Motor robustness determination method and device, computer equipment, medium and product

Abstract

The invention relates to a motor robustness determination method and device, computer equipment, a medium and a product. The method comprises the following steps: constructing a probability distribution model of each design parameter according to design parameter information of each design parameter of a to-be-evaluated motor; based on the probability distribution model of each design parameter, randomly sampling each design parameter, and combining the parameter sampling values of each design parameter obtained by random sampling to obtain at least two parameter combinations of the to-be-evaluated motor; wherein each parameter combination comprises a parameter sampling value of each design parameter; determining an evaluation index value of a robustness evaluation index of the to-be-evaluated motor under each parameter combination; and determining a robustness evaluation result of the to-be-evaluated motor according to the evaluation index value of the robustness evaluation index of the to-be-evaluated motor under each parameter combination. By adopting the method, the robustness of the designed motor can be accurately evaluated.

Description

Technical Field

[0001] The present application relates to the field of motor technology, and in particular to a method, apparatus, computer equipment, medium and product for determining robustness of a motor. Background Art

[0002] In the process of motor processing and manufacturing, due to the influence of uncertain factors such as material dispersion and processing machine wear, there are often certain differences between the actual structural parameters after processing and the design parameters. The change in parameters makes the actual performance of the motor different from the design performance, resulting in a decrease in the actual performance of the motor compared to the design performance.

[0003] In traditional motor optimization design, since the deviation of parameters due to the influence of uncertain factors in the processing process is not considered, it is possible to output an optimization solution that does not meet the robustness standard. In this type of solution, the motor is seriously affected by the uncertainty factors of the parameters. When the motor parameters deviate slightly from the design values, the motor performance will drop significantly. Therefore, it is necessary to evaluate the robustness of the solution during the motor design process. Summary of the invention

[0004] Based on this, it is necessary to provide a motor robustness determination method, device, computer equipment, medium and product that can accurately evaluate the robustness of the designed motor in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for determining motor robustness, comprising:

[0006] Constructing a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated;

[0007] Based on the probability distribution model of each of the design parameters, randomly sample each of the design parameters, and combine the parameter sampling values ​​of each of the design parameters obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated; wherein each of the parameter combinations includes the parameter sampling values ​​of each of the design parameters;

[0008] Determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination;

[0009] The robustness evaluation result of the motor to be evaluated is determined according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0010] In one embodiment, the design parameter information of each of the design parameters includes the actual value and the tolerance of the design parameter;

[0011] The step of constructing a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated includes:

[0012] For each design parameter, taking the actual value of the design parameter as the mean, and determining a first standard deviation according to the parameter tolerance;

[0013] Constructing a normal distribution function of the design parameter according to the mean and the first standard deviation;

[0014] According to the normal distribution function of the design parameters, a probability distribution model of the design parameters is constructed.

[0015] In one embodiment, the randomly sampling each of the design parameters based on the probability distribution model of each of the design parameters includes:

[0016] For each design parameter, constructing a probability distribution curve of the design parameter according to a probability distribution model of the design parameter;

[0017] Random sampling is performed from the probability distribution curve.

[0018] In one embodiment, determining the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination includes:

[0019] Get the trained robustness evaluation model;

[0020] For any parameter combination, the parameter combination is input into the trained robustness evaluation model to obtain the evaluation index value of the robustness evaluation index of the motor to be evaluated under the parameter combination.

[0021] In one embodiment, determining the robustness evaluation result of the motor to be evaluated according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination includes:

[0022] Determining an actual average value and an actual standard deviation of the robustness evaluation index according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination;

[0023] The robustness evaluation result of the motor to be evaluated is determined according to the actual average value, the actual standard deviation and the indicator requirement information of the robustness evaluation indicator.

[0024] In one embodiment, the method further comprises:

[0025] In the case where the motor to be evaluated is determined to be unrobust, determining a parameter error of each design parameter according to design parameter information of each design parameter of the motor to be evaluated;

[0026] An optimization strategy for the motor to be evaluated is determined based on the robustness evaluation result and the parameter errors of the design parameters.

[0027] In a second aspect, the present application further provides a motor robustness determination device, comprising:

[0028] A model building module, used to build a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated;

[0029] A parameter determination module, configured to randomly sample each of the design parameters based on a probability distribution model of each of the design parameters, and combine the parameter sampling values ​​of each of the design parameters obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated; wherein each of the parameter combinations includes the parameter sampling values ​​of each of the design parameters;

[0030] An index determination module, used to determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination;

[0031] The robustness evaluation module is used to determine the robustness evaluation result of the motor to be evaluated according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0032] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] Constructing a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated;

[0034] Based on the probability distribution model of each of the design parameters, randomly sample each of the design parameters, and combine the parameter sampling values ​​of each of the design parameters obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated; wherein each of the parameter combinations includes the parameter sampling values ​​of each of the design parameters;

[0035] Determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination;

[0036] The robustness evaluation result of the motor to be evaluated is determined according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0038] Constructing a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated;

[0039] Based on the probability distribution model of each of the design parameters, randomly sample each of the design parameters, and combine the parameter sampling values ​​of each of the design parameters obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated; wherein each of the parameter combinations includes the parameter sampling values ​​of each of the design parameters;

[0040] Determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination;

[0041] The robustness evaluation result of the motor to be evaluated is determined according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0042] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0043] Constructing a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated;

[0044] Based on the probability distribution model of each of the design parameters, randomly sample each of the design parameters, and combine the parameter sampling values ​​of each of the design parameters obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated; wherein each of the parameter combinations includes the parameter sampling values ​​of each of the design parameters;

[0045] Determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination;

[0046] The robustness evaluation result of the motor to be evaluated is determined according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0047] The above-mentioned motor robustness determination method, device, computer equipment, medium and product, according to the design parameter information of each design parameter of the motor to be evaluated, construct a probability distribution model of each design parameter; based on the probability distribution model of each design parameter, randomly sample each design parameter, and combine the parameter sampling values ​​of each design parameter obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated; wherein each parameter combination includes the parameter sampling values ​​of each design parameter; determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination; according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, determine the robustness evaluation result of the motor to be evaluated. The above-mentioned scheme, by constructing a probability distribution model of each design parameter, and by random sampling, obtains at least two parameter combinations including the parameter sampling values ​​of each design parameter of the motor to be evaluated, thereby ensuring that the evaluation index value of the obtained robustness evaluation index is closer to the actual value under the actual physical process; further, according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the accuracy of the obtained robustness evaluation result of the motor to be evaluated is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A diagram of an application environment of a method for determining robustness of a motor in one embodiment;

[0050] Figure 2 is a flow chart of a method for determining robustness of a motor in one embodiment;

[0051] Figure 3 A schematic diagram of a process for constructing a probability distribution model of each design parameter in an embodiment;

[0052] Figure 4 A schematic diagram of a process for determining evaluation index values ​​under various parameter combinations in an embodiment;

[0053] Figure 5 A schematic diagram of a process for determining a robustness evaluation result of a motor to be evaluated in one embodiment;

[0054] Figure 6 A schematic diagram of a comparison between an estimated value and a true value output by a proxy model in one embodiment;

[0055] Figure 7It is a schematic diagram of average torque distribution when design parameters are affected by tolerance in one embodiment;

[0056] Figure 8 is a flow chart of a method for determining motor robustness in another embodiment;

[0057] Fig. 9 is a structural block diagram of a device for determining robustness of a motor in one embodiment;

[0058] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The motor robustness determination method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 101 communicates with the server 102 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. Optionally, the server 102 constructs a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated; based on the probability distribution model of each design parameter, each design parameter is randomly sampled, and the parameter sampling values ​​of each design parameter obtained by random sampling are combined to obtain at least two parameter combinations of the motor to be evaluated; wherein each parameter combination includes the parameter sampling values ​​of each design parameter; determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination; according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, determine the robustness evaluation result of the motor to be evaluated. Among them, the terminal 101 can be but not limited to various personal computers, laptops, etc. The server 102 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0061] In one embodiment, Figure 2 As shown, a method for determining the robustness of a motor is provided. Figure 1 The server 102 in the example is used as an example to illustrate, and specifically includes the following steps:

[0062] S201 : constructing a probability distribution model of each design parameter according to design parameter information of each design parameter of the motor to be evaluated.

[0063] The motor to be evaluated is a motor whose design has been determined but has not been put into processing. The design parameters are various physical parameters of the motor to be evaluated. For example, the design parameters may be the inner diameter of the stator, the pole arc coefficient of the permanent magnet, the thickness of the permanent magnet, etc. The design parameter information of each design parameter is information that characterizes the characteristics of the design parameter. In the embodiment of the present application, the design parameter information of each design parameter includes but is not limited to the actual value and parameter tolerance of the design parameter; the probability distribution model of each design parameter is a model that characterizes the distribution of the design parameter when it is affected by uncertain factors such as material dispersion and processing machine wear.

[0064] Optionally, for any design parameter of the motor to be evaluated, the distribution of the design parameter may be analyzed based on the design parameter information of the design parameter to construct a probability distribution model of the design parameter.

[0065] S202 , based on the probability distribution model of each design parameter, randomly sample each design parameter, and combine the parameter sampling values ​​of each design parameter obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated.

[0066] Each parameter combination includes parameter sampling values ​​of each design parameter.

[0067] Optionally, for each design parameter, a probability distribution curve of the design parameter can be constructed according to the probability distribution model of the design parameter, and random sampling can be performed from the probability distribution curve. Exemplarily, 10,000 parameter sampling values ​​can be obtained by randomly sampling each design parameter, and the parameter sampling values ​​of each design parameter obtained by random sampling are combined to obtain 10,000 parameter combinations.

[0068] S203, determining the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0069] Among them, the robustness evaluation index is an index that can characterize the robustness of the motor. For example, the robustness evaluation index includes but is not limited to average torque and efficiency.

[0070] Optionally, a fast analysis proxy model of the structural parameter-output performance of the motor to be evaluated can be constructed, each parameter combination is used as the input of the proxy model, and the evaluation index value of the robustness evaluation index is used as the output.

[0071] It should be noted that the output performance of the proxy model includes both the optimization design objectives of the motor to be evaluated, such as average torque, efficiency, etc., and the index constraints of the motor, such as torque fluctuation, electromagnetic force density, etc.

[0072] S204, determining a robustness evaluation result of the motor to be evaluated according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0073] Optionally, based on statistical theory, a statistical analysis can be performed on the evaluation index values ​​of the robustness evaluation index of the motor to be evaluated under each parameter combination to analyze the probability distribution of the robustness evaluation index, and based on the probability distribution of the robustness evaluation index, the robustness of the motor to be evaluated is evaluated and an evaluation result is obtained.

[0074] In the above-mentioned method for determining the robustness of a motor, a probability distribution model of each design parameter is constructed according to the design parameter information of each design parameter of the motor to be evaluated; based on the probability distribution model of each design parameter, each design parameter is randomly sampled, and the parameter sampling values ​​of each design parameter obtained by random sampling are combined to obtain at least two parameter combinations of the motor to be evaluated; wherein each parameter combination includes the parameter sampling values ​​of each design parameter; the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination is determined; according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the robustness evaluation result of the motor to be evaluated is determined. The above-mentioned scheme, by constructing a probability distribution model of each design parameter, and by random sampling, obtains at least two parameter combinations including the parameter sampling values ​​of each design parameter of the motor to be evaluated, thereby ensuring that the evaluation index value of the obtained robustness evaluation index is closer to the actual value under the actual physical process; further, according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the accuracy of the obtained robustness evaluation result of the motor to be evaluated is ensured.

[0075] It should be noted that, when determining the unrobustness of the motor to be evaluated, the parameter error of each design parameter is determined according to the design parameter information of each design parameter of the motor to be evaluated; and the optimization strategy of the motor to be evaluated is determined according to the robustness evaluation result and the parameter error of each design parameter. It can be understood that by introducing the parameter error of each design parameter and optimizing the design parameters of the motor to be evaluated based on the robustness evaluation result and the parameter error of each design parameter, the accuracy of the optimized design parameters is guaranteed.

[0076] Optionally, the design parameter information of each design parameter includes the actual value and parameter tolerance of the design parameter; on this basis, in one embodiment, Figure 3 As shown, a method for constructing a probability distribution model of each design parameter is provided, which refines the above S201 and specifically includes the following steps:

[0077] S301: For each design parameter, take the actual value of the design parameter as the mean, and determine the first standard deviation according to the parameter tolerance.

[0078] Optionally, for each design parameter, the actual value of the design parameter can be regarded as the theoretical value of the parameter, and therefore, the actual value of the design parameter can be taken as the mean; further, the parameter tolerance reflects the allowable error of the actual value of the design parameter, and therefore, one third of the parameter tolerance can be taken as the first standard deviation.

[0079] S302: construct a normal distribution function of the design parameters according to the mean and the first standard deviation.

[0080] Optionally, based on the expression of the normal distribution function, the mean and the first standard deviation are substituted to construct the normal distribution function of the design parameters.

[0081] S303: construct a probability distribution model of the design parameters according to the normal distribution function of the design parameters.

[0082] Optionally, the normal distribution function of the design parameters can be used as the probability distribution model of the design parameters.

[0083] In this embodiment, a normal distribution function of the design parameters is constructed by introducing a mean value and a first standard deviation, and based on the normal distribution function, the accuracy of the constructed probability distribution model of the design parameters is ensured.

[0084] Optionally, in one embodiment, Figure 4 As shown, a method for determining the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination is provided, and the above S203 is refined, specifically including the following steps:

[0085] S401, obtaining a trained robustness evaluation model.

[0086] The robustness evaluation model is a model for calculating the index value of the robustness evaluation index of the motor.

[0087] S402, for any parameter combination, input the parameter combination into a trained robustness evaluation model to obtain an evaluation index value of the robustness evaluation index of the motor to be evaluated under the parameter combination.

[0088] Optionally, for any parameter combination, the parameter combination is input into a trained robustness evaluation model, so that the trained robustness evaluation model calculates and analyzes the parameter sampling values ​​of each design parameter in the parameter combination according to the model parameters, and uses the output of the trained robustness evaluation model as the evaluation index value of the robustness evaluation index of the motor to be evaluated under the parameter combination.

[0089] In this embodiment, by introducing the robustness evaluation model, it is ensured that the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination is more in line with the actual situation, that is, the accuracy of the evaluation index value of the determined robustness evaluation index is guaranteed.

[0090] Optionally, in one embodiment, Figure 5 As shown, a method for determining a robustness evaluation result of a motor to be evaluated is provided, which specifically includes the following steps:

[0091] S501, determining an actual average value and an actual standard deviation of the robustness evaluation index according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0092] Optionally, based on statistical theory, the actual mean value and actual standard deviation of the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination are calculated and used as the actual mean value and actual standard deviation of the robustness evaluation index.

[0093] S502, determining a robustness evaluation result of the motor to be evaluated according to the actual average value, the actual standard deviation and the indicator requirement information of the robustness evaluation indicator.

[0094] The indicator requirement information includes but is not limited to the theoretical value and allowable error of the robustness evaluation indicator.

[0095] Optionally, the distribution of robustness evaluation indicators is analyzed based on the actual average value and the actual standard deviation; if the robustness evaluation indicator is determined to fluctuate within the allowable error near the theoretical value of the evaluation indicator based on the distribution of the robustness evaluation indicator, it can be determined that the motor to be evaluated is robust. Otherwise, it can be determined that the motor to be evaluated is not robust. Further, the robustness evaluation result of the motor to be evaluated can be generated based on the actual average value and the actual standard deviation.

[0096] In this embodiment, by introducing the actual mean value and actual standard deviation of the robustness evaluation index, since the actual mean value and actual standard deviation can reflect the distribution and fluctuation of the robustness evaluation index, combined with the indicator requirement information of the robustness evaluation index, the robustness of the motor to be evaluated can be evaluated more accurately, that is, the accuracy of the determined robustness evaluation result is guaranteed.

[0097] Optionally, in order to more intuitively describe the implementation process of the motor robustness determination method provided by the present application, in the embodiment of the present application, a permanent magnet synchronous motor using a tile-type surface-mounted permanent magnet is taken as an example to illustrate the motor robustness determination method provided by the application. The design parameters of the motor to be evaluated include: stator inner diameter, permanent magnet pole arc coefficient and permanent magnet thickness. After the motor to be evaluated is optimized, the values ​​of the stator inner diameter, permanent magnet pole arc coefficient and permanent magnet thickness are 120.8mm, 0.93 and 4.9mm respectively. Studies have shown that the structural parameters of the motor conform to the normal distribution, the average value μ of the normal distribution is the theoretical design value, and the standard deviation σ is 1 / 3 of the parameter tolerance. The normal distribution probability model satisfied by the stator inner diameter, permanent magnet pole arc coefficient and permanent magnet thickness is shown in Table 1. After determining the probability distribution of different design parameters, 10,000 sets of parameters that meet the normal distribution are randomly sampled.

[0098] Table 1 Design parameter information and normal distribution of each design parameter

[0099]

[0100] Build a Kriging proxy model of the motor. The model is based on the input parameters of the stator inner diameter, permanent magnet pole arc coefficient and permanent magnet thickness, and the output parameter is the average torque. For example Figure 6 As shown in the figure, there is a comparison between the estimated value output by the Kriging proxy model and the true value obtained by finite element simulation. It can be seen that the estimated value output by the constructed Kriging proxy model is basically consistent with the true value obtained by finite element simulation, and the accuracy of the Kriging proxy model meets the requirements.

[0101] Furthermore, 10,000 sets of stator inner diameter, permanent magnet pole arc coefficient and permanent magnet thickness parameters are substituted into the Kriging proxy model, and the average torque of the motor is calculated using the Kriging proxy model to obtain 10,000 sets of specific values ​​of average torque corresponding to the 10,000 sets of parameters, such as Figure 7 The figure shows the average torque distribution of the motor to be evaluated when the design parameters are offset due to tolerance. Figure 7 It can be seen that since the stator inner diameter, permanent magnet pole arc coefficient and permanent magnet thickness structural parameters of the motor to be evaluated satisfy the normal distribution, the average torque output after Monte Carlo simulation also satisfies the normal distribution. The normal distribution of the average torque fitting is N (30.7, 0.01 2), that is, when the input design parameters are affected by uncertainty factors and offset, the average torque fluctuates around the average value of 30.7Nm, and the standard deviation of the average torque is 0.01Nm. The design index of the motor to be evaluated requires an average torque ≥ 26.5Nm. Under ideal conditions, that is, assuming that the design parameters can be processed strictly according to the design values, and ignoring the offset of the design parameters due to uncertainty factors, the theoretical design value of the average torque of the motor to be evaluated is 30.73Nm. It can be seen that when the design parameters deviate from the theoretical design values ​​due to the influence of tolerances, the average torque distribution of the motor is more concentrated, there is no situation where the design index is not met, and the decrease is less than the theoretical design value. The design parameter optimization of the motor is robust.

[0102] Figure 8 FIG. 2 is a flow chart of a method for determining motor robustness in another embodiment. Based on the above embodiment, this embodiment provides an optional example of a method for determining motor robustness. Figure 8 The specific implementation process is as follows:

[0103] S801: Taking the actual value of each design parameter as the mean of each design parameter, and determining the first standard deviation of each design parameter according to the tolerance of each parameter.

[0104] S802: construct a normal distribution function of each design parameter according to the mean and the first standard deviation of each design parameter.

[0105] S803: construct a probability distribution model of each design parameter according to the normal distribution function of each design parameter.

[0106] S804: construct a probability distribution curve of each design parameter according to the probability distribution model of each design parameter.

[0107] S805 , randomly sampling from each probability distribution curve, and combining the parameter sampling values ​​of each design parameter obtained by random sampling to obtain at least two parameter combinations including the parameter sampling values ​​of each design parameter of the motor to be evaluated.

[0108] S806, obtaining a trained robustness evaluation model.

[0109] S807, input each parameter combination into the trained robustness evaluation model to obtain the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0110] S808, determining an actual average value and an actual standard deviation of the robustness evaluation index according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0111] S809, determining a robustness evaluation result of the motor to be evaluated according to the actual average value, the actual standard deviation, and the indicator requirement information of the robustness evaluation indicator.

[0112] Optionally, when determining the instability of the motor to be evaluated, the parameter error of each design parameter is determined based on the design parameter information of each design parameter of the motor to be evaluated; and the optimization strategy of the motor to be evaluated is determined based on the robustness evaluation result and the parameter error of each design parameter.

[0113] The specific process of the above S801-S809 can be found in the description of the above method embodiment. The implementation principle and technical effect are similar and will not be repeated here.

[0114] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0115] Based on the same inventive concept, the embodiment of the present application also provides a motor robustness determination device for implementing the motor robustness determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more motor robustness determination device embodiments provided below can refer to the limitations of the motor robustness determination method above, and will not be repeated here.

[0116] In an exemplary embodiment, Fig. 9 As shown, a motor robustness determination device 900 is provided, comprising: a model building module 910, a parameter determination module 920, an index determination module 930 and a robustness evaluation module 940, wherein:

[0117] A model building module 910, for building a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated;

[0118] A parameter determination module 920 is used to randomly sample each design parameter based on a probability distribution model of each design parameter, and combine the parameter sampling values ​​of each design parameter obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated; wherein each parameter combination includes the parameter sampling values ​​of each design parameter;

[0119] An index determination module 930 is used to determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination;

[0120] The robustness evaluation module 940 is used to determine the robustness evaluation result of the motor to be evaluated according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

[0121] The above-mentioned motor robustness determination device constructs a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated; based on the probability distribution model of each design parameter, randomly samples each design parameter, and combines the parameter sampling values ​​of each design parameter obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated; wherein each parameter combination includes the parameter sampling values ​​of each design parameter; determines the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination; and determines the robustness evaluation result of the motor to be evaluated according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination. The above-mentioned scheme, by constructing a probability distribution model of each design parameter, and by random sampling, obtains at least two parameter combinations including the parameter sampling values ​​of each design parameter of the motor to be evaluated, thereby ensuring that the evaluation index value of the obtained robustness evaluation index is closer to the actual value under the actual physical process; further, according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the accuracy of the obtained robustness evaluation result of the motor to be evaluated is ensured.

[0122] In one embodiment, the design parameter information of each design parameter includes the actual value and the parameter tolerance of the design parameter; the model building module 910 is specifically used to:

[0123] For each design parameter, the actual value of the design parameter is taken as the mean, and the first standard deviation is determined according to the parameter tolerance; based on the mean and the first standard deviation, the normal distribution function of the design parameter is constructed; based on the normal distribution function of the design parameter, the probability distribution model of the design parameter is constructed.

[0124] In one embodiment, the parameter determination module 920 is specifically used to:

[0125] For each design parameter, a design parameter probability distribution curve is constructed according to the probability distribution model of the design parameter; and random sampling is performed from the probability distribution curve.

[0126] In one embodiment, the indicator determination module 930 is specifically used to:

[0127] A trained robustness evaluation model is obtained; for any parameter combination, the parameter combination is input into the trained robustness evaluation model to obtain an evaluation index value of the robustness evaluation index of the motor to be evaluated under the parameter combination.

[0128] In one embodiment, the robustness evaluation module 940 is specifically used to:

[0129] According to the evaluation index values ​​of the robustness evaluation index of the motor to be evaluated under each parameter combination, the actual mean value and the actual standard deviation of the robustness evaluation index are determined; according to the actual mean value, the actual standard deviation and the index requirement information of the robustness evaluation index, the robustness evaluation result of the motor to be evaluated is determined.

[0130] In one embodiment, the motor robustness determination device 900 is further configured to:

[0131] When the unrobustness of the motor to be evaluated is determined, the parameter error of each design parameter is determined according to the design parameter information of each design parameter of the motor to be evaluated; and the optimization strategy of the motor to be evaluated is determined according to the robustness evaluation result and the parameter error of each design parameter.

[0132] Each module in the above motor robustness determination device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0133] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the robustness of a motor is implemented.

[0134] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0135] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0136] Constructing a probability distribution model of each design parameter according to design parameter information of each design parameter of the motor to be evaluated;

[0137] Based on the probability distribution model of each design parameter, each design parameter is randomly sampled, and the parameter sampling values ​​of each design parameter obtained by random sampling are combined to obtain at least two parameter combinations of the motor to be evaluated; wherein each parameter combination includes the parameter sampling values ​​of each design parameter;

[0138] Determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination;

[0139] According to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the robustness evaluation result of the motor to be evaluated is determined.

[0140] In one embodiment, the design parameter information of each design parameter includes the actual value and the parameter tolerance of the design parameter; when the processor executes the computer program to construct the probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated, the following steps are also implemented:

[0141] For each design parameter, the actual value of the design parameter is taken as the mean, and the first standard deviation is determined according to the parameter tolerance; based on the mean and the first standard deviation, the normal distribution function of the design parameter is constructed; based on the normal distribution function of the design parameter, the probability distribution model of the design parameter is constructed.

[0142] In one embodiment, when the processor executes the computer program to randomly sample each design parameter based on the probability distribution model of each design parameter, the processor further implements the following steps:

[0143] For each design parameter, a design parameter probability distribution curve is constructed according to the probability distribution model of the design parameter; and random sampling is performed from the probability distribution curve.

[0144] In one embodiment, when the processor executes the computer program to determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the following steps are also implemented:

[0145] A trained robustness evaluation model is obtained; for any parameter combination, the parameter combination is input into the trained robustness evaluation model to obtain an evaluation index value of the robustness evaluation index of the motor to be evaluated under the parameter combination.

[0146] In one embodiment, when the processor executes the computer program to determine the robustness evaluation result of the motor to be evaluated according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the following steps are also implemented:

[0147] According to the evaluation index values ​​of the robustness evaluation index of the motor to be evaluated under each parameter combination, the actual mean value and the actual standard deviation of the robustness evaluation index are determined; according to the actual mean value, the actual standard deviation and the index requirement information of the robustness evaluation index, the robustness evaluation result of the motor to be evaluated is determined.

[0148] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0149] When the unrobustness of the motor to be evaluated is determined, the parameter error of each design parameter is determined according to the design parameter information of each design parameter of the motor to be evaluated; and the optimization strategy of the motor to be evaluated is determined according to the robustness evaluation result and the parameter error of each design parameter.

[0150] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0151] Constructing a probability distribution model of each design parameter according to design parameter information of each design parameter of the motor to be evaluated;

[0152] Based on the probability distribution model of each design parameter, each design parameter is randomly sampled, and the parameter sampling values ​​of each design parameter obtained by random sampling are combined to obtain at least two parameter combinations of the motor to be evaluated; wherein each parameter combination includes the parameter sampling values ​​of each design parameter;

[0153] Determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination;

[0154] According to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the robustness evaluation result of the motor to be evaluated is determined.

[0155] In one embodiment, the design parameter information of each design parameter includes the actual value and the parameter tolerance of the design parameter; when the processor executes the computer program to construct the probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated, the following steps are also implemented:

[0156] For each design parameter, the actual value of the design parameter is taken as the mean, and the first standard deviation is determined according to the parameter tolerance; based on the mean and the first standard deviation, the normal distribution function of the design parameter is constructed; based on the normal distribution function of the design parameter, the probability distribution model of the design parameter is constructed.

[0157] In one embodiment, when the processor executes the computer program to randomly sample each design parameter based on the probability distribution model of each design parameter, the processor further implements the following steps:

[0158] For each design parameter, a design parameter probability distribution curve is constructed according to the probability distribution model of the design parameter; and random sampling is performed from the probability distribution curve.

[0159] In one embodiment, when the processor executes the computer program to determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the following steps are also implemented:

[0160] A trained robustness evaluation model is obtained; for any parameter combination, the parameter combination is input into the trained robustness evaluation model to obtain an evaluation index value of the robustness evaluation index of the motor to be evaluated under the parameter combination.

[0161] In one embodiment, when the processor executes the computer program to determine the robustness evaluation result of the motor to be evaluated according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the following steps are also implemented:

[0162] According to the evaluation index values ​​of the robustness evaluation index of the motor to be evaluated under each parameter combination, the actual mean value and the actual standard deviation of the robustness evaluation index are determined; according to the actual mean value, the actual standard deviation and the index requirement information of the robustness evaluation index, the robustness evaluation result of the motor to be evaluated is determined.

[0163] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0164] When the unrobustness of the motor to be evaluated is determined, the parameter error of each design parameter is determined according to the design parameter information of each design parameter of the motor to be evaluated; and the optimization strategy of the motor to be evaluated is determined according to the robustness evaluation result and the parameter error of each design parameter.

[0165] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0166] Constructing a probability distribution model of each design parameter according to design parameter information of each design parameter of the motor to be evaluated;

[0167] Based on the probability distribution model of each design parameter, each design parameter is randomly sampled, and the parameter sampling values ​​of each design parameter obtained by random sampling are combined to obtain at least two parameter combinations of the motor to be evaluated; wherein each parameter combination includes the parameter sampling values ​​of each design parameter;

[0168] Determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination;

[0169] According to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the robustness evaluation result of the motor to be evaluated is determined.

[0170] In one embodiment, the design parameter information of each design parameter includes the actual value and the parameter tolerance of the design parameter; when the processor executes the computer program to construct the probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated, the following steps are also implemented:

[0171] For each design parameter, the actual value of the design parameter is taken as the mean, and the first standard deviation is determined according to the parameter tolerance; based on the mean and the first standard deviation, the normal distribution function of the design parameter is constructed; based on the normal distribution function of the design parameter, the probability distribution model of the design parameter is constructed.

[0172] In one embodiment, when the processor executes the computer program to randomly sample each design parameter based on the probability distribution model of each design parameter, the processor further implements the following steps:

[0173] For each design parameter, a design parameter probability distribution curve is constructed according to the probability distribution model of the design parameter; and random sampling is performed from the probability distribution curve.

[0174] In one embodiment, when the processor executes the computer program to determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the following steps are also implemented:

[0175] A trained robustness evaluation model is obtained; for any parameter combination, the parameter combination is input into the trained robustness evaluation model to obtain an evaluation index value of the robustness evaluation index of the motor to be evaluated under the parameter combination.

[0176] In one embodiment, when the processor executes the computer program to determine the robustness evaluation result of the motor to be evaluated according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination, the following steps are also implemented:

[0177] According to the evaluation index values ​​of the robustness evaluation index of the motor to be evaluated under each parameter combination, the actual mean value and the actual standard deviation of the robustness evaluation index are determined; according to the actual mean value, the actual standard deviation and the index requirement information of the robustness evaluation index, the robustness evaluation result of the motor to be evaluated is determined.

[0178] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0179] When the unrobustness of the motor to be evaluated is determined, the parameter error of each design parameter is determined according to the design parameter information of each design parameter of the motor to be evaluated; and the optimization strategy of the motor to be evaluated is determined according to the robustness evaluation result and the parameter error of each design parameter.

[0180] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0181] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0182] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining the robustness of a motor, characterized in that: The method comprises: Constructing a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated; Based on the probability distribution model of each of the design parameters, randomly sample each of the design parameters, and combine the parameter sampling values ​​of each of the design parameters obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated; wherein each of the parameter combinations includes the parameter sampling values ​​of each of the design parameters; Determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination; The robustness evaluation result of the motor to be evaluated is determined according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

2. The method according to claim 1, characterized in that The design parameter information of each of the design parameters includes the actual value and the parameter tolerance of the design parameter; The step of constructing a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated includes: For each design parameter, taking the actual value of the design parameter as the mean, and determining a first standard deviation according to the parameter tolerance; Constructing a normal distribution function of the design parameter according to the mean and the first standard deviation; According to the normal distribution function of the design parameters, a probability distribution model of the design parameters is constructed.

3. The method according to claim 1, characterized in that The randomly sampling each of the design parameters based on the probability distribution model of each of the design parameters comprises: For each design parameter, constructing a probability distribution curve of the design parameter according to a probability distribution model of the design parameter; Random sampling is performed from the probability distribution curve.

4. The method according to claim 1, characterized in that: The step of determining the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination includes: Get the trained robustness evaluation model; For any parameter combination, the parameter combination is input into the trained robustness evaluation model to obtain the evaluation index value of the robustness evaluation index of the motor to be evaluated under the parameter combination.

5. The method according to claim 1, characterized in that Determining the robustness evaluation result of the motor to be evaluated according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination includes: Determining an actual average value and an actual standard deviation of the robustness evaluation index according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination; The robustness evaluation result of the motor to be evaluated is determined according to the actual average value, the actual standard deviation and the indicator requirement information of the robustness evaluation indicator.

6. The method according to claim 1, characterized in that The method further comprises: In the case where the motor to be evaluated is determined to be unrobust, determining a parameter error of each design parameter according to design parameter information of each design parameter of the motor to be evaluated; An optimization strategy for the motor to be evaluated is determined based on the robustness evaluation result and the parameter errors of the design parameters.

7. A device for determining motor robustness, characterized in that: The device comprises: A model building module, used to build a probability distribution model of each design parameter according to the design parameter information of each design parameter of the motor to be evaluated; A parameter determination module, configured to randomly sample each of the design parameters based on a probability distribution model of each of the design parameters, and combine the parameter sampling values ​​of each of the design parameters obtained by random sampling to obtain at least two parameter combinations of the motor to be evaluated; wherein each of the parameter combinations includes the parameter sampling values ​​of each of the design parameters; An index determination module, used to determine the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination; The robustness evaluation module is used to determine the robustness evaluation result of the motor to be evaluated according to the evaluation index value of the robustness evaluation index of the motor to be evaluated under each parameter combination.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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