879 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 embodiment of the invention provides a network flow anomaly monitoring method and device based on a BiLSTM-Att network, and the method comprises the steps: obtaining network flow time sequence data, system resource use data and service call topological data, constructing a multi-modal time sequence feature matrix through employing a sliding time window method, calculating the importance score of each feature in the traffic feature subset, the resource feature subset and the service feature subset through an information gain algorithm, and screening the most discriminative feature based on a score threshold to construct a fusion feature subset; inputting the fusion feature subset into a set bidirectional long short-term memory-attention network, performing multi-level analysis on target features, performing comprehensive evaluation on a detection result by adopting a confidence-based multi-view decision fusion method, and generating a detection report containing an anomaly level, an anomaly type and an anomaly reason; according to the invention, the efficiency and accuracy of network flow anomaly monitoring based on the BiLSTM-Att network can be effectively improved.

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 deep learning-based deep fusion network production line fault prediction method, which comprises the following steps of: firstly, acquiring SCADA (Supervisory Control And Data Acquisition) system or sensor data of an automatic production line, and carrying out data preprocessing, feature extraction and fault prediction label construction; then a deep fusion network 1D-CNN-LSTM-Attention-DCE model combining a one-dimensional convolutional network, a long short-term memory neural network, a fusion attention mechanism and a dynamic cross entropy loss function DCE is established, the preprocessed data is input into the constructed model for training, and finally the trained model is used for fault prediction to obtain a fault prediction result. According to the method, a 1D-CNN-LSTM-Attention-DCE fault prediction model is provided, local features are extracted from time sequence data by using the 1D-CNN, then a long-time dependency relationship is captured through LSTM, and finally weight distribution of key features is enhanced through an attention mechanism, so that the processing capability of the fault prediction model on complex time sequence data is comprehensively improved, and the fault prediction efficiency is improved. And the fault of the production line can be accurately predicted within a certain time length range.

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 discloses a micro-service scheduling method and system based on edge computing, and relates to the technical field of distributed edge computing, and the method comprises the steps: collecting historical service index time sequence data and service demand description information of a micro-service; respectively inputting the historical service index time sequence data of each micro-service and the corresponding service demand description information into a time sequence analysis model to determine a predicted service index of each micro-service in a future time step; generating a resource cooperation notification according to the intermediate state data of the risk micro-service and the service demand description information of the corresponding first queue request, and broadcasting the resource cooperation notification in the edge network; and when the response reply message is detected, migrating the risk micro-service and the queue request to the second target edge node. Therefore, a decision-making mechanism based on data driving is realized, a service distribution and resource scheduling strategy in an edge network is dynamically adjusted, and the fault-tolerant capability of micro-services is enhanced through service migration between edge nodes.

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 provides an animal feeding plan generation method and system based on ingestion monitoring data. The method comprises the following steps: obtaining ingestion time sequence data of each animal in a feeding space for a feed storage box; acquiring health index sampling data of each animal; nutritional ingredients corresponding to the feed types of the feed storage boxes are obtained, so that a feed nutrition relation graph is constructed; processing the feed nutrition relation graph on the basis of a graph neural network to obtain updated node features of each feed type graph node; and defining an input state of a feeding plan generation model by using the acquired data, so as to determine an animal feeding plan corresponding to the target action through reinforcement learning. Therefore, through data driving and in combination with an advanced graph neural network and reinforcement learning technology, the feeding amount and the feeding time of the feed can be accurately calculated, and intelligent management of the animal feeding process is realized.

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.

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 multi-feature learning online video popularity prediction method based on time information perception. The method comprises the following steps: acquiring multi-feature data and playing time sequence data related to a to-be-predicted video; inputting the multi-feature data into feature sub-encoders in a preset multi-feature encoder according to categories, and determining visual, acoustic, text, social and metadata features; inputting the playing time sequence data into a hierarchical popularity sequence feature extraction network in a preset multi-feature encoder to determine popularity sequence features of the to-be-predicted video; inputting the visual, acoustic, text, social, metadata and popularity sequence features of the to-be-predicted video into a preset feature fusion network for feature fusion; and inputting the fused to-be-predicted video features into a preset popularity prediction network to obtain a prediction result. Through the technical scheme of the invention, various features related to the video popularity can be comprehensively considered, and the popularity prediction performance 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.

An apparatus and method for time synchronization in 5th generation (5G) networks are presented. Time sync Reference Unit (TRU) instances are used to calculate synchronization deltas between Radio Units (RUs) and Distributed Units (DUs) and time offsets between antennas of an RU. Time synchronization data are collected from the antennas with reference signals synchronized using precision time protocol (PTP) signals, deltas calculated, and averaged to determine a synchronization offset that is applied to adjust the timing of the RUs and DUs. An artificial intelligence (AI) model is used to process timing data, accounting for environmental and network variables. The synchronization offsets are published for application adjustments, and feedback is collected to refine the AI model and enhance future synchronization accuracy.

The invention discloses an industrial Internet of Things system anomaly detection method based on multivariable time series data. The method comprises the following steps: S1, dividing original multivariable time series data into an original training set and an original test set; s2, performing normalization processing on the original training set and the original test set, and performing time window segmentation to obtain a new training set and a new test set; s3, a GTVform model is constructed; s4, the GTVformer model trains the data in the new training set in two stages, and the two Transformer models are trained in a confrontation mode to reconstruct normal input window data; and S5, predicting the data in the new test set by using the trained GTVformer model to obtain a marking result. By combining the time-variable attention mechanism and the adversarial training method, the accuracy of multivariable time series data anomaly detection is remarkably improved, and meanwhile, the robustness of the model is enhanced.

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 a short-term load prediction method based on variational mode decomposition and a recurrent neural network, and the method specifically comprises the following steps: firstly, obtaining the load data of each charging station through processing the operation order data of the charging station; then, by analyzing the load data of the charging station, constructing a time sequence data set of historical load data of the charging station; secondly, decomposing the load flow data of the charging station based on a variational mode decomposition algorithm; thirdly, training and fitting the load flow data of the charging station based on a recurrent neural network; secondly, adopting a zebra optimization algorithm to adjust and obtain optimal parameters of the variational mode decomposition model and the recurrent neural network; and finally, performing load prediction on each modal component according to the trained recurrent neural network to obtain a load prediction result of the charging station. Noise separation and smooth decomposition processing are carried out on the load data, the load prediction precision of the charging station is improved, and effective services are provided for operation and maintenance of the charging station.

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.