39 results about "Time average" patented technology

A method, system, and article of manufacture for modifying a queue assignment for a contact center are provided. At least one computing device determines, by solving for constraints, at least one staffing change for at least one of the first queue or the second queue, using as inputs (a) first time-based service metrics, (b) second time-based service metrics, (c) data describing eligible staffing changes, and (d) at least one predetermined constraint. The at least one staffing change is a modification to the queue assignment of a subset of the computing device instances that is predicted to improve net staffing relating to service targets selected from (a) an average speed of answer (ASA) specifying an average time for one of the computing device instances to initiate handling of a particular task after the particular task has been assigned to the relevant queue and (b) a service level specifying a percentage of tasks for which handling is initiated within a specified time frame after the tasks are assigned to the queue. The at least one staffing change can be implemented by modifying the queue assignment of the subset of the computing device instances.

Disclosed are methods, systems, and computer programs for dynamically generating a development plan for a resource site. The methods for example, include receiving seismic data associated with a subsurface of the resource site. The seismic data may be associated with a propagated wavefield within the subsurface of the resource site and can include at least structural geological data associated with the resource site. The methods also include directionally determining a plurality of rate of change data based on the propagated wavefield within the subsurface. The methods further include executing an averaging operation using the plurality of rate of change data and thereby generate an impedance model. The impedance model may be used to generate a development plan which is then used for energy development operations at the resource site.

The invention discloses an information interaction optimization method and electronic equipment, and relates to the technical field of information interaction.The information interaction optimization method comprises the steps that when the first interaction duration of a substrate management controller is larger than or equal to a first interaction duration threshold value, the number of first interaction retry times is counted; when the number of times of first interaction retry is larger than or equal to a first retry number threshold value and the first interaction retry time average length corresponding to each retry is larger than or equal to a first interaction time length threshold value, the first interaction time length threshold value and the first retry number threshold value are adjusted, and a second interaction time length threshold value and a second interaction retry number threshold value are obtained; when the current interaction duration is greater than or equal to a second interaction duration threshold value, if the current interaction retry number is greater than or equal to a second interaction retry number threshold value, determining that interaction is abnormal; and triggering the substrate management controller and the target external equipment to execute an exception recovery mechanism. By shortening the waiting time and reducing the number of retry times, waste of resources is avoided, and the interaction efficiency is improved.

A reinforcement learning system is proposed in which a policy model neural network is trained to control an agent to perform a task in successive time steps, by training a control system including the policy model neural network to select a respective action for each time step which gives a high value for a reward function based on the action, and which indicates the contribution of the action to solving the task. The reward function includes a term based on a progress value output by a progress model. The progress model generates the progress value upon receiving a first observation of the state of the environment at a time step before the performance of the action, and a second observation of the state of the environment at a time step following the performance of the action. The progress value is an estimate of the average time which an ensemble of experts who produced the demonstrations would have taken to transform the environment from how it appears in the first observation to how it appears in the second observation.

The invention discloses a motor imagery electroencephalogram signal decoding method and system, and the method comprises the steps: obtaining the time sequence data of each channel of a motor imagery electroencephalogram signal, carrying out the averaging of the time sequence data along the time dimension, obtaining the features after the time average pooling, carrying out the standardization of the features in the channels, and obtaining the time sequence data of each channel of the motor imagery electroencephalogram signal. Gaussian weighting coefficients are generated through Gaussian weighting harmonic, the coefficients are broadcasted along the time dimension, weights matched with the time dimension are obtained, the weights and the motor imagery electroencephalogram signals are multiplied element by element, and the dynamically harmonic motor imagery electroencephalogram signals are obtained. Extracting multi-channel multi-scale spatial-temporal features of the signal, windowing a feature sequence, constructing a feature vector with a fixed length, and performing classification through a classifier to obtain a probability corresponding to each predefined motor imagery category; according to the method, the decoding performance can be effectively improved through cooperative work of adaptive coordination, feature heterogeneous fusion and channel attention optimization of the motor imagery electroencephalogram signals.

The invention discloses an assembly line aided design method and system based on a genetic algorithm, a medium and equipment. The method comprises the following steps: obtaining each process of an assembly line of batch products and all selectable candidate site areas; calculating a time result required for completing the batch of products in each process of the assembly line; and on the basis of a genetic algorithm and the time result, performing calculation to obtain an assembly line design scheme corresponding to each candidate site in all the candidate site areas. Therefore, the site area is used as a representative resource, and a series of assembly line optimization design schemes under various resource conditions can be provided by accurately quantifying the average time of completing batch products by the assembly line.

The invention discloses a distributed acoustic sensing earthquake monitoring data stream processing method and system, and belongs to the technical field of earthquake monitoring and data processing. The method comprises the following steps: after preprocessing original seismic monitoring data, slicing the original seismic monitoring data by adopting a sliding processing time window to obtain data fragments suitable for real-time detection; designing a self-adaptive short-time average / long-time average detection algorithm, detecting data segments, and dynamically adjusting self-adaptive parameters in the algorithm according to data characteristics; and finally, multi-channel fusion detection is performed on the initial candidate list of the seismic events, so that a final detection list of the seismic events is generated. The method is based on a distributed streaming data processing architecture, combines the innovation advantages of a self-adaptive earthquake detection algorithm, can improve the earthquake detection precision, guarantees the real-time performance of a detection result, and can provide efficient and reliable technical support for a large-scale earthquake monitoring task.

The application discloses a kind of energy storage typical working condition generation method and device based on dynamic time average sequence algorithm, the method includes the time series of multiple according to periodic sampling is formed set, initialization optimal time sequence, and time sequence in the set is successively carried out dynamic time regulation, find optimal path and increase path to optimal time sequence, calculate after sequentially, and obtain optimal time sequence mean, the above process can be obtained final optimal time sequence by repeated iteration, positive and negative value adjustment is carried out to the optimal time sequence, satisfies the charge-discharge capacity equal constraint of energy storage working condition.The working condition curve generated by the application can better extract the typical characteristics of original data, and has broad application prospect.

The invention discloses a lightweight load prediction method and system for a distribution transformer, relates to the technical field of power systems, and solves the problem that efficient and accurate lightweight prediction cannot be carried out on the load of the distribution transformer, and the method comprises the steps: analyzing the historical load of the distribution transformer in different dates in the past; binding the historical average temperature of the power utilization area in different dates in the past with the corresponding historical load; analyzing the load condition of the distribution transformer according to the date type of the power utilization area in the current date and the real-time average temperature, and analyzing to obtain a reference load interval or a reference load of the distribution transformer in the current date; predicting the load of the distribution transformer at the next time node according to the reference load interval to obtain a predicted load interval of the distribution transformer at the next time node; and analyzing the load condition of the distribution transformer at the next time node according to the predicted load interval. According to the invention, efficient lightweight prediction of the load of the distribution transformer is realized.

Systems and methods for managing one or more projects. The system includes a processor coupled to a memory. The processor is configured to receive a request for completing one or more projects. The request includes one or more features assigned for each project. The processor is further configured to communicate to one or more developers that are selected by the processor, a project workflow to complete the one or more projects. The project workflow is generated based on an optimization, by the processor, of one or more parameters for timely completing the projects. The processor is further configured to determine an average time interval taken to complete each project. In addition, the processor is configured to update the project workflow based on the average time interval determined.

The application discloses a steady-state detection method based on two-fluid six-equation transient calculation, which comprises the following steps: before a detection time point, data required for local convergence judgment and TASS judgment are calculated and arranged; time local convergence of a system is judged according to enthalpy change of the system; after the convergence judgment is completed, TASS judgment is started on a historical state of the system, so that the system is ensured to completely enter a final stable state. The application improves the reliability under a low-energy and small-step working condition through a mixed relative convergence-time average judgment method, and meanwhile, the original steady-state detection efficiency is maintained.

The application provides a frequency standard measurement method, device, computer equipment and storage medium, the method comprises: frequency source output measurement signal is carried out frequency division processing, obtains low frequency pulse signal; using high-precision time interval counter to low frequency pulse signal is carried out delay measurement and time interval counting, and is output in the form of time stamp; by setting a sliding window, using sliding average algorithm to time stamp is carried out sliding average filtering processing, all time stamps are carried out sliding difference to obtain time difference, time difference is grouped according to order and is taken average in group to obtain average time difference; by increasing the length of the sliding window, the number of samples is increased to reduce the statistical error; using average time difference, calculate frequency measurement value, relative frequency deviation and allan variance. The method of the application increases the sliding window, and the frequency measurement result obtained by the algorithm of grouping and averaging the time difference has higher measurement precision and stability.

The application relates to a method and device for determining income, a computer device, a computer readable storage medium and a computer program product. The method comprises: obtaining response indexes of a plurality of building objects, the response indexes representing response capabilities of the building objects to grid instructions, and the response indexes comprising at least one of the following: a response timeliness index, a response effectiveness index, a historical response average time index, and a historical response average effectiveness index; determining comprehensive scores of the building objects according to the response indexes of the building objects; and determining target incomes of the building objects according to the comprehensive scores of the building objects. The method can more accurately distinguish the response performances of different building objects, and can more fairly, accurately and incentively allocate incomes to the building objects according to the response performances of the different building objects.

A computer-implemented method for generating an event-averaged time-resolved spectrum from a plurality of time-resolved spectra of charged particles emitted from a surface (3) of a sample (2) on which events periodically repeat, is disclosed, the method comprising the steps of: receiving (101) a plurality of time-resolved spectra covering a plurality of events from a charged particle analyzer (1); obtaining (102) a selected portion of at least one of the plurality of time-resolved spectra; determining (103) a time point of another event among the plurality of events by matching the at least one selected portion with other portions of the plurality of time-resolved spectra to find similar portions; and generating (104) an event-averaged time-resolved spectrum of the event based on the plurality of time-resolved charged particle energy spectra and the determined time point.

The invention discloses a modal decomposition and fractal dimension fused coal mine micro-seismic arrival time pickup method. The method comprises the following steps: 1, acquiring an original micro-seismic signal by adopting a micro-seismic monitoring system; 2, obtaining a denoised micro-seismic signal; thirdly, in the sliding process of the long-time window and the short-time window, the fractal dimension of each window is obtained through a box dimension method, and a short-time fractal dimension derivative, a short-long time fractal dimension difference and a short-long time fractal dimension derivative difference corresponding to each window moment are obtained; 4, in the sliding process of the long-time window and the short-time window, the energy ratio corresponding to each window moment is obtained based on the short-time average energy / long-time average energy of each window; 5, constructing a weighted scoring function; and 6, acquiring the microseismic arrival time. The method is reasonable in design, not only can self-adaptive decomposition and denoising be realized, but also joint analysis based on fractal dimension and STA / LTA energy ratio can be realized, so that more stable and accurate microseismic arrival time pickup can be realized.

The application relates to the technical field of personnel position determination, in particular to a key personnel position determination method, device and equipment, wherein the accuracy of historical personnel position determination, a historical average time attenuation factor and an average spatial correlation degree are used to dynamically and adaptively determine the weight corresponding to the perception data type. Based on the determined weight corresponding to the perception data type, the time attenuation factor can improve the time sensitivity of the personnel position determination, and the address confidence degree is fused to finally accurately determine the comprehensive score of the address, and the key personnel position is determined based on the comprehensive score of the address, so that the working efficiency can be improved, the personnel cost can be reduced, and the response speed can be improved.

The invention discloses a flood early warning information determination method and device of a transformer substation and electronic equipment. The method comprises the steps that rainfall time sequence data of an area where a target transformer substation is located are acquired, and the rainfall time sequence data are used for representing changes of the rainfall capacity and the ponding depth of the area along with time; based on the rainfall time sequence data, a return period is determined, and the return period is used for representing the average time interval of rainfall events with the same rainfall intensity as the rainfall intensity of the area in the historical time period; determining the flood level of the target substation based on the recurrence period; based on the rainfall time sequence data, the occurrence time of the maximum ponding depth of the target transformer substation is determined, and the maximum ponding depth refers to the maximum value in ponding depths corresponding to multiple positions included in the target transformer substation; and determining flood early warning information of the target substation based on the flood level and the occurrence time. According to the method and the device, the technical problem that the flood early warning information determination result of the transformer substation is inaccurate in the prior art is solved.

A method and data processing system for identifying data anomalies in data obtained by a condition monitoring system from a fleet of industrial assets is provided. A time series of data values is obtained for each asset in the fleet and an average time series for the fleet is determined, based on the obtained time series. For a selected asset in the fleet a model of the dependency between the obtained time series and the average time series is generated, and a further time series is generated for the selected asset. Anomalous data values are identified from the further time series.

The application relates to the technical field of brain-like perception and computation, in particular to a motion compensation hardware acceleration method and device, which comprises the following steps: denoising is performed on current events and past preset time window events in an event stream based on FPGA to obtain a denoised event stream; events in the denoised event stream are divided into a plurality of time windows; pixel positions of each event in each time window are superimposed with positions of camera motion based on IMU data to obtain a warped event stream; time window event accumulation is performed on the warped event stream; a CPU saves an obtained event accumulation frame in an off-chip RAM; average time stamps of each pixel of an image are obtained from the event accumulation frame; Gaussian filtering is performed to obtain a motion compensation frame, which is output under the control of the CPU; and the application can improve the efficiency of event camera motion compensation and reduce the delay.

The invention relates to the technical field of data processing, and provides a data verification method, a robot and a storage medium, which are applied to the robot, and the robot carries a plurality of sensors. The method is used for verifying measurement data corresponding to each sensor. The method comprises the steps that a plurality of data storage areas are generated, and each data storage area corresponds to one sensor and is used for storing measurement data corresponding to the sensor; generating identification information corresponding to each piece of measurement data according to the collection timestamp corresponding to each piece of measurement data, so as to sort the multiple pieces of identification information according to the collection timestamps corresponding to the multiple pieces of identification information; in each data storage area, obtaining an average time interval corresponding to the plurality of pieces of measurement data according to the plurality of pieces of identification information; if the difference value between the average time interval and the preset time interval is smaller than the preset difference value, the multiple pieces of measurement data in the data storage area pass verification.

System and methods of this technology can provide a framework using machine learning based message generator. The framework can retrieve elapsed times according to computing devices executing an application. Once the elapsed times are retrieved, the framework can generate a total elapsed time that can indicate an average amount of time captured by the at least one computing devices. The framework can make a determination based on the total elapsed time and a threshold elapsed time. The threshold elapsed time can be generated from data associated with previous months, years, days, among other time periods. Upon determining that the total elapsed time is higher than the threshold elapsed time, the framework can generate instructions to display the metrics associated with the total elapsed time, on another computing device.

The application discloses a kind of distributed acoustic sensing earthquake monitoring data stream processing method and system, belong to earthquake monitoring and data processing technical field.The method of the application includes: after the original earthquake monitoring data is preprocessed, it is sliced using sliding processing time window, and the data segment suitable for real-time detection is obtained;Design adaptive short-time average / long-time average detection algorithm, detect the data segment, and dynamically adjust the adaptive parameter in the algorithm according to the data characteristics;Finally, the initial candidate list of earthquake event is executed multi-channel fusion detection, to generate the final detection list of earthquake event.The application is based on distributed stream data processing architecture, combined with the innovative advantage of adaptive earthquake detection algorithm, can improve the accuracy of earthquake detection while ensuring the real-time of detection result, can provide efficient and reliable technical support for large-scale earthquake monitoring task.

A method for determining the average usage of an analyte monitoring system may include the step of calculating one or more analyte measurements of a host during a first period of time. The method may include the step of calculating a duration of time that the host uses the analyte monitoring system during the first period of time. The method may include the step of calculating an average time that the host uses the analyte monitoring system during a second period of time or a percentage of the first period of time that the host uses the analyte monitoring system, in which the average time or the percentage is calculated based on at least the calculated duration. The method may include the step of generating and displaying a report indicating the average time or the percentage.

The invention discloses a bus travel time prediction method based on multi-component decomposition and attention fusion. The method comprises the steps of S1, date selection; s2, component coding; s3, feature fusion: performing weighted fusion on the riding features and the waiting features through travel attention, modeling long-range dependence between different road sections and stations by using a self-attention mechanism, and outputting global fusion features; calculating a corresponding time code by using historical average time, and performing dot product operation on the time code and the global fusion feature to generate a final comprehensive feature for prediction; and S4, carrying out loss alignment and prediction. According to the invention, the riding time and the waiting time are respectively modeled; off-line and on-line departure frequency data are fused to enhance waiting time prediction; and a multi-component loss alignment mechanism is constructed, the consistency of the formation level prediction and the overall travel level prediction is ensured, and finally the prediction precision and the scene adaptability are improved.

The invention discloses a multi-device system without a networking function and a clock calibration method thereof. The clock calibration method of the multi-device system without the networking function comprises the following steps: when a device initiates a clock calibration request, each device obtains a value of a factor parameter which has an influence on a clock; calculating the environment weight of the equipment clock according to the value of the factor parameter and the corresponding weight; each device calculates the time accuracy of the device according to the environment weight and the historical calibration weight of the clock of the device, and sends the time of the device and the time accuracy to other devices in the system; and each device calculates the average time according to the time and the time accuracy of all the devices, judges whether the own time is deviated from the average time or not, and if so, adjusts the own time to the average time. According to the invention, through the statistics of the time and the time accuracy of each device in the system, the time of other devices is automatically calibrated by using relatively accurate time, so that the cost of manual calibration is reduced.

Disclosed are methods, systems, and computer programs for dynamically generating a development plan for a resource site. The methods for example, include receiving seismic data associated with a subsurface of the resource site. The seismic data may be associated with a propagated wavefield within the subsurface of the resource site and can include at least structural geological data associated with the resource site. The methods also include directionally determining a plurality of rate of change data based on the propagated wavefield within the subsurface. The methods further include executing an averaging operation using the plurality of rate of change data and thereby generate an impedance model. The impedance model may be used to generate a development plan which is then used for energy development operations at the resource site.

The present disclosure relates to a time synchronization method and device, electronic equipment and storage medium. The method comprises: obtaining a plurality of groups of transmission information calculated between a master device and a slave device; obtaining an average time deviation and an average network delay between the master device and the slave device based on the plurality of groups of transmission information; adjusting the system time of the slave device based on the average time deviation, obtaining the network delay between the adjusted master device and the slave device, and obtaining the time deviation between the master device and the slave device if the difference between the network delay and the average network delay is less than a first threshold; obtaining the deviation between the system time and the master clock, adjusting the system time based on the deviation, and synchronizing the system time of the slave device based on the time deviation if the time deviation between the master device and the slave device is less than a second threshold. By offsetting the predictable deviation introduced and the deviation in the link, the time synchronization accuracy can be improved.

The application discloses a bus travel time prediction method based on multi-component decomposition and attention fusion, comprising the following steps: S1, date selection; S2, component coding; S3, feature fusion: the travel attention is used for weighted fusion of the riding features and the waiting features, a self-attention mechanism is used for modeling long-range dependence between different road sections and stations, and global fusion features are output; the corresponding time coding is calculated by using the historical average time, and dot product operation is performed on the global fusion features to generate comprehensive features finally used for prediction; and S4, loss alignment and prediction. The application respectively models the riding time and the waiting time, fuses offline and online departure frequency data to enhance the waiting time prediction, constructs a multi-component loss alignment mechanism, ensures the consistency of component-level and overall travel-level prediction, and finally improves the prediction accuracy and scene adaptability.

The present invention discloses a method and related device for accelerating the calculation of electromagnetic transient simulation of power systems. The method uses the time averaged to each parallel computing unit as a limit, sequentially placing electrical quantity calculation blocks into each parallel computing unit in descending order of calculation time. The method then uses the average difference in the cumulative time of the electrical quantity calculation blocks of the parallel computing unit with the largest cumulative electrical quantity calculation block time and the parallel computing unit with the smallest cumulative electrical quantity calculation block time as a condition. If the ratio of the average difference to the time averaged to each parallel computing unit is less than a threshold, the optimization is terminated. If it is not less than the threshold, the electrical quantity calculation blocks of the parallel computing unit with the largest cumulative electrical quantity calculation block time are reallocated. The method solves the technical problem that the existing calculation process for electromagnetic transient simulation of power systems does not fully consider the impact of the resource allocation and sequential arrangement of multiple parallel computing units on the final calculation time, making it difficult to achieve reasonable utilization of the computing hardware system and difficult to improve computing efficiency.

Certain aspects of the present disclosure provide techniques for anomaly-robust time-averaged radio frequency (RF) exposure compliance continuity. A method that can be performed by a user equipment (UE) generally includes transmitting, at a first transmission power, a first signal based on time-averaged RF exposure measurements during a time window, and storing RF exposure information associated with the time window. The method can also include detecting that an anomaly event associated with the UE has occurred, and in response to the detection of the event, transmitting, at a second transmission power, a second signal based at least in part on the stored RF exposure information.