34 results about "Time coefficient" patented technology

The invention provides a sub-season heat wave prediction method, which comprises the following steps of: determining all optimal prediction factors associated with any air temperature main mode time coefficient under preset prediction time efficiency according to a pre-obtained air temperature main mode of a target area and an air temperature main mode time coefficient-prediction time efficiency-optimal prediction factor relation table; and acquiring data of all the optimal forecasting factors through real-time non-band-pass filtering, inputting a pre-trained sub-season air temperature forecasting model, and acquiring a predicted air temperature abnormal field in the target area under preset time efficiency. And predicting the sub-season heat wave event in the target area according to the predicted air temperature abnormal field. According to the method, the interaction between multiple scales and multiple variables is considered by using the strong data mining capability of the convolutional neural network, the influence of the interannual scale signal on the large-scale sub-seasonal variation rate is fully considered, and the potential correlation between the early-stage multi-scale signal and the future air temperature is mined.

The invention relates to the field of space-time missing signal reconstruction, in particular to a modal parameter identification method, device and equipment for a space-time missing vibration signal, and the method comprises the following steps: 1, extracting modal parameters, giving a two-dimensional missing signal X, carrying out the PCA initialization of the missing signal through PCA modal decomposition, obtaining initial data, carrying out the dimension reduction of the signal, and extracting a principal component; 2, performing SRCNN and PCA joint optimization, taking a modal parameter space modal matrix U and a time coefficient matrix V of the PCA as constraint conditions of the SRCNN, and reconstructing missing data by utilizing the nonlinear mapping capability of the SRCNN and combining the global features of the PCA; and step 3, alternately optimizing the SRCNN parameter and the PCA parameter, and reconstructing a signal and a modal parameter. A vibration signal can be effectively reconstructed from the space-time field, the noise of the signal can be reduced, and the signal reconstruction precision can be improved.

The invention relates to a rainfall objective prediction method based on an intelligent algorithm, and the method comprises the steps: decomposing the midsummer rainfall observation data of a time sequence based on an empirical orthogonal function, obtaining a spatial mode and a time coefficient, calculating a circulation field mean value of a target time period based on an atmospheric circulation field monthly value data set predicted by a climate mode, and obtaining the rainfall objective prediction result. The method comprises the following steps: obtaining a circulation abnormal field through a difference value with a same-period climate state circulation field, carrying out correlation analysis on a time coefficient and the circulation abnormal field, identifying and extracting a first correlation region with high correlation, carrying out correlation analysis on the time coefficient and atmosphere reanalysis data, obtaining a second correlation region with high correlation, and identifying and extracting a second correlation region with high correlation. And extracting a circulation anomaly average value of a similar region in the first related region and the second related region as an input feature, training a prediction model according to the input feature and a corresponding time coefficient, obtaining the corresponding time coefficient according to the prediction model and circulation anomaly feature data in the second time period, and performing reduction to obtain rainfall prediction data. And the accuracy of rainfall prediction is improved.

A traffic distribution method may include acquiring traffic data on a quantity of inbound traffic to each of a plurality of servers; calculating a coefficient of variation of the traffic data for a first unit time duration, an interval average of coefficients of variation for a second unit time duration, and an interval standard deviation of the coefficients of variation for the second unit time duration, wherein the second unit time duration includes a plurality of first unit time durations; performing a real-time calculation on the coefficient of variation of the traffic data for the first unit time duration to calculate a real-time coefficient of variation; determining whether a traffic distribution imbalance state across the plurality of servers occurs, based on comparing the real-time coefficient of variation with the interval average and the interval standard deviation; and performing traffic distribution across the plurality of servers, based on the determination.

The invention discloses a multi-information system operation state batch monitoring method and system, belongs to the technical field of system operation state monitoring, and can simultaneously monitor the operation states of a plurality of information systems and provide quantitative data for managers. Comprising the steps of obtaining operation parameters of a plurality of information systems in a target time period; performing timestamp marking on the acquired operation parameters in the target time period, and storing the marked data in a temporary storage unit; according to the data marked by the timestamp corresponding to each system read from the temporary storage unit, the operation state evaluation index of each system is calculated, and the evaluation index comprises a system stability coefficient, a resource utilization rate coefficient and a response time coefficient; ranking the operation states calculated by the information systems according to the system stability coefficient, the resource utilization rate coefficient and the response time coefficient corresponding to each system to obtain a ranking result; and sending the ranking result to a monitoring terminal of an administrator.

The present invention provides a sub-seasonal heat wave prediction method, comprising: determining all optimal prediction factors associated with any temperature main mode time coefficient under a preset forecast time according to a pre-obtained temperature main mode of the target area and a temperature main mode time coefficient-forecast time effect-optimal prediction factor relationship table. Data of all optimal prediction factors are obtained by real-time non-bandpass filtering, input into a pre-trained sub-seasonal temperature forecast model, and a predicted temperature anomaly field under a preset time effect in the target area. Sub-seasonal heat wave events in the target area are predicted based on the predicted temperature anomaly field. The present invention utilizes the powerful data mining capabilities of convolutional neural networks to consider the interactions between multi-scale and multi-variable variables, fully considers the impact of interannual-scale signals on large-scale sub-seasonal variability, and explores the potential correlation between previous multi-scale signals and future temperatures.

The application discloses a wheel speed correction method for intelligent driving, and the speed time coefficient calibration method under different road conditions and variable load conditions is used in an IMU+wheel speed fusion odometer scheme, wheel speed error is reduced, and the precision of the local odometer is improved. In addition, the final result is to generate a speed scale coefficient lookup table. Through calibration, the GNSS signal is fixed in the lookup table, the situation without GNSS can be met, and because there is no online calibration calculation, the computing power is saved, the computing power requirement of the operation platform is relatively low, and the embedded scheme is promoted.

The embodiment of the invention provides a motor overload detection method and device, electronic equipment and a storage medium, and relates to the technical field of motor overload detection, and the electronic equipment can obtain the current current value of a motor and the duration corresponding to the current current value; determining a thermal time coefficient corresponding to the current current value based on a pre-calibrated corresponding relation; according to the determined thermal time coefficient and the duration, determining the heat variation of the motor in the duration; and determining the current accumulated heat based on the heat variation and the accumulated heat, and determining whether the motor is overloaded based on the size relationship between the current accumulated heat and a preset threshold value. Thus, the thermal time coefficient corresponding to the current current value can be quickly determined in a table look-up mode, and then the heat variation is calculated based on the duration and the determined thermal time coefficient without complex differential or exponential operation, so that the required calculation amount can be reduced, and the determination efficiency and the real-time performance can be improved.

The application discloses a kind of instantaneous performance prediction method and system of flapping wing energy harvesting system, it is related to ocean renewable energy utilization and unsteady fluid dynamics technical field, including the following steps: the motion parameter of flapping wing energy harvesting system in current movement period, the flow parameter of the flow where it is located and time variable are input to prediction model, obtain unsteady flow field prediction result;Unsteady flow field prediction result is modally decomposed, and unsteady flow field prediction result is converted into multiple dominant modes and the time coefficient corresponding to each dominant mode;Multiple time coefficients corresponding to multiple dominant modes are used to construct the dynamics characteristic for representing the dominant unsteady dynamics behavior of system;Dynamics characteristic is input to pre-trained instantaneous performance prediction model, and the instantaneous performance of current cycle is obtained.At the same time that data-driven prediction efficiency is significantly improved, the reliability of prediction result in physical consistency, stability and engineering explainability is guaranteed.

This invention discloses a data-driven reduced-order model method for predicting unsteady flow in a turbomachinery under static-to-dynamic interference. The main steps include: Step 1, calculating the unsteady flow field at a typical operating point; Step 2, extracting the initial modes, corresponding eigenvalues, and initial time coefficients for the typical operating point; Step 3, normalizing the initial modes to obtain dimensionless initial modes; Step 4, calculating the compressor characteristic curves using a steady-state method to obtain the characteristic curves and corresponding steady flow fields; Step 5, solving for the predicted dynamic modes; and Step 6, predicting the unsteady flow field information at different operating points. This invention calculates the unsteady-to-static interference flow field under a typical operating point and uses a data-driven reduced-order model to predict the unsteady pulsation information of the target operating point. This achieves the consideration of unsteady time-averaged effects in steady-state calculations without iteratively solving unsteady control equations, effectively solving the problems of high computational cost and long calculation cycles in unsteady calculations.

This invention relates to a data processing method and storage medium for ice-elastic plate compression tests in the field of ship structural mechanics. The method includes the following steps: performing a compression test by compressing an ice sample with an elastic plate; after the compression test, obtaining the test pressure value and strain gauge test values ​​of the ice sample under compression; selecting a preset order mode shape using the modal superposition method to obtain a fitted deflection function; determining the fitted strain value corresponding to each test point of the elastic plate based on the fitted deflection function; calculating the time coefficient by combining the strain gauge test values ​​and the fitted strain values ​​using the least squares method to obtain the fitted deflection distribution function of the elastic plate; and calculating the compression energy of the elastic plate and the ice sample based on the fitted deflection distribution function and the test pressure value. This method focuses on the deformation of the elastic plate to explore the interaction between ice and the structure from an energy perspective, considering the feedback influence of structural deformation on the action process, thus forming a universal method for predicting ice loads.

The invention discloses a vortex type fluid machinery flow field prediction method, and relates to the field of flow field prediction, and the method comprises the steps: carrying out the grid division of a fluid region in a geometric model; performing CFD calculation on the divided fluid region to obtain unsteady flow field data of each grid in each time step within a preset time period after the flow field is stable; interpolating the unsteady flow field data of each grid to a static background grid corresponding to the fluid region; carrying out POD decomposition on the flow field data in the static background grid after interpolation; clustering the time coefficients to obtain a plurality of categories; respectively inputting all time coefficients in the category into a time sequence prediction model corresponding to the category to obtain predicted time coefficients corresponding to all spatial modals in the category; and predicting the flow field of the target vortex type fluid machine according to the prediction time coefficients corresponding to all the spatial modes and all the spatial modes. The computing cost can be reduced, the computing efficiency can be improved, and the requirement for hardware performance is not high.

The invention provides a computing equipment control method and device, electronic equipment and a medium, and belongs to the technical field of computing equipment control. The method comprises the steps that a task real-time coefficient is determined based on a response requirement field of a target task; determining an average response time and a thread wakeup frequency based on the task behavior data of the target task, and calculating a task real-time score based on the average response time and the thread wakeup frequency; determining a dynamic task type of the target task based on the task real-time coefficient and the task real-time score; determining a target resource allocation strategy based on the dynamic task type and the task priority of the target task; selecting a target resource from the device hardware resource data of the computing device based on the resource demand information of the target task and a target resource allocation strategy; and scheduling a target resource of the computing device to enable the computing device to execute the target task through the target resource. The problems of unreasonable resource allocation and low resource utilization rate in the prior art can be solved.

The disclosed embodiment discloses a consensus method, device and blockchain system of a blockchain system, wherein the method includes: obtaining the hash value of the latest chain block of the blockchain system; generating a verifiable random number of each node based on the private key of each node in the blockchain system and the hash value of the latest chain block, and adding the verifiable random number of each node to each to-be-chained block generated by each node; determining the waiting time of each to-be-chained block relative to the broadcast time of the latest to-be-chained block based on the verifiable random number and time coefficient of each node; determining the broadcast time of each to-be-chained block based on the broadcast time of the latest to-be-chained block and the waiting time of each to-be-chained block relative to the broadcast time of the latest to-be-chained block; broadcasting each to-be-chained block based on the broadcast time of each to-be-chained block. The disclosed embodiment can significantly reduce the computing resources consumed by nodes in the consensus process and improve the consensus efficiency of the blockchain system.

The application discloses a cloud-computing-based intelligent management system for servo motor controllers and relates to the technical field of servo motor management. For each device, the data response time of the operation data of the servo motor is obtained to obtain a time coefficient; the specific data value of the operation data is obtained to calculate a data deviation coefficient; the task end time is used to calculate a task progress coefficient; the corresponding device priority coefficient is calculated according to the time coefficient, the data deviation coefficient and the task progress coefficient, and the operation data of the servo motor of the corresponding device is uploaded to a cloud platform in the order from large to small according to the priority coefficient, so that the operation data of the servo motors of multiple devices is uploaded to the cloud platform in the order from large to small according to the priority coefficient to manage the servo motor controllers of the devices, the occupation of network bandwidth is good, data transmission delay or packet loss and other problems are reduced, the data received by the cloud platform is consistent with the actual data, and the control accuracy of the servo motors of the partial or multiple devices is ensured.

The invention discloses a distributed thermal process fault diagnosis method based on dynamic information fusion, and the method comprises the steps: carrying out the dynamic hidden variable analysis of space-time separation of a temperature data set of a distributed thermal process, and solving a dynamic hidden variable, a dynamic space basis function and a dynamic residual error; calculating a dynamic hidden variable score and a corresponding residual error based on the dynamic hidden variable and the dynamic space basis function; decomposing the dynamic residual error through static PCA to obtain a static time coefficient matrix and a static space basis function matrix; calculating hidden variable scores and residual errors in the dynamic and static processes, defining dynamic subspace statistics, static subspace statistics and residual error subspace statistics, and performing multi-scale fusion on the three defined statistics to construct multi-scale statistics; when the multi-scale statistical magnitude is greater than a reference threshold value, judging that a fault occurs; and contribution degree analysis is carried out on the residual subspace statistics, and a fault position is positioned. According to the invention, rapid detection and accurate positioning of faults can be realized.

The invention discloses a rapid assessment method for effective power capability of strong impact load on typical members, and the method comprises the following steps: for rectangular cross-section opposite-edge constraint members, bringing key characteristic parameters such as pressure peak value and action time of strong impact uniformly distributed load into a unified calculation framework; a peak pressure ratio coefficient and a natural vibration period of a loaded component are calculated by acquiring a pressure peak value and action time of a strong impact uniform load and an elastic modulus, density, component span and section height of the loaded component, a time coefficient is determined according to the action time and the natural vibration period, and then an effective acting ratio is calculated based on the peak pressure ratio coefficient and the time coefficient. Effective power capability rapid comparison of different strong impact uniformly distributed loads acting on the same component and effective power capability rapid comparison prediction of the same strong impact uniformly distributed load acting on different components are realized. According to the method, an evaluation result with consistent comparability can be obtained, and the efficiency of engineering scheme type selection, parameter optimization and rapid decision can be remarkably improved.

The present invention discloses a real-time ionospheric TEC modeling method based on spherical harmonics and historical data constraints. The method comprises the following steps: determining the corresponding historical constrained year, month, and universal time using the real-time year, month, and universal time; dividing the historical CODE-TEC data by year and month based on the historical CODE-TEC data, taking the average hourly TEC value over 24 hours per month, and determining the historically constrained CODE-TEC based on the historically constrained year, month, and universal time; obtaining the global historically constrained CODE-TEC using the real-time year, month, and universal time, combining it with the real-time observed local TEC data to obtain real-time coefficients through spherical harmonic fitting; and substituting the real-time coefficients into the spherical harmonics to construct a real-time ionospheric TEC model for the local area. The method can quickly and accurately construct a regional ionospheric TEC model, which is of great value to both ionospheric scientific research and space weather monitoring.

The invention discloses a dynamic platform control method and device, a storage medium and a system, and belongs to the technical field of servo control, and the method comprises the steps: obtaining the current coordinates and fixed point coordinates of a dynamic platform, calculating the control flow of the servo in a period of time according to the obtained data, and enabling the control flow to comprise the control value of the dynamic platform at each moment; calculating a control compensation predicted value according to a preset time coefficient and the control value, and compensating the control value of the dynamic platform at the next moment according to the control compensation predicted value to obtain a new control value; on the basis of the new control value, the control flow is used as a sliding window, and the control flow is updated through a head interpolation method; according to the updated control flow, a control instruction is sent to perform corresponding control on the dynamic platform; according to the method, the compensation prediction value is predicted and controlled by accumulating the dynamic platform control value queues according to the issued control value, the compensation effect is optimized for specific conditions by adjusting the preset correlation coefficient, and the accuracy of dynamic platform control is improved.

A steam generator heat transfer tube wear prediction method based on numerical simulation and nuclear power plant overhaul data comprises the steps that a wear coefficient in an overhaul time node is decomposed into a time coefficient constant and a space basis function form, and the degree of freedom is reduced; in combination with a numerical simulation vibration curve and actual wear overhaul data of the nuclear power plant, correcting the actual wear overhaul data through measurement errors, and iterating an equation set to finally obtain a time constant coefficient meeting requirements; the obtained wear coefficient is subjected to space and time dimension expansion, and the wear depth amount of a certain spatial position point during future overhaul is calculated in combination with a vibration curve obtained through numerical simulation. The problems of large deviation between a simulation result and a real working condition, difficulty in accurately obtaining a wear coefficient, insufficient reliability of a prediction result and the like in an existing wear prediction technology are solved.

The application discloses a kind of dynamic platform control method, device, storage medium and system, belong to the control technical field of servo, method includes obtaining the current coordinate of dynamic platform and fixed point coordinate, according to the data of acquisition, the control flow of servo in a period of time is calculated, control flow includes the control value of dynamic platform at each time;According to the control compensation predicted value calculated by preset time coefficient and control value, the control value of dynamic platform at next time is compensated according to control compensation predicted value and new control value is obtained;Based on new control value, control flow is used as a sliding window, and control flow is updated by head insertion method;According to the control instruction issued according to updated control flow, the corresponding control is carried out to dynamic platform;The application is accumulated in the form of dynamic platform control value queue, according to the control value that has been issued, the control compensation predicted value is predicted, and the compensation effect is optimized by adjusting the preset correlation coefficient according to the specific situation, the accuracy of dynamic platform control is improved.

The invention provides a project evaluation method and device, equipment and a storage medium, and the method comprises the steps: responding to a project evaluation request for a target project, and collecting the abnormal test data of the target project; determining an abnormal evaluation parameter corresponding to the abnormal test data according to the number of abnormal devices, the total sampling number and the total number of abnormal times in the abnormal test data; determining project evaluation parameters of the target project according to an abnormal state coefficient corresponding to the abnormal test data, a repair time coefficient and the abnormal evaluation parameters; and outputting a project evaluation result corresponding to the target project based on the project evaluation parameter. The quality evaluation index of the target project is systematically and automatically generated, so that the project management efficiency and accuracy are improved.

The invention relates to a rainfall prediction method and device and computer equipment. The method comprises the following steps: determining a rainfall seasonal characteristic prediction result of a target area in a preset prediction time period according to a pre-established rainfall seasonal characteristic prediction model and historical data of each prediction factor corresponding to a target rainfall time coefficient of the target area; the rainfall seasonal feature prediction result comprises a plurality of rainfall seasonal feature prediction samples; determining a rainfall event simulation result of the target area in the prediction time period according to a pre-constructed rainfall event simulation generation model and rainfall historical data of the target area; the rainfall event simulation result comprises a plurality of rainfall event simulation sub-results; according to the rainfall seasonal characteristic prediction result and the rainfall event simulation result, determining a rainfall prediction result of the target area in the prediction time period; the rainfall prediction result comprises a plurality of rainfall prediction sub-results. By adopting the method, the reliability of rainfall prediction can be improved.

The invention discloses a big data identification scheduling method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining a data set and a current scheduling load, identifying the total amount of data, identifying time efficiency and feature information, and obtaining the number of feature items and the number of occurrences; obtaining an initial priority according to the total data amount, the number of feature items and the occurrence frequency, obtaining reference time according to the total data amount, obtaining a time coefficient corresponding to the data set according to the reference time and the timeliness corresponding to each data block, and obtaining a quantization priority according to the initial priority corresponding to the data set and the time coefficient; obtaining quantization priorities corresponding to a plurality of data sets to form a quantization priority queue; and if the current scheduling load exceeds the load threshold, obtaining a correction factor according to the current scheduling load and the total data amount, forming a target priority queue according to the correction factor, and completing the scheduling of the plurality of data sets according to the target priority queue. The method has the advantages of multi-dimensional coupling, resource adaptation and load balancing.

The invention relates to the technical field of IP distribution, and particularly discloses a multi-user fixed IP distribution control method for a cloud office system, and the method comprises the following steps: S1, screening out a first-level user according to the number of user applications, and calculating an average time interval; s2, calculating a mean value of the use duration, calculating a time coefficient of a first-level user, obtaining a number requirement of the first-level user, and calculating a mean value of the number requirement and a use coefficient; and S3, sorting the first-level users to obtain a use coefficient sequence, calculating a first quantity requirement in sequence, calculating the host digit meeting the constraint, and performing distribution according to the calculated host digit. In conclusion, the multi-user fixed IP allocation control method for the cloud office system is designed to reasonably allocate a limited number of IPs, so that the probability of occurrence of network conflicts is reduced, and the user experience is improved.

The invention discloses an estimation method and system for a virtual inertia compensation upper limit of a power system, and computer equipment, and aims to solve the problem that the power system is small in interference and unstable due to the fact that an energy storage system compensates too high virtual inertia. The method comprises the steps that firstly, an inertia time constant, an equivalent damping coefficient and relevant parameters of a synchronous unit speed regulator of a power system are collected; then determining a frequency sampling period of the energy storage system and a virtual inertia time coefficient for describing the size of virtual inertia; and establishing a power system frequency discrete response model, and estimating a virtual inertia compensation upper limit described by a frequency sampling period and a system inertia time constant. According to the estimation method for the virtual inertia compensation upper limit of the power system, a foundation is laid for proper configuration of virtual inertia and reasonable setting of virtual inertia control parameters under a system view angle.

The present invention discloses a method and device for evaluating the power supply safety state of high-speed railways. The method includes: obtaining the original record data of faults and accidents on high-speed railways; extracting the time features and spatial features at the time of faults and accidents from the original record data of faults and accidents; using a spatio-temporal clustering analysis method to divide the levels of faults and accidents according to the time features to obtain the time coefficients at the time of accidents and faults; using a spatio-temporal clustering analysis method to divide the levels of faults and accidents according to the spatial features to obtain the spatial coefficients at the time of accidents and faults; determining the power supply safety index of high-speed railways according to the time and spatial coefficients at the time of accidents and faults; and evaluating the safety state of the high-speed railway power supply system according to the safety index. The present invention can quantitatively evaluate the safety state of the high-speed railway power supply system based on fault and accident data, and has scientific guiding significance for improving the safety performance of the high-speed railway power supply system.

The invention discloses a servo motor controller intelligent management system based on cloud computing, and relates to the technical field of servo motor management, for each device, data response time of operation data of a servo motor is obtained to obtain a time coefficient; acquiring a specific data value of the operation data and calculating a data deviation coefficient; calculating a task progress coefficient at the task ending time; calculating priority coefficients of the corresponding devices according to the time coefficients, the data deviation coefficients and the task progress coefficients, and uploading the operation data of the servo motors of the corresponding devices to a cloud platform according to a descending order of the priority coefficients, the operation data of the servo motors of the multiple devices can be uploaded to the cloud platform according to the descending order of the priority coefficients to manage the servo motor controllers of the devices, so that the occupation of the network bandwidth is good, the problems of data transmission delay or packet loss and the like are reduced, the data received by the cloud platform is consistent with the actual data, and the user experience is improved. And accurate control of servo motors of part of or multiple pieces of equipment is ensured.

The application discloses a large-scale assimilation method for ocean dynamic downscaling. The application is based on a to-be-corrected variable in a high-resolution model, selects reference data, uses the EOF expansion of a space-time matrix, obtains a large-scale spatial mode of the to-be-corrected variable of the high-resolution model, projects the to-be-corrected variable and the reference data to the large-scale mode to respectively obtain time coefficients, and realizes the approximation of the large-scale signal of the high-resolution model to the large-scale signal of the reference data based on the obtained time coefficients, thereby improving the simulation of the large-scale signal of the high-resolution model. The application has the advantages that the large-scale signal is obviously improved through the EOF approximation of the large-scale signal of the high-resolution model, the small-scale signal which is not limited can also become more accurate through the interaction of the large-scale and the small-scale in the nonlinear coupling, the decoupling problem of the high-resolution nested simulation of the ocean model is solved, and the simulation capability of the small-scale process in the high-resolution model is improved.

This invention discloses a resource-constrained project scheduling method, system, equipment, and medium, belonging to the field of intelligent workshop scheduling technology. The method is based on generated process sequence codes. By creating a decoding sequence set and waiting process groups, the decoding sequence set is updated to determine the decoding sequence set corresponding to the updated process sequence codes. The start time, end time, and total time for completing all processes for each process are determined. Minimizing the maximum completion time for workers is used as the optimization objective. A genetic simulated annealing algorithm is used to obtain the individual with the highest fitness value. Based on the start time, end time, and total time for completing all processes for the individual with the highest fitness value, the total processing time coefficient and the most recent processing time coefficient for each worker are determined, and shifts are scheduled for each worker. This method can improve the utilization rate of human resource scheduling.