A method and system for detecting three-dimensional profile of a spherical optical lens

Abstract

The application discloses a kind of spherical optical lens three-dimensional profile detection method and system, it is related to optical precision detection technical field, including, towards spherical optical lens emission probe laser beam, by dynamic adjustment probe laser beam incidence angle, collect the nonlinear optical characteristic signal of penetration coating layer and the polarized optical characteristic signal of coating layer reflection;Based on nonlinear optical characteristic signal through curvature inversion algorithm generates substrate curvature spatial distribution data, extracts the thickness distribution characteristics of coating layer and substrate surface optical response characteristics by polarized feature analysis technique;Through refractive index compensation algorithm correction substrate curvature spatial distribution data, and weighted fusion thickness distribution characteristics of coating layer and substrate surface optical response characteristics, generate three-dimensional point cloud coordinate set;The application effectively improves the reconstruction accuracy of substrate curvature distribution;And realized the analysis of substrate surface real optical characteristics.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical precision detection, and in particular to a method and system for detecting the three-dimensional profile of a spherical optical lens. BACKGROUND

[0002] With the wide application of optical imaging systems in industrial detection, aerospace, biomedicine and other fields, higher and higher requirements are put forward for the geometric precision and surface quality of optical elements. As one of the core components of the optical system, the three-dimensional profile precision of the spherical optical lens directly affects the light path transmission characteristics and imaging quality. In recent years, non-contact measurement technologies based on laser interference, confocal scanning and structured light projection have gradually become mainstream means and are widely used in the topography detection of optical elements. In particular, high-precision instruments such as laser scanning confocal microscope (LSCM) and white light interferometer (WLI) have shown good performance in sub-micron surface topography characterization.

[0003] There are two main problems in the prior art: first, the existence of the coating layer affects the penetration and reflection characteristics of the optical signal, causing the traditional detection method to be unable to effectively separate the optical response of the coating layer and the substrate, thereby causing errors in the calculation of the radius of curvature; second, the thermal deformation caused by changes in the ambient temperature is not fully compensated, especially in high-precision detection scenarios, small temperature fluctuations can cause significant measurement deviations. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a method for detecting the three-dimensional profile of a spherical optical lens to solve the problem of reduced measurement accuracy caused by signal interference of the coating layer and thermal deformation in the prior art.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a method for detecting the three-dimensional profile of a spherical optical lens, which comprises emitting a probe laser beam towards the spherical optical lens, collecting a nonlinear optical characteristic signal penetrating the coating layer and a polarized optical characteristic signal reflected by the coating layer by dynamically adjusting the incident angle of the probe laser beam; generating substrate curvature spatial distribution data based on the nonlinear optical characteristic signal through a curvature inversion algorithm, extracting the thickness distribution characteristics of the coating layer and the optical response characteristics of the substrate surface through polarized feature analysis technology; correcting the substrate curvature spatial distribution data through a refractive index compensation algorithm, and weighting and fusing the thickness distribution characteristics of the coating layer and the optical response characteristics of the substrate surface to generate a three-dimensional point cloud coordinate set; real-time monitoring the temperature field distribution of the surface of the spherical optical lens, compensating the thermal deformation of the three-dimensional point cloud coordinate set, and generating a three-dimensional profile detection report after surface fitting and topological defect analysis.

[0008] As a preferred scheme of the three-dimensional profile detection method of the spherical optical lens, wherein: the probe laser beam is emitted to the spherical optical lens, the nonlinear optical characteristic signal penetrating the coating layer and the polarized optical characteristic signal reflected by the coating layer are collected by dynamically adjusting the incident angle of the probe laser beam, and the specific steps are as follows,

[0009] The probe laser beam of the first wavelength is emitted to the surface area of the spherical optical lens, the nonlinear optical characteristic signal generated by penetrating the coating layer is collected by dynamically adjusting the incident angle of the probe laser beam to the first optimal angle;

[0010] The probe laser beam of the second wavelength is emitted to the same surface area of the spherical optical lens, and the polarized optical characteristic signal reflected by the coating layer is collected by dynamically adjusting the incident angle of the probe laser beam to the second optimal angle.

[0011] As a preferred scheme of the three-dimensional profile detection method of the spherical optical lens, wherein: the base curvature space distribution data is generated based on the nonlinear optical characteristic signal by the curvature inversion algorithm, and the specific steps are as follows,

[0012] The nonlinear optical characteristic signal is subjected to signal band separation processing, the base characteristic response component penetrating the coating layer is extracted, and the window function is used to suppress the noise reflected by the coating layer, and the nonlinear optical response component is generated;

[0013] Based on the nonlinear optical response component, the base local curvature radius value is calculated by the curvature inversion algorithm, and the initial space distribution model of the base curvature is constructed;

[0014] According to the initial space distribution model, the base curvature distribution deviation value of the edge annular area of the spherical optical lens is corrected by the gradient-signal-to-noise ratio joint optimization algorithm, and the base curvature space distribution data is generated.

[0015] As a preferred scheme of the three-dimensional profile detection method of the spherical optical lens, wherein: the thickness distribution characteristics of the coating layer and the base surface optical response characteristics are extracted by the polarization characteristic analysis technology, and the specific steps are as follows,

[0016] The nonlinear optical response component is subjected to polarization state separation, and the full polarization response data coupled by the coating layer and the base is extracted;

[0017] Based on the full polarization response data, the thickness distribution characteristics of the coating layer are extracted by the polarization harmonic coherence analysis method, and the transmission matrix of the coating layer is constructed;

[0018] According to the transmission matrix of the coating layer, the optical response component of the coating layer is stripped from the full polarization response data, and the base surface optical response characteristics are obtained.

[0019] As a preferred embodiment of the spherical optical lens three-dimensional contour detection method of the present invention, the specific steps for generating the three-dimensional point cloud coordinate set are as follows:

[0020] A refractive index compensation algorithm is applied to the spatial distribution data of substrate curvature to generate corrected substrate curvature data.

[0021] Based on the corrected substrate curvature data, the thickness distribution characteristics of the coating layer and the optical response characteristics of the substrate surface are spatially fused using a weighted fusion algorithm to generate a comprehensive substrate morphology feature dataset.

[0022] A 3D point cloud mapping is performed on the comprehensive feature dataset of the base topography to generate a 3D point cloud coordinate set.

[0023] As a preferred embodiment of the three-dimensional contour detection method for spherical optical lenses described in this invention, the specific steps for real-time monitoring of the temperature field distribution on the surface of the spherical optical lens and thermal deformation compensation of the three-dimensional point cloud coordinate set are as follows:

[0024] The temperature field distribution data of the surface of the spherical optical lens is acquired in real time by an infrared thermal imager, and the temperature field distribution data is spatially differentiated by a two-dimensional discrete gradient operator to generate a temperature gradient matrix.

[0025] Based on the temperature gradient matrix, the thermal deformation compensation coefficient is calculated through a thermal-optical coupling model, and the 3D point cloud coordinate set is corrected pixel by pixel for thermal expansion to generate thermally compensated 3D point cloud coordinates.

[0026] As a preferred embodiment of the 3D contour detection method for spherical optical lenses described in this invention, the specific steps for generating a 3D contour detection report after surface fitting and topological defect analysis are as follows:

[0027] Zernike polynomial surface fitting is performed on the thermally compensated 3D point cloud coordinate set to generate an ideal reference surface and calculate the surface deviation.

[0028] Based on the surface shape deviation, the geometric features of surface defects are identified by morphological image processing algorithms, and compared with the preset defect judgment threshold to generate defect classification statistics.

[0029] By integrating the ideal reference surface, surface deviation, and defect classification statistics, a three-dimensional contour inspection report is generated.

[0030] Secondly, the present invention provides a three-dimensional contour detection system for a spherical optical lens, comprising a laser acquisition module, a signal decoupling module, a point cloud generation module, and a thermal compensation analysis module. The laser acquisition module is used to emit a probe laser beam towards the spherical optical lens and, by dynamically adjusting the incident angle of the probe laser beam, acquire nonlinear optical characteristic signals penetrating the coating layer and polarization optical characteristic signals reflected from the coating layer. The signal decoupling module is used to generate spatial distribution data of substrate curvature based on the nonlinear optical characteristic signals using a curvature inversion algorithm, and to extract the thickness distribution characteristics of the coating layer and the optical response characteristics of the substrate surface using polarization feature analysis technology. The point cloud generation module is used to correct the spatial distribution data of substrate curvature using a refractive index compensation algorithm, and to weightedly fuse the thickness distribution characteristics penetrating the coating layer and the optical response characteristics of the substrate surface to generate a three-dimensional point cloud coordinate set. The thermal compensation analysis module is used to monitor the temperature field distribution on the surface of the spherical optical lens in real time, perform thermal deformation compensation on the three-dimensional point cloud coordinate set, and generate a three-dimensional contour detection report after surface fitting and topological defect analysis.

[0031] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the three-dimensional contour detection method for spherical optical lenses as described in the first aspect of the present invention.

[0032] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the three-dimensional contour detection method for spherical optical lenses as described in the first aspect of the present invention.

[0033] The beneficial effects of this invention are as follows: by performing frequency band separation and window function noise reduction on the nonlinear optical response components, combined with curvature inversion algorithm and gradient-signal-noise ratio joint optimization strategy, the reconstruction accuracy of substrate curvature distribution is effectively improved; furthermore, the coating thickness distribution is extracted by polarization harmonic coherence analysis method, and the influence of coating peeling on substrate optical response based on transfer matrix is ​​realized to analyze the true optical properties of substrate surface. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the 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.

[0035] Fig. 1 This is a flowchart of a method for detecting the three-dimensional contour of a spherical optical lens.

[0036] Fig. 2 This is a flowchart of the detection laser beam acquisition and signal acquisition process for a three-dimensional contour detection method for spherical optical lenses.

[0037] Fig. 3 This is a flowchart of the optical signal decoupling and feature extraction process for a 3D contour detection method for spherical optical lenses.

[0038] Fig. 4 This is a flowchart of the three-dimensional point cloud generation and thermal compensation analysis for a three-dimensional contour detection method for spherical optical lenses. Detailed Implementation

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0040] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0041] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0042] Reference Figs. 1-4 As an embodiment of the present invention, this embodiment provides a method for detecting the three-dimensional contour of a spherical optical lens, comprising the following steps:

[0043] S1: Emit a probe laser beam toward the spherical optical lens. By dynamically adjusting the incident angle of the probe laser beam, nonlinear optical characteristic signals penetrating the coating layer and polarization optical characteristic signals reflected by the coating layer are collected.

[0044] S1.1: A probe laser beam of the first wavelength is emitted toward the surface area of ​​the spherical optical lens. The incident angle of the probe laser beam is dynamically adjusted to the first optimized angle to collect the nonlinear optical characteristic signal generated by penetrating the coating layer.

[0045] The specific process includes: when emitting a probe laser beam of the first wavelength towards the surface area of ​​the spherical optical lens, a tunable laser is used to output a probe laser beam of a specific wavelength. The incident angle of the probe laser beam is dynamically adjusted to the first optimized angle through a precision rotating platform, so that the probe laser beam passes through the coating layer of the spherical optical lens under optimal penetration conditions. During the penetration process, the probe laser beam undergoes nonlinear optical interaction with the substrate material of the spherical optical lens, generating characteristic signals containing nonlinear optical effects such as second harmonics and sum frequencies. A high-sensitivity photodetector array is used to receive the nonlinear optical characteristic signals after penetrating the coating layer. The detector array synchronously acquires signal strength and phase information according to spatial coordinate distribution. During the acquisition process, bandpass filtering technology is used to separate the target nonlinear optical characteristic signals, and lock-in amplification technology is used to improve the signal-to-noise ratio. Finally, the nonlinear optical characteristic signals are obtained.

[0046] The first optimized angle is determined by conducting spectral response tests and transmittance experiments on the coating material of the spherical optical lens, and combining the functional relationship curve between the nonlinear optical signal intensity and the incident angle. The incident angle that maximizes the signal-to-noise ratio of the nonlinear optical characteristic signal is selected as the first optimized angle.

[0047] The optimal penetration conditions are determined experimentally based on the material properties of the coating layer of the spherical optical lens and the nonlinear optical response characteristics of the substrate material, which maximizes the signal-to-noise ratio of the nonlinear optical characteristic signal.

[0048] S1.2: A second wavelength probe laser beam is emitted toward the same surface area of ​​the spherical optical lens. By dynamically adjusting the incident angle of the probe laser beam to the second optimized angle, the polarization optical characteristic signal reflected by the coating layer is acquired.

[0049] The specific process includes: when emitting a second-wavelength probe laser beam to the same surface area of ​​the spherical optical lens, a polarization-tunable laser source is used to output a linearly polarized probe laser beam of a specific wavelength. The incident angle of the probe laser beam is dynamically adjusted to a second optimized angle through a precision rotating platform, so that the probe laser beam acts on the coating layer of the spherical optical lens under optimal reflection conditions. During the reflection process, the interaction between the probe laser beam and the coating layer causes a change in polarization state, generating a polarization optical characteristic signal containing information on ellipticity angle and azimuth angle. A polarization-sensitive detector array is used to receive the polarization optical characteristic signal reflected by the coating layer. The detector array synchronously acquires Stokes parameters according to the spatial coordinate distribution. During the acquisition process, rotation compensator technology is used to eliminate instrument polarization effects, and synchronous detection technology is used to improve measurement accuracy. The final polarization optical characteristic signal contains information on the thickness distribution and interface characteristics of the coating layer of the spherical optical lens.

[0050] The second optimization angle is selected by measuring the reflectivity curve and polarization state change curve of the coating layer for linearly polarized light of different wavelengths, and selecting the incident angle that maximizes the intensity of the polarization optical characteristic signal and optimizes the signal-to-noise ratio.

[0051] The optimal reflection conditions are determined by analyzing the polarization reflection characteristic curve of the coating material and the sensitivity of the Stokes parameter. Through experimental calibration, the signal-to-noise ratio of the polarization optical characteristic signal and the measurement sensitivity are simultaneously optimized by the combination of incident angle and polarization state.

[0052] S2: Based on nonlinear optical feature signals, the curvature inversion algorithm is used to generate spatial distribution data of substrate curvature, and polarization feature analysis technology is used to extract the thickness distribution characteristics of the coating layer and the optical response characteristics of the substrate surface.

[0053] S2.1: Perform signal band separation processing on the nonlinear optical characteristic signal, extract the substrate characteristic response component that penetrates the coating layer, and use a window function to suppress the noise reflected by the coating layer to generate the nonlinear optical response component.

[0054] The specific process includes: using a digital filter bank to decompose the acquired nonlinear optical characteristic signal into multiple sub-bands according to frequency components; analyzing the spectral characteristics of each sub-band to identify the characteristic frequency bands corresponding to the substrate characteristic response components that penetrate the coating layer; extracting the signal components within the characteristic frequency bands through a bandpass filter while retaining the phase information of the nonlinear optical effect; applying a Hanning window function to the extracted signal components to suppress boundary effects and spectral leakage caused by coating layer reflection; coherently superimposing the sub-band signals after window function processing to reconstruct the complete nonlinear optical response components; and finally generating nonlinear optical response components that effectively highlight the characteristic response of the substrate material while minimizing the interference of coating layer reflection noise.

[0055] S2.2: Based on the nonlinear optical response components, the local curvature radius of the substrate is calculated using a curvature inversion algorithm, and an initial spatial distribution model of the substrate curvature is constructed. The expression is:

[0056] ;

[0057] in, Represents the spatial coordinates of a spherical optical lens The radius of curvature of the base at that location, Represents the spatial coordinates of a spherical optical lens. This represents the second-order nonlinear polarizability of the substrate material. Indicates the wavelength of a nonlinear optical signal. Represents the spatial coordinates of a spherical optical lens The intensity of the nonlinear optical signal after purification Represents the solid angle of a sphere. Indicates the refractive index of the substrate material and the coating layer. Represents the spatial coordinates of a spherical optical lens The dynamic scattering angle deviation between the incident laser and the local substrate normal. Represents the gradient correction coefficient for nonlinear signals. Represents the spatial coordinates of a spherical optical lens The squared magnitude of the spatial gradient of the intensity of the purified nonlinear optical signal.

[0058] The specific process includes: establishing a three-dimensional sampling grid for the spherical optical lens based on the spatial coordinate information in the nonlinear optical response components; utilizing the physical relationship between the nonlinear optical signal intensity and curvature, establishing a local curvature calculation equation at each spatial coordinate point, including the second-order nonlinear polarizability, the wavelength of the nonlinear optical signal, and the intensity of the purified nonlinear optical signal; correcting the angular influence between the incident laser and the local substrate normal by solving the geometric relationship between the spherical solid angle and the dynamic scattering angle deviation; compensating for the refraction of the nonlinear optical signal propagation path by combining the equivalent refractive index difference between the substrate material and the coating layer; introducing a nonlinear signal gradient correction coefficient to weight the square of the magnitude of the spatial gradient of the purified nonlinear optical signal intensity to eliminate calculation errors caused by signal fluctuations; calculating the local curvature radius value of the substrate point by point using the mathematical expression of the curvature inversion algorithm; arranging the curvature calculation results of all spatial coordinate points according to the sampling grid to construct an initial spatial distribution model of the substrate curvature; and the initial spatial distribution model fully characterizing the three-dimensional morphological features of the spherical optical lens substrate.

[0059] S2.3: Based on the initial spatial distribution model, the base curvature distribution deviation value of the annular region at the edge of the spherical optical lens is calculated using the gradient-signal-noise ratio joint optimization algorithm, and the spatial distribution data of the base curvature is generated, expressed as follows:

[0060] ;

[0061] in, Represents the spatial coordinates of a spherical optical lens The deviation value of the base curvature distribution at that location, This represents the theoretical curvature value based on the substrate material properties and geometric constraints. Represents spatial coordinates The weighting factor is dynamically adjusted based on signal quality. This indicates the difference in refractive index between the substrate material and the coating layer.

[0062] The specific process includes: first, comparing the calculated substrate curvature radius value with the theoretical curvature value based on substrate material properties and geometric constraints point by point to obtain the initial deviation; then, applying a weighting factor dynamically adjusted according to signal quality at the spatial coordinates to the initial deviation to compensate for measurement errors caused by signal attenuation in the edge annular region; simultaneously, considering the influence of the equivalent refractive index difference between the substrate material and the coating layer on optical path propagation, correcting the calculation results for refraction effects; using the deviation after weighting and refraction correction as the substrate curvature distribution deviation value; finally, using a gradient-signal-noise ratio joint optimization algorithm to iteratively optimize the substrate curvature distribution deviation value, eliminating local distortions and achieving a smooth transition of curvature distribution, ultimately outputting accurate spatial distribution data of substrate curvature.

[0063] The theoretical curvature value of the substrate material properties and geometric constraints is the ideal curvature distribution obtained by combining the intrinsic parameters of the material with the design geometric equations of the optical lens.

[0064] S2.4: Perform polarization state separation on the nonlinear optical response components to extract the full polarization response data of the coupling between the coating layer and the substrate.

[0065] The specific process includes: using a polarization beam splitter to decompose the nonlinear optical response components into multiple sub-components according to the polarization direction; acquiring the polarization state information of each sub-component, including linear and circular polarization components, using a Stokes parameter measuring instrument; processing the polarization transmission process at the interface between the coating layer and the substrate using Jones matrix operations to distinguish between the reflection response of the coating layer and the interaction response between the coating layer and the substrate after penetrating the coating layer; extracting the elliptic polarization characteristics generated by the coupling between the coating layer and the substrate using a polarization phase-sensitive detection device, and separating the full polarization response data that simultaneously reflects the interface characteristics of the coating layer and the nonlinear optical effects of the substrate; and finally obtaining the full polarization response data that completely preserves the polarization characteristic information of the coupling between the coating layer and the substrate.

[0066] S2.5: Based on the full polarization response data, the thickness distribution characteristics of the coating layer are extracted by polarization harmonic coherence analysis, and the transmission matrix of the coating layer is constructed.

[0067] The specific process includes: using Fourier transform to decompose the fully polarized response data into different harmonic components; using coherent demodulation technology to separate the characteristic harmonic components directly related to the coating thickness; establishing the mapping relationship between the characteristic harmonic phase and the coating thickness through a least-squares fitting algorithm to obtain the thickness value at each spatial coordinate point; deriving the optical transmission characteristics of the coating by combining Jones matrix operations, and converting the thickness distribution characteristics into an equivalent optical transmission matrix; finally, the constructed coating transmission matrix fully characterizes the optical transmission behavior of the coating at different spatial locations.

[0068] S2.6: Based on the transmission matrix of the coating layer, the optical response components of the coating layer are extracted from the full polarization response data to obtain the optical response characteristics of the substrate surface.

[0069] The specific process includes: performing matrix analysis on the transfer matrix of the coating layer and the full polarization response data to inversely derive the polarization state before it is incident on the coating layer; eliminating the modulation effect of the coating layer on the polarization state through polarization state deconvolution processing to restore the original polarization response at the substrate interface; subtracting the optical response component contributed by the coating layer from the full polarization response data using a differential algorithm to retain the optical response characteristics generated only by the substrate surface; and finally obtaining the optical response characteristics of the substrate surface, which completely removes the interference of the coating layer and accurately reflects the intrinsic optical properties of the substrate surface.

[0070] The optical response component refers to the optical characteristic response portion of the polarized light signal generated by the interaction between the coating layer and the substrate.

[0071] S3: Correct the spatial distribution data of substrate curvature through refractive index compensation algorithm, and generate a three-dimensional point cloud coordinate set by weighted fusion of the thickness distribution characteristics of the coating layer and the optical response characteristics of the substrate surface.

[0072] S3.1: Apply the refractive index compensation algorithm to the spatial distribution data of the substrate curvature to generate corrected substrate curvature data.

[0073] The specific process includes: obtaining the refractive index distribution parameters of the coating layer of the spherical optical lens; establishing a correction relationship between the refractive index of the coating layer and the curvature of the substrate; performing convolution operations on the curvature value of each spatial coordinate point in the substrate curvature spatial distribution data with the refractive index of the coating layer at the corresponding position to analyze the curvature measurement deviation caused by the refractive index difference; quantifying the influence of the abrupt change in the refractive index of the coating layer interface on the substrate curvature measurement using Fresnel transmission theory; correcting the curvature deviation components in the substrate curvature spatial distribution data point by point using the iterative least squares method; weighted fusion of the corrected curvature values ​​with the original substrate curvature spatial distribution data to eliminate the systematic error caused by the refractive index disturbance of the coating layer; and finally generating corrected substrate curvature data that accurately reflects the true geometric characteristics of the spherical optical lens substrate.

[0074] S3.2: Based on the corrected substrate curvature data, the thickness distribution characteristics of the coating layer and the optical response characteristics of the substrate surface are spatially fused using a weighted fusion algorithm to generate a comprehensive feature dataset of substrate morphology.

[0075] The specific process includes: establishing a spatial correspondence between the coating thickness variation and the optical response characteristics of the substrate surface; using an adaptive weight allocation method to dynamically adjust the fusion weight coefficients based on the signal-to-noise ratio of the coating thickness distribution characteristics, assigning higher weights to coating features in areas with high thickness measurement accuracy, and increasing the fusion ratio of optical response characteristics in areas with a superior signal-to-noise ratio of the substrate surface optical response characteristics; achieving multi-scale feature matching between thickness distribution characteristics and optical response characteristics through spatial convolution operations to eliminate local measurement inconsistencies; and finally generating a comprehensive feature dataset of substrate morphology that simultaneously contains high-precision geometric curvature information and surface optical property parameters, fully characterizing the three-dimensional morphology and surface optical properties of the substrate.

[0076] S3.3: Perform 3D point cloud mapping on the comprehensive feature dataset of the base topography to generate a 3D point cloud coordinate set.

[0077] The specific process includes: extracting spatial coordinate information and geometric feature parameters from the comprehensive feature dataset of the substrate morphology when performing 3D point cloud mapping; converting the spatial distribution data of substrate curvature into a curvature field distribution in 3D space; deriving the height compensation amount at each spatial location based on the coating thickness distribution characteristics; converting the 2D plane coordinates into point cloud coordinates in a 3D Cartesian coordinate system using a coordinate transformation algorithm; processing the transition region between discrete sampling points using a bilinear interpolation method; adaptively adjusting the point cloud density based on the optical response characteristics of the substrate surface; arranging the processed 3D spatial coordinates in an ordered manner according to spatial topological relationships; and finally generating a 3D point cloud coordinate set that accurately represents the 3D geometric morphology of the spherical optical lens surface.

[0078] S4: Real-time monitoring of the temperature field distribution on the surface of the spherical optical lens, thermal deformation compensation of the three-dimensional point cloud coordinate set, and generation of a three-dimensional contour detection report after surface fitting and topological defect analysis.

[0079] S4.1: The temperature field distribution data of the surface of the spherical optical lens is acquired in real time by an infrared thermal imager, and the temperature field distribution data is spatially differentiated by a two-dimensional discrete gradient operator to generate a temperature gradient matrix.

[0080] The specific process includes: acquiring temperature field distribution data of the spherical optical lens surface in real time using an infrared thermal imager; obtaining the temperature value of each pixel on the lens surface at a fixed sampling interval using a high-resolution infrared detector; converting the acquired temperature field distribution data into a two-dimensional digital matrix, with matrix elements corresponding to the temperature measurement values ​​of spatial coordinate points; applying a two-dimensional discrete gradient operator to perform spatial differentiation on the temperature field distribution data matrix to obtain the temperature change rate of each pixel in the horizontal and vertical coordinate directions; processing the matrix edge data points using the central difference method to ensure the boundary accuracy of the differentiation operation; combining the temperature change rate components in the horizontal and vertical coordinate directions to form a temperature gradient matrix, with matrix elements containing the temperature gradient amplitude and direction information of the corresponding spatial coordinate points; and finally generating a temperature gradient matrix that fully characterizes the spatial variation features of the temperature field on the spherical optical lens surface.

[0081] S4.2: Based on the temperature gradient matrix, the thermal deformation compensation coefficient is calculated using a thermal-optical coupling model, and a pixel-by-pixel thermal expansion correction is performed on the 3D point cloud coordinate set to generate the thermally compensated 3D point cloud coordinates. The expression is as follows:

[0082] ;

[0083] in, Represents the spatial coordinates of a spherical optical lens Thermal deformation compensation coefficient at the location (0.8≤ ≤1.2), The base of a spherical optical lens. This refers to the coating layer of a spherical optical lens. This represents the coefficient of thermal expansion of the substrate of a spherical optical lens. Represents the spatial coordinates of the spherical optical lens substrate. The amount of local temperature change at that location This indicates the refractive index of the coating layer in a spherical optical lens. This represents the coefficient of thermal expansion of the coating layer in a spherical optical lens. Represents the spatial coordinates of the spherical optical lens substrate. The temperature gradient vector at that point, This indicates the physical thickness of the coating layer on a spherical optical lens. Represents the nonlinear thermal coupling coefficient. This indicates the local temperature variation of the spherical lens coating and the spherical lens substrate. The nonlinear thermal stress accumulation term under the following conditions This represents the thermal relaxation time constant.

[0084] The specific process includes: performing a dot product operation between the temperature gradient vector at the spatial coordinates in the temperature gradient matrix and the thermal expansion coefficient of the spherical optical lens substrate to obtain the linear thermal expansion component; calculating the modulation effect of the coating layer on heat conduction by combining the refractive index and physical thickness of the spherical optical lens coating layer; describing the thermal impedance effect between the spherical optical lens substrate and the spherical optical lens coating layer through a nonlinear thermal coupling coefficient; introducing a thermal relaxation time constant to perform time-dimensional weighting of local temperature changes to reflect the relaxation characteristics of the thermal accumulation effect; superimposing the nonlinear thermal stress accumulation terms of the spherical optical lens coating layer and the spherical optical lens substrate under local temperature changes with the linear component to generate a thermally induced deformation compensation coefficient at the spatial coordinates; using the thermally induced deformation compensation coefficient to perform pixel-by-pixel coordinate correction on the three-dimensional point cloud coordinate set to offset the deformation error caused by thermal expansion; and finally generating thermally compensated point cloud data to eliminate measurement deviations caused by temperature field changes and accurately reflect the true geometric shape of the spherical optical lens.

[0085] Furthermore, the temperature gradient matrix and corresponding 3D point cloud coordinate sets of the spherical optical lens under different temperature conditions were collected as training samples. The theoretical thermal deformation fields of the spherical optical lens substrate and the spherical optical lens coating layer under each temperature condition were obtained using finite element analysis software. A network architecture for mapping the temperature gradient matrix and the thermal deformation field was established. The input layer receives parameters such as the temperature gradient vector, the thermal expansion coefficient of the spherical optical lens substrate, and the refractive index of the spherical optical lens coating layer. The output layer predicts the thermal deformation compensation coefficient. The L-BFGS (Limited Memory Quasi-Newton Optimization) algorithm is used to minimize the mean square error between the predicted thermal deformation compensation coefficient and the theoretical thermal deformation field. Parameters such as the nonlinear thermal coupling coefficient and the thermal relaxation time constant are adjusted through cross-validation. Training is completed when the average relative error between the predicted thermal deformation compensation coefficient and the finite element analysis results is less than 5%. The final obtained thermal-optical coupling model can accurately reflect the nonlinear relationship between the temperature gradient matrix and thermal deformation.

[0086] S4.3: Perform Zernike polynomial surface fitting on the thermally compensated 3D point cloud coordinate set to generate an ideal reference surface and calculate the surface deviation, expressed as:

[0087] ;

[0088] ;

[0089] in, This represents the ideal reference surface generated by Zernike polynomial surface fitting in spatial coordinates. The height value at that location, Represents the order index of a Zernike polynomial ( ≥0), This indicates the highest-order Zernike polynomial used for surface fitting. Indicates the first The fitting coefficients corresponding to the Zernike polynomial of order 1 Indicates the first Zernike polynomial of order 1 Indicates the normalized extreme radius. Indicates the polar angle. Indicates the index number of the point cloud data. Indicates the first surface deviation at each coordinate point Indicates the first The measured height values ​​of each coordinate point This represents the ideal reference surface generated by Zernike polynomial surface fitting in spatial coordinates. The height value at that location, Indicates the first Planar coordinates of a coordinate point.

[0090] The specific process includes: when fitting the thermally compensated 3D point cloud coordinate set to a Zernike polynomial surface, converting the spatial coordinates in the 3D point cloud coordinate set into normalized polar coordinates; solving for the fitting coefficients corresponding to each order of Zernike polynomials using the least squares method to achieve optimal matching between the Zernike polynomial combination and the measured point cloud data; reconstructing the ideal reference surface using the obtained fitting coefficients; calculating the height value generated by the Zernike polynomial surface fitting at each spatial coordinate point; comparing the measured height value of each coordinate point with the height value of the ideal reference surface at the corresponding position point by point to obtain the surface deviation; and finally obtaining the surface deviation matrix to fully characterize the morphological difference between the thermally compensated 3D point cloud coordinate set and the ideal reference surface.

[0091] The ideal reference surface is the optimally fitted mathematical surface obtained by fitting the thermally compensated 3D point cloud coordinate set with Zernike polynomials, and serves as a reference benchmark for evaluating actual surface shape deviations.

[0092] S4.4: Based on the surface deviation, the geometric features of surface defects are identified through morphological image processing algorithms, and compared with the preset defect judgment threshold to generate defect classification statistics.

[0093] The specific process includes: converting the surface deviation matrix into a grayscale image format; using morphological opening operations to eliminate minor noise interference while maintaining the integrity of the main defect features; extracting the geometric features of the defect region through connected component analysis, including area, perimeter, major axis length, and minor axis length; obtaining shape features such as the roundness and convex hull area ratio of each defect region; comparing the extracted geometric features of the surface defects with preset defect judgment thresholds item by item to determine the defect type; statistically analyzing the quantity and distribution characteristics of different types of defects to generate classification statistics containing defect type, size, and location information; and finally obtaining the defect classification statistics to fully characterize the defect distribution on the surface of the spherical optical lens, providing a quantitative basis for quality assessment.

[0094] The preset defect judgment threshold is a critical value of defect size and shape parameters determined through experimental statistics and empirical analysis based on the quality requirements of optical component industry standards and specific application scenarios.

[0095] S4.5: Integrates ideal reference surface, surface deviation, and defect classification statistics to generate a three-dimensional contour inspection report.

[0096] The specific process includes: spatially superimposing the mathematical expression of the ideal reference surface with the surface deviation matrix to reconstruct the actual three-dimensional morphology of the surface; mapping the location information in the defect classification statistics to the corresponding coordinate region of the three-dimensional morphology using data fusion technology; generating a three-dimensional color contour map containing curvature distribution, surface deviation cloud map, and defect markers using a visualization rendering algorithm; recording the maximum surface deviation value, root mean square deviation value, and statistical results of the number of various defects in the report according to the ISO standard format; and finally generating a three-dimensional contour inspection report that includes both quantitative analysis data and intuitive morphology display, fully reflecting the surface quality of the spherical optical lens.

[0097] This embodiment also provides a three-dimensional contour detection system for a spherical optical lens, including: a laser acquisition module, a signal decoupling module, a point cloud generation module, and a thermal compensation analysis module. The laser acquisition module is used to emit a probe laser beam towards the spherical optical lens, and to acquire nonlinear optical characteristic signals penetrating the coating layer and polarization optical characteristic signals reflected by the coating layer by dynamically adjusting the incident angle of the probe laser beam. The signal decoupling module is used to generate spatial distribution data of substrate curvature based on the nonlinear optical characteristic signals through a curvature inversion algorithm, and to extract the thickness distribution characteristics of the coating layer and the optical response characteristics of the substrate surface through polarization feature analysis technology. The point cloud generation module is used to correct the spatial distribution data of substrate curvature through a refractive index compensation algorithm, and to weightedly fuse the thickness distribution characteristics penetrating the coating layer and the optical response characteristics of the substrate surface to generate a three-dimensional point cloud coordinate set. The thermal compensation analysis module is used to monitor the temperature field distribution on the surface of the spherical optical lens in real time, to perform thermal deformation compensation on the three-dimensional point cloud coordinate set, and to generate a three-dimensional contour detection report after surface fitting and topological defect analysis.

[0098] This embodiment also provides a computer device applicable to the three-dimensional contour detection method for spherical optical lenses, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the three-dimensional contour detection method for spherical optical lenses as proposed in the above embodiment.

[0099] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0100] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for detecting the three-dimensional contour of a spherical optical lens as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0101] In summary, this invention effectively improves the reconstruction accuracy of substrate curvature distribution by performing frequency band separation and window function noise reduction on nonlinear optical response components, combined with curvature inversion algorithm and gradient-signal-noise ratio joint optimization strategy; furthermore, it extracts coating thickness distribution using polarization harmonic coherence analysis method, and realizes the analysis of true optical properties of substrate surface based on the influence of coating peeling on substrate optical response based on transfer matrix.

[0102] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting the three-dimensional contour of a spherical optical lens, characterized in that: include, A probe laser beam is emitted toward a spherical optical lens. By dynamically adjusting the incident angle of the probe laser beam, nonlinear optical characteristic signals penetrating the coating layer and polarization optical characteristic signals reflected by the coating layer are collected. Based on nonlinear optical characteristic signals, a curvature inversion algorithm is used to generate spatial distribution data of substrate curvature. Polarization feature analysis is then used to extract the thickness distribution characteristics of the coating layer and the optical response properties of the substrate surface. The specific steps are as follows: The nonlinear optical characteristic signal is processed by signal frequency band separation to extract the substrate characteristic response component that penetrates the coating layer, and a window function is used to suppress the noise reflected by the coating layer to generate the nonlinear optical response component. Based on nonlinear optical response components, the local curvature radius of the substrate is calculated using a curvature inversion algorithm, and an initial spatial distribution model of the substrate curvature is constructed. Based on the initial spatial distribution model, the deviation value of the base curvature distribution in the annular region at the edge of the spherical optical lens is corrected by the gradient-signal-noise ratio joint optimization algorithm, and the spatial distribution data of the base curvature is generated. Polarization state separation is performed on the nonlinear optical response components to extract the full polarization response data of the coupling between the coating layer and the substrate; Based on the full polarization response data, the thickness distribution characteristics of the coating layer were extracted by polarization harmonic coherence analysis, and the transmission matrix of the coating layer was constructed. Based on the transmission matrix of the coating layer, the optical response components of the coating layer are extracted from the full polarization response data to obtain the optical response characteristics of the substrate surface. The spatial distribution data of substrate curvature is corrected by refractive index compensation algorithm, and the thickness distribution characteristics of coating layer and optical response characteristics of substrate surface are weighted and fused to generate a three-dimensional point cloud coordinate set. The temperature field distribution on the surface of the spherical optical lens is monitored in real time, thermal deformation compensation is performed on the three-dimensional point cloud coordinate set, and a three-dimensional contour detection report is generated after surface fitting and topological defect analysis.

2. The method for detecting the three-dimensional contour of a spherical optical lens as described in claim 1, characterized in that: The process involves emitting a probe laser beam towards the spherical optical lens, dynamically adjusting the incident angle of the probe laser beam, and acquiring nonlinear optical characteristic signals penetrating the coating layer and polarization optical characteristic signals reflected from the coating layer. The specific steps are as follows: A probe laser beam of the first wavelength is emitted toward the surface area of ​​the spherical optical lens. By dynamically adjusting the incident angle of the probe laser beam to the first optimized angle, nonlinear optical characteristic signals generated by penetrating the coating layer are collected. A second-wavelength probe laser beam is emitted toward the same surface area of ​​the spherical optical lens. By dynamically adjusting the incident angle of the probe laser beam to a second optimized angle, the polarization optical characteristic signal reflected by the coating layer is acquired.

3. The method for detecting the three-dimensional contour of a spherical optical lens as described in claim 1, characterized in that: The specific steps for generating the 3D point cloud coordinate set are as follows: A refractive index compensation algorithm is applied to the spatial distribution data of substrate curvature to generate corrected substrate curvature data. Based on the corrected substrate curvature data, the thickness distribution characteristics of the coating layer and the optical response characteristics of the substrate surface are spatially fused using a weighted fusion algorithm to generate a comprehensive substrate morphology feature dataset. A 3D point cloud mapping is performed on the comprehensive feature dataset of the base topography to generate a 3D point cloud coordinate set.

4. The three-dimensional contour detection method for spherical optical lenses as described in claim 3, characterized in that: The real-time monitoring of the temperature field distribution on the surface of the spherical optical lens and the thermal deformation compensation of the three-dimensional point cloud coordinate set are as follows: The temperature field distribution data of the surface of the spherical optical lens is acquired in real time by an infrared thermal imager, and the temperature field distribution data is spatially differentiated by a two-dimensional discrete gradient operator to generate a temperature gradient matrix. Based on the temperature gradient matrix, the thermal deformation compensation coefficient is calculated through a thermal-optical coupling model, and the 3D point cloud coordinate set is corrected pixel by pixel for thermal expansion to generate thermally compensated 3D point cloud coordinates.

5. The three-dimensional contour detection method for spherical optical lenses as described in claim 4, characterized in that: After surface fitting and topological defect analysis, a three-dimensional contour detection report is generated. The specific steps are as follows: Zernike polynomial surface fitting is performed on the thermally compensated 3D point cloud coordinate set to generate an ideal reference surface and calculate the surface deviation. Based on the surface shape deviation, the geometric features of surface defects are identified by morphological image processing algorithms, and compared with the preset defect judgment threshold to generate defect classification statistics. By integrating the ideal reference surface, surface deviation, and defect classification statistics, a three-dimensional contour inspection report is generated.

6. A three-dimensional contour detection system for spherical optical lenses, based on the three-dimensional contour detection method for spherical optical lenses according to any one of claims 1 to 5, characterized in that: It includes a laser acquisition module, a signal decoupling module, a point cloud generation module, and a thermal compensation analysis module. The laser acquisition module is used to emit a probe laser beam toward the spherical optical lens. By dynamically adjusting the incident angle of the probe laser beam, it acquires the nonlinear optical characteristic signals that penetrate the coating layer and the polarization optical characteristic signals reflected by the coating layer. The signal decoupling module is used to generate spatial distribution data of substrate curvature based on nonlinear optical characteristic signals using a curvature inversion algorithm. It then extracts the thickness distribution characteristics of the coating layer and the optical response properties of the substrate surface using polarization feature analysis technology. The specific steps are as follows. The nonlinear optical characteristic signal is processed by signal frequency band separation to extract the substrate characteristic response component that penetrates the coating layer, and a window function is used to suppress the noise reflected by the coating layer to generate the nonlinear optical response component. Based on nonlinear optical response components, the local curvature radius of the substrate is calculated using a curvature inversion algorithm, and an initial spatial distribution model of the substrate curvature is constructed. Based on the initial spatial distribution model, the deviation value of the base curvature distribution in the annular region at the edge of the spherical optical lens is corrected by the gradient-signal-noise ratio joint optimization algorithm, and the spatial distribution data of the base curvature is generated. Polarization state separation is performed on the nonlinear optical response components to extract the full polarization response data of the coupling between the coating layer and the substrate; Based on the full polarization response data, the thickness distribution characteristics of the coating layer were extracted by polarization harmonic coherence analysis, and the transmission matrix of the coating layer was constructed. Based on the transmission matrix of the coating layer, the optical response components of the coating layer are extracted from the full polarization response data to obtain the optical response characteristics of the substrate surface. The point cloud generation module is used to correct the spatial distribution data of substrate curvature through a refractive index compensation algorithm, and to weightedly fuse the thickness distribution characteristics of the penetrating coating layer and the optical response characteristics of the substrate surface to generate a three-dimensional point cloud coordinate set. The thermal compensation analysis module is used to monitor the temperature field distribution on the surface of the spherical optical lens in real time, perform thermal deformation compensation on the three-dimensional point cloud coordinate set, and generate a three-dimensional contour detection report after surface fitting and topological defect analysis.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the three-dimensional contour detection method for spherical optical lenses according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the three-dimensional contour detection method for spherical optical lenses according to any one of claims 1 to 5.

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