399 results about "Timing data" patented technology

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 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 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 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 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 provides a time sequence data storage method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining time sequence data and metadata information of the time sequence data, the metadata information comprising a first point location identifier, a data source and a data type corresponding to the time sequence data; according to the data source and the data type, determining a storage strategy of the time series data, including a target storage period and a target storage position; according to the storage strategy and the first point position identification, a target storage instance corresponding to the time sequence data is matched from a storage instance set, all storage instances in the storage instance set are combined and constructed according to different storage strategies and the point position identification, and all the storage instances are parallel storage instances; and storing the time sequence data to a time sequence database through the target storage instance. By determining a dynamic storage strategy driven by metadata and a parallel storage instance, the problem of performance bottleneck during high-concurrency writing of massive time series data is solved, and the real-time performance and reliability of time series data storage are ensured.

The invention belongs to the field of time synchronization and data processing, and discloses a satellite and Rowland timing data fusion method and related device.Firstly, original data are intercepted and subjected to mean value removal to eliminate baseline offset, and a frequency domain matrix is reconstructed by combining frequency domain conversion with singular value decomposition; according to the method, periodic term interference signals in a frequency domain are accurately recognized and filtered out through a dynamic threshold strategy, then effective components are reserved through time domain conversion, finally, a noise covariance matrix and observation model parameters are dynamically adjusted based on an adaptive Kalman filtering algorithm, and dynamic weight fusion of double-source data is achieved. By the adoption of the method, the defects that a traditional weighted average method is insensitive in fixed weight, Kalman filtering parameters are rigid and global interference suppression of wavelet transformation is insufficient are effectively overcome, the suppression capacity for non-stationary noise and periodic interference is remarkably improved, fused data have the high precision of a satellite system and the anti-interference characteristic of a Rowland system, and the method is suitable for being applied to the field of satellite communication. And finally, high-reliability and high-stability time synchronization performance is realized.

The invention discloses an intelligent cutting control method and system for blue laser, and the method comprises the steps: carrying out the time domain analysis of the power time sequence data of a blue laser in real time, and obtaining the power fluctuation data; matching the power fluctuation data with a preset defect feature model of the blue laser to determine the defect occurrence probability of the blue laser; under the condition that the defect occurrence probability is determined to be greater than a preset probability threshold value, according to the cutting parameters and the material parameters, determining an initial power compensation parameter and a kerf quality optimization parameter through a compensation parameter calculation model; performing multi-target iterative optimization on the initial power compensation parameter, the kerf quality optimization parameter and the power fluctuation data through a self-adaptive deviation adjustment algorithm to generate a dynamic power control signal; and adjusting the output power and the beam focusing position of the blue laser according to the dynamic power control signal so as to correct the cutting path of the blue laser in real time.

The invention discloses an Internet enterprise multi-mode identity verification method and system, and belongs to the technical field of Internet enterprise security, and the method comprises the steps: obtaining user historical behavior data, current transaction request data, equipment environment parameters and initial biological signal data, carrying out the risk assessment, and generating a verification path; obtaining a personalized verification sequence instruction, collecting a user face dynamic video stream, a real-time voice stream and response action time sequence data, carrying out cross-modal association comparison with a user reference biological feature template, outputting a biological feature confidence matrix, carrying out association analysis in combination with the obtained structured identity feature vector and risk assessment, and obtaining a personalized verification result; and generating a verification decision feature vector to judge a verification result state, and obtaining a pass instruction, a rejection instruction or a manual auditing request instruction. According to the method, dynamic risk-driven multi-modal verification path generation, cross-modal biological feature association decision and incremental learning mechanisms are adopted, so that the optimal balance between security and user experience can be realized in a complex network environment.

The invention discloses a hydroelectric equipment anomaly detection method based on physical mechanism guidance and sequential topological entropy fluctuation characteristics, and belongs to the technical field of hydroelectric equipment monitoring and fault diagnosis. The method comprises the steps that multi-source sensor data of hydroelectric equipment is collected and preprocessed; constructing a physical weighted distance function in combination with an equipment physical mechanism, and embedding time sequence data into a high-dimensional point cloud space; extracting persistent homology features through a sliding window, generating a persistent graph sequence and calculating topological feature indexes; a persistence graph entropy fluctuation index is provided, and anomaly detection is realized by quantifying time sequence fluctuation of topological entropy; and finally, visual output and an alarm mechanism are combined to assist diagnosis. According to the method, equipment physical characteristics and topological data analysis are fused, the problems that a traditional method is insufficient in nonlinear system modeling, insensitive to dynamic evolution and the like are solved, the early warning capacity and detection precision of early faults are improved, and the method is suitable for anomaly detection application of core equipment such as a water turbine and a generator.

This application relates to the field of electrical technologies, and provides a full even harmonic digital demodulation method based on full-phase analysis for a magnetic modulator. According to the full even harmonic digital demodulation method based on full-phase analysis for a magnetic modulator, standard voltage timing data output by the magnetic modulator is obtained when a DC calibration current with a set amplitude is fed into the magnetic modulator during calibration of the magnetic modulator; an output voltage sequence from full-phase Fourier analysis is established based on the standard voltage sequence data, and the output voltage sequence is preprocessed; comprehensive analysis is performed on the preprocessed output voltage sequence from full-phase Fourier analysis to obtain all standard even harmonic signals and current conversion coefficients.

The invention discloses an early warning method and device for multi-device time sequence data and electronic equipment. The method comprises the steps of determining a time sequence data stream; dividing the time sequence data stream according to the model identifier to obtain a plurality of sub-time sequence data streams, sliding on the sub-time sequence data streams by using the time windows according to a preset step length from the starting timestamps of the sub-time sequence data streams, and determining the time sequence data under the time window which slides each time as a target time sequence data stream; performing timestamp alignment and data filling on time sequence data in the target time sequence data stream to obtain an aligned target time sequence data stream, and predicting a time sequence data predicted value corresponding to the aligned target time sequence data stream in a future time period through a prediction model corresponding to the aligned target time sequence data stream; and determining early warning information corresponding to the target time sequence data stream according to the time sequence data predicted value. The technical problem that the accuracy of an early warning result is affected by data time sequence dislocation caused by inconsistent acquisition time of time sequence data between devices is solved.

Embodiments disclose a level shifter including a logic unit configured to receive timing data and channel data and output an edge signal and a channel signal at a time point defined in the timing data, and a channel selection unit configured to select at least one channel from among a plurality of connected channels according to the channel signal and transmit the edge signal, and a display device including the same.

The invention provides an operation and maintenance data processing method and device applied to a sound barrier and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring image data and thermal imaging data for a target sound barrier sent by an unmanned aerial vehicle; performing edge processing on the image data and the thermal imaging data to obtain structured data; acquiring time sequence data of the image data and the thermal imaging data acquired by the unmanned aerial vehicle; inputting the time sequence data and the structured data into the LSTM model to obtain sound barrier abnormal data; and adding the sound barrier abnormal data to a preset digital twinborn model corresponding to the target sound barrier, generating a target inspection task, and carrying out operation and maintenance on the target sound barrier according to the target inspection task. By implementing the technical scheme provided by the invention, the data processing accuracy of operation and maintenance of the sound barrier can be conveniently improved.

Described herein are techniques for performing timing and frequency synchronization at a digitizer for use at a ground station or a remote terminal of a satellite communication system. A reference input is received at a reference port of the digitizer, the reference input being a PPS signal or a GNSS signal from which the PPS signal is derived. The PPS signal is compared to a clock signal generated by a DCO. The DCO is controlled to lock a frequency of the clock signal to a multiple of a frequency of the PPS signal. Sub-second timing data is generated using the PPS signal and the clock signal. The PPS signal is used to compute a seconds component of the sub-second timing data. The clock signal is used to compute a sub-second component of the sub-second timing data.

The invention relates to the technical field of overvoltage control, and particularly discloses an overvoltage control method and device for an IGBT module, and the method comprises the steps: carrying out the time sequence correlation analysis of collector current time sequence data through employing a deep learning algorithm after receiving a turn-off instruction, extracting the short-time fluctuation characteristics of collector current, and obtaining the short-time fluctuation characteristics of the collector current; and the initial gate resistance value is combined to dynamically predict the collector-emitter voltage peak amplitude possibly generated in the turn-off process. And based on comparison between the predicted voltage spike amplitude and a preset threshold value, an initial gate driving strategy is generated to guide the turn-off operation. In the turn-off execution stage, collector-emitter voltage data flow is monitored in real time and dynamically compared with a safety threshold value, and therefore closed-loop optimization adjustment is conducted on a driving strategy. According to the method, prospective evaluation and self-adaptive control of the overvoltage risk are realized through intelligent prediction of the turn-off transient voltage spike, the switching loss can be optimized while the overvoltage is suppressed, and the limitation of traditional fixed parameter control is broken through.

The invention relates to the field of computer and auxiliary equipment repair, in particular to a mold structure design method and system based on the digital twin technology, and the method comprises the following steps: obtaining a three-dimensional geometric model of a target mold; generating an initial digital twin simulation model based on the three-dimensional geometric model; according to region division of simulation sub-models in the initial digital twin simulation model, sensing modules are arranged in corresponding cavity regions in the entity mold; correcting the corresponding simulation sub-models based on the real-time state data corresponding to the simulation sub-models, and generating a target digital twin simulation model; utilizing the target digital twin simulation model to simulate the thermal-mechanical coupling condition of the cavity in the corresponding area based on the opening time sequence data of the cooling cavity flow valve in the corresponding area of each simulation sub-model in the current injection period; and respectively generating opening time sequence data of the flow valve in the corresponding area of each simulation sub-model in the next injection period according to the thermal-mechanical coupling condition of the cavity in each area.

The invention relates to the technical field of oil and gas field data management, and discloses an oil and gas field interval time sequence data management method based on a swan mongolian system. The method comprises the following steps: acquiring open time sequence data in real time through a distributed data acquisition module of a swan gap system, calculating data point density of a target area and comparing the data point density with a safety threshold; when the density exceeds the limit, evaluating data source interaction by using a statistical analysis model, and outputting a data coupling effect hidden danger state; analyzing and processing the environmental factor data by adopting a time sequence, and generating potential interference degree evaluation; determining whether to trigger an overall data optimization process or not according to the hidden danger state and interference evaluation; and during triggering, individual path complexity evaluation is executed on each data source, a priority sequence is calculated and adjusted, oil and gas field equipment is controlled according to the sequence, and dynamic management of open time sequence data is realized. According to the method, the real-time performance, accuracy and flexibility of data management are improved, and the production requirements of modern oil and gas fields are met.

The invention discloses a chronic obstructive pulmonary disease dynamic risk prediction method based on a time sequence convolutional network, and the method comprises the steps: obtaining static baseline data and dynamic time sequence data of a patient through multi-source data collection, and achieving the data synchronization through timestamp alignment; carrying out one-hot coding on the static data, constructing a feature matrix for the dynamic data, and carrying out standardization processing; a personalized context vector is constructed based on the static features, and multi-source dynamic time sequence feature adaptive fusion is realized by using an attention mechanism; performing time sequence dependency feature extraction by adopting a causal expansion convolutional network, and capturing a long-term dependency relationship through residual block stacking and exponential expansion rate design; and inputting the extracted time sequence features into a classifier, and outputting the dynamic risk probability of acute exacerbation of the chronic obstructive pulmonary disease patient. According to the method, self-adaptive feature fusion is realized in combination with personalized context vectors and an attention mechanism, a long-term time sequence dependency relationship is captured by adopting a causal expansion convolutional network, and a high-precision chronic obstructive pulmonary disease dynamic risk prediction model is constructed.

In an industrial processes, a properly instrumented line facilitates capture of data including detected steps, application input, and execution graph transitions, that permit the creation of empirical models of process timing. In this context, a process controlled by individual applications, e.g., at manufacturing workstations, provides a proxy for overall process timing by dividing a workflow into a number of discrete steps completed at each workstation, and further into any number of sub-steps, each controlled by a user and explicitly completed, e.g., by user interactions with widgets or other controls of the application. These applications provide a useful framework for modeling execution timing by providing an initial, implicit model for workflow (based on application control logic) that also facilitates automated detection of process sub-steps based on execution flow, as well as detection and measurement of the contributions of individual widgets and / or combinations of widgets to the process timing. By gathering data in this manner, mixed statistical distributions can be applied based on individual timing data for each possible sub-step, widget, process step, and the like performed with each application.

The invention discloses a parameter optimization method for photoresist, which comprises the following steps: collecting process multi-source data, preprocessing to construct a data set, and generating environment and equipment disturbance vectors; photoresist material data are extracted, multiple features are fused in a dimensionality reduction mode, and photoresist dynamic vectors are generated; acquiring process time sequence data, inputting the process time sequence data into the improved CycleNet network, and extracting process dynamic characteristics; constructing a tensor input solving unit, calculating the acid concentration and the developing thickness, and predicting the imaging quality; the photoresist dynamic vector is evolved, and a complete disturbance set is generated in combination with the multiple disturbance vectors; and constructing a robust optimization target, evaluating a parameter imaging effect, and obtaining optimal process parameter output. According to the method, robust optimization and stable control of key parameters of the photoetching process are realized by constructing a parameter optimization process fusing photoresist dynamic evolution, process time sequence disturbance and multi-source disturbance perception.

The invention discloses a capacitor voltage transformer on-line verification and state early warning method and system, and the method comprises the steps: synchronously collecting the secondary side time sequence data of a target CVT and other CVTs of the same bus with the target CVT through a power monitoring system; based on secondary side time sequence data of a plurality of CVTs on the same bus, screening out a CVT equipment group with consistent state through group consistency analysis, and generating a virtual standard reference value for error calculation; calculating a relative deviation between the secondary voltage measurement value of the target CVT and the virtual standard reference value as a first evaluation result; performing feature extraction on the secondary side time sequence data to obtain a feature vector representing the operation state of the target CVT, inputting the feature vector into a pre-trained intelligent evaluation model to obtain an error prediction value or a health state score of the target CVT, and taking the error prediction value or the health state score as a second evaluation result; and fusing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation conclusion of the running state of the target CVT, and if the state level exceeds a normal range, generating early warning information.

The invention relates to a chronic disease stage prediction method and device based on a dynamic physiological time sequence and wearable equipment, and the method comprises the steps: obtaining a historical health state pre-stored in a resident holographic health record of a target resident; collecting dynamic physiological time sequence data of a current time window of the target resident; and inputting the dynamic physiological time sequence data and the historical health state into a pre-trained health management vertical class large model, and generating a chronic disease outcome risk prediction value, so as to achieve the purpose of constructing a chronic disease accurate prevention and control system from static diagnosis to dynamic prediction and from single disease management to common disease evolution comprehensive evaluation.

A method and system for proactively monitoring the health of a robot end-of-arm tool based on timing of response to gripping commands. A part presence or other sensor provides a signal when a robot tool successfully grips or ungrips a workpiece. The time between each grip or ungrip command and its completion is recorded by the robot controller. Timing data for all robots in a facility are collected by a data collection device and forwarded to an analytic data center, where the timing data is analyzed for each end-of-arm tool. Alerts are sent advising of issues which have been identified on grippers when grip times exceeding a threshold or a deterioration trend in grip time performance is detected, and all analytic data is provided to a web portal for customer viewing and action. Response timing for other types of end-of-arm tools besides grippers may be similarly analyzed for proactive tool repair / replacement.

The invention provides a time sequence data mutation detection method and system based on multi-stage dynamic optimization and a storage medium, and belongs to the field of radiotherapy equipment state monitoring and quality control. The invention provides the equipment state change point detection method which does not need penalty parameters and is based on'multi-stage dynamic optimization '. According to the method, on the basis of Dynp and RBF models in a ruptures tool library, the accuracy and stability of change point detection are effectively improved through candidate change point supplementation, local refinement, redundant change point deletion and other stage processing. The method can be used for detecting time sequence data mutation related to quality control in the radiotherapy equipment, and the stability and safety of the radiotherapy equipment in the using process are guaranteed.

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 embodiment of the invention provides a hard real-time load dynamic isolation method and device based on a cloud native scheduler, and the method comprises the steps: deploying a delay monitoring process on each computing node in a cluster to monitor node delay data and aggregate a node time sequence state, and receiving node time sequence data sent by each node through the cloud native scheduler, the method comprises the following steps: acquiring node real-time context data of nodes, acquiring node context data of each node, judging through point time sequence data and the node real-time context data to obtain a delay exceeding node, generating a scheduling decision for the delay exceeding node, receiving the scheduling decision by a node agent process deployed on the delay exceeding node, and sending the scheduling decision to the node agent process. CPU core isolation and hard real-time task migration are carried out on the delay exceeding nodes, a resource allocator based on topological information is constructed, CPU and memory resource allocation is carried out on the migrated hard real-time tasks, load dynamic isolation of the hard real-time tasks is achieved, and the running stability of the hard real-time tasks in the delay sensitive environment can be improved.

Systems and methods are disclosed herein for training machine learning models using precision data based on movement and timing of data collection. The system trains the machine learning model to predict object locations in an environment. The training data includes location data, timing data, and motion data, collection of which is triggered by a user input indicating that a particular object was located. The system generates precision parameters for the locations by assigning initial values to entries within the training data and may lower precision parameters based on the timing data indicating that an object was located faster than a threshold time or based on the motion data indicating that a rate of motion exceeded a threshold rate when a corresponding object was located. The system may update the training data with the lowered precision parameters and train the machine learning model to predict the object locations within the environment.