Optimization Method and System for Structural Parameters of Defocus Lenses Based on Multiple Optical Parameters

Abstract

The present invention relates to the technical field of lens parameter optimization. A method and system for optimizing the structural parameters of defocus lenses based on multiple optical parameters, including: determining the lens-wearing user and the optimization target, collecting the eye parameters of the lens-wearing user to obtain a user eye parameter group, determining an optimization structure category group based on the optimization target, constructing a prevention effect function, a visual effect function, and a wearing comfort function, and generating a fitness function. According to the optimization structure category group, a standard structural parameter group is determined. Based on the standard structural parameter group, the user eye parameter group, and the fitness function, the particle swarm is initialized to obtain an initial position group and an initial fitness group. Based on the initial position group and the initial fitness group, the particle swarm is iterated to obtain an optimal structural parameter group. The present invention can improve the comprehensive performance of defocus lenses and can realize personalized lens design according to different needs.

Description

Technical Field

[0001] The present invention relates to the technical field of lens parameter optimization, and particularly to a method and system for optimizing the structural parameters of defocus lenses based on multiple optical parameters. Background Art

[0002] With the increasing prevalence of myopia, defocus lenses, as an effective means of myopia prevention and control, the optimization design of their structural parameters has become particularly important. Defocus lenses can effectively prevent the development of myopia by adjusting the focusing characteristics of light. However, due to significant differences in the eye parameters of different users, a single lens design is difficult to meet personalized needs. Therefore, optimizing the structural parameters of defocus lenses with multiple optical parameters can significantly improve the prevention effect, visual quality, and wearing comfort of the lenses, thereby better meeting the diverse needs of users.

[0003] Traditional technologies usually design defocus lenses based on fixed parameter templates or single optimization objectives. Although this method can achieve a certain myopia prevention and control effect, it lacks a comprehensive consideration of the user's eye parameters, resulting in the optimization results being unable to meet the specific needs of different users. Moreover, this method has a single optimization objective and cannot balance the myopia prevention effect, visual effect, and wearing comfort simultaneously. Summary of the Invention

[0004] The present invention provides a method and system for optimizing the structural parameters of defocus lenses based on multiple optical parameters, and its main purpose is to improve the comprehensive performance of defocus lenses and enable personalized lens design for different needs.

[0005] To achieve the above objective, a method for optimizing the structural parameters of defocus lenses based on multiple optical parameters provided by the present invention includes:

[0006] Receiving a parameter optimization instruction, determining the lens-wearing user and the optimization objective based on the parameter optimization instruction, collecting the eye parameters of the lens-wearing user to obtain a user eye parameter group, where the optimization objective includes myopia prevention, and the user eye parameter group includes: corneal curvature radius, eye axis length, pupil diameter, and accommodation lag;

[0007] Determining an optimization structure category group based on the optimization objective, where the optimization structure category group includes: defocus area curvature radius, defocus area diameter, central thickness, transition zone gradient, and aspheric coefficient;

[0008] Constructing a prevention effect function, where the variables in the prevention effect function include: actual defocus amount, defocus area, and accommodation lag;

[0009] Constructing a visual effect function, where the variables in the visual effect function include: defocus area curvature radius, defocus area diameter, aspheric coefficient, and transition zone gradient;

[0010] Construct a wearing comfort function, where the variables in the wearing comfort function include: central thickness, lens weight, and edge curvature radius;

[0011] Generate a fitness function based on the prevention effect function, visual effect function, and wearing comfort function, and confirm a standard structure parameter group according to the optimized structure category group;

[0012] Initialize each particle in the pre-constructed particle swarm based on the standard structure parameter group, user eye parameter group, and fitness function to obtain an initial position group and an initial fitness group. Among them, the particle swarm is set with an initial velocity group, and the initial position group and the initial fitness group respectively include multiple initial positions and multiple initial fitnesses. Among them, the initial position is the optimized structure category group, and the initial fitness is the output value of the fitness function;

[0013] Iterate the particle swarm based on the initial position group and the initial fitness group to obtain an optimal structure parameter group.

[0014] Optionally, the collection of eye parameters of the lens-wearing user to obtain a user eye parameter group includes:

[0015] Use a pre-constructed corneal topographer to perform non-contact scanning on the lens-wearing user to obtain a group of principal curvature values, where the group of principal curvature values includes: horizontal curvature value and vertical curvature value;

[0016] Calculate a group of principal curvature radii based on the group of principal curvature values, and calculate the corneal curvature radius according to the group of principal curvature radii. Among them, the group of principal curvature radii includes: horizontal curvature radius and vertical curvature radius, and the horizontal curvature radius and the vertical curvature radius are respectively the reciprocals of the horizontal curvature value and the vertical curvature value, and the corneal curvature radius is the average value of the horizontal curvature radius and the vertical curvature radius;

[0017] Measure the axial length of the lens-wearing user to obtain the axial length, and measure the pupil diameter of the lens-wearing user under a preset light intensity to obtain the pupil diameter. Among them, the axial length is measured by the laser interferometry method, and the pupil diameter is measured by the dynamic infrared pupillometer measurement method;

[0018] Use a pre-constructed open automatic optometer to measure the accommodation lag of the lens-wearing user at a preset fixed fixation distance;

[0019] Summarize the corneal curvature radius, axial length, pupil diameter, and accommodation lag to obtain a user eye parameter group.

[0020] Optionally, the construction of the prevention effect function includes:

[0021] Calculate the actual defocus amount based on the radius of curvature of the defocus area and the radius of curvature of the cornea, and calculate the defocus area based on the axial length of the eye, the preset standard axial length, and the diameter of the defocus area, where the defocus area refers to the area of the retina covered by the defocus area;

[0022] Construct a prevention effect function using the following formula:

[0023] ,

[0024] where, represents the prevention effect function, represents the actual defocus amount, represents the preset maximum tolerable defocus amount, represents the defocus area, represents the preset retinal area, represents the accommodative lag.

[0025] Optionally, the construction of the visual effect function includes:

[0026] Calculate the reference MTF value based on the actual defocus amount, and perform simulation using the preset optical ray tracing software to obtain a set of Zernike coefficients;

[0027] Construct a visual effect function using the following formula according to the actual defocus amount, aspheric coefficient, reference MTF value, and set of Zernike coefficients:

[0028] ,

[0029] where, represents the visual effect function, represents the exponential with the natural constant as the base, represents the aspheric coefficient, and respectively represent the preset defocus aberration threshold and aspheric aberration threshold, represents the reference MTF value, represents the preset polynomial weight coefficient, represents the th Zernike coefficient in the set of Zernike coefficients, represents the number of Zernike coefficients in the set of Zernike coefficients.

[0030] Optionally, the performing simulation using the preset optical ray tracing software to obtain a set of Zernike coefficients includes:

[0031] Input the radius of curvature of the defocus area, the diameter of the defocus area, the aspheric coefficient, and the transition zone gradient into the optical ray tracing software to obtain the target simulation software;

[0032] Perform wavefront analysis using the target simulation software to obtain the Zernike polynomial, where the Zernike polynomial includes multiple initial terms, and the initial terms correspond to a permutation sequence number in the Zernike polynomial;

[0033] Extract the initial terms in the Zernike polynomial with permutation sequence numbers between 4 and 11 to obtain the target Zernike polynomial, where the target Zernike polynomial includes multiple target terms, and each target term has a corresponding coefficient;

[0034] Identify the coefficients of each target term in the target Zernike polynomial to obtain the Zernike coefficient group.

[0035] Optionally, the constructing the wearing comfort function includes:

[0036] Set the lens manufacturing threshold, where the lens manufacturing threshold includes: maximum thickness, minimum thickness, maximum weight, and minimum radius of curvature;

[0037] Calculate the edge radius of curvature according to the aspherical coefficient and the defocus area diameter;

[0038] According to the lens manufacturing threshold, the central thickness, and the edge radius of curvature, construct the wearing comfort function using the following formula:

[0039] ,

[0040] where, represents the wearing comfort function, and represent the maximum thickness and the minimum thickness respectively, represents the central thickness, represents the lens weight, represents the maximum weight, represents the minimum radius of curvature, represents the edge radius of curvature.

[0041] Optionally, the initializing each particle in the pre-constructed particle swarm to obtain the initial position group and the initial fitness group includes:

[0042] Extract particles in the particle swarm in sequence;

[0043] Obtain the particle sequence number of the particle in the particle swarm, where the particle sequence number is the permutation sequence number of the particle in the particle swarm, and the particle sequence number is not greater than the total number of particles in the particle swarm;

[0044] Calculate the sequence number offset according to the preset average sequence number and the particle sequence number, where the average sequence number is the average of the particle sequence numbers of all particles in the particle swarm;

[0045] Based on the serial number offset, perform parameter deviation on each standard structure parameter in the standard structure parameter group to obtain a deviated structure parameter group, where parameter deviation means adding or subtracting a value based on the standard structure parameter;

[0046] Denote the deviated structure parameter group as the initial position group, and substitute the initial position group and the user eye parameter group into the fitness function to obtain an initial fitness;

[0047] Respectively summarize the initial positions and initial fitnesses of each particle in the particle swarm to obtain an initial position group and an initial fitness group.

[0048] Optionally, the iterating the particle swarm based on the initial position group and the initial fitness group to obtain an optimal structure parameter group includes:

[0049] Perform the following operations on each particle in the particle swarm:

[0050] Identify the current position, current velocity, and current fitness of the particle in the initial position group, initial velocity group, and initial fitness group respectively, obtain the historical fitness set of the particle, and supplement the current fitness to the historical fitness set to obtain a single - body fitness set;

[0051] Based on the single - body fitness set, determine the optimal fitness, where the optimal fitness is the single - body fitness with the largest value in the single - body fitness set;

[0052] Identify the optimal single - body position corresponding to the optimal fitness, identify the optimal population position of the particle swarm, and calculate the position change rate of the optimal population position;

[0053] Use the current velocity, optimal single - body position, optimal population position, and current position to update the particle state to obtain an updated position and an updated velocity;

[0054] Based on the updated position, user eye parameter group, and fitness function, calculate the updated fitness, and respectively summarize the updated position, updated velocity, and updated fitness to obtain an updated position group, an updated velocity group, and an updated fitness group;

[0055] Take the updated position group, updated velocity group, and updated fitness group as the initial position group, initial velocity group, and initial fitness group respectively, and return to the step of performing the following operations on each particle in the particle swarm until the position change rate is not greater than a preset change rate threshold, and the optimal population position when the position change rate is not greater than the preset change rate threshold is the optimal structure parameter group.

[0056] Optionally, the calculating the position change rate of the optimal population position includes:

[0057] Determine the current iteration number of the optimal population position, and query the periodic population position group based on the current iteration number and the preset statistical iteration period, where the number of periodic population positions in the periodic population position group is the same as the statistical iteration period;

[0058] Supplement the optimal population position to the periodic population position group to obtain the historical population position group, and calculate the position change rate of the historical population position group.

[0059] To achieve the above object, the present invention also provides an optimization system for the structural parameters of a defocus lens based on multiple optical parameters, including:

[0060] An optimization target receiving module, configured to receive a parameter optimization instruction, determine the lens-wearing user and the optimization target based on the parameter optimization instruction, collect the eye parameters of the lens-wearing user to obtain a user eye parameter group, where the optimization target includes preventing myopia, and the user eye parameter group includes: corneal curvature radius, eye axis length, pupil diameter, and accommodation lag;

[0061] An optimization category determination module, configured to determine an optimization structure category group based on the optimization target, where the optimization structure category group includes: defocus area curvature radius, defocus area diameter, central thickness, transition zone gradient, and aspheric coefficient;

[0062] A target function construction module, configured to construct a prevention effect function, where the variables in the prevention effect function include: actual defocus amount, defocus area, and accommodation lag, construct a visual effect function, where the variables in the visual effect function include: defocus area curvature radius, defocus area diameter, aspheric coefficient, and transition zone gradient, construct a wearing comfort function, where the variables in the wearing comfort function include: central thickness, lens weight, and edge curvature radius, generate a fitness function based on the prevention effect function, visual effect function, and wearing comfort function, and confirm a standard structural parameter group according to the optimization structure category group;

[0063] An optimal parameter selection module, configured to initialize each particle in the pre-constructed particle swarm based on the standard structural parameter group, user eye parameter group, and fitness function to obtain an initial position group and an initial fitness group, where the particle swarm is provided with an initial velocity group, and the initial position group and the initial fitness group respectively include a plurality of initial positions and a plurality of initial fitnesses, where the initial position is the optimization structure category group, and the initial fitness is the output value of the fitness function, and perform iteration on the particle swarm based on the initial position group and the initial fitness group to obtain an optimal structural parameter group.

[0064] To solve the above problems, the present invention also provides an electronic device, where the electronic device includes:

[0065] A memory, storing at least one instruction;

[0066] A processor that executes instructions stored in the memory to implement the method for optimizing the structural parameters of a defocus lens based on multiple optical parameters described above.

[0067] To solve the above problems, the present invention also provides a computer-readable storage medium storing at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the method for optimizing the structural parameters of a defocus lens based on multiple optical parameters described above.

[0068] To solve the problems described in the background art, the present invention first collects the eye parameters of users, enabling personalized optimization of lens design, ensuring that the lens parameters match the physiological characteristics of users, thereby improving the preventive effect and visual comfort of the lens. Then, it determines the optimization structure category group, which can clarify the key variables in lens design, providing a clear direction for subsequent function construction and optimization, ensuring the pertinence of the optimization process. Next, it constructs a preventive effect function, a visual effect function, and a wearing comfort function respectively. Among them, the preventive effect function can directly reflect the preventive effect of the lens on myopia by quantifying variables such as the actual defocus amount, defocus area, and accommodation lag, ensuring that the optimized lens can effectively prevent myopia. The visual effect function can evaluate the impact of the lens on the visual quality of users by quantifying variables such as the curvature radius of the defocus area and the diameter of the defocus area, ensuring that the optimized lens does not significantly reduce the visual experience of users while providing a myopia preventive effect. The wearing comfort function can evaluate the wearing comfort of the lens by quantifying variables such as the central thickness and lens weight, ensuring that the optimized lens does not increase the wearing burden of users while meeting the preventive and visual effects. Further, a fitness function is obtained by combining the above functions. This fitness function integrates the preventive effect, visual effect, and wearing comfort into a comprehensive evaluation system, which can comprehensively evaluate the performance of the lens, providing a unified optimization goal for subsequent optimization algorithms, ensuring that the optimized defocus lens not only maximizes the purpose of preventing myopia but also takes into account the visual effect and wearing comfort of the lens, thereby improving the comprehensive performance of the defocus lens. In addition, through particle swarm initialization, diverse initial solutions can be provided for the optimization algorithm, avoiding the optimization process from falling into a local optimum. At the same time, by combining user parameters and the fitness function, the optimization process is ensured to be targeted and efficient from the beginning. Finally, the particle swarm optimization algorithm can efficiently explore the parameter space through iterative search to find the optimal solution that satisfies the fitness function, ensuring that the optimization result of the lens structural parameters is both scientific and practical, while significantly improving the optimization efficiency. Therefore, the present invention can improve the comprehensive performance of defocus lenses and can achieve personalized lens design according to different needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1Schematic flowchart of a method for optimizing structural parameters of a defocus lens based on multiple optical parameters provided by an embodiment of the present invention;

[0070] Figure 2 Functional module diagram of a system for optimizing structural parameters of a defocus lens based on multiple optical parameters provided by an embodiment of the present invention;

[0071] Figure 3 Schematic structural diagram of an electronic device for implementing the method for optimizing structural parameters of a defocus lens based on multiple optical parameters provided by an embodiment of the present invention.

[0072] Explanation of reference numerals:

[0073] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0074] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0076] An embodiment of the present application provides a method for optimizing structural parameters of a defocus lens based on multiple optical parameters. The execution subject of the method for optimizing structural parameters of a defocus lens based on multiple optical parameters includes but is not limited to at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for optimizing structural parameters of a defocus lens based on multiple optical parameters can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0077] Refer to Figure 1 As shown, it is a schematic flowchart of a method for optimizing structural parameters of a defocus lens based on multiple optical parameters provided by an embodiment of the present invention. In this embodiment, the method for optimizing structural parameters of a defocus lens based on multiple optical parameters includes:

[0078] S1. Receive a parameter optimization instruction, determine the lens-wearing user and the optimization target based on the parameter optimization instruction, collect the eye parameters of the lens-wearing user to obtain a user eye parameter group, where the optimization target includes preventing myopia, and the user eye parameter group includes: corneal curvature radius, eye axis length, pupil diameter, and accommodation lag.

[0079] It is understandable that the parameter optimization instruction refers to an instruction manually initiated for optimizing the structural parameters of personalized defocus lenses for a specific user. Herein, the lens-wearing user refers to the specific user pointed out in the parameter optimization instruction, and the optimization objective refers to the purpose of optimizing the structural parameters of the defocus lens this time. For example, for children or adolescents, an important purpose of wearing defocus lenses is to prevent myopia. Therefore, improving the effect of preventing myopia is the optimization objective here.

[0080] It should be explained that the corneal radius of curvature refers to the radius of curvature of the central area of the anterior surface of the cornea. It can represent the degree of corneal curvature and optical refractive power, and it can be obtained through non-contact scanning with a corneal topographer. Among them, a corneal topographer is a non-contact ophthalmic device used to measure and analyze the shape and curvature distribution of the corneal surface, such as Pentacam or Orbscan. The axial length of the eye refers to the straight-line distance from the vertex of the anterior surface of the cornea to the fovea centralis of the retina. It can represent the overall optical length of the eyeball, and it is a core indicator of the degree of myopia development. Based on the principle of optical low-coherence reflectometry (such as IOLMaster or Lenstar), and using laser interferometry, the axial length of the eye can be directly measured: , wherein, represents the axial length of the eye, represents the speed of light, represents the time difference of the round-trip of the laser in the eyeball, represents the average refractive index of the medium in the eyeball, and 1.354 can be selected as this average refractive index. The pupil diameter refers to the opening diameter of the pupil under specific lighting conditions. Herein, the specific lighting conditions refer to the preset lighting intensity, which can represent the ability of the human eye to adjust the light flux in different environments. The pupil diameter of the human eye under specific lighting conditions can be measured using a dynamic infrared pupillometer. The accommodation lag refers to the difference between the actual accommodation ability of the eye and the theoretically required accommodation ability. It can represent the degree of inefficiency of the eye accommodation system, and the accommodation lag can be measured using an open-type autorefractor. The above-mentioned corneal radius of curvature is used to determine the physical fitting degree between the lens and the cornea, the axial length of the eye is used to determine the imaging position of the retinal defocus area, the pupil diameter is used to restrict the diameter of the defocus area, so as to avoid pupil clipping of the defocus signal, and the accommodation lag is used to guide the dynamic compensation of the defocus amount to offset the myopia progression caused by insufficient accommodation. Therefore, by measuring the above-mentioned user eyeball parameter group, defocus lenses with different structural parameters can be designed for different users.

[0081] Specifically, the collection of eyeball parameters for the lens-wearing user to obtain the user eyeball parameter group includes:

[0082] Using a pre-built corneal topographer to perform non-contact scanning on the lens-wearing user to obtain a group of principal curvature values, wherein the group of principal curvature values includes: horizontal curvature value and vertical curvature value;

[0083] Calculate the principal curvature radius group based on the principal curvature value group, and calculate the corneal curvature radius according to the principal curvature radius group. Among them, the principal curvature radius group includes: the horizontal curvature radius and the vertical curvature radius, and the horizontal curvature radius and the vertical curvature radius are the reciprocals of the horizontal curvature value and the vertical curvature value respectively. The corneal curvature radius is the average value of the horizontal curvature radius and the vertical curvature radius;

[0084] Measure the axial length of the lens-wearing user to obtain the axial length, and measure the pupil diameter of the lens-wearing user under a preset light intensity to obtain the pupil diameter. Among them, the method of measuring the axial length is the laser interferometry method, and the method of measuring the pupil diameter is the dynamic infrared pupillometer measurement method;

[0085] Use a pre-built open autorefractor to measure the accommodation lag of the lens-wearing user at a preset fixed fixation distance;

[0086] Summarize the corneal curvature radius, axial length, pupil diameter, and accommodation lag to obtain a user eyeball parameter group.

[0087] It can be understood that the horizontal curvature value refers to the value representing the degree of curvature of the cornea of the lens-wearing user in the horizontal direction, and the vertical curvature value refers to the value representing the degree of curvature of the cornea of the lens-wearing user in the vertical direction. The light intensity refers to a manually set constant. Optionally, the light intensity is set to 60 lux. The dynamic infrared pupillometer refers to a device that non-contact measures the pupil diameter and its dynamic changes using infrared light. The open autorefractor refers to a non-contact ophthalmic examination device that can quickly and accurately obtain parameters such as refractive state (such as myopia, hyperopia, astigmatism) and accommodation function (such as accommodation lag) by measuring the refraction and accommodation response of the eye to light, for example: GrandSeiko WAM-5500. The fixed fixation distance refers to the distance between the lens-wearing user and the open autorefractor.

[0088] S2. Determine the optimization structure category group based on the optimization objective. Among them, the optimization structure category group includes: the defocus area curvature radius, the defocus area diameter, the central thickness, the transition zone gradient, and the aspheric coefficient.

[0089] It is understandable that the radius of curvature of the defocus area refers to the radius of curvature of the optical surface of the defocus optical area of the lens, which determines the actual defocus amount and is the core parameter of the optical intervention intensity for myopia prevention and control. The diameter of the defocus area refers to the physical diameter of the defocus optical functional area on the lens, which determines the coverage range of the defocus signal on the retina and directly affects the spatial sufficiency of the prevention and control effect. The central thickness refers to the minimum thickness at the geometric center of the lens, which determines the weight and wearing comfort of the lens. If the central thickness is too thick, it will cause a sense of compression, and if it is too thin, it will limit the design of the defocus area optical power. The transition zone gradient refers to the curvature change rate from the defocus area to the surrounding non-defocus area, which determines the smoothness of visual transition. If the gradient is too large, it will cause astigmatism and dizziness during the adaptation period. The aspheric coefficient represents the degree of deviation of the lens surface from a spherical surface. The larger the aspheric coefficient, the greater the degree of deviation of the lens surface from a spherical surface, which determines the correction ability of higher-order aberrations (such as spherical aberration and coma) and the edge curvature distribution. It is the key index to balance visual quality and wearing conformity. Therefore, by optimizing the categories in the above optimization structure category group, the goal of maximizing the myopia prevention effect while taking into account the wearing comfort and visual clarity of the lens can be achieved.

[0090] S3. Construct a prevention effect function, where the variables in the prevention effect function include: actual defocus amount, defocus area, and accommodation lag.

[0091] It is understandable that the prevention effect function refers to the function of the lens made under the corresponding values of the current optimization structure category group for the myopia prevention effect. The larger the output value of this function, the better the myopia prevention effect. The actual defocus amount refers to the distance difference between the optical center of the defocus area of the lens and the retina, and the defocus area refers to the area of the retina covered by the defocus area.

[0092] Specifically, the construction of the prevention effect function includes:

[0093] Calculating the actual defocus amount based on the radius of curvature of the defocus area and the radius of curvature of the cornea, and calculating the defocus area based on the eye axis length, preset standard eye axis length, and the diameter of the defocus area, where the defocus area refers to the area of the retina covered by the defocus area;

[0094] Constructing a prevention effect function using the following formula:

[0095] ,

[0096] where, represents the prevention effect function, represents the actual defocus amount, represents the preset maximum tolerable defocus amount, represents the defocus area, represents the preset retinal area, represents the accommodation lag.

[0097] It is understandable that since the defocus amount is determined by the difference in optical power between the curvature radius of the defocus area of the lens and the corneal curvature radius, the actual defocus amount can be calculated through the curvature radius of the defocus area and the corneal curvature radius: , where represents the actual defocus amount, represents the curvature radius of the defocus area, represents the corneal curvature radius. Since the retina is a spherical structure, the projected area of the defocus area needs to be scaled according to the eye axis length. Therefore, the defocus area can be calculated through the standard eye axis length and the defocus area diameter: , where represents pi, represents the defocus area diameter, represents the eye axis length, represents the standard eye axis length.

[0098] Furthermore, the above prevention effect function represents the comprehensive efficacy of the defocus lens for myopia prevention and control. In this function, The item is the normalized defocus intensity term. By constraining the actual defocus amount within a safe range, optionally, the maximum tolerable defocus amount is set to 4.0D, which represents the contribution of the defocus amount to the prevention and control effect. The item is the retinal coverage efficiency term, which represents the spatial sufficiency of optical intervention by quantifying the effective coverage ratio of the defocus signal on the retina. The item is the accommodation lag compensation factor, which sets that the actual defocus amount needs to be greater than the accommodation lag amount.

[0099] S4. Construct a visual effect function, where the variables in the visual effect function include: the curvature radius of the defocus area, the defocus area diameter, the aspheric coefficient, and the transition zone gradient.

[0100] It is understandable that the visual effect function refers to the function of the imaging quality of the lens made under the corresponding values of the current optimized structure category group. The larger the value output by this function, the higher the imaging quality of the lens made.

[0101] Specifically, the construction of the visual effect function includes:

[0102] Based on the actual defocus amount, calculate the reference MTF value, and use the preset optical ray tracing software for simulation to obtain the Zernike coefficient group;

[0103] According to the actual defocus amount, the aspheric coefficient, the reference MTF value, and the Zernike coefficient group, construct a visual effect function using the following formula:

[0104] ,

[0105] Among them, represents the visual effect function, represents the exponential function with the natural constant as the base, represents the aspherical coefficient, and respectively represent the preset defocus aberration threshold and aspherical aberration threshold, represents the reference MTF value, represents the preset polynomial weight coefficient, represents the th Zernike coefficient in the Zernike coefficient group, represents the number of Zernike coefficients in the Zernike coefficient group.

[0106] It can be understood that the reference MTF value refers to the basic resolution index of the lens without aberration interference, which can reflect the inherent influence of the defocus amount on the imaging quality. The formula for calculating the reference MTF value is: . The optical ray tracing software can be selected as zmax. The Zernike coefficient group refers to the combination of the coefficients of each polynomial in the Zernike polynomial. The term in the above visual effect function: , this term squares and sums the coefficients in the Zernike coefficient group, and can represent the total energy of the high-order aberrations introduced by the lens (such as coma, spherical aberration, etc.). The larger the value of this term, the worse the visual effect.

[0107] Furthermore, the defocus aberration threshold refers to the artificially set defocus amount constant. When the actual defocus aberration exceeds this defocus aberration threshold, the imaging quality of the lens will significantly decline. The aspherical aberration threshold refers to the maximum aspherical aberration (such as coma, astigmatism, etc.) set artificially. When the aspherical coefficient exceeds this aspherical aberration threshold, it will cause obvious distortion or blurring in the lens imaging. Optionally, the defocus aberration threshold and the aspherical aberration threshold are respectively set to 1.0 D and 0.2.

[0108] Specifically, the simulation using the preset optical ray tracing software to obtain the Zernike coefficient group includes:

[0109] Input the curvature radius of the defocus area, the diameter of the defocus area, the aspherical coefficient, and the transition zone gradient into the optical ray tracing software to obtain the target simulation software;

[0110] Use the target simulation software to perform wavefront analysis to obtain the Zernike polynomial. Among them, the Zernike polynomial includes multiple initial terms, and the initial terms correspond to an arrangement serial number in the Zernike polynomial;

[0111] Extract the initial terms of the Zernike polynomial with arrangement serial numbers between 4 and 11 to obtain the target Zernike polynomial, where the target Zernike polynomial includes multiple target terms, and each target term has a corresponding coefficient;

[0112] Identify the coefficients of each target term in the target Zernike polynomial to obtain the Zernike coefficient group.

[0113] It is understandable that the target simulation software refers to the optical tracing software after numerical input, and the optical tracing software can be selected as: zmax. The physical meanings represented by the initial terms of the Zernike polynomial with arrangement serial numbers between 4 and 11 are: defocus, astigmatism (0 degrees), astigmatism (45 degrees), coma (x-axis), coma (y-axis), trefoil (0 degrees), spherical aberration, and trefoil (45 degrees).

[0114] S5. Construct a wearing comfort function, where the variables in the wearing comfort function include: central thickness, lens weight, and edge curvature radius.

[0115] It is understandable that the wearing comfort function refers to the function of the wearing comfort of the lens made under the numerical values corresponding to the current optimized structure category group. The lens weight refers to the weight of the lens made according to the numerical values corresponding to the current optimized structure category group, and the edge curvature radius refers to the numerical value representing the degree of curvature of the lens edge region, which is jointly determined by the aspheric coefficient and the defocus zone diameter and is used to control the fitting degree of the lens edge to the eyelid to avoid frictional discomfort.

[0116] Specifically, the construction of the wearing comfort function includes:

[0117] Set the lens manufacturing threshold, where the lens manufacturing threshold includes: maximum thickness, minimum thickness, maximum weight, and minimum curvature radius;

[0118] Calculate the edge curvature radius according to the aspheric coefficient and the defocus zone diameter;

[0119] According to the lens manufacturing threshold, central thickness, and edge curvature radius, construct the wearing comfort function using the following formula:

[0120] ,

[0121] where, represents the wearing comfort function, and represent the maximum thickness and minimum thickness respectively, represents the central thickness, represents the lens weight, represents the maximum weight, represents the minimum curvature radius, Represents the edge curvature radius.

[0122] It can be understood that in the above wearing comfort function, the central thickness is determined by the defocus area diameter and the defocus area curvature radius, and in actual lens manufacturing, this central thickness also needs to meet the requirements of lens manufacturing, that is, this central thickness needs to be controlled between the maximum thickness and the minimum thickness, where the maximum thickness and the minimum thickness are both artificially set constants. Similarly, the above lens weight is also restricted by lens manufacturing, that is, this lens weight should be less than the maximum weight, and the calculation method of the lens weight is: , where represents the density of the lens. The above edge curvature radius is jointly affected by the aspheric coefficient and the defocus area diameter, and its calculation formula is: , where the minimum curvature radius refers to the minimum bending radius of the lens edge allowed by ergonomics. For example, the minimum curvature radius is set to 8 mm, so this edge curvature radius should be less than the minimum curvature radius, otherwise it will cause friction on the eye skin during wearing.

[0123] Furthermore, in the above wearing comfort function, represents the linear thickness term, and its meaning is: the compliance degree of the lens central thickness within the process allowable range. The larger the value of this linear thickness term, the thinner the lens and the higher the comfort. Optionally, the maximum thickness and the minimum thickness are set to 1.5 mm and 0.8 mm respectively. represents the weight term, and its meaning is: the exponential penalty for overweight lenses. The larger the value of this weight term, the closer the lens weight is to the safety threshold (i.e., the maximum weight) and the lower the comfort. Optionally, the maximum weight is set to 15 g. represents the edge fitting term, and its meaning is: the non-linear suppression of insufficient edge curvature. The larger the value of this edge fitting term, the smoother the lens edge and the less likely it is to cause frictional discomfort during wearing.

[0124] S6. Generate a fitness function based on the prevention effect function, the visual effect function, and the wearing comfort function, and confirm the standard structure parameter group according to the optimized structure category group.

[0125] It can be understood that the fitness function is expressed as: , where respectively refer to the coefficients artificially set to represent the weights of the prevention effect, the visual effect, and the wearing comfort. Optionally, are respectively set to: 0.6, 0.2, and 0.2. This fitness function is the objective function in the subsequent particle swarm optimization iteration steps. The standard structure parameter group refers to the combination of the standard structure parameters corresponding to each category in the optimized structure category group, where the standard structure parameters are artificially set parameters.

[0126] S7. Initialize each particle in the pre-constructed particle swarm based on the standard structure parameter group, the user's eye parameter group, and the fitness function to obtain an initial position group and an initial fitness group. Among them, the particle swarm is set with an initial velocity group, and the initial position group and the initial fitness group respectively include multiple initial positions and multiple initial fitness values. Among them, the initial position is the optimized structure category group, and the initial fitness is the output value of the fitness function.

[0127] It is understandable that the initial velocity group refers to the combination of the initial velocities of each particle in the particle swarm set artificially.

[0128] Specifically, the step of initializing each particle in the pre-constructed particle swarm to obtain an initial position group and an initial fitness group includes:

[0129] Extract particles in the particle swarm in sequence;

[0130] Obtain the particle serial number of the particle in the particle swarm. Among them, the particle serial number is the arrangement serial number of the particles in the particle swarm, and the particle serial number is not greater than the total number of particles in the particle swarm;

[0131] Calculate the serial number offset according to the preset average serial number and the particle serial number. Among them, the average serial number is the average value of the particle serial numbers of all particles in the particle swarm;

[0132] Based on the serial number offset, perform parameter deviation on each standard structure parameter in the standard structure parameter group to obtain a deviated structure parameter group. Among them, parameter deviation means adding or subtracting a value based on the standard structure parameter;

[0133] Record the deviated structure parameter group as the initial position group, and bring the initial position group and the user's eye parameter group into the fitness function to obtain the initial fitness;

[0134] Summarize the initial positions and initial fitness values of each particle in the particle swarm respectively to obtain an initial position group and an initial fitness group.

[0135] It is understandable that the average serial number refers to the average value of the particle serial numbers corresponding to all particles in the particle swarm. The serial number offset refers to a value representing the degree of offset between the particle serial number and the average serial number. The calculation formula of the serial number offset can be: , where represents the serial number offset, represents the particle serial number, represents the average serial number.

[0136] It should be explained that the detailed steps of performing parameter deviation on each standard structure parameter in the standard structure parameter group based on the serial number offset are: use the following formula to calculate the offset structure parameter: , where represents the offset structure parameter, represents the standard structure parameter, represents a symbol or a symbol , the set meaning is to increase the diversity generated by the offset structure parameter, and the symbol can be determined by a random number function or a symbol .

[0137] S8. Based on the initial position group and the initial fitness group, iterate the particle swarm to obtain the optimal structure parameter group.

[0138] It can be understood that the optimal structure parameter group refers to the structure parameter group corresponding to the optimal position in the particle swarm obtained after iteration.

[0139] Specifically, the iterating the particle swarm based on the initial position group and the initial fitness group to obtain the optimal structure parameter group includes:

[0140] Perform the following operations on each particle in the particle swarm:

[0141] Identify the current position, current velocity, and current fitness of the particle in the initial position group, initial velocity group, and initial fitness group respectively, obtain the historical fitness set of the particle, and supplement the current fitness to the historical fitness set to obtain the individual fitness set;

[0142] Based on the individual fitness set, determine the optimal fitness, where the optimal fitness is the individual fitness with the largest value in the individual fitness set;

[0143] Confirm the optimal individual position corresponding to the optimal fitness, identify the optimal population position of the particle swarm, and calculate the position change rate of the optimal population position;

[0144] Use the current velocity, optimal individual position, optimal population position, and current position to update the particle state to obtain the updated position and updated velocity;

[0145] Based on the updated position, user eye parameter group, and fitness function, calculate the updated fitness, and summarize the updated position, updated velocity, and updated fitness respectively to obtain the updated position group, updated velocity group, and updated fitness group;

[0146] Take the updated position group, updated velocity group, and updated fitness group as the initial position group, initial velocity group, and initial fitness group respectively, and return to the step of performing the following operations on each particle in the particle swarm until the position change rate is not greater than a preset change rate threshold, and the optimal population position when the position change rate is not greater than the preset change rate threshold is the optimal structure parameter group.

[0147] It is understandable that the current position, current velocity, and current fitness respectively refer to the position, velocity, and fitness of the particle in the current iteration step. Among them, each time the step of updating the particle state by using the current velocity, the optimal individual position, the optimal population position, and the current position is executed, it is regarded as one iteration. The historical fitness set refers to the set of current fitness generated by the particle in all iteration processes. If the current particle has not been iterated yet, this historical fitness set is an empty set. The individual fitness set refers to the historical fitness set supplemented with the current fitness. The optimal individual position refers to the position corresponding to the optimal fitness. The optimal population position refers to the maximum value among the optimal individual positions corresponding to all particles in the particle swarm. The position change rate refers to the numerical value of the change degree of the optimal population position. The smaller this position change rate is, the higher the convergence degree of the current iteration. When this position change rate is not greater than the change rate threshold, it indicates that the current particle swarm has completed the iteration.

[0148] It should be explained that the calculation method of the updated fitness is the same as that of the initial fitness, which will not be elaborated here. The step of updating the particle state by using the current velocity, the optimal individual position, the optimal population position, and the current position refers to updating the position and velocity of the particle. The update formulas are as follows: and , where represents the updated velocity, represents the inertia weight, represents the current velocity, and respectively represent the individual learning factor and the population learning factor, both of which are set manually. and both represent random numbers, and they are between (0, 1). represents the optimal individual position, represents the current position, represents the optimal population position, represents the updated position. Since the above positions represent a group of structural parameter categories, that is, in the calculation of the above update formula, each value in the position should be calculated one by one.

[0149] Specifically, calculating the position change rate of the optimal population position includes:

[0150] Determine the current iteration number of the optimal population position, and query the periodic population position group based on the current iteration number and the preset statistical iteration period, where the number of periodic population positions in the periodic population position group is the same as the statistical iteration period;

[0151] Supplement the optimal population position to the periodic population position group to obtain the historical population position group, and calculate the position change rate of the historical population position group.

[0152] It can be understood that the current iteration number refers to the number of iterations that the particle swarm has performed when the optimal population position is obtained, and the statistical iteration period refers to a constant preset by humans, and the unit of the statistical iteration period is the number of times. The periodic population position group refers to the combination of the optimal population positions obtained in the previous iterations starting from the current iteration number, where represents the statistical iteration period. The historical population position group refers to the periodic population position group after supplementing the optimal population position.

[0153] Importantly, the step of calculating the position change rate can be: classifying the historical population position group based on the optimization structure category group to obtain a population position group set, where the population position group set includes 5 population position groups, which respectively correspond to the radius of curvature of the defocus area, the diameter of the defocus area, the central thickness, the transition zone gradient, and the aspherical coefficient. Then, calculate the standard deviation of each population position group in the population position group set to obtain a standard deviation set, and based on the standard deviation set, calculate the position change rate using the following formula:

[0154] ,

[0155] where represents the position change rate, represents the -th position weight coefficient in the preset position weight coefficient group, represents the -th standard deviation in the standard deviation set.

[0156] To solve the problems described in the background art, the present invention first collects the user's eye parameters, enabling personalized optimization of lens design, ensuring that the lens parameters match the user's physiological characteristics, thereby improving the preventive effect and visual comfort of the lens. Then, it determines the optimized structure category group, which can clarify the key variables of lens design, providing a clear direction for subsequent function construction and optimization, ensuring the pertinence of the optimization process. Next, it constructs the preventive effect function, visual effect function, and wearing comfort function respectively. Among them, the preventive effect function can directly reflect the effect of the lens on myopia prevention by quantifying variables such as the actual defocus amount, defocus area, and accommodation lag amount, ensuring that the optimized lens can effectively prevent myopia. The visual effect function can evaluate the impact of the lens on the user's visual quality by quantifying variables such as the curvature radius of the defocus area and the diameter of the defocus area, ensuring that the optimized lens does not significantly reduce the user's visual experience while providing a myopia prevention effect. The wearing comfort function can evaluate the wearing comfort of the lens by quantifying variables such as the central thickness and lens weight, ensuring that the optimized lens does not increase the user's wearing burden while meeting the preventive and visual effects. Further, the fitness function is obtained by combining the above functions. This fitness function integrates the preventive effect, visual effect, and wearing comfort into a comprehensive evaluation system, which can comprehensively evaluate the performance of the lens, providing a unified optimization goal for subsequent optimization algorithms, ensuring that the optimized defocus lens not only maximizes the purpose of myopia prevention but also takes into account the visual effect and wearing comfort of the lens, thereby improving the comprehensive performance of the defocus lens. In addition, through particle swarm initialization, diverse initial solutions can be provided for the optimization algorithm, avoiding the optimization process from falling into a local optimum, and at the same time, combining user parameters and the fitness function to ensure that the optimization process is targeted and efficient from the beginning. Finally, the particle swarm optimization algorithm can efficiently explore the parameter space through iterative search to find the optimal solution that meets the fitness function, ensuring that the optimization result of the lens structure parameters is both scientific and practical, and significantly improving the optimization efficiency. Therefore, the present invention can improve the comprehensive performance of defocus lenses and can achieve personalized lens design according to different needs.

[0157] As Figure 2 shown, it is a functional module diagram of an optimization system for the structural parameters of a defocus lens based on multiple optical parameters provided by an embodiment of the present invention.

[0158] The optimization system 100 for the structural parameters of defocus lenses based on multiple optical parameters according to the present invention can be installed in an electronic device. According to the functions to be achieved, the optimization system 100 for the structural parameters of defocus lenses based on multiple optical parameters can include an optimization target receiving module 101, an optimization category determining module 102, an objective function constructing module 103, and an optimal parameter selecting module 104. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0159] The optimization target receiving module 101 is configured to receive a parameter optimization instruction, determine a lens-wearing user and an optimization target based on the parameter optimization instruction, collect eye parameters of the lens-wearing user, and obtain a user eye parameter group. Among them, the optimization target includes myopia prevention, and the user eye parameter group includes: corneal curvature radius, eye axis length, pupil diameter, and accommodation lag.

[0160] The optimization category determining module 102 is configured to determine an optimization structure category group based on the optimization target. Among them, the optimization structure category group includes: defocus area curvature radius, defocus area diameter, central thickness, transition zone gradient, and aspheric coefficient.

[0161] The objective function constructing module 103 is configured to construct a prevention effect function, where the variables in the prevention effect function include: actual defocus amount, defocus area, and accommodation lag; construct a visual effect function, where the variables in the visual effect function include: defocus area curvature radius, defocus area diameter, aspheric coefficient, and transition zone gradient; construct a wearing comfort function, where the variables in the wearing comfort function include: central thickness, lens weight, and edge curvature radius; generate a fitness function based on the prevention effect function, the visual effect function, and the wearing comfort function, and confirm a standard structure parameter group according to the optimization structure category group.

[0162] The optimal parameter selecting module 104 is configured to initialize each particle in a pre-constructed particle swarm based on the standard structure parameter group, the user eye parameter group, and the fitness function, and obtain an initial position group and an initial fitness group. Among them, the particle swarm is set with an initial velocity group, and the initial position group and the initial fitness group respectively include multiple initial positions and multiple initial fitnesses. Among them, the initial position is the optimization structure category group, and the initial fitness is the output value of the fitness function. Iterate the particle swarm based on the initial position group and the initial fitness group to obtain an optimal structure parameter group.

[0163] Specifically, each module in the optimization system 100 for the structural parameters of defocus lenses based on multiple optical parameters in the embodiment of the present invention adopts the same as the above-mentioned Figure 1The technical means are the same as those of the method for optimizing the structural parameters of the defocusing lens based on multiple optical parameters described in [reference], and can produce the same technical effects, which will not be elaborated here.

[0164] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing the method for optimizing the structural parameters of the defocusing lens based on multiple optical parameters provided by an embodiment of the present invention.

[0165] The electronic device 1 may include a processor 10, a memory 11, and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for the method for optimizing the structural parameters of the defocusing lens based on multiple optical parameters.

[0166] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as the mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 also includes the internal storage unit of the electronic device 1 and the external storage device. The memory 11 can not only be used to store application software installed in the electronic device 1 and various types of data, such as the code of the program for the method for optimizing the structural parameters of the defocusing lens based on multiple optical parameters, but also be used to temporarily store data that has been output or will be output.

[0167] In some embodiments, the processor 10 may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing programs or modules stored in the memory 11 (such as the program for the method for optimizing the structural parameters of the defocusing lens based on multiple optical parameters, etc.), and calling the data stored in the memory 11, to perform various functions of the electronic device 1 and process data.

[0168] The bus 12 can be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to implement the connection and communication between the memory 11 and at least one processor 10, etc.

[0169] Figure 3 Only the electronic device with components is shown. Those skilled in the art can understand that Figure 3 The shown structure does not constitute a limitation on the electronic device 1, and it may include fewer or more components than shown, or combine some components, or have different component arrangements.

[0170] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for powering each component. Preferably, the power source can be logically connected to the at least one processor 10 through a power management system, so as to implement functions such as charging management, discharging management, and power consumption management through the power management system. The power source may also include any components such as one or more DC or AC power sources, a recharge system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0171] Further, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0172] Optionally, the electronic device 1 may also include a user interface. The user interface can be a display (Display), an input unit (such as a keyboard (Keyboard)). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0173] The program of the method for optimizing the structural parameters of defocus lenses based on multiple optical parameters stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following:

[0174] Receive a parameter optimization instruction, determine the lens-wearing user and the optimization target based on the parameter optimization instruction, collect the eye parameters of the lens-wearing user to obtain a user eye parameter group. Among them, the optimization target includes preventing myopia, and the user eye parameter group includes: corneal curvature radius, eye axis length, pupil diameter, and accommodation lag;

[0175] Determine an optimization structure category group based on the optimization target. Among them, the optimization structure category group includes: defocus area curvature radius, defocus area diameter, central thickness, transition zone gradient, and aspheric coefficient;

[0176] Construct a prevention effect function. Among them, the variables in the prevention effect function include: actual defocus amount, defocus area, and accommodation lag;

[0177] Construct a visual effect function. Among them, the variables in the visual effect function include: defocus area curvature radius, defocus area diameter, aspheric coefficient, and transition zone gradient;

[0178] Construct a wearing comfort function. Among them, the variables in the wearing comfort function include: central thickness, lens weight, and edge curvature radius;

[0179] Generate a fitness function based on the prevention effect function, visual effect function, and wearing comfort function, and confirm a standard structural parameter group according to the optimization structure category group;

[0180] Initialize each particle in the pre-constructed particle swarm based on the standard structural parameter group, user eye parameter group, and fitness function to obtain an initial position group and an initial fitness group. Among them, the particle swarm is set with an initial velocity group, and the initial position group and the initial fitness group respectively include multiple initial positions and multiple initial fitnesses. Among them, the initial position is the optimization structure category group, and the initial fitness is the output value of the fitness function;

[0181] Iterate the particle swarm based on the initial position group and the initial fitness group to obtain an optimal structural parameter group.

[0182] Specifically, the specific implementation method of the above instructions by the processor 10 can refer to Figures 1 to 3 the description of the relevant steps in the corresponding embodiment, which will not be elaborated here.

[0183] Further, if the modules / units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory).

[0184] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor of an electronic device, it can implement:

[0185] Receiving a parameter optimization instruction, determining a lens-wearing user and an optimization target based on the parameter optimization instruction, collecting eye parameters of the lens-wearing user to obtain a user eye parameter group, where the optimization target includes preventing myopia, and the user eye parameter group includes: corneal curvature radius, eye axis length, pupil diameter, and accommodation lag;

[0186] Determining an optimization structure category group based on the optimization target, where the optimization structure category group includes: defocus area curvature radius, defocus area diameter, central thickness, transition zone gradient, and aspheric coefficient;

[0187] Constructing a prevention effect function, where the variables in the prevention effect function include: actual defocus amount, defocus area, and accommodation lag;

[0188] Constructing a visual effect function, where the variables in the visual effect function include: defocus area curvature radius, defocus area diameter, aspheric coefficient, and transition zone gradient;

[0189] Constructing a wearing comfort function, where the variables in the wearing comfort function include: central thickness, lens weight, and edge curvature radius;

[0190] Generating a fitness function based on the prevention effect function, the visual effect function, and the wearing comfort function, and confirming a standard structure parameter group according to the optimization structure category group;

[0191] Initializing each particle in a pre-constructed particle swarm based on the standard structure parameter group, the user eye parameter group, and the fitness function to obtain an initial position group and an initial fitness group, where the particle swarm is set with an initial velocity group, and the initial position group and the initial fitness group respectively include a plurality of initial positions and a plurality of initial fitnesses, where the initial position is the optimization structure category group, and the initial fitness is the output value of the fitness function;

[0192] Iterate the particle swarm based on the initial position group and the initial fitness group to obtain the optimal structural parameter group.

[0193] In several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and there may be other partitioning methods in actual implementation.

[0194] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0195] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0196] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An optimization method for the structural parameters of a defocusing lens based on multiple optical parameters, characterized in that The method includes: Receiving a parameter optimization instruction, determining the lens-wearing user and the optimization objective based on the parameter optimization instruction, collecting the eye parameters of the lens-wearing user to obtain a user eye parameter group, where the optimization objective includes preventing myopia, and the user eye parameter group includes: corneal curvature radius, axial length, pupil diameter, and accommodation lag; Determining an optimization structure category group based on the optimization objective, where the optimization structure category group includes: defocus area curvature radius, defocus area diameter, central thickness, transition zone gradient, and aspheric coefficient; Constructing a prevention effect function, where the variables in the prevention effect function include: actual defocus amount, defocus area, and accommodation lag; Constructing a visual effect function, where the variables in the visual effect function include: defocus area curvature radius, defocus area diameter, aspheric coefficient, and transition zone gradient; Constructing a wearing comfort function, where the variables in the wearing comfort function include: central thickness, lens weight, and edge curvature radius; Generating a fitness function based on the prevention effect function, the visual effect function, and the wearing comfort function, and determining a standard structure parameter group according to the optimization structure category group; Initializing each particle in the pre-constructed particle swarm based on the standard structure parameter group, the user eye parameter group, and the fitness function to obtain an initial position group and an initial fitness group, where the particle swarm is set with an initial velocity group, and the initial position group and the initial fitness group respectively include multiple initial positions and multiple initial fitnesses, where the initial position is the optimization structure category group, and the initial fitness is the output value of the fitness function; Iterating the particle swarm based on the initial position group and the initial fitness group to obtain an optimal structure parameter group.

2. The method for optimizing the structural parameters of a defocusing lens based on multiple optical parameters according to claim 1, wherein The collecting the eye parameters of the lens-wearing user to obtain a user eye parameter group includes: Performing non-contact scanning on the lens-wearing user using a pre-constructed corneal topographer to obtain a principal curvature value group, where the principal curvature value group includes: horizontal curvature value and vertical curvature value; Calculating a principal curvature radius group based on the principal curvature value group, and calculating the corneal curvature radius according to the principal curvature radius group, where the principal curvature radius group includes: horizontal curvature radius and vertical curvature radius, and the horizontal curvature radius and the vertical curvature radius are respectively the reciprocals of the horizontal curvature value and the vertical curvature value, and the corneal curvature radius is the average of the horizontal curvature radius and the vertical curvature radius; Measuring the axial length of the lens-wearing user to obtain the axial length, and measuring the pupil diameter of the lens-wearing user under a preset light intensity to obtain the pupil diameter, where the axial length is measured by laser interferometry, and the pupil diameter is measured by dynamic infrared pupillometry; Measuring the accommodation lag of the lens-wearing user using a pre-constructed open-type autorefractor at a preset fixed fixation distance; Summarizing the corneal curvature radius, axial length, pupil diameter, and accommodation lag to obtain a user eye parameter group.

3. The method for optimizing the structural parameters of a defocusing lens based on multiple optical parameters according to claim 2, wherein The constructing the prevention effect function includes: Calculate the actual defocus amount based on the radius of curvature of the defocus area and the radius of curvature of the cornea, and calculate the defocus area based on the axial length of the eye, the preset standard axial length of the eye, and the diameter of the defocus area, where the defocus area refers to the area of the retina covered by the defocus area; Construct a prevention effect function using the following formula: ， Among them, represents the prevention effect function, represents the actual defocus amount, represents the preset maximum tolerable defocus amount, represents the defocus area, represents the preset retinal area, represents the accommodation lag amount.

4. The method for optimizing the structural parameters of a defocusing lens based on multiple optical parameters according to claim 3, wherein, The construction of the visual effect function includes: Based on the actual defocus amount, calculate the reference MTF value, and use the preset optical ray tracing software for simulation to obtain a Zernike coefficient group; According to the actual defocus amount, the aspheric coefficient, the reference MTF value, and the Zernike coefficient group, construct a visual effect function using the following formula: ， Among them, represents the visual effect function, represents the exponential power with the natural constant as the base, represents the aspherical coefficient, and respectively represent the preset defocus aberration threshold and aspherical aberration threshold, represents the reference MTF value, represents the preset polynomial weight coefficient, represents the th Zernike coefficient in the Zernike coefficient group, represents the number of Zernike coefficients in the Zernike coefficient group.

5. The method for optimizing the structural parameters of a defocusing lens based on multiple optical parameters according to claim 4, characterized in that, The use of the preset optical ray tracing software for simulation to obtain a Zernike coefficient group includes: Input the radius of curvature of the defocus area, the diameter of the defocus area, the aspheric coefficient, and the transition zone gradient into the optical ray tracing software to obtain the target simulation software; Use the target simulation software to perform wavefront analysis to obtain a Zernike polynomial, where the Zernike polynomial includes multiple initial terms, and each initial term corresponds to a permutation number in the Zernike polynomial; Extract the initial terms in the Zernike polynomial whose permutation numbers are between 4 and 11 to obtain the target Zernike polynomial, where the target Zernike polynomial includes multiple target terms, and each target term has a corresponding coefficient; Identify the coefficients of each target term in the target Zernike polynomial to obtain the Zernike coefficient group.

6. The method for optimizing the structural parameters of a defocusing lens based on multiple optical parameters according to claim 5, wherein The construction of the wearing comfort function includes: Set the lens manufacturing threshold, where the lens manufacturing threshold includes: the maximum thickness, the minimum thickness, the maximum weight, and the minimum radius of curvature; Calculate the edge radius of curvature according to the aspheric coefficient and the diameter of the defocus area; According to the lens manufacturing threshold, the central thickness, and the edge radius of curvature, construct a wearing comfort function using the following formula: ， Among them, represents the wearing comfort function, and represent the maximum thickness and the minimum thickness respectively, represents the central thickness, represents the lens weight, represents the maximum weight, represents the minimum radius of curvature, represents the edge radius of curvature.

7. The method for optimizing the structural parameters of a defocusing lens based on multiple optical parameters according to claim 6, characterized in that The initialization of each particle in the pre-constructed particle swarm to obtain the initial position group and the initial fitness group includes: Extract particles in the particle swarm in sequence; Obtain the particle number of the particle in the particle swarm, where the particle number is the permutation number of the particles in the particle swarm, and the particle number is not greater than the total number of particles in the particle swarm; Calculate the serial number offset according to the preset average serial number and the particle number, where the average serial number is the average of the particle numbers of all particles in the particle swarm; Based on the serial number offset, perform parameter deviation on each standard structure parameter in the standard structure parameter group to obtain a deviated structure parameter group, where the parameter deviation means adding or subtracting a value based on the standard structure parameter; Record the deviated structure parameter group as the initial position group, and substitute the initial position group and the user eyeball parameter group into the fitness function to obtain the initial fitness; Summarize the initial positions and initial fitnesses of each particle in the particle swarm respectively to obtain the initial position group and the initial fitness group.

8. The method for optimizing the structural parameters of a defocusing lens based on multiple optical parameters according to claim 7, wherein The iteration of the particle swarm based on the initial position group and the initial fitness group to obtain the optimal structure parameter group includes: Perform the following operations on each particle in the particle swarm: Identify the current position, current velocity, and current fitness of the particle in the initial position group, initial velocity group, and initial fitness group respectively, obtain the historical fitness set of the particle, and supplement the current fitness to the historical fitness set to obtain the monomer fitness set; Determine the optimal fitness based on the monomer fitness set, where the optimal fitness is the monomer fitness with the largest value in the monomer fitness set; Identify the optimal monomer position corresponding to the optimal fitness, identify the optimal population position of the particle swarm, and calculate the position change rate of the optimal population position; Use the current velocity, optimal monomer position, optimal population position, and current position to update the particle state to obtain the updated position and updated velocity; Calculate the updated fitness based on the updated position, user eye parameter group, and fitness function, and summarize the updated position, updated velocity, and updated fitness respectively to obtain the updated position group, updated velocity group, and updated fitness group; Use the updated position group, updated velocity group, and updated fitness group as the initial position group, initial velocity group, and initial fitness group respectively, and return the steps of performing the following operations on each particle in the particle swarm until the position change rate is not greater than the preset change rate threshold, and the optimal population position when the position change rate is not greater than the preset change rate threshold is the optimal structure parameter group.

9. The method for optimizing the structural parameters of a defocusing lens based on multiple optical parameters according to claim 8, wherein, The calculating the position change rate of the optimal population position includes: Determine the current iteration number of the optimal population position, and query the periodic population position group based on the current iteration number and the preset statistical iteration period, where the number of periodic population positions in the periodic population position group is the same as the statistical iteration period; Supplement the optimal population position to the periodic population position group to obtain the historical population position group, and calculate the position change rate of the historical population position group.

10. An off-focus lens structure parameter optimization system based on multiple optical parameters, characterized in that, The system includes: An optimization target receiving module, configured to receive a parameter optimization instruction, determine a lens-wearing user and an optimization target based on the parameter optimization instruction, collect eye parameters of the lens-wearing user to obtain a user eye parameter group, where the optimization target includes myopia prevention, and the user eye parameter group includes: corneal curvature radius, eye axis length, pupil diameter, and accommodation lag; An optimization category determination module, configured to determine an optimization structure category group based on the optimization target, where the optimization structure category group includes: defocus area curvature radius, defocus area diameter, central thickness, transition zone gradient, and aspheric coefficient; A target function construction module, configured to construct a prevention effect function, where the variables in the prevention effect function include: actual defocus amount, defocus area, and accommodation lag, construct a visual effect function, where the variables in the visual effect function include: defocus area curvature radius, defocus area diameter, aspheric coefficient, and transition zone gradient, construct a wearing comfort function, where the variables in the wearing comfort function include: central thickness, lens weight, and edge curvature radius, generate a fitness function based on the prevention effect function, visual effect function, and wearing comfort function, and confirm a standard structure parameter group according to the optimization structure category group; The optimal parameter selection module is used to initialize each particle in the pre-constructed particle swarm based on the standard structure parameter group, the user's eye parameter group and the fitness function, so as to obtain an initial position group and an initial fitness group. Among them, the particle swarm is set with an initial velocity group, and the initial position group and the initial fitness group respectively include a plurality of initial positions and a plurality of initial fitnesses. Among them, the initial position is the optimized structure category group, and the initial fitness is the output value of the fitness function. Based on the initial position group and the initial fitness group, the particle swarm is iterated to obtain the optimal structure parameter group.

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