52 results about "Time average" patented technology

The invention relates to the technical field of electrical fault detection, in particular to an electromechanical equipment health state diagnosis method and device based on machine learning, and the method comprises the following steps: collecting the current, voltage and temperature parameters of equipment, calculating the real-time average value and standard deviation of each parameter through numerical processing, and obtaining a parameter monitoring result; and monitoring a result based on the parameters. The current, voltage and temperature parameters of the equipment are collected in real time, numerical processing is carried out on each parameter, the real-time average value and the standard deviation are calculated, and accurate basic data support is provided for state monitoring. Based on the monitoring data, parameter abnormity is judged through threshold analysis, abnormal changes can be dynamically recognized in the equipment operation process, and the timeliness and accuracy of fault discovery are improved. In combination with past performance data, the future change trend of parameters is predicted through a regression algorithm, early warning for equipment operation can be formed, and problem expansion or out-of-control is avoided.

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.

Methods and systems use an additional determination to generate predictions of future data processing load predictions. Specifically, the methods and systems generate additional predictions based on other data sets and data calculation methodologies (e.g., data with non-homogeneous time dependencies). The methods and systems then use these additional predictions to determine whether or not the prediction of the real-time average of data processing loads is an outlier. Thus, the methods and systems generate a time-specific data processing prediction.

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 present invention provides a target detection and tracking method, system, and storage medium based on an event camera. The method includes: reading a DVS event sequence and performing background noise reduction processing; setting a time window; initializing Kalman filter parameters; converting the DVS event sequence within the time window into a normalized average time plane; performing target event denoising in the normalized average time plane; detecting the target position and simultaneously predicting the target position using a Kalman filter, but the initial position is determined by the detection; calculating the distance between the target position detected at the current moment and the target position at the previous moment, balancing the detected position with the position predicted by the Kalman filter based on the distance information, thereby determining the target position at the current moment; performing trajectory smoothing; moving to the next time window and repeating the above steps until the DVS event sequence ends. The present invention has the characteristics of low latency, avoiding noise interference, avoiding inaccurate position detection, and preventing target frame jitter.

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 provides a frequency scale measurement method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the frequency division processing of a measurement signal outputted by a frequency source, and obtaining a low-frequency pulse signal; carrying out delay measurement and time interval counting on the low-frequency pulse signal by using a high-precision time interval counter, and outputting the low-frequency pulse signal in a timestamp form; the method comprises the following steps of: setting a sliding window, carrying out sliding average filtering processing on timestamps by utilizing a sliding average algorithm, carrying out sliding action difference on all the timestamps to obtain time differences, grouping the time differences in sequence, and averaging in groups to obtain average time differences; the number of measured samples is increased by increasing the length of the sliding window, so that statistical errors are reduced; a frequency measurement value, a relative frequency deviation and an Arron variance are calculated using the average time difference. According to the method provided by the invention, the sliding window is increased, and the frequency measurement result is obtained by using the algorithm of grouping and averaging the time difference, so that the method has higher measurement precision and stability.

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.

The present invention discloses a fractional multi-mode frequency divider circuit based on space-time averaging and phase interpolation technology, comprising: a third-order noise shaping modulation module, a dynamic element matching and phase selection module, a phase switching frequency divider module, and a pipelined phase interpolation module; the third-order noise shaping modulation module is used to perform time averaging on the input frequency division ratio to generate an instantaneous frequency division ratio; the dynamic element matching and phase selection module is used to perform spatial averaging on the instantaneous frequency division ratio and generate a frequency divider control word and a phase interpolator control word; the phase switching frequency divider module is used to generate a clock signal corresponding to the frequency division ratio based on the frequency divider control word; and the pipelined phase interpolation module is used to perform phase interpolation on the clock signal to generate a final clock signal with an accurate phase. The embodiments of the present invention utilize the mutual cooperation of the various modules to achieve the integration of space-time averaging technology and phase interpolation technology, thereby improving the limitations of space-time averaging technology.

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 specifically relates to a phased array radar beam residence scheduling method based on an improved time pointer, and belongs to the technical field of phased array radar resource management. Firstly, an adaptive beam residence scheduling method based on a time pointer under a phased array radar is introduced into an alternative list so as to improve the time utilization rate of a system and the scheduling success rate of radar tasks. Particularly, when the number of the tasks in the previous scheduling interval is small and the number of the tasks in the next scheduling interval is large in the tasks in the two adjacent scheduling intervals, greater advantages can be exerted. Meanwhile, in consideration of an expected execution moment criterion, a time offset rate comparison link is added before task scheduling, and a non-general task with a small time offset rate is selected for execution of two tasks with the first two comprehensive priorities in all tasks which can be executed at the current moment. According to the method, the time utilization rate and the task scheduling success rate can be improved, the average time migration rate is reduced, the calculation complexity is small, and the real-time performance can be guaranteed.

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.