Subjective optometry prediction method combining wavefront aberration and optical signal

Abstract

The invention provides a subjective optometry prediction method combining wavefront aberration and optical signals. According to the method, eyeball wavefront aberration data are measured through a wavefront sensor, cornea, crystal and retina biological parameters are obtained by combining multi-view-field scanning of a galvanometer system, and after high-credibility data points are screened, a gradient refractive index eyeball three-dimensional model is constructed by adopting an NURBS algorithm; using finite element analysis to optimize cornea stress distribution, iteratively correcting the model through a deformation energy function, and dynamically compensating eyeball motion errors based on eye movement tracking data; and generating a multi-field-angle visual chart image through ray tracing simulation, and predicting a subjective optometry result in combination with an image quality evaluation algorithm. According to the method, high-precision modeling, dynamic error suppression and intelligent simulation technologies are fused, the optometry prediction accuracy, the personalized adaptation degree and the dynamic scene adaptability are remarkably improved, and an efficient and reliable solution is provided for refraction correction.

Description

Technical Field

[0001] The present invention relates to the technical field of medical optometry, and particularly relates to a subjective optometry prediction method combining wavefront aberration and optical signals. Background Art

[0002] An autorefractor is a digital objective optometry device, and its accuracy has a good guiding effect on myopia prevention and control as well as vision correction. However, currently, conventional autorefractors only measure optometry data in the visual axis direction, and there is no direct matching relationship between the optometry data in the simple visual axis direction and subjective optometry data. For adolescent vision screening, to obtain objective and subjective optometry reference data, it is necessary to perform autorefractor and visual acuity chart tests simultaneously, which requires a large amount of manpower and material resources, and the data is not comprehensive enough. Since the eyeball is not a strictly symmetric structure and there are differences in refractive power in different parts, a refractive power measurement scheme for multiple field angles and a subjective optometry prediction corresponding to the field angle are required.

[0003] Wavefront aberration is divided into low-order wavefront aberration and high-order wavefront aberration. Among them, low-order wavefront aberration is often used to correct the effects brought by myopia or hyperopia to patients. However, high-order wavefront aberration, including coma, spherical aberration, and trefoil aberration, etc., will also affect the visual quality of patients and weaken the visual performance of the eye structure. Precise measurement of the higher-order aberration of the subject can correct the subject's vision more accurately.

[0004] Regarding the current status of vision screening, most require subjective and objective optometry to be performed simultaneously, which not only consumes a large amount of manpower and material resources, but also has a single detection field angle, weak data correlation, and is easily affected by the state during detection. Summary of the Invention

[0005] In view of the above problems, the present invention proposes a subjective optometry prediction system combining wavefront aberration and optical signals, which is used to solve the problem that autorefraction and biometry cannot be performed simultaneously, and also provides optometry data in different field angle directions and corresponding subjective optometry prediction data.

[0006] The specific solution of the present invention is as follows: A subjective optometry prediction method combining wavefront aberration and optical signals, comprising the following steps: S1, measuring wavefront aberration data of the subject's eyeball through a wavefront sensor; S2, controlling a galvanometer system to scan and measure different regions of the subject's eye to obtain multi-field biological parameters; S3, screening the wavefront aberration data and multi-field biological parameters, extracting data points with a credibility greater than a credibility threshold, and constructing a three-dimensional eyeball model through the NURBS algorithm; S4. Assign a gradient refractive index to the three-dimensional model, simulate the light ray paths at different field angles of view, and generate a simulated eye chart image corresponding to each field angle of view; S5. Use an image quality assessment algorithm to evaluate the simulated image and output the predicted subjective refraction result.

[0007] Furthermore, the method further includes calculating the corneal stress distribution by combining finite element analysis, establishing a deformation energy function, and making the energy function converge by iteratively optimizing the control points and weight parameters to correct the three-dimensional model.

[0008] By combining finite element analysis to calculate the corneal stress distribution and establishing a deformation energy function, it is possible to more accurately simulate the deformation of the cornea under actual stress conditions, making the constructed three-dimensional model more realistic and reliable. Optimizing the control points and weight parameters to make the energy function converge can further improve the accuracy of the model, thus providing a more accurate basis for subsequent subjective refraction prediction and improving the credibility of the subjective refraction result.

[0009] Furthermore, the method further includes real-time collecting eye movement tracking data, updating the coordinates of the NURBS control points through affine transformation, and dynamically compensating for the eye movement error.

[0010] Real-time collecting eye movement tracking data and using affine transformation to update the coordinates of the NURBS control points can dynamically compensate for the eye movement error during the measurement. This can ensure that when the eyeball moves involuntarily, the constructed three-dimensional model of the eyeball still maintains a high degree of accuracy, avoiding data deviation caused by eye movement, and making the final subjective refraction prediction result more truly reflect the actual vision of the subject.

[0011] Furthermore, the specific steps of S1 include: Emitting detection light from a laser light source to the subject's eyeball, and forming a distorted wavefront after reflection by the retina; Using a microlens array to divide the distorted wavefront into sub-wavefronts, and detecting the spot offset of the sub-wavefronts through a CCD; Performing wavefront phase reconstruction on the spot offset using the Southwell reconstruction model and Zernike polynomials to obtain low-order and high-order aberration parameters.

[0012] Performing wavefront phase reconstruction using components such as a laser light source, a microlens array, and a CCD in combination with the Southwell reconstruction model and Zernike polynomials can accurately obtain the low-order and high-order aberration parameters of the subject's eyeball. Compared with traditional measurement methods, this method can describe wavefront aberrations more comprehensively and in detail, providing richer and more accurate aberration information for subsequent vision correction and subjective refraction prediction, and helping to improve the accuracy and personalization of refraction.

[0013] Further, S2 specifically includes: controlling the angle of incident light through galvanometer deflection, collecting optical coherence signals from the corneal center, upper side, lower side, nasal side, and temporal side, and extracting corneal curvature, lens position, and retinal distance parameters for each region.

[0014] Controlling the angle of incident light through galvanometer deflection and collecting optical coherence signals from multiple regions of the cornea can obtain more comprehensive multi-field-of-view biological parameters. This helps to more accurately understand the refractive characteristics of different parts of the eye, provides more sufficient data support for constructing an accurate three-dimensional model of the eye, and enables the subjective refraction prediction at different field-of-view angles to be more in line with the actual visual conditions of the subject.

[0015] Further, the method further includes generating a personalized correction plan based on wavefront aberration data: Fitting a correction surface based on multi-point corneal measurement data, optimizing the spherical curvature, cylindrical curvature, and astigmatism angle of the lens to minimize the wavefront aberration after correction.

[0016] Generating a personalized correction plan based on wavefront aberration data takes into account multi-point corneal measurement data and fits a correction surface, optimizing parameters such as the spherical curvature, cylindrical curvature, and astigmatism angle of the lens. This can make the design of the correction lens more conform to the actual structure and aberration conditions of the subject's eye, achieve more accurate vision correction, effectively reduce the residual wavefront aberration after correction, improve the visual quality of the subject, and reduce the visual discomfort after glasses fitting.

[0017] Further, S5 specifically includes: Calculating the letter regions in each row of the image according to the edge detection algorithm and extracting the ROI; Performing histogram equalization and adaptive binarization processing on the ROI region; Evaluating the image quality using a sharpness quantization index.

[0018] Extracting the ROI using the edge detection algorithm and performing histogram equalization and adaptive binarization processing can effectively remove background interference, enhance image contrast and sharpness, and make the letter regions in the simulation image clearer and more distinguishable. Evaluating the image quality using a sharpness quantization index can more accurately determine the smallest letter that the subject can distinguish at different field-of-view angles, thereby more precisely outputting the predicted subjective refraction result and improving the accuracy of refraction prediction.

[0019] Further, updating the NURBS control point coordinates through affine transformation to dynamically compensate for eye movement errors specifically includes the following steps: Defining a global coordinate system and a local coordinate system; Converting the control points into homogeneous coordinate form and establishing a dynamic transformation matrix; Updating the control point coordinates in real time for motion prediction compensation.

[0020] Define a global coordinate system and a local coordinate system, establish a dynamic transformation matrix, and update the coordinates of the control points in real time for motion prediction compensation, which can correct the errors caused by eye movement more precisely, further improve the construction accuracy of the three-dimensional eye model, and ensure the reliability and accuracy of the subjective refraction prediction results.

[0021] Furthermore, the construction of the three-dimensional eye model by the NURBS algorithm specifically includes: Extract the boundary feature point sets of the anterior and posterior surfaces of the cornea, and the anterior and posterior surfaces of the lens; Perform spatial grid division on the feature point sets, and the grid density is positively correlated with the local curvature; Determine the principal curvature direction within the grid through principal component analysis, and insert the initial control points along the curvature gradient direction; Construct a bivariate NURBS surface expression; Generate a non-uniform knot vector by the cumulative chord length method to complete the reconstruction of the three-dimensional model of the eye surface.

[0022] Extracting the boundary feature point sets of the surfaces such as the cornea and the lens and performing grid division, and using principal component analysis to determine the principal curvature direction and insert the initial control points and other operations can more accurately simulate the complex shape and curvature changes of the eye surface. Generating a non-uniform knot vector by the cumulative chord length method to complete the model reconstruction can make the three-dimensional eye model more truly reflect the geometric structure of the subject's eye, provide a more accurate model basis for subsequent ray path simulation and subjective refraction prediction, and improve the credibility and accuracy of the prediction results.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows: By integrating wavefront aberration measurement and multi-field biometric scanning, combining the NURBS algorithm to construct a gradient refractive index three-dimensional eye model, and using finite element analysis to optimize the corneal stress distribution, the present invention realizes high-precision eye modeling and reduction of biomechanical characteristics; Through the galvanometer multi-angle scanning and eye movement tracking dynamic compensation technology, the eye movement error is effectively suppressed, and the reliability of dynamic detection is improved; Based on ray tracing, a multi-field angle simulation visual acuity chart image is generated, combined with the image quality evaluation algorithm and the Zernike aberration separation technology, to realize the prediction of subjective refraction results and the generation of personalized correction schemes (such as optimizing the spherical and cylindrical lens parameters, astigmatism angle); At the same time, by using non-uniform knot vector generation, principal component analysis curvature interpolation and adaptive image processing algorithms, both computational efficiency and data robustness are considered. This solution deeply integrates objective biometric measurement and subjective vision simulation, and has significant advantages in terms of refraction accuracy, personalized adaptability, and dynamic scene adaptability, providing a prediction model closer to the real visual experience for refractive correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To more clearly illustrate the technical solutions in the embodiments of the present drawings or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present drawings. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0025] Figure 1 It is the flowchart of the method of the present invention; Figure 2 It is the three-dimensional reconstruction measurement point position diagram; Figure 3 It is the schematic diagram of the deflection of light in the human eye; Figure 4 It is the predicted subjective refraction image and the corrected subjective refraction image; Figure 5 It is the schematic diagram of the system function. Detailed implementation manners

[0026] In order to make the purpose, technical solutions and advantages of the present invention clearer, the following will describe and explain the present invention in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0027] Term explanation: Subjective refraction: It is an examination achieved through the interaction and communication between the optometrist and the customer. The subjective feelings of the customer are required to judge the end point of the examination, corresponding to objective examinations such as retinoscopy and computerized refraction.

[0028] Hartmann-Shack wavefront sensor: The Hartmann-Shack wavefront sensor, a device used to detect the light wavefront.

[0029] Southwell reconstruction model: A mathematical model used in structural mechanics or numerical analysis, especially applied in solving stress analysis or displacement calculation problems of complex structures. The core idea of this model is to gradually approach the solution of the problem through the iterative relaxation method, belonging to a classic numerical calculation method.

[0030] Zernike polynomials: Also known as the Zernike polynomials, they consist of a complete set of polynomials with an infinite number. They have two variables and are continuously orthogonal within the unit circle. It should be noted that the Zernike polynomials are only orthogonal in the continuous region inside the unit circle and generally do not have the orthogonal property at discrete coordinates inside the unit circle. Usually, people use the form of power series expansion to describe the aberration of an optical system. Since the form of the Zernike polynomials is consistent with the aberration polynomials observed in optical detection, they are often used to describe the wavefront characteristics.

[0031] Mooney-Rivlin equation: A common equation used to describe elastic materials, proposed by Mooney and Rivlin. The Mooney-Rivlin equation can be used to describe materials with non-linear large deformation behavior. This equation is based on the relationship between the stress tensor and the strain tensor and usually adopts a binomial form.

[0032] Levenberg-Marquardt algorithm: A method for estimating the least squares of regression parameters in non-linear regression. Proposed by D.W. Marquardt in 1963, it was developed based on a paper by K. Levenbevg in 1944. This method is a combination of the steepest descent method and the linearization method (Taylor series). Because the steepest descent method is suitable for the case where the parameter estimate value is far from the optimal value at the beginning of iteration, while the linearization method, i.e., the Gauss-Newton method, is suitable for the later stage of iteration within the range where the parameter estimate value is close to the optimal value. Combining the two methods can find the optimal value more quickly.

[0033] As Figure 1 shown, the present invention provides a subjective optometry prediction method combining wavefront aberration and optical signals, specifically including the following steps: S1, measuring the wavefront aberration data of the subject's eyeball through a wavefront sensor.

[0034] Specifically, a Hartmann-Shack wavefront sensor is used to measure the irregular wavefront aberration on the surface of the eyeball. The wavefront sensor includes a helium-neon laser, an acousto-optic modulator, a neutral filter, a spatial filter, a CCD camera, a microlens array, and a polarization beam splitter.

[0035] Specifically, the following method is used to measure the wavefront aberration data: Emitting a detection light from a laser light source to the subject's eyeball, and forming a distorted wavefront after reflection by the retina; Using the microlens array to divide the distorted wavefront into sub-wavefronts, and detecting the spot offset of the sub-wavefronts through a CCD; The Southwell reconstruction model and Zernike polynomials are used to perform wavefront phase reconstruction on the spot offset to obtain low-order and high-order aberration parameters.

[0036] For an ideal wavefront, a standard dot matrix can be formed on the CCD. Since the human eye is not an ideal wavefront, the reflected light wave has an inclination angle, and the focal point will have a corresponding shift. The shift amount is proportional to the slope of the actual wavefront locally.

[0037] Specifically, the Hartmann-Shack wavefront sensor calculates the offset for R phase points to be estimated, and uses the Southwell reconstruction model to process the slopes in the x and y directions respectively.

[0038] The slope in the x direction: ; The slope in the y direction: ; Obtain equations: ; ; where, W(x,y) represents the wavefront phase distribution; represents the slope in the x direction at the position (i, j); represents the slope in the y direction at the position (i, j); represents the phase value at the position (i, j); P x 、P y are pixel pitches, used to convert the slope into a phase difference.

[0039] Use Zernike polynomials for phase reconstruction: ; where, is the i-th Zernike expansion coefficient, reflecting the contribution of each order of aberration; represents the i-th Zernike polynomial; imax represents the number of polynomials; represents the reconstructed wavefront phase distribution.

[0040] Furthermore, the present invention can also generate a corresponding optimal optometry plan according to the wavefront aberration data, and simulate appropriate glasses prescription data through a program.

[0041] Specifically, the formula for the front surface of the simulated spectacle lens is: ; Among them, Z1 represents the 1st Zernike polynomial; is the spherical curvature; represents the square of the radial distance, indicating that the front surface is a rotationally symmetric optical surface (such as a spherical or aspherical surface); Application scenario: used for lenses to correct myopia or hyperopia, the spherical curvature is the same in any direction.

[0042] The formula for simulating the back surface of the spectacle lens is: ; Among them, Z2 represents the 2nd Zernike polynomial; is the cylindrical curvature; This formula only contains y 2 , indicating that the back surface is a non-rotationally symmetric cylindrical or toroidal surface (such as a cylindrical lens), and the cylindrical curvature only acts in the y direction; Application scenario: used for lenses to correct astigmatism, and the cylindrical curvature is independently controlled in different directions.

[0043] According to the actually introduced astigmatic angle θ and the above parameters, the wavefront aberration of the lens can be obtained, the best spherical-cylindrical power can be obtained, and the most suitable , and θ for the subject can be calculated, where θ is obtained by converting from the Cartesian coordinate system (x, y) to the polar coordinate system above, so that the vision correction effect after glasses fitting reaches the best state.

[0044] Considering that the change in the pupil light input caused by the change between the optometry dark room environment and the actual outdoor environment will affect the actual glasses fitting effect, the present invention adds the conversion function between the large pupil Zernike coefficient and the small pupil Zernike coefficient, specifically manifested as when the pupil diameter is 6 mm to 7 mm during measurement, it is converted to the Zernike of a normal pupil of 4 mm, and its related formula is: ; Among them, is the phase correction factor, related to the phase symmetry of the Zernike polynomial, ensuring the consistency of the aberration phase during pupil scaling; is the nth order Zernike polynomial coefficient under the large pupil; is the nth order Zernike polynomial coefficient under the small pupil; is the coefficient of the (n + 2i)-th order Zernike polynomial under large pupil; n is the radial order; m is the azimuthal order; N is the maximum Zernike order corresponding to the large pupil; represents traversing all high-order aberration orders; : a constant determined by n and i; : a constant determined by n and j; : a constant determined by n and j.

[0045] S2 controls the galvanometer system to scan and measure different regions of the subject's eyes to obtain multi-field biological parameters.

[0046] Specifically, as Figure 2 shown, the galvanometer is deflected by voltage to precisely control the incident light angle, and multi-point optical coherence signals are collected at the corneal center (N1), upper side (N2), lower side (N3), nasal side (N4), and temporal side (N5). Taking the central region image and signal as the reference line, parameters such as corneal curvature, lens position, retinal distance, corneal thickness, and lens thickness in each region are synchronously extracted.

[0047] Specifically, the galvanometer system obtains multi-point data in the N2 - N5 regions by scanning the optical signals of the eye axis in five directions, and then calculates three-dimensional structure parameters such as the distance from the posterior corneal surface to the anterior lens surface and the distance from the posterior lens surface to the retina, and finally fits an eyeball model including corneal shape, lens shape, and retinal shape.

[0048] S3 screens the wavefront aberration data and multi-field biological parameters, extracts data points with credibility greater than the credibility threshold, and constructs a three-dimensional eyeball model through the NURBS algorithm.

[0049] Specifically, first establish an initial reference plane: Measure the optical coherence signal at the pupil center N1 point, and extract three-dimensional structure parameters such as the corneal starting point and the corneal fitting surface; taking the N1 point as the origin of the polar coordinate system, combined with the initial angle of the galvanometer and the signal position, establish the initial reference point in the polar coordinate system.

[0050] Building an accurate three-dimensional eye model of a subject requires accurate measurement data. Considering the influence of eye movement and muscle response during the subject's measurement on the measurement data, it is necessary to use the method of Kernel Density Estimation (KDE) to extract the data points in the measurement data group whose credibility is greater than the credibility threshold. That is, in the coordinate system where the data is stored, the KDE method is used to find the points in the distribution data whose probability density estimation value is greater than the credibility threshold. The corresponding formula in the polar coordinate system is: ; where, is the angular coordinate of the i-th sample point; is the radial coordinate of the i-th sample point; represents the probability density estimation value at the point in the polar coordinate system; g represents the total number of sample points participating in the density estimation; and are the bandwidth parameters in the radial and angular directions, controlling the range of action of the kernel function; and are the kernel functions in the radial and angular directions, used to calculate the probability density of the data points and screen out the high-credibility data.

[0051] After screening out the high-credibility data, the data is classified according to its position angle, and the data points on the same surface but different positions (cornea, lens, retina) are selected for surface fitting.

[0052] Construct a three-dimensional eye model through the NURBS algorithm, specifically including: Extract the boundary feature point sets of the anterior and posterior surfaces of the cornea, and the anterior and posterior surfaces of the lens; Perform spatial grid division on the feature point sets, and the grid density is positively correlated with the local curvature; Determine the principal curvature direction within the grid through principal component analysis, and insert the initial control points along the curvature gradient direction .

[0053] Construct a bivariate NURBS surface expression: ; where, is the control point, determining the shape of the surface; represents the three-dimensional coordinate vector of the point on the surface when the parameters are (u, v); is the weight parameter, adjusting the influence degree of the control point on the surface; , are B-spline basis functions in the u and v directions respectively, defined by the knot vector, which control the local continuity of the surface, where p and q are the polynomial degrees in the corresponding directions; I is the upper limit of the control point index in the u direction; J is the upper limit of the control point index in the v direction.

[0054] The non-uniform knot vector is generated by the cumulative chord length method to complete the reconstruction of the three-dimensional model of the eyeball surface.

[0055] Its knot vector satisfies the non-uniformity condition: ; where is the ordered point sequence after parameterization, ensuring that the knot distribution matches the point cloud density; represents the Euclidean distance between the i-th point and the (i + 1)-th point; represents the sum of the distances of all adjacent point pairs; is the number of adjacent point pairs in the point sequence; h represents the maximum value of the control point index.

[0056] Furthermore, the corneal stress distribution is calculated by combining finite element analysis, and the deformation energy function is established: ; where k1 and k2 are the principal curvatures, reflecting the local bending degree of the cornea; A represents the overall area of the corneal surface; represents the total bending energy of the corneal surface; represents the fitting degree of the model to the measured biological data; G k is the measured point; is the model prediction point, that is, the point on the surface; α and β are weight coefficients, α is used to control the influence on the total energy, and β is used to control the influence on the total energy; By iteratively optimizing the control points and the weight parameters the energy function is converged to correct the three-dimensional model.

[0057] Specifically, using the finite element model Mooney-Rivlin constitutive equation, the cornea is defined as a hyperelastic material, and the Levenberg-Marquardt algorithm is used to minimize the energy function until the convergence threshold is less than .

[0058] Correspondingly, the optical signals corresponding to the data points in the N2-N5 regions are mapped to the polar coordinate system, and combined with the reference data of point N1, a complete eyeball model including the irregular curvature of the posterior surface of the cornea is constructed.

[0059] S4. Assign a gradient refractive index to the three-dimensional model, simulate the light ray paths at different field angles, as Figure 3 shown, and generate the simulated eye chart images corresponding to each field angle.

[0060] Specifically, set the myopia vision simulation and the hyperopia vision simulation. The size of the myopia eye chart is , and the size of the hyperopia eye chart is ; in the simulation software, the hyperopia eye chart is set 5 m in front of the eye to be examined, and the myopia eye chart is set 25 cm in front of the eye to be examined.

[0061] S5. Use an image quality assessment algorithm to evaluate the simulated images and output the predicted subjective refraction results, as Figure 4 shown.

[0062] Extract the ROI: According to the edge detection algorithm, calculate the letter regions in each row of the image to exclude background interference; Perform histogram equalization and adaptive binarization on the ROI region to eliminate uneven brightness and enhance the contrast of the letter boundaries; Use a sharpness quantization index to evaluate the image quality, and use the structural similarity index (SSIM) or the peak signal-to-noise ratio (PSNR) to evaluate the image recognizability, and output the predicted subjective vision value.

[0063] Furthermore, there is eye movement interference during the measurement process. Therefore, update the NURBS control point coordinates through affine transformation to dynamically compensate for the eye movement error. The specific implementation method is as follows: Define the global eyeball coordinate system and the local corneal coordinate system. The mapping relationship between the two is as follows: ; where R is the rotation matrix, calculated based on the eye movement tracking data; T is the translation vector, determined by the pupil center displacement.

[0064] Convert the control points into homogeneous coordinate form and establish a dynamic transformation matrix.

[0065] Specifically, eye movement changes are obtained based on real-time data, where the rotation components are respectively , , , and the translation components are , , . By combining the rotation components and the translation components, a dynamic change matrix is established as: ; The coordinates of the control points are updated in real time for motion prediction compensation.

[0066] Specifically, the updated control points are: ; Among them, is the old control point; is the new control point.

[0067] Correspondingly, a motion prediction compensation algorithm is added, and a first-order linear extrapolation is used to predict the transformation matrix at the next moment: ; Among them, is the prediction smoothing factor to adjust the prediction sensitivity; represents the change of the affine matrix over time, which is matrix; is the affine transformation matrix at time t; is the preset affine transformation matrix at the next moment.

[0068] As Figure 5 shown, the specific implementation process of the present invention is as follows: After the system is started, through dynamic image feedback and a position sensor, the movement of the subject's jaw support is synchronously tracked and the three-dimensional movement of the fuselage is measured to locate the positions of the binocular pupils; when the eye image signal is obtained, the image center signal is overlapped with the pupil center signal, and the optical coherence measurement device emits weak light to lock the signal on the anterior surface of the cornea. The host computer controls the movement in the depth direction through the feedback signal until the signal on the anterior surface reaches the preset interval.

[0069] After the signal on the anterior surface of the cornea is located, the optical coherence device stops emitting light, and the near-infrared LED of the wavefront aberration measurement system emits detection light; the retinal diffuse reflection light forms a distorted wavefront, which is focused by the lens group and the microlens array and then received by the CCD. The offset of the dot array is analyzed to construct a differential equation set related to the wavefront slope, and the slopes of each wavefront are solved; based on the combination of Zernike polynomials, the expansion coefficients are calculated to reconstruct the wavefront aberration parameters.

[0070] After the wavefront measurement is completed, the optical coherence device emits light again to obtain the backscattering signals of each surface of the eyeball, and extracts the reference data of the eye axis (axial length, lens thickness, anterior chamber depth, corneal thickness, etc.); with the corneal apex as the origin of the polar coordinate system (r-axis), the motor positioning is synchronized with the signal on the anterior corneal surface to establish a polar coordinate reference; the scanning galvanometer scans the upper, lower, nasal, and temporal sides of the cornea in sequence, calculates the polar coordinate depth through the optical signal distance difference, and determines the angle information by combining the galvanometer angle and the distance from the corneal apex to the CCD to construct the initial corneal surface model; the galvanometer scans step by step along the boundary points to fill the point cloud data in the polar coordinate system and complete the refined corneal modeling; fuse signals such as corneal thickness and lens thickness, and fit the curves of the posterior corneal surface, lens, and retina; use finite element analysis to calculate the corneal stress in real time, dynamically update the surface coordinate points, and synchronously fit each tissue structure and assign refractive indices (cornea 1.376, aqueous humor / vitreous body 1.336, lens 1.416).

[0071] The host computer generates a personalized three-dimensional eyeball model, locates each tissue structure through feature recognition, and completes the refractive index assignment; the built-in simulation algorithm generates light focusing images at multiple field angles of view, and calls the myopia (115mm×105mm, 25cm) / hyperopia (787mm×1092mm, 5m) visual acuity chart; performs edge detection and contrast normalization processing on the simulation images, and identifies the smallest distinguishable visual target based on the structural similarity index (SSIM) to output the predicted visual acuity value.

[0072] It should be noted that the present invention is not limited to the above embodiments. The above embodiments are only examples, and embodiments with the same structure and the same function and effect as the technical idea within the technical scope of the present invention are all included in the technical scope of the present invention. In addition, within the scope not departing from the gist of the present invention, various modifications that can be thought of by those skilled in the art to the embodiments, and other ways constructed by combining some constituent elements in the embodiments are also included in the scope of the present invention.

Claims

1. A subjective optometry prediction method combining wavefront aberration and optical signals, characterized in that, The following steps are involved: S1, measuring the wavefront aberration data of the subject's eyeball through a wavefront sensor; S2, controlling the galvanometer system to scan and measure different areas of the subject's eyes to obtain multi-field biological parameters; S3, screening the wavefront aberration data and multi-field biological parameters, extracting data points with a credibility greater than a credibility threshold, and constructing a three-dimensional model of the eyeball through a NURBS algorithm; S4, assigning gradient refractive index to the three-dimensional model, simulating light paths at different viewing angles, and generating a simulated image of the vision chart corresponding to each viewing angle; S5, using an image quality assessment algorithm to evaluate the simulated image and output a predicted subjective optometry result.

2. The subjective optometry prediction method combining wavefront aberration and optical signal according to claim 1, characterized in that, The method also includes calculating corneal stress distribution in combination with finite element analysis, establishing a deformation energy function, converging the energy function by iteratively optimizing control points and weight parameters, and correcting the three-dimensional model.

3. The subjective optometry prediction method combining wavefront aberration and optical signal according to claim 1, characterized in that, The method also includes collecting eye tracking data in real time, updating the coordinates of NURBS control points through affine transformation, and dynamically compensating for eye movement errors.

4. The subjective optometry prediction method combining wavefront aberration and optical signal according to claim 1, characterized in that, The S1 specifically includes: The detection light is emitted to the subject's eyeball through a laser light source, and a distorted wavefront is formed after being reflected by the retina; The distorted wavefront is divided into sub-wavefronts by using a micro-array lens, and the light spot offset of the sub-wavefronts is detected by using a CCD; The Southwell reconstruction model and Zernike polynomials are used to reconstruct the wavefront phase of the spot offset to obtain low-order and high-order aberration parameters.

5. The subjective optometry prediction method combining wavefront aberration and optical signal according to claim 1, characterized in that The S2 specifically includes: controlling the incident light angle by galvanometer deflection, collecting optical coherence signals from the center, upper side, lower side, nasal side, and temporal side of the cornea, and extracting the corneal curvature, lens position, and retinal distance parameters of each area.

6. The subjective optometry prediction method combining wavefront aberration and optical signal according to claim 1, characterized in that The method further includes generating a personalized correction scheme based on the wavefront aberration data: The correction surface is fitted according to the corneal multi-point measurement data to optimize the lens' spherical curvature, cylindrical curvature and astigmatism angle to minimize the wavefront aberration after correction.

7. The subjective optometry prediction method combining wavefront aberration and optical signal according to claim 1, characterized in that The S5 specifically includes: According to the edge detection algorithm, the letter area of each line in the image is calculated and the ROI is extracted; Perform histogram equalization and adaptive binarization processing on the ROI area; Assess image quality using the sharpness quantification metric.

8. The subjective optometry prediction method combining wavefront aberration and optical signal according to claim 3, wherein The method of updating the coordinates of the NURBS control points by affine transformation to dynamically compensate for the eye movement error specifically includes the following steps: Define the global coordinate system and the local coordinate system; Convert the control points into homogeneous coordinates and establish a dynamic change matrix; Update control point coordinates in real time to perform motion prediction compensation.

9. The subjective optometry prediction method combining wavefront aberration and optical signal according to claim 1, characterized in that, The method of constructing the three-dimensional eyeball model by using the NURBS algorithm specifically includes: Extracting boundary feature point sets of the anterior and posterior surfaces of the cornea and the anterior and posterior surfaces of the lens; The feature point set is spatially gridded, and the grid density is positively correlated with the local curvature; The principal curvature direction in the grid is determined by principal component analysis, and initial control points are inserted along the curvature gradient direction; Construct a two-variable NURBS surface expression; The cumulative chord length method is used to generate non-uniform node vectors to complete the reconstruction of the three-dimensional model of the eyeball surface.

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