Methods, devices, equipment, and storage media for optimizing gear parameters in speed reducers

Abstract

This invention provides a method, apparatus, device, and storage medium for optimizing gear parameters in a speed reducer. The method includes: acquiring a sample set of gear pairs in the speed reducer, as well as the gear parameters and modification ranges of the gear parameters; inputting a portion of the sample set as training samples into a preset speed reducer simulation model to obtain first simulation values ​​for each training sample under various operating conditions, the first simulation values ​​including transmission error and peak-to-peak value of transmission error; calculating the mean and variance of the transmission error and peak-to-peak value of the training sample under different operating conditions based on the first simulation values ​​of the same training sample, and determining them as second simulation values; performing fitting calculations based on the training samples and the second simulation values ​​to determine a surrogate model; and constructing a gear parameter optimization model based on the surrogate model and the modification range, so that the gear parameter optimization model outputs optimized values ​​for each gear parameter based on the second simulation values. This invention not only improves the NVH performance of the speed reducer but also enhances the versatility of gear parameters.

Description

Technical Field

[0001] This invention relates to the technical field of gear optimization, specifically to a method, apparatus, device, and storage medium for optimizing gear parameters in a reducer. Background Technology

[0002] With the rapid development of the automotive industry, vehicle power transmission systems are increasingly moving towards higher speeds, lighter weight, and electrification. As a major component of the vehicle power transmission system, the reducer mainly plays the role of reducing speed and increasing torque during power transmission. If the NVH (Noise, Vibration, and Harshness) performance of a reducer is poor, the reducer may produce a whistling sound during vehicle operation, affecting the user's driving experience. The NVH performance problems of a reducer are mainly caused by the transmission error of the reducer's gear pair. Transmission error is an important parameter for evaluating the quality of gear meshing. When the fluctuation of transmission error decreases, the vibration and noise during gear meshing will decrease, effectively reducing the whistling noise of the reducer. Therefore, optimizing gear parameters and micro-modifying the gears are commonly used to reduce the transmission error of the reducer and improve its NVH performance. However, when optimizing the transmission error of a reducer, on the one hand, because the same reducer cannot be suitable for various vehicle models under different operating conditions, the NVH performance of the reducer varies across different vehicle models, and the gear parameters are not universal; on the other hand, because micro-modifying the gears involves many variables of gear parameters, the optimization process is highly dimensional and inefficient.

[0003] Chinese patent CN115481499A discloses a method and system for optimizing gear transmission errors. It determines and reduces transmission errors based on the characteristic parameters to be optimized by importing samples of gear parameters into a gear transmission error calculation model. However, while this scheme optimizes transmission errors, it does not explain how the characteristic parameters to be optimized are applicable to various working conditions, resulting in poor parameter versatility. Chinese patent CN106763642B provides a noise reduction method and a gear reducer for electric vehicles. It determines micro-modification parameters based on transmission errors occurring during gear machining, and then modifies the gears of the reducer according to these parameters. However, this scheme has low modification efficiency, and the gear modification does not consider the working conditions, failing to simultaneously address the NVH performance of the reducer under various operating conditions.

[0004] Therefore, optimizing the gear parameters of the reducer to improve their versatility is an urgent problem to be solved. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the present invention provides a method, apparatus, device and storage medium for optimizing gear parameters of a reducer, so as to solve at least one of the above-mentioned technical problems.

[0006] In a first aspect, the present invention provides a method for optimizing gear parameters in a speed reducer, comprising: acquiring a sample set of gear pairs in a speed reducer, as well as gear parameters of the gear pairs and a modification range of the gear parameters, wherein the sample is a combination of any value of each gear parameter within the modification range; inputting a portion of the sample set as training samples into a preset speed reducer simulation model to obtain a first simulation value of each training sample under each operating condition, wherein the operating condition is the vehicle torque when the speed reducer is working, and the first simulation value includes transmission error and peak-to-peak value of transmission error; calculating the mean and variance of the transmission error and peak-to-peak value of the same training sample under different operating conditions, respectively, and determining them as second simulation values; performing fitting calculations based on the training samples and the second simulation values ​​to determine a surrogate model; constructing a gear parameter optimization model based on the surrogate model and the modification range, such that the gear parameter optimization model outputs optimized values ​​of each gear parameter based on the second simulation values.

[0007] In one embodiment of the present invention, the step of using a portion of the samples in the sample set as training samples and inputting them into a preset reducer simulation model to obtain the first simulation value of each training sample under each operating condition includes: constructing a Taguchi inner and outer surface based on the training samples and the operating conditions, wherein the training samples constitute the inner surface of the Taguchi inner and outer surface, and the operating conditions constitute the outer surface of the Taguchi inner and outer surface; and inputting each training sample and each operating condition into the reducer simulation model based on the Taguchi inner and outer surface, so that the reducer simulation model outputs the first simulation value of each training sample under each operating condition.

[0008] In one embodiment of the present invention, the surrogate model is divided into a first surrogate model, a second surrogate model, a third surrogate model, and a fourth surrogate model. The second simulation value includes the mean of the transmission error output by the first surrogate model, the variance of the transmission error output by the second surrogate model, the mean of the peak-to-peak value of the transmission error output by the third surrogate model, and the variance of the peak-to-peak value of the transmission error output by the fourth surrogate model. The step of determining the surrogate model by fitting the training samples to the second simulation value includes: fitting the training samples to the mean and variance of the transmission error respectively to determine the first surrogate model and the second surrogate model; and fitting the training samples to the mean and variance of the peak-to-peak value of the transmission error respectively to determine the third surrogate model and the fourth surrogate model.

[0009] In one embodiment of the present invention, the step of constructing a gear parameter optimization model based on the surrogate model and the modification range, so that the gear parameter optimization model outputs optimized values ​​of each gear parameter according to the second simulation value, includes: weighting and calculating the transmission error and the peak-to-peak value of the transmission error output by the first surrogate model and the second surrogate model respectively, to determine the objective function of the transmission error; weighting and calculating the transmission error and the peak-to-peak value of the transmission error output by the third surrogate model and the fourth surrogate model respectively, to determine the objective function of the peak-to-peak value of the transmission error; constructing the gear parameter optimization model according to the objective function of the transmission error, the objective function of the peak-to-peak value of the transmission error, the modification range of each gear parameter, and the constraint condition, wherein the constraint condition is that the peak-to-peak value of the transmission error is within a preset range threshold; obtaining the initial value of each gear parameter and inputting the initial value into the gear parameter optimization model to obtain an optimization set of the gear parameters, wherein the optimization set is formed by combining any combination of the optimized values ​​of each gear parameter within the modification range; selecting the combination of the optimized values ​​in the optimization set to determine the optimized value of each gear parameter.

[0010] In one embodiment of the present invention, after determining the surrogate model by fitting the training samples and the second simulation values, the method includes: using the remaining samples of the sample set as verification samples, inputting them into the reducer simulation model and each of the surrogate models, and outputting the first simulation value and the second simulation value of each verification sample, wherein the first simulation value of the verification sample includes the first transmission error and the first transmission error peak-to-peak value, and the second simulation value of the verification sample includes the mean and variance of the second transmission error, and the mean and variance of the second transmission peak-to-peak value; calculating the average relative error of each surrogate model based on the first simulation value and the second simulation value of each verification sample; wherein the mean and variance of the first transmission error of each verification sample, and the mean and variance of the first transmission error peak-to-peak value are calculated. The mean and variance of the peak-to-peak values ​​of the errors are calculated; the average relative error of the first surrogate model is determined by averaging the relative errors between the mean of the first transmission error and the mean of the second transmission error corresponding to each of the verification samples; the average relative error of the second surrogate model is determined by averaging the relative errors between the variances of the first transmission error and the variances of the second transmission error corresponding to each of the verification samples; the average relative error of the third surrogate model is determined by averaging the relative errors between the mean of the first transmission error and the mean of the second transmission error corresponding to each of the verification samples; and the average relative error of the fourth surrogate model is determined by averaging the relative errors between the variances of the first transmission error and the variances of the second transmission error corresponding to each of the verification samples.

[0011] In one embodiment of the present invention, after calculating the average relative error of each surrogate model based on the first simulation value and the second simulation value of each of the verification samples, the method includes: comparing the average relative error of each surrogate model with a preset error threshold; if the average relative error of the surrogate model is greater than the preset error threshold, then adding the training samples and reconstructing the surrogate model; if the average relative error of the surrogate model is less than or equal to the preset error threshold, then constructing the gear parameter optimization model.

[0012] In one embodiment of the present invention, after constructing a gear parameter optimization model based on the surrogate model and the modification range, and after the gear parameter optimization model outputs optimized values ​​of each gear parameter according to the second simulation value, the method includes: inputting the optimized values ​​and the initial values ​​of each gear parameter into the reducer simulation model, so as to output the transmission error corresponding to the optimized value and the transmission error corresponding to the initial value under each working condition through the reducer simulation model; comparing the transmission error corresponding to the optimized value and the transmission error corresponding to the initial value under each working condition, and determining the optimization result of the gear parameter based on the comparison result; if the optimization result does not meet the preset expectation, increasing the training samples and reconstructing the gear parameter optimization model.

[0013] In a second aspect, the present invention also provides a gear reducer parameter optimization device, comprising: a sample module, used to acquire a sample set of gear pairs in a reducer, as well as gear parameters of the gear pairs and a modification range of the gear parameters, wherein the sample is a combination of any value of each gear parameter within the modification range; a first simulation module, used to input a portion of the samples in the sample set as training samples into a preset reducer simulation model to obtain a first simulation value of each training sample under each working condition, wherein the working condition is the vehicle torque when the reducer is working, and the first simulation value includes transmission error and peak-to-peak value of transmission error; a second simulation module, used to calculate the mean and variance of the transmission error and peak-to-peak value of the same training sample under different working conditions, respectively, and determine them as second simulation values; a fitting module, used to perform fitting calculations based on the training samples and the second simulation values ​​to determine a surrogate model; and an optimization module, used to construct a gear parameter optimization model based on the surrogate model and the modification range, so that the gear parameter optimization model outputs optimized values ​​of each gear parameter based on the second simulation values.

[0014] In a third aspect, the present invention also provides an electronic device, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the reducer gear parameter optimization method as described in the above embodiments.

[0015] In a fourth aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer processor, causes the computer to perform the reducer gear parameter optimization method as described in the above embodiments.

[0016] The beneficial effects of this invention are as follows: This invention proposes a method, apparatus, device, and storage medium for optimizing gear parameters in a reducer. First, by inputting training samples into a preset reducer simulation model, the transmission error and peak-to-peak value of the training samples under various operating conditions are obtained, avoiding the uniformity of gear parameter optimization. Multiple operating conditions are considered during the optimization process, ensuring the universality of the gear parameters. Second, based on a surrogate model—that is, the mean and variance of the transmission error and peak-to-peak value corresponding to various operating conditions—and a gear parameter optimization model constructed with the modification range, the optimized values ​​of each gear parameter are obtained through the gear parameter optimization model, quickly completing the optimization of the reducer gear parameters. Furthermore, the optimized values ​​of each gear parameter are the optimal solutions, improving the NVH performance of the reducer and demonstrating high optimization efficiency. Third, because the optimized gear parameters are universal, the reducer can be compatible with various vehicle models, significantly reducing vehicle R&D costs and time.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0019] Figure 1 This is a schematic diagram illustrating the implementation environment of a reducer gear parameter optimization method according to an exemplary embodiment of the present invention;

[0020] Figure 2 This is a flowchart illustrating a method for optimizing gear parameters of a reducer, as shown in an exemplary embodiment of the present invention.

[0021] Figure 3This is a schematic diagram illustrating a toothed drum shape in an exemplary embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram illustrating the tooth profile angle in an exemplary embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram illustrating a toothed drum shape, as shown in an exemplary embodiment of the present invention;

[0024] Figure 6 This is a schematic diagram illustrating the tooth profile angle in an exemplary embodiment of the present invention;

[0025] Figure 7 This is a schematic diagram illustrating the optimization process in an exemplary embodiment of the present invention;

[0026] Figure 8 This is a schematic diagram illustrating the overall process of gear parameter optimization design in an exemplary embodiment of the present invention;

[0027] Figure 9 This is a block diagram illustrating a gear reducer parameter optimization device according to an exemplary embodiment of the present invention;

[0028] Figure 10 This is a schematic diagram illustrating the structure of a computer system suitable for implementing the electronic device of the present invention, as shown in an exemplary embodiment of the present invention. Detailed Implementation

[0029] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0030] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0031] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0032] In related technologies, reducers are only matched to one type of vehicle. That is, the gear parameters of the reducer gears are usually optimized only for a single working condition. As a result, the reducer transmission system needs to be redesigned during the development of each vehicle model, which greatly increases the vehicle's R&D costs.

[0033] Therefore, in order to solve the above problems, this invention considers multiple working conditions in the early stage of reducer development. By incorporating the influencing factor of working conditions into the gear parameter optimization simulation process, gear parameters suitable for multiple working conditions are obtained. Based on these gear parameters, the gear pair is modified so that the same reducer can be applied to multiple vehicle models.

[0034] Please see Figure 1 This is a schematic diagram illustrating the implementation environment of a reducer gear parameter optimization method, which is an exemplary embodiment of the present invention.

[0035] Reference Figure 1 As shown, the implementation environment includes a vehicle 101 and a reducer gear parameter optimization device 102. The reducer gear parameter optimization device 102 can perform reducer gear parameter optimization. This function optimizes the gear parameters of the reducer gear pair adapted to the vehicle 101 during development, thereby improving the NVH performance of the reducer in the vehicle 101, preventing the user experience from being affected by the squealing of the reducer gear transmission system, and ensuring that the optimized gear parameters can accommodate various operating conditions, resulting in high versatility of the reducer.

[0036] Please see Figure 2 The flowchart illustrates a method for optimizing gear parameters in a reducer, as shown in an exemplary embodiment of the present invention. This method can be applied to... Figure 1 The implementation environment shown is specifically configured in the reducer gear parameter optimization device 102. This method can also be applied to other exemplary implementation environments and specifically configured in other devices; this embodiment does not limit the implementation environment to which the method is applicable.

[0037] like Figure 2 As shown, in an exemplary embodiment, the reducer gear parameter optimization method includes at least steps S210 to S250, which are described in detail below:

[0038] Step S210: Obtain the sample set of gear pairs in the reducer, as well as the gear parameters and the modification range of the gear parameters. The sample is a combination of any value of each gear parameter within the modification range.

[0039] Specifically, the gear parameters of the primary and secondary gear pairs in the reducer are optimized separately. Although the modification ranges of the primary and secondary gear pairs are generally similar, the final modification results of the secondary and primary gear pairs are different. Combining them for optimization would involve a large number of design variables, i.e., gear parameters, leading to increased optimization complexity. Therefore, this embodiment optimizes the gear parameters of the primary and secondary gear pairs separately, making the modification of the primary and secondary gear pairs independent of each other. Each gear pair consists of a driving gear and a driven gear, and gear modification includes tooth direction modification and tooth profile modification. It should be noted that the gear parameters in this embodiment are microscopic parameters of the gears and do not involve the optimization of macroscopic gear parameters. The modification range of each gear parameter can be modified according to actual needs and is not limited thereto.

[0040] In one embodiment of the present invention, taking a single-stage gear pair as an example, the parameters of each gear and their corresponding modification ranges are shown in Table 1:

[0041] Table 1

[0042]

[0043] As shown in Table 1, the 16 gear parameters x1-x of this first-stage gear pair are... 16 and gear parameters x1-x 16 Each parameter corresponds to a specific modification range, and the modification includes both the left and right tooth surfaces of the gear. Specifically, gear parameters x1-x8 are related to the driving gear; gear parameters x1-x4 are gear parameters for tooth profile modification related to the driving gear; gear parameters x5-x8 are gear parameters for tooth direction modification related to the driving gear; gear parameters x9-x... 12 Gear parameters related to tooth profile modification of the driven gear, gear parameter x 13 -x 16 For gear parameters related to tooth profile modification of the driven gear, gear parameter x 13 -x 16 These are the gear parameters related to the tooth profile modification of the driven gear. That is, the gear parameters x1-x... 16 Take any value within each of the respective modification ranges, and the resulting combination of 16 values ​​is taken as a sample. Then, integrate multiple samples to obtain a sample set. The combination of values ​​of each gear parameter in each sample is different.

[0044] Please see Figures 3 to 6This is a schematic diagram showing the parameters of each gear in this invention, wherein... Figure 3 Cβ in the table refers to the gear parameters related to the tooth profile in tooth profile modification, such as the gear parameters x6, x8, and x in Table 1. 14 and x 16 ; Figure 4 fHβ in the table represents the gear parameters related to tooth inclination in tooth profile modification, such as the gear parameters x5, x7, and x in Table 1. 13 and x 15 ; Figure 5 In this context, Ca represents the gear parameters related to the tooth profile and the crown shape during tooth profile modification, such as the gear parameters x2, x4, and x in Table 1. 10 and x 12 ; Figure 6 In this context, fHa represents the gear parameters related to tooth profile inclination during tooth profile modification, such as the gear parameters x1, x3, x9, and x in Table 1. 11 .

[0045] In one embodiment of the present invention, the gear parameter optimization process of the second-stage gear pair differs from that of the first-stage gear pair only in the gear parameters; all other aspects are the same, and this embodiment will not elaborate further.

[0046] In one embodiment of the present invention, in order to reflect the characteristics of the sample space regarding gear parameters with as few samples as possible, the Latin hypercube experimental design method can be used to sample each gear parameter to make the sampling more uniform, thereby reducing the number of simulations and improving the simulation accuracy, thus obtaining a sample set. The number of samplings, i.e., the number of samples, can be adjusted according to actual needs. If higher simulation accuracy is required, the number of samples can be increased. This embodiment does not limit the sampling method or the number of samples.

[0047] By determining the sample set as described above, the accuracy of simulation calculations is improved, ensuring the precision of gear parameter optimization.

[0048] Step S220: A portion of the samples in the sample set are used as training samples and input into the preset reducer simulation model to obtain the first simulation value of each training sample under each working condition. The working condition is the vehicle torque when the reducer is working. The first simulation value includes the transmission error and the peak-to-peak value of the transmission error.

[0049] Specifically, determining the first simulation value includes: constructing the Taguchi inner and outer surfaces based on training samples and operating conditions, wherein the training samples constitute the inner surface of the Taguchi inner and outer surfaces, and the operating conditions constitute the outer surface of the Taguchi inner and outer surfaces; and inputting each training sample and each operating condition into the reducer simulation model based on the Taguchi inner and outer surfaces, so that the reducer simulation model outputs the first simulation value of each training sample under each operating condition.

[0050] In one embodiment of the present invention, in order to enable the same reducer to operate under different torque conditions and avoid noise from the reducer failing to meet usage standards due to changes in vehicle torque, multiple different operating conditions need to be considered during simulation calculations. Therefore, it is necessary to calculate the transmission error and peak-to-peak value of the transmission error for training samples under multiple operating conditions. Since the squealing noise of gears in current vehicle electric drive assemblies is mainly caused by the periodic changes in transmission error during gear meshing, resulting in order-characteristic noise, the transmission error affects the NVH performance of the reducer. The smaller the transmission error, the smoother the gear rotation and the lower the noise. Therefore, the transmission error and peak-to-peak value of the transmission error are used as optimization targets. For example, the peak-to-peak value of the first-order transmission error and the first-order transmission error of the first-stage gear pair of the reducer are used as optimization targets. It should be understood that the selection of operating conditions needs to simultaneously consider both relatively large and relatively small torques; that is, the operating conditions must include the maximum torque and minimum torque operating conditions involved in the reducer, so as to enable the same reducer to meet a wider range of operating conditions and improve the versatility of gear parameters.

[0051] In one embodiment of the present invention, the preset reducer simulation model can be built based on Romax (transmission system simulation), which is a model for dynamic simulation analysis of reducers.

[0052] In one embodiment of the present invention, all parameters that may affect the transmission error are considered in the reducer simulation model to improve the reliability of the first simulation value output by the reducer simulation model. The reducer simulation model should at least include components such as the electric drive assembly housing, the reducer gear shaft system, the left and right half shafts, and the electric drive suspension bracket. Its input training samples, i.e., different combinations of gear parameter values, output the transmission error and peak-to-peak value of each training sample under different operating conditions. For example, if there is one training sample with four operating conditions, then each training sample corresponds to four transmission errors and four peak-to-peak values ​​of transmission errors.

[0053] In one embodiment of the present invention, a majority of the samples in the sample set are typically used as training samples, and the remaining samples are used as validation samples to ensure the reliability of the simulation calculations and the feasibility of the gear parameter optimization results. For example, the sample set includes 200 samples, of which 180 are used as training samples and the remaining 20 are used as validation samples.

[0054] By combining various operating conditions for simulation calculations, the above method avoids optimizing gear parameters for only a single operating condition. While ensuring the reliability of the reducer simulation model, it improves the versatility of gear parameter optimization, making the NVH performance of the same reducer applicable to multiple operating conditions of multiple vehicle models.

[0055] Step S230: Based on the first simulation values ​​of the same training sample under different working conditions, calculate the mean and variance of the transmission error and the peak-to-peak value of the transmission error corresponding to the training sample, and determine them as the second simulation values.

[0056] Specifically, after obtaining the first simulated value, namely the simulated value of the transmission error and the peak-to-peak value of the transmission error, the mean of the transmission error, the variance of the transmission error, the mean of the peak-to-peak value of the transmission error, and the variance of the peak-to-peak value of the transmission error are calculated for each training sample. For example, when there are four operating conditions, the mean of the four transmission errors corresponding to the same training sample under the four operating conditions is calculated to obtain the mean of the transmission error corresponding to that training sample; the variance of the four transmission errors corresponding to the same training sample under the four operating conditions is calculated to obtain the variance of the transmission error corresponding to that training sample; the mean of the peak-to-peak values ​​of the four transmission errors corresponding to the same training sample under the four operating conditions is calculated to obtain the mean of the peak-to-peak values ​​of the transmission error corresponding to that training sample; and the variance of the peak-to-peak values ​​of the four transmission errors corresponding to the same training sample under the four operating conditions is calculated to obtain the variance of the peak-to-peak values ​​of the transmission error corresponding to that training sample. In other words, regardless of the number of operating conditions, the second simulation value of each training sample only includes four values: the mean of the transmission error, the variance of the transmission error, the mean of the peak-to-peak values ​​of the transmission error, and the variance of the peak-to-peak values ​​of the transmission error.

[0057] By combining the transmission errors under various working conditions in the above manner, the optimization process of transmission errors involves multiple working conditions, laying the foundation for the universality of gear parameter optimization.

[0058] Step S240: Perform fitting calculations based on the training samples and the second simulation values ​​to determine the surrogate model.

[0059] Specifically, determining the surrogate models includes: fitting the training samples to the mean and variance of the propagation error, respectively, to determine the first and second surrogate models; fitting the training samples to the mean and variance of the peak-to-peak values ​​of the propagation error, respectively, to determine the third and fourth surrogate models. The surrogate models are divided into the first, second, third, and fourth surrogate models, and the second simulation values ​​include the mean of the propagation error output by the first surrogate model, the variance of the propagation error output by the second surrogate model, the mean of the peak-to-peak values ​​of the propagation error output by the third surrogate model, and the variance of the peak-to-peak values ​​of the propagation error output by the fourth surrogate model. Each surrogate model is independent of the others.

[0060] In one embodiment of the present invention, the curve fitting between the training samples and the second simulated value can be automatically performed using existing software, which will not be elaborated upon in this embodiment.

[0061] Specifically, after determining the surrogate models, the process includes: using the remaining samples in the sample set as validation samples, inputting them into the reducer simulation model and each surrogate model, and outputting the first and second simulation values ​​for each validation sample. The first simulation value of the validation sample includes the first transmission error and the peak-to-peak value of the first transmission error, and the second simulation value of the validation sample includes the mean and variance of the second transmission error, as well as the mean and variance of the peak-to-peak value of the second transmission error. The average relative error of each surrogate model is calculated based on the first and second simulation values ​​of each validation sample. The average relative error of each surrogate model is compared with a preset error threshold. If the average relative error of the surrogate model is greater than the preset error threshold, training samples are added, and the surrogate model is reconstructed. If the average relative error of the surrogate model is less than or equal to the preset error threshold, a gear parameter optimization model is constructed.

[0062] In one embodiment of the present invention, to ensure the accuracy of the surrogate models, each surrogate model needs to be tested. Specifically, validation samples are used to check whether the average relative error of each surrogate model exceeds a preset error threshold. For example, the preset error threshold can be set to 10%. When the average relative error of any surrogate model is less than or equal to 10%, the accuracy of that surrogate model is acceptable. When the average relative error of any surrogate model is greater than 10%, the accuracy of that surrogate model is poor, and more training samples are needed to improve its accuracy. The preset error threshold can be adjusted according to actual conditions. For example, if higher accuracy is required for the surrogate models, the preset error threshold can be set to 5%, and the preset error thresholds for each surrogate model can differ to some extent.

[0063] The average relative error of each proxy model was determined using the following method:

[0064] Calculate the mean and variance of the first transmission error for each validation sample, as well as the mean and variance of the peak-to-peak value of the first transmission error; average the relative errors between the mean of the first transmission error and the mean of the second transmission error for each validation sample to determine the average relative error of the first surrogate model; average the relative errors between the variances of the first transmission error and the variances of the second transmission error for each validation sample to determine the average relative error of the second surrogate model; average the relative errors between the mean of the first transmission error and the mean of the second transmission error for each validation sample to determine the average relative error of the third surrogate model; average the relative errors between the variances of the first transmission error and the variances of the second transmission error for each validation sample to determine the average relative error of the fourth surrogate model.

[0065] In embodiments of the present invention, the average relative error of each proxy model is calculated as the average relative error between the proxy model and the reducer simulation model. For example, assuming there are 20 verification samples, the values ​​of each gear parameter in each verification sample are substituted into the reducer simulation model and the first proxy model, respectively. Based on the first transmission error of each working condition corresponding to the verification sample output by the reducer simulation model, the mean of the first transmission error corresponding to the verification sample is calculated. The mean of the first transmission error is compared with the mean of the second transmission error of the same verification sample output by the first proxy model to calculate the relative error of the first transmission error and the second transmission error corresponding to each verification sample. Then, the average of the relative errors of the first transmission error and the second transmission error corresponding to the 20 verification samples is taken as the average relative error of the first proxy model. The average relative error of other proxy models can be obtained in the same way.

[0066] The above methods ensured the calculation accuracy of each proxy model and improved the precision of gear parameter optimization.

[0067] Step S250: Construct a gear parameter optimization model based on the surrogate model and the modification range, so that the gear parameter optimization model outputs the optimized values ​​of each gear parameter according to the second simulation value.

[0068] Specifically, the optimized value is determined in the following way:

[0069] The transmission error and peak-to-peak value of the transmission error output by the first and second surrogate models are weighted and summed to determine the objective function of the transmission error. The transmission error and peak-to-peak value of the transmission error output by the third and fourth surrogate models are weighted and summed to determine the objective function of the peak-to-peak value of the transmission error. Based on the objective function of the transmission error, the objective function of the peak-to-peak value of the transmission error, the modification range of each gear parameter, and the constraints, a gear parameter optimization model is constructed. The constraint is that the peak-to-peak value of the transmission error is within a preset threshold range. The initial values ​​of each gear parameter are obtained and input into the gear parameter optimization model to obtain the optimization set of gear parameters. The optimization set is formed by combining any combination of optimization values ​​of each gear parameter within the modification range. The combination of optimization values ​​in the optimization set is selected to determine the optimization value of each gear parameter.

[0070] In one embodiment of the present invention, the gear parameter optimization model is expressed by the formula:

[0071] Formula (1) for min(T,P)

[0072]

[0073]

[0074]

[0075] In Equation (1), T represents the transmission error of the gear pair of the reducer, P represents the peak-to-peak value of the transmission error of the gear pair of the reducer, and min represents minimizing the transmission error and the peak-to-peak value of the transmission error; in Equation (2), T represents the transmission error of the gear pair of the reducer, Tμ is the mean of the transmission error, Tσ is the variance of the transmission error; λ is the weight of the transmission error, 0≤λ≤1; in Equation (3), P represents the peak-to-peak value of the transmission error of the reducer, Pμ is the mean of the peak-to-peak value of the transmission error, Pσ is the variance of the peak-to-peak value of the transmission error, and λ is the weight of the peak-to-peak value of the transmission error, 0≤λ≤1; in Equation (4), Xi is the gear parameter, Xil is the upper limit of the gear parameter, Xiu is the lower limit of the gear parameter, n represents the nsigma optimization performed, for example, n is 6 when performing 6sigma optimization, σ represents the variance of the transmission error and the peak-to-peak value of the transmission error, p μ +np σ <0.2 is the constraint condition for the peak-to-peak value of the propagation error, that is, 0.2 is the preset range threshold for the peak-to-peak value of the propagation error, which can be adjusted according to actual needs.

[0076] In one embodiment of the present invention, the weights of the mean and variance corresponding to the transmission error and the peak-to-peak value of the transmission error can be adjusted according to actual needs. For example, when the noise is as small as possible, the larger the weight, the smaller the noise. If it is desired that the noise is more stable under different operating conditions, the weight can be increased adaptively.

[0077] In one embodiment of the present invention, an optimized set of gear parameters is obtained, and the optimized values ​​of each gear parameter that are relatively optimal in the optimized set are selected. The judgment of relative optimality is adjusted according to actual needs. For example, the closer the transmission errors of each working condition are, the more optimal the optimized value of each gear parameter is.

[0078] Please see Figure 7 This is a schematic diagram illustrating the optimization process in an exemplary embodiment of the present invention. Figure 7 X1-X 16 Let T_s and T_μ be the gear parameters, representing the surrogate models for the variance and mean of the transmission error, i.e., the first surrogate model and the second generation model, respectively; let P_s and P_μ be the surrogate models for the variance and mean of the peak-to-peak value of the transmission error, i.e., the third surrogate model and the fourth surrogate model, respectively; let Output_T_s and Output_T_μ be the outputs of the surrogate models for the variance and mean of the transmission error, and let Output_P_s and Output_P_μ be the outputs of the surrogate models for the variance and mean of the transmission error; let Objective_Min_T be the objective function for the transmission error, let Objective_Min_P be the objective function for the peak-to-peak value of the transmission error, and let Constraint_P be the constraint condition for the peak-to-peak value of the transmission error.

[0079] Continue to refer to Figure 7 As shown, first set the gear parameters X1-X 16 Initial values ​​for each parameter are input as T_s, T_μ, P_s, and P_μ, respectively, and output as Output_T_s, Output_T_μ, Output_P_s, and Output_P_μ, respectively. Objective_Min_T (the objective function for propagating the error) is determined using Output_T_s and Output_T_μ, and Objective_Min_P (the objective function for propagating the peak-to-peak value of the error) is determined using Output_P_s and Output_P_μ. Finally, based on Objective_Min_P, Objective_Min_T, and the constraint condition Constraint_P, the gear parameters X1-X are obtained. 16 The optimized set is selected by choosing the optimized values ​​of gear parameters X1-X1.

[0080] In one embodiment of the present invention, after the gear parameter optimization model is constructed, the target particle swarm optimization algorithm can be used to perform optimization analysis and calculation to obtain the Pareto optimal set, that is, the optimized set of gear parameters.

[0081] The above method enables the rapid calculation of optimized values ​​for a set of gear parameters applicable to various working conditions using the established gear parameter optimization model, thereby improving the optimization efficiency and versatility of gear parameters.

[0082] Specifically, after determining the optimized values, the process includes: inputting the optimized and initial values ​​of each gear parameter into the reducer simulation model, so that the reducer simulation model outputs the transmission error corresponding to the optimized value and the transmission error corresponding to the initial value under each working condition; comparing the transmission error corresponding to the optimized value and the transmission error corresponding to the initial value under each working condition, and determining the optimization result of the gear parameters based on the comparison result; if the optimization result does not meet the preset expectation, then adding training samples and reconstructing the gear parameter optimization model.

[0083] In one embodiment of the present invention, the optimized transmission error under various operating conditions generally shows a decreasing trend compared to the transmission error before optimization, but this does not mean that the transmission error under every operating condition will decrease after optimization. Taking four operating conditions as an example, assuming that the transmission errors of the four operating conditions before optimization are 1, 2, 3, and 4 respectively; after optimization, they may be 2.2, 2.5, 2.3, and 2.3 respectively.

[0084] The above methods ensure the accuracy of the gear parameter optimization model, improve the precision and versatility of gear parameter optimization, and enable the same reducer to be matched with multiple vehicle models without the need to develop corresponding reducers for each vehicle model, thus reducing R&D costs and time.

[0085] Please see Figure 8 This is a schematic diagram illustrating the overall process of gear parameter optimization design, as shown in an exemplary embodiment of the present invention. Figure 8 As shown, the overall process of gear parameter optimization includes: first, determining the number of gear parameters and operating conditions involved in the optimization, as well as the optimization objective and constraints. The optimization objective is the transmission error and the peak-to-peak value of the transmission error, and the constraint is that the peak-to-peak value of the transmission error is within a preset threshold range. Then, samples of gear parameters are extracted and divided into training and validation samples. Each validation sample and each operating condition is then substituted into the reducer simulation model to obtain the first simulation value for each training sample under each operating condition. The second simulation value is calculated from the first simulation value to construct each surrogate model, and the accuracy of the surrogate model is verified using the validation samples. If the accuracy of each surrogate model meets the requirements, the gear optimization model is built. If not, more training samples are extracted, and the surrogate model is rebuilt. After the gear parameter optimization model is built, an optimization set of gear parameters is obtained based on the model. The relatively optimal optimization values ​​of each gear parameter are selected, and the optimization results are verified based on these values. If the optimization results meet expectations, the gear pair of the reducer is modified according to the optimization values ​​of each gear parameter. If the optimization results do not meet expectations, more samples are extracted again, and the gear parameter optimization model is rebuilt. It should be understood that other specific optimization details have been described in the above embodiments and will not be repeated here.

[0086] Please see Figure 9 This is a block diagram illustrating a gear reducer parameter optimization device according to an exemplary embodiment of the present invention. This device can be applied to... Figure 1 The implementation environment shown in this embodiment does not limit the implementation environment to which the device is applicable.

[0087] like Figure 9 As shown, the exemplary reducer gear parameter optimization device includes: a sample module 901, a first simulation module 902, a second simulation module 903, a fitting module 904, and an optimization module 905.

[0088] The sample module 901 is used to obtain the sample set of the gear pair in the reducer, as well as the gear parameters of the gear pair and the modification range of the gear parameters. The sample is a combination of any value of each gear parameter within the modification range.

[0089] The first simulation module 902 is used to take a portion of the samples in the sample set as training samples and input them into the preset reducer simulation model to obtain the first simulation value of each training sample under each working condition. The working condition is the vehicle torque when the reducer is working. The first simulation value includes the transmission error and the peak-to-peak value of the transmission error.

[0090] The second simulation module 903 is used to calculate the mean and variance of the transmission error and the peak-to-peak value of the transmission error corresponding to the training sample under different working conditions based on the first simulation value of the same training sample, and determine it as the second simulation value.

[0091] The fitting module 904 is used to correct the vehicle's second fuel level based on the current mileage, fuel consumption, and corrected fuel level, and to determine the vehicle's current fuel level.

[0092] The optimization module 905 is used to construct a gear parameter optimization model based on the surrogate model and the modification range, so that the gear parameter optimization model outputs the optimized values ​​of each gear parameter according to the second simulation value.

[0093] It should be noted that the reducer gear parameter optimization device and the reducer gear parameter optimization method provided in the above embodiments belong to the same concept. The specific way of performing each step has been described in detail in the system embodiments, and will not be repeated here.

[0094] Embodiments of the present invention also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device enables the reducer gear parameter optimization method provided in the above embodiments.

[0095] Please see Figure 10 A schematic diagram of a computer system suitable for implementing embodiments of the present invention is shown. It should be noted that... Figure 10 The computer system 1000 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0096] like Figure 10 As shown, the computer system 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from Storage Unit 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004.

[0097] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0098] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of the present invention.

[0099] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the aforementioned reducer gear parameter optimization method. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0100] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0101] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0102] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for optimizing gear parameters in a speed reducer, characterized in that, include: Obtain a sample set of gear pairs in the reducer, as well as the gear parameters of the gear pairs and the modification range of the gear parameters, wherein the sample is a combination of any value of each gear parameter within the modification range; A portion of the samples in the sample set are used as training samples and input into a preset reducer simulation model to obtain the first simulation value of each training sample under each working condition. The working condition is the vehicle torque when the reducer is working. The first simulation value includes the transmission error and the peak-to-peak value of the transmission error. Based on the first simulation values ​​of the same training sample under different working conditions, the mean and variance of the transmission error and the peak-to-peak value of the transmission error corresponding to the training sample are calculated respectively, and determined as the second simulation value. The proxy model is determined by fitting the training samples with the second simulation value. The proxy model is divided into a first proxy model, a second proxy model, a third proxy model, and a fourth proxy model. A gear parameter optimization model is constructed based on the surrogate model and the modification range, and the gear parameter optimization model outputs the optimized values ​​of each gear parameter according to the second simulation value; wherein, the transmission error and the peak-to-peak value of the transmission error output by the first surrogate model and the second surrogate model are weighted and calculated to determine the objective function of the transmission error; the transmission error and the peak-to-peak value of the transmission error output by the third surrogate model and the fourth surrogate model are weighted and calculated to determine the objective function of the peak-to-peak value of the transmission error; the gear parameter optimization model is constructed based on the objective function of the transmission error, the objective function of the peak-to-peak value of the transmission error, the modification range of each gear parameter, and the constraint condition, wherein the constraint condition is that the peak-to-peak value of the transmission error is within a preset range threshold; the initial value of each gear parameter is obtained and input into the gear parameter optimization model to obtain the optimization set of the gear parameters, the optimization set being formed by combining any combination of the optimized values ​​of each gear parameter within the modification range; the combination of the optimized values ​​in the optimization set is selected to determine the optimized value of each gear parameter.

2. The method for optimizing gear parameters of a reducer as described in claim 1, characterized in that, The step of using a portion of the samples in the sample set as training samples and inputting them into a preset reducer simulation model to obtain the first simulation value of each training sample under each operating condition includes: The Taguchi inner and outer surfaces are constructed based on the training samples and the operating conditions, wherein the training samples constitute the inner surface of the Taguchi inner and outer surfaces, and the operating conditions constitute the outer surface of the Taguchi inner and outer surfaces. Based on the Taguchi inner and outer surfaces, each training sample and each operating condition are input into the reducer simulation model, so that the reducer simulation model outputs the first simulation value of each training sample under each operating condition.

3. The method for optimizing gear parameters of a reducer as described in claim 1, characterized in that, The second simulation value includes the mean of the transmission error output by the first proxy model, the variance of the transmission error output by the second proxy model, the mean of the peak-to-peak value of the transmission error output by the third proxy model, and the variance of the peak-to-peak value of the transmission error output by the fourth proxy model. The step of determining the surrogate model by fitting the training samples with the second simulation values ​​includes: The training samples are fitted to the mean and variance of the propagation error respectively to determine the first surrogate model and the second surrogate model; The training samples are fitted with the mean and variance of the peak-to-peak values ​​of the propagation error, respectively, to determine the third surrogate model and the fourth surrogate model.

4. The method for optimizing gear parameters of a reducer as described in claim 3, characterized in that, After determining the surrogate model by fitting the training samples with the second simulation values, the process includes: The remaining samples in the sample set are used as verification samples and input into the reducer simulation model and each of the surrogate models. The first simulation value and the second simulation value of each verification sample are output respectively. The first simulation value of the verification sample includes the first transmission error and the peak-to-peak value of the first transmission error. The second simulation value of the verification sample includes the mean and variance of the second transmission error, and the mean and variance of the peak-to-peak value of the second transmission error. The average relative error of each surrogate model is calculated based on the first simulation value and the second simulation value of each of the verification samples. Specifically, the mean and variance of the first transmission error of each of the verification samples, as well as the mean and variance of the peak-to-peak value of the first transmission error, are calculated. The average relative error of the first surrogate model is determined by averaging the relative errors between the mean of the first transmission error and the mean of the second transmission error corresponding to each of the verification samples. The average relative error of the second surrogate model is determined by averaging the relative errors between the variance of the first transmission error and the variance of the second transmission error corresponding to each of the verification samples. The average relative error of the third surrogate model is determined by averaging the relative errors between the mean of the first transmission error and the mean of the second transmission error corresponding to each of the verification samples. The average relative error of the fourth surrogate model is determined by averaging the relative errors between the variances of the first and second transmission errors corresponding to each of the verification samples.

5. The method for optimizing gear parameters of a reducer as described in claim 4, characterized in that, After calculating the average relative error of each surrogate model based on the first simulation value and the second simulation value of each of the verification samples, the process includes: The average relative error of each of the aforementioned proxy models is compared with a preset error threshold. If the average relative error of the proxy model is greater than the preset error threshold, then the training samples are increased and the proxy model is reconstructed. If the average relative error of the proxy model is less than or equal to the preset error threshold, then the gear parameter optimization model is constructed.

6. The method for optimizing gear parameters of a reducer as described in claim 1, characterized in that, After constructing a gear parameter optimization model based on the proxy model and the modification range, and making the gear parameter optimization model output the optimized values ​​of each gear parameter according to the second simulation value, the process includes: The optimized values ​​and initial values ​​of each gear parameter are respectively input into the reducer simulation model, so that the transmission error corresponding to the optimized value and the transmission error corresponding to the initial value under each working condition are output through the reducer simulation model; The transmission error corresponding to the optimized value under each of the aforementioned working conditions is compared with the transmission error corresponding to the initial value, and the optimization result for the gear parameters is determined based on the comparison result. If the optimization result does not meet the preset expectation, the training samples are increased and the gear parameter optimization model is reconstructed.

7. A gear parameter optimization device for a reducer, characterized in that, include: The sample module is used to obtain a sample set of gear pairs in the reducer, as well as the gear parameters of the gear pairs and the modification range of the gear parameters. The sample is a combination of any value of each gear parameter within the modification range. The first simulation module is used to take a portion of the samples in the sample set as training samples and input them into a preset reducer simulation model to obtain the first simulation value of each training sample under each working condition. The working condition is the vehicle torque when the reducer is working. The first simulation value includes the transmission error and the peak-to-peak value of the transmission error. The second simulation module is used to calculate the mean and variance of the transmission error and the peak-to-peak value of the transmission error corresponding to the training sample under different working conditions based on the first simulation value of the same training sample under different working conditions, and determine them as the second simulation value. The fitting module is used to perform fitting calculations based on the training samples and the second simulation values ​​to determine the surrogate model, which is divided into a first surrogate model, a second surrogate model, a third surrogate model, and a fourth surrogate model. An optimization module is used to construct a gear parameter optimization model based on the surrogate model and the modification range, so that the gear parameter optimization model outputs optimized values ​​for each gear parameter according to the second simulation value; wherein, the transmission error and the peak-to-peak value of the transmission error output by the first surrogate model and the second surrogate model are weighted and summed to determine the objective function of the transmission error; the transmission error and the peak-to-peak value of the transmission error output by the third surrogate model and the fourth surrogate model are weighted and summed to determine the objective function of the peak-to-peak value of the transmission error; the gear parameter optimization model is constructed based on the objective function of the transmission error, the objective function of the peak-to-peak value of the transmission error, the modification range of each gear parameter, and the constraint condition, wherein the constraint condition is that the peak-to-peak value of the transmission error is within a preset range threshold; the initial value of each gear parameter is obtained and input into the gear parameter optimization model to obtain an optimization set of the gear parameters, wherein the optimization set is formed by combining any combination of the optimized values ​​of each gear parameter within the modification range; the combination of the optimized values ​​in the optimization set is selected to determine the optimized value of each gear parameter.

8. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the gear reduction parameter optimization method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that enables the computer to execute the gear parameter optimization method for the reducer as described in any one of claims 1 to 6.

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