Image generation method and device based on thermonuclear theory on Riemannian manifold

Abstract

The invention discloses an image generation method and device based on a thermonuclear theory on a Riemannian manifold, and the method comprises the steps: training a geometric encoder and a geometric decoder through limiting Riemannian measurement, learning the representation of an image in a low-dimensional manifold, mapping the distribution of target data into the special distribution on the low-dimensional manifold through a diffusion equation, and carrying out the recognition of the image. And the inverse diffusion process is learned by using the neural network, so that the learning precision and learning efficiency are improved. When an image generation task is carried out, sampling is carried out from special distribution on a low-dimensional manifold, target data distribution on a dimensional manifold is estimated through a thermonuclear generation model on a Riemannian manifold, and the target data distribution is restored to image distribution of a high-dimensional Euclidean space through a geometric decoder.

Description

Technical Field

[0001] The present invention belongs to the technical field of image generation, and in particular relates to an image generation method and device based on heat kernel theory on a Riemann manifold. Background Art

[0002] Image generation refers to the use of computer algorithms and techniques to create new images by processing and learning existing information. This technology can include generating new images from scratch or modifying, enhancing or synthesizing existing images. In image generation, computer systems can learn from large amounts of image data and extract patterns and features from them in order to generate realistic and artistic images. The development of this field has had a significant impact on many fields, including computer vision, medical image processing, and artistic creation.

[0003] The core of image generation technology lies in the learning and inference capabilities of the model, which is usually achieved using deep learning and neural network models. These models can understand the characteristics and structure of images through large-scale data training, and can generate new images comparable to real images. The application of this technology is very wide, including computer graphics, virtual reality, medical image processing, image enhancement, video game development and other fields. With the continuous advancement of technology, image generation technology will continue to bring more innovation and convenience to people's lives and work.

[0004] The traditional diffusion model training process usually involves complex steps and requires a step-by-step reverse simulation of the diffusion process, which requires a lot of computing resources and time; processing high-dimensional data will encounter convergence difficulties, and the quality of the generated data may be affected by the complexity of the model; the diffusion model may lose the diversity of the original data during the generation process, resulting in a lack of diversity in the generated data. Summary of the invention

[0005] In view of the problems existing in the prior art, the present application provides an image generation method and device based on the heat kernel theory on a Riemann manifold.

[0006] According to a first aspect of an embodiment of the present application, there is provided an image generation method based on heat kernel theory on a Riemann manifold, comprising:

[0007] Obtain a target image data set in a high-dimensional Euclidean space, and preprocess the target image data set;

[0008] Considering the Riemannian geometry structure of the target image data distribution, the geometric encoder and geometric decoder are trained by unsupervised learning method;

[0009] Mapping the target image data set to a low-dimensional Riemann manifold through a trained geometric encoder to obtain a target data distribution on the low-dimensional manifold;

[0010] Mapping the target data distribution to a normal distribution on a low-dimensional manifold by a diffusion equation, and learning the inverse diffusion process from the normal distribution to the target data distribution by using a heat kernel generation model on a Riemannian manifold;

[0011] Image generation is completed by sampling from the normal distribution on the low-dimensional manifold, estimating the data distribution on the high-dimensional manifold through the heat kernel generation model on the Riemann manifold, and restoring the image distribution to the high-dimensional Euclidean space through the geometric decoder.

[0012] Furthermore, the target graphic data set at least includes images of the category to be generated.

[0013] Furthermore, the geometric encoder is used to convert high-dimensional image data into a low-dimensional manifold representation, and the geometric decoder is used to reconstruct the low-dimensional manifold representation into high-dimensional image data.

[0014] Furthermore, the loss function used in the training of the geometric encoder and the geometric decoder includes the reconstruction loss, the LIPIS loss, and the local distortion metric of the mapping f between Riemann manifolds, and the local distortion metric is:

[0015]

[0016] Among them, d i are the elements of the diagonal matrix D, λ i (z) represents the eigenvalue of the Riemannian metric G(z), and z is the local coordinate of the low-dimensional Riemannian manifold with dimension m.

[0017] Furthermore, the diffusion equation is:

[0018]

[0019] in, is a point in the latent space, t∈[0,T], T refers to the end of the diffusion time, p t (x t ) refers to the probability distribution at time t, f t (x t ) is the drift coefficient, σ t is the variance of the normal distribution at time t.

[0020] Furthermore, in the learning process of the heat kernel generation model on the Riemann manifold, the loss function is the heat kernel score matching loss, specifically:

[0021]

[0022] in, is a point in the latent space, t∈[0,T], T refers to the end of the diffusion time, f t (x t )=s θ (x t ,t) is the drift coefficient.

[0023] According to a second aspect of an embodiment of the present application, there is provided an image generation device based on the heat kernel theory on a Riemann manifold, comprising:

[0024] An acquisition module is used to acquire a target image data set in a high-dimensional Euclidean space and preprocess the target image data set;

[0025] The first training module is used to consider the Riemannian geometry structure of the target image data distribution and train the geometry encoder and the geometry decoder through an unsupervised learning method;

[0026] A mapping module, used to map the target image data set to a low-dimensional Riemann manifold through a trained geometric encoder to obtain a target data distribution on the low-dimensional manifold;

[0027] A second training module is used to map the target data distribution into a normal distribution on a low-dimensional manifold by a diffusion equation, and learn the inverse diffusion process from the normal distribution to the target data distribution by using a heat kernel generation model on a Riemann manifold;

[0028] The image generation module is used to sample from the normal distribution on the low-dimensional manifold, estimate the data distribution on the high-dimensional manifold through the heat kernel generation model on the Riemann manifold, and restore the image distribution to the high-dimensional Euclidean space through the geometric decoder, thereby completing the image generation.

[0029] According to a third aspect of an embodiment of the present application, a computer program product is provided, comprising a computer program / instruction, which implements the method described in the first aspect when executed by a processor.

[0030] According to a fourth aspect of an embodiment of the present application, there is provided an electronic device, including:

[0031] one or more processors;

[0032] A memory for storing one or more programs;

[0033] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in the first aspect.

[0034] According to a fifth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer instructions are stored, and when the instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0035] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0036] It can be seen from the above embodiments that the present application trains the geometric encoder and the geometric decoder by restricting the Riemannian metric, learns the representation of the image on the low-dimensional manifold, maps the target data distribution to the special distribution on the low-dimensional manifold through the diffusion equation, and uses the neural network to learn the inverse diffusion process to improve its learning accuracy and learning efficiency. When performing the image generation task, sampling is performed from the special distribution on the low-dimensional manifold, the target data distribution on the dimensional manifold is estimated by the heat kernel generation model on the Riemannian manifold, and the image distribution is restored to the high-dimensional Euclidean space through the geometric decoder. The diffusion equation can provide a more efficient training method, reduce the consumption of computing resources and time; the characteristics of Riemannian geometry can help the heat kernel generation model on the Riemannian manifold to better converge in high-dimensional space, improve the stability and effect of training; using the structure of Riemannian geometry, the heat kernel generation model on the Riemannian manifold can more accurately capture the complexity of the data, thereby generating higher quality and diverse images.

[0037] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] Figure 1 The present invention is a flowchart of an image generation method based on the heat kernel theory on a Riemann manifold according to an exemplary embodiment.

[0040] Figure 2 It is a block diagram of an image generation device based on the heat kernel theory on a Riemann manifold according to an exemplary embodiment.

[0041] Figure 3 The diagram is a schematic diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0042] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0043] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0044] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0045] The present application provides an image generation method based on the heat kernel theory on Riemann manifold, restricts the Riemannian metric to learn the geometric encoder and geometric decoder, learns the representation of the image on the low-dimensional manifold, and performs heat diffusion and learning inverse diffusion processes on the Riemannian manifold to improve its learning accuracy and learning efficiency. The sampling process is to sample from a special distribution on the low-dimensional manifold, estimate the target data distribution on the Riemannian manifold through the heat kernel generation model on the Riemannian manifold, and restore the image distribution to the high-dimensional Euclidean space through the geometric decoder.

[0046] Please refer to Figure 1 As shown, the following steps are included:

[0047] (1) Obtain a target image dataset in a high-dimensional Euclidean space and preprocess the target image dataset;

[0048] Specifically, this application selects the MNIST dataset and the CIFAR-10 dataset. The MNIST dataset is a commonly used handwritten digital dataset consisting of digital images from 0 to 9. Each image is a grayscale image with a size of 28x28 pixels. This dataset contains 60,000 training images and 10,000 test images, and the training images and test images are merged; the CIFAR-10 dataset includes 10 different categories of color images, each with 6,000 images. These categories include airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. The size of each image is 32x32 pixels. The above images are merged as the target graphic dataset, and the preprocessing operations performed on them may include one or more of image center cropping, image random cropping, and image enhancement.

[0049] It should be noted that the target graphic data set at least includes images of the category to be generated, and in addition to the images of the category to be generated, may also include images of other categories.

[0050] (2) Considering the Riemannian geometric structure of the target image data distribution, the geometric encoder and geometric decoder are trained through unsupervised learning methods;

[0051] First, an autoencoder that preserves the geometric mapping between two Riemannian manifolds. is a Riemannian manifold of dimension m, with local coordinates Riemannian metric G(z)∈R m×m , is a Riemannian manifold of dimension n, with local coordinates Riemannian metric H(x)∈R n×n . Let the geometry decoder For smooth mapping, use f in local coordinates: The differential of the mapping f is represented by the Jacobian matrix

[0052] Intuitively, the mapping that maintains independence means that the values ​​of the metrics in different coordinate axis directions are independent of each other. In local coordinates, the mapping f between Riemann manifolds is: is an independent mapping if and only if:

[0053]

[0054] Throughout the process, it is assumed that both the latent space and the data space are Riemann manifolds, where the latent space is assigned the diagonal metric, the ambient data space is assigned the Riemannian metric H(x), and the pullback metric is defined as G(z).

[0055] In order to model the generation of heat kernels in low-dimensional manifolds, we need to make the pullback metric G(z) = D, where D is a diagonal matrix, then f: is an independent mapping if and only if:

[0056]

[0057] If J f (z) T H(f(z))J f All eigenvalues ​​of (z) are equal to 1, then the mapping f is an isometric mapping, and D is a diagonal matrix. Here we use {λ i (z)} represents the eigenvalue of the Riemannian metric G(z), and a local distortion metric of f can be defined, which measures how far the mapping f is from the local isometry, as shown below:

[0058]

[0059] This local metric can be compared with a probability metric v (in In units of ) to define a global distortion measure:

[0060]

[0061] Since the effect of the measure is limited to the support set of v, if the global distortion measure of f is zero, then f is an independence preserving mapping with respect to the support set of v. is considered as a local distortion metric, but such a metric is not coordinate invariant. Local distortion metrics defined in terms of eigenvalues ​​are coordinate invariant and are therefore geometrically well-defined. This is a general strategy for constructing coordinate invariant functionals on Riemannian manifolds and a general formula for coordinate invariant distortion measures.

[0062] In addition, reconstruction loss and LIPIS loss are added to maintain the realism of the image.

[0063] The geometric autoencoder is a model for learning low-dimensional representations of data. It adds restrictions on latent space measurement to the traditional autoencoder architecture, making the encoded representation more geometrically meaningful. The input of the geometric encoder is a high-dimensional space. For image data X, the input is a tensor of (H, W, C), where n = H × W × C, where H and W are the height and width of the image, and C is the number of channels. The output of the geometric encoder is a low-dimensional manifold The representation z on the image is a vector of dimension m. The input data is encoded into a low-dimensional representation through several layers of neural networks (such as convolutional layers and fully connected layers). Correspondingly, the input of the geometric decoder is the representation z on the low-dimensional manifold, which is decoded into high-dimensional data output through several layers of neural networks (such as fully connected layers and convolutional layers). It is a reconstructed high-dimensional image data sample. The shape of the reconstructed image data is (H, W, C), which should be consistent with the shape of the input. Based on the traditional autoencoder, the geometric autoencoder adds constraints on the geometric structure of the latent space. The constraint used here is the local distortion metric.

[0064] (3) mapping the target image data set to a low-dimensional Riemann manifold through a trained geometric encoder to obtain a target data distribution on the low-dimensional manifold;

[0065] The target image dataset is mapped to a low-dimensional Riemann manifold to obtain the distribution of the target data on the low-dimensional manifold. The purpose of this method is to represent the high-dimensional image data as a low-dimensional manifold structure through a geometric encoder, so that the data representation is more compact and easier to process. This processing method helps to model and analyze the data at a lower dimension and can help discover the potential structure and patterns in the data.

[0066] Assuming the distribution of images is p(x), the geometric autoencoder maps the data distribution to p(z).

[0067] (4) mapping the target data distribution into a normal distribution on a low-dimensional manifold by means of a diffusion equation, and learning the inverse diffusion process from the normal distribution to the target data distribution by means of a heat kernel generation model on a Riemannian manifold;

[0068] The geometric autoencoder is used to design the latent space, and the pullback metric is used to define the Riemannian metric of the Riemannian manifold in the original space, and the heat kernel generation algorithm of the general manifold is designed. According to the above, it is necessary to learn a Riemannian metric G(z) on the latent space, that is, the pullback metric, and design the heat kernel generation algorithm based on this. Set H(x) = D = I, where I is the unit matrix, then the Riemannian metric of the latent space can be directly defined, that is:

[0069]

[0070] Stable Diffusion regards the space learned by the autoencoder as a Euclidean space, which is unreasonable because its Riemann metric is not an identity matrix. Therefore, this application first designs a geometric autoencoder to project the original space into a Euclidean space, and designs a heat kernel generation algorithm on this space.

[0071] First, we consider a point in the latent space An ordinary differential equation for t∈[0,T]:

[0072]

[0073] Where T refers to the end point of the diffusion time, which is usually 1, and f t (x t ) is the drift coefficient;

[0074] It describes the range from x0 to x T A continuous reversible transformation of , if x0 is a random variable, then the x in the whole process t They are also random variables. Their distribution changes can be described by the following equation inferred from the Fokker-Planck equation:

[0075]

[0076] Among them, p t (x t ) refers to the probability distribution at time t. Let

[0077]

[0078] We can get a heat equation for probability density:

[0079]

[0080] The initial value is p(x,0)=p0, p0 represents the normal distribution, and p(x,0) is the distribution of x at time 0. By using Fourier transform, the heat conduction equation can be converted into an ordinary differential equation to obtain the solution distribution p t (x t ). We can perform Fourier transform on both ends of the above equation and transform it into the frequency domain, then we have:

[0081]

[0082] Where i is the meaning of the complex imaginary unit, and w is the frequency;

[0083] Then we can get an ordinary differential equation:

[0084]

[0085] We can get an analytical solution from this ordinary differential equation:

[0086]

[0087] in σ t is the variance of the normal distribution at time t. Through the inverse Fourier transform, we can get the solution distribution p t (x t ):

[0088]

[0089] Then we can get the probability transfer density, that is, the analytical solution of the heat kernel:

[0090]

[0091] Thus, the heat kernel score matching loss can be obtained, and a neural network can be used to fit the drift coefficient f t (x t )=s θ (x t ,t):

[0092]

[0093] Complete hot core generation model on Cain space.

[0094] It should be noted that there is no restriction on the neural network here, and it only needs to meet the input and output dimensional requirements.

[0095] The heat core generation model uses the heat core score matching loss as the loss function and is trained through the Adam optimizer. When the optimized model reaches or exceeds the evaluation index, the trained heat core generation model on the Riemann manifold is obtained.

[0096] (5) From low-dimensional manifold ( The image is generated by sampling from a normal distribution on the Euclidean space of Riemannian manifold, estimating the data distribution on the high-dimensional manifold through the heat kernel generation model on the Riemannian manifold, and restoring the image distribution to the high-dimensional Euclidean space through the geometric decoder.

[0097] Corresponding to the aforementioned embodiment of the image generation method based on the heat kernel theory on Riemann manifold, the present application also provides an embodiment of the image generation device based on the heat kernel theory on Riemann manifold.

[0098] Figure 2 It is a block diagram of an image generation device based on the heat kernel theory on a Riemann manifold according to an exemplary embodiment.

[0099] Reference Figure 2 , the device may include:

[0100] An acquisition module 21 is used to acquire a target image data set in a high-dimensional Euclidean space and preprocess the target image data set;

[0101] A first training module 22 is used to train a geometric encoder and a geometric decoder by an unsupervised learning method considering the Riemannian geometric structure of the target image data distribution;

[0102] A mapping module 23, used for mapping the target image data set to a low-dimensional Riemann manifold through a trained geometric encoder to obtain a target data distribution on the low-dimensional manifold;

[0103] A second training module 24 is used to map the target data distribution to a normal distribution on a low-dimensional manifold by a diffusion equation, and learn the inverse diffusion process from the normal distribution to the target data distribution by using a heat kernel generation model on a Riemann manifold;

[0104] The image generation module 25 is used to sample from the normal distribution on the low-dimensional manifold, estimate the data distribution on the high-dimensional manifold through the heat kernel generation model on the Riemann manifold, and restore the image distribution to the high-dimensional Euclidean space through the geometric decoder, thereby completing the image generation.

[0105] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0106] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0107] Accordingly, the present application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-mentioned image generation method based on the heat kernel theory on the Riemann manifold.

[0108] Accordingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned image generation method based on the heat kernel theory on the Riemann manifold. Figure 3 As shown in FIG. 1 , a hardware structure diagram of an image generation device based on the heat kernel theory on a Riemann manifold provided by an embodiment of the present invention is provided in any device with data processing capability, except Figure 3 In addition to the processor, memory and network interface shown, any device with data processing capability in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capability, which will not be described in detail.

[0109] Accordingly, the present application also provides a computer-readable storage medium on which computer instructions are stored, and when the instructions are executed by the processor, the image generation method based on the heat kernel theory on the Riemann manifold as described above is implemented. The computer-readable storage medium can be an internal storage unit of any device with data processing capabilities described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium can also include both an internal storage unit of any device with data processing capabilities and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and can also be used to temporarily store data that has been output or is to be output.

[0110] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0111] It will be appreciated that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. An image generation method based on heat kernel theory on Riemann manifold, characterized in that: include: Obtain a target image data set in a high-dimensional Euclidean space, and preprocess the target image data set; Considering the Riemannian geometry structure of the target image data distribution, the geometric encoder and geometric decoder are trained by unsupervised learning method; Mapping the target image data set to a low-dimensional Riemann manifold through a trained geometric encoder to obtain a target data distribution on the low-dimensional manifold; Mapping the target data distribution to a normal distribution on a low-dimensional manifold by a diffusion equation, and learning the inverse diffusion process from the normal distribution to the target data distribution by using a heat kernel generation model on a Riemannian manifold; Image generation is completed by sampling from the normal distribution on the low-dimensional manifold, estimating the data distribution on the high-dimensional manifold through the heat kernel generation model on the Riemann manifold, and restoring the image distribution to the high-dimensional Euclidean space through the geometric decoder.

2. The method according to claim 1, characterized in that The target graphic data set at least includes images of a category to be generated.

3. The method according to claim 1, characterized in that The geometric encoder is used to convert high-dimensional image data into low-dimensional manifold representation, and the geometric decoder is used to reconstruct the low-dimensional manifold representation into high-dimensional image data.

4. The method according to claim 1, characterized in that: The loss functions used in the training of the geometric encoder and the geometric decoder include reconstruction loss, LIPIS loss, and the local distortion metric of the mapping f between Riemann manifolds, where the local distortion metric is: Among them, d i are the elements of the diagonal matrix D, λ i (z) represents the eigenvalue of the Riemannian metric G(z), and z is the local coordinate of the low-dimensional Riemannian manifold with dimension m.

5. The method according to claim 1, characterized in that: The diffusion equation is: in, is a point in the latent space, T refers to the end of the diffusion time, p t (x t ) refers to the probability distribution at time t, f t (x t ) is the drift coefficient, σ t is the variance of the normal distribution at time t.

6. The method according to claim 1, characterized in that In the learning process of the heat kernel generation model on the Riemann manifold, the loss function is the heat kernel score matching loss, specifically: in, is a point in the latent space, T refers to the end of the diffusion time, f t (x t )=s θ (x t ,t) is the drift coefficient.

7. An image generation method based on heat kernel theory on Riemann manifold, characterized in that: include: An acquisition module is used to acquire a target image data set in a high-dimensional Euclidean space and preprocess the target image data set; The first training module is used to consider the Riemannian geometry structure of the target image data distribution and train the geometry encoder and the geometry decoder through an unsupervised learning method; A mapping module, used to map the target image data set to a low-dimensional Riemann manifold through a trained geometric encoder to obtain a target data distribution on the low-dimensional manifold; A second training module is used to map the target data distribution into a normal distribution on a low-dimensional manifold by a diffusion equation, and learn the inverse diffusion process from the normal distribution to the target data distribution by using a heat kernel generation model on a Riemann manifold; The image generation module is used to sample from the normal distribution on the low-dimensional manifold, estimate the data distribution on the high-dimensional manifold through the heat kernel generation model on the Riemann manifold, and restore the image distribution to the high-dimensional Euclidean space through the geometric decoder, thereby completing the image generation.

8. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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