Method suitable for gradient spectacle lens evaluation and corresponding device

Abstract

A computer-implemented method suitable for evaluating a progressive spectacle lens and a corresponding computer are provided. Measurement data indicative of at least one of color or transmittance along a plurality of measurement points across at least one line of a spectacle lens is received. The method further includes calculating scalar values representing differences between measurement data of adjacent or overlapping sets of measurement points of the plurality of measurement points. The spectacle lenses are then evaluated based on the scalar values.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202480014071.9 (22) Application Date 2024.03.14 (30) Priority Data 23161709.3 2023.03.14 EP (85) PCT International Application Entering National Phase Date 2025.08.21 (86) PCT International Application Application Data PCT / EP2024 / 056871 2024.03.14 (87) PCT International Application Publication Data WO2024 / 189165 EN 2024.09.19 (71) Applicant Carl Zeiss Optics International Ltd. Address Aalen, Germany (72) Inventors F. Semeraro M. Boareto S. Marnati (74) Patent Agency China Patent Agency (Hong Kong) Ltd. 72001 Patent Attorney Lu Jiang Liu Chunyuan (51) Int.Cl. G01M 11 / 02 (2006.01) G01J 3 / 46 (2006.01) G02C 7 / 10 (2006.01) (54) Invention Title: Method and Apparatus Suitable for Evaluating Gradient Spectacular Lenses (57) Abstract: A computer-implemented method and corresponding computer suitable for evaluating gradient spectacular lenses are provided. Measurement data is received, indicating at least one of color or transmittance at a plurality of measurement points along at least one line spanning the spectacular lens. The method further includes calculating scalar values ​​that represent the differences between measurement data of adjacent or overlapping groups of measurement points. The spectacular lens is then evaluated based on the scalar values. Claims 2 pages, Description 13 pages, Drawings 11 pages, CN 121039478 A 2025.11.28 CN 1 21 03 94 78 A 1. A computer-implemented method suitable for evaluating a gradient eyeglass lens, the method comprising: receiving measurement data (40) indicating at least one of color and transmittance of a plurality of measurement points (31) along at least one line spanning the gradient eyeglass lens (30; 80; 90; 1000), characterized in that: calculating scalar values ​​(43) representing differences between measurement data (40) of adjacent or overlapping groups of measurement points (50A, 50B), each group of measurement points (50A, 50B) including at least one of the plurality of measurement points (31), and evaluating the gradient eyeglass lens (30; 80; 90; 1000) based on the scalar values; The feature is that evaluating the gradient eyeglass lens includes: comparing the scalar values ​​with at least one threshold, and comparing the position along the at least one line where the scalar value intersects with the at least one threshold with a nominal value.2. The method of claim 1, wherein evaluating the spectacle lens based on these scalar values ​​further comprises comparing a curve defined by scalar values ​​varying with position along the at least one line with a target curve. 3. The method of claim 2, wherein the target curve comprises a Gaussian distribution. 4. The method of claim 2 or 3, wherein the method further comprises obtaining a quality grade of the gradient of the gradient spectacle lens based on the deviation of the scalar value curve from the target curve. 5. The method of any one of claims 1 to 4, wherein the at least one line comprises a plurality of parallel lines, and each of the adjacent measurement point groups (50A, 50B) comprises at least one measurement point from each of the plurality of lines. 6. The method of any one of claims 1 to 5, wherein the measurement data indicates color, and the scalar values ​​include one of a ΔECMC(1:1) value or a ΔECMC(2:1) value. 7. The method as claimed in any of the preceding claims, characterized in that one of the measurement point groups (50A, 50B) is located in the preceding region of the graduated eyeglass lens, and the other measurement point group (50A, 50B) is located in the following region of the graduated eyeglass lens, wherein the following region is adjacent to or overlaps with the preceding region. 8. The method of claim 7, further comprising: generating a scalar value curve including the scalar values ​​(43) and a plurality of position values, wherein each of the plurality of scalar values ​​(43) is associated with a position value among the plurality of position values, and wherein each of the plurality of position values ​​is associated with a region of the gradient eyeglass lens located between a corresponding preceding region and a corresponding following region or located at an overlap between the corresponding preceding region and the corresponding following region, wherein the corresponding preceding region and the corresponding following region overlap or are adjacent to each other, and evaluating the gradient eyeglass lens (30; 80; 90; 1000) based on the scalar value curve. 9. The method of claim 8, wherein evaluating the gradient eyeglass lens based on the scalar value curve comprises: providing at least one threshold and at least one corresponding nominal position value for the at least one threshold, and comparing the scalar value of the scalar value curve with the at least one threshold. 10. The method of claim 9, further comprising comparing the position value of the plurality of position values ​​along the at least one line where the scalar value curve intersects the at least one threshold with the at least one corresponding nominal position value.11. The method of any one of claims 7 to 10, wherein the scalar values ​​(43) represent the differences between measurement data (40) of adjacent measurement point groups (50A, 50B), each measurement point group (50A, 50B) including at least one of the plurality of measurement points (31), wherein the subsequent region (n+1) is adjacent to the preceding region. 12. The method of any one of claims 7 to 10, wherein the scalar values ​​(43) represent the differences between measurement data (40) of overlapping measurement point groups (50A, 50B), each measurement point group (50A, 50B) including at least two measurement points from the plurality of measurement points (31), wherein the subsequent region overlaps with the preceding region by at least one point in each measurement point group (50A, 50B), wherein the overlap between the subsequent region and the preceding region forms an overlapping region associated with the region, and wherein at least one point in each measurement point group (50A, 50B) is outside the overlapping region. 13. The method of any one of claims 7 to 12, wherein evaluating the gradient eyeglass lens based on the scalar value curve further comprises: providing a plurality of target values ​​and a plurality of position values ​​defining a target curve associated with the gradient eyeglass lens, wherein each of the plurality of target values ​​is associated with a corresponding position value among the plurality of position values, and comparing each scalar value of the scalar value curve at each position value with each value of the target curve at the corresponding position value. 14. The method of claim 13, wherein the target curve further comprises a Gaussian distribution of the plurality of target values. 15. The method of claim 13 or 14, wherein the method further comprises obtaining a quality grade of the gradient of the gradient eyeglass lens based on the deviation between at least one scalar value of the scalar value curve and at least one target value of the plurality of target values ​​of the target curve. 16. The method of any one of claims 1 to 15, characterized in that the measurement data (40) indicates color and transmittance (t), these scalar values ​​(43) including a ΔECMC (1:1) value or a ΔECMC (2:1) value and a difference (Δt) between transmittances defined by the difference between the measurement data (40). 17. A method suitable for producing graduated eyeglass lenses, the method comprising: manufacturing graduated eyeglass lenses, and evaluating the graduated eyeglass lenses using the method of any one of claims 1 to 16. 18. A computer program including instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 16. 19. A computer-readable storage medium storing the computer program of claim 16.20. A data carrier signal carrying a computer program as claimed in claim 16. 21. An apparatus including a processor configured to perform the following steps: receiving (20) measurement data (40) indicating at least one of color and transmittance of a plurality of measurement points (31) along at least one line spanning a gradient eyeglass lens (30; 80; 90; 1000), characterized in that: calculating (21) scalar values ​​(43) representing differences between measurement data of adjacent or overlapping groups of measurement points (50A, 50B), each group of measurement points (50A, 50B) including at least one of the plurality of measurement points, and evaluating the gradient eyeglass lens (30; 80; 90; 1000) based on these scalar values, characterized in that evaluating the gradient eyeglass lens includes comparing these scalar values ​​with at least one threshold, and comparing the location along the at least one line where the scalar values ​​intersect with the at least one threshold with a nominal value. 22. The apparatus of claim 21, wherein the processor is further configured to perform the steps of any one of claims 2 to 16. Claims 2 / 2 Page 3 CN 121039478 A Method and corresponding apparatus suitable for evaluating gradient eyeglass lenses Technical Field

[0001] This application relates to a computer-implemented method suitable for evaluating gradient eyeglass lenses, and to a corresponding apparatus. Background Art

[0002] Gradient eyeglass lenses are lenses that do not have a uniform color but have color variations on their surface. Such gradient eyeglass lenses can be used, for example, in sunglasses, and typically include one or more dark areas with low light transmittance and one or more light areas with high light transmittance. For example, in a gradient eyeglass lens for sunglasses, when the sunglasses are worn, the upper portion, referred to as the top portion or simply the top (facing the top of the head in the wearing position), is darker, and the lower portion, referred to as the bottom portion or the bottom, is lighter, with a transition area in between.

[0003] When manufacturing such gradient eyeglass lenses (e.g., for sunglasses), it is desirable to assess whether the tinting has been applied to the lens according to the required specifications, such as whether it is in the correct position and / or whether it has the correct gradient width.

[0004] For example, in a typical manufacturing process, the production tinting bath is programmed according to the target gradient, and the lens is subjected to the tinting bath accordingly. The actual gradient color applied to the lens is then measured and compared to the target; if the measured gradient does not adequately correspond to the target, the lens can be rejected, and / or the manufacturing process can be adjusted.

[0005] Conventionally, the width of the gradient color on the lens is measured by visually observing the lens on a lightbox with the aid of a dedicated ruler. This leads to a degree of subjectivity in the observation, such as the problem of the precise location of the start and end of the gradient.For example, the human eye has limitations in detecting small color changes, which can lead to measurement results varying depending on the operator performing the measurement.

[0006] The Smart Shade device (formerly offered by Smart Vision but now discontinued) was specifically designed for analyzing the gradient of eyeglass lenses. It returned a percentage match value of surface area relative to a reference gradient lens, without providing any further information about the lens being measured. The reference gradient lens was considered to be a lens of the target design, i.e., a sample of a lens manufactured as required.

[0007] Therefore, even if a defective lens can be identified due to a low percentage match, it is difficult to adjust production solely based on that result, as further information (e.g., the width of dark areas, transition areas, and clear areas, and the location of these areas on the lens) is unavailable. Furthermore, it is necessary to identify a reference point on the lens being inspected for measurement, which the Smart Shade device cannot identify if it is not identical between the reference and sample lenses.

[0008] Starting from this conventional solution for automatic lens measurement, the object of the present invention is to provide an improved method for evaluating graduated eyeglass lenses, which can provide more information about the graduated aspect (e.g., the location of the graduated aspect) and can be applied to different types of graduated lenses.

[0009] "ISO 12311:2013 Personal protective equipment – ​​Test methods for sunglasses and related eyewear" describes the measurement of the uniformity of light transmittance of filters with stripes or graduated areas having different light transmittances in section 7.2.1.1.

[0010] "ISO 12312-1:2013 Eye and face protection – Sunglasses and related eyewear – Part 1: General specification 1 / 13 pages 4 CN 121039478 A Sunglasses" describes the general requirements for transmittance in section 5.3.

[0011] JP 2009 109442 A describes a method, apparatus, and method for manufacturing eyeglass lenses for inspecting stains on them.

[0012] JP 2004 326018 A describes a method for dyeing plastic lenses and an ink used in the method for dyeing plastic lenses.

[0013] DE 10 2013 003558 A1 describes an apparatus and method for evaluating the coloring of spectacle lenses.

[0014] Starting from the background art, the object of the present invention is to provide an improved method for evaluating graduated spectacle lenses.

[0015] According to a first aspect of the invention, a computer-implemented method suitable for evaluating a graduated eyeglass lens is provided, the method comprising: receiving measurement data indicating at least one of color and transmittance of a plurality of measurement points along at least one line spanning the graduated eyeglass lens, characterized in that: calculating scalar values ​​representing differences between measurement data of adjacent or overlapping groups of measurement points, each group of measurement points including at least one of a plurality of measurement points; and evaluating the graduated eyeglass lens based on the scalar values.

[0016] According to a second aspect of the invention, a computer-implemented method suitable for evaluating a graduated eyeglass lens is provided, the method comprising: receiving measurement data indicating at least one of color and transmittance of a plurality of measurement points along at least one line spanning the graduated eyeglass lens, characterized in that: calculating scalar values ​​representing differences between measurement data of adjacent or overlapping groups of measurement points, each group of measurement points including at least one of a plurality of measurement points; evaluating the graduated eyeglass lens based on the scalar values; and further comprising: evaluating the graduated eyeglass lens by comparing the scalar values ​​with at least one threshold; and comparing the location along at least one line where the scalar values ​​intersect with at least one threshold with a nominal value.

[0017] Receiving the measurement data may be accomplished, for example, by uploading the measurement data to a computer performing the method or to a computer-accessible storage device (such as a cloud storage device).

[0018] Providing scalar values ​​helps in the quantitative evaluation of graduated eyeglass lenses because the magnitude of the scalar value is directly related to the gradation: a high scalar value indicates a large difference between the measurement data of adjacent measurement point groups, thus indicating a large gradation, while a low scalar value indicates the opposite, a small gradation.

[0019] A scalar is a value that can be represented by a single real number. In contrast, measurement data indicating color contains multiple values ​​at each measurement point. To represent color, at least three values ​​are typically used. For example, color can be represented by three values ​​in the RGB (red, green, blue) system. Other systems used to represent color, such as the L*a*b color space or the CMY color space, can also be used. In this case, the measurement data for each point can be represented as a vector that typically has at least three components, making analysis based directly on the measurement data more difficult than analysis based on scalar values.

[0020] A measurement point group includes one or more measurement points from a plurality of measurement points. For example, in the case where at least one line across the eyeglass lens is a single line, each group may include one or more adjacent points along that line. Two point sets are adjacent if they are close to each other along at least one line and have no common measurement points; two point sets are overlapping if they include common measurement points.For example, in the case of a single line, when each group of points includes three measurement points, the groups can be shifted forward by two points each time, so that two successive groups of points overlap at one point. These are just some numerical examples, only pages 2 / 13 of the specification, CN 121039478 A. The advantage of including more than one measurement point in each group of points is that it produces a certain smoothing or averaging effect, so that the measurement tolerance of each measurement point can be a statistical average.

[0021] At least one line may also include two or more parallel lines. In this case, each group of measurement points may include one or more points of each line, wherein the points of each line have the same position along the direction of the line. For example, in some embodiments, three to seven lines, such as five lines, may be used. The more lines there are, the longer the measurement time required to capture measurement data may be, but the averaging effect described above can be increased and the measurement can be made more accurate. The number of lines, from three to seven, is a good trade-off between measurement accuracy and the required measurement time.

[0022] If the groups of measurement points share one or more measurement points, the groups of measurement points overlap. The term “difference between measurement data of adjacent or overlapping measurement point groups” also covers cases where more than two measurement point groups are used to calculate scalar values.

[0023] A scalar value is essentially a function of the length direction along at least one line and can be evaluated in a variety of ways. In other words, the function represents the scalar value that varies with position along the line.

[0024] In cases where the measurement data indicates color, the so-called ΔECMC(1:1) value can be used to calculate the scalar value. This method is defined, for example, in ASTM standard D2244-22 (see its equations (21) and (22) for details), and additional information can be found in ASTM E308-99. The advantage of the ΔECMC(1:1) metric defined in the standard is that it provides a standardized and easily understood metric for color difference. For this purpose, if the measurement data is already in the LCh color space (brightness, chromaticity, and hue), ΔECMC(1:1) can be calculated based on the standard equations given in the standard. If color is measured in RGB, it can be converted to LCh via the L*a*b color space defined in EN ISO 116464-4. Alternatively, other scalar values ​​also defined in the aforementioned standard can be used instead of ΔECMC(1:1), such as ΔECMC(2:1). The advantage of using ΔECMC(a:b) values ​​(where a=1 or 2 and b=1) is that human perception can be considered by choosing a and b. Alternatively, ΔE, as defined in EN ISO 11664-4 (which generally defines the L*a*b* color space), can be used. Furthermore, these measures are well-defined standard measures and therefore easily understood.In some other embodiments, color values ​​can be converted to grayscale values, for example by calculating the grayscale value gs for each measurement point according to gs = (R + G + B) / 3 (where R, G, and B are the red, green, and blue values, respectively), averaging the grayscale values ​​over the corresponding group of measurement points, and calculating the difference between the average values ​​of adjacent groups of points. When the measurement data indicates transmittance, the same calculations explained above for grayscale values ​​can be used.

[0025] The measurement data can be obtained by conventional measuring devices, such as the UTM 5064 device (light transmittance uniformity) provided by AD Engineering.

[0026] In some embodiments where color is used to calculate scalar values, however, in addition to color, the measurement data can also indicate the transmittance of the graduated lens at multiple measurement points. Besides displaying scalar values ​​or color information, transmittance (preferably averaged over a group of measurement points) can also be displayed to the user. This provides the user with additional information.

[0027] For evaluation, the obtained scalar values ​​can be normalized, for example, so that the highest scalar value is 1 (or 100%). This facilitates data processing, for example, by using a general threshold as explained below. However, scalar values ​​can also be evaluated without such normalization, for example, by setting a threshold based on the maximum value of the scalar value.

[0028] As briefly mentioned above, evaluating spectacle lenses based on scalar values ​​can include comparing the scalar values ​​to one or more thresholds. Scalar values ​​above a threshold indicate high variance in the measured data, thus indicating the presence of a transition region with high color gradation, while scalar values ​​defined below the corresponding threshold indicate low variance. In this way, distinct regions can be identified, such as transition regions between specific locations along at least one line and regions outside of such transition regions. These regions can be converted to widths in mm and compared to the recorded value (mm) of a standard target, and the variance in width regions can be evaluated using a pass / fail (PASS / FAIL) method with a tolerance of + / - 3 mm. For example, in the case of a typical graduated lens with a dark top and a light bottom, the bottom region, the transition region, and the top region can be identified. The location of the region boundary (which may be the location where the scalar value intersects with the threshold) can then be compared with the nominal value of the corresponding lens type being examined.

[0029] Some types of lenses (e.g., so-called Halo lenses, which have a light inner side and a dark outer side, or so-called FOOD lenses, which have a dark bottom portion and a dark top portion separated by a light inner portion) may have more than one transition region along at least one line, and different transition regions may use the same or different thresholds.Evaluations for different transition regions can also be performed separately to allow for more precise analysis of each transition region. Specifically, scalar values ​​can then be normalized individually for each transition region, and in some embodiments, the same threshold can be used for each (individually normalized) transition region. In other embodiments, different thresholds can be used.

[0030] In a preferred embodiment, all normalized scalar values ​​can use the same threshold, for example, a threshold between 0.7 and 0.9 (70% to 90%). This makes evaluation easier. In other embodiments, different lens types can use different thresholds.

[0031] In some embodiments, nominal values ​​for the locations of the aforementioned region boundaries can be stored for different lens types. In other embodiments, they can be manually entered. In some embodiments, the lens type can be provided along with the measurement data, for example, read by the corresponding measuring device based on a label on the lens.

[0032] To obtain measurement data, the lens can be positioned such that at least one line coincides with the gradient direction. For a certain type of lens, such as the Halo lens described above, at least one line can extend substantially through the center of the lens. This ensures that the width of the measurement area (dark, light, transition) can be measured reproducibly.

[0033] In some embodiments, to assist the operator, a curve representing the color value can be displayed along the line along with the measurement data. Furthermore, in some embodiments, the aforementioned color curve along at least one line can be displayed in the color graph to provide additional information to the operator.

[0034] Some embodiments may include smoothing the curve represented by the scalar values, for example, by applying a low-pass filter. Smoothing the curve can reduce the effects from measurement noise and small deviations. In other embodiments, the unsmoothed curve can be evaluated to monitor small-scale variations in the differences in the measurement data.

[0035] In addition to or instead of evaluating the gradation of the spectacle lens based on one or more thresholds, deviations from the curve represented by the scalar values ​​can be compared to the ideal curve, whether for the entire curve or only for a specific portion of the curve, such as the portion above another threshold (e.g., 0.5 (50%)). For example, in this way, deviations from the ideal transition can be determined. The ideal curve for the transition can be a Gaussian curve (distribution), and the deviation between the curve given by the scalar value and the Gaussian curve (distribution) can be determined, for example, by the following formula: I(GD) is the deviation exponent relative to the reference Gaussian curve. The numerator calculates the sum of the absolute differences between the reference Gaussian curve (y'i value) and the scalar value yi (e.g., ΔECMC(1:1) value) in the graphical region where the Y-axis (vertical axis) is greater than another threshold (e.g., 0.5). In the denominator, the sum of Y>0.50 values ​​for the G curve is calculated.

[0036] When the scalar value and the Gaussian curve completely overlap (best case), the value of the I(GD) index is 0%.

[0037] When the difference between the scalar value and the Gaussian curve is greatest (worst case), the value of the I(GD) index is 100%.

[0038] In an embodiment, the target curve may include or may correspond to a Gaussian curve (distribution). In other embodiments, other predefined target curves may be used.

[0039] Based on the deviation from the ideal curve, a quality grade can be obtained and output (e.g., a high deviation results in a low quality value, and correspondingly, a low deviation results in a high quality value). Similarly, a quality value can be generated based on the deviation between the aforementioned transition position and the nominal position. Based on the quality value, for example, the manufactured lens may be accepted or discarded, and / or, in the case of a low quality value, the manufacturing apparatus may be recalibrated.

[0040] Therefore, according to another aspect, a method suitable for producing and controlling graduated eyeglass lenses is provided, the method comprising: manufacturing graduated eyeglass lenses, and evaluating graduated eyeglass lenses using any of the methods discussed above.

[0041] In addition to the measurements discussed above, the method can be implemented as software on a computer device, for example in the form of an Excel spreadsheet suitable for performing the above calculations. Therefore, a computer program including instructions is provided, which, when executed by a computer, cause the computer to perform the methods described above (in addition to the measurements themselves). The instructions can be stored in the memory or other storage device of a computer in the form of a computer program. A computer-readable storage medium storing such a computer program and a data carrier signal carrying the computer program are also provided. Furthermore, a computer program stored on a non-transitory tangible computer-readable storage medium is provided, the computer program including instructions that, when executed by a computer, cause the computer to perform the methods described above. It should be noted that the term "computer" above does not necessarily mean a single entity, but more devices can be coupled to each other and exchange data to perform the methods described above. For example, scalar values ​​can be calculated on one device and then sent to another device for evaluation, such as comparison with a threshold.

[0042] According to another aspect, an apparatus (e.g., a computer) including a processor is provided, the processor being configured to perform the methods described above. A system is also provided, comprising a measuring device for acquiring measurement data and a computer, wherein the measuring device provides the measurement data to the computer.

[0043] Furthermore, a data processing system is provided, comprising a processor and a storage medium coupled to the processor, wherein the processor is adapted to perform the steps of the methods discussed above based on a computer program stored on the storage medium.

[0044] In an embodiment, a computer-implemented method suitable for evaluating graduated eyeglass lenses may include receiving measurement data via a first computer interface, the measurement data indicating at least one of color and transmittance at a plurality of measurement points along at least one line spanning the graduated eyeglass lens. The computer-implemented method of this embodiment may further include calculating, via a computer processor, a scalar value representing a difference between measurement data from adjacent or overlapping groups of measurement points, each group of measurement points including at least one of a plurality of measurement points. Additionally, in this embodiment, one group of measurement points may be associated with a preceding region of the graduated eyeglass lens, and another group of measurement points may be associated with a subsequent region of the graduated eyeglass lens, wherein the subsequent region may be adjacent to or overlap with the preceding region. A preceding region may be defined as the spatial region where the measurement points of one group of measurement points are located. A subsequent region may be defined as the spatial region where the measurement points of another (subsequent) group of measurement points are located. Scalar values ​​may be calculated for regions defined by overlapping or adjacent points of one group of measurement points and another group of measurement points. The region used to calculate the scalar values ​​may identify or correspond to location values. The preceding and subsequent regions can be square or rectangular regions comprising multiple rows and columns of measurement points in their respective measurement point groups. The preceding and subsequent regions can have the same size. The measurement point groups associated with the preceding region and the subsequent measurement point groups associated with the subsequent region can have the same number of measurement points. The region used to calculate a scalar value can be rectangular or square. The region used to calculate a scalar value can have the same shape as the preceding and subsequent regions. Alternatively, the region used to calculate a scalar value can have a different shape compared to the preceding and subsequent regions. For example, the preceding and subsequent regions can be squares (e.g., each comprising 5×5 equally spaced measurement points), while the region used to calculate a scalar value can be rectangular (e.g., comprising 3×5 equally spaced measurement points). The spatial coordinates corresponding to the center position of the region used to calculate the scalar value can define the location value used for calculating the scalar value. (See page 5 / 13 of the specification, 8 CN 121039478 A) Furthermore, in this embodiment, the computer-implemented method may further include generating a scalar value curve comprising a scalar value and a plurality of position values ​​using a computer processor, wherein each of the plurality of scalar values ​​may be associated with a position value among the plurality of position values. Additionally, in this embodiment, each of the plurality of position values ​​may be associated with a region of the graduated lens located between a preceding region and a following region, wherein the preceding and following regions may overlap or be adjacent to each other.Additionally, in this embodiment, the computer-implemented method may further include: providing a scalar value curve for evaluating one or more characteristics of a progressive lens via a second computer interface, and evaluating the progressive lens based on the scalar value curve by a computer processor. Additionally, in this embodiment, evaluating the progressive lens may include: providing at least one threshold and at least one corresponding nominal position value for the at least one threshold via a third computer interface, and comparing the scalar value of the scalar value curve with the at least one threshold. In this embodiment, evaluating the progressive lens may further include comparing the position value from a plurality of position values ​​along at least one line where the scalar value curve intersects with at least one threshold with at least one corresponding nominal position value.

[0045] A preceding region may be defined as the region from which a set of measurement points is obtained. A subsequent region may be defined as the region from which another (subsequent) set of measurement points is obtained. The preceding region may overlap with the subsequent region, forming region n by the overlapping point. Alternatively, the preceding region may be adjacent to the subsequent region, forming region n by the adjacent point. The preceding and subsequent regions may be regions among a plurality of regions arranged along a line spanning the lens. In other words, a set of measurement points can be measured in a preceding region, and another set of measurement points can be measured in a subsequent region. Overlapping or adjacent regions can be defined by overlapping or adjacent points of the measurement point sets. For example, each of the preceding and subsequent regions can include a 5×5 point array, wherein the preceding and subsequent regions overlap by 3×5 point arrays, thereby defining the overlapping region, and wherein the 2×5 point arrays of the preceding and subsequent regions can be outside the overlapping region. For example, a scalar value for a region can be calculated based on 5×5 + 5×5 = 50 measurement points. Then, a scalar value curve can be obtained for each location along at least one line, each location associated with each region. In an embodiment, the scalar value curve can be compared with a target curve, and a quality degree can be derived based on the deviation between the curves.

[0046] In an embodiment, evaluating a graduated eyeglass lens based on a scalar value curve may further include: providing, via a fourth computer interface, a plurality of target values ​​and a plurality of position values ​​defining a target curve associated with the graduated eyeglass lens, wherein each of the plurality of target values ​​may be associated with a corresponding position value among the plurality of position values, and comparing each scalar value of the scalar value curve at each position value with each value of the target curve at the corresponding position value.

[0047] In an embodiment, the target curve may further include a Gaussian distribution of the plurality of target values.

[0048] In an embodiment, the quality degree of the gradient of the graduated eyeglass lens can be obtained based on the deviation of at least one scalar value of a scalar value curve from at least one target value of a plurality of target values ​​of a target curve, wherein the deviation can be calculated by a computer processor.

[0049] In an embodiment, measurement data for evaluating the graduated eyeglass lens can be provided via a first computer interface. In an embodiment, additional data for further evaluating the graduated eyeglass lens (e.g., values ​​associated with a target curve, Gaussian distribution, target value, threshold, nominal position value) can be provided via a second computer interface. Output data generated from the evaluation can be generated by a computer processor. Output data generated from the evaluation can be provided via a third computer interface to facilitate the manufacture of the graduated eyeglass lens.

[0050] “Computer processor” may be simply referred to as “processor”.

[0051] “Computer-implemented method” may be simply referred to as “method”.

[0052] In conjunction with the cited prior art, the technical advantages provided by the features of the independent claim may relate to the following.

[0053] An improvement to the second aspect may involve evaluating a graduated eyeglass lens by comparing a scalar value with at least one threshold.

[0054] Due to the features of the first and / or second aspects, an advantageous combination of evaluating graduated eyeglass lenses based on both transmittance and color can be facilitated.

[0055] The feature in the second aspect involving comparing a nominal value with a position where the scalar value intersects with at least one threshold along at least one line can provide further advantages for evaluating graduated eyeglass lenses, making the evaluation more accurate, robust, and reliable.

[0056] Further technical advantages of the features defined in the dependent claims, in conjunction with the cited prior art, may involve the following.

[0057] Advantageously, evaluating an eyeglass lens based on a scalar value may further include comparing a curve defined by the scalar value varying with position along at least one line with a target curve, which further contributes to the accuracy, robustness, and reliability of the evaluation.

[0058] Evaluating the spectacle lens by comparing a curve defined by a scalar value varying with position along at least one line to a target curve allows for a more detailed evaluation of the graduated spectacle lens, as the deviation of the position where the scalar value matches the target value of color and / or transmittance relative to the target position can be accurately characterized.

[0059] Advantageously, the target curve may further comprise a Gaussian distribution. A target curve implemented with a Gaussian distribution provides an ideal evaluation metric; therefore, comparing the intersection of the curve defined by the scalar value with the target curve implemented with a Gaussian distribution can further improve the method for evaluating graduated spectacle lenses.

[0060] The quality gradation of a graduated eyeglass lens can advantageously be obtained based on the deviation of a scalar value curve from a target curve, thus providing another level of detail when evaluating graduated eyeglass lenses.

[0061] Measurement data including color and measurement data including transmittance can advantageously help in evaluating graduated eyeglass lenses based on a combination of color and transmittance. Obtaining quality gradation based on a combination of measurement data including color and transmittance can further benefit the evaluation of graduated eyeglass lenses and thus can help in the manufacture of graduated eyeglass lenses.

[0062] When evaluating graduated eyeglass lenses based on sets of measurement points, obtaining one set of measurement points in a preceding region and another set of measurement points in a subsequent region can allow for reduction of noise in the measurement data.

[0063] In an example, evaluating a graduated eyeglass lens may include generating a scalar value curve, which includes a scalar value and a plurality of position values, wherein each of the plurality of scalar values ​​may be associated with a position value among the plurality of position values, and wherein each of the plurality of position values ​​may be associated with a region of the graduated eyeglass lens located between a corresponding preceding region and a corresponding following region, wherein the corresponding preceding region and the corresponding following region overlap or are adjacent to each other. In this example, the scalar value curve may provide further improvements in evaluating graduated eyeglass lenses. In this example, evaluating graduated eyeglass lenses based on the scalar value curve may further reduce measurement noise and provide a more systematic, reliable and robust evaluation metric. Brief Description of the Drawings

[0064] Preferred embodiments will be explained with reference to the accompanying drawings.

[0065] FIG1 is a block diagram of a system according to an embodiment.

[0066] FIG2 is a flowchart illustrating a method according to an embodiment.

[0067] FIGS. 3 to 13 (including corresponding sub-graphs (A, B, C, D)) are various diagrams for illustrating the method shown in FIG2. Specification 7 / 13 pages 10 CN 121039478 A Detailed Description

[0068] FIG1 illustrates a system according to an embodiment. The system of FIG1 includes a measuring device 10 and a computer 11. The measuring device 10 is configured to measure the transmittance and color of a gradient eyeglass lens along at least one line.

[0069] This is illustrated in FIG3, which shows a gradient eyeglass lens 30, wherein (as can be better seen in illustration 32) the measurement is performed at measurement points 31 on five parallel lines. The gradient eyeglass lens is oriented such that these lines are parallel to the gradient direction, which is from left to right in FIG3. In the embodiment of FIG1, the measuring device 10 is the UTM measuring device described above and provides measurement data in the form of RGB (red, green, blue) values ​​of color and a transmittance value T (in percentage). Returning to FIG1, the measuring device 10 provides the measurement data to the computer 11.As described above, computer 11 may be a single computer device or may include multiple devices communicating with each other. Computer 11 is configured to perform the method shown in FIG2 by programming its processor accordingly.

[0070] In FIG2, in step 20, the method begins by receiving measurement data from measuring device 10. Receiving measurement data may be via a direct connection, via the Internet, or by transmitting measurement data on a data carrier.

[0071] In step 21, the method includes the step of calculating scalar values ​​based on adjacent or overlapping sets of measurement data. Examples will be shown with reference to FIG4 and FIG5.

[0072] FIG4 shows an example process flow for generating scalar data. Measurement data 40 is obtained in the form of RGB values ​​and transmittance T values ​​based on graduated eyeglass lens 30. In the example of FIG4, this measurement data is provided for each measurement point, for example in tabular form. In the process flow of FIG4, computer 11 first converts the data into L*a*b color data 41, and then further converts it into LCh color data 42. Then, based on the LCh color data 42, the ΔECMC(1:1) scalar value 43 is calculated as a scalar value for adjacent point groups or overlapping point groups.

[0073] Figure 5 shows an example of two measurement point groups 50A and 50B of measurement point 31. In this example, five points of each of the five lines shown form a point group, and successive measurement point groups 50A and 50B (also represented as LCHn-1 and LCHn+1 in Figure 5) with three overlapping points are selected, the value ΔECMC(1:1)n is calculated and assigned to region n. Other measurement point groups or single adjacent points can also be used, but as mentioned above, selecting point groups with multiple points provides averaging and reduces measurement noise.

[0074] Returning to Figure 2, at 22, the scalar value can be further processed. One example is normalization, where the maximum scalar value is assigned to a value of 1 or 100%. Figure 6 shows an example measurement plot of curve 60 for the normalized ΔECMC(1:1) value α, where the range is from α=0 to α=1 (100%) from the bottom to the top of the gradient lens. Curves 61 to 64 show the raw measurement data, where curve 61 shows the transparency T, curve 62 shows the red (R) component, curve 63 shows the green (G) component, and curve 64 shows the blue (B) component. As can be seen, the peak of curve 60 (α=1) is approximately midway between the relatively high and relatively low values ​​in curves 61 and 64. Therefore, curve 60 can be used as a quantitative measure of the gradient. The ripples visible on the left and right sides of the curves are caused by measurement noise or shading tolerances. Due to this ripple, in some embodiments, scalar values ​​below 30% of the maximum scalar value are ignored.

[0075] Figure 12A illustrates the smoothing process, in which the calculated scalar value curve 1200 is smoothed to curve 1201 by filtering, and Figure 12B illustrates the smoothing process, in which the scalar value curve 1211 is smoothed to curve 1212. The remainder of Figures 12A and 12B will be explained further below.

[0076] Returning to Figure 2, in step 23, the scalar value may be compared to a threshold after normalization or smoothing. A simple example is shown in Figure 7, where curve 70 represents the scalar value, curve 71 represents the measured transmittance, and curves 72 to 74 represent the measured red, green, and blue values, respectively. The threshold represented by line 75 is set as a reference α. The points where curve 70 intersects line 75 are marked by vertical dashed lines 78 and 77, which represent the boundaries between the regions of the graduated lens. To the left of line 78, there is a light-colored, highly transparent area at the "bottom." Between line 78 and line 77, there is a transitional area with a high difference between successive measurement point groups. To the right of line 77, there is a dark-colored area at the top of the lens. Therefore, by using curve 70 and selecting the corresponding value of reference α according to line 75, the corresponding segment of the lens can be quantitatively determined.

[0077] In step 24 of FIG2, the position where the scalar value intersects with the threshold (in the case of FIG7, the position indicated by lines 77 and 78) can be compared with the nominal value. This will be shown for different types of graduated lenses with respect to FIG8A, FIG8B, FIG9A, FIG9B and FIG10A to FIG10D. The pass / fail method described above is then used.

[0078] Figure 8A shows a standard graduated lens 80 with a light-colored bottom region 81, a transition region 82, and a dark-colored top region 83, wherein the light-colored bottom region 81 is separated from the transition region 82 by line 84, and the transition region 82 is separated from the top region 83 by line 85 (these lines are for illustration only and do not exist in the actual lens). In use, the light-colored bottom region 81 is worn at the bottom and the dark-colored region is worn at the top, but they are shown from left to right in Figure 8A (and similar representation is used in the following figures).

[0079] Figure 8B shows an example measurement graph of the lens of Figure 8A. Curve 86 shows the normalized ΔECMC(1:1) value, as previously explained for curves 60 and 70 of Figures 6 and 7. Curve 87 shows the transmittance, and curves 88 to 810 show the measurement results for the red, green, and blue components, respectively, similar to the measurement results already explained for curves 61 to 64 of Figure 6.

[0080] Line 815 represents the threshold, corresponding to line 75 in Figure 7. Vertical dashed lines 811 and 812 show the position where curve 86 intersects with line 815, corresponding to lines 78 and 77 in Figure 7.

[0081] Vertical lines 813 and 814 indicate the nominal locations of the boundaries of the transition regions. As can be seen, in this example, the position of dashed line 811 coincides well with line 813, while dashed line 812 deviates from line 814. The amount of deviation can be considered a measure of lens quality. The positions of lines 813 and 814 can be determined, for example, based on the specifications of the respective lenses or by measuring lenses that have been evaluated as correct by other means (e.g., reference lenses) using a ruler.

[0082] A threshold, as indicated by line 815, is selected such that lines 811 and 812 represent the positions of lines 84 and 85 of FIG. 8A, i.e., the boundaries of transition region 82.

[0083] FIG. 9A and FIG. 9B illustrate embodiments of so-called Halo lenses. FIG. 9A shows an example Halo lens 90 having a generally circular light-colored inner region 92 and an annular dark-colored outer region 91, which are separated by an annular transition region. When measured along the line, as shown in Figure 9A, there appear to be two transition regions, TRANS1 and TRANS2. One is the region where the transition occurs from the annular dark outer region 91 to the circular light inner region 92, and the other is the region where the transition occurs from the circular light inner region 92 back to the annular dark outer region 91. These two transition regions are actually the parts of the same annular transition region that intersect twice. In Figure 9A, transition region TRANS2 is defined by lines 93 and 94, and transition region TRANS1 is defined by lines 95 and 96.

[0084] Figure 9B shows the corresponding measurement curves. Curve 97 shows the normalized ΔECMC(1:1) value as a function of position along the measurement line, curve 98 shows the transmittance, and curves 99, 910, and 911 show the red, green, and blue values, respectively. As can be seen, curve 97 has two peaks corresponding to the two transition regions TRANS1 and TRANS2, which intersect with these two transition regions when measured along the line.

[0085] Line 920 shows the threshold value of the normalized ΔECMC(1:1). Dashed lines 912 and 913 indicate the locations where curve 97 intersects line 920 at the first peak (ideally corresponding to lines 93 and 94 in Figure 9A), and dashed lines 914 and 915 indicate the locations where curve 97 intersects line 920 at the second peak, corresponding to lines 95 and 96 in Figure 9A.

[0086] Lines 916 and 917 for the first transition region and lines 918 and 919 for the second transition region represent nominal values. In this case, the nominal values ​​essentially coincide with the measured values, except that line 912 shows a slight deviation from line 916. Therefore, this is an example of a lens that can receive a good quality rating.Instruction manual, pages 9 / 13, 12 CN 121039478 A

[0087] Figures 10A and 10B show examples of lenses 1000 with different types of tinting. The lens has dark regions 1002 and 1003 at the top and bottom, and a light-colored region 1001 in the interior. Compared to the lens 900 of Figure 9A, these regions are not circular, but rather follow each other from top to bottom and extend across the corresponding width of the lens, as shown in Figure 10A. The dark region 1002 is separated from the inner light-colored region 1001 by a transition region trans 2, and the inner light-colored region 1001 is separated from the dark region 1003 by a transition region trans 1. Lines 1004 and 1005 on one side and lines 1006 and 1007 on the other side indicate the boundaries of the transition regions.

[0088] Figure 10B shows a first measurement diagram of the lens 1000 of Figure 10A. Curve 1008 shows the normalized ΔECMC(1:1) value as a function of position. Lines 1009 and 1010 show the nominal positions of the boundaries of the transition region trans 2 in Figure 10A, corresponding to the nominal positions of lines 1004 and 1005 in Figure 10A, and lines 1011 and 1012 show the nominal positions of the boundaries of the transition region trans 1 in Figure 10A, corresponding to the nominal positions of lines 1006 and 1007 in Figure 10A. The nominal position refers to the position according to the intended lens design.

[0089] Line 1015 represents the threshold when using a single threshold. As can be seen, only the right peak of curve 1008 intersects line 1015, resulting in dashed lines 1013 and 1014 representing the measured positions of the boundaries of the transition region TRANS1, with some deviation from the nominal positions 1011 and 1012.

[0090] For the transition region TRANS2, the peak of curve 1008 is lower and therefore does not intersect line 1015. On the other hand, using a value that intersects with the two peaks will result in an overly wide measured transition region TRANS1 (right peak).

[0091] Therefore, in the preferred embodiments shown in Figures 10C and 10D, different thresholds and / or individual normalizations are used to evaluate the peaks individually. In Figures 10C and 10D, the same reference numerals are used for features already discussed with reference to Figure 10B, and will not be discussed further.

[0092] Figure 10C shows the evaluation of the right peak of curve 1008 of Figure 10B, the portion of the curve corresponding to the right peak being represented by curve 1008A. Line 1018 corresponds to a reference value, and dashed lines 1016 and 1017 correspond to the locations where curve 1008A intersects with line 1018, i.e., the measured locations of the boundary of the transition region TRANS1. The threshold represented by line 1018 may correspond to the threshold represented by line 1015.

[0093] Figure 10D shows the evaluation of the left peak of curve 1008.The corresponding curve portion is represented by curve 1008B in Figure 10D. Line 1021 represents the threshold. In the example of Figure 10B, the peak value of curve 1008B is normalized to 1 or 100%. In this case, the same reference value as in Figure 10C can actually be used, but this actually corresponds to using a different threshold due to the different scaling factor used for normalization. Here, the location where curve 1008B intersects with line 1021 is shown by dashed lines 1019 and 1020, which in this case coincide well with the nominal values ​​according to lines 1009 and 1010. In this way, the two transition regions can be reliably measured.

[0094] Returning to Figure 2, in step 25, in addition to the measurement curves discussed above, a two-dimensional curve graph with color coordinates of the curves according to the measured values ​​can also be displayed to the operator as additional information. Figure 11 shows the corresponding embodiment. Here, reference numeral 1100 generally denotes a measurement curve, which can be any measurement curve previously discussed with respect to Figures 6, 7, 8B, 9B, or 10B to 10D. Additionally, blocks 1101 and 1102 represent color coordinates along the measured lines. In block 1101, color coordinates are shown in a*-b* of the L*a*b color space, and curve 1103 shows the corresponding values ​​obtained based on the measured values ​​(red, green, and blue values). Block 1102 shows the colors in C-h of the LCh color space, and curve 1104 shows the colors along one or more measured lines based on the measurement results. In this way, the operator can better visualize color transitions.

[0095] Figures 12A and 12B illustrate the smoothing of the discussed curves, as well as the measured values ​​and evaluations. In Figure 12A, in addition to curves 1200 and 1201 already discussed, curve 1202 represents the measured transmittance, and curves 1203, 1204, and 1205 represent the measured red, green, and blue values. In the case of multiple lines, these values ​​can be the average of these lines (e.g., the five lines in Figures 3 and 5).

[0096] Line 1206 represents the threshold. Figures 12A and 12B both show examples of standard graduated lenses similar to the standard graduated lens shown in Figure 8A, i.e., having a single transition zone. In Figure 12A, lines 1207 and 1208 show the nominal location of the boundary of the transition zone, and dashed lines 1209 and 1210 show the values ​​obtained by measuring the intersection of the smoothed curve 1201 and the line 1206 representing the threshold.

[0097] Figure 12B shows another example, where curve 1213 represents the measured transmittance, and curves 1214, 1215 and 1216 represent the measured values ​​of the red, green and blue components.In some embodiments, smoothing is applied only to lenses with a transition region having a minimum width (e.g., at least 10 mm).

[0098] Line 1217 represents a threshold. Lines 1218 and 1219 represent the nominal location of the transition region, and dashed lines 1220 and 1221 represent the location of the boundary obtained by measurement. As can be seen, in this case, the transition region is smaller than the transition region according to the nominal value.

[0099] The location of the transition region can be determined by the above measurement and compared with the nominal value. As described above, additionally or alternatively, in step 26 of FIG2, the quality of the gradient of the transition region can be determined.

[0100] For example, in terms of normalized ΔECMC(1:1), a perfect gradient can correspond to a Gaussian curve, the general formula of which is: where is the variance and is the center value. For gradients, measurements are taken from the bottom, corresponding to the width of the bottom plus half the width of the transition area (for standard gradient lenses), equal to the width of the transition area multiplied by the reference constant used. This constant can be set to 14 / 17, which helps to better identify the Gaussian curve, and 14 / 17 has proven to be a suitable value.

[0101] The function f gives the value corresponding to the ΔECMC(1:1) value for position x (if the lens is square, then take the lens diameter / lens height). As shown in Figure 13, this Gaussian curve can be compared with the measured value of normalized ΔECMC(1:1).

[0102] In Figure 13, curve 1300 corresponds to normalized ΔECMC(1:1), and curve 1301 corresponds to the Gaussian curve (distribution). In some embodiments, if the above formula does not fit well, the curve and curve can be manually adjusted. To compare the curves, the deviation index from the reference Gaussian curve can be calculated as described above. The area (integral) of the curve whose value is greater than a threshold (e.g., greater than 0.5) (i.e., above line 1302) can be determined. The deviation index can be converted into a percentage value, and quality can be measured based on this deviation index. For example, quality can be defined as follows: 0%–5%: Excellent; 5%–10%: Good; 10%–15%: Acceptable; 15%–20%: Average; >20%: Very Poor. The above thresholds are just examples, and other values ​​can be selected.

[0103] Any of the following lines can be referred to as target curves: line 75 in Figure 7, line 815 in Figure 8B, line 920 in Figure 9B, line 1015 in Figure 10B, line 1018 in Figure 10C, line 1021 in Figure 10D, line 1302 in Figure 13, and / or line 1206 in Figure 12 (indicating a predetermined threshold). Target curves can include curve 1301 corresponding to a Gaussian curve (distribution) as described in the context of Figure 13.Instruction manual, pages 11 / 13, CN 121039478 A

[0104] In the example, the measurement data 40 shown in FIG5 can be received via a first computer interface (not shown in FIG5). The scalar value 43 shown in FIG5 can be calculated by a computer processor (not shown in FIG5). The graduated eyeglass lenses 30, 80, 90, and 1000 in FIG3, 8, 9, and 10 can be evaluated by computer processors (not shown in FIG3, 8, 9, and 10).

[0105] Returning to FIG5, FIG5 shows region n, the preceding region (n-1), and the following region (n+1). The preceding region (n-1) corresponds to the region where measurement point group 50A is located, and the following region (n+1) corresponds to the region where measurement point group 50B is located. In Figure 5, the preceding region (n-1) overlaps with the subsequent region (n+1), forming region n; that is, region n is the overlapping area of ​​the preceding region (n-1) and the subsequent region (n+1). In Figure 5, the rectangle marked by the dashed line and associated with measurement point group 50A indicates the preceding region (n-1). In Figure 5, the rectangle marked by the solid line and associated with measurement point group 50B indicates region (n+1). Measurement point group 50A is measured in the preceding region (n-1), and measurement point group 50B is measured in the subsequent region (n+1). Figure 5 illustrates a preceding region (n-1) and a following region (n+1), each comprising a 5×5 array of measurement points. The preceding region (n-1) overlaps with the following region (n+1) by 3×5 points, thus defining an overlapping region n. The 2×5 points of the preceding region (n-1) and the 2×5 points of the following region (n+1) are outside the overlapping region n. For example, as shown in Figure 5, a scalar value for region (n) can be calculated based on 5×5 + 5×5 = 50 measurement points. Then, a scalar value can be obtained for each location associated with each region (n), resulting in a curve of the scalar value versus location along a line spanning the spectacle lens. The location is the center of region n in the horizontal direction in Figure 5. The scalar value curve can be compared to a target curve, and the quality grade can be derived based on the deviation between the curves.

[0106] As shown in Figures 4 and 5, the scalar value 43 can represent the difference between the measurement data 40 of the corresponding preceding region (n-1) and the following region (n+1) overlapping measurement point groups 50A and 50B. Each measurement point group 50A and 50B includes at least one measurement point among a plurality of measurement points 31, wherein the following region (n+1) may be adjacent to the preceding region (n-1).

[0107] As shown in Figures 4 and 5, the scalar value 43 represents the difference between the measurement data 40 of the overlapping measurement point groups 50A and 50B. Each measurement point group 50A and 50B includes twenty-five measurement points forming a plurality of measurement points 31, wherein the subsequent region (n+1) overlaps with the preceding region (n-1) by fifteen points in each measurement point group 50A and 50B, and wherein the overlap between the subsequent region (n+1) and the preceding region (n-1) forms an overlapping region (n) including the fifteen points, and wherein ten points in each measurement point group 50A and 50B are outside the overlapping region.

[0108] Figure 5 shows an example in which at least one line includes five parallel lines, and each measurement point group in adjacent measurement point groups 50A and 50B includes five measurement points from each parallel line, corresponding to twenty-five measurement points in each measurement point group 50A and 50B.

[0109] Figure 5 illustrates an example where measurement data 40 indicates color and scalar value 43 includes a ΔECMC (1:1) value. In other embodiments, measurement data 40 may indicate color and transmittance (t), and scalar value 43 may include a ΔECMC (1:1) value or a ΔECMC (2:1) value and a difference (Δt) between transmittances defined by the difference between measurement data 40.

[0110] The scalar value curve may be compared with any of the target curves described in the context of line 75 in Figure 7, line 815 in Figure 8B, line 920 in Figure 9B, line 1015 in Figure 10B, line 1018 in Figure 10C, line 1021 in Figure 10D, line 1302 in Figure 13, and / or line 1206 in Figure 12. Line 75 in Figure 7, line 815 in Figure 8B, line 920 in Figure 9B, line 1015 in Figure 10B, line 1018 in Figure 10C, line 1021 in Figure 10D, line 1302 in Figure 13, and / or line 1206 in Figure 12 (indicating a predetermined threshold) may each be referred to as a target curve. In an embodiment, the target curve may also include a curve 1301 corresponding to a Gaussian curve (distribution) as described in the context of Figure 13.

[0111] One possible mode of implementing the claimed invention may be defined by the following provisions.

[0112] Clause 1. A computer-implemented method suitable for evaluating a gradient eyeglass lens, the method comprising: receiving measurement data (40) indicating at least one of color and transmittance of a plurality of measurement points (31) along at least one line spanning the gradient eyeglass lens (30; 80; 90; 1000), characterized in that: calculating scalar values ​​(43) representing differences between measurement data of adjacent or overlapping groups of measurement points (50A, 50B), each group of measurement points (50A, 50B) including at least one of the plurality of measurement points, and evaluating the gradient eyeglass lens (30; 80; 90; 1000) based on the scalar values.

[0113] Clause 2. The method of Clause 1, characterized in that evaluating the gradient eyeglass lens comprises comparing the scalar values ​​with at least one threshold.

[0114] Clause 3. The method of Clause 2, further characterized in that the position where the scalar value intersects the at least one line with the at least one threshold is compared with a nominal value.

[0115] Clause 4. The method of any one of Clauses 1 to 3, characterized in that evaluating the spectacle lens based on these scalar values ​​further includes comparing a curve defined by the scalar values ​​varying with position along the at least one line with a target curve.

[0116] Clause 5. The method of Clause 4, characterized in that the target curve comprises a Gaussian distribution.

[0117] Clause 6. The method of Clause 4 or 5, characterized in that the method further includes obtaining a graded quality of the gradient spectacle lens based on the deviation of the scalar value curve from the target curve.

[0118] Clause 7. The method of any one of Clauses 1 to 6, characterized in that the at least one line comprises a plurality of parallel lines, and each group of adjacent measurement points (50A, 50B) comprises at least one measurement point from each of the plurality of lines.

[0119] Clause 8. The method of any one of Clauses 1 to 7, characterized in that the measurement data indicates color, and these scalar values ​​include one of a ΔECMC(1:1) value or a ΔECMC(2:1) value.

[0120] Clause 9. A method suitable for producing graduated eyeglass lenses, the method comprising: manufacturing graduated eyeglass lenses, and evaluating the graduated eyeglass lenses using the method of any one of Clauses 1 to 8.

[0121] Clause 10. A computer program including instructions that, when executed by a computer, cause the computer to perform the method of any one of Clauses 1 to 9.

[0122] Clause 11. A computer-readable storage medium storing the computer program as described in Clause 10.

[0123] Clause 12. A data carrier signal carrying a computer program as described in Clause 10.

[0124] Clause 13. An apparatus including a processor configured to perform the following steps: receiving measurement data (40) indicating at least one of color and transmittance of a plurality of measurement points (31) along at least one line spanning the gradient eyeglass lens (30; 80; 90; 1000), characterized in that: calculating scalar values ​​(43) representing differences between measurement data of adjacent or overlapping groups of measurement points (50A, 50B), each group of measurement points (50A, 50B) including at least one of the plurality of measurement points, and evaluating the gradient eyeglass lens (30; 80; 90; 1000) based on the scalar values.

[0125] Clause 14. The apparatus of Clause 13, wherein evaluating the gradient eyeglass lens based on these scalar values ​​further comprises comparing a curve defined by scalar values ​​varying with position along the at least one line with a target curve.

[0126] Clause 15. The computer of Clause 13 or 14, wherein evaluating the gradient eyeglass lens comprises comparing these scalar values ​​with at least one threshold. Instruction Manual Page 13 / 13 16 CN 121039478 A Figure 1 Figure 2 Instruction Manual Appendix 1 / 11 Page 17 CN 121039478 A Figure 3 Figure 4 Instruction Manual Appendix 2 / 11 Page 18 CN 121039478 A Figure 5 Figure 6 Instruction Manual Appendix 3 / 11 Page 19 CN 121039478 A Figure 7 Figure 8A Instruction Manual Appendix 4 / 11 Page 20 CN 121039478 A Figure 8B Figure 9A Instruction Manual Appendix 5 / 11 Page 21 CN 121039478 A Figure 9B Figure 10A Instruction Manual Appendix 6 / 11 Page 22 CN 121039478 A Figure 10B Figure 10C Instruction Manual Appendix 7 / 11 Page 23 CN 121039478 A Figure 10D Figure 11 Instruction Manual Appendix 8 / 11 Page 24 CN 121039478 A Figure 12A: Appendix to the instruction manual, page 9 / 11, 25 CN 121039478 A Figure 12B: Appendix to the instruction manual, page 10 / 11, 26 CN 121039478 A Figure 13: Appendix to the instruction manual, page 11 / 11, 27 CN 121039478 A

Claims

1. A computer-implemented method suitable for evaluating progressive spectacle lenses, the method comprising: Receive measurement data (40) indicating at least one of color and transmittance at a plurality of measurement points (31) along at least one line crossing the gradient eyeglass lens (30; 80; 90; 1000), characterized in that: Calculate scalar values ​​(43) that represent the differences between measurement data (40) of adjacent or overlapping measurement point groups (50A, 50B), each measurement point group (50A, 50B) including at least one of the plurality of measurement points (31), and evaluate the gradient eyeglass lens (30; 80; 90; 1000) based on these scalar values. The feature is that evaluating the gradient eyeglass lens includes: comparing the scalar values ​​with at least one threshold, and comparing the position along the at least one line where the scalar value intersects with the at least one threshold with a nominal value.

2. The method as described in claim 1, characterized in that, Evaluating the spectacle lens based on these scalar values ​​further includes comparing a curve defined by scalar values ​​that vary with position along at least one line with a target curve.

3. The method as described in claim 2, characterized in that, The target curve includes a Gaussian distribution.

4. The method as described in claim 2 or 3, characterized in that, The method further includes obtaining the quality grade of the gradient of the graduated eyeglass lens based on the deviation between the scalar value curve and the target curve.

5. The method according to any one of claims 1 to 4, characterized in that, The at least one line comprises multiple parallel lines, and each measurement point group in adjacent measurement point groups (50A, 50B) comprises at least one measurement point from each of the multiple lines.

6. The method according to any one of claims 1 to 5, characterized in that, The measurement data indicates color; these scalar values ​​include ΔE. CMC (1:1) value or ΔE CMC (2:1) is one of the values.

7. The method as described in any one of the preceding claims, characterized in that, One of these measurement point groups (50A, 50B) is located in the preceding region of the graduated eyeglass lens, and the other measurement point group (50A, 50B) is located in the following region of the graduated eyeglass lens, wherein the following region is adjacent to or overlaps with the preceding region.

8. The method as described in claim 7, characterized in that, The method further includes: Generate a scalar value curve, which includes the scalar values ​​(43) and a plurality of position values, wherein each of the plurality of scalar values ​​(43) is associated with a position value among the plurality of position values, and wherein each of the plurality of position values ​​is associated with a region of the graduated eyeglass lens located between or at the overlap between a corresponding preceding region and a corresponding following region, wherein the corresponding preceding region and the corresponding following region overlap or are adjacent to each other, and The graded eyeglass lens (30; 80; 90; 1000) is evaluated based on this scalar value curve.

9. The method as described in claim 8, Its features are, The evaluation of the graded eyeglass lens based on this scalar value curve includes: Provide at least one threshold and at least one corresponding nominal position value for the at least one threshold. The scalar value of the scalar curve is compared with the at least one threshold.

10. The method as described in claim 9, characterized in that, The method further includes The position value where the scalar value curve intersects the at least one threshold among the plurality of position values ​​along the at least one line is compared with the at least one corresponding nominal position value.

11. The method according to any one of claims 7 to 10, wherein, These scalar values ​​(43) represent the differences between measurement data (40) of adjacent measurement point groups (50A, 50B), each measurement point group (50A, 50B) including at least one of the plurality of measurement points (31). The subsequent region (n+1) is adjacent to the preceding region.

12. The method according to any one of claims 7 to 10, wherein, These scalar values ​​(43) represent the differences between the measurement data (40) of the overlapping measurement point groups (50A, 50B), each measurement point group (50A, 50B) including at least two of the plurality of measurement points (31). Wherein, the subsequent region overlaps with the preceding region by at least one point in each measurement point group (50A, 50B), wherein the overlap between the subsequent region and the preceding region forms an overlapping region associated with the region, and wherein at least one point in each measurement point group (50A, 50B) is outside the overlapping region.

13. The method according to any one of claims 7 to 12, characterized in that, Evaluating the graded eyeglass lens based on this scalar value curve further includes: Provides multiple target values ​​and multiple position values ​​for defining a target curve associated with the gradient eyeglass lens, wherein each of the multiple target values ​​is associated with a corresponding position value among the multiple position values, and Compare each scalar value of the scalar curve at each position value with each value of the target curve at the corresponding position value.

14. The method as described in claim 13, characterized in that, The target curve further includes a Gaussian distribution of the multiple target values.

15. The method as described in claim 13 or 14, characterized in that, The method further includes obtaining the quality grade of the gradient of the gradient eyeglass lens based on the deviation between at least one scalar value of the scalar value curve and at least one target value of a plurality of target values ​​of the target curve.

16. The method according to any one of claims 1 to 15, characterized in that, The measurement data (40) indicates color and transmittance (t), and these scalar values ​​(43) include ΔE. CMC (1:1) value or ΔE CMC The difference (Δt) between the (2:1) value and the transmittance defined by the difference between the measured data (40).

17. A method suitable for producing graduated spectacle lenses, the method comprising: Manufacturing a gradient eyeglass lens, and evaluating the gradient eyeglass lens using the method described in any one of claims 1 to 16.

18. A computer program comprising instructions that, when executed by a computer, cause the computer to perform the method as claimed in any one of claims 1 to 16.

19. A computer-readable storage medium storing the computer program as described in claim 16.

20. A data carrier signal carrying a computer program as described in claim 16.

21. An apparatus including a processor configured to perform the following steps: Receive (20) measurement data (40) indicating at least one of color and transmittance at a plurality of measurement points (31) along at least one line spanning at least one of the graduated eyeglass lenses (30; 80; 90; 1000), characterized in that: Calculate (21) scalar values ​​(43) representing the differences between measurement data of adjacent or overlapping measurement point groups (50A, 50B), each measurement point group (50A, 50B) including at least one of the plurality of measurement points, and The graded eyeglass lens (30; 80; 90; 1000) is evaluated based on these scalar values. The feature is that evaluating the gradient eyeglass lens includes comparing these scalar values ​​with at least one threshold, and comparing the position along the at least one line where the scalar value intersects with the at least one threshold with a nominal value.

22. The apparatus as claimed in claim 21, characterized in that, The processor is further configured to perform the steps as described in any one of claims 2 to 16.

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