20 results about "Time coefficient" patented technology

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.

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 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.

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 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 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.

The invention belongs to the technical field of gas turbine turbomachinery cascade modeling and simulation, and particularly relates to a cascade unsteady flow field prediction method at an unknown moment. According to the method, accurate prediction of the cascade unsteady flow field at an unknown moment can be achieved, and the specific process of the method comprises the steps that based on the cascade flow field at a known moment, a flow field reduced-order model is established to select a front-order mode representing a main flow structure of the flow field, and meanwhile, a time coefficient model of the cascade flow field is established; constructing a flow field prediction model based on the time coefficient model of the cascade flow field; training the machine learning model by using the time coefficient of the known cascade flow field, and predicting an unknown time coefficient by using the trained machine learning model; and obtaining a cascade unsteady flow field prediction value at an unknown moment based on the flow field prediction model through a selected mode and a predicted time coefficient.