Optical system bolt tightening torque optimization method considering multi-source uncertainty influence

Abstract

The invention provides an optical system bolt tightening torque optimization method considering multi-source uncertainty influence, and relates to the technical field of optical equipment, the method comprises the following steps: multiple groups of initial data are acquired, each group of initial data comprises N initial torque combinations, and N is a positive integer greater than 1; processing the N initial torque combinations to obtain the energy concentration ratio of each initial torque combination; based on the energy concentration ratio of each initial torque combination, evaluation parameters corresponding to each group of initial data are calculated, and the evaluation parameters are used for representing the distribution condition of the energy concentration ratios of the N initial torque combinations included in each group of initial data; and setting the initial data with the highest evaluation parameter in the multiple groups of initial data as target data, wherein the N initial torque combinations included in the target data are used for assembling an optical system. The assembly precision of the optical system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical devices, and particularly relates to an optimization method for the bolt tightening torque of an optical system considering the influence of multi-source uncertainties. Background Art

[0002] The optical system is a key component of high-precision opto-mechanical systems such as space cameras, seekers, and telescopes. The assembly stability of the optical system directly affects the use effect of subsequent devices. In the related art, the manufacturing precision of the optical system is improved to enhance the assembly precision during the assembly process. However, the assembly of the optical system is still affected by the torque during the assembly process. The precision of the optical system assembled with different torques is different, and the influence of the torque is not considered in the related art, resulting in the problem of low assembly precision of the optical system.

[0003] It can be seen that there is a problem of low assembly precision of the optical system in the related art. Summary of the Invention

[0004] Embodiments of the present invention provide an optimization method for the bolt tightening torque of an optical system considering the influence of multi-source uncertainties to solve the problem of low assembly precision of the optical system in the related art.

[0005] To solve the above problems, the present invention is implemented as follows:

[0006] In a first aspect, embodiments of the present invention provide an optimization method for the bolt tightening torque of an optical system considering the influence of multi-source uncertainties, including:

[0007] Obtain multiple groups of initial data, each group of initial data including N initial torque combinations, where N is a positive integer greater than 1;

[0008] Process the N initial torque combinations to obtain the energy concentration degree of each initial torque combination;

[0009] Based on the energy concentration degree of each initial torque combination, calculate the evaluation parameter corresponding to each group of initial data, and the evaluation parameter is used to characterize the distribution of the energy concentration degrees of the N initial torque combinations included in each group of initial data;

[0010] Set the initial data with the highest evaluation parameter among the multiple groups of initial data as the target data, and the N initial torque combinations included in the target data are used for assembling the optical system.

[0011] In one embodiment, the process of processing the N initial torque combinations to obtain the energy concentration degree of each initial torque combination includes:

[0012] Process the N initial torque combinations based on a preset torque directivity surrogate model to obtain the energy concentration of each initial torque combination;

[0013] Among them, the preset torque directivity surrogate model is obtained in the following manner:

[0014] Collect sample data, where the sample data includes a plurality of sample torque combinations, as well as the friction coefficient and pose deviation corresponding to each sample torque combination;

[0015] Input the friction coefficient and pose deviation into a preset simulation model in sequence to obtain the sample energy concentration corresponding to each sample torque;

[0016] Train an initial torque directivity surrogate model based on the plurality of sample torque combinations and the sample energy concentration corresponding to each sample torque to obtain the preset torque directivity surrogate model.

[0017] In one embodiment, the training of the initial torque directivity surrogate model based on the plurality of sample torque combinations and the sample energy concentration corresponding to each sample torque to obtain the preset torque directivity surrogate model includes:

[0018] Train an initial torque directivity surrogate model based on the plurality of sample torque combinations and the sample energy concentration corresponding to each sample torque to obtain an intermediate torque directivity surrogate model;

[0019] Calculate the test parameters corresponding to the intermediate torque directivity surrogate model;

[0020] When the test parameters meet the preset conditions, set the intermediate torque directivity surrogate model as the preset torque directivity surrogate model.

[0021] In one embodiment, the test parameters include at least one of the coefficient of determination, root mean square error, and mean relative error;

[0022] The preset conditions include at least one of the following:

[0023] The coefficient of determination is less than a preset coefficient of determination threshold;

[0024] The root mean square error is less than a preset root mean square error threshold;

[0025] The mean relative error is less than a preset relative error threshold.

[0026] In one embodiment, the collection of the sample data includes:

[0027] Collect the multiple sample torque combinations within a preset range based on a target method, as well as the friction coefficient and pose deviation corresponding to each sample torque combination;

[0028] Wherein, the target method is stratified random sampling or Latin hypercube sampling.

[0029] In one embodiment, calculating the evaluation parameter corresponding to each group of initial data based on the energy concentration degree of each initial torque combination includes:

[0030] Determine the maximum energy concentration degree and the minimum energy concentration degree of the N initial torque combinations corresponding to each group of initial data;

[0031] Based on the maximum energy concentration degree and the minimum energy concentration degree, calculate the median energy concentration degree and the energy concentration degree range corresponding to each group of initial data;

[0032] Based on the median energy concentration degree and the energy concentration degree range, calculate the evaluation parameter corresponding to each group of initial data.

[0033] In a second aspect, an optical system bolt tightening torque optimization device considering the influence of multi-source uncertainties provided by an embodiment of the present invention includes:

[0034] An acquisition module, configured to acquire multiple groups of initial data, each group of initial data includes N initial torque combinations, and N is a positive integer greater than 1;

[0035] A processing module, configured to process the N initial torque combinations to obtain the energy concentration degree of each initial torque combination;

[0036] A calculation module, configured to calculate the evaluation parameter corresponding to each group of initial data based on the energy concentration degree of each initial torque combination, and the evaluation parameter is used to characterize the distribution of the energy concentration degrees of the N initial torque combinations included in each group of initial data;

[0037] A setting module, configured to set the initial data with the highest evaluation parameter among the multiple groups of initial data as target data, and the N initial torque combinations included in the target data are used for assembling the optical system.

[0038] In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor, and when the computer program is executed by the processor, it implements the steps of the optical system bolt tightening torque optimization method considering the influence of multi-source uncertainties as described in the first aspect above.

[0039] Fourthly, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for optimizing the bolt tightening torque of an optical system considering multi-source uncertainty effects as described in the first aspect above are implemented.

[0040] Fifthly, an embodiment of the present invention further provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the steps of the method for optimizing the bolt tightening torque of an optical system considering multi-source uncertainty effects as described in the first aspect above are implemented.

[0041] In an embodiment of the present invention, multiple groups of initial data are obtained, and each group of initial data includes N initial torque combinations, where N is a positive integer greater than 1; the N initial torque combinations are processed to obtain the energy concentration degree of each initial torque combination; based on the energy concentration degree of each initial torque combination, an evaluation parameter corresponding to each group of initial data is calculated, and the evaluation parameter is used to characterize the distribution of the energy concentration degrees of the N initial torque combinations included in each group of initial data; the initial data with the highest evaluation parameter among the multiple groups of initial data is set as the target data, and the N initial torque combinations included in the target data are used for assembling the optical system. In this way, by calculating the evaluation parameter through the energy concentration degree of each initial torque combination, the evaluation parameter can evaluate the distribution of the energy concentration degrees of each initial torque combination in each group of initial data, and then the target data with the optimal energy concentration degree distribution is selected through the evaluation parameter. Assembling the optical system with the N initial torque combinations included in the target data can effectively improve the assembly accuracy of the optical system. Description of the Drawings

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments of the present invention will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0043] Figure 1 is a flowchart of a method for optimizing the bolt tightening torque of an optical system considering multi-source uncertainty effects provided by an embodiment of the present invention;

[0044] Figure 2 is a sampling schematic diagram of torque combinations, friction coefficients, and pose deviations provided by an embodiment of the present invention;

[0045] Figure 3 is one of the test schematic diagrams of the intermediate torque directivity surrogate model provided by an embodiment of the present invention;

[0046] Figure 4It is the second test schematic diagram of the intermediate torque directivity proxy model provided by the embodiment of the present invention;

[0047] Figure 5 It is the third test schematic diagram of the intermediate torque directivity proxy model provided by the embodiment of the present invention;

[0048] Figure 6 It is the flow schematic diagram of the bolt tightening torque optimization of the optical system considering the influence of multi-source uncertainty provided by the embodiment of the present invention;

[0049] Figure 7 It is the test result schematic diagram of the target data provided by the embodiment of the present invention;

[0050] Figure 8 It is the structural diagram of an optical system bolt tightening torque optimization device considering the influence of multi-source uncertainty provided by the embodiment of the present invention;

[0051] Figure 9 It is the structural diagram of an electronic device provided by the embodiment of the present invention. Specific embodiments

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] Please refer to Figure 1 , Figure 1 It is the flowchart of an optical system bolt tightening torque optimization method considering the influence of multi-source uncertainty provided by the embodiment of the present invention. As Figure 1 shown, it includes the following steps:

[0054] Step 101, obtain multiple groups of initial data, and each group of initial data includes N initial torque combinations, where N is a positive integer greater than 1.

[0055] The above multiple groups of initial data are N initial torque combinations randomly obtained from the original data, and the original data is sampled within a preset torque range and parameter range. By grouping the original data, multiple groups of initial data are obtained, and then the multiple groups of initial data are evaluated to select the optimal group of initial data as the target data, so that the assembly accuracy of the optical system adjusted by the target data is higher.

[0056] Among them, the original data can be sampled within a preset torque range and parameter range by means of stratified random sampling (SRS) or Latin hypercube sampling (LHS).

[0057] Step 102: Process the N initial torque combinations to obtain the energy concentration degree of each initial torque combination.

[0058] The above energy concentration degree is the energy concentration degree of the optical system. Among them, the higher the energy concentration degree, the higher the assembly accuracy of the optical system can be understood. In this embodiment, the N initial torque combinations in a set of initial data are processed to obtain the energy concentration degree of each initial torque combination in this set of initial data, and the adjustment effect after adjusting the optical system through each initial torque combination in this set of initial data is determined through the energy concentration degree.

[0059] In some embodiments, the N initial torque combinations can be processed by a preset torque directivity surrogate model to obtain the energy concentration degree of each initial torque combination.

[0060] In some embodiments, it may be to obtain parameters such as the friction coefficient and position deviation corresponding to each initial torque combination, and input all the parameters such as the friction coefficient and position deviation corresponding to each initial torque combination into the simulation model to obtain the energy concentration degree of each initial torque combination.

[0061] Step 103: Based on the energy concentration degree of each initial torque combination, calculate the evaluation parameter corresponding to each set of initial data. The evaluation parameter is used to characterize the distribution of the energy concentration degrees of the N initial torque combinations included in each set of initial data.

[0062] The above evaluation parameter is used to evaluate the distribution of the energy concentration degrees of each initial torque combination in a set of initial data. The higher the evaluation parameter, the more concentrated the distribution of the energy concentration degrees of each initial torque combination in this set of initial data, and the better the assembly accuracy after adjusting the optical system through each initial torque combination in this set of initial data.

[0063] It should be noted that the energy concentration degrees of each initial torque combination in a set of initial data are distributed within a certain range. The evaluation parameter can evaluate the range of the energy concentration degrees of each initial torque combination in a set of initial data, and / or evaluate the overall situation of the energy concentration degrees of each initial torque combination in a set of initial data.

[0064] Among them, in some embodiments, based on the energy concentration degree of each initial torque combination, the evaluation parameter corresponding to each group of initial data can be calculated. For example, the range of the energy concentration degree can be calculated through the energy concentration degree of each initial torque combination, and then the evaluation parameter can be calculated according to the range of the energy concentration degree. For example, the range of the energy concentration degree is multiplied by a preset coefficient to calculate the evaluation parameter. At this time, the evaluation parameter is used to evaluate the range of the energy concentration degree of each initial torque combination in a group of initial data.

[0065] In other embodiments, based on the energy concentration degree of each initial torque combination, the evaluation parameter corresponding to each group of initial data can be calculated. For example, the average value or median value of the energy concentration degree can be calculated through the energy concentration degree of each initial torque combination, and the average value or median value of the energy concentration degree is set as the evaluation parameter. At this time, the evaluation parameter is used to evaluate the overall situation of the energy concentration degree of each initial torque combination in a group of initial data.

[0066] Step 104: Set the initial data with the highest evaluation parameter among the multiple groups of initial data as the target data. The N initial torque combinations included in the target data are used to assemble the optical system.

[0067] The above target data is the initial parameter with the highest evaluation parameter, and the evaluation parameter is used to evaluate the distribution of the energy concentration degree of each initial torque combination in each group of initial data. The target data is the data with the optimal energy concentration degree distribution. Assembling the optical system through the N initial torque combinations included in the target data can effectively improve the assembly accuracy of the optical system.

[0068] In the embodiment of the present invention, multiple groups of initial data are obtained. Each group of initial data includes N initial torque combinations, and N is a positive integer greater than 1. The N initial torque combinations are processed to obtain the energy concentration degree of each initial torque combination. Based on the energy concentration degree of each initial torque combination, the evaluation parameter corresponding to each group of initial data is calculated. The evaluation parameter is used to characterize the distribution of the energy concentration degree of the N initial torque combinations included in each group of initial data. The initial data with the highest evaluation parameter among the multiple groups of initial data is set as the target data. The N initial torque combinations included in the target data are used to assemble the optical system. In this way, the evaluation parameter is calculated through the energy concentration degree of each initial torque combination. The evaluation parameter can evaluate the distribution of the energy concentration degree of each initial torque combination in each group of initial data. Then, the target data with the optimal energy concentration degree distribution is selected through the evaluation parameter. Assembling the optical system through the N initial torque combinations included in the target data can effectively improve the assembly accuracy of the optical system.

[0069] In one embodiment, the processing of the N initial torque combinations to obtain the energy concentration degree of each initial torque combination includes:

[0070] Process the N initial torque combinations based on a preset torque directivity surrogate model to obtain the energy concentration of each initial torque combination;

[0071] Among them, the preset torque directivity surrogate model is obtained through the following method:

[0072] Collect sample data, where the sample data includes multiple sample torque combinations, as well as the friction coefficient and pose deviation corresponding to each sample torque combination;

[0073] Input the friction coefficient and pose deviation into a preset simulation model in sequence to obtain the sample energy concentration corresponding to each sample torque;

[0074] Train an initial torque directivity surrogate model based on the multiple sample torque combinations and the sample energy concentration corresponding to each sample torque to obtain the preset torque directivity surrogate model.

[0075] The above preset torque directivity surrogate model can be a model constructed based on the Gaussian Process Regression (GPR) method. The preset torque directivity surrogate model establishes the relationship between the torque combination and the energy concentration, and the energy concentration corresponding to each initial torque combination can be calculated through the preset torque directivity surrogate model.

[0076] In the embodiment of the present invention, by collecting sample data, where the sample data includes multiple sample torque combinations, as well as the friction coefficient and pose deviation corresponding to each sample torque combination; input the friction coefficient and pose deviation into a preset simulation model in sequence to obtain the sample energy concentration corresponding to each sample torque; train an initial torque directivity surrogate model based on the multiple sample torque combinations and the sample energy concentration corresponding to each sample torque, so as to obtain the preset torque directivity surrogate model.

[0077] The above collection of sample data is specifically sampling within a reasonable parameter value range to obtain different sample torque combinations, as well as the friction coefficient and pose deviation corresponding to each sample torque combination.

[0078] In one embodiment, the collection of sample data includes:

[0079] Collect the multiple sample torque combinations, as well as the friction coefficient and pose deviation corresponding to each sample torque combination within a preset range based on a target method;

[0080] Among them, the target method is stratified random sampling or Latin hypercube sampling.

[0081] Exemplarily, the torque for tightening the bolts of the optical system is T ∈ [600, 800], with the unit of N*mm; the friction coefficient is divided into the thread friction coefficient and the end face friction coefficient, both taking values in the range of 0.15 - 0.2; the pose deviation includes the eccentricity errors of the primary mirror along the x-axis and y-axis and the tilt errors around the x-axis and y-axis. The eccentricity errors of the primary mirror along the x-axis and y-axis are ±0.01 mm, and the tilt errors around the x-axis and y-axis are ±0.0167 mm. Among them, the uncertainty of the friction coefficient is measured in an interval manner, and the uncertainty of the pose deviation is measured in a probabilistic manner.

[0082] For example, as Figure 2 shown, sample data is obtained by means of stratified random sampling or Latin hypercube sampling. The sample data set contains 13 feature variables, which can be further divided into 3 bolt tightening torques, 6 friction coefficients (distinguishing the thread friction coefficient and the end face friction coefficient), and 4 pose deviations of the primary mirror (distinguishing eccentricity and tilt). In this way, sample data is obtained through sampling, so as to facilitate the training of the model with the sample data and the sample energy concentration to obtain a preset torque directivity proxy model.

[0083] The above-mentioned preset simulation model can simulate the sample energy concentration of the optical system under the conditions of the friction coefficient and the pose deviation according to the friction coefficient and the pose deviation corresponding to each sample torque combination.

[0084] In the embodiment of the present invention, the feature variables are the sample torque combinations, the friction coefficient, and the pose deviation, and the target variable is the sample energy concentration. The initial torque directivity proxy model is trained with multiple sample torque combinations and the sample energy concentration corresponding to each sample torque to obtain a preset torque directivity proxy model.

[0085] In some embodiment modes, after obtaining the friction coefficient and the pose deviation corresponding to each sample torque combination, the friction coefficient and the pose deviation corresponding to each sample torque combination are normalized to obtain a normalization matrix to eliminate the influence of different dimensions and improve the accuracy of the preset torque directivity proxy model obtained by training.

[0086] In one embodiment, the training of the initial torque directivity proxy model with the multiple sample torque combinations and the sample energy concentration corresponding to each sample torque to obtain the preset torque directivity proxy model includes:

[0087] Training the initial torque directivity proxy model with the multiple sample torque combinations and the sample energy concentration corresponding to each sample torque to obtain an intermediate torque directivity proxy model;

[0088] Calculating the test parameters corresponding to the intermediate torque directivity proxy model;

[0089] When the test parameters meet the preset conditions, set the intermediate torque directivity surrogate model as the preset torque directivity surrogate model.

[0090] In the embodiment of the present invention, based on a plurality of sample torque combinations and the sample energy concentration corresponding to each sample torque, train the initial torque directivity surrogate model to obtain an intermediate torque directivity surrogate model; calculate the test parameters corresponding to the intermediate torque directivity surrogate model; when the test parameters meet the preset conditions, complete the model training, and at this time set the intermediate torque directivity surrogate model as the preset torque directivity surrogate model.

[0091] In some embodiments, after sampling a plurality of sample torque combinations and calculating the sample energy concentration corresponding to each sample torque combination, divide the plurality of sample torque combinations into training set data and test set data according to a preset ratio, train the initial torque directivity surrogate model with the training set data to obtain an intermediate torque directivity surrogate model; calculate the test parameters corresponding to the intermediate torque directivity surrogate model through the test set data.

[0092] In one embodiment, the test parameters include at least one of the coefficient of determination (R 2 ), root mean square error (RMSE), and mean relative error (MRE);

[0093] The preset conditions include at least one of the following:

[0094] The coefficient of determination is less than a preset coefficient of determination threshold;

[0095] The root mean square error is less than a preset root mean square error threshold;

[0096] The mean relative error is less than a preset relative error threshold.

[0097] For example, taking a certain SLR optical system as an example, the R 2 value of the test set data is 0.9684, and the corresponding RMSE value is only 0.0167. In order to intuitively display the prediction ability of the intermediate torque directivity surrogate model, Figure 3 shows the comparison of the prediction results of the intermediate torque directivity surrogate model for the test set (the test set has a total of 34 groups of data). It can be seen from Figure 3 that for most data, the predicted values coincide with or are very close to the true values, and only a few data points have certain deviations. This shows that the intermediate torque directivity surrogate model has high reliability and accuracy in predicting imaging quality.

[0098] Furthermore, as Figure 4As shown, in order to test the robustness and reliability of the intermediate torque directivity surrogate model and reduce the accidental errors caused by random factors, the dataset is randomly divided again according to the ratio of 8:2 (repeated 10 times), and the corresponding relative error data are calculated, as Figure 4 shown. It can be seen that the maximum relative error is 2.5%, the minimum relative error is 1.29%, and the average value is 1.95%. This indicates that under the conditions of multiple repeated tests, the average prediction accuracy of the intermediate torque directivity surrogate model reaches 98.05%, and the surrogate model shows high accuracy and consistency in predicting the imaging quality of the optical system.

[0099] Furthermore, as Figure 5 shown, in order to evaluate the performance of the intermediate torque directivity surrogate model and the accuracy of the optimal solution, and at the same time reduce the random errors that may be introduced in a single test, the present invention conducts 20 repeated tests on the intermediate torque directivity surrogate model. Each test will obtain the optimal energy concentration and the optimal torque combination results, and the corresponding relative error data are calculated. The results are as Figure 5 shown. By evaluating the optimal solutions of multiple tests, the maximum relative error is 4.24%, the minimum relative error is 0.37%, and the average relative error is only 2.32%. This indicates that the optimization result of the intermediate torque directivity surrogate model is extremely close to the true optimal solution, performs excellently in dealing with multi-factor perturbations and nonlinear optimization problems, and shows high robustness and consistency in multiple independent tests.

[0100] In one embodiment, based on the energy concentration of each initial torque combination, the evaluation parameters corresponding to each group of initial data are calculated, including:

[0101] Determine the maximum energy concentration and the minimum energy concentration of the N initial torque combinations corresponding to each group of initial data;

[0102] Based on the maximum energy concentration and the minimum energy concentration, calculate the median energy concentration and the energy concentration range corresponding to each group of initial data;

[0103] Based on the median energy concentration and the energy concentration range, calculate the evaluation parameters corresponding to each group of initial data.

[0104] It should be noted that the distribution of the energy concentration can be determined from the range of the energy concentration and / or the overall situation of the energy concentration. To improve the evaluation accuracy of the evaluation parameters, in the embodiments of the present invention, the range of the energy concentration and / or the overall situation of the energy concentration are combined to calculate the evaluation accuracy.

[0105] In an embodiment of the present invention, the maximum energy concentration and the minimum energy concentration of N initial torque combinations corresponding to each group of initial data are determined; based on the maximum energy concentration and the minimum energy concentration, the median energy concentration and the energy concentration range corresponding to each group of initial data are calculated; and based on the median energy concentration and the energy concentration range, the evaluation parameter corresponding to each group of initial data is calculated.

[0106] Among them, the maximum energy concentration and the minimum energy concentration can represent the range of the energy concentration of N initial torque combinations of a group of initial data, and the median energy concentration can represent the overall situation of the energy concentration of N initial torque combinations of a group of initial data.

[0107] In some embodiments, it may be to calculate the median energy concentration and the energy concentration range corresponding to each group of initial data, then calculate the difference between the median energy concentration and the energy concentration range, and set half of the difference as the evaluation parameter.

[0108] In some embodiments, based on the maximum energy concentration and the minimum energy concentration, the median energy concentration and the energy concentration range corresponding to each group of initial data are calculated, and then the median energy concentration and the energy concentration range are weighted to obtain the evaluation parameter corresponding to each group of initial data.

[0109] In some embodiments, based on the maximum energy concentration and the minimum energy concentration, the average energy concentration and the energy concentration range corresponding to each group of initial data are calculated, and then the average energy concentration and the energy concentration range are weighted to obtain the evaluation parameter corresponding to each group of initial data.

[0110] Furthermore, the steps of the embodiment of the present invention can be implemented by a double-layer nested uncertainty optimization solution algorithm. Among them, the outer layer of the algorithm uses the Bayesian optimization (BO) algorithm to solve the design variables, and the inner layer of the algorithm is based on the Gaussian Process Regression (GPR) surrogate model and uses the Monte Carlo simulation method (MCS) to calculate the energy concentration values corresponding to each design vector under the influence of various uncertain factors, so as to obtain the median and the interval range of the energy concentration corresponding to different groups of initial data, thereby realizing the determination of the target data.

[0111] Specifically, as Figure 6As shown, first, a sample torque combination is obtained by sampling in the SRS or LHS manner. The sample energy concentration is obtained by combining with a preset simulation model. An initial torque directivity surrogate model is established based on the GPR method. The initial torque directivity surrogate model is adjusted by the sample torque combination and the sample energy concentration to obtain a preset torque directivity surrogate model for the inner layer.

[0112] After obtaining the preset torque directivity surrogate model for the inner layer, through the BO algorithm for the outer layer, the number of explorations is set. Each exploration calculates a set of initial data to obtain an evaluation parameter. Specifically, the upper bound f i L (X) (maximum value) and the lower bound f i R (X) (minimum value) of the energy concentration of N initial torque combinations of a set of initial data are calculated by the inner layer algorithm. The evaluation parameter is calculated from the upper and lower bounds of the energy concentration.

[0113] After calculating the evaluation parameter for the current initial data each time, compare it with the existing evaluation parameters to obtain the initial data corresponding to the current optimal evaluation parameter.

[0114] Repeat the exploration until the number of explorations Iter reaches the set value Iter max , at this time, the evaluation parameters of all sets of initial data are calculated. The initial data corresponding to the evaluation parameter is used as the target data, so as to adjust the optical system through the N initial torque combinations [X1, X2,..., X n T included in the target data.

[0115] To verify the effectiveness of the BO algorithm in optimizing the torque combination to improve the energy concentration, a set of optimal torque combinations (i.e., the N initial torque combinations included in the target data) are obtained using the algorithm of the present invention. At the same time, other torque combinations are evenly sampled within the tightening torque value range. With different torque combinations as inputs, different median energy concentrations and energy concentration ranges can be obtained through the inner layer algorithm loop, as shown in the following table and Figure 7 shown.

[0116]

[0117] The results show that the optimized torque combination not only increases the median of the energy concentration by an average of 6.13%, but also reduces the fluctuation radius by an average of 14.05%. This indicates that the torque combination optimized by the algorithm provided by the invention not only effectively improves the level of the energy concentration, but also significantly reduces the volatility of the results, thus achieving the goal of reducing the sensitivity to uncertain variables.

[0118] Please refer to Figure 8 ,​Figure 8 This is the structural diagram of an optimization device for the bolt tightening torque of an optical system considering the influence of multi-source uncertainties provided by an embodiment of the present invention. As Figure 8 shown, the optimization device 800 for the bolt tightening torque of the optical system considering the influence of multi-source uncertainties includes:

[0119] An acquisition module 801, configured to acquire multiple groups of initial data, each group of initial data including N initial torque combinations, where N is a positive integer greater than 1;

[0120] A processing module 802, configured to process the N initial torque combinations to obtain the energy concentration degree of each initial torque combination;

[0121] A calculation module 803, configured to calculate an evaluation parameter corresponding to each group of initial data based on the energy concentration degree of each initial torque combination, where the evaluation parameter is used to characterize the distribution of the energy concentration degrees of the N initial torque combinations included in each group of initial data;

[0122] A setting module 804, configured to set the initial data with the highest evaluation parameter among the multiple groups of initial data as target data, and the N initial torque combinations included in the target data are used to assemble the optical system.

[0123] In one embodiment, the processing module 802 includes:

[0124] A processing unit, configured to process the N initial torque combinations based on a preset torque directivity surrogate model to obtain the energy concentration degree of each initial torque combination;

[0125] wherein, the preset torque directivity surrogate model is obtained through the following method:

[0126] Collect sample data, where the sample data includes multiple sample torque combinations, and the friction coefficient and pose deviation corresponding to each sample torque combination;

[0127] Input the friction coefficient and pose deviation into a preset simulation model in sequence to obtain the sample energy concentration degree corresponding to each sample torque;

[0128] Train an initial torque directivity surrogate model based on the multiple sample torque combinations and the sample energy concentration degree corresponding to each sample torque to obtain the preset torque directivity surrogate model.

[0129] In one embodiment, the training of the initial torque directivity surrogate model based on the multiple sample torque combinations and the sample energy concentration degree corresponding to each sample torque to obtain the preset torque directivity surrogate model includes:

[0130] Training the initial moment directivity surrogate model based on the multiple sample moment combinations and the sample energy concentration corresponding to each sample moment to obtain an intermediate moment directivity surrogate model;

[0131] Calculating the test parameters corresponding to the intermediate moment directivity surrogate model;

[0132] When the test parameters meet the preset conditions, setting the intermediate moment directivity surrogate model as the preset moment directivity surrogate model.

[0133] In one embodiment, the test parameters include at least one of the coefficient of determination, root mean square error, and mean relative error;

[0134] The preset conditions include at least one of the following:

[0135] The coefficient of determination is less than the preset coefficient of determination threshold;

[0136] The root mean square error is less than the preset root mean square error threshold;

[0137] The mean relative error is less than the preset relative error threshold.

[0138] In one embodiment, the collecting sample data includes:

[0139] Collecting the multiple sample moment combinations within a preset range based on a target method, as well as the friction coefficient and pose deviation corresponding to each sample moment combination;

[0140] Wherein, the target method is stratified random sampling or Latin hypercube sampling.

[0141] In one embodiment, the calculation module 803 includes:

[0142] A determination unit, configured to determine the maximum energy concentration and the minimum energy concentration of the N initial moment combinations corresponding to each group of initial data;

[0143] A first calculation unit, configured to calculate the median energy concentration and the energy concentration range corresponding to each group of initial data based on the maximum energy concentration and the minimum energy concentration;

[0144] A second calculation unit, configured to calculate the evaluation parameter corresponding to each group of initial data based on the median energy concentration and the energy concentration range.

[0145] The optical system bolt tightening torque optimization device considering the influence of multi-source uncertainty provided by the embodiments of the present invention can implement each process of the above-mentioned optical system bolt tightening torque optimization method considering the influence of multi-source uncertainty. The technical features correspond one by one and can achieve the same technical effects. To avoid repetition, they will not be elaborated here.

[0146] It should be noted that the optical system bolt tightening torque optimization device considering the influence of multi-source uncertainty in the embodiments of the present invention can be a device, or a component, an integrated circuit, or a chip in an electronic device.

[0147] The embodiments of the present invention also provide an electronic device. Refer to Figure 9 , Figure 9 is a schematic structural diagram of an electronic device provided by the embodiments of the present invention. The electronic device includes a memory 901, a processor 902, and a program or instruction running on the memory 901. When the program or instruction is executed by the processor 902, it can implement Figure 1 any step in the corresponding optical system bolt tightening torque optimization method embodiment considering the influence of multi-source uncertainty and achieve the same beneficial effects, which will not be elaborated here.

[0148] Among them, the processor 902 can be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or a complex programmable logic device (CPLD).

[0149] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above-mentioned optical system bolt tightening torque optimization method embodiment considering the influence of multi-source uncertainty can be completed by hardware related to program instructions, and the program can be stored in a readable medium.

[0150] The embodiments of the present invention also provide a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, it can implement the above-mentioned Figure 1Any step in the embodiment of the optimization method for the bolt tightening torque of the optical system considering multi-source uncertainty effects, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here. The storage medium, such as Read-Only Memory (ROM), Random Access Memory (RAM), magnetic disk or optical disc, etc.

[0151] The embodiment of the present invention also provides a computer program product, including computer instructions, which when executed by a processor, implement the steps in Figure 1 the optimization method for the bolt tightening torque of the optical system considering multi-source uncertainty effects as described above, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0152] The terms "first", "second", etc. in the embodiments of the present invention are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. In addition, the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices. In addition, in this application, the use of "and / or" means at least one of the connected objects. For example, A and / or B and / or C means including A alone, B alone, C alone, as well as the cases where A and B exist, B and C exist, A and C exist, and A, B, and C all exist.

[0153] It should be noted that in this article, the term "include", "comprise" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not clearly listed, or also includes elements inherent to such process, method, article or device. Without more limitations, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including that element.

[0154] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, air conditioner, or a second terminal device, etc.) to execute the methods of various embodiments of the present application.

[0155] The embodiments of the present application are described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them belong to the protection scope of the present application.

Claims

1. An optimization method for the bolt tightening torque of an optical system considering the influence of multi-source uncertainties, characterized in that, Including: Obtain multiple sets of initial data, each set of initial data including N initial torque combinations, where N is a positive integer greater than 1; Process the N initial torque combinations to obtain the energy concentration degree of each initial torque combination; Based on the energy concentration degree of each initial torque combination, calculate the evaluation parameter corresponding to each set of initial data, and the evaluation parameter is used to characterize the distribution of the energy concentration degrees of the N initial torque combinations included in each set of initial data; Set the initial data with the highest evaluation parameter among the multiple sets of initial data as the target data, and the N initial torque combinations included in the target data are used for assembling the optical system.

2. The method according to claim 1, characterized in that, The process of processing the N initial torque combinations to obtain the energy concentration degree of each initial torque combination includes: Process the N initial torque combinations based on a preset torque directivity surrogate model to obtain the energy concentration degree of each initial torque combination; Wherein, the preset torque directivity surrogate model is obtained through the following method: Collect sample data, the sample data including multiple sample torque combinations, and the friction coefficient and pose deviation corresponding to each sample torque combination; Input the friction coefficient and pose deviation into a preset simulation model in sequence to obtain the sample energy concentration degree corresponding to each sample torque; Based on the multiple sample torque combinations and the sample energy concentration degree corresponding to each sample torque, train an initial torque directivity surrogate model to obtain the preset torque directivity surrogate model.

3. The method according to claim 2, wherein The training of the initial torque directivity surrogate model based on the multiple sample torque combinations and the sample energy concentration degree corresponding to each sample torque to obtain the preset torque directivity surrogate model includes: Train an initial torque directivity surrogate model based on the multiple sample torque combinations and the sample energy concentration degree corresponding to each sample torque to obtain an intermediate torque directivity surrogate model; Calculate the test parameters corresponding to the intermediate torque directivity surrogate model; When the test parameters meet the preset conditions, set the intermediate torque directivity surrogate model as the preset torque directivity surrogate model.

4. The method according to claim 3, wherein The test parameters include at least one of the coefficient of determination, root mean square error, and mean relative error; The preset conditions include at least one of the following: The coefficient of determination is less than a preset coefficient of determination threshold; The root mean square error is less than a preset root mean square error threshold; The mean relative error is less than a preset relative error threshold.

5. The method according to claim 2, characterized in that, The collection of sample data includes: Collect the multiple sample torque combinations, and the friction coefficient and pose deviation corresponding to each sample torque combination within a preset range based on a target method; Wherein, the target method is stratified random sampling or Latin hypercube sampling.

6. The method according to any one of claims 1 to 5, characterized in that The calculation of the evaluation parameter corresponding to each set of initial data based on the energy concentration degree of each initial torque combination includes: Determine the maximum energy concentration degree and the minimum energy concentration degree of the N initial torque combinations corresponding to each set of initial data; Based on the maximum energy concentration degree and the minimum energy concentration degree, calculate the energy concentration median and energy concentration range corresponding to each set of initial data; Based on the median of the energy concentration and the energy concentration range, an evaluation parameter corresponding to each group of initial data is calculated.

7. An optical system bolt tightening torque optimization device considering the influence of multi-source uncertainties, characterized in that, Including: An acquisition module, configured to acquire multiple groups of initial data, where each group of initial data includes N initial torque combinations, and N is a positive integer greater than 1; A processing module, configured to process the N initial torque combinations to obtain the energy concentration of each initial torque combination; A calculation module, configured to calculate an evaluation parameter corresponding to each group of initial data based on the energy concentration of each initial torque combination, and the evaluation parameter is used to characterize the distribution of the energy concentration of the N initial torque combinations included in each group of initial data; A setting module, configured to set the initial data with the highest evaluation parameter among the multiple groups of initial data as target data, and the N initial torque combinations included in the target data are used for assembling the optical system.

8. An electronic device, characterized in that, Including: A processor, a memory, and a program stored on the memory and executable on the processor, and when the program is executed by the processor, the steps of the optical system bolt tightening torque optimization method considering the influence of multi-source uncertainty as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the optical system bolt tightening torque optimization method considering the influence of multi-source uncertainty as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that, Including computer instructions, and when the computer instructions are executed by a processor, the steps of the optical system bolt tightening torque optimization method considering the influence of multi-source uncertainty as described in any one of claims 1 to 6 are implemented.

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