Honeycomb net film coating method of bionic honeycomb net film lens

Abstract

The invention relates to the technical field of spectacle lenses, in particular to a honeycomb net-shaped film coating method of a bionic honeycomb net-shaped lens. Firstly, 3D image data of a substrate is obtained, secondly, multiple layers of films are sequentially plated in a high-vacuum environment, and plating of each layer of film is accompanied by generation of the 3D image data and recognition of a defect area. And identifying a defect area by comparing the 3D image data of the substrate with the 3D image data of the first layer of film. Analyzing the honeycomb structure data of the second layer of film through a defect identification model, identifying honeycomb structure defects, and calculating a structure defect difference coefficient; then the light transmittance of the lens is detected in real time through a multispectral fusion technology, and a comprehensive light transmittance defect coefficient is generated; meanwhile, a sensor is used for monitoring ambient light intensity and incident light intensity, and a light intensity defect coefficient is generated; and finally, judging the quality of the lens by combining the structure defect difference coefficient, the light transmission defect coefficient and the light intensity defect coefficient through the identified defect area position.

Description

Technical Field

[0001] The present invention relates to the technical field of spectacle lenses, and specifically to a coating method for a honeycomb mesh film layer of a bionic honeycomb retina lens. Background Art

[0002] In recent years, with the progress of optical technology and the increasing demand for visual health of people, the functions of spectacle lenses need to have anti-reflection, ultraviolet protection, enhanced optical performance, etc. while having vision correction. Although the coating technology on the surface of traditional lenses can improve optical performance, in the case of long-term strong light irradiation or complex optical environments, problems such as poor durability and attenuation of optical performance are likely to occur in the lens coating layer.

[0003] In order to further improve the optical performance of lenses, bionic technology has become a research hotspot in the optical field. The bionic honeycomb structure is widely used in the lens field because of its unique structural characteristics that can improve mechanical strength while effectively regulating the reflection and transmission of light. The honeycomb mesh film layer can not only enhance the light transmittance and anti-reflection of the lens, but also enhance the structural stability and durability of the film layer. However, the bionic honeycomb retina layer coating technology in the lens field is still in its infancy, and existing technologies have problems such as poor uniformity of the honeycomb retina layer, great difficulty in structural control during the coating process, and relatively large light transmittance in practical applications, and cannot guarantee the quality of the coating.

[0004] Therefore, a coating method for a honeycomb mesh film layer of a bionic honeycomb retina lens is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a coating method for a honeycomb mesh film layer of a bionic honeycomb retina lens. First, 3D image data of the lens substrate is obtained. Secondly, multiple layers of films are sequentially deposited in a high-vacuum environment, and the deposition of each layer of film is accompanied by the generation of 3D image data and the identification of defect areas. The defect areas are identified by comparing the 3D image data of the substrate and the first layer of film. Then, the honeycomb structure data of the second layer of film is analyzed by a defect identification model to identify honeycomb structure defects and calculate the structural defect difference coefficient. Subsequently, the light transmittance of the lens is detected in real time by multi-spectral fusion technology to generate a comprehensive light transmittance defect coefficient; at the same time, a sensor is used to monitor the ambient light intensity and the incident light intensity on the eye to generate a light intensity defect coefficient. Finally, the quality of the lens is judged based on the position of the identified defect areas and in combination with the structural defect difference coefficient, the comprehensive light transmittance defect coefficient, and the light intensity defect coefficient.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A coating method for a honeycomb mesh film layer of a bionic honeycomb retina lens, comprising:

[0008] S1. Obtain the data of the lens substrate and generate the substrate 3D image data;

[0009] S2. Coat the first layer of film on the lens substrate and generate the first 3D image data;

[0010] Obtain the first defect area based on the substrate 3D image data and the first 3D image data;

[0011] S3. Coat the second layer of film on the first layer of film and generate the second image data set; the second image data set includes the second 3D image and the honeycomb structure data;

[0012] Obtain the second defect area based on the first 3D image data and the second 3D image data;

[0013] Construct a honeycomb mesh film layer defect recognition model to recognize the defects in the honeycomb structure data, obtain the third defect area, and generate the structural defect difference coefficient;

[0014] S4. Coat the third layer of film on the second layer of film to generate the third 3D image data; and detect the light transmittance and the intensity of the light entering the eye of the first lens coated with three layers of film;

[0015] Obtain the fourth defect area based on the second 3D image data and the third 3D image data;

[0016] Obtain the light transmittance defect coefficient of different bands through the transmittance of different bands at different angles; obtain the comprehensive light transmittance defect coefficient through multi-spectral feature fusion; obtain the light intensity defect coefficient through the intensity of the ambient light entering the eye;

[0017] S5. Coat the fourth layer of film on the third layer of film to generate the fourth 3D image data;

[0018] Obtain the fifth defect area based on the third 3D image data and the fourth 3D image data;

[0019] S6. Identify all the defect areas, judge whether the defects are in the edge area or the central area, and generate the lens quality difference coefficient by integrating the structural defect difference coefficient, the comprehensive light transmittance defect coefficient and the light intensity defect coefficient.

[0020] Preferably, obtain the first layer of film information data based on the substrate 3D image data and the first 3D image data; obtain the data points exceeding the standard through the 3D point cloud network of the first layer of film information data and the first layer of film standard information data, and obtain the first defect area;

[0021] Based on the first 3D image data and the second 3D image data, second-layer film information data is obtained; the second-layer film information data and the second-layer film standard information data are used to obtain out-of-standard point data through a 3D point cloud network, and a second defect area is obtained;

[0022] Based on the second 3D image data and the third 3D image data, third-layer film information data is obtained; the third-layer film information data and the third-layer film standard information data are used to obtain out-of-standard point data through a 3D point cloud network, and a fourth defect area is obtained;

[0023] Based on the third 3D image data and the fourth 3D image data, fourth-layer film information data is obtained; the fourth-layer film information data and the fourth-layer film standard information data are used to obtain out-of-standard point data through a 3D point cloud network, and a fifth defect area is obtained;

[0024] Based on the above defect areas, a comprehensive defect area coefficient is obtained. The specific calculation formula for the comprehensive defect area coefficient is:

[0025]

[0026] where DA is the comprehensive defect area coefficient, DA i is the defect area coefficient of the i-th layer of film, ω i is the defect area coefficient weight of the i-th layer of film, f i is the characteristic function of the i-th layer of film, considering the influence of specific film properties, such as film thickness and optical properties.

[0027] Preferably, the construction process of the honeycomb mesh film layer defect recognition model:

[0028] Data acquisition module: Obtain the bionic honeycomb structure data of the second layer of film; the bionic honeycomb structure data includes the bionic honeycomb structure geometry, pore diameter, pore depth, arrangement of honeycomb units, thickness of the second film layer, and curvature of the second film layer;

[0029] Preprocessing module: Perform denoising, filtering, and image enhancement processing on the bionic honeycomb structure data;

[0030] Feature extraction module: Extract honeycomb structure geometric features and film layer surface texture features based on the bionic honeycomb structure data; the honeycomb structure geometric features include pore diameter, pore depth, arrangement of honeycomb units, and distance between honeycomb units; the film layer surface texture features include gray-level co-occurrence matrix and frequency domain features;

[0031] Defect recognition module: Identify and mark the defect positions in the honeycomb film layer through the honeycomb structure geometric features and the film layer surface texture features;

[0032] Defect Output Module: Output the structural defect difference coefficient according to the defect position in the marked honeycomb shape.

[0033] Preferably, the specific steps for calculating the structural defect difference coefficient are as follows:

[0034] Compare the geometric characteristics of the theoretical honeycomb structure of the bionic honeycomb structure with the geometric characteristics of the actually detected honeycomb structure, calculate the differences in the gray-level co-occurrence matrix, pore diameter, pore depth, and distance between honeycomb cells, and the deviation between the arrangements of honeycomb cells; through the comprehensive differences and deviations, obtain the structural defect difference coefficient; the specific calculation formula for the structural defect difference coefficient is:

[0035] DDC = ω 1 ·ΔGLCM + ω 2 ·ΔDi + ω 3 ·ΔDe + ω 4 ·ΔDis + ω 5 ·ΔArr;

[0036] Wherein, DDC is the structural defect difference coefficient, ω 1 is the difference weight of the gray-level co-occurrence matrix, ΔGLCM is the difference of the gray-level co-occurrence matrix, ω 2 is the difference weight of the pore diameter, ΔDi is the difference of the pore diameter, ω 3 is the difference weight of the pore depth, ΔDe is the difference of the pore depth, ω 4 is the difference weight of the distance between honeycomb cells, ΔDis is the difference of the distance between honeycomb cells, ω 5 is the deviation weight between the arrangements of honeycomb cells, ΔArr is the deviation between the arrangements of honeycomb cells.

[0037] Preferably, the specific steps for generating the light transmittance defect coefficients of different bands are as follows:

[0038] Measure and record the first light intensity, the first light type, the second light intensity, and the second light type at different incident angles in real time through a spectral analyzer; the first light intensity and the first light type are the light that has not passed through the lens; the second light intensity and the second light type are the light that has passed through the lens;

[0039] Determine the light transmittance of different bands by the ratio of the second light intensity to the first light intensity;

[0040] Based on the comparison between the light transmittance of different bands and the standard values of the light transmittance of different bands, obtain the light transmittance defect coefficients of each band; generate the comprehensive light transmittance defect coefficient based on the light transmittance defect coefficients of each band.

[0041] Preferably, the specific formula for the comprehensive light transmittance defect coefficient is:

[0042] CTDC(θ) = ω UV·ΔT UV (θ) + ω BL ·ΔT BL (θ) + ω IR ·ΔT IR (θ);

[0043] Wherein, CTDC(θ) is the comprehensive light transmittance defect coefficient, θ is the light source incident at different angles, ω UV is the weight of the light transmittance defect coefficient in the ultraviolet band, ΔT UV (θ) is the light transmittance defect coefficient in the ultraviolet band, ω BL is the weight of the light transmittance defect coefficient in the blue light band, ΔT BL (θ) is the light transmittance defect coefficient in the blue light band, ω IR is the weight of the light transmittance defect coefficient in the infrared band, ΔT IR (θ) is the light transmittance defect coefficient in the infrared band.

[0044] Preferably, a light intensity adjusting device is placed in front of the lens to simulate different lighting conditions and set different light intensity levels; the ambient light intensity of the current environment is detected and recorded in real time by an ambient light sensor behind the lens; an incident light intensity sensor is placed behind the lens at the same time to detect the light intensity reaching the eye after passing through the lens, simulating the actual incident light intensity under the lens.

[0045] Preferably, under different ambient light intensities, the light intensity transmitted through the lens is detected in real time, and the incident light intensity sensor is used to record the actual incident light intensity reaching the eye after passing through the lens. The specific calculation formula of the actual incident light intensity is:

[0046] I EYE (θ) = CTDC(θ) · I EN (θ);

[0047] Wherein, I EYE (θ) is the actual incident light intensity under different angle light sources, I EN (θ) is the ambient light intensity monitored in real time;

[0048] Obtain the light intensity defect coefficient based on the actual incident light intensity and the standard incident light intensity;

[0049] The specific formula of the light intensity defect coefficient is:

[0050]

[0051] Where I(θ) is the light intensity defect coefficient under different angle light sources, I F_EYE (θ) is the standard incident light intensity under different angle light sources.

[0052] Preferably, the central region is set as a circular region centered at the optical center of the lens, with a radius being a fixed ratio of the total radius of the lens, and the remaining part is the edge region;

[0053] Obtain the positions of all defect regions, and determine whether the defects are in the central region; if they are in the central region, the quality of this lens is unqualified, while the defects in the edge region have less impact on the lens.

[0054] Preferably, the lens quality difference coefficient is specifically expressed as:

[0055] Q = α 1 DDC + α 2 CTDC + α 3 I + α 4 DA;

[0056] Among them, Q is the lens quality difference coefficient, α 1 is the structure defect difference coefficient weight, DDC is the structure defect difference coefficient, α 2 is the comprehensive light transmittance defect coefficient weight, CTDC is the comprehensive light transmittance defect coefficient, α 3 is the light intensity defect coefficient weight, I is the light intensity defect coefficient, α 4 is the comprehensive defect region coefficient weight, DA is the comprehensive defect region coefficient;

[0057] When the lens quality difference coefficient is smaller, the lens quality is better.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] 1. Through the irradiation of a multi-band light source, the present invention measures the light transmittance of the lens coated with a honeycomb mesh film layer at different angles and under different lights respectively, and obtains the light transmittance defect coefficients of different light wave bands by calculating the intensity ratio of the incident light and the transmitted light. Through the multi-spectral fusion algorithm, the light transmittance defect coefficients of different light wave bands are fused to generate the comprehensive light transmittance defect coefficient. When identifying the optical defects when different light wave bands pass through the lens, this method also provides a more comprehensive evaluation of the optical performance of the lens through data fusion.

[0060] 2. The present invention detects the incident light intensity into the eye under light sources at different angles for the lens coated with a honeycomb mesh film layer, and obtains the light intensity defect coefficients under light sources at different angles. First, by setting a light intensity sensor behind the lens, the light intensity reaching the human eye through the lens under the simulated recorded environmental light conditions is recorded. Combining the light sources at different light intensity levels and the measurement of the incident light intensity at multiple angles of the lens, the optical performance of the lens in the actual use environment is evaluated.

[0061] 3. First, the present invention analyzes the 3D image data of each layer of the lens to identify the defective areas and their positions in the film layer and the honeycomb structure of the honeycomb mesh film layer. The identification of the area position determines the evaluation result of the lens quality. The defects in the optical center of the lens have a significant impact on the optical performance and are serious quality problems, while the defects in the edge area have a relatively small impact on the overall visual effect. This method enhances the ability to identify and process local defects of the lens and also accurately classifies the lens defects. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a schematic flow chart of a coating method for a honeycomb mesh film layer of a bionic honeycomb retina lens according to the present invention;

[0063] Figure 2 is a schematic structural diagram of a defect identification model for a honeycomb mesh film layer according to the present invention;

[0064] Figure 3 is a schematic diagram of a defect of a honeycomb mesh film layer according to the present invention;

[0065] Figure 4 is a schematic structural diagram of a coefficient of difference in lens quality according to the present invention.

[0066] In the figure: 1, aperture defect; 2, honeycomb shape defect one; 3, honeycomb shape defect two. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0068] Please refer to Figure 1 , the present invention provides a coating method for a honeycomb mesh film layer of a bionic honeycomb retina lens, and the technical solution is as follows:

[0069] S1. Obtain the data of the lens substrate and generate the substrate 3D image data, where the substrate 3D image data includes the lens substrate curvature, substrate thickness, and substrate smoothness;

[0070] S2. In a high-vacuum environment, coat the first layer of film on the lens substrate and generate the first 3D image data; the first layer of film is the base layer;

[0071] According to the substrate 3D image data and the first 3D image data, obtain the first defective area;

[0072] Further, the current 3D image and the previous layer's 3D image are analyzed through a convolutional neural network to obtain the current film layer information data; based on the current film layer information data and the standard coating information data, the out-of-standard point data is obtained through a 3D point cloud network, thereby obtaining the defective area;

[0073] A 3D point cloud network is a deep learning model for processing and analyzing three-dimensional point cloud data. A point cloud is a data set composed of a large number of three-dimensional coordinate points, which is widely used to represent the shape and spatial distribution of objects. Among them, point cloud data is usually obtained by laser scanners, depth cameras or three-dimensional models generated by computers, and each point represents a position in three-dimensional space. Typical architectures of 3D point cloud networks include convolutional neural networks and graph neural networks, which can effectively capture the geometric features and topological structures in the point cloud.

[0074] Based on the base 3D image data and the first 3D image data, the first layer of film information data is obtained; the first layer of film information data and the first layer of film standard information data are passed through a 3D point cloud network to obtain out-of-standard point data, and the first defective area is obtained;

[0075] Based on the first 3D image data and the second 3D image data, the second layer of film information data is obtained; the second layer of film information data and the second layer of film standard information data are passed through a 3D point cloud network to obtain out-of-standard point data, and the second defective area is obtained;

[0076] Based on the second 3D image data and the third 3D image data, the third layer of film information data is obtained; the third layer of film information data and the third layer of film standard information data are passed through a 3D point cloud network to obtain out-of-standard point data, and the fourth defective area is obtained;

[0077] Based on the third 3D image data and the fourth 3D image data, the fourth layer of film information data is obtained; the fourth layer of film information data and the fourth layer of film standard information data are passed through a 3D point cloud network to obtain out-of-standard point data, and the fifth defective area is obtained.

[0078] For the analysis of the film layer information data, the standard deviation of the film layer can be calculated using a formula to determine whether parameters such as film layer thickness and smoothness exceed the standard range. The defective area recognition formula is:

[0079]

[0080] where D i is the defect metric (standard deviation of thickness, radian or smoothness) of the i-th layer of film, x ij is the j-th point cloud data of the i-th layer of film, μ i is the average value of the i-th layer of film, and n is the total number of point cloud data.

[0081] The defect detection method of the present invention, which combines a convolutional neural network and a 3D point cloud network, improves the accuracy and efficiency of film layer defect recognition. At the same time, this method can make the quality control of the film layer in the production process more scientific and effective, and improve the consistency and reliability of the product.

[0082] S3. Deposit a second layer of film on the first layer of film and generate a second image dataset; the second image dataset includes a second 3D image and honeycomb structure data; the second layer of film is a biomimetic honeycomb structure layer;

[0083] Obtain a second defect area according to the first 3D image data and the second 3D image data;

[0084] By acquiring the 3D image data of each layer of film, the defect area of each layer of film is located. Using a convolutional neural network (CNN) and 3D point cloud network technology, this algorithm can perform refined analysis on the complex geometric shapes and surface texture features of each layer of film, and then identify tiny structural defects.

[0085] Construct a honeycomb network film layer defect recognition model to identify defects in the honeycomb structure data, obtain a third defect area, and generate a structural defect difference coefficient;

[0086] Further, the honeycomb network film layer defect recognition model refers to Figure 2 , and the specific construction process is as follows:

[0087] Data acquisition module: Acquire the biomimetic honeycomb structure data of the second layer of film; the biomimetic honeycomb structure data includes the geometric shape of the biomimetic honeycomb structure, pore diameter, pore depth, arrangement mode of honeycomb units, thickness of the second film layer, and radian of the second film layer;

[0088] Preprocessing module: Perform denoising, filtering, and image enhancement processing on the biomimetic honeycomb structure data;

[0089] Feature extraction module: Extract honeycomb structure geometric features and film layer surface texture features based on the biomimetic honeycomb structure data; the honeycomb structure geometric features include pore diameter, pore depth, arrangement mode of honeycomb units, and distance between honeycomb units; the film layer surface texture features include gray level co-occurrence matrix and frequency domain features;

[0090] Defect recognition module: Identify and mark the defect positions in the honeycomb film layer through the honeycomb structure geometric features and film layer surface texture features;

[0091] As Figure 3As shown, when the aperture defect 1 occurs, voids appear in the honeycomb structure, making the intensity of the light entering the lens uneven; when there are defects in the honeycomb structure, such as honeycomb shape defect 1 and honeycomb shape defect 2, the defective part not only reflects light incompletely but also has a serious impact on the quality of the lens.

[0092] Defect output module: Outputs the structural defect difference coefficient based on the defect geometric features, location, and degree in the marked honeycomb shape.

[0093] In this embodiment, a honeycomb mesh layer defect recognition model is constructed to identify the honeycomb mesh layer defects, thereby effectively extracting the geometric features of the honeycomb structure, including parameters such as aperture, hole depth, and honeycomb unit arrangement method. Through this model, the quality of the honeycomb structure is accurately evaluated, and potential abnormalities in the structure are identified, thus ensuring the stability of the film layer in terms of mechanical properties and optical properties.

[0094] Furthermore, the specific calculation steps of the structural defect difference coefficient are as follows:

[0095] Compare the theoretical honeycomb structure geometric features of the bionic honeycomb structure with the actually detected honeycomb structure geometric features, and calculate the differences in gray-level co-occurrence matrix, aperture, hole depth, and distance between honeycomb units and the deviation in the honeycomb unit arrangement method.

[0096] Obtain the structural defect difference coefficient by synthesizing the differences and deviations.

[0097] The specific calculation formula of the structural defect difference coefficient is:

[0098] DDC = ω 1 ·ΔGLCM + ω 2 ·ΔDi + ω 3 ·ΔDe + ω 4 ·ΔDis + ω 5 ·ΔArr;

[0099] Where DDC is the structural defect difference coefficient, ω 1 is the gray-level co-occurrence matrix difference weight, ΔGLCM is the gray-level co-occurrence matrix difference, ω 2 is the aperture difference weight, ΔDi is the aperture difference, ω 3 is the hole depth difference weight, ΔDe is the hole depth difference, ω 4 is the distance difference weight between honeycomb units, ΔDis is the distance difference between honeycomb units, ω 5 is the deviation weight between honeycomb unit arrangement methods, ΔArr is the deviation between honeycomb unit arrangement methods.

[0100] In this embodiment, the quality of the film layer structure is reflected through comprehensive multi-parameter evaluation. Among them, the difference in the gray-level co-occurrence matrix reflects the change in the surface texture of the film layer, and the deviation in the arrangement of honeycomb cells further supplements the structural stability. Through the weighted integration of different parameters, the structural defect difference coefficient proposed by the present invention can more comprehensively and accurately evaluate the structural defects of the film layer.

[0101] S4. Deposit a third layer of film on the second layer of film to generate third 3D image data; and detect the light transmittance and the intensity of incident light into the eye of the first lens coated with three layers of film; the third layer of film is an anti-reflection layer;

[0102] According to the second 3D image data and the third 3D image data, obtain a fourth defect area;

[0103] By respectively adjusting the incident angles of light sources with different wavelength bands with respect to the first lens, and measuring in real time the light transmittance of the first lens at different angles, obtain the light transmittance defect coefficients for different wavelength bands; obtain the comprehensive light transmittance defect coefficient through multi-spectral feature fusion;

[0104] Monitor the ambient light intensity in real time through a sensor, simulate the intensity of incident light into the eye under the lens under different lighting conditions, and obtain the light intensity defect coefficient;

[0105] Further, the specific steps for generating the light transmittance defect coefficients for different wavelength bands are as follows:

[0106] Measure and record in real time the intensity of the first light, the type of the first light, the intensity of the second light, and the type of the second light at different incident angles through a spectral analyzer; the intensity of the first light and the type of the first light are the light that has not passed through the lens; the intensity of the second light and the type of the second light are the light that has passed through the lens;

[0107] Determine the light transmittance for different wavelength bands by the ratio of the intensity of the second light to the intensity of the first light;

[0108] Based on the comparison between the light transmittance for different wavelength bands and the standard values of the light transmittance for different wavelength bands, obtain the light transmittance defect coefficients for each wavelength band;

[0109] Generate the comprehensive light transmittance defect coefficient based on the light transmittance defect coefficients for each wavelength band.

[0110] By adjusting the incident angles of light sources with different wavelength bands with respect to the lens, measure in real time the light transmittance of the honeycomb mesh film layer lens, and obtain the light transmittance defect coefficients for different wavelength bands such as ultraviolet light, blue light, and infrared light. Compared with the traditional single-wavelength light transmittance detection, this method generates the comprehensive light transmittance defect coefficient through multi-wavelength spectral feature fusion, more comprehensively and meticulously reflecting the light transmittance performance of the lens in each wavelength band, and ensuring the stability of the optical performance of the lens under different light conditions.

[0111] Furthermore, the specific formula for the comprehensive light transmittance defect coefficient is as follows:

[0112] CTDC(θ) = ω UV ·ΔT UV (θ) + ω BL ·ΔT BL (θ) + ω IR ·ΔT IR (θ);

[0113] Wherein, CTDC(θ) is the comprehensive light transmittance defect coefficient, θ is the light source incident at different angles, ω UV is the weight of the light transmittance defect coefficient in the ultraviolet band, ΔT UV (θ) is the light transmittance defect coefficient in the ultraviolet band, ω BL is the weight of the light transmittance defect coefficient in the blue light band, ΔT BL (θ) is the light transmittance defect coefficient in the blue light band, ω IR is the weight of the light transmittance defect coefficient in the infrared band, ΔT IR (θ) is the light transmittance defect coefficient in the infrared band.

[0114] In this embodiment, the comprehensive light transmittance defect coefficient generated by the multi-spectral feature fusion technology quantifies the light transmittance differences of light in different bands. This method makes the results of defect detection more intuitive and quantifiable, avoids the errors of subjective judgment in traditional qualitative detection, and at the same time, the comprehensive light transmittance defect coefficient provides a quantitative standard for the quality control of the lens.

[0115] Furthermore, a light intensity adjustment device is placed in front of the lens to simulate different lighting conditions and set different light intensity levels; an ambient light sensor is used to detect and record the current ambient light intensity in real time behind the lens; an incident light intensity sensor is placed behind the lens at the same time to detect the light intensity reaching the eye after passing through the lens, simulating the actual incident light intensity under the lens.

[0116] In this embodiment, by combining the ambient light sensor and the incident light intensity sensor, the actual visual experience of the lens under different ambient light intensities is simulated, the incident light intensity after the light passes through the lens is dynamically detected, and the performance of the lens under different lighting conditions such as strong light and weak light is effectively evaluated, so as to obtain the light intensity defect coefficient.

[0117] Furthermore, under different ambient light intensities, the light intensity passing through the lens is detected in real time, and the incident light intensity sensor is used to record the actual incident light intensity reaching the eye after passing through the lens. The specific formula for the actual incident light intensity is as follows:

[0118] I EYE (θ) = CTDC(θ)·I EN (θ);

[0119] Among them, I EYE (θ) is the actual incident light intensity under light sources at different angles, and I EN (θ) is the ambient light intensity monitored in real time;

[0120] The light intensity defect coefficient is obtained based on the actual incident light intensity and the standard incident light intensity.

[0121] In this embodiment, by simulating the actual visual experience under different lighting conditions and comparing with the standard incident light intensity, the light intensity defect coefficient is obtained. This provides theoretical support for further optimizing the light transmittance design of the lens, provides feedback for the improvement of optical design in the lens manufacturing process, and further improves the visual comfort and optical performance of the lens.

[0122] S5. Deposit a fourth layer of film on the third layer of film to generate fourth 3D image data; the fourth layer of film is a protective layer;

[0123] According to the third 3D image data and the fourth 3D image data, a fifth defect area is obtained;

[0124] S6. Identify all defect areas, determine whether the defect is in the edge area or the center area, and generate a lens quality difference coefficient by integrating the structural defect difference coefficient, the comprehensive light transmittance defect coefficient, and the light intensity defect coefficient, refer to Figure 4 .

[0125] Further, the center area is set as a circular area centered on the optical center of the lens, with a radius of a fixed proportion of the total radius of the lens, and the remaining part is the edge area;

[0126] Obtain the positions of all defect areas and determine whether the defect is in the center area; if it is in the center area, the quality of this lens is unqualified, while the defect in the edge area has a smaller impact on the lens.

[0127] In this embodiment, the lens is divided into the center area and the edge area, which greatly improves the reliability and scientificity of the detection result. The defect in the optical center area of the lens is directly determined to be unqualified, while the defect in the edge area is weighted according to its impact on the optical performance. This differential processing method avoids over-detection of insignificant defects, making the lens quality evaluation more accurate and reasonable.

[0128] Further, the lens quality difference coefficient is specifically expressed as:

[0129] Q = α 1 DDC + α 2 CTDC + α 3 I + α 4 DA;

[0130] Among them, Q is the lens quality difference coefficient, and α 1 is the weight coefficient of the structural defect difference coefficient, DDC is the structural defect difference coefficient, and α 2 is the weight coefficient of the comprehensive light transmittance defect coefficient, CTDC is the comprehensive light transmittance defect coefficient, and α 3 is the weight coefficient of the light intensity defect coefficient, I is the light intensity defect coefficient, and α 4 is the weight coefficient of the comprehensive defect area coefficient, and DA is the comprehensive defect area coefficient;

[0131] When the lens quality difference coefficient is smaller, the lens quality is better.

[0132] Among them, the values of the structural defect difference coefficient, the comprehensive light transmittance defect coefficient, the light intensity defect coefficient, and the comprehensive defect area coefficient are all between 0 and 1. 0 indicates no defect, and 1 indicates a serious defect; the weight coefficients in the present invention are all obtained by training with historical data. The value of the lens quality difference coefficient Q increases with the increase of each defect parameter, which indicates a decrease in the lens quality. For specific data, refer to Table 1;

[0133] Table 1 Lens Quality Difference Coefficient

[0134]

[0135] In this embodiment, by calculating the lens quality difference coefficient, not only the accuracy of film layer defect detection is improved, but also reliable technical support is provided for film layer quality control, which has broad application prospects and remarkable practical value in the manufacturing process of multi-layer film structures.

[0136] The present invention first uses 3D image data and a convolutional neural network to analyze each layer of the film and identify defects in the lens at different coating stages. This not only improves the reliability of the production process but also provides a solid foundation for subsequent optical performance evaluation. Secondly, the method introduces multi-spectral analysis technology to generate a comprehensive light transmittance defect coefficient by measuring the light transmittance in different bands in real time, ensuring that the lens maintains good light transmittance performance under various lighting conditions and enhancing the user's visual experience. In addition, the ambient light intensity and the incident light intensity are monitored in real time, making the actual optical effect of the lens more real and effective. Finally, by comprehensively considering the structural defect difference coefficient, the light transmittance defect coefficient, and the light intensity defect coefficient, the generated lens quality difference coefficient provides a scientific basis for the overall performance evaluation of the lens. When this coefficient is smaller, it indicates that the lens quality is higher, meeting the high-standard requirements of users for the lens.

[0137] Example Two

[0138] A method for coating a honeycomb mesh film layer of a bionic honeycomb mesh retina lens, comprising:

[0139] S1. Obtain the data of the lens substrate and generate the substrate 3D image data;

[0140] S2. Deposit the first layer of film on the lens substrate and generate the first 3D image data;

[0141] Obtain the first defect area based on the substrate 3D image data and the first 3D image data;

[0142] S3. Deposit the second layer of film on the first layer of film and generate the second image data set; the second image data set includes the second 3D image and the honeycomb structure data;

[0143] Obtain the second defect area based on the first 3D image data and the second 3D image data;

[0144] Construct a honeycomb mesh film layer defect recognition model to identify defects in the honeycomb structure data, obtain the third defect area, and generate a structural defect difference coefficient;

[0145] S4. Deposit the third layer of film on the second layer of film to generate the third 3D image data; and detect the light transmittance and the intensity of the light entering the eye of the first lens coated with three layers of film;

[0146] Obtain the fourth defect area based on the second 3D image data and the third 3D image data;

[0147] Obtain the light transmittance defect coefficient in different bands through the transmittance at different angles in different bands; obtain the comprehensive light transmittance defect coefficient through multi-spectral feature fusion; obtain the light intensity defect coefficient through the intensity of the light entering the eye in the environment;

[0148] S5. Deposit the fourth layer of film on the third layer of film to generate the fourth 3D image data;

[0149] Obtain the fifth defect area based on the third 3D image data and the fourth 3D image data;

[0150] S6. Identify all defect areas, determine whether the defects are in the edge area or the center area, and generate a lens quality difference coefficient by synthesizing the structural defect difference coefficient, the comprehensive light transmittance defect coefficient, and the light intensity defect coefficient.

[0151] Obtain the first layer of film information data based on the substrate 3D image data and the first 3D image data; obtain the data of the points exceeding the standard through the 3D point cloud network of the first layer of film information data and the first layer of film standard information data, and obtain the first defect area;

[0152] Based on the first 3D image data and the second 3D image data, second-layer film information data is obtained; the second-layer film information data and the second-layer film standard information data are input into a 3D point cloud network to obtain out-of-standard point data, thereby obtaining a second defect area;

[0153] Based on the second 3D image data and the third 3D image data, third-layer film information data is obtained; the third-layer film information data and the third-layer film standard information data are input into a 3D point cloud network to obtain out-of-standard point data, thereby obtaining a fourth defect area;

[0154] Based on the third 3D image data and the fourth 3D image data, fourth-layer film information data is obtained; the fourth-layer film information data and the fourth-layer film standard information data are input into a 3D point cloud network to obtain out-of-standard point data, thereby obtaining a fifth defect area.

[0155] Based on the above defect areas, by considering the specific properties of the film layers, a comprehensive defect area coefficient is obtained. The specific calculation formula for the comprehensive defect area coefficient is:

[0156]

[0157] where DA is the comprehensive defect area coefficient, DA i is the defect area coefficient of the i-th layer of film, ω i is the weight of the defect area coefficient of the i-th layer of film, and f i is the characteristic function of the i-th layer of film, considering the influence of specific properties of the film layer, such as film layer thickness and optical properties.

[0158] In this embodiment, through the calculation of the comprehensive defect area coefficient, not only the defect area coefficient of each layer of film is combined with its weight, but also the characteristic function of the film layer is introduced, considering optical properties such as film layer thickness, refractive index, and light transmittance, more comprehensively reflecting the influence of each layer of film on the overall quality of the lens.

[0159] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for coating a honeycomb mesh film layer of a bionic honeycomb mesh lens, characterized in that: include: S1. Obtain lens substrate data and generate substrate 3D image data; S2. coating the lens substrate with a first layer of film and generating first 3D image data; Obtaining a first defect area according to the substrate 3D image data and the first 3D image data; S3. Plating a second film on the first film to generate a second image data set; the second image data set includes a second 3D image and honeycomb structure data; obtaining a second defect area according to the first 3D image data and the second 3D image data; Constructing a honeycomb mesh film layer defect recognition model to perform defect recognition on the honeycomb structure data, obtaining a third defect area, and generating a structural defect difference coefficient; S4. coating a third layer of film on the second layer of film to generate third 3D image data; and performing light transmittance and eye light intensity detection on the first lens coated with the three layers of film; obtaining a fourth defect area according to the second 3D image data and the third 3D image data; The comprehensive transmittance defect coefficient is obtained by fusing the transmittance of different bands at different angles and multi-spectral characteristics; the light intensity defect coefficient is obtained by the ambient light intensity. S5. Plating a fourth film on the third film to generate fourth 3D image data; obtaining a fifth defect area according to the third 3D image data and the fourth 3D image data; S6. Identify all defect areas, determine the defect locations, and generate a lens quality difference coefficient by combining the structural defect difference coefficient, the comprehensive light transmittance defect coefficient, and the light intensity defect coefficient.

2. The method for coating a honeycomb mesh film layer of a bionic honeycomb mesh lens according to claim 1, characterized in that: According to the first 3D image data and the second 3D image data, second film information data is obtained; the second film information data and the second film standard information data are combined through a 3D point cloud network to obtain point data exceeding the standard, and a second defect area is obtained; According to the second 3D image data and the third 3D image data, a third layer of film information data is obtained; the third layer of film information data and the third layer of film standard information data are combined through a 3D point cloud network to obtain data of points exceeding the standard, thereby obtaining a fourth defect area; According to the third 3D image data and the fourth 3D image data, a fourth layer of film information data is obtained; the fourth layer of film information data and the fourth layer of film standard information data are combined through a 3D point cloud network to obtain data of points exceeding the standard, thereby obtaining a fifth defect area; Based on the above defect area, a comprehensive defect area coefficient is obtained, and the specific calculation formula of the comprehensive defect area coefficient is: Among them, DA is the comprehensive defect area coefficient, DA i is the defect area coefficient of the i-th film, ω i is the defect area coefficient weight of the i-th film, f i is the characteristic function of the i-th film layer, taking into account the specific performance influence of the film layer, such as film thickness and optical properties.

3. The method for coating a honeycomb mesh film layer of a bionic honeycomb mesh lens according to claim 1, characterized in that: The construction process of the honeycomb network film layer defect recognition model: Data acquisition module: acquiring bionic honeycomb structure data of the second layer of membrane; the bionic honeycomb structure data includes the bionic honeycomb structure geometry, pore diameter, pore depth, arrangement of honeycomb units, thickness of the second membrane layer and curvature of the second membrane layer; Preprocessing module: performing denoising, filtering and image enhancement processing on the bionic honeycomb structure data; Feature extraction module: extracting honeycomb structure geometric features and membrane surface texture features based on the bionic honeycomb structure data; the honeycomb structure geometric features include pore size, pore depth, honeycomb unit arrangement, and distance between honeycomb units; the membrane surface texture features include grayscale co-occurrence matrix and frequency domain features; Defect recognition module: Identify and mark the defect locations in the honeycomb membrane layer through the geometric features of the honeycomb structure and the surface texture features of the membrane layer; Defect output module: Output the structural defect difference coefficient based on the defect position in the marked honeycomb shape.

4. The method for coating a honeycomb mesh film layer of a bionic honeycomb mesh lens according to claim 3, characterized in that: The specific steps for calculating the structural defect difference coefficient are as follows: According to the comparison between the theoretical honeycomb structure geometric features of the bionic honeycomb structure and the actually detected honeycomb structure geometric features, the gray level co-occurrence matrix, the pore size, the pore depth and the difference between the distances between the honeycomb units and the deviation between the honeycomb unit arrangements are calculated; the structural defect difference coefficient is obtained by combining the differences and deviations; the specific calculation formula of the structural defect difference coefficient is: DDC=ω1·ΔGLCM+ω2·ΔDi+ω3·ΔDe+ω4·ΔDis+ω5·ΔArr; Among them, DDC is the structural defect difference coefficient, ω1 is the gray level co-occurrence matrix difference weight, ΔGLCM is the gray level co-occurrence matrix difference, ω2 is the aperture difference weight, ΔDi is the aperture difference, ω3 is the hole depth difference weight, ΔDe is the hole depth difference, ω4 is the difference weight of the distance between honeycomb units, ΔDis is the difference in the distance between honeycomb units, ω5 is the deviation weight between honeycomb unit arrangements, and ΔArr is the deviation between honeycomb unit arrangements.

5. The method for coating a honeycomb mesh film layer of a bionic honeycomb mesh lens according to claim 1, characterized in that: Through the transmittance of different bands at different angles, the transmittance defect coefficient of different bands is obtained; through the fusion of multi-spectral features, the comprehensive transmittance defect coefficient is obtained; The specific steps of determining the transmittance defect coefficient of different wavebands are as follows: The spectrum analyzer is used to measure and record in real time the first light intensity, the first light type, the second light intensity and the second light type at different incident angles; the first light intensity and the first light type are the light that has not passed through the lens; the second light intensity and the second light type are the light that has passed through the lens; Determine the light transmittance in different wavelength bands by the ratio of the second light intensity to the first light intensity; Based on the comparison between the transmittance of different bands and the standard value of transmittance of different bands, the transmittance defect coefficient of each band is obtained; and based on the transmittance defect coefficient of each band, a comprehensive transmittance defect coefficient is generated.

6. The method for coating a honeycomb mesh film layer of a bionic honeycomb mesh lens according to claim 5, characterized in that: The specific formula of the comprehensive light transmittance defect coefficient is: CTDC(θ)=ω UV ·ΔT UV (θ)+ω BL ·ΔT BL (θ)+ω IR ·ΔT IR (i); Among them, CTDC(θ) is the comprehensive transmittance defect coefficient, θ is the light source incident at different angles, ω UV is the transmittance defect coefficient weight of the ultraviolet band, ΔT UV (θ) is the transmittance defect coefficient in the ultraviolet band, ω BL is the transmittance defect coefficient weight of the blue light band, ΔT BL (θ) is the transmittance defect coefficient of blue light band, ω IR is the infrared band transmittance defect coefficient weight, ΔT IR (θ) is the transmittance defect coefficient in the infrared band.

7. The method for coating a honeycomb mesh film layer of a bionic honeycomb mesh lens according to claim 1, characterized in that: A light intensity adjustment device is placed in front of the lens to simulate different lighting conditions and set different light intensity levels; an ambient light sensor is used behind the lens to detect and record the light intensity of the current environment in real time; and an eye light intensity sensor is also placed behind the lens to detect the light intensity that reaches the eye after passing through the lens, simulating the actual eye light intensity under the lens.

8. The method for coating a honeycomb mesh film layer of a bionic honeycomb mesh lens according to claim 7, characterized in that: Under different ambient light intensities, the intensity of light passing through the lens is detected in real time, and an eye light intensity sensor is used to record the actual eye light intensity reaching the eye after passing through the lens. The specific calculation formula of the actual eye light intensity is: I EYE (θ)=CTDC(θ)·I EN (i); Among them, I EYE (θ) is the actual light intensity under different light angles, I EN (θ) is the ambient light intensity monitored in real time; Obtaining a light intensity defect coefficient based on actual eye-entering light intensity and standard eye-entering light intensity; The specific formula of the light intensity defect coefficient is: Among them, I(θ) is the light intensity defect coefficient under different light sources, I F_EYE (θ) is the standard eye light intensity under different light angles.

9. The method for coating a honeycomb mesh film layer of a bionic honeycomb mesh lens according to claim 1, characterized in that: The central area is set as a circular area with the optical center of the lens as the center, the radius is a fixed proportion of the total radius of the lens, and the remaining part is the edge area; the positions of all defective areas are obtained to determine whether the defects are in the central area; if they are in the central area, the quality of this lens is unqualified, and the defects in the edge area have little effect on the lens.

10. The method for coating a honeycomb mesh film layer of a bionic honeycomb mesh lens according to claim 1, characterized in that: The lens quality difference coefficient is specifically expressed as: Q=α1DDC+α2CTDC+α3I+α4DA; Among them, Q is the lens quality difference coefficient, α1 is the structural defect difference coefficient weight, DDC is the structural defect difference coefficient, α2 is the comprehensive transmittance defect coefficient weight, CTDC is the comprehensive transmittance defect coefficient, α3 is the light intensity defect coefficient weight, I is the light intensity defect coefficient, α4 is the comprehensive defect area coefficient weight, and DA is the comprehensive defect area coefficient; The smaller the lens quality difference coefficient is, the better the lens quality is.

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