Design optimization method and system for artificial lens

Abstract

The present application relates to the technical field of lens design, and discloses a design optimization method and system for an artificial lens, the method comprising: collecting geometric parameter data of the artificial lens; calculating spherical aberration, coma and astigmatism values ​​at different viewing distances by using a ray tracing algorithm, and quantitatively analyzing the imaging quality of the artificial lens by using a wavefront error evaluation function and a modulation transfer function to obtain an optical performance evaluation result; performing a finite element analysis on the artificial lens, constructing a three-dimensional structural model, and performing stress-strain analysis and dynamic loading analysis on the three-dimensional structural model to obtain stress distribution data and deformation data; inputting the optical performance evaluation result, stress distribution data and deformation data into a multi-objective optimization function, generating multiple sets of parameter design combinations by means of random perturbations, and performing optimization calculations and parameter space searches on the multiple sets of parameter design combinations to obtain a target parameter design combination, thereby improving the efficiency and accuracy of the artificial lens design.

Description

Technical Field

[0001] The present application relates to the technical field of lens design, and in particular to a design optimization method and system for an artificial lens. Background Art

[0002] As an important implant to replace the cloudy lens in cataract surgery, the optical and mechanical properties of intraocular lenses directly affect the patient's postoperative visual quality and service life. The traditional intraocular lens design optimization process mainly relies on experience and repeated experiments, lacks a systematic optimization method, and it is difficult to take into account the optimization goals of optical and mechanical properties at the same time. Especially in the design of multifocal intraocular lenses, due to their complex surface morphology and strict optical requirements, the selection of design parameters often requires a trade-off between multiple performance indicators.

[0003] At present, the design optimization of intraocular lenses has problems such as incomplete parameter collection, single optical evaluation method, and inaccurate prediction of mechanical properties. In terms of parameter collection, existing methods often ignore the accurate measurement of key parameters such as edge thickness and optical zone diameter, resulting in insufficient modeling accuracy; in terms of optical performance evaluation, usually only the imaging quality at a single viewing distance is considered, and the optical performance at different viewing distances and incident angles is not fully reflected; in terms of mechanical performance analysis, there is a lack of dynamic loading response analysis of intraocular lenses in actual intraocular use environments. Summary of the invention

[0004] The present application provides a method and system for optimizing the design of an artificial lens, thereby improving the efficiency and accuracy of the artificial lens design.

[0005] A first aspect of the present application provides a method for optimizing the design of an intraocular lens, the method comprising:

[0006] Collecting geometric parameter data of intraocular lens;

[0007] Based on the geometric parameter data, the spherical aberration, coma and astigmatism values ​​at different viewing distances are calculated using a ray tracing algorithm, and the imaging quality of the intraocular lens is quantitatively analyzed through a wavefront error evaluation function and a modulation transfer function to obtain an optical performance evaluation result;

[0008] According to the geometric parameter data and the optical performance evaluation results, finite element analysis is performed on the intraocular lens to construct a three-dimensional structural model, and stress-strain analysis and dynamic loading analysis are performed on the three-dimensional structural model to obtain stress distribution data and deformation data;

[0009] The optical performance evaluation results, the stress distribution data and the deformation data are input into a multi-objective optimization function, multiple groups of parameter design combinations are generated by random perturbation, and the multiple groups of parameter design combinations are optimized and searched in parameter space to obtain a target parameter design combination.

[0010] A second aspect of the present application provides a design optimization system for an intraocular lens, the design optimization system for an intraocular lens comprising:

[0011] An acquisition module, used for acquiring geometric parameter data of the intraocular lens;

[0012] A quantitative analysis module, for calculating the spherical aberration, coma and astigmatism values ​​at different viewing distances based on the geometric parameter data using a ray tracing algorithm, and performing quantitative analysis on the imaging quality of the intraocular lens through a wavefront error evaluation function and a modulation transfer function to obtain an optical performance evaluation result;

[0013] A construction module, used to perform finite element analysis on the intraocular lens according to the geometric parameter data and the optical performance evaluation result, construct a three-dimensional structural model, and perform stress-strain analysis and dynamic loading analysis on the three-dimensional structural model to obtain stress distribution data and deformation data;

[0014] A generation module is used to input the optical performance evaluation results, the stress distribution data and the deformation data into a multi-objective optimization function, generate multiple groups of parameter design combinations by random perturbation, and perform optimization calculations and parameter space searches on the multiple groups of parameter design combinations to obtain target parameter design combinations.

[0015] The third aspect of the present application provides an electronic device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the electronic device executes the above-mentioned artificial lens design optimization method.

[0016] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for optimizing the design of an intraocular lens.

[0017] Compared with the prior art, the present application has the following beneficial effects: by adopting a multi-dimensional geometric parameter acquisition method, combined with the collaborative measurement of a shape measuring device and a precision thickness gauge, the surface profile, thickness distribution and optical zone size data of the intraocular lens are comprehensively acquired, thereby improving the integrity and accuracy of the basic data; based on the combined application of a ray tracing algorithm and a wavefront error evaluation function, a comprehensive evaluation of the optical performance at different viewing distances and incident angles is achieved, and at the same time, the imaging quality of the intraocular lens is accurately reflected through the quantitative analysis of the modulation transfer function; a three-dimensional structural model is constructed using the finite element analysis method, and the mechanical properties of the intraocular lens in an actual use environment are comprehensively evaluated through a combination of static and dynamic loading analysis, thereby improving the accuracy of performance prediction; a multi-objective optimization function and a random perturbation mechanism are introduced, and a fuzzy rule system is used to screen and optimize the parameter combination, thereby effectively solving the problem of collaborative optimization of optical and mechanical properties; through the adaptive division of the parameter space and the iterative optimization strategy, the efficiency of parameter search and the reliability of the optimization results are significantly improved, thereby improving the efficiency and accuracy of intraocular lens design. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0020] Figure 1 is a flow chart of a design optimization method for an intraocular lens provided by an embodiment of the present invention;

[0021] Figure 2 is a schematic block diagram of the structure of an intraocular lens design optimization system provided by an embodiment of the present invention;

[0022] Figure 3 It is a schematic block diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0025] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0026] It should be further understood that the term "and / or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. Figure 1 , an embodiment of the design optimization method of the intraocular lens in the embodiment of the present application includes:

[0027] Step 100, collecting geometric parameter data of the intraocular lens;

[0028] It is understandable that the execution subject of the present application may be an intraocular lens design optimization system, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0029] Specifically, the intraocular lens is placed on an optical platform, and the platform is ensured to be stable and have sufficient accuracy to support high-precision optical measurement. The optical device of the parallel light beam is adjusted so that it is incident vertically on the first surface of the intraocular lens. The vertical incidence of the parallel light beam is a key step, which can ensure the accuracy of the optical path and provide high-quality measurement data for the shape measurement device. In this process, the shape measurement device collects the curvature radius data of the first surface by optical imaging or interference and generates the contour curve of the first surface based on this. The intraocular lens is flipped 180 degrees on the optical platform, and deformation or displacement of the lens due to the flipping action is avoided during the process. After flipping, the second surface of the intraocular lens is irradiated with a parallel light beam with vertical incidence, and the curvature radius of the second surface is measured again by the shape measurement device. The contour curve of the second surface is generated by the collected curvature data. In these two measurements, the relative position and angle of the shape measurement device are ensured to be consistent to avoid data mismatch due to differences in measurement conditions. The center position of the intraocular lens is axially positioned, and the positioning accuracy determines the accuracy of the thickness data. A precision thickness gauge is used to measure the thickness of the center point, and high-precision center thickness data is obtained through contact or non-contact measurement technology. On this basis, in order to fully describe the thickness distribution characteristics of the intraocular lens, 8 measurement points are evenly selected along the edge circumference direction. The evenly distributed measurement points can cover the entire edge area to ensure that the edge thickness data is representative. Similarly, a precision thickness gauge is used to measure the thickness of each measurement point to obtain complete edge thickness data. The boundary of the optical zone of the intraocular lens is located. The edge contour of the optical zone is accurately identified by image processing or optical detection means, and the diameter of the optical zone is measured according to the edge contour. The contour curve of the first surface, the contour curve of the second surface, the center thickness data, the edge thickness data, and the optical zone diameter data are integrated to obtain geometric parameter data. The integration process uses computer modeling tools to ensure that data from different sources have a unified format and coordinate system.

[0030] Step 200: Based on the geometric parameter data, the spherical aberration, coma and astigmatism values ​​at different viewing distances are calculated using a ray tracing algorithm, and the imaging quality of the intraocular lens is quantitatively analyzed through a wavefront error evaluation function and a modulation transfer function to obtain an optical performance evaluation result;

[0031] Specifically, the first surface contour curve and the second surface contour curve of the intraocular lens are extracted from the geometric parameter data. The contour curve is converted into feature data that can describe the optical surface morphology, that is, optical surface data, through the optical surface feature recognition technology. Parallel light is incident on the position corresponding to the optical surface data at multiple incident angles. The incident angle setting of the light needs to cover the full field of view of the intraocular lens, so as to ensure a comprehensive analysis of the optical performance. For each incident light, its initial position, direction vector and incident angle are recorded to generate complete incident light data. Based on the incident light data, a ray tracing algorithm is used to simulate the propagation path of the light at different viewing distances. The calculation process of the light path needs to consider the refraction behavior of the light when passing through the optical surface, which involves the law of refraction in geometric optics, and accurately model the complex light deflection caused by the curved surface shape. Through this process, ray tracing data is generated, including the position, direction and focusing characteristics of each light after passing through the intraocular lens. Optical aberration is calculated based on the ray tracing data. Spherical aberration is calculated for the ray tracing data, and the magnitude of the spherical aberration is quantified by comparing the axial distance between the ideal imaging position and the actual imaging position. The ideal imaging position is the expected light convergence point in the optical design, while the actual imaging position is the result of ray tracing calculation. The difference between the two directly reflects the influence of spherical aberration. Then the coma is calculated, and the coma value is determined by analyzing the focal position difference between the meridian plane light and the sagittal plane light. The existence of coma will lead to asymmetric aberration, and its calculation result is helpful to evaluate the edge imaging quality of the intraocular lens. At the same time, the astigmatism is calculated, and the degree of astigmatism is quantified by the focal length difference between the meridian plane and the sagittal plane. Astigmatism reflects the focusing difference of light in different directions and has a direct impact on the effect of vision correction. The spherical aberration data, coma data and astigmatism data are input into the wavefront error evaluation function for calculation. The wavefront error evaluation function quantifies the overall optical performance of the intraocular lens through the root mean square value (RMS) of the wavefront deviation. The calculation process of RMS wavefront error involves statistical analysis of wavefront deviation, which reflects the degree of phase shift of the intraocular lens to the incident light wave. Spatial frequency analysis is performed based on the wavefront error data, and the spatial resolution of the intraocular lens is calculated by the modulation transfer function (MTF). The modulation transfer function value evaluates the imaging quality of the optical system by quantifying its response to different spatial frequencies. A higher MTF value means that the lens can better restore details and contrast. The above steps provide the results of the optical performance evaluation, including the impact of spherical aberration, coma and astigmatism on imaging quality, as well as the overall optical performance described by wavefront error and modulation transfer function value.

[0032] Spherical aberration data, coma data and astigmatism data are input into the wavefront error evaluation function, and the combined aberration data is generated by linearly combining the optical aberration data. The weight coefficient of the linear combination is set according to the relative influence of different aberrations on the imaging quality, ensuring that the combined aberration data can accurately reflect the overall optical deviation of the intraocular lens. The combined aberration data is expanded by Zernike polynomials, and the complex optical aberrations are decomposed into a set of orthogonal polynomial basis functions to generate a wavefront function coefficient matrix. Zernike polynomials are an important tool in optical system analysis. Their expansion results are convenient for describing the wavefront morphology and can provide quantitative analysis of aberrations of different orders. The calculation result of the wavefront function coefficient matrix is ​​a high-dimensional data structure, and each coefficient corresponds to the aberration contribution of a specific order and mode, which can accurately describe the distribution characteristics of the wavefront deviation. According to the generated wavefront function coefficient matrix, the root mean square value (RMS) of the wavefront deviation is calculated. The RMS wavefront error data reflects the overall amplitude of the wavefront deviation and is a key indicator for measuring the optical quality of intraocular lenses. During the calculation process, the deviation values ​​in the vertical and horizontal directions are evaluated respectively. The wavefront error data in these directions can reveal the differences in the imaging quality of the intraocular lens in different directions. The wavefront error data is processed by two-dimensional Fourier transform in the x-axis and y-axis directions to generate a spatial frequency distribution matrix. The two-dimensional Fourier transform can convert the wavefront error from the spatial domain to the frequency domain and reveal its distribution characteristics at different spatial frequencies. Based on the spatial frequency distribution matrix, the frequency response at multiple incident angles is calculated. The frequency response data of each incident angle can reflect the transfer characteristics of light at different spatial frequencies when passing through the intraocular lens. In order to unify the evaluation criteria, the angle response data is normalized so that it takes values ​​between 0 and 1, which is convenient for comparing the responses at different incident angles. Within the preset spatial frequency range, the modulation transfer function (MTF) data is calculated by analyzing the normalized response data. MTF data describes the imaging quality of the intraocular lens at different spatial frequencies and is an important indicator for measuring the clarity of the imaging system. Based on the MTF data, the spatial resolution at multiple viewing distances is calculated to generate viewing distance resolution data. The calculation of spatial resolution can directly quantify the imaging ability of the intraocular lens at different viewing distances. Based on the viewing distance resolution data, the imaging quality within the field of view is comprehensively evaluated and calculated to obtain the optical performance evaluation results.

[0033] Step 300: Perform finite element analysis on the intraocular lens according to the geometric parameter data and the optical performance evaluation results, construct a three-dimensional structure model, and perform stress-strain analysis and dynamic loading analysis on the three-dimensional structure model to obtain stress distribution data and deformation data;

[0034] It should be noted that, according to the first surface contour curve and the second surface contour curve in the geometric parameter data, as well as the center thickness data and the edge thickness data, the three-dimensional modeling tool is used to reconstruct the intraocular lens. The reconstruction process accurately captures the geometric shape and size of the intraocular lens, and generates its complete three-dimensional geometric model through surface fitting and parametric modeling technology. The three-dimensional geometric model is meshed by finite element. The model is meshed by tetrahedral units. The unit density is controlled during the meshing process, and a higher density mesh is used for key areas (such as the optical center and the edge area) to capture the stress concentration and deformation details of these areas. After the meshing is completed, the nodes of the tetrahedral mesh unit are numbered to generate mesh unit data. Based on the actual material properties of the intraocular lens, the material properties of the mesh unit data are defined. Parameters such as the elastic modulus, Poisson's ratio and density of the material are set to accurately reflect the mechanical response of the intraocular lens under mechanical loading. The definition of these material parameters needs to be based on experimental measurement data or existing literature to ensure the physical authenticity of the simulation results. After the material property definition is completed, a complete three-dimensional structural model is generated. According to the wavefront error data in the optical performance evaluation results, boundary constraints and load conditions are applied to the three-dimensional structural model. Boundary constraints are used to simulate the fixed or constrained state of the intraocular lens in actual use, such as edge fixation or partial area compression. Load conditions simulate the mechanical force or external interference force on the optical surface during imaging based on the characteristics of the wavefront error. After applying boundary and load conditions, static structural analysis is performed on the three-dimensional structural model. Finite element solution is used to calculate the principal stress, equivalent stress and strain components of the intraocular lens. These data can quantify the stress distribution and deformation in different regions, reveal potential mechanical weaknesses, and obtain stress distribution data. Displacement loading is applied to the three-dimensional structural model to simulate the mechanical behavior of the intraocular lens under complex loading conditions such as compression deformation, bending deformation and torsion deformation. By analyzing its stress-strain response, deformation data is obtained, which reflects the overall deformation characteristics of the intraocular lens under different loading modes, and whether there is uneven deformation or excessive stress concentration. In order to evaluate the mechanical reliability of the intraocular lens, dynamic response analysis is performed based on the three-dimensional structural model. In the dynamic loading analysis, the dynamic behavior of the intraocular lens under vibration or impact conditions is simulated by setting the motion displacement amplitude and frequency. The dynamic loading data reflects the dynamic stress distribution and deformation state of the intraocular lens. These data are input into the fatigue analysis module to evaluate its fatigue life under cyclic loads and reveal the structural stability and failure risk of the intraocular lens in long-term use. Through the above steps, a comprehensive mechanical properties evaluation result including stress distribution data and deformation data is finally obtained.

[0035] Step 400: Input the optical performance evaluation results, stress distribution data and deformation data into the multi-objective optimization function, generate multiple sets of parameter design combinations through random perturbation, and perform optimization calculations and parameter space searches on the multiple sets of parameter design combinations to obtain target parameter design combinations.

[0036] Specifically, the optical performance evaluation results, stress distribution data and deformation data are standardized to eliminate the dimensional differences of different performance indicators and obtain standardized performance indicator data. The optimization objective function is constructed for the standardized performance indicator data. The optimization objective function takes into account both the optical performance objectives and the mechanical performance objectives. The optical performance objective function (such as wavefront error minimization) and the mechanical performance objective function (such as stress distribution homogenization and deformation minimization) are integrated by a weighted combination method. The setting of the weight coefficient is adjusted according to the specific design requirements to highlight the priority of optical performance or mechanical performance. The construction of the multi-objective optimization function transforms the entire optimization problem into a quantifiable mathematical problem. In order to explore the design parameter space, the wavefront error data in the optical performance evaluation results is introduced as a disturbance source of the probability mode to randomly perturb the design parameters. The random perturbation of the wavefront error can simulate the performance fluctuations of the intraocular lens under different use conditions and generate more diverse design parameter combinations. According to the parameter perturbation data, the value range of the design parameters is divided into intervals, and multiple sets of parameter design combinations are generated through the combination generation algorithm. Fuzzy rule judgment is performed on multiple sets of parameter design combinations to eliminate invalid combinations that do not meet the constraints. The setting of fuzzy rules is based on the physical constraints of optical and mechanical properties, such as the wavefront error must not exceed a certain threshold, or the stress concentration that exceeds the material limit must not appear in the stress distribution. Through fuzzy rule judgment, the number of invalid parameter combinations is effectively reduced, the efficiency of optimization calculation is improved, and the remaining feasible parameter combinations are ensured to be physically feasible. The selected feasible parameter combinations are input into the multi-objective optimization function for parameter sensitivity analysis to identify the degree of influence of the design parameters on the optimization target, thereby locally refining the parameter search space. Through sensitivity analysis, the search strategy of the optimization algorithm is dynamically adjusted, and the optimization resources are concentrated to conduct in-depth exploration in the parameter interval that has a greater impact on the objective function, thereby improving the accuracy and reliability of the optimization results. After obtaining the optimization search data, the iterative calculation stage of the parameter space is entered. Through iterative algorithms, such as genetic algorithms or particle swarm optimization algorithms, the design parameter combinations are gradually updated, and the value of the optimization objective function is evaluated in each round of iteration. Convergence criteria and termination conditions are set during the iterative calculation process, such as the change in the objective function value is less than the preset threshold, or the maximum number of iterations is reached. When the termination condition is met, the iteration process stops and the iterative calculation data is output. According to the performance index score of the iterative calculation data, the optimal target parameter design combination is selected.

[0037] The wavefront error data in the optical performance evaluation results are used as the input parameters of the perturbation probability distribution function. The construction of this distribution function needs to be set according to the statistical characteristics of the wavefront error, such as selecting normal distribution, Gaussian distribution or other function forms suitable for describing the wavefront error distribution. By fitting the wavefront error data, an accurate perturbation probability distribution is generated to ensure that the random perturbation process has physical meaning and conforms to the actual performance evaluation results. The perturbation probability distribution is applied to the geometric parameters of the intraocular lens. The first surface profile curve and the second surface profile curve are randomly sampled and perturbed. By randomly generating sampling points within the range of the perturbation probability distribution, each point on the curve is offset calculated to simulate the random changes of the curve morphology under manufacturing errors or external influences. These surface profile perturbation data generated by random sampling can capture the diversity of surface characteristics under different perturbation conditions. At the same time, based on the same perturbation probability distribution, the center thickness data and edge thickness data of the intraocular lens are randomly perturbed. The thickness perturbation is achieved by generating perturbation values ​​according to the probability distribution based on the original thickness value, and superimposing the perturbation values ​​with the original data to generate thickness perturbation data. The surface profile perturbation data and thickness perturbation data are combined to construct a parameter correlation matrix to capture the relationship between different parameters, such as the coupling effect between the surface profile and thickness data. By quantifying the parameter correlation, the synergistic variation characteristics of the design parameters under actual conditions can be more accurately described. Based on the parameter correlation matrix, complete parameter perturbation data are generated. In the parameter discretization process, the value range of the design parameters is divided into multiple sub-intervals according to the parameter perturbation data. Interval division helps to reduce the complexity of the optimization calculation, and by refining the parameter space, the performance differences of the design parameters in different intervals are captured to obtain parameter interval data. Based on the parameter interval data, the initial parameter combination is generated using the orthogonal experimental design method. Orthogonal experimental design is an effective experimental design method that can reduce the number of required calculation samples while ensuring a wide range of parameter coverage. By constructing an orthogonal table, a set of initial parameter combinations are generated, which have uniform distribution characteristics and can ensure the comprehensiveness and representativeness of the optimization search. Uniformly distributed parameter sampling data are generated based on the initial parameter combinations. Multiple sets of parameter design combinations are generated by combining and generating calculations on the parameter sampling data.

[0038] In the embodiments of the present application, a multi-dimensional geometric parameter acquisition method is adopted, combined with the collaborative measurement of a shape measuring device and a precision thickness gauge, to comprehensively obtain the surface profile, thickness distribution and optical zone size data of the intraocular lens, thereby improving the integrity and accuracy of the basic data; based on the combined application of a ray tracing algorithm and a wavefront error evaluation function, a comprehensive evaluation of the optical performance at different viewing distances and incident angles is achieved, and at the same time, the imaging quality of the intraocular lens is accurately reflected through the quantitative analysis of the modulation transfer function; a three-dimensional structural model is constructed using a finite element analysis method, and the mechanical properties of the intraocular lens in an actual use environment are comprehensively evaluated through a combination of static and dynamic loading analysis, thereby improving the accuracy of performance prediction; a multi-objective optimization function and a random perturbation mechanism are introduced, and a fuzzy rule system is used to screen and optimize the parameter combination, thereby effectively solving the problem of collaborative optimization of optical and mechanical properties; through the adaptive division of the parameter space and the iterative optimization strategy, the efficiency of parameter search and the reliability of the optimization results are significantly improved, thereby improving the efficiency and accuracy of intraocular lens design.

[0039] In a specific embodiment, the process of executing step 100 may specifically include the following steps:

[0040] Placing the intraocular lens on an optical platform, irradiating the intraocular lens with a vertically incident parallel light beam, collecting the curvature radius data of the first surface through a shape measuring device, and obtaining a first surface profile curve;

[0041] After the intraocular lens is turned over 180 degrees, a vertically incident parallel light beam is used to illuminate the intraocular lens, and the curvature radius data of the second surface is collected by a shape measuring device to obtain a second surface contour curve;

[0042] The center position of the intraocular lens was measured axially, and the thickness data was collected at the center point using a precision thickness gauge to obtain the center thickness data. Eight measurement points were evenly selected along the circumference of the edge of the intraocular lens, and the thickness of each measurement point was measured using a precision thickness gauge to obtain the edge thickness data.

[0043] Locate the boundary of the optical zone of the intraocular lens, extract the edge contour of the optical zone and measure the diameter of the optical zone to obtain the optical zone diameter data;

[0044] The first surface profile curve, the second surface profile curve, the center thickness data, the edge thickness data and the optical zone diameter data are integrated to obtain geometric parameter data.

[0045] Specifically, the intraocular lens is firmly placed on an optical platform to ensure that its position remains parallel to the platform. In order to collect the curvature radius data of the first surface, a parallel light beam is vertically incident on the first surface of the intraocular lens. The curvature of the first surface is measured with high precision by a shape measuring device (such as an optical profiler or an interferometer). The offset of the incident light beam and the reflected light beam is recorded, and according to the curvature radius formula:

[0046] ;

[0047] Calculate the radius of curvature In the formula Indicates the curvature of the surface at the measurement point, which is calculated by the relationship between the light offset measured by the optical device and the known distance. Based on the curvature radius data of the sampling point, the contour curve of the first surface is generated by interpolation or fitting algorithm The intraocular lens is flipped 180 degrees, and its position on the optical platform is kept stable to ensure that the flipping process does not cause deformation or displacement of the lens morphology. The parallel light beam is readjusted to make it perpendicular to the second surface, and the curvature radius of the second surface is measured in the same way using the shape measurement device. The formula is also:

[0048] ;

[0049] in is the curvature value of the second surface. The contour curve of the second surface is also generated through the curvature radius data of the sampling point In order to collect the thickness data of the intraocular lens, a precision thickness gauge is used to perform axial measurement of the center position. Align the probe of the thickness gauge with the center point of the intraocular lens and record its center thickness. The result of this measurement is expressed as:

[0050] ;

[0051] in and Represent the height coordinates of the first surface and the second surface at the center position respectively. In order to more comprehensively describe the thickness distribution, 8 measurement points are evenly selected in the circumferential direction of the lens edge. , use precision thickness gauge to measure thickness point by point . These data describe the thickness variation from center to edge and are used to analyze the thickness uniformity of the lens during the manufacturing process. The optical zone of the intraocular lens is accurately measured by border positioning technology. The optical zone is the most important area of ​​the lens and its border directly affects the imaging performance. The outline of the optical zone is extracted using image processing algorithms (such as edge detection) Calculate the diameter of the optical zone from the point cloud data of the contour line , the formula is:

[0052] ;

[0053] in and are the coordinates of any two points on the optical zone contour line. After all measurements are completed, the first surface contour curve , the second surface profile curve , Center thickness data , edge thickness data and optical zone diameter data These data are uniformly represented as geometric parameter data vectors :

[0054] .

[0055] In a specific embodiment, the process of executing step 200 may specifically include the following steps:

[0056] Performing optical surface feature recognition on the first surface profile curve and the second surface profile curve of the geometric parameter data to obtain optical surface data;

[0057] The parallel light is incident at a position corresponding to the optical surface data at a plurality of incident angles to obtain incident light data, and the light path of the incident light data is traced at a plurality of viewing distances to obtain light tracing data;

[0058] Perform spherical aberration calculation on the ray tracing data, calculate the axial distance between the ideal imaging position and the actual imaging position, and obtain spherical aberration data;

[0059] Perform coma calculation on the ray tracing data, calculate the focus position difference between the meridian plane ray and the sagittal plane ray, and obtain the coma data;

[0060] Perform astigmatism calculation on the ray tracing data, calculate the focal length difference between the meridian plane and the sagittal plane, and obtain the astigmatism data;

[0061] The spherical aberration data, coma data and astigmatism data are input into the wavefront error evaluation function to calculate the root mean square value of the wavefront deviation to obtain the wavefront error data. The wavefront error data is then subjected to spatial frequency analysis and modulation transfer function value calculation to obtain the optical performance evaluation result.

[0062] Specifically, the first surface profile curve and the second surface profile curve are extracted from the geometric parameter data, and the optical surface features are identified. Through the mathematical modeling method, the profile curve is fitted into a high-order polynomial or a quadratic surface model to obtain a characteristic equation that can describe the optical surface morphology. Assume that the first surface profile curve is represented by the function , and the second surface profile curve is represented by express, and are the coordinates on the surface, and is the height of the surface at these coordinate points. Through feature recognition, we can extract the curvature distribution After obtaining the optical surface data, parallel light is projected onto the optical surface at multiple incident angles for simulation. Assume that the initial propagation direction of the light is represented by the vector Indicates that is the direction cosine. By adjusting the incident angle , defining a collection of rays at different angles. For each ray, use the characteristic equation of the optical surface The intersection point and refraction direction of the light on the surface are calculated using the law of refraction to generate incident light data. This process is described by the geometric optics equation:

[0063] ;

[0064] in and are the refractive indices of air and intraocular lenses, is the angle of incidence, is the refraction angle. Based on the incident light data, the propagation path of the light inside the intraocular lens is traced. Using the ray tracing algorithm, the propagation and refraction behavior of the light between the surfaces is recursively calculated to obtain the ray tracing data, including the final focus position of each light. For a viewing distance , the theoretical imaging point of the light is ,in , and the actual imaging point Obtained directly from tracing data. Specific calculation of optical aberrations is performed based on ray tracing data. Spherical aberration calculation describes the axial deviation between the ideal imaging position and the actual imaging position. Spherical aberration The formula is:

[0065] ;

[0066] in is the axial coordinate of the actual imaging point, is the axial coordinate of the ideal imaging point. Next is the coma calculation, which is used to quantify the focal position difference between the meridian plane ray and the sagittal plane ray. Assume that the actual focal coordinates corresponding to the meridian plane and sagittal plane are and , then the coma The expression is:

[0067] ;

[0068] The presence of coma will lead to asymmetric aberrations, which will have a significant impact on image quality. The calculation of astigmatism focuses on the focal length difference between the meridian plane and the sagittal plane. and sagittal focal length , get astigmatism Values:

[0069] ;

[0070] in and are the focal lengths of the meridian plane and sagittal plane, respectively, which can be calculated from the ray tracing path and the focal coordinates. , coma data and astigmatism data Input the wavefront error evaluation function and calculate the root mean square (RMS) of the wavefront deviation. The formula for the wavefront error RMS is:

[0071] ;

[0072] in Indicates The wavefront deviation of a ray, is the mean of the wavefront deviations of all rays, is the total number of rays. Perform spatial frequency analysis on the wavefront error data and convert the wavefront error from the spatial domain to the frequency domain through two-dimensional Fourier transform to obtain the frequency distribution :

[0073] ;

[0074] in and is a spatial frequency variable. Based on the frequency distribution, the modulation transfer function (MTF) is calculated to describe the imaging quality of the optical system at different frequencies. The calculation formula of MTF is:

[0075] ;

[0076] in is the spatial frequency, is the frequency response amplitude at that frequency, is the amplitude of zero frequency. The above calculations are combined to obtain the optical performance evaluation results, including wavefront error RMS, spatial frequency distribution and MTF value. These results can fully reflect the imaging performance of the intraocular lens.

[0077] In a specific embodiment, the step of inputting spherical aberration data, coma data and astigmatism data into a wavefront error evaluation function to calculate the root mean square value of wavefront deviation to obtain wavefront error data, and performing spatial frequency analysis and modulation transfer function value calculation on the wavefront error data to obtain the optical performance evaluation result may specifically include the following steps:

[0078] Substitute the spherical aberration data, coma data and astigmatism data into the wavefront error evaluation function, perform linear combination on the optical difference data to obtain combined optical difference data, and perform Zernike polynomial expansion on the combined optical difference data to obtain a wavefront function coefficient matrix;

[0079] The root mean square value of the deviation is calculated according to the wavefront function coefficient matrix to obtain wavefront error data, and the wavefront error data includes a vertical direction deviation value and a horizontal direction deviation value;

[0080] The wavefront error data is processed by two-dimensional Fourier transform in the x-axis and y-axis directions to obtain a spatial frequency distribution matrix;

[0081] Calculating the frequency responses of multiple incident angles according to the spatial frequency distribution matrix to obtain angle response data, normalizing the angle response data, and calculating the optical transfer function within a preset spatial frequency range to obtain modulation transfer function data;

[0082] The spatial resolution at multiple viewing distances is calculated according to the modulation transfer function data to obtain the viewing distance resolution data. Based on the viewing distance resolution data, the imaging quality within the field of view is comprehensively evaluated and calculated to obtain the optical performance evaluation result.

[0083] Specifically, the spherical aberration data, coma data and astigmatism data are substituted into the wavefront error evaluation function for processing. These data are expressed by a linear combination formula, and the combined optical aberration data Calculated using the following formula:

[0084] ;

[0085] in , , They are the data distribution of spherical aberration, coma and astigmatism, , , are weight coefficients that reflect the relative impact of these aberrations on the wavefront error. These weights are set by experimental calibration or design goals. The calculated combined aberration data Describes the comprehensive deviation distribution of the wavefront in the entire optical system. The combined light difference data is expanded by Zernike polynomials, and the Zernike polynomials is a set of orthogonal basis functions describing optical aberrations, which has the form:

[0086] ;

[0087] in and are the radial and angular orders, respectively, is a radial polynomial, and is the radial distance and angle in polar coordinates. Convert to polar coordinates , solve the Zernike coefficients by the least squares method :

[0088] ;

[0089] The expanded wavefront function coefficient matrix The components of the combined light difference data on each order of Zernike polynomials are described to provide a parametric representation for further analysis. Based on the wavefront function coefficient matrix, the root mean square (RMS) of the wavefront error is calculated, which quantifies the overall amplitude of the wavefront deviation. The formula for RMS error is:

[0090] ;

[0091] in is the optical aperture area, is the wavefront average. By decomposing the calculation process into the vertical and horizontal directions, the vertical deviation is obtained respectively. and horizontal deviation , describing the distribution characteristics of the wavefront error in different directions. according to and The two-dimensional Fourier transform is performed in the direction to reveal the characteristics of the wavefront error in the frequency domain. The formula for the two-dimensional Fourier transform is:

[0092] ;

[0093] in is the frequency distribution matrix, and is a spatial frequency variable. The frequency distribution matrix can reveal the components of the wavefront error at different frequencies, providing a basis for calculating the angle response and modulation transfer function. Based on the frequency distribution matrix, the frequency response of multiple incident angles is calculated, and the angle response data is normalized so that its value is limited to the range of 0 to 1. The normalization formula is:

[0094] ;

[0095] in is the frequency and the angle of incidence The response value under normalized Used to unify the response evaluation at different frequencies and angles. Based on the normalized angular response data, the modulation transfer function (MTF) is calculated within the preset spatial frequency range:

[0096] ;

[0097] in, is the frequency The frequency distribution amplitude under is the zero frequency amplitude. MTF describes the imaging capability of the system at different spatial frequencies. Using the modulation transfer function data, the spatial resolution at multiple viewing distances is calculated. The formula for spatial resolution is:

[0098] ;

[0099] in is the wavelength of light, NA is the numerical aperture, and the viewing distance resolution data is calculated based on the NA values ​​corresponding to different viewing distances. Based on the resolution data of multiple viewing distances, the imaging quality within the field of view is evaluated by the weighted average method:

[0100] ;

[0101] in is the weight of each view distance, is the resolution corresponding to the viewing distance. These calculation results are combined into the optical performance evaluation results.

[0102] In a specific embodiment, the process of executing step 300 may specifically include the following steps:

[0103] Performing three-dimensional entity reconstruction according to the first surface contour curve, the second surface contour curve, the center thickness data and the edge thickness data in the geometric parameter data to obtain a three-dimensional geometric model of the intraocular lens;

[0104] Divide the three-dimensional geometric model into tetrahedral mesh units, and number the unit nodes of the tetrahedral mesh units to obtain mesh unit data;

[0105] Define the material properties of the grid unit data, set the elastic modulus, Poisson's ratio and density parameters, and obtain a three-dimensional structural model;

[0106] According to the wavefront error data in the optical performance evaluation results, boundary constraints and load conditions are imposed on the three-dimensional structure model to obtain loading constraint data;

[0107] Perform static structural analysis on the three-dimensional structural model and loading constraint data, calculate the principal stress, equivalent stress and strain components, and obtain stress distribution data;

[0108] Based on the three-dimensional structural model, displacement loading is applied to calculate the stress-strain response of the intraocular lens under compression deformation, bending deformation and torsion deformation to obtain deformation data;

[0109] According to the stress distribution data, the dynamic response analysis of the three-dimensional structural model is carried out, the motion displacement amplitude and frequency are set, and the dynamic loading data are obtained. The dynamic loading data is input into the fatigue analysis module, and the stress and deformation state of the intraocular lens under cyclic load is calculated to obtain the stress distribution data and deformation data.

[0110] Specifically, a three-dimensional geometric model of the intraocular lens is constructed based on the geometric parameter data. , the second surface profile curve , Center thickness data and edge thickness data , using computer-aided design (CAD) tools for 3D reconstruction. The core of 3D reconstruction is to combine surface data with thickness information and generate a solid model through parametric modeling. . Its shape is expressed by the following relationship:

[0111] ;

[0112] in is the local thickness distribution, satisfying the boundary condition At the center point, At the edge point. The generated 3D model After that, it is meshed to suit finite element analysis. The three-dimensional geometry is meshed using tetrahedral elements, ensuring that the mesh density is high enough to capture the complex geometric characteristics of the lens. During meshing, the volume formula of the tetrahedral element is used. :

[0113] ;

[0114] in are the coordinate vectors of the four nodes of the tetrahedral element; is the cross product of two vectors, representing the base area normal vector of the cell; It is the dot product, which indicates the contribution in the height direction. By numbering all grid unit nodes, grid unit data is generated. , which contains node locations and unit topology. After the meshing is completed, the material properties of the mesh unit data are defined. Intraocular lenses are usually made of flexible materials with an elastic modulus of , Poisson's ratio and density is a key material parameter. For example, assuming that the material of the lens is a certain material, then and The density is obtained through experiments. Calculated based on material volume and mass:

[0115] ;

[0116] in is the quality of the material, is the total volume of the 3D geometric model. After the definition is completed, the complete 3D structural model is obtained. . Based on the wavefront error data in the optical performance evaluation results, boundary constraints and load conditions are imposed on the three-dimensional structure model. Boundary constraints are used to fix the edge or specific point of the lens to prevent free movement. Load conditions simulate the optical pressure or external stress that the lens is subjected to during imaging. Assume that the load is uniformly distributed pressure , and its force is expressed as:

[0117] ;

[0118] in is the surface area to which the load is applied. Based on the above model and loading constraint data, a static structural analysis is performed to calculate the stress distribution of the lens. The goal of the static analysis is to solve the displacement and the stress tensor The equilibrium equation is:

[0119] ;

[0120] in is the volume force, is the divergence of the stress tensor, indicating the balance of internal forces, is the stress tensor, which is given by the strain and the material stiffness matrix Determined by a linear relationship

[0121] ;

[0122] By solving the equilibrium equation, we can obtain the principal stress , equivalent stress and strain components Apply displacement loading to the 3D structural model to simulate the mechanical behavior under compression, bending and torsion deformation conditions. For example, applying an axial compression displacement , calculate the stress-strain response in compression deformation; similarly, by applying a bending moment or torsional moment , analyze the response under corresponding deformation conditions. Deformation energy under compression and bending for:

[0123] ;

[0124] After the static analysis is completed, the dynamic response analysis of the 3D structure model is performed based on the stress distribution data. and frequency , simulating the dynamic behavior under vibration loading conditions. The dynamic equation is:

[0125] ;

[0126] in, is the mass matrix; is the damping matrix; is the stiffness matrix; u, , ü are displacement, velocity and acceleration respectively; It is a time-dependent external force. Through numerical solution, dynamic loading data is obtained, including the time-varying characteristics of stress and deformation. The dynamic loading data is input into the fatigue analysis module to simulate the mechanical behavior of the lens under cyclic load. The core of fatigue analysis is to calculate the stress amplitude according to the cyclic stress amplitude. and the fatigue life curve of the material to calculate the cumulative damage and failure risk. Cumulative Damage The formula is:

[0127] ;

[0128] in, is the cumulative damage factor; It is Number of times loaded; is the fatigue life of the material. The analysis results include stress distribution data and deformation data, which provide a reference for design optimization.

[0129] In a specific embodiment, the process of executing step 400 may specifically include the following steps:

[0130] Performing data standardization processing on the optical performance evaluation results, stress distribution data and deformation data to obtain standardized performance index data;

[0131] The optimization objective function is constructed for the standardized performance index data, and the optical performance objective function and the mechanical performance objective function are weightedly combined to obtain a multi-objective optimization function;

[0132] The wavefront error data in the optical performance evaluation results is used as a disturbance source of the probability mode to randomly perturb the design parameters to obtain parameter perturbation data, and based on the parameter perturbation data, the value range of the design parameters is divided into intervals and parameter combinations are generated to obtain multiple groups of parameter design combinations;

[0133] Perform fuzzy rule judgment on multiple sets of parameter design combinations, eliminate parameter combinations that do not meet the constraints, and obtain feasible parameter combinations;

[0134] Input feasible parameter combinations into the multi-objective optimization function to perform parameter sensitivity analysis, locally refine the parameter search space, and obtain optimized search data;

[0135] Perform parameter space iterative calculation on the optimization search data, set convergence criteria and termination conditions, obtain iterative calculation data, and select the optimal target parameter design combination based on the performance indicator score of the iterative calculation data.

[0136] Specifically, the optical performance evaluation results, stress distribution data and deformation data are standardized to ensure that the data of different performance indicators have a unified scale. The standardized value of The calculation formula is:

[0137] ;

[0138] in and are the minimum and maximum values ​​of the indicator respectively. Through this process, the differences in dimension and value range are eliminated, so that all performance indicators fall within the interval [0,1], and standardized performance indicator data is obtained. Based on the standardized performance indicators, a multi-objective optimization function is constructed. The optical performance objective function and mechanical properties objective function Perform weighted combination to obtain the multi-objective optimization function :

[0139] ;

[0140] in and is a weight coefficient used to balance the importance of optical performance and mechanical performance. The optical performance objective function is defined based on the root mean square (RMS) value of the wavefront error:

[0141]

[0142] The mechanical properties objective function is defined based on the ratio of the maximum equivalent stress to the allowable stress:

[0143] ;

[0144] Through this combination, the optimization function can comprehensively reflect the advantages and disadvantages of optical and mechanical properties. The wavefront error data in the optical performance evaluation results is used as the disturbance source of the probability model to randomly perturb the design parameters. The wavefront error data is used as a probability distribution function to Indicates that, assuming it obeys a normal distribution, the probability density function is:

[0145] ;

[0146] in is the mean of the wavefront errors, is the standard deviation. , randomly sample the design parameters and generate parameter perturbation data. These data are used to define the range of parameter values ​​and divide the parameter space into intervals. For example, for parameter The initial range , generate multiple subintervals based on the perturbation:

[0147]

[0148] Based on these intervals, multiple sets of parameter combinations are generated. After multiple sets of parameter design combinations are generated, fuzzy rules are used to judge them and eliminate combinations that do not meet the constraints. Fuzzy rules are based on specific physical limitations and design requirements. For example, if the wavefront error caused by a certain combination exceeds the allowable range Or the equivalent stress exceeds the allowable value , then the combination is eliminated. Through fuzzy rule judgment, invalid design combinations are significantly reduced, thereby improving optimization efficiency. The selected feasible parameter combinations are input into the multi-objective optimization function for parameter sensitivity analysis to identify which parameters have the most significant impact on the change of the optimization objective function. For example, by calculating the objective function Design parameters The partial derivative of ,Evaluate The results of sensitivity analysis are used to locally refine the parameter search space and focus more computing resources on key parameters. After completing the refinement of the parameter search space, iterative calculations are performed on the optimized search data. The initial parameter combination is set to , gradually update the design parameter combination through optimization algorithms (such as genetic algorithms or particle swarm optimization algorithms) The iterative update rule is expressed as:

[0149] ;

[0150] in is the learning rate, is the gradient of the objective function. During the iteration, a convergence criterion is set, for example, the change in the objective function value is less than the threshold or the maximum number of iterations is reached :

[0151] Convergence: or ;

[0152] When the convergence condition is met, the final iterative calculation data is output. According to the performance index score of the iterative calculation data, the optimal target parameter design combination is selected.

[0153] In a specific embodiment, the execution step uses the wavefront error data in the optical performance evaluation result as a disturbance source of the probability mode, randomly perturbs the design parameters to obtain parameter perturbation data, and divides the value range of the design parameters into intervals and generates parameter combinations based on the parameter perturbation data. The process of obtaining multiple sets of parameter design combinations can specifically include the following steps:

[0154] Using the wavefront error data in the optical performance evaluation results as input parameters of the disturbance probability distribution function to obtain the disturbance probability distribution;

[0155] Performing random sampling disturbance on the first surface profile curve and the second surface profile curve according to the disturbance probability distribution to obtain surface profile disturbance data;

[0156] Perform random parameter perturbation on the center thickness data and the edge thickness data according to the perturbation probability distribution to obtain thickness perturbation data;

[0157] The surface profile disturbance data and the thickness disturbance data are combined to construct a parameter correlation matrix to obtain parameter disturbance data, and the design parameters are discretized based on the parameter disturbance data to divide the parameter value range into multiple sub-intervals to obtain parameter interval data;

[0158] Based on the parameter interval data, an orthogonal experimental design is performed to obtain an initial parameter combination, and uniformly distributed parameter sampling data is generated according to the initial parameter combination;

[0159] Parameter combination generation calculation is performed on the parameter sampling data to obtain multiple groups of parameter design combinations.

[0160] Specifically, the wavefront error data in the optical performance evaluation results is used as the input parameter of the perturbation probability distribution function to construct a mathematical model describing the wavefront error distribution. Assuming that the wavefront error data is a discrete data set whose distribution is approximately normal, then the probability density function is It is expressed as:

[0161] ;

[0162] in represents the wavefront error value, is the mean of the wavefront errors, is the standard deviation of the wavefront error. By fitting the wavefront error data, we can determine and , and obtain the complete perturbation probability distribution According to the disturbance probability distribution, the first surface profile curve and the second surface profile curve Perform random sampling perturbation. For each point on the curve , by randomly sampling from Generate a perturbation value in , and add it to the original surface height to get the perturbed surface height:

[0163] ;

[0164] ;

[0165] in and They are respectively from Through this step, the surface profile disturbance data is generated. and , which can reflect the potential morphological changes of the lens surface during manufacturing or use. and edge thickness data Perform random parameter perturbations according to the same perturbation probability distribution. Assume that the original value of the center thickness is ,from Generate disturbance values ​​in , then the thickness after disturbance is:

[0166] ;

[0167] For edge thickness data , then calculate:

[0168] ;

[0169] Thickness data after disturbance and It can reflect the random changes of thickness distribution under different conditions. , and thickness perturbation data , Combine and construct parameter correlation matrix Each row of the parameter correlation matrix represents a perturbation sample, and the column represents the corresponding parameter value. Assume that each parameter takes samples, the matrix form is:

[0170] ;

[0171] Based on the parameter correlation matrix , discretize the design parameters and divide the parameter value range into multiple sub-intervals. The initial range , which can be divided into Sub-intervals:

[0172] ;

[0173] Discretized parameter interval data It can reflect the variation range and distribution characteristics of parameters. Based on the parameter interval data, the orthogonal experimental design method is used to generate the initial parameter combination. The orthogonal experimental design constructs an orthogonal table. ,exist factors (parameters) and Generate representative parameter combinations at each level (interval), and each row in the orthogonal table represents an initial parameter combination. Generate uniformly distributed parameter sampling data based on the initial parameter combination. Uniformly distributed sampling increases the representativeness and diversity of parameter samples by randomly generating data points in each parameter interval to ensure that all intervals are covered. Perform combination generation calculations on the parameter sampling data to ultimately obtain multiple sets of parameter design combinations.

[0174] In this embodiment, the following steps are also included: constructing mutually independent optical performance proxy models and mechanical performance proxy models for multiple groups of parameter design combinations, and stacking and combining the proxy models to obtain a stacked proxy model; performing deep learning training on the stacked proxy model according to the parameter feature matrix of the multiple groups of parameter design combinations to obtain a trained stacked proxy model; inputting feasible parameter combinations into the trained stacked proxy model to perform rapid performance prediction calculations to obtain performance prediction data; based on the performance prediction data, optimizing the feasible parameter combinations, constructing an optimization parameter pool, and obtaining parameter optimization level data; performing refined calculations on high-level parameter combinations in the parameter optimization level data, and using the original physical model to verify their performance indicators to obtain verified performance data; feeding back the verified performance data to the stacked proxy model for online updating and optimization to obtain an updated stacked proxy model; adaptively refining the parameter search space according to the updated stacked proxy model to narrow the search range and obtain refined search data; inputting the refined search data into the multi-objective optimization function to perform the final parameter optimization calculation to obtain the global optimal target parameter design combination.

[0175] The above describes the design optimization method of the intraocular lens in the embodiment of the present application. The following describes the design optimization system 10 of the intraocular lens in the embodiment of the present application. Figure 2 In one embodiment of the present application, an intraocular lens design optimization system 10 includes:

[0176] An acquisition module 11 is used to acquire geometric parameter data of an intraocular lens;

[0177] The quantitative analysis module 12 is used to calculate the spherical aberration, coma and astigmatism values ​​at different viewing distances based on the geometric parameter data using a ray tracing algorithm, and to quantitatively analyze the imaging quality of the intraocular lens through a wavefront error evaluation function and a modulation transfer function to obtain an optical performance evaluation result;

[0178] A construction module 13 is used to perform finite element analysis on the intraocular lens according to the geometric parameter data and the optical performance evaluation results, construct a three-dimensional structure model, and perform stress-strain analysis and dynamic loading analysis on the three-dimensional structure model to obtain stress distribution data and deformation data;

[0179] The generation module 14 is used to input the optical performance evaluation results, stress distribution data and deformation data into the multi-objective optimization function, generate multiple sets of parameter design combinations through random perturbation, and perform optimization calculations and parameter space searches on the multiple sets of parameter design combinations to obtain target parameter design combinations.

[0180] Through the coordinated cooperation of the above-mentioned components, by adopting a multi-dimensional geometric parameter acquisition method, combined with the coordinated measurement of the shape measurement device and the precision thickness gauge, the surface profile, thickness distribution and optical zone size data of the intraocular lens are comprehensively acquired, thereby improving the integrity and accuracy of the basic data; based on the combined application of the ray tracing algorithm and the wavefront error evaluation function, a comprehensive evaluation of the optical performance at different viewing distances and incident angles is achieved, and at the same time, the quantitative analysis of the modulation transfer function accurately reflects the imaging quality of the intraocular lens; the finite element analysis method is used to construct a three-dimensional structural model, and the mechanical properties of the intraocular lens in the actual use environment are comprehensively evaluated by combining static and dynamic loading analysis, thereby improving the accuracy of performance prediction; the multi-objective optimization function and random perturbation mechanism are introduced, and the parameter combination is screened and optimized using the fuzzy rule system, which effectively solves the problem of coordinated optimization of optical and mechanical properties; through the adaptive division of the parameter space and the iterative optimization strategy, the efficiency of parameter search and the reliability of the optimization results are significantly improved, thereby improving the efficiency and accuracy of intraocular lens design.

[0181] See also Figure 3 , Figure 3This is a schematic block diagram of the structure of an electronic device 300 provided in an embodiment of the present application. The electronic device 300 includes a processor 301 and a memory 302. The processor 301 and the memory 302 are connected via a device bus 303, wherein the memory 302 may include a non-volatile storage medium and an internal memory.

[0182] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor 301, the processor 301 can execute any of the above-mentioned methods for optimizing the design of an intraocular lens.

[0183] The processor 301 is used to provide computing and control capabilities to support the operation of the entire electronic device 300 .

[0184] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 301, the processor 301 can execute any of the above-mentioned methods for optimizing the design of the intraocular lens.

[0185] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the present application scheme, and does not constitute a limitation on the electronic device 300 involved in the present application scheme. The specific electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0186] It should be understood that the processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0187] It should be noted that those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the electronic device 300 described above can refer to the corresponding process of the aforementioned artificial lens design optimization method, which will not be repeated here.

[0188] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors implement the design optimization method of the artificial lens provided in the embodiment of the present application.

[0189] The computer-readable storage medium may be an internal storage unit of the electronic device 300 in the aforementioned embodiment, such as a hard disk or memory of the electronic device 300. The computer-readable storage medium may also be an external storage device of the electronic device 300, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the electronic device 300.

[0190] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0191] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable an electronic device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0192] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the design of an intraocular lens, characterized in that: The method comprises: Collecting geometric parameter data of an artificial lens; specifically comprising: placing the artificial lens on an optical platform, irradiating the artificial lens with a vertically incident parallel light beam, collecting curvature radius data of a first surface through a shape measuring device, and obtaining a first surface contour curve; flipping the artificial lens 180 degrees, irradiating the artificial lens with a vertically incident parallel light beam, and collecting curvature radius data of a second surface through a shape measuring device, and obtaining a second surface contour curve; axially measuring the center position of the artificial lens, and collecting thickness data at the center point with a precision thickness gauge to obtain center thickness data, and evenly selecting 8 measurement points along the circumferential direction of the edge of the artificial lens, and measuring the thickness of each measurement point with a precision thickness gauge to obtain edge thickness data; locating the boundary of the optical zone of the artificial lens, extracting the edge contour of the optical zone and measuring the diameter of the optical zone to obtain optical zone diameter data; integrating the first surface contour curve, the second surface contour curve, the center thickness data, the edge thickness data, and the optical zone diameter data to obtain geometric parameter data; Based on the geometric parameter data, the spherical aberration, coma and astigmatism values ​​at different viewing distances are calculated using a ray tracing algorithm, and the imaging quality of the intraocular lens is quantitatively analyzed through a wavefront error evaluation function and a modulation transfer function to obtain an optical performance evaluation result; According to the geometric parameter data and the optical performance evaluation results, finite element analysis is performed on the intraocular lens to construct a three-dimensional structural model, and stress-strain analysis and dynamic loading analysis are performed on the three-dimensional structural model to obtain stress distribution data and deformation data; The optical performance evaluation results, the stress distribution data and the deformation data are input into a multi-objective optimization function, multiple groups of parameter design combinations are generated by random perturbation, and the multiple groups of parameter design combinations are optimized and searched in parameter space to obtain a target parameter design combination.

2. The method for designing and optimizing an intraocular lens according to claim 1, characterized in that: Based on the geometric parameter data, the spherical aberration, coma and astigmatism values ​​at different viewing distances are calculated using a ray tracing algorithm, and the imaging quality of the intraocular lens is quantitatively analyzed through a wavefront error evaluation function and a modulation transfer function to obtain an optical performance evaluation result, including: Performing optical surface feature recognition on the first surface profile curve and the second surface profile curve of the geometric parameter data to obtain optical surface data; Incident parallel light at a plurality of incident angles to a position corresponding to the optical surface data to obtain incident light data, and perform light path tracing on the incident light data at a plurality of viewing distances to obtain light tracing data; Performing spherical aberration calculation on the ray tracing data, calculating the axial distance between the ideal imaging position and the actual imaging position, and obtaining spherical aberration data; Performing coma calculation on the ray tracing data, calculating the focus position difference between the meridian plane ray and the sagittal plane ray, and obtaining coma data; Performing astigmatism calculation on the ray tracing data, calculating the focal length difference between the meridian plane and the sagittal plane, and obtaining astigmatism data; The spherical aberration data, the coma data and the astigmatism data are input into a wavefront error evaluation function to calculate the root mean square value of the wavefront deviation to obtain wavefront error data, and spatial frequency analysis and modulation transfer function value calculation are performed on the wavefront error data to obtain an optical performance evaluation result.

3. The method for designing and optimizing an intraocular lens according to claim 2, characterized in that: The step of inputting the spherical aberration data, the coma data and the astigmatism data into a wavefront error evaluation function to calculate the root mean square value of the wavefront deviation to obtain the wavefront error data, and performing spatial frequency analysis and modulation transfer function value calculation on the wavefront error data to obtain an optical performance evaluation result includes: Substituting the spherical aberration data, the coma data and the astigmatism data into a wavefront error evaluation function, linearly combining the optical difference data to obtain combined optical difference data, and performing Zernike polynomial expansion on the combined optical difference data to obtain a wavefront function coefficient matrix; Calculating the root mean square value of the deviation according to the wavefront function coefficient matrix to obtain wavefront error data, wherein the wavefront error data includes a vertical direction deviation value and a horizontal direction deviation value; Performing a two-dimensional Fourier transform on the wavefront error data in the x-axis and y-axis directions to obtain a spatial frequency distribution matrix; Calculating the frequency responses of multiple incident angles according to the spatial frequency distribution matrix to obtain angle response data, normalizing the angle response data, and calculating the optical transfer function within a preset spatial frequency range to obtain modulation transfer function data; The spatial resolutions at multiple viewing distances are calculated according to the modulation transfer function data to obtain viewing distance resolution data, and based on the viewing distance resolution data, a comprehensive evaluation calculation is performed on the imaging quality within the field of view to obtain an optical performance evaluation result.

4. The method for designing and optimizing an intraocular lens according to claim 3, characterized in that: According to the geometric parameter data and the optical performance evaluation result, the intraocular lens is subjected to finite element analysis to construct a three-dimensional structure model, and the three-dimensional structure model is subjected to stress strain analysis and dynamic loading analysis to obtain stress distribution data and deformation data, including: Performing three-dimensional entity reconstruction according to the first surface contour curve, the second surface contour curve, the center thickness data and the edge thickness data in the geometric parameter data to obtain a three-dimensional geometric model of the intraocular lens; Dividing the three-dimensional geometric model into tetrahedral mesh units, and numbering the unit nodes of the tetrahedral mesh units to obtain mesh unit data; Defining material properties of the grid unit data, setting elastic modulus, Poisson's ratio and density parameters, and obtaining a three-dimensional structural model; According to the wavefront error data in the optical performance evaluation result, boundary constraint conditions and load conditions are applied to the three-dimensional structure model to obtain loading constraint data; Performing static structural analysis on the three-dimensional structural model and the loading constraint data, calculating principal stress, equivalent stress and strain components, and obtaining stress distribution data; Applying displacement loading based on the three-dimensional structural model, calculating the stress-strain response of the intraocular lens under compression deformation, bending deformation and torsional deformation, and obtaining deformation data; According to the stress distribution data, dynamic response analysis is performed on the three-dimensional structural model, the motion displacement amplitude and frequency are set, dynamic loading data is obtained, and the dynamic loading data is input into the fatigue analysis module to calculate the stress and deformation state of the artificial lens under cyclic load to obtain stress distribution data and deformation data.

5. The method for designing and optimizing an intraocular lens according to claim 4, characterized in that: The step of inputting the optical performance evaluation result, the stress distribution data and the deformation data into a multi-objective optimization function, generating multiple sets of parameter design combinations by random perturbation, and performing optimization calculation and parameter space search on the multiple sets of parameter design combinations to obtain a target parameter design combination includes: Performing data standardization processing on the optical performance evaluation result, the stress distribution data and the deformation data to obtain standardized performance index data; An optimization objective function is constructed for the standardized performance index data, and an optical performance objective function and a mechanical performance objective function are weightedly combined to obtain a multi-objective optimization function; Using the wavefront error data in the optical performance evaluation result as a disturbance source of a probability mode, randomly perturbing the design parameters to obtain parameter perturbation data, and dividing the value range of the design parameters into intervals and generating parameter combinations based on the parameter perturbation data to obtain multiple groups of parameter design combinations; Performing fuzzy rule judgment on the multiple sets of parameter design combinations, eliminating parameter combinations that do not meet constraint conditions, and obtaining feasible parameter combinations; Inputting the feasible parameter combination into the multi-objective optimization function to perform parameter sensitivity analysis, locally refine the parameter search space, and obtain optimization search data; Perform parameter space iterative calculation on the optimization search data, set convergence criteria and termination conditions, obtain iterative calculation data, and select the optimal target parameter design combination based on the performance index score of the iterative calculation data.

6. The method for designing and optimizing an intraocular lens according to claim 5, characterized in that: The wavefront error data in the optical performance evaluation result is used as a disturbance source of a probability mode to randomly perturb the design parameters to obtain parameter perturbation data, and based on the parameter perturbation data, the value range of the design parameters is divided into intervals and parameter combinations are generated to obtain multiple groups of parameter design combinations, including: Using the wavefront error data in the optical performance evaluation result as an input parameter of a disturbance probability distribution function to obtain a disturbance probability distribution; Performing random sampling disturbance on the first surface profile curve and the second surface profile curve according to the disturbance probability distribution to obtain surface profile disturbance data; Performing random parameter perturbation on the center thickness data and the edge thickness data according to the perturbation probability distribution to obtain thickness perturbation data; The surface profile disturbance data and the thickness disturbance data are combined to construct a parameter correlation matrix to obtain parameter disturbance data, and the design parameters are discretized based on the parameter disturbance data to divide the parameter value range into multiple sub-intervals to obtain parameter interval data; Performing an orthogonal test design based on the parameter interval data to obtain an initial parameter combination, and generating uniformly distributed parameter sampling data according to the initial parameter combination; Parameter combination generation calculation is performed on the parameter sampling data to obtain multiple groups of parameter design combinations.

7. A system for optimizing the design of an intraocular lens, characterized in that: Used to perform the design optimization method of an intraocular lens according to any one of claims 1 to 6, the design optimization system of the intraocular lens comprising: An acquisition module, used for acquiring geometric parameter data of the intraocular lens; A quantitative analysis module, for calculating the spherical aberration, coma and astigmatism values ​​at different viewing distances based on the geometric parameter data using a ray tracing algorithm, and performing quantitative analysis on the imaging quality of the intraocular lens through a wavefront error evaluation function and a modulation transfer function to obtain an optical performance evaluation result; A construction module, used to perform finite element analysis on the intraocular lens according to the geometric parameter data and the optical performance evaluation result, construct a three-dimensional structural model, and perform stress-strain analysis and dynamic loading analysis on the three-dimensional structural model to obtain stress distribution data and deformation data; A generation module is used to input the optical performance evaluation results, the stress distribution data and the deformation data into a multi-objective optimization function, generate multiple groups of parameter design combinations by random perturbation, and perform optimization calculations and parameter space searches on the multiple groups of parameter design combinations to obtain target parameter design combinations.

8. An electronic device, characterized in that: The electronic device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the electronic device to execute the intraocular lens design optimization method according to any one of claims 1-6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the design optimization method of an intraocular lens as described in any one of claims 1 to 6 is implemented.

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