832 results about "Time windows" patented technology

The invention relates to the technical field of edge computing, in particular to a safety production monitoring method based on edge collaboration, which comprises the following steps: collecting multi-node clock pulses, calculating adjacent time differences to form a pulse difference parameter set, carrying out statistical distribution, setting a dynamic tolerance boundary, and constructing an effective deviation parameter set; the method comprises the following steps of: generating a weighted smooth deviation result by combining clock stability and link delay weighted normalization, sorting monitoring data according to a timestamp to identify duplication abrupt change exception, constructing a cross-node exception time sequence by associating nodes and time information, and judging a safety state transition result of equipment. A time difference parameter is constructed through multi-node reference pulse arrival time, a dynamic deviation constraint is formed by combining distribution characteristics, clock stability and link measurement weighted smoothing are introduced, cross-node time reference consistency is kept, operation data unified time sequence rearrangement is carried out, a sudden change event reliably reappears in a time window, and an abnormal identifier is associated with a node identity. And forming a cross-node event evolution link.

The invention discloses a computing power distribution method based on a dynamic window and multi-version correction, and relates to the technical field of intelligent resource scheduling, the method comprises the following steps: constructing a scheduling state set by monitoring a to-be-scheduled task set and a platform resource state of a cloud platform resource scheduling system in real time; based on time window dynamic judgment, extracting a stable and effective task set; calculating a scheduling priority coefficient in combination with task resource intensity, urgency and historical completion performance, determining a priority scheduling set and executing a first version scheduling scheme; the difference between the current scheduling version and the historical optimal scheme is evaluated through the residual vector and the offset coefficient, and whether version correction needs to be carried out or not is judged; and based on resource use feedback in the scheduling execution process, calculating a feedback change rate and judging whether a scheduling execution state is stable or not, and if the scheduling execution state is unstable, dynamically adjusting scheduling parameters. According to the method, accurate allocation of computing power resources, adaptive scheduling optimization and abnormal risk control are realized.

The invention discloses a method for processing an uncorrectable error of bus equipment and a server, relates to the technical field of servers, and aims to reduce the reporting frequency of the uncorrectable error by setting a suppression identifier of the uncorrectable error of the bus equipment and a time window of uncorrectable error statistics, and improve the reporting efficiency of the uncorrectable error when the uncorrectable error is triggered for the first time. According to the method, uncorrectable errors are reported, the uncorrectable errors are captured, it is ensured that initial errors are not missed, and when the error count of a time window is larger than a reporting threshold value, reporting is carried out again, error missing processing of error aggravation is avoided, redundant error reporting is reduced, an error reporting storm is inhibited through an inhibiting identifier, and the error reporting efficiency is improved. Therefore, the influence of frequent error triggering on the availability of a server system and normal operation of other services is reduced, the reliability of the server is improved, and the technical problems that frequent error triggering cannot be corrected, other service requirements are covered and the availability of the server system is influenced in the related technology are solved.

The invention discloses a heterogeneous computing power resource dynamic cooperative scheduling method and system and a computer device. The method comprises the steps of obtaining multi-dimensional feature information of a to-be-scheduled task, current state information of each node in a heterogeneous computing power resource pool and scheduling environment state information; training a time sequence model by adopting historical associated data, inputting the multi-dimensional feature information, the current state information and the scheduling environment state information into the trained model to obtain associated trend information in a future preset time window, and then combining the multi-dimensional feature information and the current state information of all the nodes to obtain the scheduling environment state information. A multi-objective optimization function fusing task delay, cost consumption and energy consumption is constructed, the function is optimized based on a preset strategy, a target decision strategy is obtained, a scheduling instruction set is generated after analysis, allocation information and target nodes are determined according to the scheduling instruction set, and tasks are issued. According to the method, dynamic collaborative scheduling of heterogeneous computing power resources can be realized, and the overall utilization efficiency of the resources is accurately, efficiently and effectively improved.

The invention provides a stream computing resource scheduling method and system based on a dynamic time window, and relates to the field of stream computing resource scheduling, and the method comprises the following steps: constructing a master-slave event time monitoring network to obtain a task feature vector, and establishing an adaptive time window scheduling mechanism to dynamically adjust the window size; and constructing a multi-level task priority queue, carrying out state transition by adopting a remote direct memory access mechanism of incremental transmission, and finally coordinating computing nodes through a distributed task arrangement protocol to complete resource switching. According to the method, computing resource competition can be effectively reduced, the flow computing task processing efficiency is improved, dynamic optimal configuration of resources is realized, and the system throughput is enhanced.

The invention relates to a time series data acquisition method and device based on a dynamic time window and fingerprint deduplication. The method comprises the following steps: establishing a sliding time window with a variable length by taking the current system time as a reference, and only querying incremental time sequence data in the window; adaptively zooming the window by calculating the ratio of the collected data volume to the expected volume in real time; setting the trigger interval as half of the length of the sliding time window to form a time overlap, thereby capturing out-of-order data; key features of each piece of time series data are extracted, and data fingerprints are generated; a bloom filter is used for primary screening, and accurate duplicate removal is carried out in a key-value storage system; compared with an existing fixed polling scheme, the method has the advantages that resources can be dynamically adjusted along with data flow rate, system load is reduced, real-time performance is improved, data integrity is guaranteed by overlapped windows, zero repeated acquisition is guaranteed by fingerprint two-stage duplicate removal, and the method is applicable to efficient incremental synchronization scenes of transactional log-free time sequence databases such as InfluxDB and the like.

The invention discloses a computing power server load prediction method and system, and belongs to the field of data processing, the computing power server load prediction method comprises the following steps: collecting historical load data of a computing power server, and preprocessing the historical load data, the preprocessing comprising data cleaning, time window construction, and data set division; constructing an LSTM (Long Short Term Memory) model, and training the LSTM model; predicting a future computing power server load through an LSTM model; compared with the prior art, the method has the beneficial effects that the overall computing resource utilization rate is effectively improved by collecting the historical load data of the computing power server, training the LSTM model, finally predicting the future computing power server load through the LSTM model and allocating the computing resources according to the predicted data; meanwhile, abnormal tasks are recognized, and the abnormal tasks are processed in time when appearing, so that other tasks are prevented from being affected.

The invention discloses an operation data processing method and system based on a compliance verification library and a risk control plan, and belongs to the technical field of data compliance and risk control, and the method comprises the steps: obtaining an execution plan of a target operation task and risk control strategy information of the execution plan, analyzing operation data items, distributing corresponding compliance verification time windows and levels, and forming an operation data verification scheduling table; according to the verification level and the business domain, a rule set is extracted from a compliance verification library, and rules comprise predefined rules, model output rules and historical violation rules; field-level matching, logic consistency verification and cross-source linkage verification are executed in the verification time window; when the verification result meets the triggering condition, generating an abnormal event, and executing response operations such as delay, recheck or interception; and finally, writing the execution state and the verification result into an abnormal log queue for risk control strategy parameter updating or re-evaluation. According to the method, the dynamic compliance verification and strategy closed-loop optimization of the operation data are realized, and the intelligent compliance control capability of the system is improved.

The invention relates to a server-side satellite precision product anomaly detection method and system and a terminal, specifically, a previous time window of a current epoch is obtained, the time window comprises a plurality of epochs, the residual mean value and the root-mean-square of each satellite in each epoch in the time window are calculated, and the residual mean value and the root-mean-square of each satellite in each epoch in the time window are calculated; normalizing the residual mean value and the root-mean-square to obtain the characteristics of the satellite in epochs; acquiring features of all the satellites in all epochs in the previous time window to form a feature set, performing outlier detection on the feature set by adopting an LOF method, and determining the LOF score of the satellite at the current epoch moment by utilizing the features of the current epoch of the satellite and the feature set after removing the satellite of which the LOF score is greater than a preset value from the feature set; the LOF scores of the satellites are sent to the PPP user side, when the PPP user side carries out ambiguity fixing, all the satellites are tried to be fixed firstly, if fixing fails, the satellite with the maximum LOF value is removed from the ambiguity set, fixing is tried again, and continuous circulation is carried out till the ambiguity is fixed successfully.

The invention discloses a micro burst traffic detection method, network element equipment, a computer readable storage medium and a computer program product, and relates to the technical field of communication, the method is applied to the network element equipment, the network element equipment is provided with a microsecond-level timer, and the method comprises the following steps: counting the number of forwarding message bytes of each sub-time window in a sliding time window; performing mean value calculation on the forwarding message byte numbers of all the sub-time windows in the sliding time window to obtain a sub-window byte number mean value of the sliding time window; determining a sub-window byte number threshold value of the sliding time window based on the sub-window byte number mean value; and determining the state of the sub-time window of which the forwarding message byte number is greater than the byte number threshold value of the sub-window as a flow micro-burst state. According to the invention, the size of the micro-burst flow and the corresponding time delay can be accurately detected in a fine-grained manner, and the detection precision of the micro-burst flow is effectively improved.

Techniques are described for deploying software applications associated with document management. The techniques may include a system comprising processing circuitry implemented by one or more processors, the processing circuitry having access to a memory. The processing circuitry may be configured to determine, for a time window, a current version of a widget and a next version of the widget to implement a software application of the one or more software applications. The processing circuitry may further be configured to output instructions to execute the current version of the widget in response to receiving a first request for the software application. The processing circuitry may further be configured to output instructions to execute the next version of the widget based on a number of responses to previous requests that select the current version of the widget.

The invention discloses a multi-layer heterogeneous sequential network feature alignment and clustering method and device based on tensor self-representation. The method comprises the steps that sequential feature matrix sequences {},..., {} of data views in a multi-view heterogeneous network in time windows are obtained; based on the matrix sequence of the first time window, constructing a Laplacian regular term by introducing a self-representation learning mechanism, modeling multiple views by adopting a tensor structure and introducing a Schatten p-norm regularization mode to construct an optimization objective function for optimization, and representing shared feature representation of each data view in the first time window; and solving the function and carrying out clustering processing on the obtained function, wherein the clustering result of each time window is used for carrying out characteristic analysis on nodes in the network. According to the method, on the premise that the original structure and semantics of the network are guaranteed, unified modeling can be carried out on the incomplete multi-view heterogeneous network, and feature expression is aligned.

The invention relates to an access control method and system for an industrial internet server. The method comprises the following steps: analyzing a plurality of round-trip delays and a plurality of response delays to obtain a failure coefficient of a target equipment execution instruction; determining an execution delay index according to the deviation and the failure coefficient between the response duration of each process variable of the target equipment for the plurality of instructions in the same time window and the corresponding preset duration; according to the relevancy between the execution delay indexes of the time windows and the change characteristic values of the process variables, the influence index of instruction delay execution on the process variables is determined; calculating a risk coupling coefficient corresponding to the instruction sequence according to the delay difference of each adjacent instruction in the instruction sequence; and adjusting the number of access instructions of the server according to the influence index and the risk coupling coefficient. According to the method, the interlocking amplification risk of instruction delay and process variable fluctuation can be effectively inhibited, and the stability and reliability of an industrial internet access control system are guaranteed.

The invention discloses an asynchronous hierarchical federated learning method, system, device and medium, and relates to the technical field of federated learning, and the method comprises the steps: clustering devices with similar training time in the same cluster at a client-edge end by using a mode based on a dynamic time window, and carrying out the clustering of the devices in the same cluster; dynamically grouping the equipment and asynchronously receiving local model update uploaded by the client; and at the edge end-central server, the performance evaluation and the historical participation degree of the edge server are dynamically scored, and the weight of the edge server participating in the global model aggregation is dynamically adjusted according to the score, so that a plurality of edge aggregation models are subjected to new global aggregation to obtain the global model; according to the method, the equipment is dynamically grouped, the local model is updated, the problem of equipment heterogeneity in the equipment is solved, the weight of the edge server participating in global model aggregation is dynamically adjusted according to the score, the heterogeneity condition existing in data is counteracted, and the federal learning aggregation efficiency is greatly improved.

A method performed by a wireless transmit / receive unit (WTRU) may comprise: receiving, from a base station, configuration information; receiving, from an application layer, one or more uplink protocol data units (PDUs); determining a buffering delay for transmitting the one or more uplink PDUs in resources associated with configured grant (CG) physical uplink shared channel (PUSCH) occasions; and on a condition that the buffering delay is greater than a delay threshold and that there are no available resources associated with semi-persistent scheduling (SPS) physical downlink shared channel (PDSCH)occasions within a time window, transmitting an indication of a time offset for the time window. The method may further comprise transmitting, to the base station, in a CG PUSCH occasion, the one or more uplink PDUs.

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.

A method, system and electronic device for regulating the top cover pressure of a vertical hydro-generator set, and a storage medium. The method comprises: setting a monitoring time window, and collecting thrust bearing pad temperatures at different time points within the monitoring time window; acquiring a top cover pressure influence factor set at each time point; inputting the top cover pressure influence factor set corresponding to each time point into a pre-built top cover pressure influence correction database for optimal matching to obtain a bearing pad temperature correction factor corresponding to each time point; using the bearing pad temperature correction factor corresponding to the same time point to perform temperature influence correction on the corresponding thrust bearing pad temperature so as to obtain a corrected thrust bearing pad temperature; and on the basis of a preset pad temperature threshold, performing over-temperature calculation on the corrected thrust bearing pad temperature at each time point.

The invention provides an industrial single-chip microcomputer automatic control method based on time sequence event triggering, and the method comprises the steps: carrying out the multi-period sampling and time window division of an original signal inputted by an industrial field sensor or a switching value, and extracting the time distribution characteristics of the signal; performing signal stability judgment and pseudo edge elimination based on the signal time distribution characteristics, and outputting a stable edge event set; performing event confirmation delay and multi-level logic judgment on the stable edge event set to generate a preliminary time sequence trigger signal; environment characteristic data are collected, real-time feedback of an actuator is obtained, and system judgment parameters are dynamically corrected; and generating a control command and verifying an execution result based on the preliminary time sequence trigger signal and actuator feedback, the method can dynamically distinguish real signals and noise under vibration interference, simultaneously ensures the real-time performance and trigger reliability of the system, and solves the problems of insufficient edge detection stability and false trigger phenomenon caused by mechanical vibration.

The invention relates to the technical field of data processing, in particular to an automatic equipment data acquisition method and system, and the method comprises the steps: obtaining the voltage data and temperature data of each piece of equipment; dividing the voltage data and the temperature data of each device into a plurality of time windows; acquiring a change trend coefficient of each device in each time window, and acquiring a temperature influence coefficient of each device in each time window; acquiring a voltage transient coefficient of each device in each time window; fractal dimensions of all the devices in all the time windows are obtained; acquiring an electromagnetic disturbance coefficient of each device in each time window; acquiring a data acquisition disturbance value of each device in each time window; and the disturbance coupling index of each device in each time window is obtained, and then the filtering window parameter of each device in each time window is obtained, so that adaptive filtering of the voltage data is realized. By analyzing the environmental interference degrees of different devices in different time periods, adaptive filtering is performed on the acquired data, so that the accuracy of data acquisition is improved.

The invention discloses an electric power total-factor unified load prediction method and system based on a time sequence large model, and relates to the technical field of machine learning, and the method comprises the steps: triggering a prediction process through a timed task, and obtaining historical load data and external covariable data; constructing a time sequence sample required by training and prediction according to a time window, and performing batch pulling and processing according to a fixed number of days when the data size exceeds a single-batch threshold value; performing data standardization and data cleaning on the data, and generating structured time sequence input; loading a time sequence large model as a unified prediction engine, and inputting a load sequence and an external covariable into a covariable fusion component for collaborative modeling; and outputting fine-grained prediction results of the electric power elements in the target time period at the same time under a single model framework, and post-processing and storing the prediction results. Through the technical scheme of the invention, the model fragmentation and maintenance cost is reduced, the generalization and stability are improved, and the operation stability and the engineering availability are improved.

Support for distributed database backtracking, in which the database is reverted or iteratively reverted to a previous state, is provided. Log sequence number ranges and storage offset ranges for use in reconstructing the previous state are determined and used in the reconstruction. Clock sampling records can be generated indicating a feasible backtracking window with time points indicating allowable times to which backtracking can be performed. Appropriate log records, snapshots and checkpoints for database states are identified and organized with backtracking information record and timeline. A minimal and necessary set of log records, snapshots and checkpoints enabling database reconstruction within a time window is maintained. Measures are taken to maintain the necessary data to support backtracking even after prior backtrackings have occurred in the backtracking time window.

The invention discloses a dynamic scheduling method for a multi-satellite collaborative observation task, which mainly comprises the following steps of: dispersing a scheduling period into time slices according to minimum overlapping observation time, and uniformly managing resources and time windows; a multi-objective optimization algorithm is used for initially scheduling fixed tasks, and the task completion rate and the minimum relay frequency are considered; detecting a satellite idle time slice based on the initial scheduling result and constructing an index; the method comprises the following steps of: preferentially performing plug-in scheduling on a temporary task, starting multi-satellite relay if a single satellite cannot complete the task, triggering collaborative rescheduling if the tasks fail, merging the fixed task and the temporary task for re-optimization, and simultaneously minimizing disturbance to the fixed task. According to the method, the resource utilization rate and the dynamic response capability are improved through the strategy of'insertion priority and rescheduling understanding ', the temporary task can be efficiently processed on the premise of guaranteeing fixed task execution, interference on an original fixed task plan is reduced, and the method is suitable for complex and changeable observation scenes.

The invention discloses a polling arbitration method and a polling arbitration system for dynamically updating weight by an EWMA algorithm, and the method comprises the following steps: monitoring and receiving a request signal sent by each AHB bus channel on a matrix bus in real time; taking a preset arbitration period as a time window, collecting the current request access times of each AHB bus channel, reading the historical request times stored in the last period, calculating the request variation of each channel, and performing uniform quantization; and on the basis of the historical weight value and the quantized value, an exponentially weighted moving average value is calculated through an EWMA algorithm. When the method is used, the actual weight value of each AHB channel can be adaptively and dynamically adjusted every fixed arbitration period number, resource allocation and system fairness are improved, and it is ensured that an arbiter can more accurately reflect the actual demand of each AHB channel on a matrix bus.

The invention discloses a server data access identity authentication system, which relates to the technical field of data access identity authentication, and comprises a session credibility calculation component, which is configured to acquire multi-dimensional data of a user terminal through a distributed verification node cluster to obtain session context credibility, the dynamic time window generation component is configured to generate an authentication effective time window, the dynamic challenge difficulty adjustment component is configured to dynamically determine a behavior challenge difficulty coefficient, the challenge generation component is configured to obtain a challenge verification result, and the verification result processing component is configured to process the challenge verification result according to the behavior challenge difficulty coefficient and the challenge verification result. And the access authorization component is configured to decide whether to grant server data access authority to a user based on session context credibility and a challenge verification result, and the access authorization component is configured to decide whether to grant server data access authority to the user based on session context credibility and a challenge verification result. And the calculation efficiency and the user experience are optimized.

The invention relates to the technical field of centrifugal machines, in particular to an automatic discharging control method and system for a top-suspended scraper discharging centrifugal machine, and the method comprises the steps that vibration data and rotating speed data of a rotary drum of the centrifugal machine are obtained; dividing the whole data acquisition time into a plurality of time windows; according to the peak pulse characteristic, the periodic characteristic, the frequency change characteristic and the high-frequency characteristic of the vibration data of each time window, acquiring a comprehensive abnormal coefficient of each time window, and acquiring an abnormal influence coefficient of each time window in combination with the dispersion degree of the rotating speed data of each time window; and according to the change characteristics of the abnormal influence coefficient of the adjacent window of each time window, the unloading abnormal coefficient of each time window is obtained, and then the optimized prediction range parameter of each time window is obtained, so that the scraper movement control of the next time window is optimized. And the unloading efficiency of the centrifugal machine is improved by adaptively adjusting the prediction range parameter of each time window.

A method comprises receiving a current dataset for a current time window from at least one sensor in a wellbore created in a subsurface formation, wherein the current dataset comprises values of a number of current features of the subsurface formation at a spatial location in the wellbore. The method includes selecting at least one previous time window from a number of previous time windows that includes a previously cached dataset that was detected by the at least one sensor or a different sensor in the wellbore and that spatially overlaps with the spatial location for the current dataset. The method includes merging the current dataset with the previously cached dataset to create a merged dataset. The method includes selecting a machine learning model from a plurality of machine learning models for the spatial location in the wellbore based on the merged dataset.

The invention provides an alarm processing method and device, equipment, a storage medium and a program product, and relates to the field of artificial intelligence. The method comprises the following steps: extracting a plurality of alarms in a historical time period based on a preset time window size to obtain a historical alarm transaction corresponding to each time window in a plurality of time windows; wherein the alarms comprise event type alarms and business index type alarms, and the event type alarms and the business index type alarms respectively comprise a plurality of alarm types; determining a frequent item set combination according to the historical alarm transaction corresponding to each time window in the plurality of time windows, and constructing an alarm association knowledge graph; wherein the alarm association knowledge graph comprises a plurality of nodes, and each node corresponds to one alarm type; according to the to-be-queried current event type alarm set, the alarm association knowledge graph is queried, and the business index type alarm corresponding to each event type alarm in the current event type alarm set is determined, so that the business index influenced by the system fault is accurately determined.

The invention discloses an electric energy error compensation method and device. The method comprises the following steps: acquiring a voltage sequence and a current sequence in a time window; preprocessing the voltage sequence and the current sequence, and constructing an input feature vector; stacking the input feature vectors in a time window to construct an input tensor, inputting the input tensor into a working condition classification network, and predicting output working condition priori, confidence and context representation; splicing the input tensor and the corresponding working condition priori and context representation to construct an extended input sequence, taking the extended input sequence as the input of an error compensation network, and outputting a mean value of compensation quantity; a model composed of the working condition classification network and the error compensation network is trained, and the trained model is used for electric energy error compensation; the method has the advantages of being rigorous in error calculation method, high in precision under complex working conditions, low in field operation risk and intelligent and self-adaptive.

The present disclosure provides a data query method. In the method, a first query result of a first database in response to a first query request is obtained. The first database stores data within a first time window. A second query result of a second database is obtained when the first query result does not meet a preset check condition. The second database stores data within a second time window. A start time of the second time window being after a start time of the first time window. A target query result based on at least one of the first query request and the second query result is generated by processing circuity. An update frequency of the second database is higher than an update frequency of the first database.

The invention provides a communication method and a communication device, which are applied to the technical field of communication. For example, the communication method is adapted to a communication scene in which an LTM mechanism is triggered, and the method comprises the following steps: network equipment uniformly calculates the window length of a time window based on sending time information of reference signals of a plurality of candidate cells; the parameter of the window length of the time window is issued to the UE through the first message, and the measurement starting moment information is issued to the UE through the second message, so that the UE measures a plurality of candidate cells in the first time window (determined based on the parameter of the window length of the time window and the measurement starting moment information), redundant measurement and power consumption of the UE can be remarkably reduced, and the user experience is improved. And the measurement efficiency can be improved. According to the embodiment of the invention, the first message and the second message can be compatible with a signaling structure in the existing 3GPP standard, for example, on the premise of keeping the original LTM signaling architecture unchanged, a measurement time sequence control mechanism of the embodiment of the invention is introduced, so that the first message and the second message are compatible with the existing LTM process.