Automatic evaluation method, device and computer-readable storage medium for solar cell defects causing performance loss

Abstract

The present invention discloses an automatic evaluation method for performance loss caused by defects in solar cells, the method comprising: reading the electroluminescence spectrum, external quantum efficiency and electroluminescence images of solar cells at different injection current densities, determining the exact position of the solar cell in the electroluminescence image; defining the defect area in the electroluminescence image corresponding to a certain injection current density; calculating the predicted electroluminescence intensity of each defect area and replacing the original electroluminescence intensity of the defect area; outputting the predicted or actual volt-ampere characteristics of the solar cell according to the relationship curve between the injection current density and the predicted or actual internal voltage, and performing a comparison and evaluation of the performance loss. The present invention also discloses an automatic evaluation system for performance loss caused by defects in solar cells. The present invention can quantitatively obtain the device performance loss caused by defects in solar cells, which is beneficial for manufacturing personnel to improve the process in a targeted manner and reduce the R&D cost of high-efficiency solar cells.

Description

Technical Field

[0001] The invention belongs to the technical field of battery detection, and relates to an automatic evaluation method, device and computer-readable storage medium for the volt-ampere characteristics of a solar cell. Background Art

[0002] In order to obtain solar cells with higher conversion efficiency, it is necessary to constantly change process parameters in actual production to compare the performance of samples produced by different manufacturing processes. However, the solar cell materials themselves and in the process of production and manufacturing will inevitably produce or introduce some defects, resulting in abnormally reduced conversion efficiency, affecting the reliability of performance comparisons of different samples. Therefore, trying to artificially eliminate the impact of defects on solar cells and quantify the performance loss of solar cells caused by defects will help manufacturers to improve the process in a targeted manner and reduce the R&D cost of high-efficiency solar cells. Summary of the invention

[0003] In order to solve the deficiencies in the prior art, the present invention proposes an automatic evaluation system and method for performance loss caused by solar cell defects based on electroluminescent imaging detection technology and image restoration technology.

[0004] The present invention provides an automatic evaluation method for performance loss caused by solar cell defects, comprising the following steps:

[0005] Step 1: reading the electroluminescence spectrum, external quantum efficiency and electroluminescence image of the solar cell at different injection current densities;

[0006] The electroluminescent image of the solar cell is obtained by taking a picture with a CCD camera or a CMOS camera, the electroluminescent spectrum is obtained by a spectrometer, and the external quantum efficiency is obtained by a solar cell external quantum efficiency tester; the range of the injected current density is 0 to the short-circuit current density of the solar cell; the short-circuit current density of the solar cell is related to the material type and manufacturing process of the solar cell.

[0007] Step 2: After thresholding the input electroluminescent image by Gaussian filtering and Otsu algorithm, the circumscribed rectangular area corresponding to the white outline in the thresholded image is the accurate position of the solar cell in the electroluminescent image;

[0008] The Gaussian filter refers to a linear smoothing algorithm used in the noise reduction process of image processing, thereby improving the burrs on the edge of the solar cell and obtaining an electroluminescent image with a higher signal-to-noise ratio; the window size of the Gaussian filter is S, where S is a positive integer; the Otsu algorithm is an algorithm for adaptively selecting a threshold value, and using this method, the program can automatically set a threshold value for the electroluminescent image after Gaussian filtering, thereby obtaining a thresholded image; the setting of the threshold value is related to the grayscale characteristics of the image, and when this threshold value is used, the black and white areas of the thresholded image meet the maximum inter-class variance, and the thresholding error probability is minimized. Specifically, the Otsu algorithm is considered to be the best algorithm for threshold selection in image segmentation, with simple calculations and no influence from image brightness and contrast, so it has been widely used in digital image processing.

[0009] The Otsu algorithm divides an image into two parts, background (black) and foreground (white), according to the grayscale characteristics of the image. "Variance" is usually used as a measure of the uniformity of grayscale distribution. The larger the inter-class variance between the background and the foreground, the greater the difference between the two parts (i.e., the black and white parts) that constitute the image. Therefore, the segmentation (segmentation, i.e., threshold selection) that maximizes the inter-class variance means the minimum probability of misclassification.

[0010] Therefore, the Otsu algorithm selects the threshold value that maximizes the inter-class variance.

[0011] Step 3: According to the accurate position of the solar cell obtained in step 2, select the electroluminescent image under a certain injection current density, and traverse the entire electroluminescent image to obtain all defect areas D1 to D i ;

[0012] The defect area is defined as the electroluminescence intensity of the area that satisfies the following conditions: 1) There is a point P with the lowest electroluminescence intensity in the area i , click P i The intensity value of the specified area N1 is lower than 1 / k1 of the average intensity value, k1 is a given coefficient; 2) The intensity values ​​of all points in the area are lower than 1 / k2 of the average intensity value of the specified area N2, k2 is a given coefficient; i is a positive integer;

[0013] The designated areas N1 and N2 are based on point P i As the center, specify a square or circular area with different side lengths or radii, and the defect area D i Included in N1 and N2; for the same electroluminescent image, the side lengths or radii of multiple defect areas remain consistent, and N1 of different defect areas or N2 of different defect areas do not overlap; the coefficients k1 and k2 are both real numbers greater than 1.

[0014] Step 4: All defect areas D1 to D1 obtained in step 3 i, replacing the electroluminescence intensity of the defective area with the predicted electroluminescence intensity of the defective area one by one, and obtaining the predicted electroluminescence image of the solar cell when it is defect-free;

[0015] The defect area D i The predicted electroluminescence intensity is defined as: i The average electroluminescent intensity of the parts outside.

[0016] Step 5: Select the corresponding electroluminescent images in order from small to large injection current density, and obtain the predicted electroluminescent image under each injection current density according to the method of step 3 and step 4;

[0017] Step 6: Calculate the actual and predicted electroluminescent intensity of the battery at each injection current density, obtain the relationship curve between the injection current density and the actual or predicted internal voltage according to the reciprocal relationship between the internal voltage of the solar cell and the actual or predicted electroluminescent intensity and the internal voltage, and output the actual or predicted ideal volt-ampere characteristic curve of the solar cell;

[0018] The ideal electroluminescence intensity of the battery is defined as: the average electroluminescence intensity of all pixels on the ideal electroluminescence image of the battery;

[0019] Step 7: According to the actual or predicted volt-ampere characteristic curve obtained in step 5, the actual or predicted performance parameters of the battery are obtained, and the difference between the actual performance parameters and the predicted performance parameters is compared and evaluated. The difference is the loss of performance caused by the battery defect.

[0020] The degree to which the predicted ideal volt-ampere characteristic is better than the actual volt-ampere characteristic represents the impact of defects on the quality of solar cells; the actual electroluminescence intensity of the battery is defined as: the average electroluminescence intensity of all pixels on the actual electroluminescence image of the battery.

[0021] The relationship between the actual or predicted electroluminescent intensity and the internal voltage of the battery is:

[0022]

[0023] Where V(I) is the internal voltage of the solar cell when the current I is injected, k is the Boltzmann constant, T is the Kelvin temperature, q is the electron charge, is the actual or predicted electroluminescent intensity, F(I) is the attenuation factor, A(I) is the calibration factor, R n (I) is the pixel value of pixel n, R min (I) is the lowest pixel value of the image, t(I) is the exposure time, S is the cell area, EQE is the experimentally measured external quantum efficiency of the solar cell, <eqe> EL is the average quantum efficiency of the cell within the wavelength range of the electroluminescence spectrum, is the distribution of photon density and photon energy in the blackbody radiation spectrum, h is Planck's constant, c is the speed of light in vacuum, E g is the bandgap width of the battery, E is the energy; when the solar cell is working under light, I sun＝q ∫EQE(E)S AM1.5G (E)dE,I sun For the spectrum of air quality AM1.5G AM1.5G Current density under the conditions, internal voltage V(I) and (I sun -I) is the volt-ampere characteristic of a solar cell under illumination conditions.

[0024] The solar cell performance parameters used for comparative evaluation include one or more of the following parameters: volt-ampere characteristic curve, open circuit voltage, fill factor, conversion efficiency, etc.

[0025] The computer automatically finds the intersection of the volt-ampere characteristic curve and the voltage axis, which is the open circuit voltage;

[0026] Fill factor = the area of ​​the largest rectangle formed by each point on the volt-ampere characteristic curve and the coordinate axis / (open circuit voltage × I sun );

[0027] Conversion efficiency = fill factor × (open circuit voltage × I sun ) / power density of air quality AM1.5G solar radiation; the power density of air quality AM1.5G solar radiation is 1000W / m 2 .

[0028] The present invention also provides an automatic evaluation device for performance loss caused by solar cell defects, comprising: a memory and a processor;

[0029] The memory stores a computer program, and when the computer program is executed by the processor, the above-mentioned automatic evaluation method is implemented.

[0030] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned automatic evaluation method is implemented.

[0031] The beneficial effects of the present invention are: using electroluminescent image restoration technology, the influence of defects on solar cells is artificially eliminated, and the performance loss of solar cells caused by defects can be quantified, which is beneficial for manufacturers to make targeted improvements to the process and reduce the research and development costs of high-efficiency solar cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The system block diagram of the automatic evaluation system for performance loss caused by solar cell defects of the present invention is shown.

[0033] Figure 2 The present invention is a flow chart of a method for automatically evaluating performance loss caused by defects in a solar cell.

[0034] Figure 3 GaAs solar cell captured by CCD camera at a forward injection current density of 20mA / cm 2 Actual electroluminescent image when .

[0035] Figure 4 The GaAs cell output from the ideal volt-ampere characteristic recovery module of the battery is injected with a forward current density of 20 mA / cm 2 Ideal electroluminescent image when .

[0036] Figure 5 The ideal volt-ampere characteristic curve of a GaAs solar cell predicted by the method of the present invention.

[0037] Figure 6 This is a comparison chart of the ideal volt-ampere characteristic curve (part) of a GaAs solar cell and the ideal open-circuit voltage and actual results. DETAILED DESCRIPTION

[0038] The invention is further described in detail with reference to the following specific embodiments and drawings. The process, conditions, experimental methods, etc. for implementing the present invention, except for the contents specifically mentioned below, are common knowledge and common common sense in the art and are not particularly limited by the present invention.

[0039] like Figure 1 As shown, an automatic evaluation system for performance loss caused by solar cell defects of the present invention comprises: an input module, a battery edge detection module, a battery ideal volt-ampere characteristic recovery module and a battery volt-ampere characteristic evaluation module.

[0040] The input module is used to read the electroluminescence spectrum, external quantum efficiency and a set of electroluminescence images collected under different forward injection current densities of the solar cell.

[0041] The cell edge detection module is used to determine the exact location of the solar cell in the electroluminescent image.

[0042] The battery ideal volt-ampere characteristic recovery module is used to identify defective areas on the surface of solar cells, predict the ideal luminous intensity of each defective area and recover the defects. According to the relationship between the electroluminescent intensity and the internal voltage of the battery, the ideal volt-ampere characteristic curve of the solar cell is obtained.

[0043] The battery volt-ampere characteristic evaluation module is used to call the relationship between the electroluminescence intensity and the internal voltage of the battery, obtain the actual volt-ampere characteristic curve of the solar cell from the original electroluminescence image, and compare the ideal and actual volt-ampere characteristics of the solar cell to evaluate the ideal quality of the solar cell under a specific process;

[0044] The solar cell volt-ampere characteristics used for comparative evaluation include one or more of the following parameters: volt-ampere characteristic curve, open circuit voltage, fill factor and conversion efficiency.

[0045] like Figure 2 As shown, the automatic evaluation method for performance loss caused by solar cell defects of the present invention comprises the following steps:

[0046] Step 1: The input module reads the electroluminescence spectrum, external quantum efficiency and electroluminescence image of the solar cell under different injection current densities, and transmits the above data information to the cell edge detection module;

[0047] Step 2: After the battery edge detection module performs threshold processing on the input electroluminescent image through Gaussian filtering and Otsu algorithm, the circumscribed rectangular area corresponding to the white outline in the thresholded image is obtained, which is the accurate position of the solar cell in the electroluminescent image;

[0048] Step 3: According to the accurate position of the solar cell obtained in step 2, the battery ideal volt-ampere characteristic recovery module selects an electroluminescent image corresponding to a certain injection current density, traverses the entire electroluminescent image to obtain all defect areas D1 to D i ;

[0049] Step 4: All defect areas D1 to D1 obtained in step 3 i , the battery ideal volt-ampere characteristic recovery module replaces the electroluminescence intensity of the defective area with the predicted electroluminescence intensity one by one to obtain the ideal electroluminescence image of the solar cell;

[0050] Step 5, the battery ideal volt-ampere characteristic recovery module selects the corresponding electroluminescent images in order of the injected current density from small to large, obtains the ideal electroluminescent image at each injected current density according to the method of step 3 and step 4, calculates the ideal electroluminescent intensity of the battery at each injected current density, obtains the relationship curve between the injected current density and the ideal internal voltage according to the reciprocal relationship between the ideal electroluminescent intensity and the internal voltage of the solar cell, and outputs the ideal volt-ampere characteristic of the solar cell;

[0051] Step 6. The battery volt-ampere characteristic evaluation module traverses the actual electroluminescence image under each injection current density, and outputs the actual volt-ampere characteristic of the solar cell based on the reciprocal relationship between the actual electroluminescence intensity and the internal voltage of the solar cell, and compares and evaluates it with the ideal volt-ampere characteristic obtained in step 5 to obtain the difference between the actual volt-ampere characteristic and the ideal volt-ampere characteristic.

[0052] Example

[0053] Taking the automatic evaluation of the ideal volt-ampere characteristics of a 4cm×2cm gallium arsenide (GaAs) solar cell as an example, the specific steps are as follows:

[0054] First, the input module is fed with a current ranging from 0 to 25 mA / cm 2 A group of electroluminescence images of GaAs solar cells taken by a CCD camera under forward injection current density, as well as the electroluminescence spectrum and external quantum efficiency of the cells.

[0055] Furthermore, the cell edge detection module uses a Gaussian filter with a window size of 25 to improve the burrs on the edge of the solar cell and obtain an electroluminescent image with a higher signal-to-noise ratio; then uses the Otsu algorithm to automatically set a threshold for the electroluminescent image to achieve threshold processing of the input electroluminescent image; then intercepts the circumscribed rectangular area corresponding to the white outline in the thresholded image as the actual position of the GaAs solar cell in the electroluminescent image, such as Figure 3 shown.

[0056] Furthermore, the battery ideal volt-ampere characteristic recovery module utilizes the automatic evaluation method for performance loss caused by solar cell defects to identify the luminous intensity of the defective area on the surface of the solar cell, and predicts its ideal luminous intensity, and obtains the ideal volt-ampere characteristic curve of the solar cell based on the relationship between the electroluminescent intensity and the internal voltage of the battery. It includes the following steps:

[0057] Step 1: Transmit the input electroluminescence spectrum, external quantum efficiency and electroluminescence images at different injection current densities to the cell edge detection module to obtain a set of electroluminescence images of GaAs solar cells;

[0058] Step 2: Specify Figure 3 The injection current density shown is 20 mA / cm 2 If the electroluminescence intensity of a certain area on the image satisfies the following conditions at the same time, it is defined as a defect area D i (i is an integer greater than 1): 1. There is a point P with the lowest electroluminescence intensity in the region i , click P i The intensity value is lower than that of P i The average intensity value of the square area with a side length of 10 is 0.85; second, the intensity values ​​of all points in the area are lower than the intensity value of P i 0.95 of the average intensity value of a square area with a center and a side length of 6;

[0059] Step 3: Calculate P i The defect area D is excluded from the square area with a side length of 6 and a center of i The average electroluminescence intensity of the defect area D i Predicted electroluminescence intensity;

[0060] Step 4: Traverse the image and find all defect areas D1 to D2 that meet the conditions. i , D1 to D i The predicted electroluminescence intensity is restored one by one, and the Figure 4 The GaAs cell shown in the figure is injected with a current density of 20 mA / cm 2 The ideal electroluminescent image when

[0061] Step 5: Select the corresponding electroluminescence images in order of injection current density from small to large, and obtain the ideal electroluminescence image under each injection current density according to the method of step 3 and step 4. According to the reciprocal relationship between the electroluminescence intensity and the internal voltage of the solar cell, the ideal electroluminescence image is obtained as follows: Figure 5 The relationship curve between the injected current density and the ideal internal voltage is shown, and the ideal volt-ampere characteristic of the solar cell is output;

[0062] Step 6: Traverse the actual electroluminescence image under each injection current density, output the actual volt-ampere characteristics of the solar cell according to the reciprocal relationship between the electroluminescence intensity and the internal voltage of the solar cell, and compare and evaluate it with the ideal volt-ampere characteristics obtained using step 5.

[0063] Furthermore, the battery ideal volt-ampere characteristic evaluation module outputs Figure 6 The diagram shows a comparison of the ideal and actual volt-ampere characteristics of a solar cell, including a volt-ampere characteristic curve (part) and an open-circuit voltage.

[0064] See also Figure 6 The ideal volt-ampere characteristic curve of the solar cell obtained by the method of the present invention is shifted to the right compared with the actual volt-ampere characteristic curve, and the ideal open circuit voltage is higher than the actual open circuit voltage. This shows that defects have a certain impact on the quality of solar cells. If these defects are eliminated, the open circuit voltage can be increased by at least 0.17mV.

[0065] Example 2

[0066] This embodiment proposes an automatic evaluation system for performance loss caused by solar cell defects, including: an input module, a battery edge detection module, a battery ideal volt-ampere characteristic recovery module and a battery volt-ampere characteristic evaluation module.

[0067] The input module is used to read the electroluminescence spectrum, external quantum efficiency and a set of electroluminescence images collected under different forward injection current densities of the solar cell.

[0068] The read solar cell electroluminescence image is obtained by taking a photo with a CCD camera or a CMOS camera, the electroluminescence spectrum is obtained by a spectrometer, and the external quantum efficiency is obtained by a solar cell external quantum efficiency tester.

[0069] The cell edge detection module is used to determine the exact position of the solar cell in the electroluminescent image.

[0070] The position of the solar cell in the electroluminescent image is defined as: after the input electroluminescent image is thresholded using Gaussian filtering and Otsu's algorithm, the circumscribed rectangular area corresponding to the white outline in the thresholded image.

[0071] The battery ideal volt-ampere characteristic recovery module is used to identify defective areas on the surface of the solar cell, predict the ideal luminous intensity of each defective area and recover the defects, and obtain the ideal volt-ampere characteristic curve of the solar cell based on the relationship between the electroluminescent intensity and the internal voltage of the battery.

[0072] The battery volt-ampere characteristic evaluation module is used to call the relationship between the electroluminescence intensity and the internal voltage of the battery, obtain the actual volt-ampere characteristic curve of the solar cell from the original electroluminescence image, and compare the ideal and actual volt-ampere characteristics of the solar cell to evaluate the ideal quality of the solar cell under a specific process.

[0073] The protection content of the present invention is not limited to the above embodiments. Without departing from the spirit and scope of the inventive concept, changes and advantages that can be thought of by those skilled in the art are included in the present invention and are protected by the attached claims.< / eqe>

Claims

1. A method for automatically evaluating performance loss caused by solar cell defects, characterized in that: The steps include: Step 1, reading the electroluminescence spectrum, external quantum efficiency and electroluminescence image of the solar cell at different injection current densities; Step 2: After thresholding the input electroluminescent image by Gaussian filtering and Otsu algorithm, the circumscribed rectangular area corresponding to the white outline in the thresholded image is the accurate position of the solar cell in the electroluminescent image; Step 3: According to the accurate position of the solar cell obtained in step 2, select the electroluminescent image corresponding to a certain injection current density, and traverse the entire electroluminescent image to obtain all defect areas D1 to D i ; Step 4: All defect areas D1 to D1 obtained in step 3 i , replacing the electroluminescence intensity of the defective area with the predicted electroluminescence intensity of the defective area one by one, and obtaining the predicted electroluminescence image of the solar cell when it is defect-free; Step 5: Select the corresponding electroluminescent images in order from small to large injection current density, and obtain the predicted electroluminescent image under each injection current density according to the method of step 3 and step 4; Step 6: Calculate the actual and predicted electroluminescent intensity of the battery at each injection current density, obtain the relationship curve between the injection current density and the actual or predicted internal voltage according to the reciprocal relationship between the internal voltage of the solar cell and the actual or predicted electroluminescent intensity, and output the actual or predicted ideal volt-ampere characteristic curve of the solar cell; Step 7: According to the actual or predicted volt-ampere characteristic curve obtained in step 5, the actual or predicted performance parameters of the battery are obtained, and the difference between the actual performance parameters and the predicted performance parameters is compared and evaluated. The difference is the loss of performance caused by the battery defect.

2. The automatic evaluation method according to claim 1, characterized in that: In step 1, the electroluminescent image of the solar cell is captured by a CCD camera or a CMOS camera, the electroluminescent spectrum is obtained by a spectrometer, and the external quantum efficiency is obtained by a solar cell external quantum efficiency tester; the range of the injected current density is 0 to the short-circuit current density of the solar cell; the short-circuit current density of the solar cell is related to the material type and manufacturing process of the solar cell.

3. The automatic evaluation method according to claim 1, characterized in that: In step 2, the Gaussian filter refers to a linear smoothing algorithm, which is used in the noise reduction process of image processing, thereby improving the burrs on the edge of the solar cell and obtaining an electroluminescent image with a higher signal-to-noise ratio; the window size of the Gaussian filter is S, where S is a positive integer; the Otsu algorithm is an algorithm for adaptively selecting a threshold value, which is used to automatically set a threshold value for the electroluminescent image, thereby obtaining a thresholded image.

4. The automatic evaluation method according to claim 1, characterized in that: In step 3, the defect region is defined as the electroluminescence intensity of the region that satisfies the following conditions: 1) There is a point P with the lowest electroluminescence intensity in the region i , click P i The intensity value is lower than 1 / k1 of the average intensity value of the specified area N1, where k1 is a given coefficient; 2) The intensity values ​​of all points in the area are lower than 1 / k2 of the average intensity value of the specified area N2, where k2 is a given coefficient and i is a positive integer.

5. The automatic evaluation method according to claim 4, characterized in that: The designated areas N1 and N2 are based on point P i As the center, specify a square or circular area with different side lengths or radii, and the defect area D i Included in N1 and N2; for the same electroluminescent image, the side lengths or radii of multiple defect areas remain consistent, and N1 of different defect areas or N2 of different defect areas do not overlap; the coefficients k1 and k2 are both real numbers greater than 1.

6. The automatic evaluation method according to claim 1, characterized in that: In step 4, the predicted electroluminescent intensity of the defective area is defined as i The average electroluminescent intensity of the parts outside.

7. The automatic evaluation method according to claim 1, characterized in that: In step 6, the relationship between the actual or predicted electroluminescent intensity and the internal voltage of the solar cell is: Where V(I) is the internal voltage of the solar cell when the current I is injected, k is the Boltzmann constant, T is the Kelvin temperature, q is the electron charge, is the actual or predicted electroluminescent intensity, F(I) is the attenuation factor, A(I) is the calibration factor, R n (I) is the pixel value of pixel n, R min (I) is the lowest pixel value of the image, t(I) is the exposure time, S is the cell area, EQE is the experimentally measured external quantum efficiency of the solar cell, <eqe> EL is the average quantum efficiency of the cell within the wavelength range of the electroluminescence spectrum, is the distribution of photon density and photon energy in the blackbody radiation spectrum, h is Planck's constant, c is the speed of light in vacuum, E g is the bandgap width of the battery, E is the energy; when the solar cell is working under light, I sun =q∫EQE(E)S AM1.5G (E)dE,I sun For the spectrum of air quality AM1.5G AM1.5G Current density under the conditions, internal voltage V(I) and (I sun -I) is the volt-ampere characteristic of a solar cell under illumination conditions.< / eqe> 8. The automatic evaluation method according to claim 1, characterized in that: In step 7, the solar cell performance parameters used for comparative evaluation include one or more of the following parameters: volt-ampere characteristic curve, open circuit voltage, fill factor and conversion efficiency; The computer automatically finds the intersection of the volt-ampere characteristic curve and the voltage axis, which is the open circuit voltage; Fill factor = the area of ​​the largest rectangle formed by each point on the volt-ampere characteristic curve and the coordinate axis / (open circuit voltage × I sun ); Conversion efficiency = fill factor × (open circuit voltage × I sun ) / power density of air quality AM1.5G solar radiation; the power density of air quality AM1.5G solar radiation is 1000W / m 2 .

9. An automatic evaluation device for the ideal volt-ampere characteristics of a solar cell whose performance loss is caused by defects in the solar cell, characterized in that: include: Memory and processor; The memory stores a computer program, and when the computer program is executed by the processor, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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