Freeform quantized visual off-axis contact lens and method of design

Abstract

The application relates to the field of corneal molding lenses, and discloses a free-axial quantitative visual defocus corneal molding lens and a design method thereof. The design method comprises the following steps: obtaining corneal defocus parameters and a plurality of partition curvature parameters of a target cornea, wherein the partition curvature parameters are the curvatures of the target cornea at corresponding target axial positions; constructing a plurality of partition function models based on the partition curvature parameters and the corneal defocus parameters; performing boundary smoothing processing on the partition function models to obtain a corneal function model; and generating corresponding corneal molding lens design parameters based on the corneal function model. The embodiment of the application can flexibly obtain the partition curvature parameters and more accurately fit the complex morphology of the cornea.

Description

Technical Field

[0001] This application relates to the field of orthokeratology lens technology, and in particular to a free-axis quantitative visual defocus orthokeratology lens and its design method. Background Technology

[0002] Orthokeratology lenses are rigid corneal contact lenses that use reverse geometry to cause corneal epithelial cells to migrate during wear, thereby redistributing the corneal epithelial cells to change the geometry of the anterior surface of the cornea to correct vision.

[0003] Currently, orthokeratology lenses are typically designed with a base curve, a reversal curve, a fitting curve, and a peripheral curve, arranged from the center outwards. When worn, the base curve is typically manipulated by factors such as the fluid forces of tear film between the lens and the cornea to create positive pressure on the anterior surface of the cornea. This pressure may cause corneal epithelial cells to migrate, altering the corneal refractive power. However, existing orthokeratology lenses do not adequately adapt to individual differences, potentially leading to poor reshaping results or a strong foreign body sensation after wearing. Summary of the Invention

[0004] The purpose of this application is to provide a free-axis quantitative visual defocus orthokeratology lens and its design method, which can flexibly obtain the curvature parameters of the zones and more accurately fit the complex shape of the cornea.

[0005] This application provides a method for designing orthokeratology lenses, including:

[0006] Obtain corneal defocus parameters and multiple zone curvature parameters of the target cornea; the zone curvature parameters are the curvature of the target cornea on the corresponding target axis.

[0007] Based on the partition curvature parameters and the corneal defocus parameters, multiple partition function models are constructed;

[0008] The boundary smoothing process is applied to the partition function model to obtain the corneal function model;

[0009] Based on the corneal function model, corresponding orthokeratology lens design parameters are generated.

[0010] In some embodiments, the number and / or range of the target axes are adjustable.

[0011] In some embodiments, a method for determining the partition curvature parameter includes:

[0012] In response to the received axis configuration operation, a plurality of the target axis positions are determined;

[0013] The curvature of the cornea on the target axis is determined to obtain the curvature parameter of the partition.

[0014] In some embodiments, constructing multiple partition function models based on the partition curvature parameter and the corneal defocus parameter includes:

[0015] Obtain the initial partitioning function model; the initial partitioning function model is obtained based on the even-order aspherical formula and includes the aspherical coefficients and polynomial coefficients to be solved.

[0016] Based on the partition curvature parameters and the corneal defocus parameters, a set of constraint equations is established;

[0017] Solve the constraint equations and assign values ​​to the non-spherical coefficients and polynomial coefficients in the initial partitioning function model based on the solution results to obtain the partitioning function model.

[0018] In some embodiments, the boundary smoothing process performed on the partitioning function model includes:

[0019] The partitioning function model is sampled to obtain a sampling direction dataset and its corresponding multidimensional radial observation data matrix; the sampling direction dataset contains multiple sampling points distributed at an angle;

[0020] Angle normalization processing is performed on the sampling direction dataset and the preset target resampling direction dataset respectively to obtain the normalized original sampling direction set and the normalized target direction set;

[0021] The normalized original sampling direction set and its corresponding multidimensional radial observation data matrix are reordered to establish a monotonically increasing sampling direction sequence and its corresponding ordered observation data matrix.

[0022] Based on the sampling direction sequence and the ordered observation data matrix, a continuously distributed data matrix is ​​generated using periodic extension and conformal interpolation methods to obtain the corneal function model.

[0023] In some embodiments, generating a continuously distributed resampled data matrix based on the sampling direction sequence and the ordered observation data matrix using periodic extension and conformal interpolation methods includes:

[0024] The ordered observation data matrix and its corresponding sampling direction sequence are periodically extended to generate an extended sampling direction sequence and its corresponding extended observation data matrix containing complete periodic neighborhood information.

[0025] For each dimension of the multidimensional radial observation data matrix, the extended sampling direction sequence is used as the interpolation node, and the data sequence corresponding to that dimension in the extended observation data matrix is ​​used as the node value. An interpolation algorithm with shape preservation characteristics is used to perform fitting calculation on the normalized target direction set to obtain the interpolation result of that dimension on the target direction set.

[0026] The interpolation results from all dimensions are aggregated to generate the corneal function model.

[0027] In some embodiments, generating corresponding orthokeratology lens design parameters based on the corneal function model includes:

[0028] Based on the corneal diameter parameters of the target cornea, the base curve diameter parameters are determined;

[0029] Based on the corneal function model, the minimum curvature and its corresponding axial direction are determined.

[0030] Based on the minimum curvature and the target correction degree, the curvature parameter of the base arc BC is calculated;

[0031] Based on the corneal function model, the circumferential symmetry of the target cornea is evaluated and the corneal defocus parameters are verified to determine the positioning arc design type parameters and the reversal arc zone design parameters.

[0032] By integrating the minimum curvature, the axis direction, the base curve BC curvature parameter, the corneal diameter parameter, the base curve area diameter parameter, the verified corneal defocus parameter, the positioning arc design type parameter, and the reversal arc area design parameter, the orthokeratology lens design parameters are obtained.

[0033] This application also provides a free-axis quantified visual defocus orthokeratology lens, which is designed using the above-described orthokeratology lens design method.

[0034] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method for designing orthokeratology lenses.

[0035] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for designing orthokeratology lenses.

[0036] The beneficial effects of this application are as follows: By dividing the target cornea into multiple regions, constructing multiple independent regional function models for each region, and performing boundary smoothing, a continuous and accurate corneal function model is generated. Based on this model, corresponding orthokeratology lens design parameters are then generated. This allows for flexible acquisition of regional curvature parameters, more accurately fitting the complex morphology of the cornea. This enables orthokeratology lens design to more precisely match the actual morphology and individual needs of the patient's cornea, especially when dealing with complex or irregular corneal topography. It can more accurately capture curvature information in key directions, thereby improving the accuracy of the regional function model construction, ultimately optimizing the design effect of the orthokeratology lens, and enhancing the accuracy and comfort of correction. Attached Figure Description

[0037] Figure 1 This diagram illustrates the application environment of the orthokeratology lens design method provided in this embodiment.

[0038] Figure 2 This is a flowchart of the design method for orthokeratology lenses provided in the embodiments of this application.

[0039] Figure 3 This is a flowchart of a method for constructing multiple partition function models provided in an embodiment of this application.

[0040] Figure 4 This is a flowchart of a method for boundary smoothing of a partition function model provided in an embodiment of this application.

[0041] Figure 5 This is a flowchart of a method for generating orthokeratology lens design parameters provided in an embodiment of this application.

[0042] Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and drawings are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0045] 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 application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application. Furthermore, the information, data, and signals involved in the embodiments of this application are all authorized by relevant parties or have been fully authorized by all parties, and the collection, use, and processing of related data comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0046] In traditional orthokeratology lens design, the lack of precise quantification of individual corneal geometric differences across different axes, particularly the insufficient modeling of the coupling relationship between regional curvature parameters and corneal defocus parameters, prevents lens designs from adapting to the asymmetric characteristics of the cornea. This results in uneven pressure distribution in the base curve region, affecting the controllability of corneal epithelial cell migration and lens stability. Key performance indicators such as the predictability of orthokeratology results and wearing comfort are significantly constrained. For example, when customizing lenses for patients with uneven corneal astigmatism, the curvature of the target cornea at a specific axis deviates significantly from the global average. Existing design methods, using fixed axis configurations or simplified curvature models, cannot accurately characterize the actual curvature changes of the cornea at the target axis. Consequently, during lens wear, the tear fluid hydrodynamic distribution in the reverse and adaptation curve regions becomes unbalanced, generating abnormal positive pressure in localized areas. This causes corneal epithelial cell migration paths to deviate from the expected path, resulting in fluctuating orthokeratology results and increased foreign body sensation after wear.

[0047] If the above problems are not resolved, the individualized fitting capability of orthokeratology lenses will continue to be limited. The resulting instability in the reshaping effect may prevent vision correction from achieving clinical expectations. At the same time, abnormal mechanical interaction between the lens and the cornea will increase the risk of corneal epithelial damage or inflammatory response, further affecting the safety and reliability of the treatment process.

[0048] Based on this, embodiments of this application provide a free-axis quantitative visual defocus orthokeratology lens, its design method, device and medium. By constructing multiple partition function models and performing boundary smoothing processing, a continuous and accurate corneal function model is generated. The orthokeratology lens designed in this way can better adapt to individual corneal characteristics, improve the shaping effect and wearing comfort.

[0049] Figure 1 This diagram illustrates the application environment of the orthokeratology lens design method provided in this embodiment. (See also...) Figure 1This method is applied to a corneal reshaping lens design system. The system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be at least one of a mobile phone, tablet, laptop, or vehicle-mounted terminal. The server 120 can be a standalone server or a server cluster consisting of several servers. The terminal 110 sends the corneal defocus parameters and multiple zone curvature parameters of the target cornea to the server 120. The server 120 acquires the corneal defocus parameters and multiple zone curvature parameters of the target cornea, constructs multiple zone function models based on the zone curvature parameters and corneal defocus parameters, performs boundary smoothing on the zone function models to obtain a corneal function model, and generates corresponding corneal reshaping lens design parameters based on the corneal function model. The zone curvature parameters represent the curvature of the target cornea at the corresponding target axis.

[0050] It should be understood that Figure 1 The application scenarios shown are merely examples. In practical applications, the orthokeratology lens design method provided in this application embodiment can also be applied to other scenarios. For example, the above-described orthokeratology lens design method can be directly applied to terminal 110. Terminal 110 is used to obtain the corneal defocus parameters and multiple zone curvature parameters of the target cornea. Based on the zone curvature parameters and corneal defocus parameters, multiple zone function models are constructed. The zone function models are then subjected to boundary smoothing processing to obtain a corneal function model. Based on the corneal function model, corresponding orthokeratology lens design parameters are generated.

[0051] See Figure 2 In one embodiment, a method for designing orthokeratology lenses is provided. The subject executing the method may be a terminal device or a server, including but not limited to steps S201 to S204.

[0052] Step S201: Obtain the corneal defocus parameters and multiple zone curvature parameters of the target cornea.

[0053] Corneal defocus parameters are parameters used to describe the refractive state of the cornea, such as myopia, astigmatism, and astigmatic axis. They are important bases for assessing an individual's visual status and guiding the design of orthokeratology lenses.

[0054] Regional curvature parameters refer to the curvature of the cornea along a corresponding target axis. In essence, regional curvature parameters describe the curvature value of the cornea in a specific region or direction. Because the cornea is not a perfectly symmetrical sphere, its curvature may differ in different regions and directions; these regional curvature parameters provide a more precise reflection of the corneal geometry.

[0055] In practical applications, corneal defocus parameters can be obtained in several ways. For example, professional optometry equipment, such as a comprehensive or automated refractometer, can be used to examine the user's eyes and obtain defocus parameters such as myopia, astigmatism, and astigmatic axis. Another method is for the user to provide a recent optometry report from an ophthalmology hospital or optometry center, which typically records corneal defocus parameters in detail. For obtaining zonal curvature parameters, a corneal topography instrument can be used. This device scans the corneal surface and generates a detailed corneal curvature distribution map. Operators can manually select specific axes on the cornea as needed and read the curvature values ​​at these axes from the curvature distribution map to obtain the zonal curvature parameters. For example, multiple axes such as 0 degrees, 45 degrees, 90 degrees, and 135 degrees can be manually selected and the corneal curvature at these axes recorded.

[0056] Step S202: Based on the partition curvature parameters and corneal defocus parameters, construct multiple partition function models.

[0057] A regional function model is a mathematical model used to describe the geometry of different regions of the target cornea. By dividing the target cornea into multiple regions and constructing an independent function model for each region, the complex morphology of the cornea can be more accurately fitted.

[0058] When constructing a partition function model, a basic mathematical model can be chosen first, such as a polynomial function or an aspherical function, as the initial model. Then, the obtained partition curvature parameters and corneal defocus parameters are used as constraints and substituted into this initial model. By solving these constraints, the unknown coefficients in the model can be determined, thus obtaining the function model corresponding to each partition. For example, for a cornea with four partitions, a quadratic polynomial function model can be constructed for each partition, and the coefficients of each polynomial can be determined using the curvature data within that partition and the overall defocus parameter.

[0059] Step S203: Perform boundary smoothing on the partition function model to obtain the corneal function model.

[0060] The corneal function model is a unified function model that mathematically describes the entire corneal surface. This model provides a continuous and complete geometric description of the corneal surface by smoothing and integrating multiple regional function models.

[0061] Since each partition function model is constructed independently, discontinuities or unevenness may occur at partition boundaries. To obtain a continuous and complete description of the corneal surface, these partition function models need to be smoothed. One approach is to introduce transition functions at partition boundaries, allowing adjacent partition function models to connect smoothly at those boundaries. For example, mathematical methods such as spline interpolation or Bézier curves can be used to fit at partition boundaries to eliminate discontinuities, thereby generating a globally smooth corneal function model.

[0062] Step S204: Based on the corneal function model, generate the corresponding orthokeratology lens design parameters.

[0063] Orthokeratology lens design parameters refer to the specific parameters used to guide the manufacturing of orthokeratology lenses, such as base curve curvature, reversal curve design, positioning curve design type, and optical zone diameter. These parameters directly determine the lens geometry and corrective effect.

[0064] After obtaining a continuous corneal function model, this model can be used to calculate and determine various design parameters for orthokeratology lenses. For example, the minimum curvature point of the cornea and its corresponding axial direction can be extracted from the corneal function model, and the base curve (BC) curvature parameter can be calculated in conjunction with the target correction power. Simultaneously, this model can also be used to assess the circumferential symmetry of the cornea and, in conjunction with corneal defocus parameters, determine the design type parameters for the positioning arc and the design parameters for the reversal arc zone. For instance, if the corneal function model shows high corneal asymmetry, an asymmetric positioning arc may need to be designed to provide better stability.

[0065] The following example will provide a more detailed explanation of the above technical solution:

[0066] Suppose user A needs custom-made orthokeratology lenses due to myopia. First, a professional corneal topography instrument is used to measure user A's cornea and obtain its corneal defocus parameters. For example, for user A's right cornea, the myopia is -3.00D, the astigmatism is -0.50D, and the astigmatism axis is 180 degrees. Simultaneously, multiple corneal curvature parameters are obtained at eight target axes: 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 170 degrees, and 315 degrees. For example, starting from the 0-degree axis, the curvature parameters for each zone are 42.75D, 43D, 43.75D, 43D, 42.75D, 43D, 43.75D, and 43D.

[0067] Next, based on the obtained regional curvature parameters and corneal defocus parameters, multiple regional function models are constructed. Specifically, the cornea can be divided into eight quadrants, and an independent quadratic aspheric function model is constructed for each quadrant. Each model contains aspheric coefficients and polynomial coefficients to be solved. Then, the corneal curvature data within each quadrant and the overall defocus parameters are used as constraints to establish a system of constraint equations. By numerically solving this system of equations, the aspheric coefficients and polynomial coefficients in each regional function model can be determined, thus obtaining eight independent regional function models that preliminarily describe the morphology of each corneal region.

[0068] However, these eight partition function models may exhibit discontinuities at their boundaries. To obtain a continuous and smooth overall description of the corneal surface, boundary smoothing processing of these partition function models is required. Specifically, each partition function model is sampled to obtain a series of sampling direction datasets and their corresponding multidimensional radial observation data matrices. These sampling points are angularly distributed. Then, angle normalization processing is performed on the sampling direction datasets and the preset target resampling direction datasets, respectively, to obtain normalized original sampling direction sets and normalized target direction sets. Next, the normalized original sampling direction sets and their corresponding multidimensional radial observation data matrices are reordered to establish a monotonically increasing sampling direction sequence and its corresponding ordered observation data matrix. Finally, based on this sampling direction sequence and the ordered observation data matrix, a continuously distributed data matrix is ​​generated using periodic extension and conformal interpolation methods, thereby obtaining a unified and continuous corneal function model. This corneal function model can accurately describe the entire corneal surface geometry of user A.

[0069] Finally, based on this corneal function model, corresponding orthokeratology lens design parameters are generated. For example, the optical zone diameter parameter is determined based on user A's corneal diameter parameter. The minimum curvature and its corresponding axis direction are determined from the corneal function model. Combining this minimum curvature and the target correction power, the base curve (BC) curvature parameter is calculated. Simultaneously, the circumferential symmetry of user A's cornea is evaluated using the corneal function model, and the corneal defocus parameter is verified to determine the most suitable positioning arc design type parameter and reversal arc zone design parameter. For example, if the evaluation results show a certain degree of corneal asymmetry, it may be necessary to design an asymmetrical positioning arc to ensure stable lens positioning on the ocular surface. Finally, all these parameters, including the minimum curvature, axis direction, base curve (BC) curvature parameter, corneal diameter parameter, optical zone diameter parameter, verified corneal defocus parameter, positioning arc design type parameter, and reversal arc zone design parameter, are integrated to obtain a complete set of orthokeratology lens design parameters tailored to user A's individual needs.

[0070] The aforementioned method, by acquiring individualized corneal defocus parameters and multiple regional curvature parameters, can more precisely capture the complex geometry of the cornea, a significant difference from existing technologies that may rely on only a few points or simplified models for design. By constructing multiple regional function models and performing boundary smoothing, this method can generate a continuous and accurate corneal function model, overcoming the problems of insufficient fitting accuracy or boundary discontinuities that may occur in traditional methods when dealing with corneal irregularities. For example, when dealing with astigmatic or irregular astigmatic corneas, traditional methods may struggle to accurately describe their complex curvature changes, leading to poor reshaping effects. This method, through regional modeling and global smoothing, can more accurately reflect the true morphology of the cornea, thus providing a more solid data foundation for the design of orthokeratology lenses. Therefore, the orthokeratology lens design parameters generated based on this high-precision corneal function model can better adapt to individual corneal characteristics, potentially improving reshaping effects and wearing comfort, and effectively solving the problem of poor adaptation of existing orthokeratology lenses to individual differences.

[0071] In some embodiments, the number and / or range of target axes are adjustable.

[0072] This application's solution allows for adjustable number and / or range of target axes, enabling flexible selection of more or fewer axes, or concentration of axes in specific corneal regions, when acquiring corneal defocus parameters and multiple zone curvature parameters of the target cornea, based on the actual complexity of the target cornea or specific correction needs. For example, for highly irregular corneas, the number of axes can be increased to obtain denser curvature data; for regular astigmatism, the number of axes can be reduced to simplify calculations. This dynamic adjustment capability allows subsequent steps of constructing multiple zone function models based on zone curvature parameters and corneal defocus parameters, as well as boundary smoothing of the zone function models, to process more refined or targeted corneal data, thereby generating more accurate and personalized corneal function models, ultimately resulting in more optimized orthokeratology lens design parameters.

[0073] The following is a concrete example. Suppose a patient has highly irregular astigmatism in their cornea, particularly with significant curvature variations in a specific quadrant (e.g., inferotemporal). When designing orthokeratology lenses, the operator can use the software's user interface to increase the number of target axes from the default 8 to 16, and concentrate the axis range in the inferotemporal quadrant (e.g., from 225 degrees to 315 degrees) for more intensive sampling. Based on these settings, the system will acquire more detailed zone curvature parameters on these specific axes. Specifically, the user can select the "Custom Axis" mode in the software interface, then enter "Number of Axis: 16," and specify "Starting Angle: 225 degrees, Ending Angle: 315 degrees." The system will automatically calculate and display these axes, guiding measurement equipment such as a corneal topographer to acquire data on these specified axes, or extracting data for the corresponding axes from existing corneal topography data. This more refined and targeted data will be used for subsequent partition function model construction and boundary smoothing, thereby generating a corneal function model that more accurately reflects the corneal characteristics of the patient.

[0074] By allowing the number and / or range of target axes to be adjustable, the method of this application can flexibly adjust the precision of data acquisition and model construction according to individual differences in the cornea and correction needs of different patients. This enables more accurate capture of local features and irregularities of the cornea when constructing the regional function model, especially in cases of complex corneal morphology or high astigmatism. Ultimately, this helps to generate a more accurate corneal function model, thereby designing more personalized orthokeratology lenses that better conform to the actual corneal morphology of patients, significantly improving correction effects and wearing comfort.

[0075] In some embodiments, the method for determining partition curvature parameters includes: determining a plurality of target axes in response to a received axis configuration operation; determining the curvature of the cornea on the target axes to obtain partition curvature parameters.

[0076] Axis configuration refers to the act of setting, selecting, or adjusting a specific direction (i.e., axis) on the cornea used for measuring or analyzing curvature by an operator or implementing entity. This operation allows operators to flexibly define the direction of the corneal area to be focused on based on the patient's corneal characteristics, correction needs, or experience. For example, this operation can be implemented through a graphical user interface (GUI), where operators can directly click, drag, or input angle values ​​on the corneal topography map to specify the axis; or, a set of axes can be automatically recommended or generated based on a preset algorithm (e.g., based on corneal astigmatism axis, topographic feature points, etc.), allowing users to fine-tune it.

[0077] Determining multiple target axes refers to clarifying the specific directions on the cornea where curvature measurement needs to be performed, based on the results of the axis configuration operation. This step provides a precise positioning basis for subsequent curvature parameter acquisition, ensuring that the acquired curvature data matches the design requirements. For example, if the axis configuration operation involves the operator manually inputting angle values, these angle values ​​are directly used as target axes; if the axis configuration operation involves the user selecting points on the graphical interface, the execution entity converts these points into corresponding angular directions as target axes; or, the system can automatically generate them according to preset rules (such as setting an axis every 30 degrees, or densifying axes in the astigmatism axis and its perpendicular direction).

[0078] Determining the curvature of the cornea along the target axis involves measuring or calculating the degree of curvature of the cornea's horizontal meridian along the determined target axis. This step acquires the foundational data used to construct the partition function model, which directly reflects the optical properties of the cornea in key directions. For example, raw data acquired by a corneal topography instrument (such as Pentacam or Orbscan) can be used to extract the corresponding curvature values ​​at each target axis through image processing and data analysis algorithms; alternatively, if the raw data is a three-dimensional point cloud, the curvature can be determined by fitting local surfaces or calculating local normal vectors.

[0079] This application's solution dynamically determines multiple target axes by receiving axis configuration operations, and then precisely determines the corneal curvature at these target axes to obtain zonal curvature parameters. Specifically, the axis configuration operation serves as input, allowing designers to flexibly define the corneal directions of interest based on the patient's individual needs or the specific morphology of the cornea. Subsequently, the executing entity transforms the abstract configuration instructions into specific, measurable target axes according to the configuration operation. Once the target axes are defined, the system can extract or calculate the corresponding corneal curvature values ​​at these precisely defined axes using existing corneal measurement data. Finally, these curvature values ​​with clearly defined axis information are integrated to form zonal curvature parameters, serving as key inputs for subsequent construction of zonal function models. This dynamic and configurable parameter acquisition method enables orthokeratology lens design to more accurately capture the actual topographic features of the patient's cornea, especially when dealing with complex or irregular astigmatism. It avoids information loss or inaccuracies that may result from using fixed axes, thus providing more reliable and personalized basic data for subsequent zonal function model construction and orthokeratology lens design.

[0080] The following is a concrete example. Suppose a patient has a corneal astigmatism axis of 30 degrees, and their corneal topography shows significant asymmetry in the 90-degree direction. The user configures the axis through a graphical interface. For example, on the corneal topography display interface, the user can manually input angle values ​​such as 30 degrees, 90 degrees, 120 degrees, and 180 degrees, or directly drag the indicator line on the interface to select these axes. After receiving these operations, the system determines multiple target axes as 30 degrees, 90 degrees, 120 degrees, and 180 degrees. Subsequently, the executing entity determines the corneal curvature at these determined target axes from the acquired corneal topography data. For example, at the 30-degree axis, the corneal curvature is 43.50D; at the 90-degree axis, the corneal curvature is 44.25D; at the 120-degree axis, the corneal curvature is 43.75D; and at the 180-degree axis, the corneal curvature is 43.00D. Finally, these curvature values ​​and their corresponding axis information are integrated to obtain the partition curvature parameters, which can be represented as a set of (axis, curvature) pairs: (30 degrees, 43.50D), (90 degrees, 44.25D), (120 degrees, 43.75D), (180 degrees, 43.00D). These parameters are then used to construct the partition function model.

[0081] Through the above technical solution, this application enables flexible acquisition of zonal curvature parameters. This allows the design of orthokeratology lenses to more accurately match the actual shape and personalized needs of the patient's cornea, especially when dealing with complex or irregular corneal topography. It can more accurately capture curvature information in key directions, thereby improving the construction accuracy of the zonal function model, ultimately optimizing the design effect of the orthokeratology lens, and improving the accuracy and comfort of correction.

[0082] See Figure 3 In one embodiment, the method for constructing multiple partition function models includes, but is not limited to, steps S301 to S303.

[0083] Step S301: Obtain the initial partitioning function model.

[0084] The initial partitioning function model is obtained based on the even-order aspheric formula and includes aspheric coefficients and polynomial coefficients to be solved. It can be understood that the initial partitioning function model provides a general mathematical framework for describing the local geometry of the cornea. This model is initialized based on the even-order aspheric formula, which is widely used in ophthalmic optics for the mathematical expression of corneal morphology due to its ability to effectively describe the curvature changes of aspherical bodies. This formula typically contains a series of aspheric coefficients and polynomial coefficients to be solved; these coefficients are key variables for adjusting the model to accurately match actual corneal data. For example, the initial partitioning function model can be represented using Zernike polynomials or higher-order aspheric polynomials, which can fit various complex corneal surface shapes by adjusting their coefficients.

[0085] Step S302: Based on the zonal curvature parameters and corneal defocus parameters, establish a set of constraint equations.

[0086] Step S303: Solve the constraint equation system, and assign values ​​to the non-spherical coefficients and polynomial coefficients in the initial partitioning function model based on the solution results to obtain the partitioning function model.

[0087] A set of constraint equations is established to correlate the actual measured corneal data with the initial function model. These equations use the regional curvature parameters and corneal defocus parameters as known conditions, and the aspheric coefficients and polynomial coefficients in the initial regional function model as unknowns to be solved. By establishing these equations, it is ensured that the constructed model accurately reflects the curvature characteristics of the target cornea in different regions and the overall defocus state. For example, the least squares method or optimization algorithms can be used to construct the constraint equations, ensuring that the model satisfies the measured data points while maintaining overall smoothness and rationality.

[0088] By solving the established constraint equations using numerical methods, the specific values ​​of each non-spherical coefficient and polynomial coefficient in the initial partitioning function model can be determined. These values ​​are then assigned to the initial partitioning function model, thereby transforming a general mathematical model into a partitioning function model that precisely describes the local shape of a specific target cornea. For example, Gaussian elimination, iterative methods (such as Newton's method or gradient descent), or specialized optimization solvers can be used to efficiently solve these equations.

[0089] This application's approach first obtains an initial partitioning function model based on even-order aspheric formulas, providing a flexible and well-fitting framework for the mathematical description of local corneal shape. Subsequently, using actual measurement data of the target cornea—namely, partition curvature parameters and corneal defocus parameters—a rigorous set of constraint equations is established, tightly integrating the theoretical model with actual data. By solving these constraint equations and precisely assigning values ​​to the aspheric and polynomial coefficients in the initial model, a partitioning function model that accurately reflects the characteristics of the target cornea is finally obtained. This systematic modeling process ensures that each partitioning function model can accurately capture the local geometric features and optical properties of the cornea, providing high-precision and highly reliable foundational data for subsequent boundary smoothing and orthokeratology lens design.

[0090] The following is a concrete example to illustrate this. When constructing multiple partitioning function models, an initial partitioning function model can be obtained first. This model can be initialized based on a high-order even-degree aspheric formula, for example, using a Zernike polynomial containing radial and angular terms as a foundation, which includes the aspheric coefficients and polynomial coefficients to be solved. Next, based on the partition curvature parameters and corneal defocus parameters obtained from the target cornea, a system of nonlinear constraint equations is established. This system of equations can be set to minimize the sum of squared errors between the curvature and defocus values ​​calculated by the model and the actual measured values, while considering the smoothness constraints of the model parameters. For example, numerical optimization software (such as optimization libraries in MATLAB or Python) can be used to solve this system of nonlinear constraint equations to obtain a set of optimal aspheric coefficients and polynomial coefficients. Finally, these solved coefficients are assigned to the initial partitioning function model to obtain the accurate partitioning function model for the target cornea.

[0091] The aforementioned technical solution ensures a solid mathematical foundation and high accuracy in constructing the corneal partition function model. This method avoids the errors and uncertainties that may arise from simple fitting, enabling the constructed partition function model to more accurately characterize the complex morphology and optical properties of the target cornea. This provides more reliable and refined data support for subsequent corneal function model generation and orthokeratology lens design, thereby contributing to the design of orthokeratology lenses that better meet individual patient needs and offer superior corrective effects.

[0092] See Figure 4 In one embodiment, the method for performing boundary smoothing on the partition function model includes, but is not limited to, steps S401 to S404.

[0093] Step S401: Sample the partition function model to obtain the sampling direction dataset and its corresponding multidimensional radial observation data matrix.

[0094] The sampling direction dataset contains multiple sampling points distributed at an angle.

[0095] Step S402: Perform angle normalization processing on the sampling direction dataset and the preset target resampling direction dataset respectively to obtain the normalized original sampling direction set and the normalized target direction set.

[0096] Step S403: Reorder the normalized original sampling direction set and its corresponding multidimensional radial observation data matrix to establish a monotonically increasing sampling direction sequence and its corresponding ordered observation data matrix.

[0097] Step S404: Based on the sampling direction sequence and the ordered observation data matrix, a continuously distributed data matrix is ​​generated using periodic extension and conformal interpolation methods to obtain the corneal function model.

[0098] Sampling the partition function model aims to transform it into a sampling direction dataset suitable for subsequent processing. For example, data can be extracted from each partition function model by sampling multiple angularly distributed points along a predetermined radial direction (e.g., from the corneal center outwards), such as at angles of 0 degrees, 15 degrees, 30 degrees...345 degrees, using a fixed or adaptive step size to obtain radial curvature or height values. Alternatively, each partition function model can be sampled in a gridded manner in polar coordinates at certain angular intervals (e.g., every 5 or 10 degrees) and radial intervals (e.g., every 0.1 millimeters), resulting in a series of discrete curvature or height data points, forming a sampling direction dataset and a multidimensional radial observation data matrix. The multidimensional radial observation data matrix refers to a series of observation data (such as curvature values, height values, etc.) acquired radially (e.g., from the corneal center to the edge) in each sampling direction. These data constitute a vector, and multiple such vectors combined in different sampling directions form the multidimensional radial observation data matrix.

[0099] Angle normalization aims to standardize angle representations, typically mapping angles to a common range (e.g., [0, 2π) or [0, 360 degrees]) to ensure consistency in subsequent angle comparisons and interpolations. For example, all angle values ​​can be uniformly converted to radians, ensuring they fall within the range [0, 2π), such as adjusting angle values ​​greater than or equal to 2π or less than 0 by adding or subtracting 2π. Alternatively, all angle values ​​can be uniformly converted to degrees, ensuring they fall within the range [0, 360 degrees), such as through modulo operations (angle % 360).

[0100] Reordering refers to arranging the sampled data points in ascending order based on the normalized angle values. Its purpose is to prepare data for interpolation algorithms, as these algorithms typically require ordered input data points to ensure accurate and stable results. For example, standard sorting algorithms (such as quicksort and mergesort) can be used to sort the normalized original sampled direction set, and the corresponding rows or columns in the multidimensional radial observation data matrix can be adjusted simultaneously to maintain data consistency. Alternatively, an index mapping can be constructed to associate each angle in the original sampled direction set with its new position in the sorted sequence, and then the observation data matrix can be rearranged according to this index mapping.

[0101] Periodic extension and conformal interpolation combine two key techniques. Periodic extension handles cyclic angular data, while conformal interpolation ensures the interpolation curve retains the local shape characteristics of the original data. Its role is to create a smooth, continuous, and accurate representation of the entire corneal surface from discrete, potentially non-uniformly distributed sampled data, while preserving the basic features of the original corneal shape. For example, periodic extension can be achieved by adding dummy data points at both ends of the sampling direction sequence, such as copying the beginning of the sequence to the end and vice versa, to simulate periodic boundary conditions and ensure smooth transitions at the boundaries. Conformal interpolation can employ methods such as cubic spline interpolation or PCHIP (Piecewise Cubic Hermite Interpolating Polynomial). These methods consider not only the values ​​of the data points during interpolation but also their derivatives or slopes, thus avoiding unnatural oscillations or sharp inflection points on the interpolation curve and ensuring the smoothness and shape preservation of the interpolation results.

[0102] The proposed solution transforms a discrete mathematical model into processable raw data by sampling the partitioning function model. Subsequently, angle normalization is performed on the sampled direction dataset and the pre-defined target resampled direction dataset to ensure all angle data are within a uniform representation range, laying the foundation for subsequent processing. Next, the normalized raw sampled direction set and its corresponding multidimensional radial observation data matrix are reordered to establish a monotonically increasing sampled direction sequence and its corresponding ordered observation data matrix, providing the necessary data structure for the interpolation algorithm. Finally, based on the sampled direction sequence and the ordered observation data matrix, a continuously distributed data matrix is ​​generated using periodic extension and conformal interpolation methods, yielding the corneal function model. Periodic extension addresses the periodicity of the angle data, ensuring a smooth transition at the 0 / 360 degree boundary, while conformal interpolation guarantees that the interpolation result maintains the local shape characteristics of the data while generating a continuous and accurate representation of the corneal surface. This series of steps effectively integrates the discrete partition function model into a continuous corneal function model, thereby solving the discontinuity problem that may exist at the boundary of the discrete model and ensuring the smooth transition and accuracy of the entire corneal surface data.

[0103] The following is a concrete example to illustrate this. Assume the partitioning function model has been constructed and needs to be smoothly integrated. First, from each partitioning function model, 100 points are sampled radially at 0.1mm intervals in each of 36 angular directions (0°, 10°, 20°...350°), resulting in 36 radial observation data vectors. These vectors together constitute the sampling direction dataset and the multidimensional radial observation data matrix. Next, these angle values ​​are normalized to the interval [0, 360 degrees). Simultaneously, the preset target resampling direction dataset (e.g., 360 directions at 1-degree intervals) is also normalized. Then, the 36 normalized original sampling directions are arranged in ascending order, and the order of their corresponding 36 radial observation data vectors is simultaneously adjusted, forming a monotonically increasing sampling direction sequence and an ordered observation data matrix. Finally, for each radial observation data vector (i.e., each radial dimension) in the ordered observation data matrix, its corresponding sampling direction sequence is periodically extended. For example, -10 degrees (corresponding to 350 degrees) is added before 0 degrees, and 360 degrees (corresponding to 0 degrees) is added after 350 degrees. Then, using the extended sampling direction sequence as interpolation nodes and the radial observation data vector as node values, the PCHIP interpolation algorithm is used to fit and calculate the results on the normalized target direction set (0 to 359 degrees), obtaining the interpolation results for that radial dimension in all target directions. This process is repeated for all radial dimensions, and finally, the interpolation results of all dimensions are aggregated to generate a corneal function model that is continuously distributed in all target angular directions.

[0104] By sampling, normalizing, reordering, and employing periodic extension and conformal interpolation methods, this application effectively integrates discrete regional function models into a continuous corneal function model. This significantly solves the discontinuity problem that may exist at the boundaries of discrete models, ensuring a smooth transition and accuracy of data across the entire corneal surface. The resulting corneal function model can more accurately reflect the true morphology of the target cornea, providing a high-precision and continuous data foundation for the generation of subsequent orthokeratology lens design parameters. This improves the accuracy of orthokeratology lens design and helps enhance patient comfort.

[0105] In some embodiments, a continuously distributed resampled data matrix is ​​generated based on a sampling direction sequence and an ordered observation data matrix using periodic extension and conformal interpolation methods. This includes: performing periodic extension processing on the ordered observation data matrix and its corresponding sampling direction sequence to generate an extended sampling direction sequence containing complete periodic neighborhood information and its corresponding extended observation data matrix; for each dimension of the multidimensional radial observation data matrix, using the extended sampling direction sequence as interpolation nodes and the corresponding data sequence in the extended observation data matrix as node values, employing an interpolation algorithm with shape-preserving properties to perform fitting calculations on the normalized target direction set to obtain the interpolation result of that dimension on the target direction set; and aggregating the interpolation results of all dimensions to generate a corneal function model.

[0106] Periodic extension refers to copying and extending the original data sequence at both ends to simulate the periodicity of the data. Its purpose is to provide interpolation algorithms with complete periodic neighborhood information, especially at the start and end boundaries of the data sequence, avoiding interpolation errors or discontinuities caused by boundary effects. For data with periodic characteristics, direct interpolation within a finite interval may not accurately reflect its overall periodic trend, especially at the beginning and end, where uneven transitions are likely to occur. Periodic extension allows the interpolation algorithm to utilize data from the other end of the period when processing boundary data, thus ensuring the smoothness and continuity of the interpolation result throughout the entire period. This process can be achieved by copying several data points from the beginning of the original data sequence and adding them to the end of the sequence, and simultaneously copying several data points from the end of the sequence and adding them to the beginning of the sequence. For example, if the original sequence is [d1, d2, ..., dn], the extended sequence might be [dn-k+1, ..., dn, d1, d2, ..., dn, d1, ..., dk]. Alternatively, mathematical functions or algorithms can be used to generate corresponding data points in virtual regions outside the periodicity of the original data, thereby forming an extended sampling direction sequence and observation data matrix.

[0107] The extended sampling direction sequence and its corresponding extended observation data matrix are the result of periodic extension processing. The extended sampling direction sequence includes the original sampling direction sequence and the newly added sampling direction points after periodic extension at both ends. The extended observation data matrix includes the original ordered observation data matrix and the observation data corresponding to these newly added sampling direction points. Its purpose is to provide a wider interpolation domain containing complete periodic neighborhood information for subsequent interpolation algorithms, ensuring smooth transition of interpolation at periodic boundaries and avoiding interpolation distortion caused by data truncation. During periodic extension processing, the beginning part of the original sampling direction sequence is copied and appended to the end of the sequence, and the end part of the original sampling direction sequence is copied and appended to the beginning of the sequence. The corresponding observation data matrix is ​​also copied and appended in the same way to maintain the one-to-one correspondence between sampling directions and observation data.

[0108] Shape-preserving interpolation algorithms refer to interpolation functions that maintain certain geometric properties of the original data during the interpolation process, such as monotonicity, convexity, or concavity. Their role is to ensure that the interpolation result not only passes through all data points but also faithfully reflects the local variation trends of the original data, avoiding unnatural oscillations or distortions, thus generating a corneal function model that better conforms to actual physical laws. In corneal curvature data, maintaining its smoothness and local shape characteristics is crucial for accurate design. Cubic spline interpolation algorithms can be used, which generate piecewise cubic polynomials with continuous second-order derivatives at the nodes, thus ensuring the smoothness of the interpolation curve. Appropriate boundary conditions can further control its shape characteristics. Alternatively, the PCHIP (piecewise cubic Hermite interpolation polynomial) interpolation algorithm can be used, which uses cubic Hermite polynomials for interpolation in each sub-interval, ensuring that the interpolation function has continuous first-order derivatives at the nodes and maintaining the monotonicity of the original data, avoiding overshoot or undershoot, thereby better preserving the local shape characteristics of the data.

[0109] Fitting calculation refers to the process of solving for the interpolation function based on given data points and an interpolation algorithm. Its function is to calculate the data points on the target direction set within the interpolation domain defined by the extended sampling direction sequence, based on the values ​​of the extended observation data matrix. This is a crucial step in converting discrete data into a continuous function model. Through fitting calculation, smooth and shape-preserving corneal curvature values ​​can be obtained in any target direction. After selecting an interpolation algorithm with shape-preserving properties, the extended sampling direction sequence can be used as interpolation nodes, the corresponding dimension data sequence in the extended observation data matrix can be used as node values, and then the normalized target direction set can be input into the interpolation algorithm as the points to be calculated. The algorithm will output the interpolation results in these target directions.

[0110] Aggregating interpolation results across all dimensions refers to combining smooth curvature data from various radial directions into a unified, holistic corneal surface model that can be used for subsequent design parameter generation. A multidimensional radial observation data matrix typically contains multiple dimensions, each potentially representing variations in corneal curvature at different radial distances. After interpolating independently for each dimension, these independent interpolation results need to be integrated to form a complete, continuous corneal function model. The interpolation results for each dimension on the target direction set can be combined according to their corresponding radial distance or dimension index to form a two-dimensional or multidimensional data structure that can comprehensively describe the curvature distribution of the cornea across all target axes and radial distances.

[0111] The proposed solution involves periodically extending an ordered observation data matrix and its corresponding sampling direction sequence to generate an extended sampling direction sequence and its corresponding extended observation data matrix containing complete periodic neighborhood information. This extension operation allows the interpolation algorithm to utilize data from the other end of the period when processing the start and end boundaries of the original data sequence, effectively avoiding boundary effects caused by data truncation and ensuring the smoothness and continuity of the interpolation results across the entire period. Subsequently, for each dimension of the multidimensional radial observation data matrix, using the extended sampling direction sequence as interpolation nodes and the corresponding data sequence in the extended observation data matrix as node values, a shape-preserving interpolation algorithm is used to fit the normalized target direction set. This dimension-wise interpolation method combines the complete periodic information provided by periodic extension with the advantages of shape-preserving interpolation algorithms, ensuring that curvature changes in each radial dimension smoothly and faithfully reflect the local trends of the original data, avoiding unnatural oscillations or distortions. Finally, the interpolation results from all dimensions are aggregated to form a unified corneal function model. Through this synergistic effect, the proposed solution can overcome the discontinuity and distortion problems that may occur when traditional interpolation methods deal with periodic data boundaries, thereby generating a highly smooth, accurate corneal function model that conforms to physiological characteristics, laying a solid foundation for the accurate generation of subsequent orthokeratology lens design parameters.

[0112] As a specific implementation method, when performing boundary smoothing on the partition function model, the ordered observation data matrix and its corresponding sampling direction sequence can first undergo periodic extension processing. For example, if the original sampling direction sequence contains N angle points, the first K points can be copied and appended to the end of the sequence, and the last K points can be copied and appended to the beginning of the sequence, thus forming an extended sampling direction sequence containing N+2K points. The corresponding ordered observation data matrix is ​​also subjected to the same copying and appending operations to construct the extended observation data matrix. Subsequently, for each dimension of the multidimensional radial observation data matrix, for example, for the curvature value sequence representing different radial distances of the cornea, the PCHIP (piecewise cubic Hermite interpolation polynomial) algorithm can be used as an interpolation algorithm with shape preservation properties. The above extended sampling direction sequence is used as the interpolation node of the PCHIP algorithm, and the data sequence corresponding to this dimension in the extended observation data matrix is ​​used as the node value. Then, fitting calculation is performed on the preset normalized target direction set to obtain the interpolation result of this dimension on the target direction set. For example, if the target orientation set contains M angle points, then each dimension will generate M interpolated curvature values. Finally, the interpolation results from all dimensions are aggregated. For instance, these interpolation results can be organized into an M-row, D-column matrix, where M represents the number of points in the target orientation set and D represents the number of dimensions of the multidimensional radial observation data matrix. This matrix is ​​the final corneal function model, which can continuously and smoothly describe the curvature distribution of the cornea at different radial distances and across all target axes.

[0113] By employing the aforementioned technical solution, when smoothing the boundaries of the partition function model, periodic extension processing of the ordered observation data matrix and its corresponding sampling direction sequence effectively solves the problem of discontinuous interpolation at the boundaries of periodic data, ensuring that the interpolation algorithm can utilize complete periodic neighborhood information when processing data boundaries. Furthermore, for each dimension of the multidimensional radial observation data matrix, a shape-preserving interpolation algorithm is used for fitting calculations, ensuring that the interpolation results faithfully reflect the local variation trends of the original data and avoiding unnatural oscillations or distortions. Finally, by aggregating the interpolation results from all dimensions, a highly smooth, continuous, and accurate corneal function model can be generated. This significantly improves the accuracy and reliability of corneal surface modeling, providing a solid foundation for the accurate calculation of subsequent orthokeratology lens design parameters, thereby contributing to the design of orthokeratology lenses that better fit the patient's corneal morphology and achieve better corrective effects.

[0114] See Figure 5 In one embodiment, the method for generating orthokeratology lens design parameters includes, but is not limited to, steps S501 to S505.

[0115] Step S501: Determine the base curve diameter parameters based on the corneal diameter parameters of the target cornea.

[0116] Step S502: Based on the corneal function model, determine the minimum curvature and its corresponding axial direction.

[0117] Step S503: Calculate the curvature parameters of the base arc BC based on the minimum curvature and the target correction degree.

[0118] Step S504: Based on the corneal function model, the circumferential symmetry of the target cornea is evaluated and the corneal defocus parameters are verified to determine the positioning arc design type parameters and the reversal arc zone design parameters.

[0119] Step S505 integrates the minimum curvature, axis direction, base curve (BC) curvature parameters, corneal diameter parameters, base curve area diameter parameters, verified corneal defocus parameters, positioning arc design type parameters, and reversal arc area design parameters to obtain the orthokeratology lens design parameters.

[0120] Corneal diameter parameters typically refer to the horizontally visible iris diameter (HVID) or corneal white-to-white diameter of the target cornea, and are key indicators for assessing overall corneal size. Optical zone diameter parameters are the diameter of the core area on orthokeratology lenses used to achieve vision correction. Their determination requires comprehensive consideration of the patient's corneal size, pupil size, refractive status, and clinical experience. For example, corneal diameter parameters can be converted to optical zone diameter parameters using a preset scaling factor, or determined by consulting a pre-set clinical design table or applying empirical formulas based on the patient's pupil diameter data and corneal diameter parameters.

[0121] The axial direction refers to the meridian direction where the minimum curvature value is located. This parameter can be determined through mathematical analysis of the corneal function model, such as calculating its second derivative or using numerical optimization algorithms to search and identify the point of minimum curvature on the entire corneal surface and record its curvature value and corresponding angular position. Alternatively, the corneal function model can be discretely sampled to generate a dense curvature distribution map, and then image processing or data analysis methods can be used to find the region of lowest curvature and determine its center position and direction.

[0122] The base curve (BC) curvature parameter is the curvature of the innermost segment of the orthokeratology lens that directly contacts the central cornea. It is a key parameter determining the degree of fit between the lens and the central cornea. The target correction is the refractive error that the patient needs to correct. This calculation can be performed using empirical formulas or clinical design principles. For example, the base curve curvature can be equal to the minimum corneal curvature minus (the target correction multiplied by an empirical coefficient). Alternatively, an iterative optimization method can be used to calculate the appropriate base curve curvature parameter based on the target correction and the minimum corneal curvature, combined with a pre-set planarization amount or relative curvature difference between the lens and the cornea.

[0123] Circumferential symmetry assessment refers to analyzing the curvature changes or morphological characteristics of the corneal surface described by the corneal function model along different meridians to determine whether the cornea has regular symmetry or irregularity. Corneal defocus parameter verification involves comparing previously acquired corneal defocus parameters with the actual corneal morphology reflected by the corneal function model to ensure the accuracy and consistency of the defocus parameters. The positioning arc design type parameter refers to the design type of the arc segment on the orthokeratology lens used to stabilize the lens position and provide support; for example, it can be spherical, aspherical, toric, or multi-segment design. The reversal arc zone design parameter refers to the design parameters of the transition area on the orthokeratology lens located between the base arc and the positioning arc. Its function is to allow tear exchange and provide a certain negative pressure to achieve the corneal reshaping effect. Circumferential symmetry assessment can be completed by calculating the curvature difference, eccentricity, or astigmatic axis stability index of the corneal function model along different meridians. Corneal defocus parameter verification can be performed by substituting the defocus parameters into the corneal function model and comparing them with actual measurement data. Based on the evaluation and verification results, the system can automatically select or recommend appropriate positioning arc design types and reversal arc zone parameters.

[0124] This application's solution, after obtaining a precise corneal function model, transforms abstract corneal morphology data into specific orthokeratology lens design parameters through a series of structured and systematic steps. First, based on the corneal diameter parameters of the target cornea, the optical zone diameter parameters are determined, providing a foundation for subsequent lens optical zone size setting. Next, through in-depth analysis of the corneal function model, the minimum corneal curvature and its corresponding axis direction are accurately identified, which is crucial for determining the initial matching point of the lens base curve. Based on this, combined with the target correction power, the base curve (BC) curvature parameter is calculated, directly related to the lens's corrective effect. Simultaneously, the solution also utilizes the corneal function model to evaluate the circumferential symmetry of the target cornea and verify the corneal defocus parameters, ensuring the accuracy and adaptability of the design parameters. This, in turn, determines appropriate positioning arc design type parameters and reversal arc zone design parameters to guarantee lens stability and reshaping effect. Finally, all these meticulously calculated and verified parameters are integrated to form a complete set of orthokeratology lens design parameters. This method ensures comprehensiveness, accuracy, and clinical applicability from corneal morphology to lens design, effectively solving the problem of having only corneal models but lacking specific design guidance, enabling the generated lenses to better adapt to individual corneal characteristics and achieve precise correction.

[0125] As a specific implementation method, when generating orthokeratology lens design parameters, the patient's corneal diameter parameter can be obtained first, for example, 11.8 mm as measured by a corneal topography instrument. Based on this, the optical zone diameter parameter can be set to 6.0 mm, a value typically adjusted based on clinical experience and the patient's pupil size. Subsequently, the executing agent analyzes the constructed corneal function model to identify the flattest region of the cornea, for example, in the 180-degree axial direction, where its minimum curvature is 42.00D. Assuming the patient's target correction is -3.00D, the base curve (BC) curvature parameter can be calculated using empirical formulas, such as: base curve curvature parameter = minimum corneal curvature - (target correction × 0.7), resulting in: 42.00D - (-3.00D × 0.7) = 42.00D + 2.10D = 44.10D. Simultaneously, the implementing entity will evaluate the circumferential symmetry of the corneal function model. If regular astigmatism is found in the cornea, the positioning arc design type parameters can be determined as an allosteric surface design to better match the corneal morphology and provide stable positioning. The reversal arc zone design parameters can be set as an aspherical transition arc with a specific width and depth, based on the peripheral corneal morphology reflected by the corneal function model. Finally, the determined minimum curvature, axis direction, base curve (BC) curvature parameters, corneal diameter parameters, optical zone diameter parameters, verified corneal defocus parameters, positioning arc design type parameters, and reversal arc zone design parameters are integrated to form a complete set of orthokeratology lens design parameters to guide lens manufacturing.

[0126] Through the above technical solution, this application can transform the abstract corneal function model into a comprehensive, specific, and clinically guiding set of orthokeratology lens design parameters. This solution systematically determines the optical zone diameter parameters, minimum curvature and its axial direction, base curve (BC) curvature parameters, positioning arc design type parameters, and reversal arc zone design parameters, ensuring that the lens design fully considers the individual corneal geometry and refractive needs. In particular, by evaluating corneal circumferential symmetry and verifying corneal defocus parameters, the accuracy of the design parameters and the lens-corneal fit are significantly improved. This effectively solves the problem of not being able to directly generate practical design parameters with only a corneal model, enabling the generated orthokeratology lenses to more accurately correct vision and provide better wearing comfort and shaping effects.

[0127] This application also provides a free-axis quantified visual defocus orthokeratology lens, which is designed using orthokeratology lens design methods.

[0128] Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application.

[0129] The following reference Figure 6To describe an electronic device 600 according to such an embodiment of the present disclosure. Figure 6 The electronic device 600 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0130] like Figure 6 As shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0131] The storage unit stores program code, which can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the section on the design method of orthokeratology lenses described above, according to various exemplary embodiments of this disclosure.

[0132] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0133] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0134] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0135] Electronic device 600 can also communicate with one or more external devices 600' (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0136] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0137] The corneal reshaping lens design method, apparatus, device, and medium provided in this application divide the target cornea into multiple regions, construct multiple independent regional function models for each region, and perform boundary smoothing to generate a continuous and accurate corneal function model. Based on this model, corresponding corneal reshaping lens design parameters are then generated. This allows for a more accurate fit to the complex morphology of the cornea and enables flexible and personalized acquisition of regional curvature parameters. This allows the design of the corneal reshaping lens to more precisely match the actual morphology and personalized needs of the patient's cornea. Especially when dealing with complex or irregular corneal topography, it can more accurately capture curvature information in key directions, thereby improving the construction accuracy of the regional function model and ultimately optimizing the design effect of the corneal reshaping lens, enhancing the accuracy and comfort of correction.

[0138] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the methods described above according to the embodiments of this disclosure.

[0139] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0140] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0141] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0142] Exemplary embodiments of this disclosure have been specifically shown and described above. It should be understood that this disclosure is not limited to the detailed structures, arrangements, or implementations described herein; rather, this disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A method of designing a corneal reshaping lens, characterized by, include: Obtain corneal defocus parameters and multiple zone curvature parameters of the target cornea; The partition curvature parameter is the curvature of the target cornea on the corresponding target axis. Based on the partition curvature parameters and the corneal defocus parameters, multiple partition function models are constructed; The boundary smoothing process is applied to the partition function model to obtain the corneal function model; Based on the corneal function model, corresponding orthokeratology lens design parameters are generated; The boundary smoothing process for the partitioning function model includes: The partitioning function model is sampled to obtain a sampling direction dataset and its corresponding multidimensional radial observation data matrix; the sampling direction dataset contains multiple sampling points distributed at an angle; Angle normalization processing is performed on the sampling direction dataset and the preset target resampling direction dataset respectively to obtain the normalized original sampling direction set and the normalized target direction set; The normalized original sampling direction set and its corresponding multidimensional radial observation data matrix are reordered to establish a monotonically increasing sampling direction sequence and its corresponding ordered observation data matrix. Based on the sampling direction sequence and the ordered observation data matrix, a continuously distributed data matrix is ​​generated using periodic extension and conformal interpolation methods to obtain the corneal function model.

2. The method for designing orthokeratology lenses according to claim 1, characterized in that, The number and / or range of the target axes are adjustable.

3. The design method for orthokeratology lenses according to claim 1 or 2, characterized in that, The method for determining the curvature parameters of the partition includes: In response to the received axis configuration operation, a plurality of the target axis positions are determined; The curvature of the cornea on the target axis is determined to obtain the curvature parameter of the partition.

4. The design method for orthokeratology lenses according to claim 1, characterized in that, Based on the partition curvature parameters and the corneal defocus parameters, multiple partition function models are constructed, including: Obtain the initial partitioning function model; the initial partitioning function model is obtained based on the even-order aspherical formula and includes the aspherical coefficients and polynomial coefficients to be solved. Based on the partition curvature parameters and the corneal defocus parameters, a set of constraint equations is established; Solve the constraint equations and assign values ​​to the non-spherical coefficients and polynomial coefficients in the initial partitioning function model based on the solution results to obtain the partitioning function model.

5. The method for designing orthokeratology lenses according to claim 1, characterized in that, The step of generating a continuously distributed resampled data matrix based on the sampling direction sequence and the ordered observation data matrix using periodic extension and conformal interpolation methods includes: The ordered observation data matrix and its corresponding sampling direction sequence are periodically extended to generate an extended sampling direction sequence and its corresponding extended observation data matrix containing complete periodic neighborhood information. For each dimension of the multidimensional radial observation data matrix, the extended sampling direction sequence is used as the interpolation node, and the data sequence corresponding to that dimension in the extended observation data matrix is ​​used as the node value. An interpolation algorithm with shape preservation characteristics is used to perform fitting calculation on the normalized target direction set to obtain the interpolation result of that dimension on the target direction set. The interpolation results from all dimensions are aggregated to generate the corneal function model.

6. The method for designing orthokeratology lenses according to claim 1, characterized in that, The generation of corresponding orthokeratology lens design parameters based on the corneal function model includes: Based on the corneal diameter parameters of the target cornea, the base curve diameter parameters are determined; Based on the corneal function model, the minimum curvature and its corresponding axial direction are determined. Based on the minimum curvature and the target correction degree, the curvature parameter of the base arc BC is calculated; Based on the corneal function model, the circumferential symmetry of the target cornea is evaluated and the corneal defocus parameters are verified to determine the positioning arc design type parameters and the reversal arc zone design parameters. By integrating the minimum curvature, the axis direction, the base curve BC curvature parameter, the corneal diameter parameter, the base curve area diameter parameter, the verified corneal defocus parameter, the positioning arc design type parameter, and the reversal arc area design parameter, the orthokeratology lens design parameters are obtained.

7. A free-axis, quantitatively visible defocusing orthokeratology lens, characterized in that, The orthokeratology lens is designed using the design method described in any one of claims 1 to 6.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the design method of the orthokeratology lens according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the design method of the orthokeratology lens according to any one of claims 1 to 6.

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