623 results about "Timing data" patented technology

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

The invention provides a risk control big data mining method and system based on the Internet of Vehicles, and the method comprises the steps: collecting a real-time operation data flow of a target vehicle in an Internet of Vehicles environment, including vehicle state time sequence data, a driving behavior event set, external environment perception data and a vehicle-mounted communication interaction record; and then respectively extracting a combined operation feature vector, an abnormal behavior mode identifier and a dynamic environment risk factor from different data, converting to generate a structured communication feature matrix, constructing a space-time association risk analysis map based on the structured communication feature matrix, and carrying out driving risk dynamic assessment on a target vehicle through the space-time association risk analysis map. And finally, generating a risk control decision instruction set according to the result, and feeding back the risk control decision instruction set to the vehicle-mounted control terminal and the Internet of Vehicles cloud platform, thereby realizing comprehensive assessment and effective management and control of vehicle risks.

The invention discloses a source-grid-load-storage integrated optimal scheduling method and system for a micro-grid, and relates to the technical field of smart grids and micro-grids, and the method comprises the steps: firstly collecting multi-source data in a specified micro-grid, and carrying out the optimization storage of the multi-source data in a time sequence database; constructing a time sequence prediction model, predicting generating capacity and load demand according to historical data, generating a preliminary scheduling strategy according to the generating capacity and load demand, realizing comprehensive cost minimization, analyzing the strategy, evaluating deviation and abnormity, and finally correcting the strategy according to an evaluation result; comprising an intelligent data aggregation module, a time sequence data prediction module, a preliminary scheduling generation module, a preliminary scheduling strategy analysis module and a real-time optimization feedback module, influences of various data sources are considered, the model can be adjusted in real time, variable power demands and power generation conditions in a micro-grid environment can be better coped with, and the real-time optimization feedback is realized. The accuracy of a prediction result is greatly improved, so that a powerful basis is provided for scheduling decision making, and risks possibly caused by prediction deviation are avoided.

This disclosure is directed to intelligent synchronization of computing users, and associated timing data, based on parameters or data received from computing systems connected via wireless, satellite, wire-based, optical-fiber based, etc., computing networks.

The invention discloses a resource scheduling method for dynamic rendering and related equipment, and relates to the technical field of dynamic rendering, and the method comprises the following steps: obtaining time sequence data of a user touch event; based on the time sequence data, predicting a user behavior state through a behavior prediction model, and determining a current resource demand level; dynamically adjusting a resource allocation strategy and a video memory management strategy of the graphic processing unit according to the resource demand level; and executing a rendering task based on the adjusted resource allocation strategy, and performing picture output by adopting a double-buffer architecture so as to reduce delay caused by time sequence mismatch. According to the method and the device, by establishing closed-loop control of a user behavior-resource demand-supply strategy, accurate scheduling and efficient utilization of resources are realized, the problems of time sequence mismatch, resource waste and jamming in the traditional technology are solved, the rendering efficiency and the user experience are improved, and technical support is provided for high-concurrency and low-delay scenes.

The invention discloses a runoff prediction method and terminal based on a space-time heterogeneous graph neural network, and the method comprises the steps: obtaining hydrological time series data and space station information data collected by each station, and carrying out the preprocessing of the hydrological time series data and the space station information data; performing weighted fusion according to the features of the preprocessed hydrological time series data and the space station information data, generating a feature embedding vector representing a global variable time-space relationship through a graph attention network, and inputting the feature embedding vector into a full connection layer to obtain an output multi-station runoff prediction sequence. Constructing a space-time heterogeneous graph flood forecasting model; performing model parameter optimization on the space-time heterogeneous graph flood forecasting model; and carrying out runoff prediction on the hydrological time sequence data and the space station information data which are received in real time according to the optimized space-time heterogeneous graph flood forecasting model. In this way, hydrological space-time correlation characteristics can be effectively captured in forecasting, so that the accuracy and reliability of forecasting are improved.

The invention relates to the technical field of hard disk life prediction, and discloses a solid state disk life prediction method, which comprises the following steps: collecting multi-source data of a solid state disk, the multi-source data comprising health state data, I / O load data and environmental data of the solid state disk; preprocessing the multi-source data; calculating a health score of the solid state disk based on the preprocessed multi-source data, and establishing a prediction model by using the health score and the time sequence data; based on comparison between the prediction result and a set threshold value, an early warning signal is triggered according to a comparison result; and optimizing the prediction model in real time based on the comparison between the prediction result and the actual service life. By collecting the multi-source data, including the health state, the I / O load and the environment data, of the solid state disk, the problem that tiny differences of the hard disk under different operation conditions are ignored in an existing method is solved. Compared with a traditional method, the method has the advantage that the prediction accuracy is greatly improved.

The present invention relates to a computer-implemented method and system for generating a video editing project using artificial intelligence (AI) and machine learning (ML) techniques. The method includes processing a collection of video clips to generate text-based metadata, receiving selection criteria to identify relevant video clips, and generating a natural language prompt based on the selection criteria. The prompt, comprising instructions and context, is provided to a large language model (LLM), which processes the input and outputs data for constructing a video project data model. The project data model includes timing data for salient snippets within the selected video clips. A dynamic and interactive web-based user interface is rendered to visually represent the project data model, offering a timeline view and editing tools for refining the video project. This system streamlines the video editing process by integrating AI-driven content analysis with user-directed editing, resulting in a tailored video project that aligns with user-defined thematic elements.

The embodiment of the invention discloses an optical module health state detection method and device, a storage medium and a program product. The method comprises the following steps: extracting time sequence features from time sequence data of an optical module, and extracting key features from system log data of the optical module; fusing the time sequence features and the key features based on the constructed hybrid model, and generating fused features to obtain a current health degree score of the optical module; collecting a historical health degree score of the optical module in a preset time range; detecting at least one environmental parameter of the environment where the optical module is located; obtaining a dynamic threshold value of the optical module based on the historical health degree score and the environmental parameters; and judging the health state of the optical module based on the relationship between the current health degree score and the dynamic threshold. According to the method, the health state of the optical module can be comprehensively, accurately and efficiently evaluated based on multi-dimensional data features, powerful support is provided for operation and maintenance management of the optical module, and the method is of great significance in guaranteeing service continuity and reducing operation and maintenance cost.

The invention provides an intelligent inspection method and device for a database, and belongs to the technical field of databases. The method comprises the following steps: acquiring a multi-modal data set corresponding to the operation state of the inspection database by presetting a multi-source heterogeneous data acquisition mode; based on a cross-modal attention mechanism and a self-learning model, establishing a multi-dimensional data association relationship corresponding to the multi-modal data set, and in combination with historical time sequence data and unsupervised anomaly detection, determining a database health value and anomaly early warning information; wherein the multi-dimensional data association relationship is at least established among the SQL performance, the resource consumption, the transaction behavior and the business load. And generating a database optimization scheme according to the database health value, the abnormal early warning information and a preset feedback optimization mechanism, and sending the database optimization scheme to the user terminal. According to the scheme, the database is comprehensively inspected in combination with the cross-modal attention mechanism, the self-learning model, the knowledge graph association reasoning model and the time sequence prediction model, and the automation, dynamic and intelligent levels of inspection are improved.

The invention discloses a time sequence data management method of an edge computing gateway, which relates to the technical field of edge computing and industrial Internet of Things, and comprises the following steps of: respectively recording a communication bandwidth occupancy rate, a buffer area residual rate and a scheduling thread occupancy rate of the edge computing gateway in a preset fixed time period; and constructing a resource use original data matrix covering all time points in the fixed time period. According to the method, by periodically monitoring the resource use state and fusing the high-priority task scheduling performance, the scheduling resource abnormal occupancy index is dynamically generated, and intelligent sensing and scheduling optimization of the edge computing gateway on the resource pressure are achieved. When the abnormal index is increased, the system automatically triggers buffer area redistribution and low-optimal task data compression, data writing and scheduling real-time performance of key tasks are guaranteed preferentially, the problems of task starvation and data loss are effectively avoided, and the stability and the response capability of the system in a high-pressure environment are improved.

The invention relates to the technical field of industrial control security and the technical field of computers, and discloses an industrial control time series data anomaly detection method, which comprises the following steps: effectively integrating a novel auto-encoder, a generative adversarial network and a self-supervised comparative learning network, and modeling long-term dependence by using an improved long-short-term memory network, extracting short-term features by combining causal convolution and expansion convolution of a time sequence convolutional network, and accurately positioning key features in a time sequence by adopting a self-attention mechanism in the last layer. A dynamic double-optimization strategy of a generative adversarial network and a self-supervised comparative learning network is introduced to optimize parameters of the novel auto-encoder, various types of pollution data can be effectively identified, the generative adversarial network can further learn time sequence features and generate diversified abnormal sequences, the detection precision is remarkably improved, and the method is suitable for large-scale popularization and application. Therefore, the accuracy of anomaly detection is improved, and the adaptability to a complex industrial control environment is enhanced.

The invention discloses a power network topology optimization method based on time sequence data analysis, and relates to the technical field of power network topology optimization, and the method comprises the following steps: dividing a power grid into a plurality of sub-regions, and injecting disturbance signals into the sub-regions; obtaining time sequence disturbance response data, extracting oscillation characteristics, and identifying oscillation nodes; constructing an oscillation weakness evaluation model, and carrying out weakness evaluation on the oscillation nodes; correcting a phase difference matrix of the DTW based on an evaluation result, and screening a cross-regional risk propagation path; and power grid topology reconstruction is carried out according to a screening result, so that the problems of inaccurate weak node identification and low cross-regional high-risk path screening accuracy in a traditional network optimization method can be solved.

The invention provides a fault diagnosis question-answering system based on a multi-modal knowledge graph and a large language model, and the system comprises a time sequence data processing module which is used for carrying out the noise reduction of time sequence data, carrying out the feature extraction of the time sequence data after noise reduction, and representing a time sequence data body through a plurality of feature data; the multi-modal knowledge graph module is used for constructing a multi-modal knowledge graph, and the multi-modal knowledge graph comprises a text modal knowledge graph and a time sequence data modal knowledge graph; the large model fine tuning module is used for performing fine tuning on the large model by adopting a LoRA model based on different fault problems and corresponding solutions so as to obtain a question and answer large model; and the RAG question and answer module is used for enhancing the user question based on the multi-modal knowledge graph, inputting the enhanced statement into the fine-tuned large model, and outputting a result. According to the method, the fault diagnosis speed is increased, fault questions and answers are carried out on various data types, and the problem that a large model lacks professional domain knowledge is solved.

This disclosure is directed to intelligent synchronization of computing users, and associated timing data, based on parameters or data received from computing systems connected via wireless, satellite, wire-based, optical-fiber based, etc., computing networks.

The invention belongs to the technical field of field effect transistor performance prediction, and particularly relates to a reliability prediction method, device and system for a field effect transistor, and the method comprises the steps: determining the correlation between the time sequence data of environment parameters, electrical parameters and performance parameters in historical operation records and the change of the performance parameters through a correlation analysis method when the time sequence data changes; key performance parameters are screened out, then a virtual operation model of the field effect transistor is constructed before life prediction is conducted through the screened key performance parameters, and historical data are used for conducting validity verification on the virtual operation model; finally, dynamic changes of environment parameters and electrical parameters are simulated through historical data when the successfully verified virtual operation model is utilized, the degradation trend of key performance parameters is recorded, residual life prediction is carried out based on the degradation trend, prediction efficiency is improved, life prediction can more accurately reflect actual working conditions, and the service life prediction efficiency is improved. And the accuracy of residual life prediction is improved.

The invention discloses a signal data dynamic loading method based on Web graphical programming. The method comprises the steps that a server collects waveform data; the waveform data caching module caches waveform data and sends the waveform data to the adapter module and the downsampling module; the down-sampling module carries out down-sampling on the waveform data, and the down-sampled data is sent to the adapter module through the down-sampled data cache module; the database adapter writes basic information into a service database, and the time sequence database adapter writes time sequence data into a time sequence database; the browser obtains a data loading request instruction; the query module determines a query condition; the query module queries corresponding waveform target data; the query module preprocesses the waveform target data and sends the waveform target data to the browser; and the waveform display control performs graphical format conversion on the graphical waveform data and displays the graphical waveform data in a browser. The method has the advantages that dynamic loading and visual display of the real-time data can be achieved in a graphical mode, and a new solution is provided for monitoring, analyzing and processing the real-time data.

The present invention relates to the field of intelligent weak current data processing technology, specifically a multimodal data fusion method and system for an intelligent weak current monitoring system; specifically: collecting multimodal weak current data; performing two-level denoising to eliminate noise interference and retain edge details, and then generating identification items through standardization and hashing to ensure data validity; using a cross-modal adaptive attention mechanism to dynamically allocate the weights of image and time series features, combining a residual fusion strategy to capture the interaction details between modalities, and realizing efficient complementary fusion of image and sensor time series data; generating a comprehensive anomaly score through a joint decision-making mechanism of deep autoencoder reconstruction error and classification network anomaly probability, and realizing high robustness anomaly detection in combination with threshold judgment. The present invention effectively improves the anomaly detection accuracy and system reliability of weak current monitoring scenarios.

The invention relates to a response performance test method and system of a microswitch. The method comprises the following steps: inputting a multi-pulse trigger sequence to a microswitch contact and collecting contact response original data; performing digital filtering processing on the contact response original data to obtain contact response time sequence data; comprehensive response time parameters including contact mechanical action starting time, electrical initial contact time and electrical stable contact time are calculated according to the contact response time sequence data; performing temperature compensation and contact rebound characteristic extraction on the comprehensive response time parameter to obtain contact rebound characteristic evaluation data; according to the contact rebound characteristic evaluation data and the comprehensive response time parameter, the response performance stability of the microswitch under the high-frequency operation condition is calculated, and a reliability evaluation result is generated. According to the invention, the problem of time sequence measurement error caused by the influence of contact jitter in the traditional test is effectively solved, and the accuracy and consistency of contact response time measurement are remarkably improved.

The invention discloses an intelligent asthenopia control method and system based on eye movement and electroencephalogram data. The method comprises the steps that eye movement time sequence data and electroencephalogram rhythm signals of a user are synchronously obtained through a multi-mode sensing unit; inputting the eye movement time sequence data and the electroencephalogram rhythm signal into a multi-modal fusion decision model, and generating a fatigue level through feature weighting based on an attention mechanism; at least one intervention mode is dynamically selected according to the asthenopia level, and the intervention modes comprise the first mode, the second mode and the third mode; in the first mode, a display interface adjusting instruction containing natural light simulation parameters is generated, and the display interface adjusting instruction is integrated with a vegetation color system compensation algorithm; and mode 2: starting a visual training program with ecological images, and generating a dynamic guide mark simulating natural motion on an interface. According to the invention, through a data-environment-physiology multi-dimensional collaborative innovative architecture, the spanning of asthenopia regulation from passive response to ecological active restoration is realized.

The embodiment of the invention discloses a real-time state monitoring method and device for high-frequency time sequence data and a medium, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining a state monitoring task of target production equipment, carrying out the initialization configuration, building a multi-topic subscription channel corresponding to the state monitoring task, and carrying out the multi-topic subscription through the multi-topic subscription channel, acquiring multi-channel high-frequency sensing data of a field side; multi-channel high-frequency sensing data is stored to a time sequence storage node preset on the edge side by utilizing communication among multiple processes, and the time sequence storage node comprises a super table structure storage library constructed based on TD engine; and performing multi-thread analysis on the multi-channel high-frequency sensing data in the time sequence storage node according to an AI model node preset on the edge side, and determining real-time state data of the target production equipment. Through collaborative design of a multi-topic subscription channel, edge side time sequence storage optimization and multi-thread AI analysis, the high-frequency data real-time processing capability and the analysis efficiency are remarkably improved.

The invention relates to an SoC fault diagnosis method, system and device and a medium. The method comprises the following steps: acquiring on-chip sensor time sequence data, SoC multi-level software event log data and an SoC design mapping table, and performing time alignment to obtain multi-modal time axis data; performing modal feature extraction and embedding generation on the multi-modal time axis data, and performing single-modal anomaly detection on each modal feature to obtain an anomaly candidate set; on the basis of the abnormal candidate set, directional dependency measurement is calculated for the multi-modal embedded vector, time delay is recognized, a candidate causal edge list is obtained, and a heterogeneous time sequence-event causal graph is constructed in combination with the SoC design mapping table and the candidate causal edge list; and calculating the contribution degree of each node to downstream anomaly and candidate root cause probability distribution for the heterogeneous time sequence-event causality graph, and analyzing candidate root causes and corresponding contribution shares thereof to obtain a structured root cause report. By adopting the method, the SoC fault detection accuracy and the fault root cause positioning precision can be improved.

The invention relates to the technical field of reclosing, in particular to a self-adaptive reclosing optimization method and system for a power distribution network, and the method comprises the following steps: S1, collecting the historical fault data of the power distribution network, and carrying out the preprocessing of the historical fault data, and generating three-dimensional time series data; s2, according to the three-dimensional time sequence data, extracting key features by using a multi-scale convolutional neural network, and processing the key features to obtain input features; s3, establishing a Transform network model, classifying the input features to obtain samples, analyzing relation information of the samples to obtain an optimization model, classifying fault areas, generating classification labels, and predicting fault duration; and S4, for a non-permanent fault, dynamically adjusting reclosing delay time and an action time sequence by adopting the optimization model. According to the method, fault data are analyzed through deep learning, and the time and the time sequence of the reclosing action are optimized.

The invention relates to the technical field of data processing, and discloses a wind turbine generator performance simulation analysis method and device based on time sequence analysis. The method comprises the following steps: performing high-frequency sampling and self-adaptive filtering on pre-acquired operation data of a wind turbine generator to obtain a denoised multi-dimensional time sequence data set; performing wavelet packet decomposition and Hilbert-Huang transform on the multi-dimensional time sequence data set to obtain a time-frequency domain feature matrix; performing nonlinear manifold learning and sparse representation on the time-frequency domain feature matrix to obtain a low-dimensional feature vector; performing kernel principal component analysis and information entropy calculation on the low-dimensional feature vector to obtain a quantitative index of the performance of the wind turbine generator; performing dynamic time warping and sequence pattern mining on the quantitative indexes to obtain abnormal working condition characteristics of the wind turbine generator; and performing fuzzy rough set analysis and fusion processing on the abnormal working condition features to obtain performance evaluation data of the wind turbine generator. The efficiency and accuracy of wind turbine generator performance simulation analysis based on time sequence analysis are improved.

The invention relates to the technical field of equipment monitoring, in particular to an intelligent monitoring method and system for ship power equipment, and the method comprises the following steps: synchronously collecting the operation time sequence data of the equipment; based on the ship power transmission topological structure, generating a dynamic physical constraint model between devices; and inputting the collected equipment operation time sequence data into the dynamic physical constraint model, calculating a host-gear box torque transmission residual error and a gear box-shafting rotating speed matching residual error sequence, and when the zero-crossing rate sudden change frequency of any residual error sequence exceeds a preset sudden change frequency threshold value in a preset time window, triggering an early warning mechanism. Compared with the limitation that a traditional single-point vibration threshold method cannot cover a multi-node cooperative fault, the cross-equipment coupling fault recognition rate is increased, and the coverage range of the cross-equipment coupling fault is widened.

The invention provides a lower respiratory tract hospital infection risk prediction method and system based on deep learning, and relates to the field of medical electronic medical record time sequence data analysis processing and deep learning. According to the method, two diagonal masking self-attention mechanism blocks are utilized to carry out missing value filling on electronic medical record medical time sequence data; processing the demographic data in the electronic medical record by using a full-connection layer neural network, and initializing the hidden state of a decoder; constructing an encoder-decoder model based on an attention mechanism, performing model training by using the filled time sequence data and the hidden state of the initialized decoder, and constructing a lower respiratory tract hospital infection risk prediction model; and inputting the processed data into the trained lower respiratory tract hospital infection risk prediction model, and outputting a lower respiratory tract hospital infection risk prediction result of the patient. According to the method, mass data of the electronic medical record can be effectively utilized, the static characteristics and the dynamic time sequence characteristics are fused, the health state of the patient is captured, and the prediction accuracy and effectiveness are improved.

The invention discloses a fault diagnosis method and device, a medium and a program product, and relates to the technical field of computers, and the method comprises the steps: obtaining time sequence data of a computer component; the time sequence data refers to a series of ordered data points generated and / or collected by the computer component within a set time interval; extracting at least one feature reflecting the fault state of the computer component from the time sequence data to obtain a time sequence feature; performing fault diagnosis based on the time sequence characteristics through a plurality of pre-constructed models to obtain a plurality of output results; the output result comprises a diagnosis result and a diagnosis value corresponding to the diagnosis result, and the diagnosis value is a quantitative representation of the diagnosis result generated by the model; and performing fusion processing on the plurality of output results to determine a final diagnosis result. The technical problems of long diagnosis time and low accuracy are solved by extracting the time sequence characteristics representing different fault states and comprehensively diagnosing decisions by multiple models, and the technical effect of efficiently and accurately diagnosing faults is achieved.

The invention provides a missing value complementing method and system for multi-dimensional power consumption time sequence data, and relates to the technical field of power data processing, and the method comprises the steps: preprocessing the to-be-complemented multi-dimensional power consumption time sequence data into a multivariable time sequence matrix; inputting the multivariable time sequence matrix into a trained completion model, and performing missing value completion to obtain a completed multivariable time sequence matrix; wherein the completion model is a two-stage missing value interpolation model based on an auto-encoder of a bidirectional gating loop unit and a Transform variant, the auto-encoder is used for capturing time correlation among multivariate time sequence data and carrying out first-stage fitting completion on missing values, the Transform variant is used for mining a potential relationship between a time sequence and a dimension, and the Transform variant is used for carrying out second-stage fitting completion on the missing values; performing second-stage fitting completion on the missing value; according to the method, a two-stage interpolation method is adopted, the time series data modeling capability of the model is further enhanced, and the quality of damaged time series data is improved.

The disclosed systems and methods are directed to an implementation of time-based authentication with a contactless card based on remotely provisioned timing data. In one described implementation the timing information is provided by an external agent such as a client device and / or a remote verification server associated with the user account. The timing information is then incorporated into a cryptogram generation process executing on the contactless card, resulting in creation of an encrypted time-based token. The authentication request message that includes the time-based authentication token, may be further supplemented by the inclusion of the timing information separately encoded using, for example, public key cryptography. This modification of the authentication request message generated by the contactless card, enables an additional time-based filtering mechanism that may be performed by a third-party client device.

The invention discloses a water level time sequence data cleaning method based on regression statistics, which comprises the following steps: S1, sorting flood data of a historical session of a monitoring station to form a complete flood sample set; s2, establishing a function model according to the nonlinear change characteristics of the flood water level; s3, performing regression function fitting on the historical flood sample data, and calculating linear correlation between each fitting function and actual data; s4, based on a fitting result of the optimal regression function, establishing a Gaussian distribution model of a difference value; s5, determining a confidence interval of Gaussian distribution through the verification sample, and judging whether the data is abnormal data or not according to the confidence interval; s6, the detected abnormal data are corrected, and the cleaned data are obtained; and S7, processing the first N time sequence points in the flood time sequence data by adopting a moving average algorithm, and eliminating the interference of accidental fluctuation on regression fitting. The method has the advantages of being high in cleaning precision, high in adaptability and good in flood nonlinear change characteristic processing effect.