Material collapse state detection method, device and storage medium

Abstract

The present application discloses a material collapse state detection method, device and storage medium, which relates to the field of semiconductor manufacturing technology. The method comprises: obtaining a production image of a melting stage in real time; detecting the corner point information of the melting material according to the production image; determining the corner point displacement according to the production image and the corner point information; calculating the total displacement of the corner points on the production image according to the corner point displacement; and determining that the melting stage is in a material collapse state when the total displacement of the corner points reaches a target threshold. The method solves the problem that there may be no detection when judging according to the number of corner points in the prior art, and achieves the effect of being able to detect the material collapse state in real time.

Description

Technical Field

[0001] The invention relates to a material collapse state detection method, device and storage medium, belonging to the technical field of semiconductor manufacturing. Background Art

[0002] In the field of photovoltaic industry, photovoltaic cells are power generation system units made of silicon materials. Monocrystalline silicon, as the most important substrate for photovoltaic cells, is mostly produced in a single-product furnace using the direct pulling method. The production process of monocrystalline silicon mainly includes melting, seeding, necking, shouldering, equal-diameter growth and finishing. In the above-mentioned entire production process, the melting process refers to the process in which the heater in the single crystal furnace heats the quartz crucible to melt the polycrystalline silicon material in the crucible after completing the furnace loading, leak detection and other operations. This process is the process with the highest temperature in the single crystal production process, and the time required for polycrystalline silicon to melt is relatively long. Therefore, it is the process with the highest risk of accidents in the single crystal silicon production process. Accidents that have occurred include silicon leakage, crucible deformation, silicon spraying, and a large number of impurities generated during the melting process, which is one of the main reasons for the high production cost.

[0003] In the melting process, the crucible is first placed in a low crucible position and a low crucible rotation speed so that the silicon material as a whole can absorb as much heat as possible. The silicon material generally melts from the bottom first. When a solution appears at the bottom of the crucible, the crucible rotation speed should be increased to heat evenly and avoid deformation of the crucible. Since the temperature in the solution is relatively high, the silicon material in the solution melts faster, and the silicon material on the liquid surface collapses due to the loss of support, which is called "collapse". On the one hand, after the collapse, the silicon material as a whole is already in the low temperature zone at the bottom of the heater. If the silicon material is in the lower temperature part of the thermal field for a long time, the melting time will be prolonged due to insufficient heat, and the impurities in the solution will increase. When pulling the crystal, it will enter the prepared single crystal silicon rod through melt convection, resulting in an increase in the content of oxygen and other impurity elements in the finished single crystal silicon product; in addition, the extended melting time prolongs the reaction time between the quartz crucible and the silicon liquid at a higher temperature, which will increase the erosion of the quartz crucible. On the other hand, after the collapse, the upper silicon material quickly sinks to the bottom. Since the temperature of the silicon material sinking to the bottom is different from that of the silicon solution, the temperature gradient between the two changes dramatically. If the original crucible position (original heating state) is maintained, it is easy to cause silicon liquid splashing. Therefore, after the collapse of the material occurs in the melting process, the crucible position should be raised in time so that the whole pile of materials continues to be in the high temperature zone, thereby reducing the impurities in the silicon solution after complete melting, breaking the original temperature gradient, and preventing accidents such as silicon liquid splashing caused by the convection temperature gradient.

[0004] An existing method for detecting the collapsed material state includes: obtaining the image frame at the current moment, then using the Harris operator to detect feature points (i.e., corner points), and then counting the number of corner points on the image, and determining the melting state of the silicon material corresponding to the image frame at the current moment according to the number of corner points. However, in the early stage of melting, the bottom of the silicon material melts faster, and the above method cannot detect the number of corner points at the bottom of the silicon material, that is, the above method cannot detect the state of the melt. At the same time, during the melting process, the number of corner points first increases and then decreases, and the melt state cannot be fully detected only by the number of corner points. Summary of the invention

[0005] The object of the present invention is to provide a material collapse state detection method, device and storage medium to solve the problems existing in the prior art.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] According to a first aspect, an embodiment of the present invention provides a method for detecting a collapsed material state, the method comprising:

[0008] Acquire production images of the melting stage in real time;

[0009] Detecting corner point information of the melt according to the production image;

[0010] Determine corner point displacement according to the production image and the corner point information;

[0011] Calculating the total displacement of the corner points on the production image according to the corner point displacement;

[0012] When the total displacement of the corner points reaches a target threshold, it is determined that the melt stage is in a material collapse state.

[0013] Optionally, the method further includes:

[0014] Obtain the displacement of each corner point obtained in the melting stage;

[0015] Calculating the total displacement of the production image according to the acquired displacements of each corner point;

[0016] The target threshold is determined according to the total displacement of the production image.

[0017] Optionally, determining the target threshold according to the total displacement of the production image includes:

[0018] Sorting the total displacements of the acquired production images in ascending order, obtaining a first displacement in a first order and a second displacement in a second order;

[0019] calculating a displacement distance between the first displacement and the second displacement;

[0020] The target threshold is determined according to the second displacement and the displacement interval.

[0021] Optionally, detecting corner point information of the melt according to the production image includes:

[0022] Corner point information in the production image is detected by using the Shi-Tomasi corner point detection algorithm.

[0023] Optionally, determining the corner point displacement according to the production image and the corner point information includes:

[0024] The corner point displacement is determined based on the production image and the corner point information using a Lucas-Kanad algorithm.

[0025] Optionally, determining the corner point displacement according to the production image and the corner point information by using a Lucas-Kanad algorithm includes:

[0026] The production image is reduced n times to generate a first pyramid image of the production image, wherein the bottom layer of the first pyramid image is the production image; n is a positive integer;

[0027] The corner point displacement is determined by a Lucas-Kanad algorithm according to the first pyramid image, a second pyramid image corresponding to a previous frame production image, and the corner point information.

[0028] Optionally, the determining the corner point displacement according to the first pyramid image, the second pyramid image corresponding to the previous frame production image, and the corner point information by using a Lucas-Kanad algorithm includes:

[0029] Initializing the existing displacement of the n+1th layer in the second pyramid image;

[0030] For the Lth layer of the first pyramid image, obtain the first corner point displacement of the L+1th layer in the second pyramid image calculated by the Lucas-Kanad algorithm, and calculate the current displacement of the Lth layer in the second pyramid image according to the first corner point displacement and the current displacement of the L+1th layer in the second pyramid image; calculate the second corner point displacement of the Lth layer of the first pyramid image by the Lucas-Kanad algorithm, and obtain the current displacement of the Lth layer in the first pyramid image according to the current displacement of the Lth layer in the second pyramid image and the second corner point displacement; if L>1, continue to perform this step; the starting value of L is n;

[0031] If L=1, the determined existing displacement of the first layer in the first pyramid image is determined as the corner point displacement.

[0032] Optionally, the real-time acquisition of production images at the melting stage includes:

[0033] Collecting the image of the melt stage by using an image sensor;

[0034] The collected image is preprocessed and gray-scale converted, and the preprocessed gray-scale image is determined as the production image.

[0035] In a second aspect, a material collapse state detection device is provided, the device comprising a memory and a processor, the memory storing at least one program instruction, the processor implementing the method as described in the first aspect by loading and executing the at least one program instruction.

[0036] According to a third aspect, a computer storage medium is provided, wherein at least one program instruction is stored in the computer storage medium, and the at least one program instruction is loaded and executed by a processor to implement the method according to the first aspect.

[0037] By acquiring a production image of the melting stage in real time; detecting the corner point information of the melting material according to the production image; determining the corner point displacement according to the production image and the corner point information; calculating the total corner point displacement on the production image according to the corner point displacement; and determining that the melting stage is in a material collapse state when the total corner point displacement reaches a target threshold, the problem that the existing technology may not be able to detect when judging based on the number of corner points is solved, and the effect of being able to detect the material collapse state in real time is achieved.

[0038] At the same time, the corner points are detected by the Shi-Tomasi corner point detection algorithm, which improves the accuracy of corner point detection and thus improves the accuracy of subsequent collapsed material detection.

[0039] The target threshold is calculated based on the calculated displacements, so that it is possible to determine whether abnormal displacement occurs based on the magnitude relationship between the total displacement and the target threshold, thereby further ensuring the accuracy of collapse detection.

[0040] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flow chart of a method for detecting a collapsed material state provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0044] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0045] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0046] Please refer to Figure 1 , which shows a method flow chart of a collapsed material state detection method provided by an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0047] Step 101, acquiring a production image of the melting stage in real time;

[0048] In actual implementation, this step includes:

[0049] First, collecting images of the melt stage by using an image sensor;

[0050] Second, the collected image is preprocessed and grayscale converted, and the preprocessed grayscale image is determined as the production image.

[0051] Of course, according to actual processing requirements, the images captured by the image sensor can also be directly used as production images.

[0052] It should be noted that, in order to improve the detection accuracy, after the production image is acquired, the acquired production image can be preprocessed by an edge detection algorithm or a filtering algorithm, and then the preprocessed production image is used in subsequent steps.

[0053] Step 102, detecting corner point information of the melt according to the production image;

[0054] A plurality of corner points may constitute the shape of the melt in the image. In the present application, the melt state of the silicon material is represented by the corner point information.

[0055] A corner point is the intersection of two edges, and is used to indicate the position where the directions of the two edges change. Therefore, a small movement of a corner point in any direction will cause a large change in the direction and amplitude of the gradient map in the area. That is, the pixel corresponding to the local maximum in the first-order derivative (i.e., the gradient of the grayscale image) is the corner point. Based on the above idea, a window of a fixed size is made to slide slightly in any direction at a certain position on the image. If the grayscale value in the window (on the gradient map) has a large change, then there is a corner point in the area where the window is located. The specific detection steps are as follows:

[0056] First, we need to calculate the change in the internal pixel values ​​when the window (a small image fragment) is moved in both the x and y directions.

[0057] Assume that the center of a window is located at point (x, y) of the grayscale image. The grayscale value of the pixel at this position is I(x, y). If the window moves a small displacement u and v in the x and y directions respectively to a new position (x+u, y+v), the grayscale value of the pixel at this position is I(x+u, y+v). The change in grayscale value caused by the window movement is

[0058] |I(x+u,y+v)-I(x,y)|. (1.1)

[0059] Let w(x,y) be the window function at the position point (x,y), which is used to represent the weight of each pixel in the window. Among them, if the pixel at the center point of the window is a corner point, then the grayscale value of the center point changes greatly before and after the window moves, and the weight coefficient of this point is large, indicating that the contribution of this point to the grayscale change is large; while the points far away from the center of the window (corner point) have a relatively small grayscale change, and the weight coefficient is small, indicating that the contribution of this point to the grayscale change is small; therefore, w(x,y) can be set to a Gaussian distribution (binary normal distribution) with the center of the window as the origin. In this embodiment, the window function distribution obeys the Gaussian distribution as an example. The sum of the squares of the grayscale of the image in four directions can be used to describe the change in the pixel grayscale value caused by the window moving in each direction (u,v):

[0060] E(u,v)=∑ (x,y)w(x,y)×[I(x+u,y+v)-I(x,y)] 2 (1.2)

[0061] In the flat area, the grayscale change of the window in any direction is small, so the E value is small; in the edge area, the image changes little in the direction parallel to the edge, while the translation direction perpendicular to the edge will cause a great change to the image, so the E value can be large or small; at the corner point, each direction of translation will greatly change the image, so the E value is large.

[0062] Secondly, using the two-dimensional Taylor formula to expand (1.1) we can get:

[0063] I(x+u,y+v)≈I(x,y)+uI x +vI y ,

[0064] Among them, I x and I y is the partial differential of I. Substituting the above formula into (1.2), we can get:

[0065]

[0066] The matrix M is:

[0067]

[0068] Finally, Shi-Tomasi found that the stability of the corner point is related to the smaller eigenvalue of the matrix M, so the smaller eigenvalue is directly used as the corner point score of the pixel. Therefore, the corner point score formula at the pixel point (x, y) is:

[0069]

[0070] Where λ1 and λ2 are two eigenvalues ​​of the matrix M. Therefore, it is necessary to perform eigenvalue decomposition on the matrix M and find the minimum eigenvalue as the corner score. If the score is greater than the set corner threshold, it is considered to be a corner, otherwise it is not. That is to say, the specific corner detection method for detecting the corner information in the production image by the Shi-Tomasi corner detection algorithm is:

[0071] Input: grayscale image, window size 3, window function w(x,y), corner threshold;

[0072] Output: Corner point score and corner point position information corresponding to each pixel.

[0073] Step 103, determining corner point displacement according to the production image and the corner point information;

[0074] The corner point displacement is determined based on the production image and the corner point information using a Lucas-Kanad algorithm.

[0075] The Lucas-Kanad (LK) algorithm uses the corner points extracted by the Shi-Tomasi method to calculate the optical flow vector at the pixel point. This method can quickly calculate the optical flow with a small amount of calculation. The specific calculation method of the LK algorithm is as follows:

[0076] Assume that the grayscale of a target corner pixel at time t is I(x, y, t). At time t+δt, after moving (δx, δy), it has the same grayscale as at time t, that is,

[0077] I(x,y,t)=I(x+δx,y+δy,t+δt). (2.1)

[0078] The optical flow equation can be calculated for (2.1):

[0079] I x u+I y v+I t =0, (2.2)

[0080] in, and Further:

[0081]

[0082] in, Indicates the speed at which the corner pixel moves. Represents the grayscale differential of the corner pixel space.

[0083] The optical flow equation (2.2) is valid for all pixels in a window centered at point (x, y), and the pixels in the window have the same velocity (u, v). If we use an n×n window, we get n×n equations:

[0084]

[0085] Here n = 25, and the speed v can be obtained:

[0086]

[0087] In the melting process, when the time intervals of collecting images are equal, the size of the corner point displacement can be determined according to the size of the corner point velocity of the pixel point obtained above. Therefore, the calculation method of the corner point displacement determined by the Lucas-Kanad algorithm according to the production image and the corner point information is:

[0088] Input: grayscale images of the previous frame and the current frame, corner information of the previous frame image, and window size. The window size is the default value.

[0089] Output: The displacement of some corner points on the previous frame image.

[0090] From the above content, it can be seen that the LK algorithm needs to meet the assumption that the moving object moves slowly, and there may be discontinuous movement between the corner points of the molten material between adjacent image frames during material collapse, and the LK algorithm will have a large error. Therefore, in this application, the step of obtaining the corner point displacement may also include:

[0091] First, the production image is reduced n times to generate a first pyramid image of the production image, wherein the bottom layer of the first pyramid image is the production image; n is a positive integer;

[0092] In one embodiment, when the pixels of the current production image are 400×400, the object speed is [8,8]; when the image pixels are reduced to 200×200, the speed becomes [4,4]; when it is reduced to 100*100, the speed is reduced to [2,2]. By analogy, after the production image is reduced multiple times, a pyramid image can be obtained.

[0093] Second, the corner point displacement is determined by using a Lucas-Kanad algorithm according to the first pyramid image, a second pyramid image corresponding to the previous frame production image, and the corner point information.

[0094] The previous frame production image I and the current frame production image I(t+δt) are downsampled to form two image pyramids with equal number of layers. and The pixel value after downsampling is calculated using the mean or maximum value; the resolution of the sampling or each image pyramid gradually decreases from bottom to top, that is, the bottom layer has the highest resolution and the most pixels, while the top layer has the lowest resolution and the least pixels. m is the number of the topmost layer, i.e. L m =n+1.

[0095] Specific:

[0096] (1) Initializing the existing displacement of the n+1th layer in the second pyramid image;

[0097] Assume that the current displacement of the previous frame image of the Lth layer is When L = L m Initialize the existing displacement of the top layer of the previous frame image. Calculate the offset of the highest layer using the LK algorithm

[0098] (2) for the Lth layer of the first pyramid image, obtain the first corner point displacement of the L+1th layer in the second pyramid image calculated by the Lucas-Kanad algorithm, and calculate the current displacement of the Lth layer in the second pyramid image according to the first corner point displacement and the current displacement of the L+1th layer in the second pyramid image; calculate the second corner point displacement of the Lth layer in the first pyramid image by the Lucas-Kanad algorithm, and obtain the current displacement of the Lth layer in the first pyramid image according to the current displacement of the Lth layer in the second pyramid image and the second corner point displacement; if L>1, continue to perform this step; the starting value of L is n;

[0099] For the Lth layer, calculate the existing displacement of the Lth layer of the previous frame image, g L =2(g L+1 +d L+1 ) ; Use the LK algorithm to calculate the offset of the Lth layer The total displacement of the Lth layer g = (g L +d L ).

[0100] (3) If L=1, the existing displacement of the first layer in the first pyramid image is determined as the corner point displacement.

[0101] Step 104, calculating the total displacement of the corner points on the production image according to the corner point displacement;

[0102] The total displacement of the corner points of the production image can be obtained by summing up the calculated displacements of each corner point.

[0103] Step 105: When the total displacement of the corner points reaches a target threshold, it is determined that the melt stage is in a material collapse state.

[0104] Optionally, in actual implementation, this step may include:

[0105] First, obtain the displacement of each corner point obtained in the melting stage;

[0106] Second, the total displacement of all corner points on the production image is calculated based on the acquired displacements of each corner point.

[0107] Third, the target threshold is determined according to the total displacement of the corner points calculated in the previous furnace.

[0108] (1) sorting the total displacements of the corner points obtained in ascending order, and obtaining a first displacement in the first sorting order and a second displacement in the second sorting order;

[0109] In a possible implementation manner, the first sorting is one quarter, and the second sorting is three quarters. Accordingly, the first displacement is determined to be Q1, and the second displacement is Q3.

[0110] (2) calculating the displacement distance between the first displacement and the second displacement;

[0111] The displacement interval is IQR=Q3-Q1.

[0112] (3) Determine the target threshold value according to the second displacement and the displacement interval.

[0113] The target threshold is: U=Q3+a*IQR, wherein a is a coefficient, and in a possible embodiment, a=1.5.

[0114] The above uses quartiles to calculate the target threshold in real time, and the quartiles have a certain resistance, which reduces the interference of noise in the melting process.

[0115] Optionally, when it is determined that the material is in a state of collapse, a preset prompt method may be used to issue a prompt, which includes at least one of voice, indicator light, and vibration. This embodiment does not limit the specific prompt method. In addition, after the control system receives the indication of collapse, it can automatically and timely raise the crucible position to break the original temperature gradient, prevent the silicon solution from splashing due to excessive local temperature gradient, and effectively avoid the risk of damage to the crucible caused by the sharp part of the silicon material, thereby causing silicon leakage. It also improves the automation of the melting process and reduces labor costs.

[0116] In summary, by acquiring the production image of the melting stage in real time; detecting the corner point information of the melting material according to the production image; determining the corner point displacement according to the production image and the corner point information; calculating the total displacement of the corner points on the production image according to the corner point displacement; and determining that the melting stage is in a material collapse state when the total displacement of the corner points reaches the target threshold, the problem that the existing technology may not be able to detect when judging based on the number of corner points is solved, and the effect of being able to detect the material collapse state in real time is achieved.

[0117] At the same time, the corner points are detected by the Shi-Tomasi corner point detection algorithm, which improves the accuracy of corner point detection and thus improves the accuracy of subsequent collapsed material detection.

[0118] The target threshold is calculated based on the calculated total displacements, so that it is possible to determine whether abnormal displacement occurs based on the magnitude relationship between the total displacement and the target threshold, thereby further ensuring the accuracy of collapse detection.

[0119] The present application also provides a material collapse state detection device, which includes a memory and a processor, wherein the memory stores at least one program instruction, and the processor implements the above-mentioned method by loading and executing the at least one program instruction.

[0120] The present application also provides a computer storage medium, in which at least one program instruction is stored. The at least one program instruction is loaded and executed by a processor to implement the method described above.

[0121] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for detecting a collapsed material state, characterized in that: The method comprises: Acquire production images of the melting stage in real time; Detecting corner point information of the melt according to the production image; Determining the corner point displacement according to the production image and the corner point information includes: The production image is reduced n times to generate a first pyramid image of the production image, wherein the bottom layer of the first pyramid image is the production image; n is a positive integer; Determining the corner point displacement according to the first pyramid image, the second pyramid image corresponding to the previous frame production image, and the corner point information by using the Lucas-Kanad algorithm includes: Initializing the existing displacement of the n+1th layer in the second pyramid image; For the Lth layer of the first pyramid image, obtain the first corner point displacement of the L+1th layer in the second pyramid image calculated by the Lucas-Kanad algorithm, and calculate the current displacement of the Lth layer in the second pyramid image according to the first corner point displacement and the current displacement of the L+1th layer in the second pyramid image; calculate the second corner point displacement of the Lth layer of the first pyramid image by the Lucas-Kanad algorithm, and obtain the current displacement of the Lth layer in the first pyramid image according to the current displacement of the Lth layer in the second pyramid image and the second corner point displacement; if L>1, continue to perform this step; the starting value of L is n; If L=1, the determined existing displacement of the first layer in the first pyramid image is determined as the corner point displacement; Calculating the total displacement of the corner points on the production image according to the corner point displacement; When the total displacement of the corner points is greater than the target threshold, it is determined that the melt stage is in a material collapse state.

2. The method according to claim 1, characterized in that: The method further comprises: Obtain the displacement of each corner point obtained in the melting stage; Calculating the total displacement of all corner points on the production image according to the displacement of each corner point; The target threshold is determined according to the total displacement of the corner points calculated for the current furnace.

3. The method according to claim 2, characterized in that The determining the target threshold value according to the total displacement of the corner points calculated in the current heat includes: Sort the total displacements of the corner points of each frame of the image obtained in ascending order, and obtain a first displacement in the first sorting order and a second displacement in the second sorting order; calculating a displacement distance between the first displacement and the second displacement; The target threshold is determined according to the second displacement and the displacement interval.

4. The method according to claim 1, characterized in that The detecting corner point information of the melt according to the production image comprises: Corner point information in the production image is detected by using the Shi-Tomasi corner point detection algorithm.

5. The method according to any one of claims 1 to 4, characterized in that: The real-time acquisition of production images at the melting stage includes: Collecting the image of the melt stage by using an image sensor; The collected image is preprocessed and gray-scale converted, and the preprocessed gray-scale image is determined as the production image.

6. A material collapse state detection device, characterized in that: The device includes a memory and a processor, wherein the memory stores at least one program instruction, and the processor implements the method according to any one of claims 1 to 5 by loading and executing the at least one program instruction.

7. A computer storage medium, characterized in that: The computer storage medium stores at least one program instruction, and the at least one program instruction is loaded and executed by a processor to implement the method according to any one of claims 1 to 5.

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