A method and system for curvature optimization of a multi-focal lens optical design

Abstract

The application provides a curvature optimization method and system for multi-focal lens optical design, which comprises the following steps: obtaining lens design requirement parameters and optical performance indexes, establishing an initial curvature model of the lens surface and setting constraint conditions; simulating light propagation by a ray tracing algorithm, calculating imaging data and comparing to obtain a deviation value; constructing an optimization objective function with the minimum deviation value as the target, adjusting the curvature variable by an optimization algorithm after importing the constraint conditions, and generating multiple sets of curvature parameter schemes; and selecting the optimal curvature parameter through optical simulation verification, and finally generating lens surface curvature processing data. The system cooperates with multiple modules to execute the above method, realizes algorithmic optimization of the multi-focal lens surface curvature, and completes the lens optical design.

Description

Technical Field

[0001] This invention relates to the field of multifocal lens optical design technology, specifically to a curvature optimization method and system for multifocal lens optical design. Background Technology

[0002] In the field of multifocal lens optical design, the surface curvature of the lens is a core factor determining its optical performance. Traditional design methods often rely on manual experience combined with basic optical formulas to calculate curvature parameters, first presetting initial curvature values ​​for each focal zone of the lens, and then making local adjustments through simple light simulation. This design method cannot accurately take into account the imaging needs of the far, intermediate, and near focal zones of a multifocal lens, easily leading to problems such as uneven curvature transitions between focal zones and poor aberration control. Furthermore, the manually adjusted curvature parameters lack global optimization considerations, making it difficult to match the complex optical design requirements of multifocal lenses, resulting in insufficient image sharpness and limited field of view in actual use.

[0003] While existing multifocal lens curvature design technologies incorporate some algorithms for auxiliary calculations, they are mostly simple applications of single algorithms, lacking a targeted multi-objective optimization system. This fails to incorporate constraints such as lens material parameters and manufacturing process feasibility into the optimization process, resulting in insufficient accuracy and comprehensiveness in algorithm iteration. Furthermore, some technologies lack a complete optical simulation verification process, determining curvature parameters solely through theoretical calculations without simulating light propagation and imaging effects in actual usage scenarios. This leads to a disconnect between the designed curvature parameters and actual production and usage requirements, making it difficult for the lens's optical performance to meet preset targets. In addition, the traditional design process suffers from poor coordination between curvature model construction, parameter calculation, and optimization adjustments, resulting in low efficiency and an inability to meet the rapid design needs of multifocal lenses with various specifications. Summary of the Invention

[0004] To address the aforementioned technical shortcomings, the purpose of this invention is to provide a curvature optimization method and system for multifocal lens optical design, enabling algorithmic optimization of the surface curvature of multifocal lenses and completing the lens optical design.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for curvature optimization in the optical design of a multifocal lens includes the following steps:

[0007] S1. Obtain the design requirements parameters and optical performance indicators of the multifocal lens, establish the initial curvature model of the lens surface, and determine the curvature variables and constraints of the model.

[0008] S2. Simulate the propagation path of light in the initial curvature model based on the ray tracing algorithm, calculate the optical imaging data of each region of the lens, and compare the deviation value of the imaging data with the preset optical performance index.

[0009] S3. Construct a curvature optimization objective function with the goal of minimizing the deviation value, and import the lens material parameters and optical design requirements as constraints into the optimization algorithm;

[0010] S4. Iteratively solve and adjust the curvature variables of the curvature model using optimization algorithms to generate multiple sets of optimized curvature parameter schemes.

[0011] S5. Perform optical simulation verification on each curvature parameter scheme and select the optimal curvature parameter that matches the preset optical performance index.

[0012] S6. Generate surface curvature processing data for the multifocal lens based on the optimal curvature parameters to complete the optical design of the lens.

[0013] Preferably, in step S1, the method for obtaining the design requirement parameters and optical performance indicators of the multifocal lens, establishing an initial curvature model of the lens surface, and determining the curvature variables and constraints of the model is as follows: obtaining the design requirement parameters of the lens, such as the refractive power, focal point distribution position, lens diameter, and center thickness, as well as the optical performance indicators of image clarity, aberration control, and field of view; establishing an initial curvature model of the lens surface based on the aspherical surface equation, dividing the lens into far-focus, mid-focus, and near-focus curvature regions, setting independent curvature variables for each region, and determining the value range of the curvature variables and the smoothness constraints of the curvature connection of each region.

[0014] Preferably, in step S2, the method for simulating the propagation path of light in the initial curvature model based on the ray tracing algorithm, calculating the optical imaging data of each region of the lens, and comparing the deviation value of the imaging data with the preset optical performance index is as follows: using a geometric ray tracing algorithm, setting the incident angle, incident position, and number of rays to simulate the refraction and propagation path of parallel and divergent light in the initial curvature model; calculating the optical imaging data of imaging resolution, meridional aberration, sagittal aberration, and field curvature distortion of each focal region of the lens, calculating the deviation value of each imaging data with the corresponding optical performance index according to the preset weight, and generating comprehensive deviation value data.

[0015] Preferably, in step S3, the method of constructing a curvature optimization objective function with the goal of minimizing the deviation value and incorporating lens material parameters and optical design requirements as constraints into the optimization algorithm is as follows: a multi-objective curvature optimization objective function is constructed with the core objective of minimizing the comprehensive deviation value of optical imaging data in each region; the material parameters of the lens, such as refractive index, Abbe number, and dispersion coefficient, as well as the design requirements of the refractive power deviation range and the imaging sharpness threshold of each focal zone, are incorporated into the optimization algorithm as hard constraints, while the continuous rate of change of the lens surface curvature and the feasibility of the processing technology are incorporated into the optimization system as soft constraints.

[0016] Preferably, in step S4, the method of generating multiple sets of optimized curvature parameter schemes by iteratively solving and adjusting the curvature variables of the curvature model through optimization algorithms is as follows: a hybrid optimization algorithm combining gradient descent and genetic algorithms is used. The curvature variables of the initial curvature model are used as the initial solution. The gradient descent algorithm is used to quickly approximate the optimal solution interval. Then, the genetic algorithm is used to perform global search and iterative optimization of the curvature variables within the solution interval. After each iteration, the objective function value corresponding to the curvature variable is calculated. The comprehensive deviation value before and after the iteration is compared, and the curvature parameters of each curvature region are gradually adjusted until the objective function value converges to the preset threshold.

[0017] Preferably, in step S5, the method for performing optical simulation verification on each curvature parameter scheme and selecting the optimal curvature parameter that matches the preset optical performance index is as follows: establish corresponding lens curvature models for each of the multiple curvature parameter schemes generated by iterative optimization, simulate the light propagation and imaging process in actual use scenarios through an optical simulation platform; detect the compliance of the optical performance index of each scheme and the imaging transition effect at the junction of each focal area, eliminate schemes with excessive aberrations and focal area transition discontinuities, and select the curvature parameter combination with the best comprehensive optical performance.

[0018] Preferably, in step S6, the method for generating surface curvature processing data of a multifocal lens based on the optimal curvature parameters is as follows: the selected optimal curvature parameters are substituted into the surface curvature equation of the lens to generate the overall surface curvature contour data of the lens; according to the parameter format requirements of the lens processing equipment, the curvature contour data is converted into standardized processing data containing the coordinates of each processing position, curvature value, and processing tool path, while marking the curvature partition position and parameter boundary of each focal area of ​​the lens.

[0019] Preferably, during the global search and iterative optimization of the curvature variables within the solution interval, an iteration termination condition is set. The iteration termination condition includes: the objective function value converges to a preset threshold, the number of iterations reaches a preset maximum value, and the adjustment range of the curvature variables is less than a preset minimum value. If any termination condition is met, the iteration stops and the current curvature parameter scheme is output.

[0020] A curvature optimization system for multifocal lens optical design includes a parameter acquisition module, a curvature model construction module, a ray tracing calculation module, an optimization algorithm module, a simulation verification module, a processing data generation module, and a main control module. The parameter acquisition module acquires the lens design requirements parameters and optical performance indicators. The curvature model construction module establishes the initial curvature model of the lens and sets constraints. The ray tracing calculation module simulates light propagation and calculates optical imaging data and deviation values. The optimization algorithm module constructs the objective function and iteratively optimizes the curvature variables. The simulation verification module performs simulation verification on the curvature parameter scheme and selects the optimal parameters. The processing data generation module generates lens surface curvature processing data. The main control module is connected to each module to coordinate the work of each module and transmit data, completing the entire process design for lens curvature optimization.

[0021] Preferably, the ray tracing calculation module includes a ray simulation unit, an imaging data calculation unit, and a deviation value analysis unit. The ray simulation unit is used to simulate the propagation path of light in the curvature model, the imaging data calculation unit is used to calculate the optical imaging data of each region, and the deviation value analysis unit is used to calculate the deviation value between the imaging data and the performance index. The optimization algorithm module includes an objective function construction unit, a constraint condition import unit, and a hybrid optimization operation unit. The hybrid optimization operation unit integrates gradient descent algorithm and genetic algorithm for iterative solution of curvature variables and parameter adjustment.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] The parameter acquisition module accurately obtains the lens design requirements parameters and optical performance indicators. The curvature model construction module establishes an initial curvature model based on the aspherical surface equation, divides the independent focal curvature region and sets constraints, laying an accurate model foundation for subsequent curvature optimization and ensuring a high degree of matching between the model and the lens design requirements.

[0024] The ray tracing calculation module uses professional algorithms to simulate the complete propagation path of light in the curvature model, comprehensively calculates the optical imaging data of each focal zone, and quantifies the gap between imaging data and performance indicators through deviation value analysis, providing objective and accurate data basis for curvature optimization and avoiding deviations caused by subjective judgment.

[0025] A multi-objective optimization objective function with the goal of minimizing the deviation value is constructed. Various conditions such as lens material parameters, design requirements, and processing feasibility are incorporated into the constraint system. The curvature variable is solved and adjusted globally through a hybrid optimization algorithm to achieve systematic and global optimization of the curvature parameter, taking into account the optical performance requirements of each focal zone.

[0026] In the design of the optical simulation verification stage, simulation tests are conducted on multiple optimized curvature parameter schemes in actual use scenarios to comprehensively detect whether the optical performance indicators meet the standards and the focal area connection effect, accurately select the optimal curvature parameters, ensure that the designed curvature parameters meet the actual use requirements, and improve the optical performance of the lens.

[0027] Standardized lens surface curvature processing data is generated based on the optimal curvature parameters, clarifying the processing coordinates, curvature values, tool paths, and focal zone parameter boundaries. This data is directly connected to lens processing equipment to achieve seamless conversion of design data into production data, ensuring that the curvature design results can be accurately implemented in production.

[0028] The system features modular functions such as parameter acquisition, model building, ray tracing, algorithm optimization, simulation verification, and processing data generation. Each module works in coordination with the main control module and transmits data efficiently, realizing full automation of the multifocal lens curvature optimization process from model building to processing data output, which greatly improves the efficiency of lens optical design.

[0029] The entire curvature optimization method revolves around the complex optical design requirements of multifocal lenses. Through algorithms, it achieves precise and global optimization of curvature parameters, effectively solving problems such as poor curvature connection between focal areas and poor aberration control. This ensures that the optical performance of each focal area of ​​the lens matches the preset indicators and adapts to the complex optical design requirements of multifocal lenses. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method of the present invention;

[0031] Figure 2 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0032] The invention will now be further described with reference to the accompanying drawings.

[0033] like Figure 1 As shown, a curvature optimization method for multifocal lens optical design includes the following steps:

[0034] S1. Obtain the design requirements and optical performance indicators of the multifocal lens, establish an initial curvature model of the lens surface, and determine the curvature variables and constraints of the model:

[0035] The design requirements parameters for the lens, including refractive power, focal point distribution, lens diameter, and center thickness, as well as optical performance indicators such as image sharpness, aberration control, and field of view, are obtained. An initial curvature model of the lens surface is established based on the aspherical surface equation. The lens is divided into far-focus, mid-focus, and near-focus curvature regions. Independent curvature variables are set for each region, and the range of values ​​for the curvature variables and the smoothness constraints of the curvature transition between each region are determined.

[0036] S2. Simulate the propagation path of light in the initial curvature model based on the ray tracing algorithm, calculate the optical imaging data of each region of the lens, and compare the deviation value of the imaging data with the preset optical performance index:

[0037] Using a geometric ray tracing algorithm, the incident angle, incident position, and number of rays are set to simulate the refraction and propagation paths of parallel and divergent light in the initial curvature model. The optical imaging data of imaging resolution, meridional aberration, sagittal aberration, and field curvature distortion in each focal region of the lens are calculated. The deviation values ​​between each imaging data and the corresponding optical performance indicators are calculated according to preset weights to generate comprehensive deviation value data.

[0038] S3. Construct a curvature optimization objective function with the goal of minimizing the deviation value, and import the lens material parameters and optical design requirements as constraints into the optimization algorithm:

[0039] With minimizing the comprehensive deviation of optical imaging data in each region as the core objective, a multi-objective curvature optimization objective function is constructed. Material parameters such as the refractive index, Abbe number, and dispersion coefficient of the lens, as well as the design requirements for the range of refractive power deviation and the image sharpness threshold of each focal zone, are incorporated into the optimization algorithm as hard constraints. At the same time, the continuous rate of change of the lens surface curvature and the feasibility of the processing technology are incorporated into the optimization system as soft constraints.

[0040] S4. Iteratively solve and adjust the curvature variables of the curvature model using optimization algorithms to generate multiple optimized curvature parameter schemes:

[0041] A hybrid optimization algorithm combining gradient descent and genetic algorithm is adopted. The curvature variables of the initial curvature model are used as the initial solution. The gradient descent algorithm is used to quickly approach the optimal solution interval, and then the genetic algorithm is used to perform global search and iterative optimization of the curvature variables within the solution interval. After each iteration, the objective function value corresponding to the curvature variable is calculated, and the comprehensive deviation value before and after the iteration is compared. The curvature parameters of each curvature region are gradually adjusted until the objective function value converges to the preset threshold.

[0042] During the global search and iterative optimization of curvature variables in the solution interval, an iteration termination condition is set. The iteration termination condition includes: the objective function value converges to a preset threshold, the number of iterations reaches a preset maximum value, and the adjustment range of curvature variables is less than a preset minimum value. If any termination condition is met, the iteration stops and the current curvature parameter scheme is output.

[0043] S5. Perform optical simulation verification on each curvature parameter scheme, and select the optimal curvature parameter that matches the preset optical performance index:

[0044] For the multiple curvature parameter schemes generated by iterative optimization, corresponding lens curvature models are established. The light propagation and imaging process in actual use scenarios are simulated through an optical simulation platform. The optical performance indicators of each scheme are tested to see if they meet the standards, and the imaging transition effect at the junction of each focal zone is detected. Schemes with excessive aberrations and focal zone transition discontinuities are eliminated, and the curvature parameter combination with the best overall optical performance is selected.

[0045] S6. Generate surface curvature processing data for the multifocal lens based on the optimal curvature parameters to complete the optical design of the lens:

[0046] The optimal curvature parameters obtained from the screening are substituted into the surface curvature equation of the lens to generate the overall surface curvature profile data of the lens. According to the parameter format requirements of the lens processing equipment, the curvature profile data is converted into standardized processing data containing the coordinates of each processing position, curvature value, and processing tool path. At the same time, the curvature partition position and parameter boundary of each focal area of ​​the lens are marked.

[0047] like Figure 2 As shown, a curvature optimization system for multifocal lens optical design includes a parameter acquisition module, a curvature model construction module, a ray tracing calculation module, an optimization algorithm module, a simulation verification module, a processing data generation module, and a main control module. The parameter acquisition module is used to acquire lens design requirements parameters and optical performance indicators; the curvature model construction module is used to establish an initial curvature model of the lens and set constraints; the ray tracing calculation module is used to simulate light propagation and calculate optical imaging data and deviation values; the optimization algorithm module is used to construct an objective function and iteratively optimize curvature variables; the simulation verification module is used to simulate and verify the curvature parameter scheme and select the optimal parameters; the processing data generation module is used to generate lens surface curvature processing data; the main control module is connected to each module to coordinate the work of each module and transmit data, completing the entire process design of lens curvature optimization.

[0048] Furthermore, the ray tracing calculation module includes a ray simulation unit, an imaging data calculation unit, and a deviation value analysis unit. The ray simulation unit is used to simulate the propagation path of light in the curvature model, the imaging data calculation unit is used to calculate the optical imaging data of each region, and the deviation value analysis unit is used to calculate the deviation value between the imaging data and the performance index. The optimization algorithm module includes an objective function construction unit, a constraint condition import unit, and a hybrid optimization operation unit. The hybrid optimization operation unit integrates gradient descent algorithm and genetic algorithm for iterative solution of curvature variables and parameter adjustment.

[0049] Example 1

[0050] This embodiment is applied to the optical design curvature optimization of multifocal myopia correction lenses, and the specific process is as follows:

[0051] The parameter acquisition module obtains the design requirements and optical performance indicators of multifocal myopia correction lenses, clarifying design requirements such as lens diopter, focal point distribution, lens diameter, and center thickness, as well as optical performance indicators such as image clarity, aberration control, and field of view. The curvature model construction module establishes an initial curvature model of the lens surface based on the aspherical surface equation, dividing the lens into far-focus, mid-focus, and near-focus curvature regions. Independent curvature variables are set for each region, and the range of values ​​for these curvature variables and the smoothness constraints for the transition between curvature regions are determined.

[0052] The ray tracing calculation module starts working, using a geometric ray tracing algorithm to set the incident angle, incident position, and number of rays for the simulated rays. It simulates the refraction and propagation paths of parallel and divergent rays in the initial curvature model, accurately calculates the imaging resolution, meridional aberration, sagittal aberration, field curvature distortion, and other optical imaging data for each focal area of ​​the lens, calculates the deviation values ​​between each imaging data and the corresponding optical performance indicators according to preset weights, generates comprehensive deviation value data, and transmits it to the optimization algorithm module.

[0053] The optimization algorithm module takes minimizing the comprehensive deviation of optical imaging data in each region as its core objective. It constructs a multi-objective curvature optimization objective function, using material parameters such as refractive index, Abbe number, and dispersion coefficient of the lens, as well as design requirements such as the range of refractive power deviation and the image sharpness threshold for each focal zone, as hard constraints. The continuous rate of change of lens surface curvature and the feasibility of the manufacturing process are used as soft constraints, all of which are imported into a hybrid optimization algorithm. This algorithm uses the curvature variables of the initial curvature model as the initial solution, quickly approximating the optimal solution interval through a gradient descent algorithm. Then, a genetic algorithm is used to perform a global search and iterative optimization of the curvature variables within the solution interval. After each iteration, the objective function value corresponding to the curvature variable is calculated, and the comprehensive deviation value before and after the iteration is compared. The curvature parameters of each curvature region are gradually adjusted until the objective function value converges to a preset threshold, generating multiple sets of optimized curvature parameter schemes.

[0054] The simulation verification module establishes corresponding lens curvature models for each curvature parameter scheme. Using an optical simulation platform, it simulates real-world viewing scenarios, recreating the light propagation and imaging process. It checks the optical performance indicators of each scheme and the imaging transition effect at the junctions of different focal zones, eliminating schemes with excessive aberrations or focal zone transitions, and selecting the curvature parameter combination with the optimal overall optical performance. The processing data generation module substitutes the optimal curvature parameters into the lens surface equation to generate overall lens surface curvature contour data. Then, according to the parameter format requirements of the lens processing equipment, the curvature contour data is converted into standardized processing data containing coordinates of each processing position, curvature values, and processing tool paths. Simultaneously, it marks the curvature partition positions and parameter boundaries of each focal zone of the lens, completing the optical design and curvature optimization of the multifocal myopia correction lens.

[0055] Example 2

[0056] This embodiment is applied to the optical design curvature optimization of multifocal presbyopia correction lenses. The specific process is as follows:

[0057] The parameter acquisition module accurately acquires the design requirements and optical performance indicators of multifocal presbyopic lenses, covering core design requirements such as lens diopter, focal point distribution, lens diameter, and center thickness, as well as optical performance indicators such as image clarity, aberration control, field of view, and adaptability to switching between focal zones. The curvature model construction module, based on the visual requirements of presbyopic lenses, establishes an initial curvature model of the lens surface using aspherical surface equations. It rationally divides the lens into far-focus, mid-focus, and near-focus curvature regions, assigning independent curvature variables to each region, clarifying the value range of these variables, and setting smoothness constraints at the curvature transition points between regions to ensure a smooth visual transition during viewing.

[0058] The ray tracing calculation module uses a geometric ray tracing algorithm to simulate the incident state of light in different scenarios. It sets the corresponding incident angle, incident position, and number of light rays, and simulates the refraction and propagation path of parallel and divergent light in the initial curvature model. It calculates the imaging resolution, meridional aberration, sagittal aberration, field curvature distortion, and other optical imaging data of each focal area of ​​the lens at far, middle, and near distances. It calculates and integrates the deviation values ​​of each imaging data and the corresponding optical performance indicators according to preset weights to generate comprehensive deviation value data, which provides a data basis for subsequent optimization.

[0059] The optimization algorithm module aims to minimize the overall deviation value. It constructs a multi-objective curvature optimization objective function, using material parameters such as refractive index, Abbe number, and dispersion coefficient, as well as design requirements such as refractive power deviation range, image sharpness thresholds for each focal zone, and field of view boundaries, as hard constraints. Soft constraints include the continuous rate of change of lens surface curvature, manufacturing feasibility, and overall lens thickness adaptability. A hybrid optimization algorithm combining gradient descent and genetic algorithms is introduced. Starting with the initial curvature variable as the initial solution, the gradient descent algorithm is used to quickly narrow down the optimal solution range. Then, a global iterative search is performed using a genetic algorithm. After each iteration, the changes in the objective function value are compared, and the parameters of each curvature region are gradually adjusted until the iteration termination condition is met, outputting multiple optimized curvature parameter schemes.

[0060] The simulation verification module constructs a corresponding lens curvature model for each curvature parameter scheme. Using an optical simulation platform, it simulates the actual visual scenarios of presbyopic individuals viewing distant, intermediate, and near objects, comprehensively testing whether the optical performance indicators of each scheme meet the standards. It focuses on verifying whether the imaging transition at the junctions of each focal zone is natural and without abrupt changes, eliminating schemes with substandard optical performance and poor visual experience, and selecting the curvature parameter combination with the best overall performance. The processing data generation module substitutes the optimal curvature parameters into the lens surface curvature equation to generate complete lens surface curvature contour data. Then, following the standard format of lens processing equipment, it converts the contour data into standardized processing data containing processing position coordinates, curvature values, and processing tool paths. Simultaneously, it marks the curvature partition positions and parameter boundaries of each focal zone, completing the optical design and curvature optimization of the multifocal presbyopic correction lens, and outputting curvature data that can be directly used for production processing.

Claims

1. A method for curvature optimization in the optical design of multifocal lenses, characterized in that, Includes the following steps: S1. Obtain the design requirements parameters and optical performance indicators of the multifocal lens, establish the initial curvature model of the lens surface, and determine the curvature variables and constraints of the model. S2. Simulate the propagation path of light in the initial curvature model based on the ray tracing algorithm, calculate the optical imaging data of each region of the lens, and compare the deviation value of the imaging data with the preset optical performance index. S3. Construct a curvature optimization objective function with the goal of minimizing the deviation value, and import the lens material parameters and optical design requirements as constraints into the optimization algorithm; S4. Iteratively solve and adjust the curvature variables of the curvature model using optimization algorithms to generate multiple sets of optimized curvature parameter schemes. S5. Perform optical simulation verification on each curvature parameter scheme and select the optimal curvature parameter that matches the preset optical performance index. S6. Generate surface curvature processing data for the multifocal lens based on the optimal curvature parameters to complete the optical design of the lens.

2. The curvature optimization method for multifocal lens optical design as described in claim 1, characterized in that, In step S1, the method for obtaining the design requirement parameters and optical performance indicators of the multifocal lens, establishing an initial curvature model of the lens surface, and determining the curvature variables and constraints of the model is as follows: obtaining the design requirement parameters of the lens, such as the refractive power, focal point distribution position, lens diameter, and center thickness, as well as the optical performance indicators of image sharpness, aberration control, and field of view; establishing an initial curvature model of the lens surface based on the aspherical surface equation, dividing the lens into far-focus, mid-focus, and near-focus curvature regions, setting independent curvature variables for each region, and determining the value range of the curvature variables and the smoothness constraints of the curvature connection between each region.

3. The curvature optimization method for multifocal lens optical design as described in claim 1, characterized in that, In step S2, the method for simulating the propagation path of light in the initial curvature model based on the ray tracing algorithm and calculating the optical imaging data of each region of the lens, and comparing the deviation value of the imaging data with the preset optical performance index is as follows: using the geometric ray tracing algorithm, setting the incident angle, incident position and number of light rays to simulate the refraction and propagation path of parallel light and divergent light in the initial curvature model; calculating the optical imaging data of imaging resolution, meridional aberration, sagittal aberration and field curvature distortion of each focal region of the lens, calculating the deviation value of each imaging data with the corresponding optical performance index according to the preset weight, and generating comprehensive deviation value data.

4. The curvature optimization method for multifocal lens optical design as described in claim 1, characterized in that, In step S3, a curvature optimization objective function is constructed with the goal of minimizing the deviation value. The method of incorporating lens material parameters and optical design requirements as constraints into the optimization algorithm is as follows: a multi-objective curvature optimization objective function is constructed with the core objective of minimizing the comprehensive deviation value of optical imaging data in each region; the material parameters of the lens, such as refractive index, Abbe number, and dispersion coefficient, as well as the design requirements of the refractive power deviation range and the imaging sharpness threshold of each focal zone, are incorporated into the optimization algorithm as hard constraints; at the same time, the continuous rate of change of the lens surface curvature and the feasibility of the processing technology are incorporated into the optimization system as soft constraints.

5. The curvature optimization method for multifocal lens optical design as described in claim 1, characterized in that, In step S4, the method of generating multiple optimized curvature parameter schemes by iteratively solving and adjusting the curvature variables of the curvature model through optimization algorithms is as follows: a hybrid optimization algorithm combining gradient descent and genetic algorithms is used. The curvature variables of the initial curvature model are used as the initial solution. The gradient descent algorithm is used to quickly approach the optimal solution interval. Then, the genetic algorithm is used to perform global search and iterative optimization of the curvature variables within the solution interval. After each iteration, the objective function value corresponding to the curvature variable is calculated. The comprehensive deviation value before and after the iteration is compared, and the curvature parameters of each curvature region are gradually adjusted until the objective function value converges to the preset threshold.

6. The curvature optimization method for multifocal lens optical design as described in claim 1, characterized in that, In step S5, the method for verifying each curvature parameter scheme through optical simulation and selecting the optimal curvature parameter that matches the preset optical performance index is as follows: establish corresponding lens curvature models for each of the multiple curvature parameter schemes generated by iterative optimization, simulate the light propagation and imaging process in actual use scenarios through an optical simulation platform; detect the compliance of the optical performance index of each scheme and the imaging transition effect at the junction of each focal area, eliminate schemes with excessive aberrations and focal area transition discontinuities, and select the curvature parameter combination with the best comprehensive optical performance.

7. The curvature optimization method for multifocal lens optical design as described in claim 1, characterized in that, In step S6, the method for generating surface curvature processing data of a multifocal lens based on the optimal curvature parameters is as follows: the selected optimal curvature parameters are substituted into the surface curvature equation of the lens to generate the overall surface curvature contour data of the lens; according to the parameter format requirements of the lens processing equipment, the curvature contour data is converted into standardized processing data containing the coordinates of each processing position, curvature value, and processing tool path, while marking the curvature partition position and parameter boundary of each focal area of ​​the lens.

8. The curvature optimization method for multifocal lens optical design as described in claim 5, characterized in that, During the global search and iterative optimization of the curvature variables within the solution interval, iteration termination conditions are set. These conditions include: the objective function value converges to a preset threshold, the number of iterations reaches a preset maximum value, and the adjustment range of the curvature variables is less than a preset minimum value. If any one of these termination conditions is met, the iteration stops, and the current curvature parameter scheme is output.

9. A curvature optimization system for multifocal lens optical design, used to perform the method according to any one of claims 1 to 8, characterized in that, The system includes a parameter acquisition module, a curvature model construction module, a ray tracing calculation module, an optimization algorithm module, a simulation verification module, a processing data generation module, and a main control module. The parameter acquisition module is used to acquire lens design requirements parameters and optical performance indicators. The curvature model construction module is used to establish the initial curvature model of the lens and set constraints. The ray tracing calculation module is used to simulate light propagation and calculate optical imaging data and deviation values. The optimization algorithm module is used to construct the objective function and iteratively optimize the curvature variables. The simulation verification module is used to simulate and verify the curvature parameter scheme and select the optimal parameters. The processing data generation module is used to generate lens surface curvature processing data. The main control module is connected to each module to coordinate the work of each module and transmit data, completing the entire process design of lens curvature optimization.

10. The curvature optimization system for multifocal lens optical design as described in claim 9, characterized in that, The ray tracing calculation module includes a ray simulation unit, an imaging data calculation unit, and a deviation value analysis unit. The ray simulation unit is used to simulate the propagation path of light in the curvature model, the imaging data calculation unit is used to calculate the optical imaging data of each region, and the deviation value analysis unit is used to calculate the deviation value between the imaging data and the performance index. The optimization algorithm module includes an objective function construction unit, a constraint condition import unit, and a hybrid optimization operation unit. The hybrid optimization operation unit integrates gradient descent algorithm and genetic algorithm for iterative solution of curvature variables and parameter adjustment.

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