Spectacle lens specification three-dimensional measurement method, system and equipment and storage medium

Abstract

The invention relates to the technical field of lens measurement, and discloses a glasses lens specification three-dimensional measurement method, system and device and a storage medium, and the method comprises the following steps: S1, carrying out the surface scanning of a lens through a high-precision mechanical probe and an embedded camera, and obtaining three-dimensional point cloud data; s2, fitting the point cloud data by using a least square method to generate an ideal curved surface model of the lens; s3, optimizing the path planning of the probe by using a variational method, and ensuring that the acquisition sequence of each measurement point is optimal; s4, correcting errors in the primary scanning process through secondary scanning; and S5, outputting the optimized three-dimensional model and the lens specification parameters. Through the technical scheme of combining the high-precision mechanical probe and the embedded camera, higher scanning precision and data stability are achieved, so that space coordinate data and surface image information can be synchronously provided, error accumulation possibly occurring in a single system is avoided, and comprehensiveness and accuracy of lens surface data are ensured.

Description

Technical Field

[0001] The present invention relates to the field of lens measurement technology, and in particular to a method, system, device and storage medium for three-dimensional measurement of spectacle lens specifications. Background Art

[0002] In the prior art, the specification measurement of eyeglass lenses is usually carried out by manual or semi-automatic equipment. The traditional manual measurement method relies on manual operation, and the size, thickness, curvature, etc. of the lens are measured by tools such as calipers and optical instruments. Although this method is simple to operate, due to the influence of human factors, the accuracy of the measurement results is low and the measurement efficiency is low. In addition, some automated measurement systems have also been gradually applied to the specification detection of eyeglass lenses. These systems are mostly based on two-dimensional or simple three-dimensional scanning technology, which can measure the lenses relatively quickly, reduce manual operation errors, and improve production efficiency. However, the existing automated systems still have certain technical difficulties when dealing with lenses with complex shapes and large surface changes.

[0003] Although existing technologies have improved measurement efficiency to a certain extent, there are still some problems that cannot be ignored. First, many automated measurement systems are unable to accurately scan the complex curvature changes on the lens surface, resulting in defects in the accuracy of curved surface measurement, especially when there is a large difference in thickness between the edge and the middle of the lens. Secondly, existing measurement systems generally lack an effective error correction mechanism, and measurement errors will accumulate with the increase in the number of scans, affecting the final measurement results. In addition, traditional systems have weak measurement path planning and optimization capabilities, and usually cannot achieve optimal path scanning, which may lead to unnecessary redundancy and waste of time during the measurement process. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a three-dimensional measurement method, system, device and storage medium for eyeglass lens specifications, which solve the problems of insufficient accuracy, unoptimized measurement path and inability to effectively correct errors in the existing eyeglass lens specification measurement technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A three-dimensional measurement method for spectacle lens specifications, comprising the following steps: S1. Use a high-precision mechanical probe and an embedded camera to scan the surface of the lens to obtain three-dimensional point cloud data; S2. Fitting the point cloud data using the least squares method to generate an ideal surface model of the lens; S3, use variational method to optimize the probe path planning to ensure the optimal acquisition order of each measurement point; S4, correcting the error caused by unstable probe contact and slight vibration during the initial scanning process through secondary scanning; S5. Output the optimized three-dimensional model and lens specification parameters.

[0006] Preferably, in step S2, the least squares method fits the point cloud data by minimizing the following error function: Among them, f(x i ,y i ) is the fitting surface at point (x i ,y i ), z i is the actual measured z coordinate.

[0007] Preferably, in step S3, the probe path is optimized by using a variational method, and the path planning is performed by minimizing the following objective function: Where γ(t)=(x(t),y(t),z i ) is the path of the probe, f(x(t), y(t)) is the value of the fitting surface on (x(t), y(t)), z i is the actual measured z coordinate, and λ is the regularization parameter.

[0008] Preferably, in the step S4, the secondary scanning correction process performs error correction through a feedback mechanism and performs fine adjustment based on the corrected data.

[0009] Preferably, in step S5, the output three-dimensional model includes the three-dimensional surface reconstruction result and specification parameters of the lens, including but not limited to the diameter, curvature and thickness of the lens.

[0010] Preferably, in step S2, the three-dimensional surface model of the lens generated by least squares fitting is a continuous and smooth surface, and the fitting process is optimized by weighted least squares method.

[0011] Preferably, in step S3, the probe path optimized by the variational method takes into account the curvature change of the lens surface and optimizes the selection order of the measurement points each time.

[0012] A three-dimensional measurement system for spectacle lens specifications, comprising: A data acquisition module is used to scan the lens surface using a high-precision mechanical probe and an embedded camera to obtain three-dimensional point cloud data; A data processing module, used for fitting the three-dimensional point cloud data using a least squares method and optimizing a probe path using a variational method; A control module, used to adjust the path of the probe and perform a secondary scan to correct the measurement error; Output module, used to generate 3D models and specification parameters of lenses.

[0013] A three-dimensional measuring device for spectacle lens specifications, comprising: High-precision mechanical probe system, used to obtain three-dimensional point cloud data of lens surface; Embedded camera system for real-time lens image acquisition and edge detection combined with image processing technology; A data processing unit is used to receive the collected image data and fit the point cloud data, optimize the path and correct the error; a feedback control system is used to correct the measurement error and optimize the probe path through real-time feedback; The output system is used to generate and output the three-dimensional model and specification parameters of the lens.

[0014] A storage medium stores spectacle lens specification data, the data including three-dimensional point cloud data of the lens obtained by a three-dimensional measurement method, a three-dimensional surface model of the lens generated by fitting, and related lens specification parameters.

[0015] The present invention provides a method, system, device and storage medium for three-dimensional measurement of spectacle lens specifications. Beneficial effects: 1. The present invention adopts a technical solution combining a high-precision mechanical probe with an embedded camera to achieve higher scanning accuracy and data stability. Compared with the prior art solution that relies solely on a mechanical probe or an image acquisition system, the present invention can simultaneously provide spatial coordinate data and surface image information, avoiding the error accumulation that may occur in a single system and ensuring the comprehensiveness and accuracy of the lens surface data.

[0016] 2. The present invention adopts the technical solution of least square method and variational method to optimize the path, achieving more efficient and accurate measurement path planning. Compared with the traditional path optimization method, the present invention greatly reduces path redundancy by comprehensively considering the order of measurement points, path smoothness and error minimization, and avoids error transmission caused by unreasonable measurement order, effectively improving the efficiency and accuracy of measurement.

[0017] 3. The present invention introduces a secondary scanning correction process, which can detect and correct measurement errors caused by unstable probe contact and small vibrations in real time. This technology solves the common problem of scanning error accumulation in existing solutions and avoids the reduction of measurement accuracy. By precisely controlling the path and contact force of the probe, the system ensures that each measurement point can be accurately collected, thereby greatly improving the reliability of the final measurement results.

[0018] 4. The present invention automatically generates a three-dimensional model and precise specification parameters of the lens, and provides a detailed error analysis report. Compared with the prior art solution that only outputs basic data, the present invention can evaluate the measurement accuracy in real time, generate a comprehensive quality report, and help users understand the error range of each measurement point. This improvement effectively improves the accuracy requirements in the subsequent eyeglass fitting process and enhances the usability of the overall measurement system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the method steps of the present invention; Figure 2 It is a schematic diagram of the system module of the present invention; Figure 3 It is a schematic diagram of the device of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1: Please see attached Figure 1 The embodiment of the present invention provides a method for three-dimensionally measuring specifications of eyeglass lenses, comprising the following steps: S1. Use a high-precision mechanical probe and an embedded camera to scan the surface of the lens to obtain three-dimensional point cloud data; S2. Fitting the point cloud data using the least squares method to generate an ideal surface model of the lens; S3, use variational method to optimize the probe path planning to ensure the optimal acquisition order of each measurement point; S4, correcting the error caused by unstable probe contact and slight vibration during the initial scanning process through secondary scanning; S5, outputting the optimized three-dimensional model and lens specification parameters; In the step S2, the least squares method fits the point cloud data by minimizing the following error function: Among them, f(x i ,y i ) is the fitting surface at point (x i ,y i ), z i is the actual measured z coordinate; In the S3 step, the variational method optimizes the probe path and performs path planning by minimizing the following objective function: Where γ(t) = (x(t), y(t), z(t)) is the path of the probe, f(x(t), y(t)) is the value of the fitted surface on (x(t), y(t)), z i is the actual measured z coordinate, λ is the regularization parameter; In the step S4, the secondary scanning correction process performs error correction through a feedback mechanism and performs fine adjustment based on the corrected data; In step S5, the output three-dimensional model includes the three-dimensional surface reconstruction result and specification parameters of the lens, including but not limited to the diameter, curvature and thickness of the lens; In the step S2, the three-dimensional surface model of the lens generated by the least squares fitting is a continuous and smooth surface, and the fitting process is optimized by the weighted least squares method; In the step S3, the probe path optimized by the variational method takes into account the curvature change of the lens surface and optimizes the selection order of each measurement point.

[0022] Specifically, in step S1, the scanning process of the lens surface is completed by the collaboration of a high-precision mechanical probe and an embedded camera. The high-precision mechanical probe works with a displacement control system through a tiny displacement sensor to accurately move to each specified position on the lens surface. In this way, the probe can gradually contact each point of the lens and accurately record the spatial coordinates of these contact points. At the same time, the embedded camera captures images of the lens surface in real time so that these images can be further processed in the data processing module to assist in the construction of three-dimensional point cloud data.

[0023] Generally, in order to ensure scanning accuracy, the mechanical probe needs to contact the lens surface within a fixed distance and angle range. This is done through a precise electronic control system, and each contact of the probe is recorded and associated with the corresponding coordinate value.

[0024] As an option, the contact pattern between the lens surface and the probe can be monitored by a force sensor. The force sensor can detect the contact pressure between the probe and the lens surface in real time and adjust the contact strength of the probe based on the pressure feedback. In this way, deformation of the lens surface caused by excessive contact force or influence on measurement accuracy due to insufficient contact force can be avoided.

[0025] In one possible implementation, the system automatically adjusts the distance between the probe and the lens surface according to the shape characteristics of the lens. Through image recognition algorithms, the system can evaluate the geometry of the lens in real time and adjust the relative position of the mechanical probe to ensure that the measurement accuracy is not affected by changes in the curvature of the lens. The feedback mechanism in this process can effectively improve the measurement accuracy and avoid data errors caused by inappropriate contact angles or distances.

[0026] In this step, the 3D point cloud data is generated by the spatial coordinate data of each contact point of the probe. The spatial coordinates of each contact point are determined by the distance between the probe and the lens surface, the movement path of the probe, and the geometric shape of the lens surface. In order to ensure the accuracy of the contact point, the system usually needs to compare the coordinate data of each contact point with the reference coordinate system to avoid the accumulation of errors.

[0027] During data collection, the formula can be used to describe the relationship between the spatial coordinates of the contact point and the probe position. i =(x i ,y i ,z i ) is the three-dimensional coordinate of the i-th measurement point, x i ,y i ,z i are the coordinates of the point in the x, y, and z directions. i ,y i ) can be determined by the contact position between the probe and the lens surface in the horizontal plane, and z i It is determined by the vertical distance between the probe and the lens surface.

[0028] In this way, the spatial coordinate data P of each measurement point obtained in this step is i =(x i ,y i ,z i ) will become the basic data in the subsequent data fitting and path optimization process. These data are organized into a point cloud dataset as the preliminary data input of the entire measurement system.

[0029] Several technical optimization measures were taken in this step. First, the positioning system of the mechanical probe uses high-precision displacement control technology to ensure that each movement of the probe is accurate. To further reduce errors, after each contact, the system will immediately calibrate the data of the current measurement point and compare it with historical data to detect potential measurement errors in a timely manner.

[0030] Secondly, the cooperation between the embedded camera and the mechanical probe can collect the image data and geometric coordinate data of the lens at the same time. Through the image processing algorithm, the system can analyze the changes in the surface shape of the lens in real time and assist the precise movement of the mechanical probe. For example, through edge detection and contour extraction technology, the camera can obtain the edge information of the lens in real time, thereby helping the mechanical probe to better adapt to the slight curvature changes on the lens surface.

[0031] As an option, the camera data can be combined with the depth sensor data in this step to further improve the measurement accuracy. The depth sensor can provide three-dimensional depth information of the lens surface, which combined with the three-dimensional coordinate data of the mechanical probe can help improve the measurement accuracy of the entire system.

[0032] By using a high-precision mechanical probe to work in conjunction with an embedded camera, step S1 can accurately collect a large amount of 3D point cloud data in a short time. This provides a reliable data basis for subsequent steps such as fitting and path optimization. In this way, the accuracy of 3D measurement of eyeglass lens specifications can be significantly improved, and high stability and accuracy can be maintained under complex lens geometries.

[0033] In addition, this step can flexibly deal with the impact of lens surface irregularities and reduce the propagation of errors through precise mechanical control and real-time feedback mechanism. Such technical implementation ensures that lenses of different types and curvatures can be effectively measured and processed during dynamic scanning.

[0034] In step S2, the least squares method is used to optimize the fitting of the 3D point cloud data. First, the system calculates each measurement point P i =(x i ,y i ,z i ) is input into the optimization calculation to obtain the mathematical description of the lens surface. The fitting surface is completed by minimizing the error function, the goal of which is to minimize the sum of the squares of the vertical distances between all the acquisition points and the fitting surface.

[0035] In general, the least squares method is optimized using the following error function: Among them, f(x i ,y i ) is the fitting surface at point (x i ,y i ), z i is the actual measured z coordinate, and n is the total number of measurement points. The core task of the least squares method is to adjust the fitting surface function so that the value of the error function E is minimized, so that all measurement points can be as close to the fitting surface as possible.

[0036] As an option, in this step, the fitting process can be further optimized by weighted least squares. Weighted least squares introduces weight coefficients based on traditional least squares. By assigning different weights to different measurement points, the optimization results can better reflect the accuracy of important measurement points. Specifically, the system will dynamically adjust the weights according to the characteristics of the lens and the distribution of the measurement points. For example, the edge of the lens and areas with large curvature changes can be assigned larger weights to ensure the fitting accuracy of these areas.

[0037] Specifically, the weight w i It will be calculated according to the following formula: Among them, w i For point P i This method can reduce the impact of measurement points with large errors on the fitting results and improve the overall accuracy of the fitting.

[0038] In actual operation, the optimization process of the least squares method can be implemented by a variety of algorithms, common algorithms include gradient descent, Newton's method, and quasi-Newton's method. Usually, the system will use a more adaptable optimization algorithm, such as gradient descent, to gradually reduce the value of the error function until the convergence condition is reached.

[0039] During the optimization process, the system will iteratively adjust each parameter. For example, assuming that the fitting surface is a parameterized model f(x, y; a1, a2, ..., a m ), where a1, a2, ..., a m are the parameters of the surface model. The least squares method adjusts the shape of the surface according to these parameters to minimize the error. The optimization goal is to find a set of optimal parameters {a1, a2, ..., a m}, so that the error between all measured points and the fitting surface is minimized.

[0040] The termination condition of the optimization is generally set as the error function reaching a preset minimum value, or the optimization process goes through a certain number of iterations. Specifically, if the error value E is small enough, or the error does not decrease significantly within a certain number of iterations, the fitting process is considered to be completed.

[0041] The fitting performed by the least squares method in this step can transform the point cloud data into a continuous surface model of the lens surface, greatly improving the accuracy and stability of 3D reconstruction. The introduction of the weighted least squares method further optimizes the fitting accuracy of different areas, allowing the system to more accurately reflect the lens surface, especially in areas with large curvature changes, and the fitting results are more reliable.

[0042] Through this optimization process, the three-dimensional model of the lens can be ensured to be more consistent with the actual shape, thus providing reliable data for subsequent path optimization, error correction and specification parameter extraction. The application of the least squares method and its variants ensures that the method can still fit efficiently and accurately when facing large-scale data or complex geometric shapes, providing technical support for high-precision measurement needs.

[0043] In step S3, the main function of the variational method is to optimize the motion trajectory of the mechanical probe so that the probe can scan each measurement point on the lens surface in the optimal order. To achieve this goal, first, according to the fitting surface model obtained in step S2, the system will generate an initial path planning, which does not consider optimality, but simply connects each measurement point according to certain rules.

[0044] In general, path optimization is performed by minimizing the following objective function: Where γ(t) = (x(t), y(t), z(t)) is the path of the probe, f(x(t), y(t)) is the value of the fitted surface on (x(t), y(t)), z i is the actual measured z-coordinate, and λ is the regularization parameter that controls the balance between path smoothness and measurement error.

[0045] Specifically, represents the speed of the probe path. The optimization objective considers not only the smoothness of the path, but also the error between each measurement point and the fitting surface to ensure that the measurement error is minimized during the scanning process. By adjusting the regularization parameter λ, the trade-off between smoothness and error correction during the path optimization process can be controlled.

[0046] In one possible implementation, path optimization not only considers the order of each measurement point, but also considers factors such as the distance, curvature and angle between measurement points through the calculus of variations. For example, path planning can be dynamically adjusted through intelligent algorithms to minimize the turning angle between measurement points, avoid frequent direction changes of the probe, and further improve measurement efficiency.

[0047] In the process of path optimization, the variational method finds the optimal path by taking the derivative of the path function γ(t) and calculating the impact of the path change on the objective function. It mainly controls the smoothness of the path to ensure that the probe does not experience abrupt speed changes during movement. The second term λ(f(x(t),y(t))-z(t)) 2 The accuracy of the probe at each measurement point is controlled to ensure that the difference between the measured value and the fitted surface value is minimized.

[0048] In actual path optimization, the calculus of variations generates a series of path solutions that are continuously adjusted until the error function J reaches a minimum. To solve this problem, the system usually uses the Euler-Lagrange equation, which finds the optimal path by describing the rate of change of the path. In this process, each point of the path is carefully calculated to ensure that the optimization goal of the entire path is met.

[0049] In actual operation, the path optimization algorithm can adopt a variety of optimization strategies such as gradient descent, genetic algorithm or simulated annealing algorithm. Each algorithm has its advantages and disadvantages, but their goal is the same: to gradually find the optimal path through iterative calculation.

[0050] Specifically, the gradient descent method is applicable to most conventional path optimization problems. The basic idea is to adjust the path parameters in the direction of gradient descent by calculating the gradient information of the objective function until the objective function reaches the minimum value. For complex path planning problems, genetic algorithms or simulated annealing algorithms can find better solutions through global search, especially when the path space is complex and there are many local minima. These algorithms have stronger adaptability.

[0051] Path optimization not only improves measurement efficiency, but also improves measurement accuracy and stability by reducing unnecessary path redundancy. Path planning through variational methods can ensure the optimal order of measurement points, avoid unnecessary repeated scanning, and reduce the transmission and accumulation of errors. By adjusting the regularization parameter λ, the system can flexibly adjust the balance between path smoothness and error minimization according to measurement requirements.

[0052] In addition, the optimized path makes the movement of the mechanical probe smoother, reduces the load on the mechanical system, and increases the service life of the equipment. More importantly, path optimization also ensures the efficiency of the entire scanning process, optimizing the measurement process in time.

[0053] In summary, using the variational method to optimize the probe path not only improves the accuracy of three-dimensional measurement of eyeglass lenses, but also makes the entire process more efficient. By optimizing path planning, the system can adapt to lenses of different shapes and complexities, achieve accurate three-dimensional measurement, and provide reliable data support for subsequent error correction and specification parameter extraction. In step S4, the secondary scan correction is performed through a feedback mechanism. After the initial scan is completed, the system will first analyze the acquired point cloud data and surface model to evaluate the possible errors. By comparing with the ideal fitting surface, the system can identify error points in the preliminary scan, which usually appear in areas where the probe contact is unstable or where small vibrations occur during the scanning process.

[0054] In general, the second scan is optimized based on the results of the first scan. The probe will re-touch the identified error areas, scan them in detail, re-collect data and make corrections. This process can be automatically managed by the system's control module, ensuring that the probe path adjustment accurately matches the initial scan results.

[0055] As an option, the secondary scan correction process can be combined with precise vibration detection to further identify and correct errors caused by equipment vibration or external environmental factors. For example, during the scanning process, the equipment's vibration sensor can monitor the equipment's motion status in real time and dynamically adjust the probe's scanning strategy to minimize the impact of vibration on the measurement results.

[0056] Specifically, the control mechanism of the secondary scanning process will be adjusted according to the initial scanning data and the feedback information from the equipment. By analyzing the contact between the probe and the lens surface in real time, the system can intelligently determine the areas that need secondary scanning. For example, the system can perform error mapping on the scanning results to determine which areas have errors exceeding the preset error threshold, thereby automatically triggering a secondary scan of these areas.

[0057] In the implementation of the second scan, the key to error correction is how to accurately evaluate the difference between the preliminary data and the ideal surface model. To achieve this goal, the system can use the following mathematical formula to quantify the error: Among them, f(x i ,y i ) represents the ideal surface at point (x i ,y i ), (f(x i ,y i )-z i ) is the measured coordinate value in the preliminary scan data, and n is the number of measurement points. By calculating the value of the error function, the system can identify which areas have errors exceeding the preset threshold and trigger a secondary scan.

[0058] The secondary scan correction process effectively solves the measurement errors caused by unstable probe contact and small vibrations. By analyzing and correcting the initial scan results, the system can ensure the accuracy of each measurement point, thereby improving the reliability and accuracy of the entire measurement process. By adopting feedback mechanisms and vibration detection, this step can dynamically adjust the probe path in actual operation to ensure that each measurement can achieve the expected accuracy.

[0059] In addition, secondary scan correction can also reduce system errors caused by factors such as equipment vibration. When the equipment vibrates or the scan is unstable, the system can automatically correct the scan path through real-time feedback and re-measure to reduce the impact of error accumulation.

[0060] In step S5, the system first inputs the optimized point cloud data and 3D surface model obtained in step S4 into the data processing module. The task of the data processing module is to further process the optimized 3D data to generate a complete 3D model of the lens. Specifically, the system will use a 3D reconstruction algorithm to connect all the point cloud data to construct a continuous 3D surface.

[0061] Generally, the generated 3D model is represented by B-spline curves or NURBS surfaces, which are mathematical models that can accurately describe the shape and geometric features of the lens surface. When generating the 3D model, the system will make adjustments based on the density and accuracy of the fitting data to ensure that the quality of the model meets the expected standards.

[0062] As an option, if the model is more complex or contains areas with more details, the system can use multi-level subdivision technology (such as subdivision surfaces) to gradually optimize the model to make it smoother and reduce unnecessary jagged or irregular areas.

[0063] Specifically, the generated 3D model not only includes the surface shape of the lens, but also performs further calculations based on it to extract the lens's specifications. The specifications include but are not limited to the lens' diameter, curvature, thickness, etc. These parameters are key information required in the process of fitting glasses.

[0064] In one possible implementation, the system will automatically calculate multiple specifications of the lens based on the fitted three-dimensional surface. For example, by measuring the curvature of different areas on the lens surface, the system can accurately obtain parameters such as the spherical degree and cylindrical degree of the lens, which is crucial for the subsequent personalized customization of the lens. In order to ensure the accuracy of these parameters, the system will combine the actual shape of the lens and use high-precision algorithms for calculation to reduce the impact of fitting errors or data noise.

[0065] For the extraction of various specifications of the lens, the system will calculate the spatial coordinates of each key point based on the 3D model, and further obtain the lens's diameter, thickness and other geometric parameters. Specifically, the diameter can be determined by scanning the maximum lateral scale of the model; the thickness is obtained by calculating the vertical distance at different positions on the lens surface.

[0066] After completing the calculation of the 3D model and specification parameters, the system will output the final results. These results include but are not limited to the 3D model, diameter, thickness, curvature and other parameters of the lens. The output method can be a graphical interface display, data file storage (such as STL, OBJ format), etc., to ensure that the subsequent glasses fitting process can be directly used.

[0067] As an option, the system can also transmit these results in real time to relevant systems through networking with optician equipment or quality control systems for optician operation or production quality monitoring.

[0068] In order to facilitate long-term data management and subsequent operations, all 3D data and specification parameters generated in this step will be stored in the data storage medium. These data can provide important references for subsequent quality tracking, customer customization, production control, etc.

[0069] Through the processing of step S5, the system successfully converts the optimized point cloud data into a high-precision 3D model and extracts accurate lens specification parameters. This process not only improves the availability of data, but also provides high-quality data support for lens customization and subsequent eyeglass fitting.

[0070] Embodiment 2: Please see attached Figure 2 , a three-dimensional measurement system for spectacle lens specifications, comprising: A data acquisition module is used to scan the lens surface using a high-precision mechanical probe and an embedded camera to obtain three-dimensional point cloud data; A data processing module, used for fitting the three-dimensional point cloud data using a least squares method and optimizing a probe path using a variational method; A control module, used to adjust the path of the probe and perform a secondary scan to correct the measurement error; Output module, used to generate 3D models and specification parameters of lenses.

[0071] Specifically, in this embodiment, the system is composed of a data acquisition module, a data processing module, a control module and an output module. The modules are connected through a data interface to form an efficient workflow, ensuring that the entire process from preliminary three-dimensional point cloud data acquisition to the final specification parameter output can be seamlessly connected. The data acquisition module is the first link of the system, responsible for scanning the lens surface through a high-precision mechanical probe and an embedded camera to obtain three-dimensional point cloud data. Specifically, the mechanical probe scans point by point along the lens surface to obtain the spatial coordinates of each measurement point, and the embedded camera provides images in real time to assist the probe in accurately locating the measurement point. In general, the data acquisition module uses a high-precision displacement sensor to ensure that the probe is in stable contact with the lens surface and the position is accurate. During the scanning process, the probe gradually covers each key point on the lens surface, and through refined control, data errors caused by poor contact or uneven measurement paths are avoided. As an option, the data acquisition module can also integrate a real-time force sensor to detect the pressure of the probe in contact with the lens surface, thereby automatically adjusting the contact strength during the scanning process to avoid errors caused by too tight or too loose contact.

[0072] The core task of the data processing module is to process the point cloud data from the data acquisition module and generate a three-dimensional surface model of the lens by fitting it through the least squares method. Through this fitting, the system can obtain the ideal surface model that best matches the lens surface, providing basic data for subsequent path optimization and error correction.

[0073] The data processing module will also optimize the probe path using the variational method based on the fitted surface model. By optimizing the path, it is possible to ensure that the acquisition order of the measurement points is optimal, reduce unnecessary measurement redundancy, and ensure the accuracy of each measurement point. Specifically, the goal of path optimization is to minimize the total length of the path while ensuring that the error of each measurement point is minimized.

[0074] Generally, the data processing module will automatically identify and correct any deviations caused by measurement errors or unstable probe contact during the processing process, ensuring the integrity and accuracy of the entire data set.

[0075] Embodiment three: Please see attached Figure 3 , a three-dimensional measuring device for spectacle lens specifications, comprising: High-precision mechanical probe system, used to obtain three-dimensional point cloud data of lens surface; Embedded camera system for real-time lens image acquisition and edge detection combined with image processing technology; A data processing unit is used to receive the collected image data and fit the point cloud data, optimize the path and correct the error; a feedback control system is used to correct the measurement error and optimize the probe path through real-time feedback; The output system is used to generate and output the three-dimensional model and specification parameters of the lens.

[0076] Specifically, in this embodiment, the three-dimensional measurement device for spectacle lens specifications is composed of a high-precision mechanical probe system, an embedded camera system, a data processing unit, a feedback control system, and an output system. Each component is connected through a standardized interface to form an efficient and stable working platform, ensuring that the system can operate stably in various usage scenarios.

[0077] In this device, the high-precision mechanical probe system is responsible for collecting lens surface data. The core of this system is to drive the probe to move on the lens surface through a high-precision electric drive mechanism, scan and record the three-dimensional coordinates of each measurement point point by point. In order to ensure the accuracy of data collection, the movement of the probe is monitored in real time by a high-precision displacement sensor and control module to ensure the precise positioning of each measurement point.

[0078] In general, the motion path of the probe is preset by the control system to ensure that all critical areas of the lens are covered. The probe's touch force and contact angle are strictly controlled to avoid damage to the lens surface and to ensure that there is no contact instability during the scanning process.

[0079] In this device, the embedded camera system works closely with the mechanical probe system to collect real-time images of the lens surface. The main task of the camera is to image the lens surface and provide the captured image data to the data processing module for subsequent image analysis and data processing.

[0080] Specifically, the embedded camera can monitor the motion of the probe on the lens surface in real time, assisting the probe system to accurately locate each measurement point. Through image processing algorithms, the images captured by the camera can provide information about the lens edge, surface bumps and other features, further improving the accuracy of data collection.

[0081] The data processing unit is the "brain" of the entire measuring device, responsible for receiving and processing data from the mechanical probe system and the camera system. This unit mainly processes and optimizes the 3D point cloud data. By fitting the scanned point cloud data, the data processing unit can generate a 3D model of the lens and extract relevant specification parameters.

[0082] Specifically, the data processing unit uses the least squares method to fit the point cloud data and generate an ideal surface model of the lens. On this basis, the data processing unit also performs path optimization operations to ensure the optimality of the probe path and avoid unnecessary redundant scanning. The unit optimizes the path through the variational method, making the probe movement smoother and the order of the measurement points optimal.

[0083] The feedback control system is an important module in the device, which is responsible for adjusting the probe path according to the real-time feedback during data acquisition and processing. The system monitors the contact between the probe and the lens surface by connecting to various sensors (such as force sensors, displacement sensors and vibration sensors).

[0084] When the probe is not in contact with the lens surface or the vibration is beyond the allowable range, the feedback control system will automatically adjust the probe's motion path to ensure that it can accurately scan the target area. In some cases, if errors are detected in the initial scan data, the feedback control system will initiate a secondary scan correction to re-measure the area with larger errors, thereby improving the accuracy of the overall data.

[0085] The output system is the last part of the equipment, responsible for outputting the 3D model and specification parameters after data processing and optimization. Through the 3D surface model generated by the data processing unit, the output system can automatically extract the key specification parameters of the lens, such as diameter, thickness, curvature, etc.

[0086] The output system provides multiple output interfaces so that data can be output in different formats, such as STL, OBJ or CAD file formats. These files can be used for subsequent production, quality control or customer customization. In addition, the output system can also generate error analysis reports to help users intuitively understand measurement accuracy and data quality.

[0087] Embodiment 4: A storage medium stores spectacle lens specification data, the data including three-dimensional point cloud data of the lens obtained by a three-dimensional measurement method, a three-dimensional surface model of the lens generated by fitting, and related lens specification parameters.

[0088] Specifically, the storage medium is used to store the lens specification data obtained by the three-dimensional measurement method. The data content includes but is not limited to the three-dimensional point data obtained by point cloud scanning, the three-dimensional surface model of the lens generated by the least squares method, and the lens specification parameters (such as diameter, thickness, curvature, etc.) extracted based on the surface fitting results. These data are crucial for subsequent glasses fitting processes, quality control and customized production.

[0089] In general, the choice of storage media includes local storage devices (such as hard disks, solid-state drives) or cloud storage systems. For application scenarios with large amounts of data, cloud storage can provide larger storage space and convenient remote access capabilities. In addition, in order to improve data security and prevent loss, the system usually adopts a backup mechanism to store data in multiple media at the same time to ensure data integrity and reliability.

[0090] As an option, the storage medium can include the storage location of the computer executable program, ensuring that the measurement results can be called and processed in different computing environments. In actual applications, the data will be saved in standard formats such as STL, OBJ, etc., which can facilitate the subsequent display and further processing of the 3D model.

[0091] Specifically, the storage medium includes not only 3D point cloud data and 3D models, but also other auxiliary data, such as intermediate data in the error correction process, sensor feedback data in the measurement process, and data of the measurement environment (such as temperature, humidity and other influencing factors). These additional data can be used for subsequent analysis and calibration to ensure the accuracy and reliability of the measurement results.

[0092] In one possible implementation, the storage medium uses a database management system (DBMS) to organize and manage data. In this way, the three-dimensional model and specification parameters of the lens can be stored in a data table, and the measurement data of each lens can be stored as an independent record in the database for subsequent retrieval, update and use.

[0093] The stored data will adopt different data formats and structures according to actual needs. In general, the 3D point cloud data of the lens will be stored in a standard file format containing spatial coordinates, such as STL format, which is widely used in 3D printing and computer-aided design (CAD) fields. Point cloud data usually includes the 3D coordinates of each measured point, and may also include other features, such as the point's normal vector, reflection intensity, etc.

[0094] For the generated 3D surface model, the system will use standard NURBS surface or B-spline curve representation, which can efficiently store the precise shape of the lens surface. Through these formats, subsequent processing programs can easily read and modify the 3D model of the lens for more complex analysis or optimization.

[0095] In some embodiments, the lens specifications (such as diameter, curvature, thickness, etc.) will be saved as structured data, such as JSON or XML format. These formats can clearly mark the values ​​of each parameter and facilitate the transfer of data between different systems, ensuring that the required data can be accurately obtained in the production line, quality control system and customization needs.

[0096] In this embodiment, data security and backup measures are also very important. Since the measurement data of eyeglass lenses is of great significance in the process of fitting glasses, any data loss may lead to production errors or customer dissatisfaction. Therefore, the system adopts an efficient backup mechanism. When the data is stored, it will be backed up regularly to ensure that it can be quickly restored in the event of a failure.

[0097] As an option, the system can implement distributed storage, where data is stored in multiple physical locations or cloud storage at the same time, thereby reducing the risk of data loss due to a single device failure. Furthermore, the storage medium can also implement version control functions to record the historical version of each data update so that it can be backtracked or corrected when necessary.

[0098] In some embodiments, data storage may also integrate encryption technology to ensure the security of stored data. In particular, when lens data involves personalized customization and customer privacy, encryption measures can be used to prevent unauthorized access and data leakage.

[0099] The data in the storage medium can be read and used at any time in subsequent applications. For example, in customer customization requirements, the stored 3D model and specification parameters of the lens can be read and directly applied to the production line or assembly process. In the quality control and production inspection links, the stored data can be used to check whether each lens meets the design standards and quality requirements.

[0100] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional measurement method for spectacle lens specifications, characterized in that: The following steps are involved: S1. Use a high-precision mechanical probe and an embedded camera to scan the surface of the lens to obtain three-dimensional point cloud data; S2. Fitting the point cloud data using the least squares method to generate an ideal surface model of the lens; S3, use variational method to optimize the probe path planning to ensure the optimal acquisition order of each measurement point; S4, correcting the error caused by unstable probe contact and slight vibration during the initial scanning process through secondary scanning; S5. Output the optimized three-dimensional model and lens specification parameters.

2. A three-dimensional measurement method for spectacle lens specifications according to claim 1, characterized in that: In the step S2, the least squares method fits the point cloud data by minimizing the following error function: Among them, f(x i ,y i ) is the fitting surface at point (x i ,y i ), z i is the actual measured z coordinate.

3. A three-dimensional measurement method for spectacle lens specifications according to claim 1, characterized in that: In the S3 step, the variational method optimizes the probe path and performs path planning by minimizing the following objective function: Where γ(t) = (x(t), y(t), z(t)) is the path of the probe, f(x(t), y(t)) is the value of the fitted surface on (x(t), y(t)), z i is the actual measured z coordinate, and λ is the regularization parameter.

4. A three-dimensional measurement method for spectacle lens specifications according to claim 1, characterized in that: In the step S4, the secondary scanning correction process performs error correction through a feedback mechanism and performs fine adjustment based on the corrected data.

5. The method for three-dimensional measurement of spectacle lens specifications according to claim 1, characterized in that: In the step S5, the output three-dimensional model includes the three-dimensional surface reconstruction result and specification parameters of the lens, including but not limited to the diameter, curvature and thickness of the lens.

6. A method for three-dimensional measurement of spectacle lens specifications according to claim 1, characterized in that: In the step S2, the three-dimensional surface model of the lens generated by the least squares fitting is a continuous and smooth surface, and the fitting process is optimized by the weighted least squares method.

7. A method for three-dimensional measurement of spectacle lens specifications according to claim 1, characterized in that: In the step S3, the probe path optimized by the variational method takes into account the curvature change of the lens surface and optimizes the selection order of each measurement point.

8. A three-dimensional measurement system for spectacle lens specifications, according to a three-dimensional measurement method for spectacle lens specifications according to any one of claims 1 to 7, characterized in that: include: A data acquisition module is used to scan the lens surface using a high-precision mechanical probe and an embedded camera to obtain three-dimensional point cloud data; A data processing module, used for fitting the three-dimensional point cloud data using a least squares method and optimizing a probe path using a variational method; A control module, used to adjust the path of the probe and perform a secondary scan to correct the measurement error; Output module, used to generate 3D models and specification parameters of lenses.

9. A three-dimensional measurement device for spectacle lens specifications, according to the three-dimensional measurement system for spectacle lens specifications of claim 8, characterized in that: include: High-precision mechanical probe system, used to obtain three-dimensional point cloud data of lens surface; Embedded camera system for real-time lens image acquisition and edge detection combined with image processing technology; A data processing unit, used to receive the collected image data and fit the point cloud data, optimize the path and correct the error; Feedback control system for correcting measurement errors and optimizing probe paths through real-time feedback; The output system is used to generate and output the three-dimensional model and specification parameters of the lens.

10. A storage medium, characterized in that: The spectacle lens specification data are stored, and the data include three-dimensional point cloud data of the lens obtained by a three-dimensional measurement method, a three-dimensional surface model of the lens generated by fitting, and related lens specification parameters.

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