Optical performance simulation method, device and storage medium of optical assembly

Abstract

The application provides an optical performance simulation method, device and storage medium of an optical assembly. The simulation method comprises: obtaining initial values of a plurality of component parameters of the optical assembly, and establishing an initial model of the optical assembly according to the initial values of the plurality of component parameters; determining a plurality of key parameters from the plurality of component parameters according to the initial model; determining a plurality of candidate values of each key parameter according to the initial values of the plurality of key parameters; combining the candidate values of the plurality of key parameters through a robustness design algorithm to obtain a plurality of candidate parameter value groups; simulating the initial model by respectively configuring the plurality of candidate parameter value groups to obtain simulation results of optical performance indexes corresponding to the plurality of candidate parameter value groups respectively; and determining optimal parameter values from the plurality of candidate values of each key parameter through the robustness design algorithm based on the simulation results, so as to obtain an optimal parameter value group. The simulation method can improve the efficiency and accuracy of optical simulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optical assemblies, and in particular to an optical performance simulation method and device for optical assemblies and a storage medium. BACKGROUND

[0002] In the design of optical assemblies, there are strict requirements for two optical performance indicators, i.e., brightness value and brightness uniformity. Optical simulation plays a crucial role in the design and optimization of optical systems. However, due to the complexity of optical systems, the simulation process often requires a large amount of computing resources and time, and especially when considering multiple uncertainty factors, the efficiency and stability of the simulation results may be severely affected. Therefore, how to improve the efficiency of optical simulation and ensure the reliability of the results has become an important issue in the field of optical design. SUMMARY

[0003] To solve the above technical problems, the present disclosure provides an optical performance simulation method, device and storage medium for optical assemblies.

[0004] To achieve the above-mentioned purposes, in a first aspect, the present application provides an optical performance simulation method for optical assemblies, which comprises:

[0005] obtaining initial values of a plurality of component parameters of an optical assembly, and establishing an initial model of the optical assembly according to the initial values of the plurality of component parameters;

[0006] determining a plurality of key parameters from the plurality of component parameters according to the initial model;

[0007] determining a plurality of candidate values for each key parameter according to the initial values of the plurality of key parameters;

[0008] combining the candidate values of the plurality of key parameters through a robust design algorithm to obtain a plurality of candidate parameter value groups; each candidate parameter value group includes the values of the plurality of key parameters, and the value of each key parameter is one of the plurality of candidate values;

[0009] configuring the initial model with the plurality of candidate parameter value groups respectively for simulation to obtain simulation results of optical performance indicators corresponding to the plurality of candidate parameter value groups respectively;

[0010] determining an optimal parameter value for each key parameter from the plurality of candidate values through a robust design algorithm based on the simulation results of the optical performance indicators corresponding to the plurality of candidate parameter value groups respectively, thereby obtaining an optimal parameter value group; the optimal parameter value group includes the values of the plurality of key parameters, and the value of each key parameter is the corresponding optimal parameter value.

[0011] The optical performance simulation method of the optical assembly provided in the application comprises the following steps: obtaining initial values of a plurality of component parameters of the optical assembly, establishing an initial model of the optical assembly according to the initial values of the plurality of component parameters, determining a plurality of key parameters according to the initial model, determining a plurality of candidate values of each key parameter according to the initial values of the plurality of key parameters, combining the candidate values of the plurality of key parameters to obtain a plurality of candidate parameter value groups through a robust design algorithm, simulating the initial model by respectively configuring the plurality of candidate parameter value groups, obtaining simulation results of optical performance indexes corresponding to the plurality of candidate parameter value groups respectively, and determining optimal parameter values from the plurality of candidate values of each key parameter based on the simulation results of the optical performance indexes corresponding to the plurality of candidate parameter value groups respectively through the robust design algorithm, so as to obtain an optimal parameter value group. The simulation method introduces a robust design algorithm for the complexity and multivariable influencing factors of the optical system, reduces the influence of uncertain factors in the simulation process, can quickly determine the optimal parameter value group, and thus can improve the efficiency and precision of optical simulation.

[0012] In a second aspect, the application further provides an optical performance simulation device of an optical assembly, which comprises a processor and a memory; the memory is coupled with the processor, and is used for storing computer program codes, the computer program codes comprising computer instructions, which, when executed by the processor, cause the optical performance simulation device of the optical assembly to perform the optical performance simulation method of the optical assembly according to the first aspect.

[0013] In a third aspect, the application further provides a computer readable storage medium, which stores computer programs or instructions, which, when executed by an optical performance simulation device of an optical assembly, cause the optical performance simulation device of the optical assembly to perform the optical performance simulation method of the optical assembly according to the first aspect.

[0014] Additional aspects and advantages of the application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a flowchart of the optical performance simulation method of the optical assembly provided in the embodiments of the application;

[0016] Figure 2 is a first structural schematic diagram of the optical assembly provided in the embodiments of the application;

[0017] Figure 3 is Figure 2 is a sectional structural diagram of the optical assembly shown in FIG. 8 along the sectioning line k-k;

[0018] Figure 4 is a second structural schematic diagram of an optical assembly provided by an embodiment of the present application;

[0019] Figure 5 is a third structural schematic diagram of an optical assembly provided by an embodiment of the present application.

[0020] The following is a description of the reference signs:

[0021] optical assembly 100

[0022] first glass layer 11

[0023] second glass layer 12

[0024] shielding layer 13

[0025] light-reflecting member 14

[0026] light-emitting element 15

[0027] light guide strip 16

[0028] adhesive layer 17

[0029] light-in surface 161

[0030] light-out surface 162

[0031] The following detailed description will describe the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0033] In addition, the terms “first”, “second”, and the like in the specification of the present application are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms “include” and “have” and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0034] It should be noted that the features in the embodiments of the present application can be combined with each other without conflict.

[0035] Please refer to Figure 1 The present application provides an optical performance simulation method of an optical assembly, the simulation method comprising steps S1-S6, specifically as follows:

[0036] Step S1, obtaining initial values of a plurality of component parameters of the optical assembly, and establishing an initial model of the optical assembly according to the initial values of the plurality of component parameters;

[0037] Step S2, determining a plurality of key parameters from the plurality of component parameters according to the initial model;

[0038] Step S3, determining a plurality of candidate values of each key parameter according to the initial values of the plurality of key parameters;

[0039] Step S4, combining the candidate values of the plurality of key parameters by a robust design algorithm to obtain a plurality of candidate parameter value groups; each candidate parameter value group includes the values of the plurality of key parameters, and the value of each key parameter is one of the plurality of candidate values;

[0040] Step S5, configuring the initial model with the plurality of candidate parameter value groups respectively to perform simulation, and obtaining simulation results of optical performance indexes corresponding to the plurality of candidate parameter value groups respectively;

[0041] Step S6, determining an optimal parameter value from the plurality of candidate values of each key parameter based on the simulation results of the optical performance indexes corresponding to the plurality of candidate parameter value groups respectively by the robust design algorithm, thereby obtaining an optimal parameter value group; the optimal parameter value group includes the values of the plurality of key parameters, and the value of each key parameter is the corresponding optimal parameter value.

[0042] The optical performance simulation method of the optical assembly provided in the application comprises the following steps: obtaining initial values of a plurality of component parameters of the optical assembly, establishing an initial model of the optical assembly according to the initial values of the plurality of component parameters, determining a plurality of key parameters according to the initial model, determining a plurality of candidate values of each key parameter according to the initial values of the plurality of key parameters, combining the candidate values of the plurality of key parameters by a robust design algorithm to obtain a plurality of candidate parameter value groups, respectively configuring the initial model with the plurality of candidate parameter value groups to perform simulation, obtaining simulation results of optical performance indexes corresponding to the plurality of candidate parameter value groups respectively, and determining optimal parameter values from the plurality of candidate values of each key parameter based on the simulation results of the optical performance indexes corresponding to the plurality of candidate parameter value groups respectively by the robust design algorithm, so as to obtain an optimal parameter value group. The simulation method introduces a robust design algorithm in view of the complexity and multivariable influencing factors of the optical system, reduces the influence of uncertain factors in the simulation process, and can quickly determine the optimal parameter value group, thereby improving the efficiency and precision of optical simulation.

[0043] Please refer to Figures 2-5 , Figure 2 is a first structural schematic diagram of the optical assembly provided in the embodiments of the application. Figure 3 is Figure 2 is a sectional structure diagram of the optical assembly shown in FIG. 1 along a sectioning line k-k. Figure 4 is a second structural schematic diagram of the optical assembly provided in the embodiments of the application. Figure 5 is a third structural schematic diagram of the optical assembly provided in the embodiments of the application.

[0044] The optical performance simulation method of the optical assembly is introduced in detail by taking the optical assembly as an ambient light as an example. As shown in FIG. 1, the optical assembly 100 comprises a plurality of first glass layers 11, a second glass layer 12, a shielding layer 13, a light-reflecting piece 14, a plurality of light-emitting elements 15, a light guide strip 16 and an adhesive layer 17. Figure 3

[0045] Among them, the first glass layer 11 and the second glass layer 12 are stacked along a first direction, and the shielding layer 13 and the light-reflecting piece 14 are located between the first glass layer 11 and the second glass layer 12.

[0046] The light guide strip 16 is adhered to one side of the second glass layer 12 away from the first glass layer 11 through the adhesive layer 17, and the light guide strip 16 has an incident light surface 161 and an emergent light surface 162.

[0047] ​The plurality of light-emitting elements 15 are spaced apart from the light-incident surface 161 of the light guide strip 16 and are evenly arranged along the extending direction of the light guide strip 16. The light emitted by the plurality of light-emitting elements 15 can enter the light guide strip 16 through the light-incident surface 161, and the light emitted through the light-exit surface 162 can enter the second glass layer 12 from the side of the second glass layer 12 away from the first glass layer 11. The reflector 14 is used to reflect the light transmitted in the second glass layer 12 so that the reflected light exits from the side of the second glass layer 12 away from the first glass layer 11.

[0048] For example, such as Figures 2-3 As shown, the first glass layer 11 and the second glass layer 12 are stacked along the X-axis direction, and the light guide strip 16 is bonded to the second glass layer 12 through the adhesive layer 17. The light guide strip 16 extends along the Z-axis direction, and the plurality of light-emitting elements 15 are arranged along the Z-axis direction. The light-incident surface 161 of the light guide strip 16 faces the light-emitting elements 15. The X-axis direction is perpendicular to the Z-axis direction.

[0049] In the embodiments of this application, the optical component 100 includes a plurality of first glass layers 11, second glass layers 12, shielding layers 13, reflectors 14, a plurality of light-emitting elements 15, light guide strips 16, and adhesive layers 17. In other embodiments, the optical component 100 may also include other components, which are not limited here.

[0050] The initial value of the multiple component parameters of the optical assembly 100 refers to acquiring at least one parameter of each component. For example, the parameters of any component among the first glass layer 11, the second glass layer 12, the shielding layer 13, the reflector 14, the multiple light-emitting elements 15, the light guide strip 16, and the adhesive layer 17 may include the component's geometric dimensional parameters (e.g., length, width, thickness, surface shape, distance from other components, etc.) and material property parameters (e.g., material type, transmittance, reflectance, refractive index, diffusivity, etc.).

[0051] The parameters of the plurality of light-emitting elements 15 may also include the arrangement spacing L3 (i.e., the spacing between adjacent light-emitting elements 15) and light source characteristic parameters (e.g., the color, wavelength, luminous flux, etc. of the emitted light).

[0052] The number of reflective elements 14 can be multiple, and the parameters of the reflective elements 14 may also include the arrangement of the reflective elements 14. For example, Figure 4 The reflective elements 14 are arranged randomly, that is, the size of each reflective element 14 is random and the spacing between adjacent reflective elements 14 is also random; Figure 3The reflective elements 14 are arranged in a decreasing size pattern, that is, the size of each reflective element 14 gradually decreases along the direction away from the light-emitting element 15. Figure 5 The reflective elements 14 are arranged in a progressively larger manner, that is, the size of each reflective element 14 gradually increases along the direction away from the light-emitting element 15. Of course, in other embodiments, the reflective elements 14 can also be arranged in other ways, which are not limited here.

[0053] In some embodiments, determining multiple key parameters from the plurality of component parameters based on the initial model includes:

[0054] Based on the initial model and the preset key parameter mapping table, multiple key parameters are determined from the multiple component parameters.

[0055] The preset key parameter mapping table records the correspondence between different initial models and different key parameter groups, where each key parameter group includes multiple key parameters. The preset key parameter mapping table can be pre-configured by engineers.

[0056] by Figures 2-5 Taking the optical component 100 shown as an example, in some embodiments, the plurality of key parameters include at least one of the following parameters:

[0057] The surface shape of the first glass layer 11;

[0058] The arrangement spacing L3 of the plurality of light-emitting elements 15;

[0059] The luminous flux of the light-emitting element 15;

[0060] The distance L1 between the light-incident surface 161 of the light guide strip 16 and the light-emitting element 15;

[0061] The distance between the center of the light-incident surface 161 of the light guide strip 16 and the center of the light-emitting element 15 in the first direction;

[0062] The arrangement of the reflective elements 14; and,

[0063] The width L2 of the light guide strip 16.

[0064] In some embodiments, determining multiple candidate values ​​for each key parameter based on the initial values ​​of the multiple key parameters includes:

[0065] Based on the preset candidate value mapping table for each key parameter and the initial values ​​of the multiple key parameters, multiple candidate values ​​for each key parameter are determined.

[0066] The preset candidate value mapping table for each key parameter records the correspondence between different initial values ​​of the key parameter and different candidate value groups. Each candidate value group includes multiple candidate values. The preset key parameter mapping table can be pre-configured by engineers.

[0067] For example, in one embodiment, the plurality of key parameters include the surface shape of the first glass layer 11 (A), the arrangement spacing L3 of the plurality of light-emitting elements 15 (B), the luminous flux of the light-emitting elements 15 (C), the distance between the center of the light-incident surface 161 of the light guide strip 16 and the center of the light-emitting element 15 in the first direction (D), the distance L1 between the light-incident surface 161 of the light guide strip 16 and the light-emitting element 15 (E), the arrangement of the reflectors 14 (F), and the width L2 of the light guide strip 16 (G). For ease of description, different identifiers are used to represent different key parameters in the embodiments of this application. Among them, A represents the surface shape of the first glass layer 11, B represents the arrangement spacing L3 of the plurality of light-emitting elements 15, C represents the luminous flux of the light-emitting element 15, D represents the distance between the center of the light-incident surface 161 of the light guide strip 16 and the center of the light-emitting element 15 in the first direction, E represents the distance L1 between the light-incident surface 161 of the light guide strip 16 and the light-emitting element 15, F represents the arrangement of the reflector 14, and G represents the width L2 of the light guide strip 16.

[0068] For example, please refer to Table 1, which shows the correspondence between the seven key parameters A to G and their candidate values. The level value 1 for each key parameter represents its initial value, and levels 1 to 3 represent multiple candidate values ​​for that key parameter. The differences between the multiple candidate values ​​and the initial value of each key parameter are all within the tolerance range corresponding to that key parameter. Taking the luminous flux (C) of the light-emitting element 15 as an example, its initial value is 0.48 mm, and its candidate values ​​are 0.48 mm, 0.53 mm, and 0.6 mm. Taking the distance (D) between the center of the light-incident surface 161 of the light guide strip 16 and the center of the light-emitting element 15 in the first direction as an example, its initial value is centered (i.e., the distance between the center of the light-incident surface 161 of the light guide strip 16 and the center of the light-emitting element 15 in the first direction is zero), and its candidate values ​​are centered, offset by 0.5mm (i.e., the distance between the center of the light-incident surface 161 of the light guide strip 16 and the center of the light-emitting element 15 in the first direction is 0.5mm), and offset by 1.25mm (i.e., the distance between the center of the light-incident surface 161 of the light guide strip 16 and the center of the light-emitting element 15 in the first direction is 1.25mm). It should be noted that this application does not limit the number of candidate values ​​for each key parameter; the number of candidate values ​​for each key parameter can be 2, 3, 4, etc.

[0069] Table 1. Schematic diagram of the correspondence between key parameters and candidate values.

[0070]

[0071] Among them, the robust design algorithm, also known as the Taguchi design algorithm, is a method widely used in product design and experimental design. Its core objective is to find the combination of parameters that makes the product or process more stable in the operating environment. The specific steps of determining multiple candidate parameter value groups through the robust design algorithm are existing technologies and will not be elaborated here.

[0072] Please refer to Table 2, which shows multiple candidate parameter value groups obtained by combining the candidate values ​​of multiple key parameters shown in Table 1 using the robust design algorithm. Table 2 is also called an orthogonal matrix. In Table 2, each key parameter is also called a factor, and the candidate value of each key parameter is also called the level value of that factor. Standard orthogonal matrices include L8, L9, and L18. L8 is suitable for experimental designs with 3 factors, each with 2 level values; L9 is suitable for experimental designs with 4 factors, each with 3 level values; and L18 is suitable for experimental designs with 1 factor with 2 level values ​​and 7 factors with 3 level values. Since the embodiment of this application includes 7 factors, an error factor H is added to generate the standard orthogonal matrix L18. H does not represent any key parameter of the optical component and is a virtual parameter. As shown in Table 1, A has 2 candidate values, and B to G each have 3 candidate values. A complete permutation simulation requires 2*3^7 = 4374 simulations. However, using a robust design algorithm for simulation requires only 18 simulations. After simple statistical processing of the outputs from these 18 simulations, the optimal values ​​for each key parameter can be determined. Although the number of simulations is significantly reduced, the optimal results obtained from the robust design algorithm are close to those obtained from the complete permutation method. Therefore, using the robust design algorithm for simulation improves simulation efficiency.

[0073] Table 2. Schematic diagram of candidate parameter value groups

[0074]

[0075] In some embodiments, the simulation method further includes:

[0076] Based on the multiple candidate parameter value groups, multiple error parameter value groups are determined; wherein each error parameter value group includes the values ​​of the multiple key parameters, and at least one of the multiple key parameters is an error parameter, and there is a preset error between the value of each error parameter and one of its candidate values;

[0077] The initial model is configured with the multiple error parameter groups for simulation, and the simulation results of the optical performance indicators corresponding to the multiple error parameter groups are obtained.

[0078] Furthermore, the simulation results based on the optical performance indices corresponding to the multiple candidate parameter value groups are used to determine the optimal parameter value from the multiple candidate values ​​for each key parameter through a robust design algorithm, including:

[0079] Based on the simulation results of the optical performance indices corresponding to the multiple candidate parameter value groups and the multiple error parameter groups, the optimal parameter value is determined from the multiple candidate values ​​of each key parameter through a robust design algorithm.

[0080] It should be noted that in actual products, some of the key parameters of the optical components may have certain errors due to limitations in the production process. Therefore, by taking these errors into account when simulating the initial model of the optical components, the simulation results are closer to the actual product, and thus the simulation results are more accurate.

[0081] Of course, in other embodiments, the error of each key parameter may not be considered, and the optimal parameter value may be determined from the multiple candidate values ​​of each key parameter by using a robust design algorithm based solely on the simulation results of the optical performance indicators corresponding to the multiple candidate parameter value groups.

[0082] For example, please refer to Table 3, which shows the plurality of candidate parameter value groups and the plurality of error parameter value groups. In the embodiment shown in Table 3, C and G among the seven key parameters A to G are error parameters. Specifically, the error value corresponding to the candidate value of C 0.48mm is 0.53mm, i.e., the error is 0.05mm; the error value corresponding to the candidate value of C 0.53mm is 0.48mm, i.e., the error is 0.05mm; the error value corresponding to the candidate value of C 0.60mm is 0.54mm, i.e., the error is 0.06mm. The error value corresponding to the candidate value of G 10mm is 9.8mm, i.e., the error is 0.2mm; the error value corresponding to the candidate value of G 18mm is 17.8mm, i.e., the error is 0.2mm; the error value corresponding to the candidate value of G 25mm is 24.8mm, i.e., the error is 0.2mm.

[0083] Table 3. Schematic diagram of candidate parameter value groups and error parameter value groups

[0084]

[0085]

[0086] In some embodiments, the optical performance indicators include at least a first optical performance indicator, and the robust design algorithm includes a signal-to-noise ratio calculation formula. The first optical performance indicator is either luminance value or luminance uniformity.

[0087] For example, please refer to Figure 4Table 4 shows the simulation results of the brightness values ​​and brightness uniformity corresponding to the multiple candidate parameter value groups and the multiple error parameter groups, respectively. The simulation results of the brightness values ​​and brightness uniformity corresponding to each candidate parameter value group and each error parameter group can be statistical values ​​of the brightness values ​​sampled at multiple preset sampling points on the second glass layer 12 of the optical component 100.

[0088] Table 4. Simulation Results

[0089]

[0090]

[0091] In some embodiments, the step of determining the optimal parameter value from multiple candidate values ​​for each key parameter using a robust design algorithm based on the simulation results of the optical performance indices corresponding to the plurality of candidate parameter value groups includes:

[0092] Based on the simulation results of the first optical performance index corresponding to the multiple candidate parameter value groups, and according to the signal-to-noise ratio calculation formula corresponding to the first optical performance index, the signal-to-noise ratio of the first optical performance index corresponding to each candidate value of each key parameter is calculated.

[0093] The candidate value with the highest signal-to-noise ratio, which corresponds to the first optical performance index, among the candidate values ​​of each key parameter, is determined as the first preferred parameter value of that key parameter.

[0094] The optimal parameter value for each key parameter is determined based at least on the first preferred parameter value for each key parameter.

[0095] In robust design algorithms, the signal-to-noise ratio (S / N) is a key indicator for measuring product quality stability, reflecting the system's sensitivity to noise.

[0096] This application uses the signal-to-noise ratio (SNR), the first optical performance index, among the candidate values ​​of each key parameter to judge the merits of each candidate value of each key parameter, which can quickly achieve multi-parameter optimization and improve the efficiency of optical simulation.

[0097] In some embodiments, the step of calculating the signal-to-noise ratio (SNR) of the first optical performance index corresponding to each candidate value of each key parameter based on the simulation results of the first optical performance index corresponding to the plurality of candidate parameter value groups, according to the SNR calculation formula corresponding to the first optical performance index, includes:

[0098] Based on the simulation results of the first optical performance index corresponding to the multiple candidate parameter value groups and the multiple error parameter groups respectively, the signal-to-noise ratio of the first optical performance index corresponding to each candidate value of each key parameter is calculated according to the signal-to-noise ratio calculation formula corresponding to the first optical performance index.

[0099] In some embodiments, the simulation method further includes:

[0100] Based on the signal-to-noise ratio (SNR) of the first optical performance index corresponding to each candidate value of each key parameter, the range of the SNR of the first optical performance index for each key parameter is calculated; wherein, the range of the SNR of the first optical performance index for each key parameter is the difference between the maximum and minimum values ​​of the SNR of the first optical performance index corresponding to each candidate value of the key parameter.

[0101] The signal-to-noise ratio range of the first optical performance index of the multiple key parameters is sorted to obtain the first sorting result;

[0102] Based at least on the first sorting result, the multiple key parameters are divided into stable factors, adjustment factors, and minor factors that affect the robustness of the first optical performance index.

[0103] It's important to note that in robust design algorithms, noise factors, control factors, and less significant factors are three key concepts, each playing a distinct role in experimental design and quality control. Specifically, noise factors are those factors in an experiment that are uncontrollable or difficult to control, potentially increasing the variability of experimental results. In robust design algorithms, these noise factors are manipulated to induce variation, and then the optimal control factor settings that make the process or product resistant to this variation are determined. Control factors are those factors in an experiment that can be controlled and adjusted; engineers can optimize experimental results by changing the levels of these factors. Less significant factors have a relatively small impact on experimental results and may not be prioritized in experimental design. Robust design algorithms primarily focus on the major factors that significantly affect the response variable, while less significant factors may be ignored after initial analysis or considered in subsequent optimizations.

[0104] This application, based at least on the first sorting result, categorizes the multiple key parameters into stable factors, adjustment factors, and minor factors that affect the robustness of the first optical performance index. This allows for the determination of the degree of influence of each key parameter on the first optical performance index, thereby enabling engineers to change the values ​​of the adjustment factors to optimize the first optical performance index.

[0105] For example, please refer to Table 5, which uses the brightness value as an example of the first optical performance index. Table 5 shows the first ranking result obtained from the simulation results shown in Table 4. In this embodiment, the key parameters in the first ranking result are sorted according to the sorting rule of the range value from largest to smallest. Specifically, the first ranking result is DAEBHCGF.

[0106] In the robust design algorithm, the signal-to-noise ratio (SNR) is categorized into three types: high-target, low-target, and near-target. For high-target SNR, a larger SNR value is desirable, ideally approaching infinity. For low-target SNR, a smaller SNR value is desirable, ideally approaching zero. For near-target SNR, the SNR value is desired to fluctuate around a fixed target value, with minimal fluctuation being preferable. When designing the optical component 100, it is generally desirable for the brightness value of the optical component 100 to reach a target brightness value (e.g., 4 nits). Therefore, this application uses the near-target SNR calculation formula to calculate the brightness value SNR. Specifically, the signal-to-noise ratio calculation formula is SN=10*Log10(Ybar^2 / s^2), where Ybar is the mean brightness value and s is the standard deviation. Taking A as level 1 (i.e., the surface shape of the first glass layer 11 is planar) as an example, Ybar is the average value of the simulation results of the brightness values ​​corresponding to the 18 sets of values ​​with A as level 1 in Table 4, and s is the standard deviation of the simulation results of the brightness values ​​corresponding to these 18 sets of values.

[0107] Based on the principle of maximizing signal-to-noise ratio, the first preferred parameter values ​​for the eight key parameters A to H are A2B1C1D1E2F2G2H1, that is, the first preferred parameter value for A is curved surface, the first preferred parameter value for B is 20mm, the first preferred parameter value for C is 0.48mm, the first preferred parameter value for D is centered, the first preferred parameter value for E is 1.2mm, the first preferred parameter value for F is small to large, the first preferred parameter value for G is 18mm, and the first preferred parameter value for H is 1.

[0108] Table 5. Summarizing Results of the First Sorting

[0109]

[0110] In some embodiments, the simulation method further includes:

[0111] Based on the simulation results of the first optical performance index corresponding to the multiple candidate parameter value groups, the mean value of the first optical performance index corresponding to each candidate value of each key parameter is calculated.

[0112] Based on the mean value of the first optical performance index corresponding to each candidate value of each key parameter, the mean range of the first optical performance index of each key parameter is calculated; wherein, the mean range of the first optical performance index of each key parameter is the difference between the maximum and minimum values ​​of the mean values ​​of the first optical performance index corresponding to each candidate value of the key parameter.

[0113] The mean range of the first optical performance index of the multiple key parameters is sorted to obtain the second sorting result.

[0114] For example, please refer to Table 6, which uses the first optical performance index as the brightness value as an example. Table 6 shows the second ranking result obtained from the simulation results shown in Table 4. In this embodiment, the key parameters in the second ranking result are sorted according to the sorting rule of the range value from largest to smallest. Specifically, the second ranking result is BDFEHGCA.

[0115] Table 6. Scheme of Second Sorting Results

[0116]

[0117] In some embodiments, the step of classifying the plurality of key parameters into stable factors, adjustment factors, and minor factors affecting the robustness of the first optical performance index, based at least on the first sorting result, includes:

[0118] Based on the first and second sorting results, the multiple key parameters are divided into stable factors, adjustment factors, and minor factors that affect the robustness of the first optical performance index.

[0119] It should be noted that the classification results of the multiple key parameters obtained by combining the first and second sorting results are more accurate than the classification results obtained based solely on the first sorting results.

[0120] In some embodiments, the step of classifying the plurality of key parameters into stable factors, adjustment factors, and minor factors affecting the robustness of the first optical performance index based on the first ranking result and the second ranking result includes:

[0121] The key parameters that rank in the top preset positions in both the first and second sorting results are identified as stabilizing factors affecting the robustness of the first optical performance index.

[0122] The key parameters that rank only in the first sorting result or only in the second sorting result are identified as adjustment factors affecting the robustness of the first optical performance index.

[0123] Key parameters that are not ranked in the first or second sorting results within the first preset number of positions are identified as secondary factors affecting the robustness of the first optical performance index.

[0124] For example, please refer to Table 7. Table 7 takes the first optical performance index as the brightness value as an example and shows the first classification result of the multiple key parameters obtained from the first sorting result in Table 4 and the second sorting result in Table 5. In this embodiment, the first preset number of digits is the first 4 digits. In other embodiments, the first preset number of digits can also be other values, which are not limited here.

[0125] Table 7. Schematic diagram of the first classification results for key parameters.

[0126] Key parameter First ranking result Second ranking result First classification result A 2 - Adjustment factor B 4 1 Stabilizing factor C - - Secondary factor D 1 2 Stabilizing factor E 3 4 Stabilizing factor F - 3 Adjustment factor G - - Secondary factor H - - Secondary factor

[0127] In some embodiments, the optical performance index further includes a second optical performance index. The second optical performance index is the other of luminance value and luminance uniformity.

[0128] The simulation results based on the optical performance indices corresponding to the multiple candidate parameter value groups, using a robust design algorithm to determine the optimal parameter value from multiple candidate values ​​for each key parameter, also include:

[0129] Based on the simulation results of the second optical performance index corresponding to the multiple candidate parameter value groups, and according to the signal-to-noise ratio calculation formula corresponding to the second optical performance index, the signal-to-noise ratio of the second optical performance index corresponding to each candidate value of each key parameter is calculated.

[0130] The candidate value with the highest signal-to-noise ratio (SNR) among the candidate values ​​of each key parameter is determined as the second preferred parameter value for that key parameter.

[0131] The determination of the optimal parameter value for each key parameter, based at least on the first preferred parameter value for each key parameter, includes:

[0132] Based on the first and second preferred parameter values ​​for each key parameter, the optimal parameter value for each key parameter is determined.

[0133] It should be noted that, based on the simulation results of the brightness values ​​corresponding to the multiple candidate parameter value groups, the first preferred parameter value of each key parameter is determined. Based on the simulation results of the brightness uniformity corresponding to the multiple candidate parameter value groups, the second preferred parameter value of each key parameter is determined. Based on the first and second preferred parameter values ​​of each key parameter, the optimal parameter value of each key parameter is determined. Thus, when designing the optical component 100, both the brightness value and brightness uniformity can be taken into account, ensuring that both optical performance indicators reach their corresponding target values, thereby improving design efficiency.

[0134] In some embodiments, the step of calculating the signal-to-noise ratio (SNR) of the second optical performance index corresponding to each candidate value of each key parameter based on the simulation results of the second optical performance index corresponding to the plurality of candidate parameter value groups, according to the SNR calculation formula corresponding to the second optical performance index, includes:

[0135] Based on the simulation results of the second optical performance index corresponding to the multiple candidate parameter value groups and the multiple error parameter groups, the signal-to-noise ratio of the second optical performance index corresponding to each candidate value of each key parameter is calculated according to the signal-to-noise ratio calculation formula corresponding to the second optical performance index.

[0136] In some embodiments, the simulation method further includes:

[0137] Based on the signal-to-noise ratio (SNR) of the second optical performance index corresponding to each candidate value of each key parameter, the range of the SNR of the second optical performance index for each key parameter is calculated; wherein, the range of the SNR of the second optical performance index for each key parameter is the difference between the maximum and minimum values ​​of the SNR of the second optical performance index corresponding to each candidate value of the key parameter.

[0138] The signal-to-noise ratio range of the second optical performance index of the multiple key parameters is sorted to obtain a third sorting result;

[0139] Based at least on the third ranking result, the multiple key parameters are divided into stable factors, adjustment factors, and minor factors that affect the robustness of the second optical performance index.

[0140] This application, based at least on the third sorting result, categorizes the multiple key parameters into stable factors, adjustment factors, and minor factors that affect the robustness of the second optical performance index. This allows for the determination of the degree of influence of each key parameter on the second optical performance index, thereby enabling engineers to change the values ​​of the adjustment factors to optimize the second optical performance index.

[0141] For example, please refer to Table 8, which uses brightness uniformity as an example of the second optical performance index and shows the third ranking result obtained from the simulation results shown in Table 4. In this embodiment, the key parameters in the third ranking result are sorted according to the sorting rule of the range value from largest to smallest. Specifically, the third ranking result is FGEBADHC.

[0142] In the design of the optical component 100, it is generally desirable to minimize the brightness uniformity of the optical component 100. Therefore, this application uses the small signal-to-noise ratio calculation formula to calculate the brightness uniformity signal-to-noise ratio.

[0143] According to the principle of maximizing signal-to-noise ratio, the second preferred parameter values ​​for the eight key parameters A to H are A1B1C2D1E2F2G3H3, that is, the second preferred parameter value for A is planar, the second preferred parameter value for B is 20mm, the second preferred parameter value for C is 0.53mm, the second preferred parameter value for D is centered, the second preferred parameter value for E is 1.2mm, the second preferred parameter value for F is from small to large, the second preferred parameter value for G is 25mm, and the second preferred parameter value for H is 3.

[0144] Table 8. Schematic diagram of the third sorting results

[0145]

[0146] In some embodiments, the simulation method further includes:

[0147] Based on the simulation results of the second optical performance index corresponding to the multiple candidate parameter value groups, the mean value of the second optical performance index corresponding to each candidate value of each key parameter is calculated.

[0148] Based on the mean of the second optical performance index corresponding to each candidate value of each key parameter, the mean range of the second optical performance index of each key parameter is calculated; wherein, the mean range of the second optical performance index of each key parameter is the difference between the maximum and minimum values ​​of the mean of the second optical performance index corresponding to each candidate value of the key parameter.

[0149] The mean range of the second optical performance index of the multiple key parameters is sorted to obtain a fourth sorting result.

[0150] For example, please refer to Table 9, which uses the second optical performance index as the brightness value as an example and shows the fourth ranking result obtained from the simulation results shown in Table 4. In this embodiment, the key parameters in the fourth ranking result are sorted according to the sorting rule of the range value from largest to smallest. Specifically, the fourth ranking result is FGEBADCH.

[0151] Table 9. Scheme of Fourth Sorting Results

[0152] Level A B C D E F G H 1 0.59 0.5887 0.6563 0.5879 0.5961 0.8972 0.725 0.6276 2 0.6685 0.6264 0.6118 0.6663 0.5855 0.3847 0.6059 0.6378 3 0.6727 0.6198 0.6335 0.7062 0.6058 0.5568 0.6224 Extreme difference 0.0785 0.084 0.0445 0.0784 0.1207 0.5124 0.1682 0.0153 Ranking 5 4 7 6 3 1 2 8

[0153] In some embodiments, the optical performance simulation method further includes:

[0154] Based on the third and fourth ranking results, the multiple key parameters are divided into stable factors, adjustment factors, and minor factors that affect the robustness of the second optical performance index.

[0155] In some embodiments, the step of classifying the plurality of key parameters into stable factors, adjustment factors, and minor factors affecting the robustness of the second optical performance index based on the third and fourth ranking results includes:

[0156] The key parameters that rank in the top preset positions in both the third and fourth sorting results are identified as stabilizing factors affecting the robustness of the second optical performance index.

[0157] The key parameters that rank only in the first preset position in the third sorting result or only in the fourth sorting result are determined as adjustment factors affecting the robustness of the second optical performance index;

[0158] Key parameters that are not ranked in the top preset positions in both the third and fourth ranking results are identified as secondary factors affecting the robustness of the second optical performance index.

[0159] For example, please refer to Table 10. Table 10 takes brightness uniformity as the second optical performance index. Table 10 shows the second classification results of the multiple key parameters obtained from the third sorting results in Table 8 and the fourth sorting results in Table 9. In this embodiment, the first preset number of digits is the first 4 digits. In other embodiments, the first preset number of digits can also be other values, which are not limited here.

[0160] Table 10. Schematic diagram of the second classification results for key parameters.

[0161] Key parameter Third ranking result Fourth ranking result Second classification result A - - Secondary factor B 4 4 Stabilizing factor C - - Secondary factor D - - Secondary factor E 3 3 Stabilizing factor F 1 1 Stabilizing factor G 2 2 Stabilizing factor H - - Secondary factor

[0162] In some embodiments, determining the optimal parameter value for each key parameter based on a first preferred parameter value and a second preferred parameter value for each key parameter includes:

[0163] When the first preferred parameter value and the second preferred parameter value of a certain key parameter are equal, the first preferred parameter value is determined as the optimal parameter value of the key parameter.

[0164] When the first preferred parameter value and the second preferred parameter value of a certain key parameter are not equal, the preferred parameter value of the brightness uniformity of the key parameter is determined as the optimal parameter value of the key parameter; wherein, the preferred parameter value of the brightness uniformity of the key parameter is the preferred parameter value determined based on the brightness uniformity signal-to-noise ratio among the first preferred parameter value and the second preferred parameter value.

[0165] It is easy to understand that the brightness value of the optical component 100 is most affected by the luminous flux of the light-emitting element 15. Therefore, the brightness value of the optical component 100 can be adjusted to reach the target brightness value by adjusting the luminous flux of the light-emitting element 15. However, the brightness uniformity of the optical component 100 is affected by the combined influence of many component parameters. Therefore, adjusting the brightness uniformity is much more difficult than adjusting the brightness value. Therefore, when the first preferred parameter value and the second preferred parameter value of a certain key parameter are not equal, this application determines the preferred parameter value of the brightness uniformity of the key parameter as the optimal parameter value of the key parameter, which can prioritize ensuring that the optical uniformity of the optical component 100 meets the requirements.

[0166] For example, taking the first optical performance index as brightness value and the second optical performance index as brightness uniformity, as mentioned above, the first preferred parameter values ​​for the eight key parameters A to H are A2B1C1D1E2F2G2H1, and the second preferred parameter values ​​for the eight key parameters A to H are A1B1C2D1E2F2G3H3. Where the first and second preferred parameter values ​​for B, D, E, and F are equal, then the optimal parameter value for B is horizontal value 1, i.e., 20mm; the optimal parameter value for D is horizontal value 1, i.e., centered; the optimal parameter value for E is horizontal value 2, i.e., 1.2mm; and the optimal parameter value for F is horizontal value 2, i.e., increasing from small to large. If the first and second preferred parameter values ​​of A, C, and G are not equal, then the optimal parameter value of A is horizontal value 1, i.e., plane; the optimal parameter value of C is horizontal value 2, i.e. 0.53mm; and the optimal parameter value of G is horizontal value 3, i.e. 25mm. Therefore, the optimal parameter values ​​of the seven key parameters A to G are A1B1C2D1E2F2G3.

[0167] In some embodiments, the simulation method further includes:

[0168] The initial model is configured with an initial parameter value set for simulation to obtain the simulation results of the optical performance index corresponding to the initial parameter value set; wherein, the initial parameter value set includes the values ​​of the multiple key parameters, and the value of each key parameter is a corresponding initial value;

[0169] The initial model is configured with the optimal parameter value set for simulation, and the simulation results of the optical performance index corresponding to the optimal parameter value set are obtained.

[0170] Thus, by comparing the simulation results of the optical performance indicators corresponding to the initial parameter value set with the simulation results of the optical performance indicators corresponding to the optimal parameter value set, it can be verified whether the optimal parameter value set meets the design requirements.

[0171] For example, suppose that the design requirements for the optical component 100 are a brightness value of 4 nits and a brightness uniformity of ≤25%. Please refer to Table 11, which shows the simulation results of the optical performance indicators corresponding to the initial parameter value group in Table 1 and the simulation results of the optical performance indicators corresponding to the optimal parameter value group obtained by the optical performance simulation method of the optical component provided in this application. As shown in Table 11, there is a significant difference between the simulation results of the brightness value and brightness uniformity corresponding to the initial parameter value group and the target brightness value and target brightness uniformity. The simulation result of the brightness value corresponding to the optimal parameter value group is 3.83 nits, which is closer to the target brightness value of 4 nits than the simulation result of 4.54 nits corresponding to the initial parameter value group. Furthermore, the simulation result of the brightness uniformity corresponding to the optimal parameter value group is 18.9%, which is significantly lower than the simulation result of 83.7% corresponding to the initial parameter value group and also lower than the target brightness uniformity of 25%. Therefore, the optimal parameter value group can ensure that the brightness value and brightness uniformity of the optical component 100 meet the design requirements.

[0172] Table 11 Comparison of simulation results of optical performance indicators corresponding to the initial parameter value group and the optimal parameter value group

[0173]

[0174] In some embodiments, the simulation method further includes:

[0175] Based on the simulation results of the optical performance indicators corresponding to the multiple candidate parameter value groups, the predicted results of the optical performance indicators corresponding to the optimal parameter value group are calculated by the statistical analysis module.

[0176] For example, the statistical analysis module can be the predictive analysis module in Minitab software. Minitab software provides a series of statistical analysis tools, including descriptive statistics, hypothesis testing, regression analysis, quality control, etc., which can be used to discover trends from data, solve problems, and gain valuable insights. The predictive analysis module in Minitab software can process large amounts of data, quickly develop models, and generate accurate predictive models.

[0177] Thus, by comparing the simulation results of the optical performance indicators corresponding to the optimal parameter value set with the predicted results of the optical performance indicators corresponding to the optimal parameter value set, the accuracy of the initial model can be verified, and whether the optimal parameter value set meets the design requirements can be further verified.

[0178] For example, please refer to Table 12, which shows the predicted results of the optical performance indicators corresponding to the optimal parameter value group. By comparing Table 11 and Table 12, it can be seen that the predicted brightness value corresponding to the optimal parameter value group is 3.98167, which is close to the simulated value of 3.83. The predicted brightness uniformity corresponding to the optimal parameter value group is 13.84%, which is also close to the simulated result of 18.9%. Therefore, it can be further determined that the initial model is accurate and the optimal parameter value group obtained by the optical performance simulation method of the optical component provided in this application can ensure that the optical performance indicators of the optical component meet the design requirements.

[0179] Table 12 Prediction results of optical performance indicators corresponding to the optimal parameter value group

[0180] Combination scheme Brightness value Brightness uniformity A1B1C2D1E2F2G3 3.98167 13.84％ A2B1C2D1E2F2G3 4.22167 20.68％

[0181] Based on the same inventive concept, this application also provides an optical performance simulation device for an optical component, the optical performance simulation device including a processor and a memory; the memory is coupled to the processor, the memory is used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the optical performance simulation device for the optical component performs the optical performance simulation method for the optical component as described in any of the above embodiments.

[0182] Based on the same inventive concept, this application also provides a computer-readable storage medium storing a computer program or instructions, which, when executed by an optical performance simulation device for an optical component, causes the optical performance simulation device for the optical component to perform the optical performance simulation method for the optical component as described in any of the above embodiments.

[0183] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: 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 or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0184] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. 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 may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0185] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0186] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0187] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

[0188] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for simulating the optical performance of an optical component, characterized in that, The simulation method comprises: obtaining initial values of a plurality of component parameters of an optical assembly, and establishing an initial model of the optical assembly according to the initial values of the plurality of component parameters; determining a plurality of key parameters from the plurality of component parameters according to the initial model; determining a plurality of candidate values of each key parameter according to initial values of the plurality of key parameters; combining the candidate values of the plurality of key parameters through a robust design algorithm to obtain a plurality of candidate parameter value groups; each candidate parameter value group comprises values of the plurality of key parameters, and the value of each key parameter is one of the plurality of candidate values; determining a plurality of error parameter value groups according to the plurality of candidate parameter value groups; each error parameter value group comprises values of the plurality of key parameters, and at least one of the plurality of key parameters is an error parameter, and there is a preset error between the value of each error parameter and one of the candidate values of the error parameter; configuring the initial model with the plurality of candidate parameter value groups and the plurality of error parameter value groups respectively to perform simulation, and obtaining simulation results of optical performance indexes corresponding to the plurality of candidate parameter value groups and the plurality of error parameter value groups respectively; determining optimal parameter values from the candidate values of each key parameter corresponding to the maximum signal-to-noise ratio of the optical performance index through a robust design algorithm based on the simulation results of the optical performance indexes corresponding to the plurality of candidate parameter value groups and the plurality of error parameter value groups respectively, to obtain an optimal parameter value group; the optimal parameter value group comprises values of the plurality of key parameters, and the value of each key parameter is the corresponding optimal parameter value.

2. The method of claim 1, wherein, The optical performance index at least comprises a first optical performance index, and the robust design algorithm comprises a signal-to-noise ratio calculation formula; The determination of the optimal parameter values from the candidate values of each key parameter corresponding to the maximum signal-to-noise ratio of the optical performance index through a robust design algorithm based on the simulation results of the optical performance indexes corresponding to the plurality of candidate parameter value groups and the plurality of error parameter value groups respectively comprises: calculating the first optical performance index signal-to-noise ratio corresponding to each candidate value of each key parameter according to the signal-to-noise ratio calculation formula corresponding to the first optical performance index based on the simulation results of the first optical performance index corresponding to the plurality of candidate parameter value groups and the plurality of error parameter value groups respectively; determining the candidate value corresponding to the maximum first optical performance index signal-to-noise ratio among the candidate values of each key parameter as the first preferred parameter value of the key parameter; determining the optimal parameter value of each key parameter based on at least the first preferred parameter value of each key parameter.

3. The method of claim 2, wherein the optical performance of the optical assembly is simulated by: The simulation method further comprises: calculating the first optical performance index signal-to-noise ratio range of each key parameter based on the first optical performance index signal-to-noise ratio corresponding to each candidate value of each key parameter; the first optical performance index signal-to-noise ratio range of each key parameter is the difference between the maximum value and the minimum value among the first optical performance index signal-to-noise ratios corresponding to the candidate values of the key parameter. The first optical performance index signal-to-noise ratio of the plurality of key parameters is sorted to obtain a first sorting result; The plurality of key parameters are divided into stable factors, adjustment factors and secondary factors affecting the robustness of the first optical performance index according to the first sorting result.

4. The method of claim 3, wherein the optical performance of the optical assembly is simulated by: The simulation method further comprises: Based on the simulation results of the first optical performance index corresponding to the plurality of candidate parameter value groups respectively, the first optical performance index mean value corresponding to each candidate value of each key parameter is calculated; Based on the first optical performance index mean value corresponding to each candidate value of each key parameter, the first optical performance index mean value range of each key parameter is calculated; wherein the first optical performance index mean value range of each key parameter is the difference between the maximum and minimum of the first optical performance index mean value corresponding to each candidate value of the key parameter; The first optical performance index mean value range of the plurality of key parameters is sorted to obtain a second sorting result; The plurality of key parameters are divided into stable factors, adjustment factors and secondary factors affecting the robustness of the first optical performance index according to the first sorting result. According to the first sorting result and the second sorting result, the plurality of key parameters are divided into stable factors, adjustment factors and secondary factors affecting the robustness of the first optical performance index.

5. The method of claim 4, wherein the optical performance of the optical assembly is simulated by: Each key parameter in the first sorting result and the second sorting result is sorted according to the sorting rule of the range value from large to small; According to the first sorting result and the second sorting result, the plurality of key parameters are divided into stable factors, adjustment factors and secondary factors affecting the robustness of the first optical performance index. The key parameters ranked in the first preset number in both the first sorting result and the second sorting result are determined as stable factors affecting the robustness of the first optical performance index; The key parameters ranked in the first preset number in only the first sorting result or only the second sorting result are determined as adjustment factors affecting the robustness of the first optical performance index; The key parameters not ranked in the first preset number in both the first sorting result and the second sorting result are determined as secondary factors affecting the robustness of the first optical performance index.

6. The method of claim 2, wherein the optical performance of the optical assembly is simulated by: The first optical performance index is one of a brightness value and brightness uniformity.

7. The method of claim 6, wherein the optical performance of the optical assembly is simulated by: The optical performance index further comprises a second optical performance index; wherein the second optical performance index is the other of a brightness value and brightness uniformity; Based on the simulation results of the optical performance index corresponding to the plurality of candidate parameter value groups and the plurality of error parameter value groups respectively, the optimal parameter value is determined from the candidate value corresponding to the maximum optical performance index signal-to-noise ratio of the plurality of candidate values of each key parameter by a robustness design algorithm, further comprising: According to the second optical performance index corresponding to the signal-to-noise ratio calculation formula, a second optical performance index signal-to-noise ratio corresponding to each candidate value of each key parameter is calculated; The candidate value corresponding to the maximum second optical performance index signal-to-noise ratio in each candidate value of each key parameter is determined as the second preferred parameter value of the key parameter; The method further comprises: The method further comprises: The method further comprises: When the first preferred parameter value is not equal to the second preferred parameter value, the brightness uniformity preferred parameter value of the key parameter is determined as the optimal parameter value of the key parameter.

8. The method of claim 1, wherein, The simulation method further comprises: The initial model is simulated by configuring an initial parameter value set, to obtain a simulation result of an optical performance index corresponding to the initial parameter value set; wherein the initial parameter value set comprises values of the plurality of key parameters, and the value of each key parameter is a corresponding initial value; The initial model is simulated by configuring the optimal parameter value set, to obtain a simulation result of an optical performance index corresponding to the optimal parameter value set.

9. The method of claim 8, wherein the optical performance of the optical assembly is simulated by: The simulation method further comprises: Based on the simulation results of the optical performance index corresponding to each of the plurality of candidate parameter value sets, a prediction result of the optical performance index corresponding to the optimal parameter value set is calculated by a statistical analysis module.

10. The method of claim 1, wherein, The optical assembly comprises a plurality of first glass layers, a second glass layer, a shielding layer, a reflective piece, a plurality of light emitting elements, a light guide strip, and an adhesive layer; The first glass layer and the second glass layer are stacked along a first direction, and the shielding layer and the reflective piece are located between the first glass layer and the second glass layer; The light guide strip is adhered to one side of the second glass layer away from the first glass layer through the adhesive layer, the light guide strip has an entrance surface and an exit surface, the plurality of light emitting elements are arranged apart from the entrance surface of the light guide strip and uniformly arranged along the extension direction of the light guide strip, and the light emitted by the plurality of light emitting elements can enter the light guide strip through the entrance surface, and the light emitted through the exit surface can enter the second glass layer from the side of the second glass layer away from the first glass layer; The reflective piece is used for reflecting the light conducted in the second glass layer, so that the reflected light exits from the side of the second glass layer away from the first glass layer.

11. The method of claim 10, wherein the optical performance of the optical assembly is simulated by: The plurality of key parameters comprise at least one of the following parameters: The surface shape of the first glass layer; The arrangement spacing of the plurality of light emitting elements; The luminous flux of the light emitting element; The distance between the entrance surface of the light guide strip and the light emitting element; The distance between the center of the entrance surface of the light guide strip and the center of the light emitting element in the first direction; The arrangement mode of the reflective piece; The width of the light guide strip.

12. An optical performance simulation device for an optical assembly, characterized in that An optical performance simulation device of an optical assembly comprises a processor and a memory; the memory is coupled with the processor, and the memory is configured to store computer program codes, the computer program codes comprising computer instructions, which, when executed by the processor, cause the optical performance simulation device of the optical assembly to perform the optical performance simulation method of any one of claims 1-11.

13. A computer-readable storage medium, characterized in that, A computer readable storage medium stores computer programs or instructions, which, when executed by an optical performance simulation device of an optical assembly, cause the optical performance simulation device of the optical assembly to perform the optical performance simulation method of any one of claims 1-11.

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