces of target ocean observation profile data; and performing matrix processing on the target ocean observation profile data to obtain corresponding normalized data so as to construct a profile data set in the target time period. Therefore, the multi-source heterogeneous profile data can be subjected to data cleaning and format normalization in sequence, so that the consistency, accuracy and availability of the data are improved.

The invention discloses a wind turbine generator component life probability prediction method and system, and a wind power plant energy control method and system. The prediction method comprises the steps of constructing a state space model of a degradation process of a wind turbine generator component; acquiring real-time state information of the component; inputting the real-time state information into the state space model, evaluating the degradation state of the component by adopting a sequential data assimilation algorithm, and outputting posterior probability distribution of the degradation state; and based on the posterior probability distribution, forward simulating a future degradation path of the component by adopting Monte Carlo simulation until the future degradation path exceeds a preset failure threshold, and obtaining residual life probability distribution of the component. According to the method, the personalized residual life of each unit can be accurately evaluated, and an innovative wind power plant energy management control strategy is created on the basis, so that the absolute maximization of the full-life-cycle economic benefit of the wind power plant in a complex market environment is realized.

The invention provides a self-adaptive cardinality estimation method, system and device and a storage medium, and belongs to the field of database query optimization, and the method comprises the steps: constructing original connection sequence data and an optimal table sequence of a table based on a disclosed benchmark test data set as a data set, and converting the data in the data set into an original feature vector; the original feature vectors are input into a plurality of Transform models with different hyper-parameter settings for model training, a linear transformation module is included in front of each Transform model, and a plurality of cardinal number prediction results are obtained through training; and selecting a model with a minimum error as an optimal model according to a cardinality prediction result to obtain all original query feature vectors and corresponding optimal models, and learning a mapping relationship between the query features and the optimal models by utilizing a classifier. Obtaining a new SQL query input classifier, and distributing an optimal model based on the index data set; and processing the feature vector by using the optimal model to obtain a cardinality estimation result. The defect of single model query is changed, and the accuracy of a query result is improved based on feature classification query.

The invention discloses a high-concurrency and sequential data processing method for batch import class interfaces. In a request verification and forwarding stage, request data are verified, a guarantee sequence is locked based on a merchant ID after verification is passed, then the data are batched, request and batch information is persisted to a database, batch information is delivered to a corresponding message queue Topic according to a service scene, and the request ID is returned. In the consumption queue data stage, a consumer thread subscribes to messages of the message queue, processes the messages, updates a batch state, manually submits consumption offset, and dynamically adjusts the sleep duration of the thread according to a system load to realize intelligent peak clipping. And a progress result query stage: extracting data from the database based on the request ID, calculating a processing progress and a failure reason, formatting and returning the processing progress and the failure reason, and supporting front-end visual display. Through the message queue peak clipping, the differentiation sequence control strategy and the multi-dimensional data persistence, the high concurrency bearing capacity of the interface is improved, and the data consistency and reliability are guaranteed.

The invention provides a traffic prediction method and system, and belongs to the technical field of traffic prediction, and the method comprises the steps: collecting historical traffic data of a to-be-predicted user, carrying out the data processing, and obtaining discrete feature information and continuous feature information before a prediction time period in a preset period; inputting the discrete feature information into a pre-constructed first traffic prediction model for traffic prediction to obtain a first traffic prediction result corresponding to a prediction time period in a preset period; and inputting the continuous feature information and the first traffic prediction result into a pre-constructed second traffic prediction model for traffic prediction to obtain a final traffic prediction result corresponding to the prediction time period. The method has the beneficial effects that the machine learning fusion model is adopted to predict the traffic time sequence data scene, more comprehensive and more accurate prediction of the traffic time sequence data scene is realized, the limitation of a single model in a complex data scene is overcome, and the prediction accuracy and robustness are remarkably improved.