1210 results about "Time windows" patented technology

Methods, systems, and media for timing presentation of content are provided. In some embodiments, the method comprises: causing a first video content item to be presented on a user device of a user; wherein the video content item is provided by a video sharing service; identifying a promotion for the video sharing service that is to be presented to the user during a time window; determining a probability that the user will select the promotion if the promotion is presented at a first time point within the time window in connection with the first video content item; determining whether to present the promotion at the first time point based on at least on the probability; in response to determining that the promotion is to be presented at the first time point, causing the promotion to be presented on the user device at the first time point in connection with the first video content item; and, in response to determining that the promotion is not to be presented at the first time point, causing the promotion to be presented on the user device at a second time point in connection with a second video content item, wherein the second time point is within the time window and wherein the second time point is after the first time point.

The invention discloses an intelligent early warning diagnosis method based on an intelligent power plant, and the method comprises the steps: detecting an abnormality through employing an unsupervised learning method, and correcting a detection result in combination with a heuristic rule; adjusting the length of a dynamic time window according to the running state of the equipment, ensuring the requirement of data trend analysis, carrying out smoothing processing on trend data captured in the adjusted time window, and extracting fault trend characteristics; and performing abnormal distribution analysis on the fault trend characteristics, performing layered early warning on the faults according to abnormal levels, constructing a step-by-step troubleshooting model based on a Bayesian network, and analyzing fault causes step by step. According to the intelligent early warning diagnosis method, the influence of equipment faults on a power plant system can be effectively reduced, and the overall safety and stability of a power plant are improved.

The invention relates to the technical field of predictive maintenance, in particular to a predictive maintenance method and system for relay protection equipment, and the method comprises the following steps: calculating the change trend of each operation parameter in each time window based on the operation parameters of the relay equipment, and obtaining the change rate of the operation state of the relay equipment; according to the method, the running trend under different time scales is refined, the running state change rate is accurately extracted to quantify the health state of the relay equipment, and the health score attenuation rate is calculated in combination with the equipment aging degree, the contact abrasion degree and the relay action times, so that the health degree decline rate is measured, and accurate judgment of equipment maintenance requirements can be realized; and the maintenance task priority is calculated in combination with the importance weight of the equipment, so that the maintenance resource configuration is more targeted, the load fluctuation condition of the equipment is quantified, and the maintenance period is dynamically adjusted according to the set threshold value to match the load change trend of the equipment, thereby avoiding the problem of excessive maintenance or insufficient maintenance.

The invention relates to the technical field of cooperative control, in particular to a multi-machine cooperative control method and system for a bridge cable hoisting device, and the method comprises the following steps: collecting tension change characteristics through a distributed sensor array, extracting indexes through a sliding window algorithm to generate a response time window, and generating a cooperative time window through time sequence overlapping and speed trend adjustment. Calculating a compensation value by combining inertial parameters and acceleration to generate a trigger window, detecting tension and displacement difference, inputting the tension and displacement difference into a dynamic threshold function for screening and fusion, outputting a synchronous trigger condition, and generating a synchronous control instruction by encoding a device number and a tension change rate after timestamp alignment. According to the method, multi-node tension synchronous acquisition and normalization processing, dynamic response generation through a sliding window, boundary matching action triggering adjustment through an overlapping algorithm, response lag correction through inertia compensation, tension and displacement difference real-time detection, dynamic threshold screening and triggering condition fusion and timestamp alignment packaging instruction consistency are carried out; and the cooperation precision and the system robustness are improved.

The invention provides a supercomputing resource intelligent allocation method and system based on AI load monitoring, and the method comprises the steps: firstly obtaining a real-time load data set of a plurality of computing nodes in a supercomputing system, including a resource occupancy rate index and a task queue state parameter, then carrying out the load feature extraction processing of the real-time load data set, generating resource occupancy fluctuation characteristics and task queue evolution characteristics, performing resource demand analysis on the load characteristic set by using a pre-constructed resource demand prediction model, predicting resource demands of each computing node in a subsequent time window, determining resource allocation priorities and migration strategy parameters based on prediction results, and performing resource allocation according to the resource allocation priorities and the migration strategy parameters. The resource dynamic allocation instruction is generated and sent to the target computing node, the resource reallocation operation is triggered, intelligent and efficient allocation of super computing system resources is achieved, the resource utilization rate and the task execution efficiency are improved, and the overall energy efficiency ratio and stability of the system are optimized.

The invention discloses a task scheduling method and device, electronic equipment, a storage medium and a product, and relates to the technical field of distributed task scheduling, the task scheduling method and device are applied to a distributed system, the distributed system comprises all distributed equipment, and the task scheduling method comprises the steps that the task dependency relationship of all distributed tasks on the distributed system is acquired and analyzed, and an initial task execution sequence is obtained; the distributed tasks in the initial task execution sequence are subjected to priority distribution, time windows of the distributed tasks are adjusted based on the distributed priorities, a target task execution sequence is obtained, and the time windows are used for limiting the execution time of the distributed tasks; the resource state of each distributed device is monitored, at least one target device meeting the resource requirement of each distributed task is searched, each distributed task is distributed to the at least one target device, and the distributed tasks are executed according to the execution sequence of the target tasks. The technical problem that the task scheduling effect is poor is solved.

A kernel monitor can be used to mitigate ransomware activity of a host system. In some aspects, a computing system can use the kernel monitor to monitor a set of system calls generated by the host system within a time window to perform a functionality. The kernel monitor can include a respective kernel program monitoring each system call in the set of system calls. The set of system calls can be filtered by the kernel monitor to identify a subset of system calls associated with encrypting a filesystem of the host system. The computing system can determine that the subset of system calls is indicative of ransomware activity associated with the host system based on the subset of system calls exceeding a predefined threshold. Subsequently, the computing system can perform a mitigation operation to mitigate the ransomware activity.

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 relates to the technical field of blockchain network security monitoring, and particularly discloses a blockchain-based network security monitoring method and system, the system collects transaction data, block data and intelligent contract execution results from a plurality of blockchain nodes, the analysis technology calculates the abnormal degree of each time window, the change rate of data inconsistency is quantified, and the data security monitoring accuracy is improved. If a relatively high data inconsistent change rate is detected, further comparing consensus results of the nodes, calculating consensus verification differences of the nodes, identifying the nodes deviating from normal consensus behaviors, constructing a machine learning model of a support vector machine, evaluating the safety states of the nodes, and according to a result output by the model, determining the inconsistent change rate of the nodes. The safety states of the nodes are divided into normal, slight abnormality and serious abnormality, for the nodes marked as slight abnormality, the system analyzes the abnormality trend and deviation of the nodes through a time sequence, automatically marks high-priority concerned nodes and generates a review task list to remind an operator to intervene.

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.

A network management system may collect processor usage statistics from one or more network devices. The network management system may determine, for each network device of the one or more network devices, aggregate processor usage statistics across a time window based on the processor usage statistics and, based on an aggregate overall processor usage for a given network device exceeding a baseline threshold, analyze aggregate per-process processor usage for the given network device to determine one or more processes as a root cause of anomalous behavior of the given network device. The network management system may generate a remedial action to remediate the root cause.

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 intelligent substation secondary equipment maintenance control method and system, and relates to the technical field of secondary equipment maintenance control, and the method comprises the following steps: obtaining first data of a substation to construct a static matrix, and generating a dynamic topological graph; determining the security level of the controlled equipment according to the dynamic topological graph, and establishing a priority table of mapping of the equipment type and the security level; detecting concurrent instruction conflicts through a priority table, identifying an N-order virtual loop based on a dynamic topological graph, generating a minimum isolation set, and outputting a virtual terminal isolation instruction sequence with timing constraint according to a signal level; and when the state of the maintenance pressing plate or the GOOSE link is changed, the affected equipment is positioned based on the static matrix, and incremental updating of the dynamic topological graph is controlled according to the instruction sequence. According to the method, the path optimization strategy based on the cross-interval coefficient is matched with the time window constraint, so that the maintenance efficiency is guaranteed, potential safety hazards caused by disordered operation time sequences are avoided, and the maintenance process is standardized and intelligent.

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 provides a high-speed rail tunnel safety monitoring method and system. The method comprises the following steps: carrying out normalization processing on original time series data to construct a multi-dimensional time series data set; the multi-dimensional time sequence data set is input into a pre-trained LSTM model, the structural state of the tunnel key part in a future time window is predicted, and the structural state comprises the surrounding rock deformation trend, the lining stress peak value, the water seepage risk grade and the crack severity grade; and according to the prediction result of the structure state and a preset mapping relationship between the structure state and the early warning level, triggering hierarchical early warning, and generating a corresponding tunnel maintenance strategy instruction. According to the technical scheme provided by the invention, intelligent sensing, accurate prediction and active maintenance decision making of the tunnel structure state can be realized, and the timeliness and preventability of high-speed rail tunnel safety management are improved.

Embodiments of the present disclosure relate to methods, devices and computer readable media for communication. A terminal device determines a time window for monitoring a WUS from a network device. In response to receiving the WUS from the network device in the time window, the terminal device starts an on-duration operation of DRX based on a first time offset from an end of the reception of the WUS. In this way, a starting time of a DRX cycle may be dynamically determined, and a good tradeoff between latency and power consumption may be achieved. Accordingly, latency may be reduced and power consumption may also be reduced.

The invention discloses a process monitoring method, system and device for a baseboard management controller and a medium, and relates to the technical field of server management, and the method comprises the steps: continuously collecting CPU occupation information of a target process from an information collection file through a kernel interface based on a preset sampling time interval, and calculating the CPU utilization rate of the target process, and calculating a target parameter based on the CPU utilization rate in the preset sliding time window, analyzing the target parameter and the CPU utilization rate based on a preset threshold value, and determining the CPU state of the target process. According to the method, the CPU usage rate of each process can be continuously calculated by periodically sampling the CPU occupation information and the system CPU time of the target process, the CPU usage condition of the target process is analyzed based on the sliding time window, sampling and analysis can be automatically carried out, whether abnormity exists or not is monitored in real time, and the monitoring efficiency of the target process is improved.

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 an operation method and system of a digital twin factory. According to the system, the equipment state monitoring sub-module monitors and displays the equipment parameters in real time, the equipment state monitoring sub-module provides a visual equipment operation state for an operator, potential faults can be found in time, and therefore production interruption caused by the equipment faults is reduced. And secondly, the early warning function of the module can give an alarm before a fault occurs, so that a time window is provided for preventive maintenance, and the influence of fault expansion is avoided. In addition, the real-time monitoring function of the equipment state monitoring sub-module is beneficial for improving the production efficiency because the equipment state monitoring sub-module can ensure that the equipment runs in the optimal state, and the non-planned downtime is reduced. And meanwhile, the data recording and analyzing functions of the module are beneficial to improving the reliability of the equipment, and potential problems of the equipment can be identified by analyzing historical data, so that measures are taken for preventive maintenance, the service life of the equipment is prolonged, and stable operation of a factory is guaranteed.

The invention relates to the technical field of computers, in particular to a request processing method and device, electronic equipment and a storage medium, which are used for preventing illusion of a large model due to high concurrency and improving the availability of the large model. The method comprises the following steps: acquiring a to-be-processed request, initially acquiring a token corresponding to the to-be-processed request from a token bucket, and adding 1 to the total concurrent number of a current sliding time window; if the token is failed to be acquired for the first time, determining the number of retry times and the retry interval duration for re-acquiring the token based on the total concurrent number and the throughput, the average time delay and the maximum acceptable time delay of the large model; and on the basis of the number of times of retry and the retry interval duration, re-acquiring the token from the token bucket, and when the token is successfully re-acquired, reasoning the to-be-processed request on the basis of the large model, and generating and returning an answer. Thus, according to the real-time total concurrent amount, the number of retry times and the retry interval duration are dynamically adjusted, the requests are dispersed to the large model, and the response success rate and availability are improved.

The invention discloses a network equipment fault analysis method and device based on multi-dimensional characteristics, and relates to the technical field of network equipment fault analysis, and the method comprises the steps: obtaining the time sequence characteristic data of each network equipment in an Internet of Things cluster during the operation of a current time period, and forming a multi-dimensional network fault identification data set according to the time window dimension, the network equipment dimension and the feature dimension, carrying out encoding processing on the network fault identification data set by adopting a pre-trained encoder to obtain an encoding vector, inputting the encoding vector into a pre-constructed fault prediction decoder, and carrying out fault prediction on the network fault identification data set according to the pre-constructed fault prediction decoder. And outputting the fault probability of each network device in the preset time period. The network equipment fault diagnosis method and device solve the technical problems that when network equipment fault diagnosis is carried out in the related technology, due to the fact that network faults have space-time variability and diversity, single-dimension analysis cannot accurately capture the full view of the faults, and fault diagnosis is delayed and misdiagnosed.

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 provides a cumulative time error prediction method based on a time window LSTM model. The method comprises the steps of obtaining historical data of a sensor; performing time feature processing on the historical data, and selecting the historical data in a time window with a certain length as a time sequence; an LSTM model is used for modeling and training the time sequence, the input of the LSTM model is the sensor time error change condition in the time window, and the output of the LSTM model is the difference value between the sensor time at the current moment and the standard time; and correcting the sensor time according to a prediction result of the LSTM model. The actual time is independent from the time on the sensor, so that the time synchronization process is simplified; through selection of a time window, consumption of computing resources is remarkably reduced on the premise that prediction precision is guaranteed; through a correction mechanism, high-precision synchronization is achieved, computing resources are optimized, and it is ensured that sensor time and standard time are kept consistent.

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 embodiment of the invention provides a thread pool dynamic adjustment method and device based on a configuration center. The method comprises the steps that thread pool parameters are initialized, historical parameter versions are sorted through effective time, the credibility of each parameter source is calculated based on the change effect of different parameter sources, and three weighted change proportions are summed to obtain a risk value; analyzing the corresponding relationship between the effective time of each parameter in the historical version library and the system load to establish an optimal time window of parameter change, when the risk value is lower than a threshold value and is in the optimal time window, determining an adjustment step length according to the credibility of a parameter source, continuously collecting performance indexes and associating the performance indexes to corresponding parameter versions, when the performance index is degraded, recording and positioning the last stable parameter value and the last stable effective time through the version library, and executing rollback according to the last stable parameter value and the last stable effective time; according to the method, the efficiency and accuracy of dynamic adjustment of the thread pool can be effectively improved.

The invention discloses a computing task scheduling method and system based on time sequence diagram network resource state prediction, and relates to the technical field of computing resource allocation, and the method comprises the steps: collecting computing power resource state data based on computing power nodes, and modeling the computing power resource state data into time sequence heterogeneous diagram data; constructing a time sequence heterogeneous graph attention network THGAT model, and performing offline training on the THGAT model by taking node feature and edge feature prediction loss as a loss function and taking minimization of a prediction error as a target; inputting the time sequence heterogeneous graph data into a THGAT model, and predicting the resource state of a future time window; the calculation task is converted into a calculation demand vector, and a multi-objective optimization resource allocation THGATRM algorithm is adopted to perform calculation power resource allocation on the calculation task according to the resource state of the corresponding time window; and executing the computing task according to the computing power resource allocation scheme. Through the technical scheme of the invention, the accuracy of computing power resource prediction is improved, and efficient allocation and optimization of resources are realized.

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.

Systems and methods of handling sessions between client devices and one or more server based on session classifications are provided. A device identifies a time series of security metrics corresponding to requests received during a session established by a client device to access a resource provided by one or more servers. The device generates security features from the time series of security metrics based on one or more time windows. The device classifies the session as one of anomalous or genuine using the security features generated from the time series of security metrics based on the one or more time windows. The device handles subsequent requests received during the session based on the classification of the session as the one of anomalous or genuine.

The invention discloses a VAGCN-based complex industrial process operation state evaluation method. The method comprises the following steps: collecting process data and carrying out standardization processing; performing data division on the data by using a sliding window to obtain a time sequence; the method comprises the following steps: converting time sequence data into cross-time-space variable interaction graph structure data through a variable interaction graph structure, and then capturing variable interaction characteristics and local space-time dependency in each time window by using a moving window; time pooling is used, advanced features of variable interaction are extracted and then input into the operation state evaluation model, posterior probabilities of the online data belonging to different operation state evaluation grades are obtained, and a final evaluation result is a state grade corresponding to the maximum posterior probability at the current moment; and performing similarity analysis on the variable interaction graph structure in the non-optimal state and the optimality graph structure in the optimal state so as to evaluate the variable dynamic offset degree in the non-optimal state. According to the method, the interaction among the variables at different time points can be captured, and more accurate process operation state evaluation can be realized.