Optical element surface defect detection method and related equipment

Abstract

The embodiment of the present application discloses a method for detecting surface defects of optical elements and related equipment, which can effectively remove the texture features of the surface of optical elements, separate the defects on the surface of optical elements, and accurately know the defects existing in optical elements. It has the advantages of good direction recognition, accurate separation, and complete extraction of defect features, and has important application value in precision optical detection and surface metrology. The present application includes: using NSST to decompose the surface data of precision optical elements to obtain several standard sub-blocks with different scales and directions; using L0 gradient minimization to eliminate the texture values ​​of all standard sub-blocks except the highest scale sub-block to obtain several target sub-blocks containing only defects of different scales and directions, and the highest scale sub-block is the texture information of the tool marks when the optical element is processed; using NSST inverse transformation on the several target sub-blocks to obtain the data of the surface defects of the precision optical element.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of optical detection technology, and in particular to a method for detecting defects in optical surface elements and related equipment. Background Art

[0002] Nowadays, multi-resolution analysis technology has been successfully applied in the field of defect detection and feature separation. Typical examples include wavelets, curvelets, contourlets and shearlets. Among them, shearlets have better directionality than wavelets, and have simpler mathematical structures than shearlets, curvelets and contourlets, which can effectively reduce the complexity of calculations. Non-subsampled shearlets are an improved version of shearlets. When non-subsampled shearlets are used for decomposition operations, no downsampling occurs in the decomposition process, and the size of each decomposed sub-band is the same as the source surface, which is conducive to the identification and separation of defects in the surface.

[0003] With the development and progress of the manufacturing industry, the requirements for the performance and life of precision optical components are constantly increasing. Defects and damages of optical components will affect the performance and life of the entire optical system. Most defects of optical components cannot be identified by the human eye. In order to make optical components have a longer life and better performance and avoid the impact of optical components on the performance and life of the entire optical system, based on this, the application provides a method for detecting surface defects of optical components and related equipment for solving the above problems. Summary of the Invention

[0004] The embodiments of the present application provide a method and related equipment for detecting surface defects of optical elements, which can effectively remove the texture features of the surface of optical elements, separate the defects on the surface of optical elements, and accurately know the defects existing in the optical elements. It has the advantages of good direction recognition, accurate separation and positioning, and complete extraction of defect features, and has important application value in precision optical detection and surface metrology.

[0005] The first aspect of the present application provides a method for detecting surface defects of an optical element, comprising:

[0006] The surface data of precision optical components are decomposed using NSST (Non-Subsampled Shearlet Transform) to obtain several standard sub-blocks with different scales and directions.

[0007] Using L0 gradient minimization to eliminate the texture values ​​of all standard sub-blocks except the highest-scale sub-block to obtain several target sub-blocks containing only defects of different scales and directions. The highest-scale sub-block represents the tool mark texture information during surface processing of the optical element.

[0008] An NSST inverse transformation is applied to the plurality of target sub-blocks to obtain data of surface defects of the precision optical element.

[0009] Optionally, the L0 gradient minimization is used to eliminate the texture values ​​of all standard sub-blocks except the highest-scale sub-block to obtain a plurality of target sub-blocks containing only defects of different scales and directions, including:

[0010] Normalizing the plurality of standard sub-blocks;

[0011] Calculating the standard deviations of different standard sub-blocks respectively to obtain a highest-scale sub-block, wherein the highest-scale sub-block represents the texture information of tool marks during surface processing of the optical element;

[0012] It is determined that the texture values ​​of all standard sub-blocks except the highest-scale sub-block are eliminated using L0 gradient minimization to obtain several target sub-blocks containing only defects of different scales and directions.

[0013] Optionally, after respectively calculating the standard deviations of the different standard sub-blocks to obtain the highest-scale sub-block, the method further includes:

[0014] The highest scale sub-block is set to a 0 matrix.

[0015] Optionally, before decomposing the surface data of the precision component using NSST, the method further includes:

[0016] determining a detection area of ​​the precision optical element;

[0017] Setting a preset distance between two adjacent data points in the detection area;

[0018] Obtaining the number of data points in the detection area;

[0019] Surface data of knife marks, pits, and scratches in the detection area are obtained according to the preset distance and the number of data points in the detection area.

[0020] Optionally, the determining uses L0 gradient minimization to eliminate texture values ​​of all standard sub-blocks except the highest-scale sub-block to obtain a plurality of target sub-blocks containing only defects of different scales and directions, including:

[0021] The function that determines the L0 gradient minimization is:

[0022]

[0023] Among them, I P Represents the surface data at input P, ​​S P Indicates the operation result of the surface data at output P, represents the gradient at the surface data P, λ is a parameter controlling the smoothing weight, λ=nσ (n=1, 2, 3...), σ is the standard deviation.

[0024] Optionally, before eliminating the texture values ​​of all standard sub-blocks except the highest-scale sub-block using L0 gradient minimization, the method further includes:

[0025] Average grayscale, average contrast, smoothness, consistency, and entropy are used as statistical features of surface data to analyze the texture characteristics and defect numerical distribution of precision optical components at different scales and directions.

[0026] Average grayscale:

[0027] Average contrast:

[0028] Smoothness: R=1-1 / (1+σ 2 );

[0029] consistency:

[0030] entropy:

[0031] Among them, z i A random variable representing height; i represents the counting point, i = 0, 1, 2....; p(z i ) indicates the height level is z i The number of pixels; L represents the height level; μ2 represents the secondary level; μ n represents the nth order; σ 2 represents variance; σ represents standard deviation; m represents average grayscale; U represents consistency; and e represents entropy.

[0032] Optionally, after using average grayscale, average contrast, smoothness, consistency, and entropy as statistical features of the surface data, the method further includes:

[0033] Texture features at different scales and directions are reconstructed to accurately analyze features at different scales on the surface of optical components.

[0034] A second aspect of the present application provides an optical element surface defect detection system, comprising:

[0035] A decomposition unit is used to decompose the surface data of the precision optical element using NSST to obtain a number of standard sub-blocks with different scales and directions;

[0036] an elimination unit, configured to eliminate texture values ​​of all standard sub-blocks except the highest-scale sub-block using L0 gradient minimization to obtain a plurality of target sub-blocks containing only defects of different scales and directions, wherein the highest-scale sub-block represents tool mark texture information during surface machining of the optical element;

[0037] The inverse transformation unit is used to use NSST inverse transformation on the plurality of target sub-blocks to obtain data on the surface defects of the precision optical element.

[0038] A third aspect of the present application provides an optical element surface defect detection device, comprising:

[0039] processor, memory, input and output units, and buses;

[0040] The processor is connected to the memory, the input and output unit, and the bus;

[0041] The memory stores a program, and the processor calls the program to execute the optical element surface defect detection method as described in any one of the first aspects.

[0042] A fourth aspect of the present application provides a computer-readable storage medium, on which a program is stored. When the program is executed on a computer, the method according to any one of the first aspects is performed.

[0043] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0044] The optical element surface defect detection method of the present application first uses NSST to decompose the surface data of the precision optical element to obtain several standard sub-blocks with different scales and directions; then uses L0 gradient minimization to eliminate the texture values ​​of all standard sub-blocks except the highest scale sub-block to obtain several target sub-blocks containing only defects of different scales and directions, and the highest scale sub-block is the texture information of the tool mark during the surface processing of the optical element; finally, the NSST inverse transform is used on the several target sub-blocks to obtain the data of the surface defects of the precision optical element. It can be seen that the surface data is first decomposed into different scales and directions using NSST, and then the L0 gradient minimization is used on the sub-block to remove the texture features of the optical element surface, and finally the defects on the element surface are separated, so that the defects of the optical element can be accurately known, which is convenient for subsequent processing of the defects of the optical element and contributes to the normal operation of the entire optical system. The present method has the advantages of good direction recognition, accurate separation and positioning, and complete extraction of defect features, and has important application value in precision optical detection and surface measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1This is a schematic diagram of an embodiment of a method for detecting surface defects of an optical element according to the present application;

[0046] Figure 2 Schematic diagram of another embodiment of a method for detecting surface defects of an optical element according to this application

[0047] Figure 3 Schematic diagram of an embodiment of an optical element surface defect detection system of this application

[0048] Figure 4 This is a schematic diagram of an embodiment of an optical element surface defect detection device according to the present application;

[0049] Figure 5 This is a diagram showing the effect of using the L0 gradient minimization method to eliminate texture in a method for detecting surface defects of an optical element in this application;

[0050] Figure 6 This is a diagram showing the texture and defect information of each layer in the optical element surface defect detection method of this application. DETAILED DESCRIPTION

[0051] With the development and progress of the manufacturing industry, the requirements for the performance and life of precision optical components are constantly increasing. Defects and damages of optical components will affect the performance and life of the entire optical system. Most defects of optical components cannot be identified by the human eye. In order to make the optical components have a longer life and better performance, it is necessary to avoid the impact of optical components on the performance and life of the entire optical system.

[0052] Based on this, the present application provides a method and related equipment for detecting surface defects of optical elements, which can effectively remove the texture features of the surface of optical elements, separate the defects on the surface of optical elements, and accurately know the defects existing in optical elements. It has the advantages of good direction recognition, accurate separation, and complete extraction of defect features, and has important application value in precision optical detection and surface measurement.

[0053] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] The method of this application can be applied to servers, terminals or other devices with logical processing capabilities, and this application does not limit this. For the convenience of description, the following description is based on the example of the execution subject being the terminal.

[0055] See also Figure 1In a first aspect, the present application provides an embodiment of a method for detecting surface defects of an optical element, comprising:

[0056] 101. The terminal uses NSST to decompose the surface data of precision optical components to obtain several standard sub-blocks with different scales and directions;

[0057] In an embodiment of the present application, a confocal microscope is used to measure and obtain surface data of a precision optical element. After obtaining the surface data of the precision element, the surface data is decomposed using NSST to obtain a plurality of standard sub-blocks of different scales and directions, wherein the scalar of the scale is the distance, and the scalar of the direction is different directions. For example, one standard sub-block is viewed from a distance and from above, and another standard sub-block is viewed from close up and from the side. Therefore, since the scales and directions of viewing are different, a large number of standard sub-blocks are obtained by decomposition. The number of standard sub-blocks obtained by decomposition is not specifically limited in this application and can be set according to actual conditions. The decomposed standard sub-blocks contain data on defects, scratch textures, knife marks, pits, etc. on the surface of the optical element at different scales and in different directions. After obtaining a plurality of standard sub-blocks of different scales and directions by decomposition, step 102 is executed.

[0058] 102. The terminal uses L0 gradient minimization to eliminate texture values ​​of all standard sub-blocks except the highest-scale sub-block to obtain a plurality of target sub-blocks containing only defects of different scales and directions, wherein the highest-scale sub-block represents tool mark texture information during surface processing of the optical element.

[0059] In the embodiment of the present application, since the texture information of the surface of the optical element is relatively complex, it is very necessary to separate the texture of the surface of the optical element in order to completely and efficiently extract its surface defects. In the present application, the L0 gradient minimization algorithm can be used to remove the texture of the image surface, enhance the effective edge features, and has high execution efficiency. Therefore, L0 gradient minimization is used in this article to separate the surface texture information. Specifically, L0 gradient minimization is used to eliminate the texture data of all standard sub-blocks except the highest-scale sub-block. Since the highest-scale sub-block mainly contains texture information such as tool marks generated during surface processing, there are no defects on the surface. Therefore, if the highest-scale sub-block is eliminated using the L0 gradient minimization method, its surface is approximately smooth. Therefore, when the highest-scale sub-block is eliminated using the L0 gradient minimization method, no useful information will be left. Therefore, in order to speed up the calculation rate, the highest-scale sub-block needs to be released separately, and the L0 gradient minimization method is used to eliminate the texture of all standard sub-blocks except the highest-scale sub-block. After elimination, the remaining target sub-block only contains defects on the surface of the optical element, and these defects are from different scales and directions, so that defects on the optical surface element can be located.

[0060] For a visualization of the effects of texture removal using L0 gradient minimization, see Figure 5 .

[0061] 103. The terminal uses NSST inverse transformation on the plurality of target sub-blocks to obtain data on surface defects of the precision optical element.

[0062] In an embodiment of the present application, after defects of different scales and directions are obtained, the target sub-blocks containing only defects of different scales and directions are subjected to NSST inverse transformation, so that data on surface defects of precision optical elements can be obtained from the target sub-blocks.

[0063] See also Figure 2 In a second aspect, the present application provides another embodiment of a method for detecting surface defects of an optical element, comprising:

[0064] 201. The terminal decomposes the surface data of the precision optical element using NSST to obtain a plurality of standard sub-blocks with different scales and directions;

[0065] In an embodiment of the present application, a confocal microscope is used to measure and obtain surface data of a precision optical element. Specifically, the area to be inspected is first determined on the surface of the precision optical element. Then, a preset distance is set between two adjacent data points in the inspection area. In this application, the distance between two adjacent data points is set to 0.625 μm. By setting the distance between the two data points, the number of data points in the inspection area is determined to be 256×256. Then, based on the preset distance and the number of data points in the inspection area, surface data of knife marks, pits, and scratches in the inspection area are obtained. After obtaining the surface data of the precision optical element, the surface data of the precision optical element is decomposed using NSST to decompose it into several standard sub-blocks with different scales and directions.

[0066] 202. The terminal uses average grayscale, average contrast, smoothness, consistency, and entropy as statistical features of surface data to analyze texture features and defect value distribution of precision optical components at different scales and directions;

[0067] Average grayscale:

[0068] Average contrast:

[0069] Smoothness: R=1-1 / (1+σ 2 );

[0070] consistency:

[0071] entropy:

[0072] Among them, z i A random variable representing height; i represents the counting point, i = 0, 1, 2....; p(z i ) indicates the height level is z i The number of pixels; L represents the height level; μ2 represents the secondary level; μ n represents the nth order; σ 2 represents variance; σ represents standard deviation; m represents average grayscale; U represents consistency; and e represents entropy.

[0073] The above formula is used to analyze the texture characteristics and defect value distribution of optical components at different scales and directions, and the table is compiled as follows:

[0074]

[0075] The data in the table shows that the closer the smoothness is to 1, the greater the deviation in the grayscale value of the area. The data in the table shows that the grayscale value deviation of layer 3 is the largest, indicating that the texture and defect characteristics of layer 3 are more complex than those of layers 1, 2, and 4. In the consistency data, the values ​​of the largest scale and layer 3 are smaller than those of layers 1, 2, and 4, especially the largest scale has the lowest consistency. The analysis shows that the largest scale feature is mainly the surface contour of the component, and the defects are more obvious, so the consistency is relatively small. For the image information of each layer, please refer to Figure 6 .

[0076] In the table, the average grayscale and average contrast values ​​for layers 1 and 2 differ significantly from those at other scales. The values ​​for layers 1 and 2 are relatively close, while layer 3 has the highest value. Only the entropy is slightly smaller than the maximum scale, further demonstrating that the texture and defects at layer 3 are more complex. The maximum scale has the highest entropy, indicating that the surface features at this scale are more random than at other scales.

[0077] Therefore, we can see that layers 1 and 2 are primarily defects, and therefore have similar average grayscale, average contrast, and smoothness values. Layer 3, with its relatively complex texture and defect details, has the highest average grayscale, average contrast, and smoothness values, making it a critical scale for separating surface defects.

[0078] 203. The terminal reconstructs texture features of different scales and directions for accurate analysis of features of different scales on the surface of the optical element.

[0079] In the embodiment of the present application, features at different scales are present on the surface of the optical element. Large-scale information primarily represents the topography of the element surface, with deeper defects being particularly noticeable. Therefore, as shown in step 202, layers 1 and 2 primarily represent defects but lack some detail. Layer 3 primarily represents both defects and texture details, making it a critical scale for separating surface defects. Layer 4, the finest scale, primarily reconstructs texture detail information, with no defect or other information present. Therefore, layers 1, 2, and 3 are subsequently subjected to texture removal processing.

[0080] 204. The terminal uses L0 gradient minimization to eliminate texture values ​​of all standard sub-blocks except the highest-scale sub-block to obtain a plurality of target sub-blocks containing only defects of different scales and directions, wherein the highest-scale sub-block represents tool mark texture information during surface processing of the optical element.

[0081] In an embodiment of the present application, several standard sub-blocks obtained by decomposition are normalized, and then the standard deviations of different standard sub-blocks are calculated respectively to obtain the highest-scale sub-block. The highest-scale sub-block represents the texture information of the tool marks during surface processing of the optical element, and there is no defect information, that is, the fourth layer of information described in step 203. To facilitate subsequent processing, the highest-scale sub-block needs to be set to a 0 matrix, and then the L0 gradient minimization method is used to eliminate the texture values ​​of all standard sub-blocks except the highest-scale sub-block to obtain several target sub-blocks containing only defects of different scales and directions.

[0082] Among them, the function that determines the minimization of L0 gradient is:

[0083]

[0084] Among them, I P Represents the surface data at input P, ​​S P Indicates the operation result of the surface data at output P, represents the gradient at the surface data P, λ is a parameter controlling the smoothing weight, λ=nσ (n=1, 2, 3...), σ is the standard deviation.

[0085] 205. The terminal uses NSST inverse transformation on the plurality of target sub-blocks to obtain data on surface defects of the precision optical element.

[0086] In the embodiment of the present application, step 205 is as shown in the aforementioned step 103 and will not be repeated here.

[0087] Therefore, to address the complex and difficult-to-accurate detection characteristics of surface defects in precision optical components, this application first uses NSST to decompose the surface data into different scales and directions. Then, L0 gradient minimization is used on the sub-blocks to remove the texture features of the optical component surface, and finally, the surface defects of the component are isolated. Compared with traditional wavelet technology, this application's method has good directional recognition, accurate separation and positioning, and can relatively completely extract defect features, which has important application value in precision optical inspection and surface metrology.

[0088] See also Figure 3 In a second aspect, the present application provides an optical element surface defect detection system, comprising:

[0089] A decomposition unit 301 is used to decompose the surface data of the precision optical element using NSST to obtain a plurality of standard sub-blocks with different scales and directions;

[0090] Elimination unit 302, configured to eliminate texture values ​​of all standard sub-blocks except the highest-scale sub-block using L0 gradient minimization to obtain a plurality of target sub-blocks containing only defects of different scales and directions, wherein the highest-scale sub-block represents tool mark texture information during surface machining of the optical element;

[0091] The inverse transformation unit 303 is configured to perform an NSST inverse transformation on the plurality of target sub-blocks to obtain data on the surface defects of the precision optical element.

[0092] See also Figure 4 In a third aspect, the present application provides an optical element surface defect detection device, comprising:

[0093] Processor 401, memory 402, input and output unit 403 and bus 404;

[0094] The processor 401 is connected to the memory 402, the input and output unit 403 and the bus 404;

[0095] The memory 402 stores a program, and the processor 401 calls the program to execute the optical element surface defect detection method as described in any one of the first aspects.

[0096] A fourth aspect of the present application provides a computer-readable storage medium, on which a program is stored. When the program is executed on a computer, the method according to any one of the first aspects is performed.

[0097] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0099] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0100] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk, and other media that can store program code.

Claims

1. A method for detecting surface defects of an optical element, characterized in that: include: Determine the inspection area of ​​precision optical components; Setting a preset distance between two adjacent data points in the detection area; Obtaining the number of data points in the detection area; Acquire surface data of knife marks, pits, and scratches in the detection area according to the preset distance and the number of data points in the detection area; NSST is used to decompose the surface data of precision optical components to obtain several standard sub-blocks with different scales and directions. Normalizing several standard sub-blocks and calculating the standard deviations of different standard sub-blocks to obtain a highest-scale sub-block, wherein the highest-scale sub-block represents the tool mark texture information during surface processing of the optical element; The function that determines the L0 gradient minimization is: Among them, I P Represents the surface data at input P, ​​S P Indicates the operation result of the surface data at output P, represents the gradient of the surface data P, λ is the parameter controlling the smoothing weight, λ = nσ (n = 1, 2, 3...), σ is the standard deviation; The highest scale sub-block is set to a 0 matrix; NSST inverse transformation is applied to the plurality of standard sub-blocks to obtain data of surface defects of the precision optical element.

2. The optical element surface defect detection method according to claim 1, characterized in that: Before eliminating the texture values ​​of all standard sub-blocks except the highest-scale sub-block using L0 gradient minimization, the method further includes: Average grayscale, average contrast, smoothness, consistency, and entropy are used as statistical features of surface data to analyze the texture characteristics and defect numerical distribution of precision optical components at different scales and directions. Average grayscale: Average contrast: Smoothness: R = 1-1 / (1+σ 2 ); consistency: entropy: Among them, z i A random variable representing height; i represents the counting point, i = 0, 1, 2....; p(z i ) indicates the height level is z i The number of pixels; L represents the height level; μ2 represents the secondary level; μ n represents the nth order; σ 2 represents variance; σ represents standard deviation; m represents average grayscale; U represents consistency; and e represents entropy.

3. The optical element surface defect detection method according to claim 2, characterized in that: After using average grayscale, average contrast, smoothness, consistency and entropy as statistical features of the surface data, the method further includes: Texture features at different scales and directions are reconstructed to accurately analyze features at different scales on the surface of optical components.

4. An optical element surface defect detection system, characterized in that: include: a determination unit, configured to determine a detection area of ​​a precision optical component; A setting unit, configured to set a preset distance between two adjacent data points in the detection area; A first acquiring unit, configured to acquire the number of the data points in the detection area; a second acquiring unit, configured to acquire surface data of knife marks, pits, and scratches present in the detection area according to the preset distance and the number of data points in the detection area; A decomposition unit is used to decompose the surface data of the precision optical element using NSST to obtain a number of standard sub-blocks with different scales and directions; an elimination unit, configured to normalize a plurality of standard sub-blocks and calculate the standard deviations of the different standard sub-blocks to obtain a sub-block of the highest scale, wherein the sub-block of the highest scale represents the texture information of tool marks during surface processing of the optical element; The function that determines the L0 gradient minimization is: Among them, I P Represents the surface data at input P, ​​S P Indicates the operation result of the surface data at output P, represents the gradient of the surface data P, λ is the parameter controlling the smoothing weight, λ = nσ (n = 1, 2, 3...), σ is the standard deviation; a transformation unit, configured to transform the highest-scale sub-block into a zero matrix; The inverse transformation unit is used to use NSST inverse transformation on the plurality of standard sub-blocks to obtain data on the surface defects of the precision optical element.

5. An optical element surface defect detection device, characterized in that: include: processor, memory, input and output units, and buses; The processor is connected to the memory, the input and output unit, and the bus; The memory stores a program, and the processor calls the program to execute the optical element surface defect detection method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which, when executed on a computer, performs the method according to any one of claims 1 to 3.

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