540 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 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 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 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.

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.

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 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.

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.

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 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.

The invention discloses a CC attack gateway layer detection method and system based on an intelligent sliding time window, and belongs to the technical field of network security. The method comprises the following steps: analyzing the HTTP traffic of a gateway layer in real time, and extracting multi-dimensional features of the traffic and user behaviors; performing real-time aggregation and multi-dimensional analysis on the features by using a sliding time window mechanism; based on a risk score accumulation mechanism, matching the aggregation result with a pre-configured rule base to obtain a preliminary detection result and a risk score; when the initial detection is negative, further performing verification detection by using a pre-trained machine learning model; and finally, according to the risk score and a machine learning result, judging an attack and executing a progressive disposal measure. Through streaming processing, rule and model cooperation, intelligent degradation and other mechanisms, the problem that detection precision and processing performance cannot be achieved at the same time in a gateway scene in the prior art is effectively solved, and high-precision CC attack defense under the requirements of high concurrency and low delay is achieved.

The invention relates to the technical field of equipment fault root cause analysis and operation and maintenance management and control, and discloses an equipment operation and maintenance intelligent analysis and management and control method and system based on a fault root cause. Constructing a knowledge graph which contains causal association rules of all stages and is updated regularly; performing machine language processing, introducing a time dimension to form a time sequence fault mode knowledge graph, and designing an operation and maintenance large language model cue word containing a dynamic time window instruction; inputting processed map data, equipment full life cycle parameters and operation and maintenance demand natural language description, and outputting a fault root cause identification result considering full life cycle factors; and finally, automatically generating a preventive operation and maintenance scheme according to a result, updating atlas data, and dynamically adjusting the priority of the scheme in combination with a fault root cause weight and an operation and maintenance priority mapping model to form a closed-loop management and control mechanism so as to realize accurate analysis of equipment faults and beforehand efficient operation and maintenance.

The invention belongs to the technical field of data processing, and discloses a Mars probe temperature monitoring method and system based on time control factor optimization, and the method comprises the steps: obtaining simulated fire soil, and carrying out the screening processing and dense state preparation processing, and obtaining a plurality of relatively dense state samples; performing simulated temperature monitoring on the plurality of samples in the relatively dense state to obtain a plurality of temperature-time curves, and performing piecewise linear fitting to obtain thermal response characteristics of the simulated fire soil; performing optimal monitoring time period analysis and optimal monitoring duration analysis according to the thermal response characteristics to obtain an optimal passive monitoring time window and duration strategy; and determining target to-be-monitored fire soil, and performing real temperature monitoring on the target to-be-monitored fire soil according to the optimal passive monitoring time window and the duration strategy to obtain an optimal temperature monitoring result. According to the invention, the temperature of the fire soil to be measured can be accurately monitored, so that the assessment precision of the passing ability of the Mars rover is effectively improved, and the driving safety of the Mars rover is ensured.

A system for reducing input data size for use in an artificial intelligence (AI) engine for predicting a subsequent event. The system includes a computer configured to implement instructions to receive input data and time data indicative of previous events associated with users. The instructions configure the system to determine interface channels associated with modes of interface with the users, previous event characteristics, or both. The system implements instructions associating previous event data with time windows. The instructions configure the system to generate user window values for the combinations of users and time windows. The user window values indicate the interface channels and previous event characteristics of data within the respective time windows. Implementing the instructions configures the system to form a first portion of the raw input data having an association value below a threshold with respect to preceding the subsequent event and generate condensed input data.

The invention discloses a data processing method based on a multi-granularity sliding time window. The method comprises the following steps of S1, collecting multi-source time sequence data and performing data preprocessing; s2, constructing a short-layer granularity sliding window, a medium-layer granularity sliding window and a long-layer granularity sliding window, and setting an initial step length and an overlapping ratio; calculating a variable coefficient, optimizing each window parameter according to the variable coefficient, and ensuring synchronous calibration; s3, high-frequency mutation features are extracted through a short window; the middle window captures medium-term time sequence dependence; mining a long-term trend and associated characteristics through a long window; s4, constructing a cross-scale feature fusion model based on mutual information, distributing a dynamic weight, generating a fusion feature vector, performing dimensionality reduction, and outputting optimized fusion features; and S5, performing time sequence analysis or prediction based on the fusion features, and correcting each parameter of the sliding statistical window according to an analysis result. The invention further discloses a data processing system based on the multi-granularity sliding time window. According to the invention, collaborative extraction of high-frequency abrupt change and low-frequency trend can be realized.

The invention relates to a collision detection processing method and device, equipment and a medium. The method comprises the following steps: in response to a collision detection event triggered in a game scene, determining a first time window corresponding to the event based on a first thread; on the basis of a first timestamp corresponding to the first time window, acquiring spatial geometric information corresponding to all colliders contained in a detection range corresponding to the collision detection event; the second thread performs collision detection on each collision body according to the spatial geometric information, and returns a corresponding collision detection result to the first thread; and in response to a trigger event that the first time window reaches the second time stamp, the first thread determines whether collision occurs between the colliders based on the collision detection result. According to the method and the device, the collision detection calculation task is separated to the independent threads to be executed at the first timestamp, and the collision detection result is uniformly processed at the second timestamp, so that the accuracy and the stability of collision judgment logic are ensured while the game running efficiency is remarkably improved.

The invention discloses a server hardware fault detection method and system, relates to the technical field of computers, and aims to collect full-dimension key data of a server and comprehensively capture dominant faults such as temperature exceeding and hidden faults such as register fluctuation of hardware. According to the method, the defects that in the prior art, only dominant faults can be detected, and hidden faults are missed to be judged are overcome, fault detection integrity is improved, in addition, for the problems that in the prior art, no adaptive time window exists, and quantitative association is lacked, a basic time window and a dynamic adjustment mechanism are designed, and the basic window adapts to hardware response characteristics; a hardware aging / load change scene is dynamically adjusted and adapted, and misassociation of irrelevant events is avoided; and then a weighted causal correlation model of time delay distribution concentration ratio + event co-occurrence rate is constructed, and dual-threshold screening is combined, so that accidental fluctuation and fault abnormity are accurately distinguished, and the problem of misjudgment caused by rough statistics in the prior art is solved.

The invention discloses a massive heterogeneous task elastic scheduling method based on multi-granularity state awareness, which belongs to the field of cloud computing, and adopts the technical scheme that the method comprises the following steps: collecting operation data of a system in a continuous time window; processing the operation data to obtain a fusion state vector; splicing the fusion state vector and the feature vector of the current to-be-scheduled task to obtain a state required by a task scheduling decision; and generating a candidate target node set according to task feature information and system resource constraints to construct an action space, and transmitting the fusion state vector and related features of the current to-be-scheduled task as input to a strategy network based on reinforcement learning to obtain a scheduling result. The elastic scheduling method for the massive heterogeneous tasks has the beneficial effect that the elastic scheduling method for the massive heterogeneous tasks can realize multi-granularity state sensing in a long-term dynamic environment.

The invention provides a wind power equipment optimal decommissioning time window prediction method and device. According to the wind power equipment optimal decommissioning time window prediction method provided by the embodiment of the invention, the dynamic quantitative evaluation of the environmental influence in the full life cycle of the wind power equipment is realized, the optimal decommissioning time window is accurately identified through three-dimensional data fusion of technology, economy and environment and machine learning prediction, and the scientificity and environmental protection benefits of decommissioning decision are improved.

The invention is notably directed to a computer-implemented method of clustering anomalies detected in a computerized system. The proposed method makes use of an unsupervised cognitive model, executed based on input datasets to obtain clusters of anomalies. The method accesses input datasets, which correspond to detected anomalies of the computerized system. These anomalies span respective time windows. Each input dataset comprises a set of timeseries of key performance indicators. The key performance indicators of each input dataset extend over a respective time window. That is, each anomaly corresponds to a respective time window. This model includes a first stage, which includes an encoder designed to learn fixed-size representations of input datasets, and a second stage, which is a clustering stage. The model is executed based on the input datasets accessed, the first stage learning fixed-size representations of the input datasets and the second stage clustering the learned representations.

Disclosure discloses a method and a device in a node used for wireless communications. A node first receives first information, and then transmits a first signal and triggers a first timer, the first timer is expired and triggers a first procedure; the first information is used to determine a first time interval length; the first timer is started in a first time window, the first time window comprising a positive integer number of slots; a time interval between an end time of transmission of the first signal and a start of the first time window is equal to the first time interval length; the first procedure is related to the type of the first timer. By associating a start time of timing of the first timer with the first time interval length, the present disclosure optimizes the RRM and / or RLM timer design in NTN, thus improving the overall performance.

Various aspects of the present disclosure relate to PDCCH monitoring. In an aspect, a UE receives, from a network entity, an indication of a plurality of parameters for physical downlink control channel (PDCCH) monitoring. The plurality of parameters are used for PDCCH monitoring within a time window, and the time window is associated with the indication of the plurality of parameters. The UE performs PDCCH monitoring in the time window based on the plurality of parameters.

Aspects of the present disclosure are directed to systems and methods of dynamically generating individualized or personalized times for providing messages over networked environments. The computing system may obtain, for a user device, an event dataset identifying a plurality of interaction times corresponding to a plurality of interactions over an instant time window by a user with an application on the user device to address a condition of the user. The computing system may apply the event dataset to a machine learning (ML) model. The computing system may generate based on applying the event dataset to the ML model, a defined time at which a message is to be provided to the user device during a subsequent time window. The computing system may provide for presentation on the user device to address the condition of the user, the message in accordance with the defined time.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may perform UE-initiated beam reporting based on satisfaction of a triggering condition, which may be a quantity of triggering event instances within a time window. A network entity may configure the triggering condition for the UE. For example, a triggering event may be that the quality of the beam used for communication between the UE and the network entity is below a threshold. As another example, a triggering event may be that the quality of at least one new beam is a threshold better than the beam that corresponds to the activated transmission configuration indicator (TCI) state with the highest or lowest quality from among the activated TCI states for the UE.

The invention relates to a method (100) for readjusting a laser system (1), comprising the following steps: specifying (101) a time window for performing a switching of a modulation frequency; interrupting (102) the measurement within a predefined time window; the readjustment is carried out (103) within a predefined time window, the digital-to-analog converter (5) being provided to provide a precise control of the analog signal. The invention further relates to a computer program, a device and a storage medium for this purpose.

The invention discloses a tunnel illumination control method and device, equipment, a storage medium and a program product, and relates to the technical field of tunnel illumination control, and the method comprises the steps: synchronously obtaining the operation state time sequence data and distribution position data of brightness sensors disposed on the whole line of a tunnel in a monitoring time window; wherein the operation state time sequence data comprises an original illumination sampling sequence and a real-time temperature sampling sequence of each brightness sensor; inputting the operation state time sequence data and the distribution position data into an illumination value calibration model to obtain a calibration illumination value of each brightness sensor; and performing tunnel lighting control based on the calibrated illuminance value. According to the invention, accurate control of tunnel illumination is realized.

A wireless communication method and a communications apparatus are provided. The method includes: determining a first time window, where the first time window is a transmission time window of a reference signal, the first time window is determined based on a discontinuous reception (DRX) on duration time, and a length of the first time window is a preset value.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an ambient internet of things (A-IoT) device may receive a plurality of A-IoT trigger messages in a time window associated with an inventory procedure. The A-IoT device may transmit a single A-IoT access message in the time window in response to the plurality of A-IoT trigger messages, where an access response is not received in response to the A-IoT access message in the time window. Numerous other aspects are described.

The invention relates to the technical field of resistor life detection, in particular to a real-time life detection method and system for a piezoresistor. The method comprises the following steps: dividing a current impact experiment of the piezoresistor into a plurality of time windows, obtaining a plurality of related parameters representing that the resistance performance tends to be degraded in each time window, and screening out direct detection parameters measured by an instrument in a resistance degradation process; according to the relevance between the direct detection parameter and the indirect detection parameter of the multiple related parameters, obtaining the relevance significance and reference factors of the two types of detection parameters; weighting processing is carried out on the direct detection parameters to obtain variation characteristic quantities of the piezoresistor tending to deteriorate in different time windows, a prediction model is constructed through the variation characteristic quantities constructed in a self-adaptive weighting mode, and residual life detection is carried out on the target piezoresistor which is in a running state at present by utilizing the prediction model, so that the residual life of the target piezoresistor is detected. According to the scheme, the accuracy of predicting the real-time residual life of the piezoresistor in the using process is improved.