Contour-based microstructure evaluation method, medium, compensation design method, and lens

Abstract

This invention relates to the field of evaluation technology for microstructured optical surfaces, and particularly to a contour-based microstructure evaluation method, medium, compensation design method, and lens. The method acquires three-dimensional physical surface contour data of the microstructure region of the optical surface, preprocesses to remove macroscopic substrate surface shape and noise, and calculates a statistical characteristic index I representing the overall fluctuation intensity of the microstructure within the projection area of ​​the average size of the human pupil. total This invention addresses the problem that existing technologies cannot evaluate microstructures from the source of physical surface features, overcomes the incomparability of results caused by the arbitrariness of traditional parameter measurement areas, establishes a direct correlation between the intensity of physical surface feature fluctuations and the intensity of myopia prevention and control signals, and achieves a technological upgrade from experience-based trial and error to data-driven approaches, providing a reliable basis for design verification and manufacturing process control.

Description

Contour-based microstructure evaluation methods, media, compensation design methods, and lenses Technical Field

[0001] This invention relates to the field of evaluation technology for microstructured optical surfaces, and in particular to contour-based microstructure evaluation methods, media, compensation design methods, and lenses. Background Technology

[0002] In recent years, microstructured optical lenses, especially multi-point defocus lenses used for myopia control in adolescents, have attracted much attention because they can modulate incident light through microstructures to introduce low-order aberrations, causing blurring, or introduce high-order aberrations and light scattering, resulting in decreased contrast, ultimately effectively inhibiting axial elongation. However, current evaluations of the effectiveness of such lenses mainly rely on lengthy clinical studies, and a universally accepted, rapid, and effective evaluation method is lacking. Most existing evaluation methods focus on assessing optical effects such as defocus amount, wavefront aberration, MTF (modulation transfer function), and contrast, but these methods do not directly evaluate and control the fundamental source of these effects—the physical microstructure surface profile produced during manufacturing. Such optical evaluation methods have fundamental limitations: first, they can only be performed after lens manufacturing is complete, making them unsuitable for early design verification or quality control of opaque molds; second, the evaluation results reflect the final optical performance, not the physical microstructure surface profile that produces that performance, making it difficult to trace the root cause of manufacturing deviations.

[0003] In the prior art, the relevant solution is the "Evaluation Method and Lens for Microstructured Lenses" disclosed in CN 120890657 A. This method considers simple dimensional parameters such as the maximum sagitta, fill rate, optical zone diameter, and microstructure diameter of the microstructured lens, attempting to directly characterize the microstructure stimulation intensity of the lens. However, this method is limited to theoretical design-level comparisons and fails to take into account the microstructure morphological deviations in the actual manufacturing process. Therefore, this method is essentially still a theoretical design-level comparison tool and cannot reflect the microstructure morphological deviations introduced by molding (such as injection molding, compression molding, casting), hardening, coating, and other processes in actual production. Especially for complex microstructures that are non-periodic, irregular, or without independent unit boundaries superimposed on aspherical substrates, the existing parameter system is completely ineffective.

[0004] Furthermore, although a mature surface evaluation system exists in the field of ultra-precision inspection, such as parameters like Sa (arithmetic mean height of the profile), RMS or Sq (root mean square height of the profile), and St (maximum peak-to-valley height of the profile), these parameters do not reveal the intrinsic relationship between them and the optical performance of microstructured lenses and the intensity of control signals. Because the optical microstructure surface has not been optimized and improved, the aforementioned statistical parameters cannot accurately reflect the inherent fluctuation characteristics of the microstructure itself, resulting in a lack of comparability in the quantitative results between different products.

[0005] In summary, existing technologies suffer from the following main problems: First, current evaluation methods fail to evaluate microstructured lenses from the fundamental source of physical surface profile; second, existing methods do not fully consider the impact of microstructure morphological deviations introduced during actual manufacturing on product performance; and finally, existing parameters fail to accurately characterize the actual fluctuation intensity of microstructures, especially for complex microstructures. These problems limit the effectiveness and consistency of microstructured lens evaluation, urgently requiring an evaluation method that can be directly based on physical surface profile and applicable to any form of microstructure, in order to achieve an objective, quantifiable, and comparable assessment of the control signal intensity of microstructured lenses. Summary of the Invention

[0006] To address one of the aforementioned technical problems, the present invention adopts the following technical solution:

[0007] According to one aspect of the present invention, a contour-based microstructure evaluation method is provided, the method comprising the following steps:

[0008] S1: Obtain the three-dimensional physical surface contour data of the optical surface microstructure region;

[0009] S2: Preprocess the three-dimensional physical surface contour data to remove macroscopic base surface shape and measurement noise, and obtain data that only reflects the local undulations of the microstructure;

[0010] S3: Within the projection area on the optical surface corresponding to the average size of the human pupil, calculate at least one statistical characteristic index I for the preprocessed data. total I total Used to characterize the overall fluctuation intensity of microstructures; the statistical characteristic index is selected from at least one of the following: arithmetic mean height, root mean square height, peak-to-valley value, mean curvature, Gaussian curvature, principal curvature, and statistical values ​​of sag height; wherein, the statistical values ​​of sag height include mean sag height, standard deviation of sag height, or median value of sag height distribution;

[0011] S4: I total As a quantitative value of the control signal intensity of optical surface microstructure.

[0012] According to a second aspect of the present invention, a method for designing a myopia control lens compensation is provided, comprising the following steps:

[0013] Using a contour-based microstructure evaluation method, the designed surface shape and the actual surface shape after manufacturing of the target lens are evaluated respectively, and the corresponding quantitative values ​​of the control signal intensity are obtained.

[0014] Calculate the difference between the quantized value of the prevention and control signal strength of the actual surface shape after manufacturing and the quantized value of the prevention and control signal strength of the designed surface shape;

[0015] Based on the difference value, the microstructure parameters in the original design are pre-compensated so that the final product after manufacturing process can still achieve the expected signal strength control effect.

[0016] According to a third aspect of the present invention, a myopia control optical surface is provided, wherein the lens is prepared by a myopia control lens compensation design method.

[0017] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the above-described contour-based microstructure evaluation method.

[0018] According to a fifth aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the above-described contour-based microstructure evaluation method.

[0019] This invention has at least one of the following beneficial effects:

[0020] First, this invention directly acquires the three-dimensional physical surface contour data of the microstructure region on the optical surface, and removes macroscopic substrate surface shape and measurement noise in subsequent processing, thereby obtaining data that only reflects the local undulations of the microstructure. This effectively solves the problem that existing technologies cannot evaluate microstructures from the source of physical surface shape. Traditional methods mostly rely on optical performance indicators such as defocus amount and MTF. These indicators can only be measured after the lens is finished and are limited by the light transmittance of the material, making them unsuitable for early quality control of opaque molds. Evaluation methods based on simplified geometric parameters such as sag and fill rate ignore the complex morphology of the actual surface shape of the microstructure, and are particularly difficult to apply to complex microstructures that are non-periodic or have no independent unit boundaries. In contrast, this invention uses the real physical contour as input, which includes the influence of various factors such as the size, shape, height, and arrangement of the microstructure. This allows the evaluation results to truly and comprehensively reflect the modulation capability of the microstructure itself, realizing the transformation from "effect-based deduction" to "source-based quantification," and providing a reliable basis for design verification and manufacturing process control.

[0021] Secondly, in this invention, statistical characteristic index I is calculated within the projection area on the lens corresponding to the average size of the human pupil. totalThis invention gives the evaluation area a clear physiological meaning, overcoming the technical shortcomings of existing statistical parameters (such as Sa, RMS, Sq, St) that lead to incomparable results due to arbitrary selection of measurement areas. In the field of ultra-precision testing, although parameters such as Sa and RMS are widely used, they typically do not define a spatial range related to visual function, resulting in a lack of a unified benchmark for evaluation between different products or batches. This invention, however, limits the evaluation area to a projection area that matches the actual use state of the human eye, ensuring that the calculated I... total It can accurately reflect the modulation intensity of incident light by the microstructure under the wearing state, thus enabling objective comparison of lenses with different designs or manufacturing processes on the same scale, significantly improving the repeatability and practicality of the evaluation results.

[0022] Furthermore, in this invention, statistical characteristic index I is used. total This invention directly uses I as a quantitative value for the intensity of the myopia control signal, establishing a direct correlation between the intensity of physical surface shape fluctuations and the intensity of the myopia control signal, thus overcoming the deficiency in existing technologies where surface shape parameters are disconnected from optical effects. While previous surface statistical parameters could describe morphological fluctuations, no patterns related to the control mechanism were found, thus failing to guide design optimization or process adjustments. However, in this invention, due to I... total It can characterize the overall fluctuation intensity of the microstructure, and thus I total Defined as a core indicator characterizing the overall fluctuation intensity of microstructures, and used as a proxy variable for the intensity of control signals to evaluate lenses, this indicator not only possesses mathematical calculability but also a clear functional orientation. Therefore, researchers can, without relying on clinical trials, compare I... total The size can be quickly adjusted to adjust the design scheme, and the manufacturing end can also set process control thresholds accordingly, thereby realizing the technology upgrade from experience-based trial and error to data-driven technology. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 is a schematic flowchart of a contour-based microstructure evaluation method provided in an embodiment of the present invention;

[0025] Figure 2 shows the physical contour point cloud, RMS value and MTF curve of lenses 1 to 3 with different apertures and defocusing amounts in the same arrangement provided in the embodiment of the present invention.

[0026] Figure 3 shows the measurement results of the four facets of the lens and the PSD curve provided in the embodiment of the present invention;

[0027] Figure 4 shows the measurement results of the five facets of the lens and the PSD curve provided in the embodiment of the present invention;

[0028] Figure 5 shows the measurement results and PSD curve of the six-faceted lens provided in the embodiment of the present invention.

[0029] Figure 6 is a comparison of the PSD curves of lenses 4 to 6 provided in the embodiments of the present invention;

[0030] Figure 7 is a high- and low-frequency exploded view of the lens 4 provided in an embodiment of the present invention;

[0031] Figure 8 is a high- and low-frequency exploded view of the lens 5 provided in an embodiment of the present invention;

[0032] Figure 9 is a high- and low-frequency exploded view of the lens 6 provided in an embodiment of the present invention;

[0033] Figure 10 is a comparison chart of the MTF curves of lenses 4 to 6 provided in the embodiments of the present invention;

[0034] Figure 11 is a high- and low-frequency exploded view of the complex microstructure (lens 8) provided in the embodiment of the present invention;

[0035] Figure 12 is a graph showing the changes in high-frequency information during the manufacturing process of lens 7 according to an embodiment of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] As a possible embodiment of the present invention, as shown in FIG1, a contour-based microstructure evaluation method is provided, the method comprising the following steps:

[0038] S1: Obtain the three-dimensional physical surface contour data of the optical surface microstructure region.

[0039] Specifically, the optical surface can be the surface of an optical lens or the mold used to make the lens.

[0040] Three-dimensional physical surface contour data can be point cloud data; it can be obtained through the following two methods:

[0041] Firstly, three-dimensional physical surface contour data is point cloud data generated by sampling optical design models or optical design data.

[0042] On the design side: Export microstructure surface models from optical design software such as Zemax and Code V, or 3D design software such as UG, SolidWorks, and Pro E, or programming software such as Matlab, and generate theoretical point clouds through uniform mesh sampling. For example, for a regularly arranged lens array superimposed on an aspherical substrate (as shown in Figure 4), its STL or OBJ format surface file can be directly exported and converted into a point cloud, or the point cloud data can be directly exported through secondary development.

[0043] Secondly, the three-dimensional physical surface contour data is point cloud data obtained by measuring the actual lens or mold surface using a white light interferometer, confocal microscope, atomic force microscope, or profilometer.

[0044] Manufacturing process: A white light interferometer is used to scan the surface of the actual lens or mold (nickel-phosphorus, tungsten steel, glass, etc.) to obtain a measured point cloud. This method can accurately reflect the surface deviations introduced by processes such as injection molding, compression molding, casting, and coating. As shown in Figure 12, after the hardening coating, the edges of the high-frequency microstructure of lens 7 are significantly smoother, and this change can only be captured by the measured point cloud.

[0045] Existing optical evaluation methods (such as MTF) require transparent lenses, making them unsuitable for early-stage control of opaque molds. Simplified parametric methods, on the other hand, cannot describe complex structures without independent unit boundaries or that are not cylindrical or spherical, and even for simple microstructures like spheres and cylinders, only partial geometric parameters are extracted. In this step, point cloud data fully preserves the spatial morphology information of the microstructure, and regardless of its regularity, it can be used as a unified input to achieve a full-chain feasibility evaluation from design to mold to lens.

[0046] S2: Preprocess the three-dimensional physical surface contour data to remove macroscopic base surface shape and measurement noise, and obtain data that only reflects the local undulations of the microstructure.

[0047] Since microstructures are typically superimposed on spherical or aspherical substrates, the original point cloud contains large-scale curvature information, which can severely interfere with the statistical results of microstructure undulations. Therefore, a reference surface (such as a quadratic surface) consistent with the design substrate can be fitted using the least squares method, and the original point cloud can be subtracted from this reference surface to obtain a surface shape containing only the local undulations of the microstructure. Simultaneously, median filtering or the 3σ criterion can be used to remove measurement outliers. The preprocessed data accurately reflects the modulation capability of the microstructure itself, allowing for comparison of different products under the same benchmark.

[0048] S3: Within the projection area on the optical lens corresponding to the average size of the human pupil, calculate at least one statistical characteristic index I for the preprocessed data. total I total Used to characterize the overall fluctuation intensity of microstructures.

[0049] The statistical characteristic indicators are selected from at least one of the following: arithmetic mean height, root mean square height, peak-to-trough value, mean curvature, Gaussian curvature, principal curvature, and statistical values ​​of local sag; wherein, the statistical values ​​of local sag include mean sag, standard deviation of sag, or median value of sag distribution.

[0050] All of these parameters can be used to characterize the overall fluctuation intensity of the microstructure because they quantify the variation characteristics of the three-dimensional surface shape in the height direction from different perspectives:

[0051] Root mean square height (Sq, RMS): It assigns a higher weight to the magnitude deviation and can effectively reflect the "energy" level of the surface modulation.

[0052] Arithmetic mean height (Sa): Provides the average absolute value of the profile offset, reflecting the average level of the overall undulation;

[0053] Peak-to-valley value (Sz): Reflects the vertical distance between the maximum peak and the minimum valley, characterizing the extreme modulation depth;

[0054] Mean curvature: describes the average degree of curvature of a local surface, and is directly related to the ability to deflect light.

[0055] Gaussian curvature: An intrinsic measure of the degree of curvature of a surface, which is not affected by curvature pose and can more stably extract the light deflection ability of microstructure surfaces;

[0056] Principal curvature: describes the degree of curvature of a local surface along different directions, and can characterize the non-uniformity of light modulation;

[0057] Statistical values ​​of sag (such as mean sag, standard deviation, median): directly characterize the geometric height distribution of the microstructure along the optical axis and are the core factors affecting light modulation and disturbance.

[0058] Although the above indicators are calculated in different ways, they all essentially reflect the ability of microstructures to disturb incident light, and therefore can all be used as reasonable proxy variables for controlling signal strength.

[0059] Specifically, the area projected onto the optical lens corresponding to the average size of the human pupil is a circular region with a diameter of 6 millimeters. This size typically corresponds to the projection of the average human pupil size onto the lens under indoor lighting, ensuring that the evaluation results are consistent with actual wearing conditions.

[0060] Within this region, the root mean square height (RMS, Sq) is used as a statistical characteristic indicator. total Let's take an example. RMS satisfies the following conditions:

[0061] ;

[0062] in, Let be the sag of the i-th point. Let N be the average elevation within the region, and N be the total number of point cloud points within the region.

[0063] S4: I total As a quantitative value for the control signal intensity of the microstructure of optical lenses.

[0064] As shown in Figure 2, lenses 1, 2, and 3 employ the same microlens arrangement structure but with different parameters: lens 2 has a defocus amount 1.5 times that of lens 1, meaning its lens curvature and sag increase, while maintaining the same fill factor; lens 3 has a fill factor 1.7 times that of lens 1, meaning its lens aperture is larger, while maintaining the same defocus amount. The root mean square height (RMS) of their three-dimensional surface profiles was calculated, yielding the following results: 0.282 μm for lens 1, 0.403 μm for lens 2, and 0.467 μm for lens 3. The RMS value increases with increasing defocus amount and fill factor, indicating that this index effectively reflects the overall surface profile fluctuation intensity of the microstructure under changes in geometric parameters such as sag (corresponding to defocus amount) and aperture (corresponding to fill factor).

[0065] Further comparison of the modulation transfer function (MTF) curves of the three lenses reveals that: Lens 3 has a significantly lower MTF across the entire frequency range than Lens 1, especially in the low-frequency region (the area to the left of the red dashed line, spatial frequency <10 cpd); Lens 2 also has a lower MTF in the low-frequency range than Lens 1. Since a decrease in MTF reflects a reduction in the imaging contrast of the optical system, and myopia control lenses achieve their biological effects by introducing low-order aberrations (such as defocus and astigmatism) to cause image blurring, or by introducing high-order aberrations and light scattering to cause a decrease in contrast, the degree of MTF reduction can be used as an indirect characterization of the control signal strength: the lower the MTF, the stronger the control signal.

[0066] Experimental results show that increasing the microstructure fill rate has a particularly significant effect on improving the strength of the control signal, and increasing the defocus amount (sagittal height) also makes a positive contribution. It is worth noting that the above optical effects are highly consistent with the trend of RMS value changes: the RMS values ​​of lens 3 (high fill rate) and lens 2 (high sagittal height) are both greater than those of lens 1, and the order of RMS values ​​corresponds exactly to the degree of low-frequency attenuation of MTF.

[0067] The results validate the rationality of using RMS as a quantitative indicator of signal strength for epidemic prevention and control in this invention: RMS not only comprehensively reflects the overall surface undulation characteristics of the microstructure, but also has a clear correlation with actual optical performance. Therefore, this indicator can be used to quickly and quantitatively evaluate the epidemic prevention and control potential of different microstructure design schemes without relying on complex optical simulations or clinical trials, achieving an effective mapping from physical contours to functional effects.

[0068] Furthermore, the contour-based microstructure evaluation method in this embodiment is not only applicable to myopia control optical lenses with simple microstructures, but also to myopia control optical lenses with complex microstructures.

[0069] Simple microstructures refer to micro-graphic arrangements whose smallest units are simple geometric shapes such as spheres, non-spheres, and cylinders, and which often have clear periodicity and independently definable units. Typical styles include:

[0070] Periodically arranged arrays of spherical, cylindrical, or aspherical microlenses (such as hexagonal close-packed, square grid, annular, and radial arrangements).

[0071] A group of circular microlenses with a specified finite number of apertures, sagittas, and fill rates;

[0072] Cylindrical microstructures that are orderly distributed along a specific direction, etc.

[0073] The microstructures shown in lens 1, lens 2, lens 3, lens 5, and lens 6 of this invention.

[0074] Complex microstructures refer to complex micro-figures whose smallest unit is not a simple geometric shape such as a sphere, aspherical surface, or cylinder, and often lack periodicity, symmetry, or clearly defined boundaries of individual units. Typical styles include:

[0075] Non-periodic random arrangement of micro-dot arrays (such as disordered dot groups formed by laser direct writing);

[0076] Microstructures without independent unit boundaries, interwoven or merged (such as continuously undulating free-form surface microtextures).

[0077] Irregular microstructures superimposed on spherical, aspherical, or free-form substrates;

[0078] Multi-scale hybrid structure (simultaneously containing large-scale defocus units and small-scale scattering textures).

[0079] For such complex microstructures, such as lens 8 in Figure 11 and lens 7 in Figure 12, traditional evaluation methods are completely ineffective for the following reasons:

[0080] First, it fails to define "individual microstructural units." Traditional parameters (such as fill rate and defocus) are applicable only when identifiable, independent microlenses exist, a prerequisite that cannot be met in boundaryless or randomly distributed structures. Second, the parameter system loses its applicability. Even with artificial region division, the smallest unit obtained is still not a simple geometric shape such as a sphere, aspherical surface, or cylinder. Parameters like defocus, fill rate, aperture, and lens sagitta are only applicable to these simple geometric shapes, meaning the results lose physical meaning due to the irregularity and drastic changes in the local morphology of the microstructure. Third, it ignores surface detail features. Traditional methods only extract partial geometric dimensions and cannot effectively capture surface details that have a crucial impact on optical modulation, such as edge steepness and internal fluctuations.

[0081] In contrast, this invention directly processes three-dimensional physical surface contour data (such as point clouds), without requiring preset unit boundaries or structural periodicity. As long as the surface morphology can be obtained, regardless of whether the smallest unit of the microstructure is a simple geometric shape such as a sphere, non-sphere, or cylinder, or whether the microstructure is regularly arranged, its overall fluctuation intensity can be quantified through statistical indicators (such as root mean square height). Therefore, this method is universally applicable to both simple and complex microstructures, and in particular provides a feasible technical path for the objective evaluation of novel complex microstructure lenses.

[0082] As another embodiment of the present invention, a new reference value for evaluating the control signal strength of optical lens microstructure is also provided. This reference value can be combined with the above-mentioned control signal strength quantification value to evaluate the control signal strength of optical lens microstructure.

[0083] S5: Determine the spatial characteristic frequency based on the cumulative energy of the power spectral density distribution. The spatial characteristic frequency serves as another reference value for evaluating the signal strength of the optical lens microstructure.

[0084] The spatial characteristic frequency is the spatial frequency corresponding to 50% of the cumulative total energy of the power spectral density distribution.

[0085] As shown in Figure 4, spatial frequency analysis is performed on the microstructure lens surface (which can be a design model or measured data) by averaging along the X direction, Y direction, and no direction, to obtain the PSD (power spectral density) curve (in this embodiment, the PSD is obtained by averaging along no direction). This curve can characterize the intensity of each spatial frequency component of the lens surface, and the frequency corresponding to the peak value can correspond to the microstructure arrangement of the lens. Then, the area quantile line of each curve is found, for example, 50%, and the corresponding frequency is defined as the spatial characteristic frequency, which can characterize the average spatial frequency of the entire curve. The higher the spatial characteristic frequency of the lens, the finer the overall surface microstructure.

[0086] Specifically, the contents of lenses 4 to 6 are shown in Figures 3 to 5.

[0087] Lens 4 exhibits small and dense microstructures with dramatic variations in sagittal height within individual microstructures and numerous minute fluctuations within each microstructure. Overall structural information is predominantly high-frequency, with fewer low-frequency components. Peaks ① and ② of the PSD curve correspond to the distance and period of the microstructure arrangement; for example, the frequency of peak ① is around 3mm. -1 Nearby corresponds to the 0.33mm spacing between microstructures, while ③ and ④ correspond to the fine structures within a single microstructure.

[0088] Lens 5 exhibits relatively large and uniformly distributed microstructures. The sagittal variation within individual spherical microstructures is gradual, and the structural arrangement is regular. Overall structural information is predominantly low-frequency, with fewer high-frequency components. For example, the frequency of lens ① is around 0.8mm. -1 Nearby, the corresponding lens arrangement period is approximately 1.25mm.

[0089] Lens 6 has relatively large and sequentially intersecting microstructures. The sagittal height changes gradually within a single spherical microstructure, but drastic changes occur at the intersections of microstructures, exhibiting a regular structural arrangement. There is not only a periodic arrangement between lenses connected in the Y direction (①), but also a periodic arrangement between different chains in the X direction (②).

[0090] As shown in Figure 6, the PSD curves of the three typical microstructure lenses (lens 4 to 6) are superimposed to visually compare the proportions of high-frequency and low-frequency components and calculate their respective spatial characteristic frequencies. In this embodiment, the characteristic frequency corresponding to the 50% area quantile is calculated, i.e., the area under the PSD curves on both sides of the characteristic frequency line is equal. Lens 4 is 4.34mm. -1 Lens 5 is 0.78mm. -1 Lens 6 is 0.55mm. -1 Comparing the characteristic frequencies of lenses 4 and 6, it can be seen that the spatial frequency of the microstructure of lens 4 is significantly higher than that of lenses 5 and 6, which means that the microstructure of lens 4 is smaller and has more subtle surface fluctuations.

[0091] As shown in Figure 6, the curve of lens 4 in the high-frequency region (to the right of the spatial characteristic frequency) is significantly higher than that of lenses 5 and 6, indicating that its high-frequency components are significantly higher. This suggests that its microstructure is dominated by fine, small-scale undulations, which, in the context of myopia prevention and control mechanisms, mainly interferes with retinal imaging contrast through high-frequency scattering. The curves of lenses 5 and 6 in the low-frequency region (to the left of the spatial characteristic frequency) are higher, indicating that they contain more low-frequency information. This suggests that their microstructure is dominated by large-scale, slowly changing defocus units, which, in the context of myopia prevention and control mechanisms, mainly function by introducing low-order aberrations and causing image blurring.

[0092] Based on this, the higher the feature frequency, the finer and denser the microstructure, the greater the proportion of high-frequency components, and the more the control mechanism tends to be contrast-modulated.

[0093] The lower the characteristic frequency, the sparser the microstructure and the larger the unit scale. Low-frequency components dominate, and the control mechanism is more inclined to introduce low-order aberrations, causing imaging blur.

[0094] Since different prevention and control mechanisms may exhibit varying biological responses to myopia intervention, spatial characteristic frequency provides a mechanism-related quantitative dimension. This dimension can be combined with the overall RMS or high- and low-frequency component indices to differentiate and evaluate the prevention and control signal characteristics of different types of microstructured lenses. Therefore, this parameter not only has a clear physical meaning but can also serve as an auxiliary reference value, enhancing the resolution and guiding value of the evaluation system.

[0095] As another possible embodiment of the invention, step S4 can be replaced with the following steps:

[0096] S4': Perform spatial frequency decomposition on the preprocessed data to obtain its power spectral density distribution.

[0097] S5': Set a cutoff frequency. The cutoff frequency is used to distinguish between large-scale microstructures that cause low-order aberrations and thus blur the image, and small-scale microstructures that cause high-order aberrations and light scattering, thus causing a decrease in contrast.

[0098] Low-order aberrations are typically defocus and astigmatism, while high-order aberrations typically include at least coma, spherical aberration, and cloverleaf aberration. Low-frequency microstructures (i.e., large-scale microstructures) correspond to imaging blur caused by low-order aberrations, while high-frequency microstructures (i.e., small-scale microstructures) correspond to contrast reduction caused by high-order aberrations and light scattering.

[0099] Specifically, the cutoff frequency can be selected based on the characteristic dimensions of common microstructure lenses. For example, the lens diameter of current myopia control lenses is typically around 1mm, so a cutoff frequency of 2mm can be chosen. -1 This corresponds to a size of 0.5mm. In other words, periods greater than 0.5mm are considered low-frequency information, and periods less than 0.5mm are considered high-frequency information.

[0100] S6': Based on the cutoff frequency and power spectral density distribution, the physical profile of the microstructure is divided into low-frequency and high-frequency components, and the statistical characteristic index I corresponding to the low-frequency component is calculated respectively. low Statistical characteristic index I corresponding to high frequency components high .

[0101] Specifically, as shown in Figures 7 to 10, high-pass and low-pass filters were applied to lens 4, using the cutoff frequency as the boundary, to obtain high-frequency and low-frequency components. Their RMS values ​​and proportions were then calculated. The high-frequency component accounted for 82.42%, indicating that high-frequency information dominates in this lens, and that contrast reduction and light scattering are the dominant factors in the control mechanism. Furthermore, the high-frequency RMS value is quite high, indicating a strong signal.

[0102] Using the cutoff frequency as a boundary, lens 5 was subjected to high-pass and low-pass filtering respectively to obtain high-frequency and low-frequency components, and their RMS values ​​and proportions were calculated. The low-frequency component accounted for 88.43%, indicating that low-frequency information dominates in this lens, and that low-order aberrations causing image blur are the main contributors to the image blurring in the control mechanism. The magnitude of the low-frequency RMS value can be used to compare with other lens designs to quantitatively evaluate the low-order aberrations (such as defocus) of lens 5 and the intensity of the resulting image blur.

[0103] Using the cutoff frequency as a boundary, high-pass and low-pass filters were applied to lens 6 to obtain high-frequency and low-frequency components, and their RMS values ​​and proportions were calculated. The low-frequency component accounted for 86.90%, indicating that low-frequency information dominates in this lens, and low-order aberrations are the primary cause of image blurring in the control mechanism. Compared with lens 5, lens 6 has a higher low-frequency RMS value, indicating that lens 6 causes stronger low-order aberration signals and a greater degree of image blurring.

[0104] In the analysis of Figures 7 to 9, frequency decomposition yields the RMS values ​​of high-frequency and low-frequency components. The high-frequency RMS value characterizes the strength of the contrast reduction effect caused by higher-order aberrations and light scattering introduced by the optical surface, while the low-frequency RMS value characterizes the strength of image blurring caused by lower-order aberrations introduced by the optical surface. These two types of effects are the two most mainstream mechanisms in the field of myopia control, and the high-frequency and low-frequency RMS values ​​provided by this method offer an effective and feasible quantitative characterization method. For a specific lens, the respective proportions of high-frequency and low-frequency RMS values ​​can effectively reveal the main control mechanism of the lens, and the corresponding RMS values ​​can be directly compared with other lenses, providing a reasonable reference for researchers in this field to evaluate the control signal strength of specific microstructure lenses.

[0105] Furthermore, the total RMS value of the microstructure profile before decomposition can be considered as the superposition result of high-frequency and low-frequency RMS, that is, it includes the effects of imaging blurring and contrast reduction. Therefore, the magnitude of the total RMS value can be used to characterize the total intensity of the control signal on the surface of the microstructure.

[0106] As shown in Figure 10, in order to verify the correspondence between the lens physical surface shape analysis method and the actual optical effect, optical analysis was performed on the surface shapes of three lenses (lens 4-lens 6) to obtain their respective MTF modulation transfer functions. This curve can quantitatively describe the resolution of the lens for spatial frequency images.

[0107] As shown in Figure 10, in the low-frequency response region to the left of the dashed line, the MTF values ​​of lenses 5 and 6 are lower. This is because the physical surface contours of lenses 5 and 6 are dominated by low-frequency components, which mainly reduce the resolution of low-frequency images. In the high-frequency response region to the right of the dashed line, the MTF value of lens 4 is lower. This is because the physical surface contours of lens 4 are dominated by high-frequency components, which mainly reduce the resolution of high-frequency images.

[0108] Optical analysis (MTF) results for these three types of lenses show that the high-frequency and low-frequency components of the lens's physical profile affect the optical performance accordingly. Specifically, the low-frequency surface components mainly suppress the resolution of low-frequency images, while the high-frequency surface components mainly weaken the resolution of high-frequency images. This correspondence verifies a clear physical link between the spatial frequency characteristics of the microstructure and its optical modulation function.

[0109] Based on this, by setting a cutoff frequency with physiological and optical significance (e.g., 2 mm) -1 The microstructure surface is divided into high-frequency and low-frequency components, and the proportion R of the high-frequency components is calculated for each component. high R, the proportion of low-frequency components low This allows for a more refined evaluation of the strength of control signals.

[0110] When R high When the value is high, it indicates that the lens is dominated by higher-order aberrations and high-frequency scattering mechanisms, and is suitable for control strategies that require reducing the high-frequency contrast of the retina.

[0111] When R low At higher levels, it reflects that the mechanism mainly introduces low-order aberrations (such as defocus and astigmatism), and focuses more on blurring the image.

[0112] Therefore, compared to a rough assessment that relies solely on overall statistical indicators, this embodiment, through frequency decomposition and proportion quantification, can not only accurately reflect the functional differences of lenses with different microstructures, but also provide more targeted technical basis for mechanism adaptation, design optimization and clinical selection, significantly improving the resolution and application value of the evaluation system.

[0113] S7': Based on I total I low Or I high At least one of them, to generate the quantized value of the control signal intensity of the optical lens microstructure.

[0114] Specifically, the quantification value of the control signal strength includes the proportion of high-frequency components R. high Or the proportion of low-frequency components R low ,in:

[0115] ; .

[0116] First, in this embodiment, the spatial frequency decomposition of the preprocessed microstructure surface is performed, and based on a cutoff frequency with physiological and optical significance (such as 2 mm), -1 This method effectively addresses the technical deficiency of existing evaluation methods in distinguishing different prevention and control mechanisms by dividing high- and low-frequency components. Large-scale microstructures (period > 0.5 mm) primarily affect low-frequency visual information by introducing low-order aberrations locally, while small-scale fluctuations (period < 0.5 mm) reduce high-frequency contrast through scattering and high-order aberrations. Their biological effects in myopia intervention follow different pathways. Traditional overall indicators (such as RMS) only reflect the total amount of facial undulations and cannot reveal their internal composition.

[0117] This embodiment calculates the low-frequency component index I separately. low With high-frequency component index I high And derive the high-frequency proportion R high Low frequency ratio R low These parameters enable the identification and quantification of prevention and control mechanisms, providing a clear basis for more detailed mechanism-oriented lens design.

[0118] Secondly, as shown in Figure 12, this method can accurately capture the selective influence of manufacturing processes on microstructures, thereby supporting closed-loop process optimization. For example, surface treatment processes such as hardening and coating can significantly smooth the sharp edges and fine features of microstructures, leading to a substantial attenuation of high-frequency components, while having minimal impact on low-frequency profiles. Without separation of the frequency dimension, such non-uniform distortions would be masked by the overall RMS, making it difficult to pinpoint the root cause of the problem. This embodiment, however, compares the designed surface profile with the manufactured surface profile... high Changes can be quantitatively assessed to determine the degree of high-frequency loss, thereby guiding targeted pre-compensation strategies (such as enhancing edge steepness or increasing the sag in high-frequency regions) to ensure that the final product retains the expected control signal characteristics.

[0119] In summary, this embodiment not only continues the advantage of evaluating from the source of physical contours in Embodiment 1, but also further expands the evaluation dimension from "total intensity" to "frequency composition", realizing a refined analysis of the functional characteristics of microstructures.

[0120] As another possible embodiment of the present invention, a method for designing a myopia control lens compensation is also provided, comprising the following steps:

[0121] S6: Using the contour-based microstructure evaluation method in the above embodiments, the designed surface shape and the actual surface shape after manufacturing of the target lens are evaluated respectively to obtain the corresponding quantitative value of the control signal intensity.

[0122] S7: Calculate the difference between the quantized value of the prevention and control signal strength of the actual surface after manufacturing and the quantized value of the prevention and control signal strength of the designed surface.

[0123] S8: Based on the difference value, the microstructure parameters in the original design are pre-compensated so that the final product after manufacturing process can still achieve the expected signal strength control effect.

[0124] Specifically, microstructure parameters include sag, edge steepness, fill rate distribution, or local curvature, and pre-compensation includes increasing sag, enhancing edge gradient, or increasing the design weight of high-frequency components.

[0125] This embodiment aims to address the problem of reduced signal strength caused by the attenuation of microstructure surface information during manufacturing. The method introduces a physical contour-based quantitative evaluation system during the design phase to achieve reverse compensation for the impact of manufacturing processes, ensuring that the final product performance matches the design objectives.

[0126] As shown in Figure 12, taking lens 7 as an example, its manufacturing process includes: surface design → mold → unhardened lens → hardened coated lens. By performing spatial frequency decomposition on the three-dimensional surface of each stage and extracting high-frequency components (>2 mm)... -1 The RMS value of ( ) can be used to quantitatively analyze the impact of the manufacturing process on high-frequency control signals:

[0127] The design surface profile has a high-frequency RMS of 0.162 μm.

[0128] The high-frequency RMS of the mold is 0.158 μm (slight loss due to machining error).

[0129] Unhardened lens high-frequency RMS = 0.149 μm (further attenuation due to injection molding deformation);

[0130] The high-frequency RMS of the hardened coated lens is 0.096 μm (significantly reduced, mainly due to the effects of hardening, coating layer filling, and edge smoothing).

[0131] The data above indicates that high-frequency information is lost most significantly during the hardening coating stage, reaching approximately 37%, directly impacting the lens's contrast interference resistance. If this attenuation is not considered in traditional designs, the actual product's interference control performance will be far below expectations.

[0132] To address this, the present invention proposes pre-compensation of microstructure parameters during the initial design phase. For example, to mitigate high-frequency signal attenuation caused by hardening coating, the sag of the microstructure is appropriately increased or the edge steepness is enhanced during design, ensuring that the high-frequency components of the designed surface are higher than the target value. After manufacturing, although some information is weakened, the final product can still achieve the predetermined signal strength for control.

[0133] This compensation method is applicable to all myopia control lenses that rely on microstructure modulation, and is particularly critical for non-periodic, complex microstructures. By applying the physical profile evaluation results in reverse to design optimization, a technological upgrade from "experience-based trial and error" to "data-driven" approaches has been achieved, significantly improving product consistency and clinical reliability.

[0134] As another possible embodiment of the present invention, a myopia control optical lens is also provided, which is prepared by a myopia control lens compensation design method.

[0135] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0136] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0137] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0138] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as “circuit,” “module,” or “system.”

[0139] An electronic device according to this embodiment of the invention. The electronic device is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the invention.

[0140] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0141] The memory stores program code that can be executed by a processor, causing the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of the present invention.

[0142] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0143] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0144] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0145] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0146] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the present invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the present invention described in the "Exemplary Methods" section above.

[0147] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0148] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0149] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0150] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0151] Furthermore, the accompanying drawings are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes shown in the above drawings do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0152] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0153] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A contour-based microstructure evaluation method, characterized in that, The method includes the following steps: S1: acquiring actual three-dimensional physical surface contour data of the microstructure region of the optical surface; S2: preprocessing the three-dimensional physical surface contour data to remove macroscopic base surface shape and measurement noise, obtaining data that only reflects the local undulations of the microstructure; S3: calculating at least one statistical characteristic index I on the preprocessed data within the projection area on the optical surface corresponding to the average size of the human pupil. total I total The variation characteristics of the three-dimensional surface in the height direction were quantified to characterize the overall fluctuation intensity of the microstructure; the statistical characteristic index was selected from at least one of the following: arithmetic mean height, root mean square height, peak-to-valley value, mean curvature, Gaussian curvature, principal curvature, and statistical values ​​of sag height; wherein, the statistical values ​​of sag height include mean sag height, standard deviation of sag height, or median value of sag height distribution; S4: I total This serves as the quantization value of the control signal intensity for the optical surface microstructure.

2. The method according to claim 1, characterized in that, The three-dimensional physical surface contour data is point cloud data generated by sampling optical design models or optical design data.

3. The method according to claim 1, characterized in that, The three-dimensional physical surface contour data is point cloud data obtained by measuring the actual lens or mold surface using a white light interferometer, confocal microscope, atomic force microscope, or profilometer.

4. The method according to claim 1, characterized in that, Replace step S4 with the following steps: S4': Perform spatial frequency decomposition on the preprocessed data to obtain its power spectral density distribution; S5': Set a cutoff frequency, which is used to distinguish between large-scale microstructures that cause low-order aberrations and thus image blurring, and small-scale microstructures that cause high-order aberrations and light scattering and thus contrast reduction; S6': Based on the cutoff frequency and power spectral density distribution, divide the physical contour of the microstructure into low-frequency components and high-frequency components, and calculate the statistical characteristic index I corresponding to the low-frequency components respectively. low Statistical characteristic index I corresponding to high frequency components high S7': Based on I total I low Or I high At least one of them, the intensity quantization value of the control signal of the optical surface microstructure is generated.

5. The method according to claim 1, characterized in that, The projection area on the optical surface corresponding to the average size of the human eye pupil is a circular area with a diameter of 6 mm.

6. The method according to claim 4, characterized in that, The cutoff frequency is 2 mm. -1 .

7. The method according to claim 4 or 6, characterized in that, The quantitative value of the control signal strength includes the proportion of high-frequency components, R. high Or the proportion of low-frequency components R low ,in: ； 。 8. The method according to claim 4, characterized in that, Also includes: Based on the cumulative energy of the power spectral density distribution, a spatial characteristic frequency is determined; the spatial characteristic frequency serves as another reference value for evaluating the signal strength of the optical surface microstructure; the spatial characteristic frequency is the spatial frequency corresponding to 50% of the total cumulative energy of the power spectral density distribution.

9. The contour-based microstructure evaluation method according to claim 1, characterized in that, The method is applicable to myopia control optical lenses with simple or complex microstructures; wherein, the simple microstructure refers to a microstructure whose smallest unit is a simple geometric shape of sphere, aspherical, or cylindrical surface; and the complex microstructure refers to a microstructure whose smallest unit is not a simple geometric shape of sphere, aspherical, or cylindrical surface.

10. A method for designing a myopia control lens compensation, characterized in that, Includes the following steps: Using the method described in any one of claims 1 to 9, the designed surface shape and the actual surface shape after manufacturing of the target lens are evaluated respectively to obtain the corresponding quantitative value of the prevention and control signal strength; the difference between the quantitative value of the prevention and control signal strength of the actual surface shape after manufacturing and the quantitative value of the prevention and control signal strength of the designed surface shape is calculated. Based on the difference value, the microstructure parameters in the original design are pre-compensated so that the final product after manufacturing process can still achieve the expected signal strength control effect.

11. The method according to claim 10, characterized in that, The microstructure parameters include sag, edge steepness, fill rate distribution, or local curvature, and the pre-compensation includes increasing the sag, enhancing the edge gradient, or increasing the design weight of high-frequency components.

12. A myopia control optical lens, characterized in that, The lens is prepared by the compensation design method described in claim 10 or 11.

13. A non-transitory computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a contour-based microstructure evaluation method as described in any one of claims 1 to 9.

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