295 results about "Timing data" patented technology

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 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 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.

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.

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.

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.

In one embodiment, a system includes a peripheral device, which includes an interface to receive from a virtual machine (VM) running on a host device, over a communication data bus, a request for timing data derived from a time measurement dialogue, the host device maintaining a master clock time, a hardware clock to maintain a peripheral device clock time, and processing circuitry to transform the master clock time to a frame of reference of the VM, and provide to the VM, over the communication data bus, the timing data based on the peripheral device clock time, and the master clock time transformed to the frame of reference of the VM.

The invention relates to the technical field of computer vision, and provides a video embedded subtitle detection method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an audio stream associated with a video stream, carrying out the voice recognition of the audio stream, and generating first time sequence data comprising a transliteration text and corresponding time information; extracting a video frame sequence from the video stream, and performing character recognition on each video frame in the video frame sequence to generate second time sequence data comprising a recognition text and corresponding position information; and performing cross-modal time sequence alignment and text content comparison based on the first time sequence data and the second time sequence data, and determining target subtitle information embedded in the video stream according to a comparison result. According to the method, the cross-modal analysis of the audio and the video is introduced, so that the scene characters in the embedded subtitles and the video images can be effectively distinguished, the false detection rate is greatly reduced, and the subtitle detection accuracy is remarkably improved.

The application relates to the technical field of oil and gas field data management, and discloses an oil and gas field opening time sequence data management method based on a Hongmeng system. The method realizes real-time acquisition of opening time sequence data through a distributed data acquisition module of the Hongmeng system, calculates the data point density of a target area and compares the data point density with a safety threshold value; when the density exceeds the limit, a statistical analysis model is used to evaluate the interaction of data sources, and a data coupling effect hidden danger state is output; time sequence analysis is used to process environmental factor data, and a potential interference degree evaluation is generated; whether the overall data optimization process is triggered is determined according to the hidden danger state and the interference evaluation; when the overall data optimization process is triggered, individual path complexity evaluation is performed on each data source, an adjustment priority sequence is calculated, oil and gas field equipment is controlled according to the sequence, and dynamic management of opening time sequence data is realized. The method improves the real-time performance, accuracy and flexibility of data management, and adapts to the production requirements of modern oil and gas fields.

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.

The application belongs to the technical field of time series data prediction, and particularly relates to a time series data prediction optimization method and system introducing dynamic error. The method comprises the following steps: preprocessing time series data to be processed; inputting the preprocessed data into a prediction model established and trained in advance to obtain preliminary prediction data; comparing the preliminary prediction data with the preprocessed data to obtain a preliminary error; based on the preliminary error, combining historical data of different time lengths, and according to a time decay function, obtaining long-term error and recent error, and through weighted combination, obtaining a corrected prediction value to realize real-time correction of dynamic error. The application firstly applies the dynamic error correction method to time series prediction of satellite telemetry data, realizes real-time correction of prediction error, and significantly improves prediction accuracy.

The invention discloses an operating room purification environment self-adaptive regulation and control method and system based on time sequence prediction, and relates to the technical field of air conditioning. According to the operating room purification environment self-adaptive regulation and control method based on time sequence prediction, the environment state of an operating room and purification air conditioner operation time sequence data are continuously obtained in a set sliding regulation and control period, and a collaborative feature set is generated through associated feature mining; extracting a predicted collaborative feature set through a time sequence prediction model in combination with historical collaborative features; performing security domain definition based on the operation state set and a preset mapping relation library, and determining a purification control domain; according to the method, the operating room is subjected to environment regulation and control through the prediction optimization control set, so that the accuracy of environment regulation and control is improved, the problem of regulation and control lag caused by real-time fluctuation of the environment is avoided, operation parameters of a purification air conditioner are matched in advance, and then the cleanliness of the operating environment is guaranteed.

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.

Systems and methods for correcting timing information associated with captured nuclear imaging data, and for reconstructing medical images based on the corrected timing information, are disclosed. In some embodiments, an image scanning system scans a subject, detects an event, and generates timing data for the detected event. The timing data includes a first number of bits, and characterizes a time that a crystal of the image scanning system detected the event. The image scanning system shifts the first number of bits by a predetermined amount to generate a second number of bits, where the second number of bits includes at least one bit representing a lower order time value than the least significant bit of the first number of bits of the timing data. Further, the image scanning system generates timing correction data for the event based on the second timing data.

The invention provides an abnormal time series data detection method and device and a storage medium, relates to the technical field of data processing, and can improve the accuracy of time series data anomaly detection. The method comprises the following steps: processing original time sequence data based on a spectrum entropy dynamic period detection algorithm, and determining whether the original time sequence data is period time sequence data; and under the condition that the original time series data are non-periodic time series data, determining a target anomaly detection model based on a normality test algorithm KS in combination with a first algorithm and the original time series data, performing anomaly detection on the original time series data based on the target anomaly detection model, and determining an anomaly detection result, the anomaly detection result comprises whether the original time sequence data is abnormal data or not; and under the condition that the original time sequence data is periodic time sequence data, performing anomaly detection on the original time sequence data based on a second algorithm, and determining an anomaly detection result.

The invention relates to the technical field of timed task scheduling, and provides a dynamic timed task implementation method and device for passenger reservation management, and the method comprises the steps: dynamically planning a timed push task within a target time based on a flight plan and a timed data push rule, and executing the timed push task, and acquiring an actual execution result according to a preset target check period in the execution process, judging the actual execution result, and executing corresponding actual compensation according to a judgment result. According to the invention, through dynamic task planning, flexible business change coping, execution process monitoring, real-time state mastering and automatic compensation mechanism, final accuracy of data is ensured, an efficient, stable and adaptive automatic task management closed loop is formed, the automation level, the operation efficiency and the data accuracy of passenger reservation management are significantly improved, and the efficiency of passenger reservation management is improved. And a solid technical support is provided for ensuring the service quality and the operation compliance of passengers.

The application discloses a time series data compression method and device, and relates to the technical field of data compression, and the method comprises the following steps: creating a one-dimensional dictionary according to time series data; sorting the data in the one-dimensional dictionary to obtain a first sorting result, and creating a one-dimensional dictionary index according to the first sorting result; performing traversal XOR on the data in the one-dimensional dictionary to obtain a traversal XOR result, compressing the one-dimensional dictionary according to the traversal XOR result, and obtaining a compressed one-dimensional dictionary; determining a one-dimensional dictionary index sequence corresponding to a time sequence according to the one-dimensional dictionary index and the time sequence corresponding to the time series data, compressing the one-dimensional dictionary index sequence corresponding to the time sequence, and obtaining a compressed index sequence; and merging the compressed one-dimensional dictionary and the compressed index sequence to obtain compressed data of the compressed time series data. The application can further improve the compression ratio of data without losing accuracy, save storage space, and realize efficient storage.

The invention provides a landslide early warning method based on time series data anomaly detection of a crack meter. The landslide early warning method comprises a model training stage and an early warning judgment stage, the model training stage comprises the following steps: S1, acquiring crack meter monitoring data including a displacement observation value and time information, setting a mask and performing preprocessing; s2, generating code representation for the preprocessed monitoring data of the crack meter; s3, performing deep feature extraction on the coded representation by adopting a multi-layer perceptron mixer; s4, based on the deep features, enabling the anomaly precursor perception task to learn probability distribution of the anomaly detection task through a knowledge distillation mechanism, completing model collaborative optimization, and obtaining a trained early warning model; the early warning discrimination stage comprises the following steps: inputting to-be-detected crack meter monitoring data into the trained early warning model, and outputting an abnormal score; and judging whether an early warning signal corresponding to the monitoring data of the crack meter to be detected is valid or not in combination with an abnormal score threshold obtained by learning.

The invention discloses a polymerization reaction heating temperature accurate parking control method based on generative AI, and the method comprises the steps: confirming a control target in a technological process containing a polymerization reaction; analyzing and determining state characteristics influencing a control target by utilizing a process principle; performing time sequence characteristic correlation analysis by using a sliding window, and determining time sequence characteristics and control variables influencing a control target; constructing a discriminator and a training data set thereof, and training the discriminator through the training data set to obtain a trained discriminator; constructing a generator and a sample set thereof, and training the generator through the sample set and the trained discriminator to obtain a trained generator; the trained generator is applied to the technological process containing polymerization reaction, prediction time sequence data of control variables are generated, and accurate parking control over the temperature rise temperature is achieved. Accurate parking control over the temperature rise temperature of the polymerization reaction is achieved on the basis of the generation type AI, namely setting of the discriminator and the generator.

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.

The invention discloses a physical experiment data rapid analysis method and system based on image processing, and relates to the technical field of image data analysis. Comprising the following steps: processing a continuously collected original experiment image sequence, and separating a foreground region representing an experiment object; automatically identifying a physical experiment type based on the image features of the region, and synchronously obtaining a unified configuration set bound with a feature extraction strategy and a physical constraint rule; performing feature extraction in parallel according to the configuration set to generate initial time sequence data, and verifying the data based on a physical constraint rule to generate a physical consistency residual error; querying a predefined mapping relation according to a residual error mode, dynamically adjusting a feature extraction parameter or foreground region positioning, enabling the residual error to meet a preset convergence condition through iteration, and generating corrected time series data; and finally, outputting a physical quantity analysis result. According to the invention, the physical experiment data processing efficiency and the physical reliability of the result are improved.

This invention discloses a method and system for optimizing vehicle trajectories at intersections based on offline reinforcement learning. The specific steps are as follows: A communication scenario between traffic lights and connected vehicles at an intersection is established under an intelligent connected vehicle environment. A communication distance threshold between vehicles and traffic lights is set within the scenario. When a vehicle enters the control area of ​​the traffic light, both parties can exchange information. The operating trajectories of connected vehicles and signal phase timing data at the signalized intersection are collected. The vehicle's driving process at the intersection is abstracted as a Markov decision process. Vehicle travel time, vehicle energy consumption, and collision time are used as parameters of the Markov decision reward function. A suitable dataset is constructed. The vehicle agent is trained offline using reinforcement learning to obtain a suitable intersection traffic strategy. This invention can be applied to vehicle control at intersections, controlling vehicle acceleration to enable vehicles to travel along the optimal trajectory, thereby achieving energy saving, emission reduction, and improved safety performance.