Method, system and device for correcting cold reflection noise in infrared image and medium

Abstract

The invention discloses a method, a system, equipment and a medium for correcting cold reflection noise in an infrared image, belongs to the technical field of infrared image denoising, and solves the problem that information of the infrared image is lost due to the fact that details in the infrared image are excessively smoothed in an existing correction method. Obtaining an original infrared image including cold reflection noise; reading an original infrared image based on a smooth window of a Butterworth function, and inputting the read infrared image into a correction formula for correction; fitting the corrected infrared image based on a Gaussian function, and constructing a fitting function based on the corrected infrared image; constructing cold reflection features for the infrared image based on the parameters of the constructed fitting function and a Gaussian function; and performing differential processing on the cold reflection features and the original infrared image to complete correction of the cold reflection noise in the infrared image.

Description

Technical Field

[0001] The present invention relates to the technical field of infrared image denoising, and in particular to a correction method, system, equipment and medium for cold reflection noise in infrared images. Background Art

[0002] Infrared images have been widely used in a series of aspects related to national economy and people's livelihood, such as environmental monitoring, target recognition, astronomical observation, etc. However, infrared detectors are often interfered by noise during the imaging process. Among them, the cold reflection phenomenon, as a defect in infrared images, not only reduces the visualization effect of infrared images, but also affects subsequent processing applications, such as target detection, recognition and tracking.

[0003] At present, non-uniformity correction technology is mainly divided into calibration-based methods and scene-based correction methods. Although these methods can suppress noise in images to a certain extent, their suppression effect on noise caused by cold reflection is limited. After processing, cold reflection noise often leads to serious artifacts, which greatly affects the authenticity and reliability of the image. These artifacts not only interfere with the extraction of target information in the image, but also may lead to misjudgment and misleading in subsequent analysis and application. Therefore, the current correction methods are still not effective enough in improving the quality of infrared images, especially in complex environments, where the noise problem caused by cold reflection still needs to be effectively solved.

[0004] In order to solve the above technical problems, the Chinese patent CN117314791A developed by the research team of Changchun University of Science and Technology discloses "Infrared image cold reflection noise correction method based on Butterworth function fitting", which analyzes the infrared image from the perspective of noise distribution by combining the existing infrared noise image denoising method. The two-dimensional Butterworth function is used to fit the surface formed after mapping, and the geometric features of the two-dimensional Butterworth function are used to represent the infrared image data features. This can significantly improve the signal-to-noise ratio of the infrared image, thereby removing the noise in the image and improving the quality of the infrared image.

[0005] Therefore, the use of a two-dimensional Butterworth function helps to remove high-frequency noise in infrared images, but only using a two-dimensional Butterworth function to correct cold reflection noise in infrared images may over-smooth the details in the infrared images. Especially in the cold reflection area or the edge of the light spot, over-smoothing may lose important information.

[0006] In summary, the existing correction methods over-smooth the details in the infrared images, resulting in the loss of information in the infrared images. Summary of the invention

[0007] The present invention solves the problem that the information of the infrared image is lost due to excessive smoothing of the details in the infrared image in the existing correction method.

[0008] The method for correcting cold reflection noise in infrared images of the present invention comprises the following steps: Step S1, acquiring an original infrared image including cold reflection noise; Step S2, reading the original infrared image described in step S1 based on the smoothing window of the Butterworth function, and inputting the read infrared image into the correction formula for correction; Step S3, fitting the corrected infrared image in step S2 based on a Gaussian function, and constructing a fitting function based on the corrected infrared image in step S2; Step S4, constructing a cold reflection feature for the infrared image based on the parameters of the fitting function constructed in step S3 and the Gaussian function; Step S5, performing differential processing on the psychrometric feature described in step S4 and the original infrared image to complete the correction of the psychrometric noise in the infrared image.

[0009] Further, in one embodiment of the present invention, in step S2, the Butterworth function is specifically: ; in, is the Butterworth function, Infrared image Pixels coordinate, Infrared image Pixels coordinate, The center of the infrared image coordinate, The center of the infrared image coordinate, is a constant.

[0010] Furthermore, in one embodiment of the present invention, in step S2, the correction formula is specifically: ; in, To correct the formula, is the infrared image information, for The maximum value of is the distance between the information pixel and the center of the cold reflection signal, The degree of suppression of scene signals in infrared images.

[0011] Furthermore, in one embodiment of the present invention, in step S3, the Gaussian function is specifically: ; in, is a Gaussian function, is the amplitude, All are attributes, are the mean values ​​of the attributes, are the standard deviations of the attributes.

[0012] Furthermore, in one embodiment of the present invention, in step S3, constructing a fitting function based on the corrected infrared image in step S2 includes the following steps: Step S301, using a Gaussian function as a model, and using the cold reflection area retained in the corrected infrared image described in step S2 as a fitting target; Step S302, adjusting the parameters of the Gaussian function based on the pixel information of the cold reflection area in step S301; Step S303, self-optimizing the parameters of the Gaussian function adjusted in step S302 to complete the construction of the fitting function.

[0013] Further, in one embodiment of the present invention, in the step S4, the cold reflection feature of the infrared image is constructed based on the parameters of the fitting function and the Gaussian function, specifically: The parameters of the fitting function are used as the cold reflection characteristics of the infrared image after fitting in step S3. The Gaussian function is used to map the corrected infrared image in step S2 to form a curved surface, which is also used as the cold reflection characteristic.

[0014] Further, in one embodiment of the present invention, in step S5, the difference processing is specifically: ; in, For differential processing, , , , and are the parameters of the fitting function, and These are the coordinate grids of infrared images.

[0015] The correction system for cold reflection noise in infrared images of the present invention comprises the following modules: An acquisition module, for acquiring an original infrared image including cold reflection noise; A correction module, based on a smooth window of a Butterworth function, reads the original infrared image described in the acquisition module, and inputs the read infrared image into a correction formula for correction; A construction module is used to fit the corrected infrared image of the correction module based on a Gaussian function, and to construct a fitting function based on the corrected infrared image of the correction module; A feature module constructs cold reflection features for infrared images based on the parameters of the fitting function constructed by the construction module and the Gaussian function; The correction module performs differential processing on the cold reflection feature described in the feature module and the original infrared image to complete the correction of the cold reflection noise in the infrared image.

[0016] An electronic device described in the present invention comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement any of the method steps described in the above method when executing the program stored in the memory.

[0017] The computer-readable storage medium described in the present invention stores a computer program, and when the computer program is executed by a processor, the method steps of any of the above methods are implemented.

[0018] The present invention solves the problem that the existing correction method causes the loss of information of the infrared image due to excessive smoothing of the details in the infrared image. The specific beneficial effects include: 1. The correction method for cold reflection noise in infrared images described in the present invention uses two-dimensional Butterworth function alone and multiple times in the prior art, which may over-smooth the details in the image. Especially in the cold reflection area or the edge of the light spot, over-smoothing may lose important information. In order to solve the above technical problems, the present invention applies Gaussian function to fit and denoise the infrared image after smoothing with Butterworth function, which can accurately fit the shape of cold reflection noise, thereby effectively improving the quality of infrared images; 2. The correction method of cold reflection noise in infrared images described in the present invention can realize accurate correction of infrared images through the combination of Butterworth function and Gaussian function. However, when Butterworth function is used to remove noise by smoothing infrared images, it may lose the details of cold reflection area in the smoothing process, especially when the noise is similar to the characteristics of cold reflection. Therefore, in the combined use of Butterworth function and Gaussian function, how to effectively remove noise in infrared images while maintaining the fine structure of cold reflection area becomes a technical problem to be overcome by the present invention. In order to overcome the above technical problems, the present invention uses a correction formula to denoise the infrared image after the Butterworth function is used to denoise the infrared image. The correction formula not only solves the problem of smoothing and detail loss in the Butterworth function, but also through refined parameter adjustment and weighted recovery technology, the characteristics of the cold reflection area can be clearly passed to the subsequent Gaussian function to fit the infrared image step, thereby effectively improving the quality and processing accuracy of the infrared image; The method for correcting cold reflection noise in infrared images of the present invention can improve the accuracy of correction under cold reflection noise characteristics of different sizes and intensities. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 The original infrared image including cold reflection noise obtained in the first embodiment; Figure 2 This is a correction effect diagram using the existing correction method described in the first embodiment; Figure 3 This is a correction effect diagram of the method for correcting cold reflection noise in infrared images described in Implementation Example 1. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe various embodiments of the present invention in conjunction with the accompanying drawings. The embodiments described with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0021] Embodiment 1: The method for correcting cold reflection noise in an infrared image described in this embodiment comprises the following steps: Step S1, acquiring an original infrared image including cold reflection noise; Step S2, reading the original infrared image described in step S1 based on the smoothing window of the Butterworth function, and inputting the read infrared image into the correction formula for correction; Step S3, fitting the corrected infrared image in step S2 based on a Gaussian function, and constructing a fitting function based on the corrected infrared image in step S2; Step S4, constructing a cold reflection feature for the infrared image based on the parameters of the fitting function constructed in step S3 and the Gaussian function; Step S5, performing differential processing on the psychrometric feature described in step S4 and the original infrared image to complete the correction of the psychrometric noise in the infrared image.

[0022] In this implementation manner, in step S2, the Butterworth function is specifically: ; in, is the Butterworth function, Infrared image Pixels coordinate, Infrared image Pixels coordinate, The center of the infrared image coordinate, The center of the infrared image coordinate, is a constant.

[0023] In this implementation manner, in step S2, the correction formula is specifically: ; in, To correct the formula, is the infrared image information, for The maximum value of is the distance between the information pixel and the center of the cold reflection signal, The degree of suppression of scene signals in infrared images.

[0024] In this implementation manner, in step S3, the Gaussian function is specifically: ; in, is a Gaussian function, is the amplitude, All are attributes, are the mean values ​​of the attributes, are the standard deviations of the attributes.

[0025] In this embodiment, in the step S3, the step of constructing a fitting function based on the corrected infrared image in the step S2 includes the following steps: Step S301, using a Gaussian function as a model, and using the cold reflection area retained in the corrected infrared image described in step S2 as a fitting target; Step S302, adjusting the parameters of the Gaussian function based on the pixel information of the cold reflection area in step S301; Step S303, self-optimizing the parameters of the Gaussian function adjusted in step S302 to complete the construction of the fitting function.

[0026] In this implementation, in step S4, the cold reflection feature of the infrared image is constructed based on the parameters of the fitting function and the Gaussian function, specifically: The parameters of the fitting function are used as the cold reflection characteristics of the infrared image after fitting in step S3. The Gaussian function is used to map the corrected infrared image in step S2 to form a curved surface, which is also used as the cold reflection characteristic.

[0027] In this implementation manner, in step S5, the difference processing is specifically as follows: ; in, For differential processing, , , , and are the parameters of the fitting function, and These are the coordinate grids of infrared images.

[0028] In the prior art, Chinese patent CN117314791A discloses a "method for correcting cold reflection noise in infrared images based on Butterworth function fitting", which uses a two-dimensional Butterworth function to correct cold reflection noise in infrared images multiple times, thereby improving the quality of infrared images. However, this correction method may cause a loss of details in infrared images to a certain extent, especially in the accuracy of high-contrast areas or edge information.

[0029] In order to solve the above technical problems, this embodiment designs a correction method for cold reflection noise in infrared images, comprising the following steps: Step S1, in a selected scene, using a cooling type infrared imaging system to collect infrared images to obtain the original infrared image containing cold reflection noise ; Step S2, reading the original infrared image described in step S1 based on the smoothing window of the Butterworth function , remove unnecessary scene information and get the infrared image after reading After that, the infrared image will be read Enter the correction formula to make corrections; The smoothing window based on the Butterworth function reads the original infrared image in step S1 , specifically: The smoothing window of the Butterworth function reads the original infrared image Preprocessing is performed by calculating the difference between each pixel and the original infrared image The distance to the center, using the smoothing window of the Butterworth function to read the original infrared image Smoothing is performed and noise is further removed by mean filtering. The calculation formula of the Butterworth function is: ; in, is the Butterworth function, Infrared image Pixels coordinate, Infrared image Pixels Coordinates, used to identify the location of each pixel in the infrared image, The center of the infrared image coordinate, The center of the infrared image coordinates, which are used to calculate the distance of each pixel from the center of the infrared image in order to determine the frequency response at that location, It is a constant used to control the bandwidth of the filter, affecting the filtering effect, and used to determine the smoothness of the filter, affecting the denoising effect. This will result in a stronger low-frequency filtering effect.

[0030] The neighborhood mean of each pixel is calculated by using the smoothing window method of the Butterworth function, thereby further smoothing the infrared image and reducing noise.

[0031] The calculation formula of the mean filter is: ; in, is the mean filter, This is an infrared image after the smoothing window processing of the Butterworth function. The infrared image has been processed with low-frequency noise and retains more image information. is the window size, which controls the range of the filter.

[0032] The Butterworth function has good smoothness in infrared image processing and can effectively fit the edge features of cold reflection noise and reduce its sharpness. This step can help accurately identify and simulate the morphology of cold reflection noise, thus laying the foundation for subsequent infrared image processing.

[0033] Step S3, first, use the Gaussian function to fit the infrared image corrected in step S2. Specifically, the Gaussian function provides an ideal mathematical model. If you want the Gaussian function to fit the actual data of the cold reflection noise in the infrared image as much as possible, you need to complete it through a fitting process. Therefore, the least squares method is needed to optimize and solve the optimal parameters of the function in the fitting process. That is, through the least squares method, the pixel values ​​of the selected area in the infrared image are fitted to solve the optimal parameters of the function so that the Gaussian function can be as close to the actual data of the infrared image as possible. The least squares method optimizes these parameters by minimizing the sum of squares of the errors between the fitting function and the infrared image data.

[0034] Secondly, a fitting function is constructed based on the corrected infrared image described in step S2. Specifically, the Gaussian function provides an ideal mathematical model. By using the cold reflection areas retained in the corrected infrared image as fitting targets, the pixel values ​​of these areas can be used to fit the Gaussian function. In order to make the Gaussian function match the infrared image data as much as possible, it is necessary to use the pixel information in the infrared image to adjust the parameters in the Gaussian function. By adjusting the parameters in the Gaussian function, it can fit the infrared image data most accurately, thereby achieving the goal of noise removal or feature retention, and completing the construction of the fitting function.

[0035] The Gaussian function is specifically: ; in, is a Gaussian function, is the amplitude, All are attributes, are the mean values ​​of the attributes, are the standard deviations of the attributes.

[0036] The fitting function is specifically: ; in, Provide parameters for the fitting function, including the quadratic term, linear term and constant term of the data points. and The valid data points are and Coordinates, the coordinates of the extracted valid data points, used to fit the model.

[0037] ; in, is the parameter solution of the fitting function, which is used to output the parameters of the fitting function, including standard deviation, center position, etc. It is the gray value of the valid data point, including the actual data value that needs to be fitted, and is used to calculate the parameters of the fitting model.

[0038] ; in, and They are Gaussian functions in and The standard deviation of the direction is used to describe the extent of the Gaussian noise in the infrared image and help optimize the noise removal. and are the first and second parameters in the fitting function solution, respectively, used with and The spread of the coordinates is related, reflecting the Gaussian distribution of the infrared image.

[0039] The application of Gaussian function can further smooth the fitting results, making the transition of noise more natural, thereby reducing its interference with the overall infrared image. This process effectively improves the quality of infrared images by reducing the abruptness of reflected noise.

[0040] It should be noted that this embodiment uses the Butterworth function to fit the shape of the cold reflection noise, and uses the Gaussian function to process the fitted cold reflection noise, which can effectively improve the quality of the infrared image. This solves the technical problem of the "infrared image cold reflection noise correction method based on Butterworth function fitting" disclosed in Chinese patent CN117314791A. However, in the process of combining the Butterworth function with the Gaussian function, although the Butterworth function can remove high-frequency noise in the infrared image, excessive smoothing may cause the edge of the cold reflection area to become blurred, affecting the fitting accuracy of the subsequent Gaussian function to the infrared image.

[0041] In order to solve the above technical problems, this embodiment introduces the step S2 of "taking the infrared image after reading" in the process of using the Butterworth function and the Gaussian function together. Enter the correction formula for correction", and designed the correction formula, specifically: ; in, To correct the formula, is the infrared image information, for The maximum value of is the distance between the information pixel and the center of the cold reflection signal, The degree of suppression of scene signals in infrared images.

[0042] By modifying the correction formula, the details of the cold reflection area can be restored on the basis of smoothing denoising, suppressing background noise while retaining the characteristics of the cold reflection. The key to this improvement lies in the detail enhancement strategy in the correction process. Through the adaptive smoothing window and weighted inverse transformation method, special attention is paid to the cold reflection area to ensure that other parts are not overly affected while restoring details. In addition, a weighted strategy based on the characteristics of cold reflection is adopted in the correction process to further optimize the infrared image restoration effect, ensure that the geometric characteristics of the cold reflection are not lost, and maintain the accuracy of the infrared image and the accuracy of the Gaussian function fitting to the infrared image.

[0043] Step S4, constructing cold reflection features for the infrared image. Specifically, the parameters of the fitting function of the Gaussian function obtained in step S3 can be used to describe the features of the cold reflection area in the infrared image. These parameters reflect the spatial distribution, intensity and range of the cold reflection, and are an important basis for subsequent analysis. Further, the Gaussian function is used to map the corrected infrared image described in step S2 to form a fitted surface. The surface represents the spatial distribution and intensity changes of the cold reflection features in the infrared image. By fitting and analyzing the surface, the regional features related to the cold reflection can be accurately extracted, thereby improving the accuracy of cold reflection recognition and processing in the infrared image. In this process, the fitted surface not only provides the local features of the cold reflection, but also can describe the overall morphology and change law of the cold reflection through the parameters of the Gaussian function, thereby effectively realizing the modeling and application of the cold reflection features.

[0044] Step 5: Perform differential processing on the cold reflection features constructed from the infrared image and the original infrared image to remove noise from the infrared image, clearly distinguish the infrared image from the background, and improve the quality of the infrared image.

[0045] The differential processing is specifically as follows: ; in, For differential processing, , , , and are the parameters of the fitting function, and These are the coordinate grids of infrared images.

[0046] In order to better illustrate the method for correcting cold reflection noise in the infrared image described in this embodiment, a detailed description is given through the following examples: like Figure 1 As shown in FIG. , the original infrared image including cold reflection noise is obtained. Figure 2As shown in FIG. 1 , the correction result of the “infrared image cold reflection noise correction method based on Butterworth function fitting” disclosed in Chinese patent CN117314791A is used. It can be seen that the corrected infrared image still has weak noise, such as Figure 3 As shown, the correction result of the correction method for cold reflection noise in the infrared image described in this embodiment is shown. It can be seen that compared with the existing correction method, the correction method described in this embodiment has a better correction effect.

[0047] In summary, the correction method described in this embodiment performs Butterworth preprocessing on the infrared image and then uses Gaussian function fitting and denoising. Compared with other existing infrared image correction methods, the correction method described in this embodiment can significantly improve the signal-to-noise ratio of the infrared image, thereby removing noise in the infrared image and improving the quality of the infrared image.

[0048] Embodiment 2: The system for correcting cold reflection noise in infrared images described in this embodiment includes the following modules: An acquisition module, for acquiring an original infrared image including cold reflection noise; A correction module, based on a smooth window of a Butterworth function, reads the original infrared image described in the acquisition module, and inputs the read infrared image into a correction formula for correction; A construction module is used to fit the corrected infrared image of the correction module based on a Gaussian function, and to construct a fitting function based on the corrected infrared image of the correction module; A feature module constructs cold reflection features for infrared images based on the parameters of the fitting function constructed by the construction module and the Gaussian function; The correction module performs differential processing on the cold reflection feature described in the feature module and the original infrared image to complete the correction of the cold reflection noise in the infrared image.

[0049] Embodiment 3: An electronic device described in this embodiment includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the method steps described in the first embodiment when executing the program stored in the memory.

[0050] Embodiment 4: A computer-readable storage medium described in this embodiment stores a computer program, and when the computer program is executed by a processor, the method steps described in embodiment 1 are implemented.

[0051] The above is a detailed introduction to the correction method, system, device and medium for cold reflection noise in infrared images proposed in the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for correcting cold reflection noise in infrared images, characterized in that: The following steps are involved: Step S1, acquiring an original infrared image including cold reflection noise; Step S2, reading the original infrared image described in step S1 based on the smoothing window of the Butterworth function, and inputting the read infrared image into the correction formula for correction; Step S3, fitting the corrected infrared image in step S2 based on a Gaussian function, and constructing a fitting function based on the corrected infrared image in step S2; Step S4, constructing a cold reflection feature for the infrared image based on the parameters of the fitting function constructed in step S3 and the Gaussian function; Step S5, performing differential processing on the psychrometric feature described in step S4 and the original infrared image to complete the correction of the psychrometric noise in the infrared image.

2. The method for correcting cold reflection noise in infrared images according to claim 1, characterized in that: In the step S2, the Butterworth function is specifically: ； in, is the Butterworth function, Infrared image Pixels coordinate, Infrared image Pixels coordinate, The center of the infrared image coordinate, The center of the infrared image coordinate, is a constant.

3. The method for correcting cold reflection noise in infrared images according to claim 1, characterized in that: In the step S2, the correction formula is specifically: ； in, To correct the formula, is the infrared image information, for The maximum value of is the distance between the information pixel and the center of the cold reflection signal, The degree of suppression of scene signals in infrared images.

4. The method for correcting cold reflection noise in infrared images according to claim 1, characterized in that: In the step S3, the Gaussian function is specifically: ； in, is a Gaussian function, is the amplitude, All are attributes, are the mean values ​​of the attributes, are the standard deviations of the attributes.

5. The method for correcting cold reflection noise in infrared images according to claim 1, characterized in that: In the step S3, the step of constructing a fitting function based on the corrected infrared image in the step S2 includes the following steps: Step S301, using a Gaussian function as a model, and using the cold reflection area retained in the corrected infrared image described in step S2 as a fitting target; Step S302, adjusting the parameters of the Gaussian function based on the pixel information of the cold reflection area in step S301; Step S303, self-optimizing the parameters of the Gaussian function adjusted in step S302 to complete the construction of the fitting function.

6. The method for correcting cold reflection noise in infrared images according to claim 1, characterized in that: In the step S4, the cold reflection feature of the infrared image is constructed based on the parameters of the fitting function and the Gaussian function, specifically: The parameters of the fitting function are used as the cold reflection characteristics of the infrared image after fitting in step S3. The Gaussian function is used to map the corrected infrared image in step S2 to form a curved surface, which is also used as the cold reflection characteristic.

7. The method for correcting cold reflection noise in infrared images according to claim 1, characterized in that: In the step S5, the differential processing is specifically: ； in, For differential processing, , , , and are the parameters of the fitting function, and These are the coordinate grids of infrared images.

8. A system for correcting cold reflection noise in infrared images, characterized in that: Includes the following modules: An acquisition module, for acquiring an original infrared image including cold reflection noise; A correction module, based on a smooth window of a Butterworth function, reads the original infrared image described in the acquisition module, and inputs the read infrared image into a correction formula for correction; A construction module is used to fit the corrected infrared image of the correction module based on a Gaussian function, and to construct a fitting function based on the corrected infrared image of the correction module; A feature module constructs cold reflection features for infrared images based on the parameters of the fitting function constructed by the construction module and the Gaussian function; The correction module performs differential processing on the cold reflection feature described in the feature module and the original infrared image to complete the correction of the cold reflection noise in the infrared image.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 7 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of claims 1 to 7 are implemented.

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