Method and system for detecting optical performance distribution of an ophthalmic lens

Abstract

The present application relates to the technical field of lens detection, and particularly relates to a method and system for detecting optical performance distribution of an ophthalmic lens, the method comprising: S10: performing a wavefront sensing scan on an effective optical area of a lens to be detected, including: performing an initial sparse sampling scan to obtain a first wavefront data set; analyzing the first wavefront data set to reconstruct a preliminary optical performance distribution, and identifying a low-confidence area and an optical parameter mutation area to form a preliminary identification result; generating and performing an enhanced scan according to the preliminary identification result, and performing encrypted sampling on the low-confidence area and the optical parameter mutation area to obtain a second wavefront data set; S20: fusing and reconstructing the first wavefront data set and the second wavefront data set to calculate an optical performance parameter distribution; and S30: generating a corresponding detection result according to the optical performance parameter distribution. The present application ensures global detection accuracy and improves detection efficiency through a closed loop of dynamic interactive scanning and real-time data analysis.

Description

Technical Field

[0001] This invention relates to the field of lens testing technology, and in particular to a method and system for testing the optical performance distribution of optometric lenses. Background Technology

[0002] Optical lenses, also known as corrective lenses, are functional optical devices used to correct refractive errors and presbyopia in the human eye. Common types include single-vision lenses, bifocal lenses, and more technically complex progressive multifocal lenses. Their core value lies in providing the wearer with clear and comfortable vision through precisely designed optical surfaces. Therefore, it is necessary to test the quality and function of the lenses and measure their optical performance.

[0003] Existing technologies typically employ either focimeter measurement or mechanical scanning measurement. However, focimeter measurement is a sampling measurement of a few discrete points, which cannot fully obtain continuous information on the changes in the optical performance of the lens surface. This fails to meet the needs for evaluating the performance of progressive lens channels and the optimization effect of freeform surfaces, which require full-domain data. On the other hand, while mechanical scanning measurement can theoretically approximate the true distribution by infinitely increasing the sampling density, the fixed scanning path lacks intelligence and cannot be dynamically adjusted according to the real-time condition of the lens being tested.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] In view of at least one of the above technical problems, the present invention provides a method and system for detecting the optical performance distribution of optometry lenses, which adopts a dynamic interactive wavefront sensing scanning and real-time data analysis closed loop to achieve high-efficiency and high-precision detection of the full-domain optical performance distribution.

[0006] This invention provides a method for detecting the optical performance distribution of optometric lenses, comprising the following steps: S10: Perform wavefront sensing scanning on the effective optical area of ​​the lens under test, including: Perform an initial sparse sampling scan to obtain the first wavefront dataset; Based on the analysis of the first wavefront dataset, a preliminary optical performance distribution is reconstructed, and low-confidence regions and abrupt changes in optical parameters are identified in the preliminary optical performance distribution, forming a preliminary identification result; Based on the preliminary identification results, an enhanced scan is generated and executed to perform encrypted sampling on the low-confidence region and the region of abrupt change in optical parameters to obtain the second wavefront dataset. S20: The first wavefront dataset and the second wavefront dataset are fused and reconstructed to calculate the continuous distribution of optical performance parameters of the lens under test within the effective optical region; S30: Generate corresponding detection results based on the distribution of the optical performance parameters.

[0007] Further, in step S10, an initial sparse sampling scan is performed to obtain the first wavefront dataset, including: Identify the lens type of the lens under test, and predict the high-probability variation area of ​​the optical performance parameters of the lens under test within the effective optical area based on the lens type; Based on the high-probability change region, a non-uniform initial scanning path is planned; the initial scanning path arranges relatively dense sampling points in the high-probability change region and relatively sparse sampling points in other regions of the effective optical region. The wavefront sensing scan is performed along the initial scanning path to obtain the first wavefront dataset.

[0008] Further, in step S10, predicting the high-probability variation region of the optical performance parameters of the lens under test within the effective optical region based on the lens type includes: Obtain the theoretical distribution data of the design optical parameters corresponding to the lens type; A two-dimensional coordinate system is established within the effective optical region, and the parameter gradient values ​​at each coordinate point in the two-dimensional coordinate system are calculated based on the theoretical distribution data. A first threshold is set, and the region formed by the coordinates of the parameter gradient values ​​exceeding the first threshold is determined as the high-probability change region.

[0009] Further, in step S10, the first threshold is set according to the prescription parameters of the lens to be tested corresponding to the lens type. The prescription parameters include at least one of spherical power, cylindrical power, or ADD value, and the first threshold increases as the absolute value of the prescription parameters increases.

[0010] Further, in step S10, low-confidence regions and abrupt changes in optical parameters are identified in the preliminary optical performance distribution, including: Based on the preliminary optical performance distribution, the corresponding theoretical wavefront slope distribution is derived. The standard deviation of the difference between the measured wavefront slope and the theoretical wavefront slope of the sampling points in the effective optical region is calculated and denoted as the wavefront aberration fitting residual of the current sampling point. The continuous region corresponding to the wavefront aberration fitting residual being greater than the preset residual threshold is defined as the low confidence region. Calculate the spatial gradient magnitudes of spherical and cylindrical power in the preliminary optical performance distribution; define the continuous region where the gradient magnitude is greater than a preset gradient threshold as the region of abrupt change in optical parameters.

[0011] Further, in step S10, based on the preliminary identification results, an enhanced scan is generated and performed, including: Based on the low-confidence region, generate a regular grid encrypted scan path covering the low-confidence region; Based on the optical parameter abrupt change region, determine the boundary direction or maximum gradient direction of the optical parameter abrupt change region, and generate a contour tracking scanning path based on the boundary direction or a linear scanning path based on the maximum gradient direction. The enhanced scan is performed sequentially using the regular grid encryption scan path, the contour tracking scan path, or the line scan path.

[0012] Further, in step S20, the first wavefront dataset and the second wavefront dataset are fused and reconstructed to calculate the continuous distribution of optical performance parameters of the lens under test within the effective optical region, including: Based on the first wavefront dataset, a parameter distribution base field covering the optical performance of the effective optical region is generated; Based on the second wavefront dataset, multiple local high-density data points are determined as parameter anchors in the low-confidence region and the optical parameter mutation region, respectively. The parameter anchor point is mapped to the corresponding spatial position of the parameter distribution base field, and the parameter value at the position of the parameter anchor point in the parameter distribution base field is corrected using the value of the parameter anchor point as the constraint target. After the correction is completed, the parameter distribution base field is smoothed and optimized to obtain the optical performance parameter distribution.

[0013] Further, in step S20, the parameter distribution substrate field is smoothed and optimized to obtain the optical performance parameter distribution, including: A boundary value problem is preset, and the corrected parameter distribution basis field is used as the initial field for solving. The spatial position and value of the parameter anchor point are used as the fixed boundary conditions in the boundary value problem. The optical performance parameter distribution that meets the requirements of full-field smoothness is obtained through numerical solution, and the smoothing optimization process is completed.

[0014] The present invention also provides a system for detecting the optical performance distribution of optometric lenses, comprising: The scanning acquisition module performs wavefront sensing scanning on the effective optical area of ​​the lens under test, including: Perform an initial sparse sampling scan to obtain the first wavefront dataset; Based on the analysis of the first wavefront dataset, a preliminary optical performance distribution is reconstructed, and low-confidence regions and abrupt changes in optical parameters are identified in the preliminary optical performance distribution, forming a preliminary identification result; Based on the preliminary identification results, an enhanced scan is generated and executed to perform encrypted sampling on the low-confidence region and the region of abrupt change in optical parameters to obtain the second wavefront dataset. The reconstruction calculation module merges and reconstructs the first wavefront dataset and the second wavefront dataset to calculate the continuous distribution of optical performance parameters of the lens under test within the effective optical region. The result generation module generates corresponding detection results based on the distribution of the optical performance parameters.

[0015] Furthermore, the scanning acquisition module includes: The identification and prediction unit identifies the lens type of the lens under test and predicts the high-probability variation area of ​​the optical performance parameters of the lens under test within the effective optical area based on the lens type. The sampling unit is planned based on the high-probability change region, and a non-uniform initial scanning path is planned; the initial scanning path arranges relatively dense sampling points in the high-probability change region and relatively sparse sampling points in other regions of the effective optical region. The data acquisition unit performs the wavefront sensing scan along the initial scanning path to acquire the first wavefront dataset.

[0016] The technical solution of this invention can achieve the following technical effects: By constructing a dynamic interactive closed loop that combines initial sparse sampling, real-time analysis and identification, and targeted enhanced scanning, adaptive optimization of detection resources is achieved, reducing unnecessary redundant measurements in regions where optical parameters change smoothly. This significantly improves overall detection efficiency while ensuring or even improving overall detection accuracy.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the method for detecting the optical performance distribution of optometry lenses in an embodiment of the present invention. Figure 2 This is a schematic diagram of the process for obtaining the first wavefront dataset in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process for predicting the high-probability variation region of optical performance parameters of the lens under test within the effective optical region based on the lens type in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the process of generating and performing enhanced scanning based on the preliminary identification results in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the process of calculating the continuous distribution of optical performance parameters of the lens under test within the effective optical region in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] This invention provides a method such as Figures 1 to 5 The method for detecting the optical performance distribution of the optometric lens shown includes the following steps: S10: Perform wavefront sensing scanning on the effective optical area of ​​the lens under test, including: Perform an initial sparse sampling scan to obtain the first wavefront dataset; Based on the analysis of the first wavefront dataset, a preliminary optical performance distribution was reconstructed, and low-confidence regions and regions of abrupt changes in optical parameters were identified in the preliminary optical performance distribution, forming the preliminary identification results; Based on the preliminary identification results, an enhanced scan was generated and executed to perform encrypted sampling of low-confidence regions and regions with abrupt changes in optical parameters, thereby obtaining the second wavefront dataset. S20: Merge and reconstruct the first and second wavefront datasets to calculate the continuous distribution of optical performance parameters of the lens under test within the effective optical region; S30: Generate corresponding detection results based on the distribution of optical performance parameters.

[0023] The specific working principle of this invention is as follows: First, the system performs an initial sparse sampling scan. The control computer drives the displacement stage so that the probe beam illuminates different positions of the lens under test sequentially along a preset path with relatively sparse sampling points. At each sampling point, the wavefront sensor synchronously collects the distorted wavefront after passing through the lens and records the wavefront slope or phase information at that point. The data set of all points constitutes the first wavefront dataset.

[0024] Next, the system performs real-time analysis and identification, processing the first wavefront dataset: using wavefront reconstruction algorithms such as Zernike polynomial fitting to calculate the preliminary optical performance distribution, including at least the preliminary spatial distribution of spherical and cylindrical power; subsequently, the system performs intelligent analysis on this preliminary distribution to identify two types of key regions: low-confidence regions, which are regions with low data reliability due to measurement noise, local scattering, or alignment deviations; and regions with abrupt changes in optical parameters, which are regions where the optical performance of the lens itself undergoes a sharp spatial change, such as the channel of a progressive multifocal lens.

[0025] Finally, the system generates and executes enhanced scans based on the recognition results. For the identified low-confidence areas and areas with abrupt changes in optical parameters, a new scanning path with encrypted sampling points is planned. The computer drives the displacement stage to guide the probe beam to perform a secondary scan on these areas, obtaining denser and more reliable wavefront data to form a second wavefront dataset. For other unrecognized areas, measurements are not repeated.

[0026] By fusing the first wavefront dataset with the second wavefront dataset, a continuous, smooth optical performance parameter distribution with significantly improved accuracy in key areas is obtained within the effective optical region. The optical performance parameter distribution may include a two-dimensional distribution map of various parameters such as spherical power, cylindrical power, astigmatic axis, and higher-order aberrations.

[0027] Based on the distribution of continuous optical performance parameters, corresponding test results are automatically generated and can be presented in various forms. For example, pseudo-color maps or contour maps can be generated and output on the display unit to intuitively show the changes of parameters such as refractive power and astigmatism across the entire lens, forming a visual distribution map. Alternatively, parameter values ​​in key areas can be automatically extracted, such as optical central power, incremental power increase in the progressive channel, and peripheral astigmatism, and compared with design values ​​or national standards to provide a qualification or failure judgment, forming a quantitative analysis report. The complete distribution data and analysis report can also be stored as a file in a specific format to form a digital file for quality traceability or further analysis.

[0028] In some embodiments of the present invention, such as Figure 2As shown, in step S10, an initial sparse sampling scan is performed to obtain the first wavefront dataset, including: Identify the lens type of the lens under test, and predict the high-probability variation area of ​​the optical performance parameters of the lens under test within the effective optical area based on the lens type; Based on the high probability of change region, a non-uniform initial scanning path is planned; the initial scanning path arranges relatively dense sampling points in the high probability of change region and relatively sparse sampling points in other regions of the effective optical region. Perform a wavefront sensing scan along the initial scan path to acquire the first wavefront dataset.

[0029] By reading the markings on the lens carrier, when the operator places the lens to be tested on the stage, the system can use the attached barcode or QR code scanner to read the code on the lens packaging bag or frame label, and directly retrieve the lens model, design type and prescription parameters from the local or cloud database. Alternatively, it can use machine vision-based optical recognition. The camera on top of the system takes an image of the lens surface, and the image processing algorithm identifies permanent markings on the lens, such as the invisible laser markings on progressive multifocal lenses, which include the brand, model and prescription cross, thereby determining the lens type and the approximate location of the optical area.

[0030] Next, based on the identified lens type, the system predicts high-probability change areas, i.e. areas where optical performance parameters are highly likely to undergo significant spatial changes. This prediction can be made based on the built-in prior knowledge base. The system retrieves the lens freeform surface design model data or the standard theoretical refractive power distribution map of the model from the database. In memory, the system quickly calculates the gradient mode field of the theoretical data within a two-dimensional coordinate system established in the effective optical area. Alternatively, for a mass-produced lens type, the system statistically analyzes the results of thousands of full inspections of this model of lens in history to determine which coordinate region has a high frequency of high gradients. This coordinate region is then preset as a high-probability change area.

[0031] Then, the system plans a non-uniform initial scanning path. The path planning algorithm takes the boundaries of the predicted high-probability change areas as input. The planning goal is to make the spatial distribution density of the sampling points proportional to the predicted importance of the area, given a fixed total number of sampling points. Ultimately, the sampling points will automatically cluster in these high-value areas to form a relatively dense distribution, while they will naturally be sparse in other areas, thus generating an irregular initial scanning path that is entirely driven by intelligent decision-making.

[0032] Finally, the system executes the initial scanning path and acquires data, driving the probe beam to arrive at each target sampling point in sequence. At each point, the wavefront sensor is triggered, acquiring and storing the wavefront slope data of that point. When all path points have been traversed, this ordered wavefront data with spatial coordinates constitutes the first wavefront dataset, greatly improving the information efficiency and value of the initial sampling, and enhancing the adaptability and universality to different lenses.

[0033] In some embodiments of the present invention, such as Figure 3 As shown, in step S10, predicting the high-probability variation region of optical performance parameters of the lens under test within the effective optical region based on the lens type includes: Obtain theoretical distribution data of the design optical parameters corresponding to the lens type; A two-dimensional coordinate system is established within the effective optical region, and the parameter gradient values ​​at each coordinate point in the two-dimensional coordinate system are calculated based on theoretical distribution data. A first threshold is set, and the region formed by the coordinates of the parameter gradient values ​​exceeding the first threshold is determined as a high-probability change region.

[0034] After identifying the lens type, the system accesses an integrated lens design database. The database stores complete digital twin data of various lens design models. For freeform or progressive lenses, the data is usually a high-resolution surface lattice or polynomial coefficients characterizing the surface. These can be calculated directly or through ray tracing and exported as theoretical numerical tables of properties such as spherical power, cylindrical power, astigmatic axis, and even aberrations at preset grid points, describing the expected spatial distribution of optical performance.

[0035] The system establishes a two-dimensional physical coordinate system with the lens's positioning reference on the testing fixture as the origin O, the horizontal direction as the X-axis, and the vertical direction as the Y-axis. The obtained theoretical data points are mapped to this physical coordinate system through coordinate transformation. Numerical methods are used to calculate the spatial gradient field of the theoretical distribution data. For the discrete grid formed by the theoretical data, the gradient is calculated at each interior point using the central difference method, comprehensively reflecting the degree of variation between spherical and cylindrical lenses at that location. By traversing all interior points, a continuous theoretical gradient amplitude distribution map covering the effective optical area can be obtained.

[0036] Finally, a dynamic first threshold is set and high-probability change regions are identified. Different prescription lenses have different absolute gradient magnitudes, and the first threshold changes with the lens. The first threshold can be linearly correlated with the absolute value of the lens prescription parameters. After setting the threshold, the system extracts all coordinate points in the theoretical gradient distribution map that satisfy the parameter gradient value exceeding the first threshold. The coordinate points naturally cluster into one or more connected regions in space. The connected regions are identified as high-probability change regions. For example, for a progressive lens, the progressive channel region between the distance and near use areas is usually defined as a high-probability change region.

[0037] In some embodiments of the present invention, in step S10, the first threshold is set according to the prescription parameters of the lens to be tested corresponding to the lens type. The prescription parameters include at least one of spherical power, cylindrical power or ADD value, and the first threshold increases as the absolute value of the prescription parameters increases.

[0038] Lens power is used to correct myopia or hyperopia; cylinder power is used to correct astigmatism; and the additional power (ADD) value is specifically used for presbyopia correction lenses, representing the positive power added to the near vision zone relative to the far vision zone. When identifying lens types, the system simultaneously retrieves these prescription parameters from a related database. By mathematically correlating thresholds with the fundamental prescription parameters of the lens, an objective and quantifiable decision-making standard is established, avoiding misjudgments or omissions caused by fixed thresholds and improving the accuracy and specificity of predicting high-probability change areas.

[0039] In some embodiments of the present invention, step S10, identifying low-confidence regions and abrupt changes in optical parameters in the preliminary optical performance distribution, includes: Based on the preliminary optical performance distribution, the corresponding theoretical wavefront slope distribution is derived. The standard deviation of the difference between the measured wavefront slope and the theoretical wavefront slope of the sampling points in the effective optical region is calculated and denoted as the wavefront aberration fitting residual of the current sampling point. The continuous region corresponding to the wavefront aberration fitting residual being greater than the preset residual threshold is defined as the low confidence region. Calculate the spatial gradient magnitudes of spherical and cylindrical power in the preliminary optical performance distribution; define the continuous regions where the gradient magnitudes are greater than the preset gradient threshold as abrupt regions of optical parameters.

[0040] First, the system inverts the theoretical wavefront slope distribution based on the preliminary distribution. Then, it compares the measured wavefront slope of each sampling point with the theoretical value and calculates the standard deviation of the difference in the local neighborhood. This yields a quantitative index characterizing the volatility and unreliability of the current sampling point data, namely the wavefront aberration fitting residual. The wavefront aberration fitting residual is then compared with an adaptively set residual threshold. The residual threshold can be dynamically adjusted according to the overall signal-to-noise ratio of this scan. The lower the signal-to-noise ratio, the higher the residual threshold is set. Finally, continuous areas with abnormally high residuals are identified as low-confidence areas, corresponding to locations where measurements are interfered with or where there is local scattering.

[0041] The system extracts the spatial distribution information of spherical and cylindrical power from the same initial distribution in parallel and calculates the comprehensive spatial gradient amplitude. The amplitude quantifies the severity of the change in refractive properties. The system compares the gradient amplitude map with another gradient threshold dynamically set according to the lens characteristics and identifies continuous regions where the gradient amplitude exceeds the threshold as regions of abrupt changes in optical parameters, such as channels in progressive multifocal lenses. This provides spatial guidance for subsequent highly targeted enhancement scans.

[0042] In some embodiments of the present invention, such as Figure 4 As shown, in step S10, based on the preliminary identification results, an enhanced scan is generated and performed, including: Based on the low-confidence areas, generate a regular grid encrypted scan path that covers the low-confidence areas; Based on the region of abrupt change in optical parameters, determine the boundary orientation or maximum gradient direction of the region of abrupt change in optical parameters, and generate a contour tracking scanning path based on the boundary orientation or a linear scanning path based on the maximum gradient direction. The regular grid encryption scan path, contour tracing scan path, or line scan path are executed sequentially as an enhanced scan.

[0043] First, address the low-confidence regions. These regions are typically irregularly shaped and may be formed due to random measurement noise, momentary occlusion, or local surface scattering. To confirm the true situation, it is necessary to improve the signal-to-noise ratio and spatial sampling coverage of the low-confidence regions. This can be achieved by obtaining the outline polygon of the low-confidence region, calculating the minimum bounding rectangle, and then planning a regular matrix grid that completely covers the rectangle within the rectangle at a density much higher than the initial scan. This ensures that no matter where the defect or noise point is located within the region, it can be surrounded and captured by a sufficiently dense set of measurement points. This allows for the averaging out of random noise or the clear revelation of local anomalies through subsequent data fusion.

[0044] The system then processes regions with abrupt changes in optical parameters that have clear physical meaning and geometric characteristics, such as the banded areas of a progressive channel or the boundaries of astigmatic regions. It performs regional feature analysis, extracts the binarized image of the region, calculates geometric moments, or employs edge detection algorithms to determine whether the overall structure presents a narrow, elongated boundary or has a consistent internal orientation field. If determined to be a boundary, the system extracts the main contour line and generates a contour tracing scan path, which consists of a series of sampling points evenly distributed along the contour line. If determined to have strong directionality, the system extracts the maximum gradient direction and generates one or more linear scan paths that traverse the region along the maximum gradient direction. For example, for a curved progressive channel, the system may generate a curved path meandering along the center of the channel; for a steeply changing astigmatic region boundary, it may generate a short, straight path perpendicular to the boundary.

[0045] Finally, the system executes the planned path in an orderly manner, integrating the regular grid path and the feature path into an ordered sequence of scanning points, driving the probe beam to arrive at each enhanced sampling point in sequence, and the wavefront sensor synchronously collects data, thereby completing the entire enhanced scanning task. This achieves precise matching between scanning resources and problem types, greatly improving the efficiency and effectiveness of enhanced scanning, ensuring that the output data is highly compatible with subsequent analysis requirements, and optimizing the overall data processing flow.

[0046] In some embodiments of the present invention, such as Figure 5 As shown, in step S20, the first wavefront dataset and the second wavefront dataset are fused and reconstructed to calculate the continuous distribution of optical performance parameters of the lens under test within the effective optical region, including: Based on the first wavefront dataset, a parameter distribution base field covering the effective optical region is generated to represent the optical performance. Based on the second wavefront dataset, multiple local high-density data points were identified as parameter anchors in both low-confidence regions and regions of abrupt changes in optical parameters. The parameter anchor points are mapped to the corresponding spatial locations in the parameter distribution basis field, and the parameter values ​​at the locations of the parameter anchor points in the parameter distribution basis field are corrected using the values ​​of the parameter anchor points as the constraint targets. After calibration, the parameter distribution base field is smoothed and optimized to obtain the optical performance parameter distribution.

[0047] First, a baseline field of optical performance parameter distribution covering the effective optical region is generated. Using the first wavefront dataset as input, standard wavefront reconstruction algorithms, such as Zernike polynomial fitting or Fourier transform, are used to process the first wavefront dataset. Since the sampling points of the first wavefront dataset are relatively sparse but cover the entire lens, the reconstructed distribution can reflect the global trend and basic outline of optical performance, forming a preliminary and continuous parameter distribution baseline field. However, the accuracy and reliability may be insufficient in the identified low-confidence regions and regions of abrupt changes in optical parameters.

[0048] Based on the second wavefront dataset, multiple local high-precision data points were determined as parameter anchor points. The second wavefront dataset was derived from targeted enhanced scans and has a high sampling density in a specific region. To extract more representative high-precision information through local data processing, we can use all the high-density sampling data in each small local neighborhood to calculate a more robust and accurate optical parameter value, such as the spherical diopter at the center of the neighborhood, through local fitting or averaging. This value, together with the precise spatial coordinates, is defined as a parameter anchor point.

[0049] Then, the parameter anchor points are mapped to the base field and corrected. All calculated parameter anchor points are mapped to the corresponding positions of the parameter distribution base field generated in the first step, according to their spatial coordinates. The precise values ​​of these anchor points are used as mandatory constraint targets to locally correct the base field. For each anchor point, the parameter value of the anchor point on the base field will be directly replaced with the value of the anchor point; or an optimization algorithm will be used to force the base field to move closer to the value of the anchor point at the current anchor point, fixing the initially drawn base field to a more accurate height at certain key positions.

[0050] Finally, the system performs smoothing optimization on the corrected base field to obtain the optical performance parameter distribution. The optical performance parameter distribution maintains the accuracy of the enhanced data at the anchor point and ensures the physical rationality and continuity of the entire domain through smoothing constraints, thus achieving an efficient unity and complementary advantages between global trends and local accuracy.

[0051] In some embodiments of the present invention, step S20 involves smoothing and optimizing the parameter distribution substrate field to obtain the optical performance parameter distribution, including: A boundary value problem is pre-defined. The corrected parameter distribution basis field is used as the initial field for solving the problem. The spatial location and value of the parameter anchor points are used as fixed boundary conditions in the boundary value problem. The optical performance parameter distribution that meets the requirements of full-field smoothness is obtained through numerical solution, and the smoothing optimization process is completed.

[0052] The system undergoes smoothing optimization, transforming it into a numerical solution of a boundary value problem with a well-defined mathematical framework. First, the governing equations are pre-defined, using either the Laplace or Poisson equations. The steady-state solution naturally possesses excellent smoothness, minimizing non-physical fluctuations across the entire field. Then, the corrected parameter distribution base field is set as the initial field for numerical iteration, providing a good starting point for the governing equations that integrates global trends and local corrections. Simultaneously, the precise spatial locations and values ​​of all parameter anchor points are defined as strictly adhered to Dirichlet boundary conditions, ensuring that the high-precision data acquired by the enhanced scan is preserved without loss during optimization. Based on this, the system utilizes finite difference... Numerical computation techniques such as the finite element method or the finite element method are used to solve the boundary value problem on the computational grid corresponding to the effective optical region. That is, under the premise that all anchor point values ​​are forcibly fixed, a distribution field that makes the entire region satisfy the above smoothing equation is found. The result obtained after convergence is obtained, and the final distribution of optical performance parameters is obtained. This ensures the global optimality and theoretical consistency of the smoothing effect, avoids filtering algorithms that rely on experience, eliminates discontinuous changes that may be caused by local correction, and the framework based on the numerical solution of mature partial differential equations has high robustness and universality. It provides the entire detection system with an efficient, reliable core optimization module that does not require complex parameter tuning for specific lens types.

[0053] Based on the same inventive concept as the optical performance distribution detection method for an optometric lens described in the foregoing embodiments, the present invention also provides an optical performance distribution detection system for an optometric lens, comprising: The scanning acquisition module performs wavefront sensing scanning on the effective optical area of ​​the lens under test, including: Perform an initial sparse sampling scan to obtain the first wavefront dataset; Based on the analysis of the first wavefront dataset, a preliminary optical performance distribution was reconstructed, and low-confidence regions and regions of abrupt changes in optical parameters were identified in the preliminary optical performance distribution, forming the preliminary identification results; Based on the preliminary identification results, an enhanced scan was generated and executed to perform encrypted sampling of low-confidence regions and regions with abrupt changes in optical parameters, thereby obtaining the second wavefront dataset. The reconstructed calculation module merges and reconstructs the first and second wavefront datasets to calculate the continuous distribution of optical performance parameters of the lens under test within the effective optical region. The results generation module generates corresponding detection results based on the distribution of optical performance parameters.

[0054] The detection system described above in this invention can effectively realize the optical performance distribution detection method of optometric lenses, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.

[0055] In some embodiments of the present invention, the scanning acquisition module includes: The identification and prediction unit identifies the lens type of the lens under test and predicts the high-probability variation area of ​​the optical performance parameters of the lens under test within the effective optical area based on the lens type. The sampling unit is planned based on the high probability of change area, and a non-uniform initial scanning path is planned. The initial scanning path arranges relatively dense sampling points in the high probability of change area and relatively sparse sampling points in other areas of the effective optical area. The data acquisition unit performs wavefront sensing scanning along the initial scanning path to acquire the first wavefront dataset.

[0056] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.

[0057] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for detecting the optical performance distribution of an optometric lens, characterized in that, Includes the following steps: S10: Perform wavefront sensing scanning on the effective optical area of ​​the lens under test, including: Perform an initial sparse sampling scan to obtain the first wavefront dataset; Based on the analysis of the first wavefront dataset, a preliminary optical performance distribution is reconstructed, and low-confidence regions and abrupt changes in optical parameters are identified in the preliminary optical performance distribution, forming a preliminary identification result; Based on the preliminary identification results, an enhanced scan is generated and executed to perform encrypted sampling on the low-confidence region and the region of abrupt change in optical parameters to obtain the second wavefront dataset. S20: The first wavefront dataset and the second wavefront dataset are fused and reconstructed to calculate the continuous distribution of optical performance parameters of the lens under test within the effective optical region; S30: Generate corresponding detection results based on the distribution of the optical performance parameters.

2. The method for detecting the optical performance distribution of optometric lenses according to claim 1, characterized in that, In step S10, an initial sparse sampling scan is performed to obtain the first wavefront dataset, including: Identify the lens type of the lens under test, and predict the high-probability variation area of ​​the optical performance parameters of the lens under test within the effective optical area based on the lens type; Based on the high-probability change region, a non-uniform initial scanning path is planned; the initial scanning path arranges relatively dense sampling points in the high-probability change region and relatively sparse sampling points in other regions of the effective optical region. The wavefront sensing scan is performed along the initial scanning path to obtain the first wavefront dataset.

3. The method for detecting the optical performance distribution of optometric lenses according to claim 2, characterized in that, In step S10, predicting the high-probability variation region of the optical performance parameters of the lens under test within the effective optical region based on the lens type includes: Obtain the theoretical distribution data of the design optical parameters corresponding to the lens type; A two-dimensional coordinate system is established within the effective optical region, and the parameter gradient values ​​at each coordinate point in the two-dimensional coordinate system are calculated based on the theoretical distribution data. A first threshold is set, and the region formed by the coordinates of the parameter gradient values ​​exceeding the first threshold is determined as the high-probability change region.

4. The method for detecting the optical performance distribution of optometric lenses according to claim 3, characterized in that, In step S10, the first threshold is set according to the prescription parameters of the lens to be tested corresponding to the lens type. The prescription parameters include at least one of spherical power, cylindrical power, or ADD value, and the first threshold increases as the absolute value of the prescription parameters increases.

5. The method for detecting the optical performance distribution of optometric lenses according to claim 1, characterized in that, In step S10, low-confidence regions and abrupt changes in optical parameters are identified in the preliminary optical performance distribution, including: Based on the preliminary optical performance distribution, the corresponding theoretical wavefront slope distribution is derived. The standard deviation of the difference between the measured wavefront slope and the theoretical wavefront slope of the sampling points in the effective optical region is calculated and denoted as the wavefront aberration fitting residual of the current sampling point. The continuous region corresponding to the wavefront aberration fitting residual being greater than the preset residual threshold is defined as the low confidence region. Calculate the spatial gradient magnitudes of spherical and cylindrical power in the preliminary optical performance distribution; define the continuous region where the gradient magnitude is greater than a preset gradient threshold as the region of abrupt change in optical parameters.

6. The method for detecting the optical performance distribution of optometric lenses according to claim 1, characterized in that, In step S10, based on the preliminary identification results, an enhanced scan is generated and performed, including: Based on the low-confidence region, generate a regular grid encrypted scan path covering the low-confidence region; Based on the optical parameter abrupt change region, determine the boundary direction or maximum gradient direction of the optical parameter abrupt change region, and generate a contour tracking scanning path based on the boundary direction or a linear scanning path based on the maximum gradient direction. The enhanced scan is performed sequentially using the regular grid encryption scan path, the contour tracking scan path, or the line scan path.

7. The method for detecting the optical performance distribution of optometric lenses according to claim 1, characterized in that, In step S20, the first wavefront dataset and the second wavefront dataset are fused and reconstructed to calculate the continuous distribution of optical performance parameters of the lens under test within the effective optical region, including: Based on the first wavefront dataset, a parameter distribution base field covering the optical performance of the effective optical region is generated; Based on the second wavefront dataset, multiple local high-density data points are determined as parameter anchors in the low-confidence region and the optical parameter mutation region, respectively. The parameter anchor point is mapped to the corresponding spatial position of the parameter distribution base field, and the parameter value at the position of the parameter anchor point in the parameter distribution base field is corrected using the value of the parameter anchor point as the constraint target. After the correction is completed, the parameter distribution base field is smoothed and optimized to obtain the optical performance parameter distribution.

8. The method for detecting the optical performance distribution of optometric lenses according to claim 7, characterized in that, In step S20, the parameter distribution substrate field is smoothed and optimized to obtain the optical performance parameter distribution, including: A boundary value problem is preset, and the corrected parameter distribution basis field is used as the initial field for solving. The spatial position and value of the parameter anchor point are used as the fixed boundary conditions in the boundary value problem. The optical performance parameter distribution that meets the requirements of full-field smoothness is obtained through numerical solution, and the smoothing optimization process is completed.

9. A system for detecting the optical performance distribution of optometric lenses, characterized in that, The method for detecting the optical performance distribution of optometric lenses as described in any one of claims 1 to 8 includes: The scanning acquisition module performs wavefront sensing scanning on the effective optical area of ​​the lens under test, including: Perform an initial sparse sampling scan to obtain the first wavefront dataset; Based on the analysis of the first wavefront dataset, a preliminary optical performance distribution is reconstructed, and low-confidence regions and abrupt changes in optical parameters are identified in the preliminary optical performance distribution, forming a preliminary identification result; Based on the preliminary identification results, an enhanced scan is generated and executed to perform encrypted sampling on the low-confidence region and the region of abrupt change in optical parameters to obtain the second wavefront dataset. The reconstruction calculation module merges and reconstructs the first wavefront dataset and the second wavefront dataset to calculate the continuous distribution of optical performance parameters of the lens under test within the effective optical region. The result generation module generates corresponding detection results based on the distribution of the optical performance parameters.

10. The optical performance distribution detection system for optometric lenses according to claim 9, characterized in that, The scanning acquisition module includes: The identification and prediction unit identifies the lens type of the lens under test and predicts the high-probability variation area of ​​the optical performance parameters of the lens under test within the effective optical area based on the lens type. The sampling unit is planned based on the high-probability change region, and a non-uniform initial scanning path is planned; the initial scanning path arranges relatively dense sampling points in the high-probability change region and relatively sparse sampling points in other regions of the effective optical region. The data acquisition unit performs the wavefront sensing scan along the initial scanning path to acquire the first wavefront dataset.

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