66 results about "Timing data" patented technology

The present application relates to the technical field of chemical production monitoring, and discloses a kind of chemical production monitoring system based on timing prediction, comprising: S1: information acquisition module gathers multiple data in production process, wherein multiple data is divided into risk timing data and conventional timing data according to process or function class, S2: risk timing data is extracted timing feature by CNN, timing prediction is carried out, S3: the standard range of RMSE in timing prediction is set, the RMSE in risk timing data prediction is obtained multiple times, and the RMSE over-range time period is extracted;Conventional timing data abnormal parameters are stored, and the correlation between the conventional timing data abnormal parameters and the risk timing data abnormal parameters is obtained by experiencing multiple data storage;When conventional timing data is in dynamic change state, conventional timing data is promoted to risk timing data, and coupled monitoring is carried out by introducing model, dynamic computing power is increased according to production line use time length, so that monitoring quantity and production line state are coupled.

This invention discloses a real-time processing and database entry method for tunnel boring machine (TBM) construction time-series data based on integrated batch processing. The method includes: acquiring the original time-series data of the TBM and its associated original ring numbers; performing standardization processing based on a pre-built semantic rule base to obtain standardized records; identifying the TBM's operating status and determining the target ring number based on the standardized records and a judgment threshold; performing differentiated data cleaning strategies on the standardized records according to the operating status and evaluating the cleaning results to obtain a comprehensive quality score; and routing the standardized records to matching processing channels based on dynamic diversion conditions including the comprehensive quality score and arrival delay, and writing them into the time-series database using the target ring number as a reference. This invention overcomes the defects of missed physical anomaly detection and reference misalignment, improving the operational condition reproduction accuracy of the entered data.

This invention relates to the field of navigation and timing technology, specifically to a GNSS timing data prediction method and related equipment. The method involves acquiring Roland timing data when GNSS timing data is invalid and preprocessing it. The preprocessed data is then input into a trained single-hidden-layer extreme learning machine model. This model calculates the hidden-layer output matrix by randomly initializing the input layer weight matrix and bias vector, and solves for the output weights using regularized least squares. The model outputs a normalized GNSS timing data prediction value, which is then denormalized to obtain the final GNSS timing prediction result. This method combines the high stability of the Roland system with the efficient computational power of the extreme learning machine to achieve accurate GNSS timing data prediction. It is suitable for enhancing the resilience of positioning, navigation, and timing systems in critical infrastructure scenarios, ensuring the continuous operation of the system even when GNSS signals are interfered with or fail.

The purpose of this invention is to address the issue of accuracy in processing spinning time-series data streams, which are characterized by high noise levels and distinct time-domain features generated during the operation of spinning workshops, by employing a fuzzy hierarchical clustering method that integrates time-domain characteristics. This method reduces the impact of noise during the classification process and considers both time-domain features and noise effects. The technical solution of this invention is to provide a fuzzy hierarchical clustering analysis method for spinning time-series data that integrates time-domain characteristics. This invention proposes a fuzzy hierarchical clustering analysis method for spinning time-series data that integrates time-domain features. It iterates between the DTS feature matrix and the MTS feature matrix, considering the time-domain characteristics and noise effects in the spinning time-series data. Without increasing time complexity, it incorporates the time-frequency characteristics and noise effects in the spinning time-series data during the iteration process. Compared with the latest methods, this invention can more accurately process newly generated time-series data in spinning manufacturing.

This application relates to the field of sports event tracking and filming technology, and discloses a method and system for sports event tracking and timing filming. The method includes: analyzing the real-time status of participants based on real-time data collected from at least two mobile high-speed cameras and real-time timing data of the event to obtain real-time status information; calculating the probability of participants at each position based on the real-time status information to construct a set of position distribution probability pipelines; analyzing the coverage value of cameras at different positions on each position distribution probability pipeline in the set of position distribution probability pipelines, and selecting the camera combination with the optimal coverage value; analyzing the real-time status information of participants, assigning at least one tracking role and tracking target to each camera in the camera combination to obtain a tracking and filming strategy; and controlling the corresponding camera to perform target tracking according to the tracking and filming strategy. This application can improve the success rate of capturing moments such as overtaking and sprinting of participants, and improve the quality of tracking and filming.

The application discloses a kind of pulsating pressure frequency domain analysis method based on transient schlieren feature point timing data, it is related to the cross technical field of intelligent fluid mechanics and aircraft aerodynamic test, comprising: S1, the transient schlieren image sequence related to isolation section is obtained by high-speed schlieren acquisition system, and the transient schlieren image sequence is preprocessed, to obtain corresponding flow field feature point timing data;S2, based on the acquisition timing of S1, the trajectory of the timing position of feature point in x direction is constructed;Based on timing position trajectory, the oscillation displacement sequence of each time is calculated, and the corresponding oscillation displacement difference sequence is obtained by differentiating processing to oscillation displacement sequence;S3, the time-domain signal in oscillation displacement difference sequence is converted into frequency-domain signal after fast fourier transform, and the corresponding power spectral density PSD curve is obtained.This application compared with traditional sensor measurement method, no flow field interference, can be adapted to complex configuration, and has greater application potential to high temperature scene.

This application provides a method and system for aligning time-series data of a stainless steel pot production line. The method includes: acquiring production data from multiple workstations of the stainless steel pot production line within a target time period and converting the production data into multiple event nodes; extracting the identity features of the stainless steel pots from the multiple event nodes; establishing candidate transfer relationships among the multiple event nodes based on the process topology relationship, flow time constraints, and identity features between the multiple workstations to form a candidate transfer network; solving the trajectory of the candidate transfer network to obtain the production history trajectory corresponding to each of the multiple stainless steel pots within the target time period; and correcting the production data with abnormal time-series relationships within the target time period based on the production history trajectory.

This invention provides a hybrid signature method and system based on quantum key distribution and PUF, belonging to the field of information security technology. The method includes: generating a quantum key through a quantum key distribution protocol and generating a derived key seed based on a physically non-cloning function, while simultaneously configuring a trusted region for location and timing; acquiring location and timing information and generating location and timing data; generating a dynamic root key and deriving classical signature key pairs and post-quantum signature key pairs; preprocessing the message to be signed and the location and timing data, matching hierarchical security policies, and selecting the corresponding signature method to generate a signature data packet; during the signature verification process, verifying the location and timing data for signature validity and legality, and verifying the signature result using the corresponding verification method selected according to the hierarchical security policy. This invention achieves dynamic generation of signature keys and hierarchical security policy scheduling, balancing digital signatures in terms of quantum attack resistance, device binding capability, and computational efficiency.

An lithographic apparatus is disclosed, comprising: an EUV source, the EUV source comprising a laser; and a control system; wherein: the control system is configured to obtain timing data of an intended exposure process; and the control system is configured to determine control data for varying an instantaneous output power of the laser, at least in dependence on the timing data, such that an average output power of the laser is maintained at a substantially constant level, or varies at a predetermined rate or at a rate slower than the predetermined rate, during the intended exposure process.

The application discloses a physical perception time-frequency semantic alignment timing prediction method and system for edge embedded devices, and belongs to the technical field of deep learning and industrial artificial intelligence. The method comprises the following steps: preprocessing historical timing data to obtain a standardized input sequence; then decoupling the sequence into a low-frequency trend component and a high-frequency detail component through causal stationary wavelet transform, so that feature extraction only depends on historical observation data; then inputting the decoupled components into a context perception prototype reprogramming module, projecting continuous physical features into a discrete semantic space of a frozen large language model by using a mixed semantic prototype library and a double-flow cross-attention mechanism; then dynamically fusing the trend and detail components through a semantic gating network to generate fused semantic features; and finally obtaining a final prediction result after splicing the fused semantic features and wavelet enhancement prompts. The application realizes high-precision and low-delay timing prediction.

The application provides a control method of a buried vibration detector, belongs to the technical field of vibration detection control, and has a sealed cubic structure and a magnetic keyless design, is waterproof and pressure-resistant, has strong ground burying stability, and has a service life of 0.5 years; time domain-frequency domain joint analysis combined with effective radius calculation can accurately distinguish between personnel, passenger cars and engineering vehicles and reduce misjudgment; a self-adaptive learning formula dynamically corrects a threshold value, adapts to different soil, temperature and humidity environments, and reduces the false report rate; standardized switching on and off and timing data uploading prolong the endurance and meet long-term unattended monitoring; positioning and transmission are stable: Beidou and GPS dual-mode positioning and cellular network positioning are accurate under a ground burying environment, and data transmission is uninterrupted; the background can realize real-time viewing of the state and historical records, and the operation and maintenance efficiency is improved.

The application discloses a tunnel smoke spread real-time prediction method based on physical priori and timing network, comprising the following steps: acquiring multi-source timing data and preprocessing, and constructing multi-working condition data set; constructing a mixed physical priori containing smoke spread length priori and smoke layer height field shape priori; adopting intrinsic orthogonal decomposition to reduce the dimension of the smoke layer height field, and extracting spatial main mode and mode coefficient; constructing a multi-task prediction model based on a causal timing convolution network, and synchronously predicting the smoke backflow length and the mode coefficient; using a mixed loss function fusing data fitting loss, physical priori loss and smoothing constraint, and combining a progressive loading mechanism to train the model; using the trained model to output the prediction result in real time, and reconstructing the smoke layer spatial distribution. The application fuses physical priori and causal timing network, realizes real-time and high-precision prediction of the tunnel fire smoke backflow length and the smoke layer spatial distribution, and has the advantages of strong physical rationality, high generalization ability and stable prediction.

The embodiment of the application provides a hybrid signature method and system based on quantum keys and PUF, and belongs to the technical field of information security. The method comprises the following steps: generating quantum keys through a quantum key distribution protocol, and generating a derived key seed based on a physically unclonable function, while completing the configuration of a trusted area of positioning and timing; acquiring positioning and timing information and generating positioning and timing data; generating a dynamic root key, and deriving a classical signature key pair and a post-quantum signature key pair; preprocessing the to-be-signed message and the positioning and timing data, matching a hierarchical security strategy, selecting a corresponding signature mode to generate a signature data packet; in the signature verification process, verifying the signature and the legality of the positioning and timing data, and verifying the signature result according to the corresponding signature mode selected according to the hierarchical security strategy. The application realizes dynamic generation of signature keys and scheduling of a hierarchical security strategy, and balances the anti-quantum attack ability, device binding ability and calculation efficiency of digital signature.

This application relates to the field of gas turbine control technology and discloses a hardware serial port redundant communication method and system for gas turbine servo modules. The method includes: collecting communication parameters, operating status parameters, and control performance parameters of the primary and backup servo modules, normalizing them, and organizing them into a sliding window timing data matrix; inputting the timing data matrix into a long short-term memory network, and iteratively calculating a fault probability prediction value through a gating mechanism; constructing a state vector based on the fault probability prediction value and operating parameters, calculating a value function through a dual-Q network, and outputting a switching decision command and strategy parameters; synchronizing control parameters according to the strategy parameters and linearly and gradually adjusting the output of the primary and backup modules, and feeding back the switching effect to the online Q network to update the value function. This application improves the fault warning accuracy, the intelligence level of switching decisions, and the control stability of the switching process in the redundant communication system for gas turbine servo modules.

The application provides a timing prediction method and device based on feature matching closed loop fusion and a storage medium. The method comprises: performing cleaning processing on original timing data to obtain available timing data; extracting timing features based on the available timing data, and performing label processing on the timing features according to a feature classification system to generate a labeled feature set; determining a base model set corresponding to each label to generate a feature model mapping table; updating weight parameters of each base model set and performing weighted fusion on a preliminary prediction result to obtain a fusion prediction result; generating a residual sequence based on the fusion prediction result and an observation value of a target timing, performing residual type determination on the residual sequence, and selecting a calibration model according to a residual type to predict a residual prediction value, and fusing the residual prediction value and the fusion prediction result to obtain a final prediction result. The application can improve prediction accuracy, enhance stability under distribution deviation, and improve multi-scene generalization ability.

This application discloses a remote control learning method. When the learning remote control receives an infrared signal from a target remote control, it determines the infrared protocol parameters and preliminary timing data of the infrared signals of the buttons to be learned. The preliminary timing data is preprocessed to obtain target timing data. A bitmap is constructed based on the infrared protocol parameters of the infrared signal, the target timing data, and a pre-stored remote control library on the learning remote control. The target timing data is decoded according to the infrared protocol parameters of the infrared signal to obtain the corresponding key values. Based on the one-to-one correspondence between key values ​​and the buttons to be learned, the bitmap is set to zero to obtain a set bitmap. Then, based on the number of 1 values ​​in the set bitmap, the infrared protocol parameters of the target remote control and the correspondence between all buttons and key values ​​are determined. The infrared protocol parameters of the target remote control and the correspondence between all buttons and key values ​​are configured on the learning remote control. This application can reduce timing data errors and reduce the number of remote control learning operations.

A method for determining relationships of entities associated with interactions may include receiving interaction data associated with interactions between first and second entities. The interaction data may include first entity identification data, second entity identification data, and relative timing data. A node may be generated for each second entity. A set of edges may be generated for each first entity to include an edge connecting the node associated with second entity identification data of each interaction to the node associated with second entity identification data of a next interaction based on the relative timing data. Sample data associated with a portion of the nodes / edges may be generated. A vector for each node of the sample may be generated. A distance between each vector and other vectors may be determined. A relationship between each second entity may be determined based on the distance. Systems and products are also disclosed.

A server transmits an encoded game frame over a network to a respective client system as a set of packets. In response to transmitting the set of packets, the server determines a bandwidth estimate based on the size of the encoded game frame and the timing data associated with the transmitted set of packets. The server then compares the bandwidth estimate to a current video bitrate of the game stream being transmitted from the server to the respective client device. In response to the comparison indicating an underutilization of the network, the server increases the encoding bitrate. Further, in response to the comparison indication an overutilization of the network, the server decreases the encoding bitrate.

The application discloses a kind of joint abnormality detection method and device for non-uniform multiple product service time series data, belong to time series abnormality detection field.The method processes the non-uniform time series data of each variable by time-aware dual-channel encoder, and the irregular sampling and missing value of observation value channel are handled by attenuation mechanism and input compensation, and the time interval channel learns event arrival mode;Subsequently, based on the hidden state representation of all variables, dynamically build variable relationship graph and use graph attention network to learn the dependence relationship between variables, update the hidden state and generate system-level joint representation vector;Finally, through joint abnormality detection module, based on reconstruction error and prediction error, system-level abnormality score and variable-level contribution degree are calculated, and abnormality detection and root cause analysis are realized.The application can effectively process non-uniform multiple time series data, accurately capture the dynamic dependence relationship between variables, play an important role in system monitoring and fault diagnosis in the fields of cloud computing, Internet of Things, etc., and has important application value.

The application discloses a kind of mobile device detection method and system based on USB direct connection, it is related to mobile device fault detection technical field.Method includes: S1: establish USB direct connection anti-interference communication link;S2: through the USB direct connection anti-interference communication link acquisition mobile device's static parameter and dynamic timing data, to the dynamic timing data is carried out noise reduction processing, obtain clean data;S3: construct multimodal standard feature library, the multimodal standard feature library includes static compliance benchmark library, dynamic timing feature template library and fault characteristic fingerprint library;S4: to the clean data with the static parameter is carried out feature fusion, after the feature of fusion is input into the LSTM model of training completion and carries out inference, obtains fault judgment result and compliance verification result;S5: output the fault judgment result, the compliance verification result and data traceability information, form detection report.The application greatly improves the precision, forward-looking and scene adaptation ability of mobile device detection.

The application belongs to the technical field of computers, and particularly relates to a time series data processing method and an electronic device. The method comprises the following steps: acquiring a plurality of data sets with the same time series attribute, wherein the data set comprises a plurality of time series data with the time series attribute; sorting the time series data in the data set according to time series nodes to obtain a data sequence; determining a sequence length for accommodating the time series nodes according to the minimum time series node and the maximum time series node in the data sequence, and determining the number of binary bits for accommodating attribute values according to the maximum attribute value in the data sequence; initializing a sub-bitmap corresponding to the data set according to the minimum time series node, the sequence length and the number of binary bits, and writing the data sequence into the sub-bitmap; the sub-bitmap is an array for storing the data set in binary format; and merging the sub-bitmaps of the plurality of data sets to obtain a main bitmap corresponding to the time series attribute. The application can improve the storage space utilization rate of the time series data.

The application discloses a kind of alignment methods of timing information data of teaching framework, it is related to timing signal processing technical field, including: based on alignment reference framework obtains teaching link and timing information type;Text feature data, behavior feature data and timing limit interval of the teaching link are obtained;When link time domain is obtained based on data extraction;Timing information data alignment is carried out in teaching framework based on link time domain;The present application is used to solve the alignment method of timing information data of existing teaching framework, in teaching timing alignment determination aspect, lack the method of doing semantic matching relying on the text feature of teaching link, cause only rely on fixed clock time division data interval, time period cannot adapt to the rhythm change of teaching time, lead to the problems, such as multiple timing data link mismatch and alignment reliability decline.

The application provides an aero-engine RUL prediction method and system based on a pulse timing dynamics network, belonging to the technical field of aero-engines, comprising constructing and training a pulse timing dynamics network, the pulse timing dynamics network comprising a plurality of stacked timing pulse convolution modules, and a residual timing coupling connection channel being arranged between adjacent timing pulse convolution modules; obtaining timing data generated in the operation process of a target device and converting the timing data into a discrete pulse sequence; inputting the discrete pulse sequence into the pulse timing dynamics network for timing feature extraction and fusion, and outputting a high-level fusion pulse sequence; and generating a target device remaining useful life prediction value based on the high-level fusion pulse sequence. The application scheme improves the aero-engine RUL low-latency reasoning accuracy and curve stability, can adapt to non-stationary and multi-stage degradation, and reduces energy consumption and time delay.

The application discloses a real-time feature extraction method and system for built-in time sequence data of an intelligent fusion terminal, relates to the technical field of data processing, and comprises the following steps: obtaining an original time sequence data stream through the intelligent fusion terminal, performing multi-modal data fusion, and constructing a fusion time sequence data stream; constructing a double-layer feature extraction window, performing real-time feature fusion extraction on the fusion time sequence data stream, and obtaining a multi-scale real-time feature vector; constructing an initial feature vector matrix based on the multi-scale real-time feature vector, performing dynamic feature weight distribution updating, and obtaining a feature vector matrix; performing state recognition response on the intelligent fusion terminal according to the feature vector matrix, and generating an auxiliary decision instruction of the intelligent fusion terminal. The application solves the technical problem that the real-time feature extraction capability of the intelligent fusion terminal for built-in time sequence data is insufficient in the prior art, and achieves the technical effect of improving the real-time feature extraction effect of the intelligent fusion terminal for built-in time sequence data.

The application discloses an optimization method and system suitable for non-uniform timing data in torsional vibration measurement, and relates to the technical field of rotating machinery state monitoring.The application comprises the following steps: receiving original square wave signals output by a rotary encoder, detecting pulse edge types, extracting accurate time stamps of each edge, and generating an original time stamp sequence; based on the original time stamp sequence, a time interval sequence is calculated, and a reference time interval is calculated based on an arithmetic average value; double-criterion abnormality identification is performed on the time interval sequence, and an abnormality type is determined; corresponding interpolation correction is performed according to the abnormality type identification result, and an optimized time stamp sequence is generated; and the optimized time stamp sequence is subjected to uniformity verification and output as input data for torsional vibration analysis.The application reconstructs a high-precision torsional vibration waveform through feature identification and deterministic interpolation compensation of the time stamp sequence, and provides reliable data basis for rotating machinery load health monitoring and fault diagnosis.

This invention discloses a multi-stage rocket timing test channel reuse system, relating to the field of aerospace systems technology. The system includes an onboard system and a ground system. The onboard system includes first- and second-stage onboard timing acquisition channel circuits and additional multi-stage onboard timing acquisition channel circuits. The ground system includes first- and second-stage time-series ground test channel circuits. The first- and second-stage onboard timing acquisition channel circuits of the onboard system are connected to the first- and second-stage time-series ground test channel circuits of the ground system via diodes one and two. The multi-stage onboard timing acquisition channel circuits of the onboard system are connected to the first- and second-stage time-series ground test channel circuits of the ground system via diodes three and four. This invention reuses the same timing acquisition channel for timing data from different stages and time periods to meet the testing requirements of rocket timing. By designing isolation circuits, different timing channels using the same test path are isolated, ensuring that each timing test process does not interfere with the others.

The application relates to the technical field of signal quality testing, and discloses a transmission signal testing method and system for a liquid crystal display product, the method comprising the following steps: collecting a PWM analog signal and a TE digital signal of a liquid crystal display product to be tested; performing analog-digital conversion on the PWM analog signal; performing edge capture on the TE digital signal; obtaining PWM discrete timing data and TE edge timestamp data; performing fast Fourier transform (FFT) analysis on the discrete timing data to obtain PWM signal characteristic data; calculating the frequency of the TE signal based on the TE edge timestamp data; statistically analyzing the period discrete degree to obtain TE signal characteristic data; comparing the two types of characteristic data with preset quality thresholds; generating a test determination result for the liquid crystal display product to be tested; and generating a test determination report based on the test determination result. The application can improve the efficiency of transmission signal testing of a liquid crystal display product.

This application relates to the field of microfluidic biosensing technology, and discloses a data acquisition and control system based on organ-on-a-chip microfluidics. The system includes a rhythm-locked loop module, a phase-controlled acquisition module, an impedance detection module, a data compensation module, and a closed-loop control module. The system generates a phase-locked clock by analyzing the pulse rhythm of the microfluidic pump drive current and chamber pressure timing data and aligning it with time-domain phase-locking. During the steady-state window of the laminar flow, it performs phase-controlled synchronous acquisition of the sensor array to obtain the raw biochemical dataset. During the low-level interval of the clock, it performs electrochemical impedance spectroscopy to obtain a fouling compensation factor. Based on this factor, it compensates and calibrates the data of each modality to obtain a biological phenotypic feature matrix, which is then input into a model predictive controller to achieve adaptive microfluidic closed-loop control. This application effectively improves the signal-to-noise ratio of data acquisition and the robustness of microenvironment control during long-term operation of organ-on-a-chip systems.

An intelligent dosing platform system for emergency medication administration comprising intelligent injection devices with emergency protocol modules, time-based tracking systems, and AI-driven prompting systems. The system analyzes time-based administration protocols for emergency medications, monitors elapsed time since previous medication administrations, and generates next dose prompts based on protocol timing requirements and elapsed time data. A field coordination module transmits administration timing data between field emergency response teams and remote coordination systems. The system supports emergency response environments including cardiac emergencies, anaphylaxis, opioid overdose response, and military field operations. Multi-modal alert systems deliver dose prompts through audible alerts, visual electronic medical record prompts, and remote physician communications. The system prevents duplicate medication administration across emergency response teams through timing data sharing while accounting for time delays caused by patient movement and transport.

A report data loading method and device based on a row-column layout, a product and a medium, the application relates to the technical field of computer data processing. In the method, the configuration metadata of a target report is analyzed to construct a layout structure containing static attributes and dynamic timing areas; the query time span, the starting query time and the sampling frequency parameters are read, the theoretical record number is determined accordingly, the pre-occupancy height of the dynamic timing area is determined in combination with the preset row height, and a fixed-height grid container is generated; a parallel data channel is established to receive fragmented timing data, and the timestamp characteristic value is extracted; the target row index is calculated based on the timestamp characteristic value, the starting query time and the sampling frequency, the fragmented timing data is directly written into the grid unit indicated by the index, and the positioning and loading of the data in the layout structure are completed. The effect of improving the report data loading efficiency is improved.