519 results about "Time segment" patented technology

The invention discloses an airport flight quantity and throughput prediction method and system, and relates to the technical field of intelligent airport management. The method comprises the following steps: firstly, determining a future target time period to be predicted, and obtaining related multi-source data according to the future target time period; preprocessing the data and extracting time sequence and non-time sequence features; processing time sequence features by using the trained long-short-term memory neural network model, processing non-time sequence features by using the extreme gradient boosting tree model, and respectively obtaining prediction results; and integrating the outputs of the two types of models through a weighted fusion strategy to obtain an accurate prediction value of the flight sortie and the single-machine passenger capacity, and further calculating to obtain a passenger throughput prediction result. According to the method and the system provided by the invention, through the architecture of multi-source data fusion and hybrid intelligent model cooperation, the prediction precision and reliability are effectively improved, and scientific decision support is provided for airport operation management.

Certain aspects of the present disclosure provide flow estimation techniques for dynamic scenes. A method generally includes obtaining a first time series sequence of sample sets of a scene (including a first and a second set of samples) over a first period of time associated with a reference time; processing, with a spatial-temporal attention module, the first time series sequence, to generate a modified first time series sequence of sample sets of the scene comprising the first set and not the second set of samples; processing, with a first neural ordinary differential equation (ODE) and a first ODE solver, the modified first time series sequence to predict a second time series sequence of sample sets; and generating a first output indicating a predicted flow of the scene from the reference time to a first selected time based on the modified first time series sequence and the second time series sequence.

This application relates to the field of communication technologies, and discloses a communication method, apparatus, and system. The method includes: A terminal determines predicted values of first-type measurement quantities of a plurality of cells at at least one time point in a first time period, where the plurality of cells include a serving cell and a candidate cell; and if the predicted values of the first-type measurement quantities of the plurality of cells at the at least one time point satisfy a reporting condition, sends measurement report information to a network device.

The invention relates to the technical field of computer processing, in particular to a Kafka cluster configuration parameter adjusting method and device, equipment and a medium, and is used for relieving problems caused by a fixed change means of code modification. The method comprises the following steps: according to related historical data of a Kafka cluster, predicting a first predicted load index in a future time period through a first time sequence model, and predicting a second predicted load index in the future time period through a second time sequence model; weighting the first predicted load indicator; weighting the second predicted load index; determining a predicted load index according to the weighted first predicted load index and the weighted second predicted load index; determining a plurality of first candidate adjustment actions according to the predicted load index; selecting a first candidate adjustment action from the plurality of first candidate adjustment actions as a first adjustment action; and adjusting a partition allocation index and a copy allocation index of the Kafka cluster configuration parameters by using the first adjustment action.

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 computer implemented methods, systems, and program instructions detect anomalies in a core network of a telecommunications network. The method comprises: receiving data representative of streams of time series data of a plurality of Key Performance Indicators (KPIs) of the performance of nodes of the core network; comparing the received time series data for each of the KPIs to predicted time series values for each KPI generated by one or more time series analysis algorithms trained with historical data for each KPI to predict the KPI over time; determining any KPIs having deviations between the received time series data and the predicted time series data during a specific time period, wherein each deviation is an anomaly; grouping the streams of time series data for each KPI determined to be deviated to generate anomaly data; using an artificially intelligent clustering algorithm to generate a plurality of clusters, wherein each cluster comprises a subset of the KPIs determined to be deviated that have been assigned to said cluster by the artificially intelligent clustering algorithm; wherein each of the clusters is identified as having an associated root cause.

A sound diagnostic system includes an image forming apparatus and an information processing apparatus. The image forming apparatus includes operating portions, a sound collector for collecting a sonic wave, an acquiring portion, in each of a plurality of time sections when the sonic wave is collected by the sound collector, for acquiring a data including sonic wave level and an operating state of the operating portions. The information processing apparatus includes a generator for generating a threshold by adding a predetermined value to a first sonic wave level based on the sonic wave in a first period. The information processing apparatus determines a cause of an abnormal sound by comparing a second sonic wave level based on the sonic wave in a second period after the first period and the threshold. The predetermined value is set so as to be smaller as the first sonic wave level is larger.

The invention discloses a virtual digital human multimedia teaching interaction method and system and a storage medium, and the method comprises the steps: calculating an audio mouth shape time difference, a mouth shape subtitle time difference, an audio subtitle time difference and a video mouth shape time difference based on a same-window data group on the basis of a unified time aperture and a window, comparing the time differences with a time difference threshold value, and outputting a dislocation alarm result, unified constraint on four types of key alignment relationships is realized; when an alarm is triggered, selecting a degradation strategy according to a dislocation alarm result and generating an execution record, and summarizing the dislocation alarm result and the execution record to form an evidence chain data packet; when the cumulative number of times in the preset window number exceeds a diffusion blocking threshold value, recording output is switched into a video stream which retains audio and subtitles and does not overlap mouth shapes; and generating a correction release stream based on the evidence chain data packet, and replacing the recorded broadcast corresponding time slice and retaining the Hash verification value when the time sequence consistency index meets the release threshold, thereby realizing drivable treatment action, suppressible diffusion and replaceable correction.

A computerized process, a system, and non-transitory computer-readable medium having computer-executable instructions for mixing audio tracks according to a template. The process may include receiving at least one request for each of a plurality of time blocks of a template; querying a catalog of songs and / or song portions in a database to compile a candidate list of songs and / or song portions that substantially meet the at least one request of a first time block of the plurality of time blocks; choosing a first song portion and a second song portion from the candidate list for the first time block; compiling the first song portion and the second song portion to form at least a portion of the first time block, including blending a temporal length of the first song portion and the second song portion; and generating an audio file with the plurality of time blocks.

Systems, methods, and instrumentalities are disclosed herein associated with timing alignment in duplex. A wireless transmit / receive unit (WTRU) may be configured to determine a guard time period. The WTRU may receive information related to a grant allocation. The grant allocation may begin at a time associated with a number of starting symbol(s) within a subband. The WTRU may determine that the time associated with the number of starting symbol(s) is within the guard time period. The WTRU may calculate the difference between the time associated with the number of starting symbols and the start time of the guard time period. The WTRU may determine to puncture a part of the transmission that uses the grant allocation based on the size of the difference between the time associated with number of starting symbol(s) and the start time of the guard time period.

The embodiment of the invention discloses a production scheduling method and system applied to multi-objective optimization in waterproof roll manufacturing, and the method comprises the steps: obtaining the state constraint information of a waterproof roll production line in a preset time period according to a preset multi-objective optimization label, and enabling the information to reflect the limiting conditions which need to be satisfied by the production of the production line; based on the determined target function type and constraint condition set of the multi-target production scheduling model, performing association mapping to obtain the multi-target production scheduling model; solving the model by adopting an improved particle swarm algorithm with an adjusted particle coding mode and a fitness function, and adjusting a particle search behavior to obtain a multi-target optimization optimal solution; and generating a multi-target production scheduling strategy for the production line according to the weight distribution relationship of each target in the optimal solution and the priority ranking result, and guiding the actual production arrangement.

The invention discloses a performance management and control method and system for a distributed system. The method comprises the following steps: receiving a plurality of link data packets of the distributed system within a preset time period; constructing a service dependency graph of the distributed system in a preset time period according to the plurality of link data packets; reasoning the service dependency graph by using a graph convolutional network model to obtain an abnormal probability of each graph node, and determining at least one target root cause path of the distributed system having an abnormality within a preset time period according to the abnormal probability of each graph node; and determining a target disposal strategy matched with each target root cause path from a preset disposal strategy library, and feeding back the target disposal strategy to the corresponding service node. The technical problems of low performance management and control efficiency and poor effect caused by incapability of efficiently processing large-scale data in a distributed system and incapability of accurately positioning a root path in a complex service scene in related technologies are solved.

The invention discloses an intelligent attendance checking method and system. The method comprises the following steps: collecting multi-dimensional non-biological characteristic data of each employee in a preset attendance checking working time period; wherein the non-biological characteristic data comprises at least two of the accumulated connection duration of the terminal equipment accessing the specified wireless network, the accumulated effective operation duration of the specified computer equipment in a local login state and the accumulated effective active duration of the specified software application; calculating the ratio of each piece of non-biological characteristic data of each employee to the duration corresponding to the preset attendance work time period, and calculating the attendance credibility score of the employee by adopting a weighted summation operation method; and judging whether the attendance credibility score is not lower than a preset threshold, and generating an attendance record according to a judgment result. The non-biological characteristic data generated by the behavior of the employee in the preset attendance working time period is used as the attendance basis, the attendance information is automatically acquired, attendance automation is realized, the employee does not need to perform active operation, the phenomenon of missing card punching is avoided, and the safety is high.

The invention provides a government affair hotline resource optimization method and device based on time sequence prediction and reinforcement learning. The method comprises the steps of obtaining historical service work order data of a preset historical time period from a government affair hotline service platform; according to the historical service work order, utilizing a time sequence prediction model to predict future service work order data in a preset future time period; and determining a resource scheduling strategy in the preset future time period by using a reinforcement learning model. According to the technical scheme provided by the invention, in order to improve the working efficiency and reduce the user waiting time and unnecessary troubles in the process that government affair hotlines serve people, the historical work order data is deeply analyzed by using the time sequence prediction model, and possible problem hotspots and service demand peak periods in the future can be more accurately predicted. The distribution strategy of manpower and technical resources is dynamically adjusted in combination with a reinforcement learning algorithm, and it is ensured that the optimal government affair hotline service response speed and service level can be kept in different time periods.

The invention discloses a multi-experiment task production scheduling method, device and equipment and a storage medium, and the method comprises the steps: carrying out the sampling of a preset number of times for each production scheduling scheme based on the prior distribution of the duration of each experiment stage, and determining the starting time of each experiment operation, constructing a global time resource scheduling problem under the constraint conditions that the time window constraint of the experimental operation, the time period of the experimental operation belongs to the idle time period of the operation equipment and the time periods of the experimental operation distributed on the same operation equipment are not overlapped, and calculating the ratio of the infeasible times of the global time resource scheduling problem to the preset times; and if the risk value is lower than the risk threshold value, taking the production scheduling scheme as a target production scheduling scheme, and performing online scheduling of the experimental tasks according to the target production scheduling scheme, so that the method is suitable for production scheduling of high-throughput and diversified experimental tasks, and the risk of equipment resource conflict is reduced.

The invention provides a time series data prediction method and device, equipment, a storage medium and a program product, and can be applied to the technical field of computers. The method comprises the following steps: acquiring first historical time sequence data; the first historical time sequence data is time sequence data generated in a first historical time period; performing trend period decomposition on the first historical time sequence data to obtain trend characteristics and period characteristics; the trend feature is used for representing the overall trend condition of the first historical time sequence data in a first historical time period, and the periodic feature is used for representing the periodic fluctuation condition of the first historical time sequence data; determining a time sequence type corresponding to the first historical time sequence data based on the trend feature and the periodic feature; the time sequence type is one of a linear type, a periodic type, a stepped type or an irregular type; and determining predicted time sequence data in the to-be-predicted time period based on a time sequence prediction strategy corresponding to the time sequence type. The prediction accuracy is improved.

Correcting time series predictions for seasonality using trends may be performed. An initial time series prediction for a period of time using a machine learning model trained on a time series may be made. Data of the time series from more than an analysis time period prior to the period of time may be obtained. The time series prediction and the obtained data of the time series may be decomposed in respective components including a trend component. The respective trend component of the obtained data may evaluated to determine whether a trend replacement criteria is satisfied. The respective trend component of the time series prediction may be replaced with a portion corresponding to the period of time in a prior analysis time period from the respective component of the obtained data of the time series to correct the time series prediction for seasonality.

The invention discloses a high-speed bus-oriented data acquisition synchronous control method, which relates to the technical field of data acquisition, and comprises the following steps of: performing interval overlapping calculation on a time period inserted by an asynchronous trigger signal and a theoretical boundary of a synchronous sampling window, and constructing a boundary disturbance alignment inference function, judging whether the sampling starting time or the sampling ending time is shifted or not, and outputting a value quantification result of sampling boundary shifting; and constructing a section consistency evaluation model according to a value quantification result of the sampling boundary offset, performing combined operation on the offset amplitude, the offset direction and the sampling window superposition ratio, and generating a time section alignment consistency grade score for describing whether the current sampling meets the time synchronization requirement or not. The problem of sampling boundary offset identification and compensation caused by asynchronous trigger signal forced insertion is solved, sampling time section alignment consistency judgment and dynamic regulation and control are achieved, and data synchronization precision and acquisition stability are guaranteed.

The present invention provides a display method, an apparatus, and an electronic device, the method including: displaying a resource allocation area, the resource allocation area including at least one dividing line dividing the resource allocation area into sub-areas corresponding to a plurality of teams waiting for allocation, each sub-area including a time axis, a plurality of sub-time periods corresponding to the plurality of time axes being consecutive time periods; and adjusting the sub-areas corresponding to the plurality of teams waiting for allocation in the resource allocation area by adjusting the sub-time periods of the corresponding sub-area in response to a touch operation on the at least one dividing line, thereby reducing the complexity of resource allocation operations.

Systems and methods are disclosed configured to enable dynamic control of content streaming and / or to control content streaming so as to reduce peak system loads. A user electronic timetable is accessed from memory and is amazed to identify future unscheduled time periods. A first learning engine classifies timetable entries into subject types. A watchlist comprising a plurality of content items of respective time lengths is accessed. A second learning engine utilizes the classification and the time lengths to generate a scheduling recommendation of at least one item of content on the user watchlist. The scheduling recommendation is transmitted over a network to the user device. If the recommendation is accessed, a timetable entry is generated corresponding to the scheduling recommendation The at least one item of content on the user watchlist is streamed to the first device of the user in accordance with the accepted scheduling recommendation.

A method and a computing device for predicting actions of a robot, the method comprising: obtaining a first state of the robot and a first action sequence corresponding to a first time period; determining, based on the first state and the first action sequence, position information of a changed tile of a first target frame; sending the position information to a second node; instructing the robot to perform the first action sequence in the first time period, obtaining a second target frame captured by a camera of the robot at a target time within the first time period; sending the second target frame to the second node, the second node comprising a second model; receiving, from the second node, intermediate data generated by the second model based on the second target frame, the position information, and first key data and first value data corresponding to the first target frame; and predicting, by a first model, a second action sequence of the robot in a second time period based on the intermediate data and a second state of the robot.

A method includes receiving a package of data comprising electrocardiogram (ECG) data and metadata. The metadata includes determined RR intervals associated with the ECG data. The method further includes displaying, on a user interface, at least a subset of the determined RR intervals and overriding the determined RR intervals associated with a time period that is greater than a time period associated with a selected RR interval.

The application discloses an intelligent scheduling control method for an automobile part automatic assembly line, and relates to the technical field of automatic control, which comprises the following steps: obtaining the task instruction response time length value and the node execution action state change value of each execution unit in a current scheduling period, and calculating the response delay value and the state fluctuation value of the corresponding node; generating a response efficiency value according to the response delay value and the state fluctuation value of each node, and generating a feedback control level label in combination with the node task attribute information; judging whether to perform compression processing based on the feedback control level label and the available idle time value of each node in the current scheduling period, and if the compression condition is met, encoding the response delay value and the state fluctuation value into a compressed feedback data frame; and completing the relay transmission of the compressed feedback data frame in a time period that meets the scheduling idle condition, so that the scheduling delay pressure control, task state accurate maintenance and instruction issuing sequence dynamic optimization of the automobile part automatic assembly line are realized.

The invention provides a prediction model acquisition method and device, equipment, a medium and a program product, relates to the technical field of artificial intelligence, and can solve the problem of relatively poor accuracy of predicting the customer migration quantity. According to the specific technical scheme, a time sequence data set corresponding to the customer migration number in a first time period is obtained, and the time sequence data set comprises customer migration parameters corresponding to each first unit duration in the first time period; based on customer migration parameters in the time sequence data set, seasonal parameters and model parameters are determined; performing model initialization by using the seasonal parameters and the model parameters, and constructing an SARIMA model; the time sequence data set, the seasonal parameters and the model parameters are used for fitting the SARIMA model, a prediction model is obtained, and the prediction model is used for predicting the migration number of customers with seasonal changes. The method is used for predicting customer migration data volume more accurately.

The invention provides a task scheduling method, a scheduling system, a client and a computing device cluster, and the method comprises the steps that the scheduling system obtains information of a first computing task, the information of the first computing task comprises first execution time of the first computing task, and the first execution time is time under a first time system; and the scheduling system determines a first time period corresponding to the first calculation task from the plurality of time periods according to the first execution time, and schedules the first calculation task when the current time is in the first time period. Wherein the multiple time periods are different time periods under the second time system, the second time system and the first time system are different timing systems, the first execution time corresponds to second execution time under the second time system, and the second execution time is located in the first time period. According to the scheme, the management complexity of the calculation task can be reduced.

The invention discloses a productivity calculation method and system oriented to process multi-resource priority allocation. The method comprises the following steps: acquiring process route information of a target product, extracting selectable resources corresponding to each process, priorities of the resources and unit capacity occupation parameters, and acquiring capacity calendar information of the resources under a preset time granularity; calculating an allocable capacity upper limit of each resource in each time period based on the capacity calendar and the unit capacity occupation parameter; allocating the requirements of each process according to the resource priorities, preferentially allocating to high-priority resources, and sequentially allocating to secondary-priority resources when the remaining requirements are not met; identifying productivity bottleneck resources based on the preliminary allocation result, and performing reallocation on the bottleneck related allocation until a preset stop condition is reached on the premise of meeting the priority and allocatable upper limit constraints; and outputting a final distribution result. According to the method, a convergent allocation result can be obtained in each resource allocatable upper limit constraint, so that the production satisfaction rate is integrally improved.

The invention relates to an appointment registration platform number placement reminding method based on a self-research algorithm and a storage medium. The method comprises the following steps: crawling number placement records of doctors in a preset time period; recording a number allocation time point, predicting that the number allocation probability at the time point is increased by a preset proportion every time the number allocation is correct at the same time in a preset period, predicting that the number allocation probability at the time point is reduced by the preset proportion every time the number allocation is wrong, and establishing a personalized number allocation model; predicting a future number allocation time point of a doctor based on a personalized number allocation model in combination with a hospital scheduling plan and a doctor vacation calendar, and generating doctor number allocation prediction time according to scheduling of predicted number allocation accuracy; and pushing the doctor number allocation prediction time to the user based on the grading reminding strategy. The number allocation time of the doctor is predicted through historical rule analysis in combination with real-time data fusion, number allocation time prediction errors can be reduced, and the situation that the user registers a window mistakenly is reduced.

The application discloses a content delivery method and device, a server and a storage medium. The method comprises the following steps: determining a target user amount of target content in a next target time period; determining target users associated with the target content in the next target time period according to the target user amount and a target user determination strategy; screening the target users associated with the target content in the next target time period to obtain to-be-delivered users, and sending the target content to the to-be-delivered users; at the end of the next target time period, updating a current real exposure amount of the target content at the end of a previous target time period by using a real exposure amount of the target content in the to-be-delivered users, returning to the first step until the real exposure amount at the end of the next target time period accumulates to reach a target exposure amount or until a corresponding accumulated time length at the end of the next target time period reaches a target time length. The method can reduce the over-exposure amount in the content delivery process.

The invention discloses a monitoring video delay sweep generation method based on scene change self-adaptive frame extraction, and belongs to the technical field of video monitoring and streaming media processing. The method comprises the following steps: acquiring a sweep strategy containing a time period, a reference interval, a difference threshold value, a forced retention period, a window length and a quality threshold value; receiving a real-time code stream or reading a storage side key frame intermediate file; calculating an inter-frame difference degree by using a brightness channel of a multi-scale structure similarity algorithm, determining a frame extraction interval in combination with a static duration, and marking candidate frames; performing quality detection according to definition, brightness, contrast, stripes and noise, and rejecting unqualified products; when a forced period is reached, a bottom frame is reserved; and performing segmented coding according to a time window, and performing assembly output at the end of a time period. According to the method, online and offline processing links are unified, key information is reserved, and redundancy and time delay are reduced.

The invention is suitable for the technical field of engineering vehicles, and provides a vehicle working condition identification method and device and a loader, and the method comprises the steps: obtaining the operation data of a target loader within a preset time period; segmenting the preset time period according to a preset duration value to obtain a plurality of sub-time periods; according to the operation data of the target loader in each sub-time period, the energy consumption proportion of a walking motor in the target loader in each sub-time period is determined, and according to the energy consumption proportion of the walking motor in each sub-time period, walking motor energy consumption proportion distribution is obtained; and based on the walking motor energy consumption proportion distribution, the working condition of the target loader in the preset time period is recognized. The working condition of the loader can be accurately identified.