A method and system for controlling the thickness of nano-coating on glass surface

Abstract

The present application provides a method and system for controlling the thickness of a nano-coating on a glass surface, which belongs to the field of detection and control technology and is used to achieve low-cost and high-precision detection and control of the thickness of a nano-coating. A control device acquires a target image, where the surface of a glass panel is coated with a nano-coating. The target image is obtained by photographing the nano-coating on the surface of the glass panel while the surface of the nano-coating is atomized; the control device discretely divides the target image into K sub-image sets; the control device analyzes the K sub-image sets separately through an AI / ML model to obtain K corresponding analysis results, and fuses the K analysis results to obtain an analysis result of the target image. The analysis result of the target image is an image of an area on the glass panel where the nano-coating is non-uniform; the control device instructs the coating device to correct the nano-coating in the non-uniform area.

Description

Technical Field

[0001] The present application relates to the field of detection and control technology, and in particular to a method and system for controlling the thickness of a nano-coating on a glass surface. Background Art

[0002] With the rapid development of materials science and surface engineering technology, the application of nano-coatings on glass surfaces is becoming increasingly widespread. For example, low-emissivity (Low-E) coatings in architectural glass, anti-fog / anti-icing coatings in automotive glass, anti-reflective and hydrophobic coatings on electronic device screens, etc., all rely on functional films with nano-scale thickness to achieve the optimization of optical, electrical or mechanical properties. The thickness of the nano-coating (usually in the range of 1-500nm) directly affects its key parameters such as light transmittance, conductivity, weather resistance and interface bonding strength. Therefore, the development of high-precision, high-efficiency and non-destructive thickness detection technology has become a core requirement for improving the quality of coating processes and the reliability of industrial applications.

[0003] Current testing methods include the following:

[0004] Spectroscopic ellipsometry, which estimates film thickness by analyzing the reflection characteristics of polarized light on a coating surface, is suitable for non-contact inspection of transparent or translucent films. However, its accuracy is limited by the accuracy of its optical model, making it less adaptable to multi-layer composite coatings or non-uniform surface structures. Furthermore, it requires complex mathematical inversion algorithms, making it difficult to meet the needs of online, real-time inspection.

[0005] Atomic force microscopy (AFM) and scanning electron microscopy (SEM) directly measure film thickness by scanning the surface topography or observing cross-sections, achieving sub-nanometer resolution. However, these methods require destructive sample preparation (e.g., SEM requires cutting a cross section and then coating it with a conductive layer), resulting in low detection efficiency and high costs, making them unsuitable for quality control during production.

[0006] Therefore, how to achieve low-cost and high-precision nanocoating thickness detection and control is a current research issue. Summary of the Invention

[0007] The embodiments of the present application provide a method and system for controlling the thickness of a nano-coating on a glass surface, so as to achieve low-cost and high-precision detection and control of the thickness of the nano-coating.

[0008] To achieve the above objectives, this application adopts the following technical solutions:

[0009] In a first aspect, an embodiment of the present application provides a method for controlling the thickness of a nano-coating on a glass surface, which is applied to a control device and includes: the control device acquires a target image, the surface of the glass panel is coated with a nano-coating, and the target image is obtained by photographing the nano-coating on the surface of the glass panel when the surface of the nano-coating is atomized; the control device discretely divides the target image into K sub-image sets, any two sub-image sets in the K sub-image sets contain different sub-images, K is an integer greater than 1, any two sub-images contained in the i-th sub-image set in the K sub-image sets are not adjacent in the target image, and any two sub-images contained in the i-th sub-image set are the same size, i is an integer from 1 to K; the control device analyzes the K sub-image sets separately through an AI / ML model to obtain corresponding K analysis results, and fuses the K analysis results to obtain an analysis result of the target image, and the analysis result of the target image is an image on the glass panel with an area where the nano-coating is non-uniformly marked; the control device instructs the coating device to correct the nano-coating in the non-uniform area.

[0010] Optionally, the control device discretely divides the target image into K sub-image sets, including: the control device discretely divides the target image into two sub-image sets according to a size of the target image being smaller than a threshold size.

[0011] Optionally, the control device discretely divides the target image into two sub-image sets based on the size of the target image being smaller than a threshold size, including: the control device divides the target image into M1*N1 grids through gridding processing based on the size of the target image being smaller than the threshold size, where M1 is the number of grids contained in a row of the target image, N1 is the number of grids contained in a column of the target image, and M1 and N1 are integers greater than or equal to 2; the control device determines the grids with odd column indexes in each row with odd row indexes in the M1*N1 grids as belonging to the first sub-image set. A sub-image of the first sub-image set, and a grid with an even column index in each row with an even row index in the M1*N1 grids, are determined as a sub-image belonging to the first sub-image set; the control device determines a grid with an even column index in each row with an odd row index in the M1*N1 grids, as a sub-image belonging to the second sub-image set, and a grid with an odd column index in each row with an even row index in the M1*N1 grids, as a sub-image belonging to the second sub-image set; wherein the two sub-image sets include the first sub-image set and the second sub-image set.

[0012] Optionally, the control device discretely divides the target image into K sub-image sets, including: the control device discretely divides the target image into three sub-image sets according to a size of the target image being greater than or equal to a threshold size.

[0013] Optionally, the control device discretely divides the target image into three sub-image sets based on the size of the target image being greater than or equal to a threshold size, including: the control device divides the target image into M2*N2 grids through gridding processing based on the size of the target image being greater than or equal to a threshold size, where M2 is the number of grids contained in a row of the target image, N2 is the number of grids contained in a column of the target image, and M2 and N2 are integers greater than or equal to 3; the control device divides the column index of a row of grids with a row index of 1 in the M2*N2 grids into grids with a starting value of 1 and a step length of the column index of 3, and divides the column index of a row of grids with a row index of 2 in the M2*N2 grids into grids with a starting value of The control device determines the grid with a starting column index of 2 and a step length of column index of 3 in a row of grids with a row index of 3 in the M2*N2 grids, and the grid with a starting column index of 1 and a step length of column index of 3 in a row of grids with a row index of 4 in the M2*N2 grids as belonging to a sub-image in the first sub-image set, and so on until traversing to a row of grids with a row index of N2 in the M2*N2 grids; the control device determines the grid with a starting column index of 2 and a step length of column index of 3 in a row of grids with a row index of 1 in the M2*N2 grids, and the grid with a starting column index of 1 and a step length of column index of 3 in the M2*N2 grids. The control device determines the grid with a starting column index of 3 and a step length of column index of 3 in a row of grids with a row index of 2 in the M2*N2 grids, the grid with a starting column index of 4 and a step length of column index of 3 in a row of grids with a row index of 3 in the M2*N2 grids, and the grid with a starting column index of 2 and a step length of column index of 3 in a row of grids with a row index of 4 in the M2*N2 grids as belonging to a sub-image in the second sub-image set, and so on until traversing to a row of grids with a row index of N2 in the M2*N2 grids; the control device determines the grid with a starting column index of 3 and a step length of column index of 3 in a row of grids with a row index of 1 in the M2*N2 grids, A grid with a starting column index of 4 and a step length of 3 in a row of grids with a row index of 2 among the M2*N2 grids, a grid with a starting column index of 5 and a step length of 3 in a row of grids with a row index of 3 among the M2*N2 grids, and a grid with a starting column index of 3 and a step length of 3 in a row of grids with a row index of 4 among the M2*N2 grids are determined to be a sub-image belonging to the second sub-image set, and then, this is deduced by analogy until a row of grids with a row index of N2 among the M2*N2 grids is traversed; wherein, the three sub-image sets include a first sub-image set, a second sub-image set, and a third sub-image set.

[0014] Optionally, the control device analyzes K sub-image sets separately through the AI / ML model to obtain corresponding K analysis results, and fuses the K analysis results to obtain the analysis result of the target image, including: for the i-th sub-image set, i traverses 1 to K, the control device randomly combines the sub-images in the i-th sub-image set in pairs to obtain P sub-image groups, and analyzes the j-th sub-image group in the P sub-image groups through the AI / ML model to obtain the analysis result of the j-th sub-image group, where j is an integer traversing 1 to P, and the analysis result of the j-th sub-image group is two sub-images with or without marked non-uniform areas of the nano-coating; the i-th analysis result in the K analysis results includes the analysis result of the j-th sub-image group; the control device splices the K analysis results in the order of discrete segmentation to obtain the analysis result of the target image.

[0015] Optionally, the control device randomly combines the sub-images in the i-th sub-image set in pairs to obtain P sub-image groups, and analyzes the j-th sub-image group in the P sub-image groups through the AI / ML model to obtain an analysis result of the j-th sub-image group, including: for the i-th sub-image set, i traverses 1 to K: the control device randomly combines the sub-images in the i-th sub-image set in pairs to obtain P sub-image groups; the control device inputs the j-th sub-image group into the feature extraction layer of the AI / ML model to obtain two feature sequences corresponding one to one to two sub-images in the j-th sub-image group output by the feature extraction layer of the AI / ML model; the control device analyzes the two feature sequences through the feature processing layer of the AI / ML model to obtain an analysis result of the j-th sub-image group.

[0016] Optionally, the control device analyzes two feature sequences through the feature processing layer of the AI / ML model to obtain an analysis result of the j-th sub-image group, including: the control device calculates the difference between each two feature elements at corresponding positions in the two feature sequences, and sets the value of each two feature elements in the two feature sequences whose difference is less than a preset threshold as a default value to obtain two corrected feature sequences; the control device interweaves the feature elements of the two corrected feature sequences according to the energy level to obtain two interleaved feature sequences, and inputs the two interleaved feature sequences into the feature processing layer of the AI / ML model to obtain the analysis result of the j-th sub-image group output by the feature processing layer of the AI / ML model.

[0017] Optionally, the control device interleaves the characteristic elements of each of the two corrected characteristic sequences according to the energy level to obtain two interleaved characteristic sequences, including: for each of the two corrected characteristic sequences, if the value of a characteristic element in the corrected characteristic sequence is larger, the energy of the characteristic element is higher; on this basis, the control device interleaves the characteristic elements in the corrected characteristic sequence whose energies belong to the same energy level in the corrected characteristic sequence in order from high to low energy levels to obtain two interleaved characteristic sequences.

[0018] In a second aspect, an embodiment of the present application provides a glass surface nano-coating thickness control system, which includes a control device and a coating device, and the control device is configured as follows: the control device acquires a target image, where the surface of the glass panel is coated with a nano-coating, and the target image is obtained by photographing the nano-coating on the surface of the glass panel when the surface of the nano-coating is atomized; the control device discretely divides the target image into K sub-image sets, where any two sub-image sets in the K sub-image sets contain different sub-images, K is an integer greater than 1, any two sub-images contained in the i-th sub-image set in the K sub-image sets are not adjacent in the target image, and any two sub-images contained in the i-th sub-image set are the same size, i is an integer from 1 to K; the control device analyzes at least two sub-image sets separately through an AI / ML model to obtain at least two corresponding analysis results, and fuses the at least two analysis results to obtain an analysis result of the target image, where the analysis result of the target image indicates an area of ​​non-uniform nano-coating in the glass panel; the control device instructs the coating device to correct the nano-coating in the non-uniform area.

[0019] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having program code stored thereon. When the program code is run by the computer, the method described in the first aspect is executed.

[0020] In summary, the above method and device have the following technical effects:

[0021] By atomizing the surface of the nanocoating, the atomized water droplets can be distributed on the surface of the nanocoating. At this time, if the thickness of the nanocoating is uneven, the distribution of the atomized water droplets may also be uneven, and may be concentrated or sparse. In view of this feature, the nanocoating on the surface of the glass panel can be photographed to obtain a target image when the surface of the nanocoating is atomized. The control device then discretely divides the target image into K sub-image sets, and then analyzes the K sub-image sets separately through the AI / ML model to obtain corresponding K analysis results, and fuses the K analysis results to obtain the analysis result of the target image. This can reduce the amount of data processed, and processing with a single sub-image as the granularity can also improve processing accuracy, so that it can accurately indicate the area where the nanocoating is uneven on the glass panel. Finally, the control device instructs the coating equipment to correct the nanocoating in the uneven area, thereby achieving low-cost and high-precision nanocoating thickness detection and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the architecture of a detection and control system provided in an embodiment of the present application;

[0023] Figure 2 A flow chart of a method for controlling the thickness of a glass surface nanocoating provided in an embodiment of the present application;

[0024] Figure 3 A schematic diagram of a method for controlling the thickness of a nano-coating on a glass surface provided in an embodiment of the present application;

[0025] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] 1. Deep neural network (DNN):

[0027] DNNs are a specific implementation of machine learning. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, enabling them to learn arbitrary mappings. Based on how the network is constructed, DNNs can be categorized as feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

[0028] The characteristic of FNN network is that neurons in adjacent layers are fully connected to each other, which makes FNN usually require a large amount of storage space and leads to high computational complexity.

[0029] CNN is a neural network specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling along the time axis) and image data (discrete sampling along two dimensions) can both be considered grid-like data. CNNs do not utilize all input information at once for computation. Instead, they use a fixed-size window to intercept a portion of the information for convolution operations, significantly reducing the computational complexity of model parameters. Furthermore, depending on the type of information intercepted by the window (e.g., people and objects in an image represent different types of information), each window can use a different convolution kernel, enabling CNNs to better extract features from the input data.

[0030] RNNs are a type of DNN that utilizes feedback time series information. Their input consists of a new input value at the current moment and their own output value at the previous moment. RNNs are suitable for capturing temporally correlated sequence features and are particularly well-suited for applications such as speech recognition and channel coding.

[0031] In the embodiment of the present invention, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein the other information and the information to be indicated have an association relationship. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can be achieved by means of the arrangement order of each piece of information that is agreed upon in advance (for example, stipulated by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0032] In addition, the specific indication method can also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the existing technology and will not be repeated in this article. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, there may be a situation where the indication methods for different information are different. In the specific implementation process, the required indication method can be selected according to specific needs. The embodiment of the present invention does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present invention should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0033] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately, and the sending period and / or sending timing of these sub-information can be the same or different. The specific sending method is not limited by the embodiment of the present invention. The sending period and / or sending timing of these sub-information can be predefined, for example, predefined according to a protocol, or can be configured by the transmitting device through sending configuration information to the receiving device.

[0034] "Pre-definition" or "pre-configuration" can be achieved by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present invention do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or an electronic device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or an electronic device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present invention.

[0035] The "protocol" involved in the embodiments of the present invention may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol in a reliable access method system for future Internet of Things devices. The embodiments of the present invention do not specifically limit this.

[0036] In the embodiments of the present invention, descriptions such as "when...", "in the case of...", "if", and "if" all mean that the device will perform corresponding processing under certain objective circumstances. They do not limit the time, nor do they require the device to perform judgment actions during implementation, nor do they mean the existence of other limitations.

[0037] In the description of the embodiments of the present invention, unless otherwise specified, " / " indicates that the objects associated with each other are in an "or" relationship. For example, A / B can mean A or B. "And / or" in the embodiments of the present invention is merely a description of the association relationship between the associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exists simultaneously, and B exists alone. A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural. In addition, to facilitate the clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit differences. At the same time, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or design. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0038] The network architecture and business scenarios described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. Ordinary technicians in this field can know that with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0039] The technical solution in this application will be described below with reference to the accompanying drawings.

[0040] See also Figure 1 , an embodiment of the present application provides a detection and control system, which may include: a control device and a coating device.

[0041] Wherein, control equipment and coating equipment can be understood as terminals, and terminal can be a terminal with communication function, or can be a chip or chip system arranged at the terminal. The terminal equipment can also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent or user device. The terminal equipment in the embodiment of the present application can be a mobile phone (mobile phone), tablet computer (Pad), a computer with wireless transceiver function. Alternatively, it can also be a server, a server cluster, a virtual server, a virtual server cluster.

[0042] The following will explain the method.

[0043] See also Figure 2 The present invention provides a method for controlling the thickness of a nano-coating on a glass surface. The method is applied to the above-mentioned detection and control system, and the process of the method includes:

[0044] S201, controlling a device to acquire a target image.

[0045] The surface of the glass panel is coated with a nano coating. The glass panel may be a rectangular panel, or this application will be described using a rectangle as an example.

[0046] Nano coating is different according to the application scene, and its specific implementation may also be different, such as metal oxide, polymer, carbon-based composite material, layer composite structure (such as gradient refractive index coating), etc., without specific limitation. The nano coating in the embodiment of the present application has low adhesion hydrophobicity, that is, the coating surface chemical composition (such as fluorine or silane group) makes it have moderate hydrophobicity (contact angle of about 90 ° ~ 120 °), and the adhesion of water droplets to the surface is low, but it is not enough to form a super-hydrophobic "lotus effect" (contact angle> 150 °). Therefore, if the nano coating surface of the glass panel is atomized, the water droplets will not quickly gather and roll off, but remain in a dispersed state. At this time, the uniformity of the thickness of the nano coating of the glass panel will affect the distribution of water droplets. For example, if the thickness of the nano coating is uneven, for non-uniform areas, that is, the thickness is not a substantially consistent area, but the thickness change is relatively large and discrete areas, so the distribution of atomized water droplets in the area is also not uniform, and concentrated or sparse situations may occur.

[0047] Based on the above characteristics, the target image can be obtained by photographing the nanocoating on the surface of the glass panel when the nanocoating surface is atomized, such as setting the camera position perpendicular to the surface of the glass panel coated with the nanocoating, and then photographing it to obtain the target image.

[0048] S202: The control device discretely divides the target image into K sub-image sets.

[0049] The sub-images contained in any two sub-image sets among the K sub-image sets are different, K is an integer greater than 1, the positions of any two sub-images contained in the i-th sub-image set among the K sub-image sets are not adjacent in the target image, and the sizes of any two sub-images contained in the i-th sub-image set are the same, i is an integer from 1 to K. The purpose of this is to enable subsequent artificial intelligence (AI) / machine learning (ML) models to process at the sub-image granularity, which can reduce the performance requirements for the AI / ML model on the one hand and improve the accuracy on the other hand. In addition, since the target image is discretely segmented, the discreteness of the segmentation will make it likely that the areas where the water mist distribution changes continuously can be segmented into different sub-image sets, so that they can be processed separately by the AI / ML model, avoiding the situation where the areas where the water mist distribution changes continuously affect the AI / ML model and cause misprocessing, thereby improving the accuracy. Specifically, it can include the following two situations:

[0050] Case 1:

[0051] The control device discretely divides the target image into two sub-image sets according to the fact that the size of the target image is smaller than the threshold size.

[0052] For example, based on the fact that the size of the target image is smaller than a threshold size, the control device divides the target image into M1*N1 grids through gridding processing, where M1 is the number of grids contained in a row of the target image, N1 is the number of grids contained in a column of the target image, and M1 and N1 are integers greater than or equal to 2. Based on this, the control device determines the grids with odd column indexes in each row with odd row indexes in the M1*N1 grids as belonging to a sub-image in the first sub-image set, and determines the grids with even column indexes in each row with even row indexes in the M1*N1 grids as belonging to a sub-image in the first sub-image set; the control device determines the grids with even column indexes in each row with odd row indexes in the M1*N1 grids as belonging to a sub-image in the second sub-image set, and determines the grids with odd column indexes in each row with even row indexes in the M1*N1 grids as belonging to a sub-image in the second sub-image set; wherein the two sub-image sets include the first sub-image set and the second sub-image set.

[0053] For ease of understanding, an example of situation 1 can be as follows Figure 3As shown in (a) in the figure, the first sub-image set includes: 3 sub-images corresponding to the 1st, 3rd, and 5th grid columns of the 1st row, 3 sub-images corresponding to the 2nd, 4th, and 6th grid columns of the 2nd row, 3 sub-images corresponding to the 1st, 3rd, and 5th grid columns of the 3rd row, 3 sub-images corresponding to the 2nd, 4th, and 6th grid columns of the 4th row, 3 sub-images corresponding to the 1st, 3rd, and 5th grid columns of the 5th row, and 3 sub-images corresponding to the 2nd, 4th, and 6th grid columns of the 6th row. The second sub-image set includes: 3 sub-images corresponding to the 2nd, 4th, and 6th grid columns of the 1st row, 3 sub-images corresponding to the 1st, 3rd, and 5th grid columns of the 2nd row, 3 sub-images corresponding to the 2nd, 4th, and 6th grid columns of the 3rd row, 3 sub-images corresponding to the 1st, 3rd, and 5th grid columns of the 4th row, 3 sub-images corresponding to the 2nd, 4th, and 6th grid columns of the 5th row, and 3 sub-images corresponding to the 1st, 3rd, and 5th grid columns of the 6th row. Any two sub-images in the first sub-image set or the second sub-image set are not adjacent, that is, they do not share a common edge, and are therefore considered discrete. The segmentation method in the above case 1 can also be understood as pseudo-random discrete segmentation.

[0054] Case 2:

[0055] The control device discretely divides the target image into three sub-image sets according to whether the size of the target image is greater than or equal to a threshold size.

[0056] For example, the control device divides the target image into M2*N2 grids through gridding processing based on the fact that the size of the target image is greater than or equal to the threshold size, where M2 is the number of grids contained in a row of the target image, and N2 is the number of grids contained in a column of the target image, and M2 and N2 are integers greater than or equal to 3.

[0057] On this basis, the control device determines the grid with a starting column index of 1 and a step length of column index of 3 in a row of grids with a row index of 1 in the M2*N2 grids, the grid with a starting column index of 2 and a step length of column index of 3 in a row of grids with a row index of 2 in the M2*N2 grids, the grid with a starting column index of 3 and a step length of column index of 3 in a row of grids with a row index of 3 in the M2*N2 grids, and the grid with a starting column index of 1 and a step length of column index of 3 in a row of grids with a row index of 4 in the M2*N2 grids as a sub-image belonging to the first sub-image set, and then, and so on, until traversing to a row of grids with a row index of N2 in the M2*N2 grids.

[0058] Similarly, the control device determines the grid whose column index starts at 2 and whose step length of the column index is 3 in the row of grids with a row index of 1 in the M2*N2 grids, the grid whose column index starts at 3 and whose step length of the column index is 3 in the row of grids with a row index of 2 in the M2*N2 grids, the grid whose column index starts at 4 and whose step length of the column index is 3 in the row of grids with a row index of 3 in the M2*N2 grids, and the grid whose column index starts at 2 and whose step length of the column index is 3 in the row of grids with a row index of 4 in the M2*N2 grids, as a sub-image belonging to the second sub-image set, and so on, until traversing to the row of grids with a row index of N2 in the M2*N2 grids;

[0059] Similarly, the control device determines the grid with a starting column index of 3 and a step length of column index of 3 in a row of grids with a row index of 1 in the M2*N2 grids, the grid with a starting column index of 4 and a step length of column index of 3 in a row of grids with a row index of 2 in the M2*N2 grids, the grid with a starting column index of 5 and a step length of column index of 3 in a row of grids with a row index of 3 in the M2*N2 grids, and the grid with a starting column index of 3 and a step length of column index of 3 in a row of grids with a row index of 4 in the M2*N2 grids as belonging to a sub-image in the second sub-image set, and then, and so on, until traversing to a row of grids with a row index of N2 in the M2*N2 grids.

[0060] The three sub-image sets include a first sub-image set, a second sub-image set and a third sub-image set.

[0061] For ease of understanding, an example of situation 2 can be as follows Figure 3As shown in (b) in the figure, at this time, the first sub-image set includes: 2 sub-images corresponding to the 1st and 4th grid columns of the 1st row, 2 sub-images corresponding to the 2nd and 5th grid columns of the 2nd row, 2 sub-images corresponding to the 1st and 4th grid columns of the 3rd row, 2 sub-images corresponding to the 2nd and 5th grid columns of the 4th row, 2 sub-images corresponding to the 1st and 4th grid columns of the 5th row, and 2 sub-images corresponding to the 2nd and 5th grid columns of the 6th row. The second sub-image set includes: 2 sub-images corresponding to the 2nd and 5th grid columns of the 1st row, 2 sub-images corresponding to the 3rd and 6th grid columns of the 2nd row, 2 sub-images corresponding to the 2nd and 5th grid columns of the 3rd row, 2 sub-images corresponding to the 3rd and 6th grid columns of the 4th row, 2 sub-images corresponding to the 2nd and 5th grid columns of the 5th row, and 2 sub-images corresponding to the 3rd and 6th grid columns of the 6th row. The third sub-image set includes: two sub-images corresponding to the 3rd and 6th grid columns of the 1st row, two sub-images corresponding to the 1st and 4th grid columns of the 2nd row, two sub-images corresponding to the 3rd and 6th grid columns of the 3rd row, two sub-images corresponding to the 1st and 4th grid columns of the 4th row, two sub-images corresponding to the 3rd and 6th grid columns of the 5th row, and two sub-images corresponding to the 1st and 4th grid columns of the 6th row. In other words, no two sub-images in the first, second, or third sub-image sets are adjacent, i.e., they have no shared edges, and are therefore considered discrete. The segmentation method in Case 2 above can also be understood as pseudo-random discrete segmentation.

[0062] S203: The control device analyzes the K sub-image sets respectively through the AI / ML model to obtain corresponding K analysis results, and fuses the K analysis results to obtain the analysis result of the target image.

[0063] The AI / ML model can be the DNN mentioned above, specifically CNN.

[0064] The analysis result of the target image is an image with the non-uniform area of ​​the nano-coating marked on the glass panel. It can be understood that the non-uniform area of ​​the nano-coating is marked on the target image.

[0065] For example, for the i-th sub-image set, i traverses 1 to K, the control device can randomly combine the sub-images in the i-th sub-image set in pairs to obtain P sub-image groups, and analyze the j-th sub-image group in the P sub-image groups through the AI / ML model to obtain the analysis result of the j-th sub-image group, where j is an integer traversing 1 to P, and the analysis result of the j-th sub-image group is two sub-images with or without marked areas of non-uniform nanocoating; the i-th analysis result in the K analysis results includes the analysis result of the j-th sub-image group.

[0066] Specifically, for the i-th sub-image set, i traverses from 1 to K:

[0067] The control device can randomly combine the sub-images in the i-th sub-image set in pairs to obtain P sub-image groups; the control device can input the j-th sub-image group into the feature extraction layer of the AI / ML model to obtain two feature sequences corresponding to the two sub-images in the j-th sub-image group output by the feature extraction layer of the AI / ML model. The feature extraction layer may include a convolution layer and a pooling layer. The control device analyzes the two feature sequences through the feature processing layer of the AI / ML model to obtain the analysis result of the j-th sub-image group. For example, the control device can calculate the difference between each two feature elements at corresponding positions in the two feature sequences (such as the difference between two feature elements with an index of 0, the difference between two feature elements with an index of 1, the difference between two feature elements with an index of 2, and so on), and set (or replace) the value of each two feature elements in the two feature sequences whose difference is less than a preset threshold to a default value, such as 0, to obtain two corrected feature sequences. It should be understood that since the feature extraction layer performs convolution and pooling processing on the two sub-images in the j-th sub-image group at the same scale, the two feature sequences have the same size, that is, they contain the same number of feature elements, each of which can be a vector and can be expressed as e j(xπ+μ). In this case, the difference can be converted into an absolute value, such as a positive number. The control device can interleave the characteristic elements of the two corrected characteristic sequences according to the energy level to obtain two interleaved characteristic sequences. For example, for each of the two corrected characteristic sequences, if the value of a characteristic element (after being converted) in the corrected characteristic sequence is larger, the energy of the characteristic element is higher. On this basis, the control device interweaves the characteristic elements of the corrected characteristic sequence that belong to the same energy level in the energy level from high to low, to obtain two interleaved characteristic sequences. The default value is the lowest energy level. For example, a modified feature sequence contains 100 feature elements across four energy levels. The interleaved feature sequence is traversed from front to back in the following order: first, the 36 feature elements in the highest energy level #1, sorted in descending order of energy; then, the 22 feature elements in the second-highest energy level #2, sorted in descending order of energy; then, the 20 feature elements in the second-lowest energy level #3, sorted in descending order of energy; and finally, the 22 feature elements in the lowest energy level #4. These 22 feature elements are all default values. This approach removes the randomness and discreteness of the feature sequence, making it orderly for AI / ML to process. Furthermore, sorting the feature sequence from high to low energy improves the processing performance of the AI / ML model, avoids the confusion of redundant features, and thus improves accuracy. Finally, the control device inputs the two interleaved feature sequences into the feature processing layer of the AI / ML model, outputting the analysis results for the jth sub-image group. Thus, the control device splices the K analysis results in the order of discrete segmentation, that is, restores the target image with the annotation, and obtains the analysis result of the target image. For example, one analysis result of the target image can be as follows: Figure 3 As shown in (c) in .

[0068] It should be understood that the number of energy levels can be fixed, such as 4, and the interval of each capacity level can be evenly divided into 4 intervals according to the difference between the characteristic element with maximum energy and the characteristic element with minimum energy (default value) in a modified characteristic sequence, and each interval corresponds to an energy level.

[0069] It should also be understood that since the control device interleaves the characteristic elements of the two corrected characteristic sequences according to the energy level, two interleaved characteristic sequences are obtained, and the two interleaved characteristic sequences are input into the feature processing layer of the AI / ML model, the analysis result of the j-th sub-image group output by the feature processing layer of the AI / ML model is obtained. In the sub-image, the values ​​of the characteristic elements extracted from the area with uniform nanocoating are also relatively stable, while the values ​​of the extracted characteristic elements in the area with non-uniform nanocoating vary greatly due to the variation in the thickness of the nanocoating. In addition, the area with non-uniform nanocoating is usually random, and it is unlikely that two areas with non-uniform nanocoating will have the same shape. Therefore, the above method can directly exclude the areas with uniform nanocoating at the same position in the two sub-images, so that only the areas with non-uniform nanocoating that may exist can be analyzed later, which can effectively reduce the amount of information.

[0070] S204: The control device instructs the coating device to correct the nano-coating in the non-uniform area.

[0071] For example, the control device may inform the coating device of information about the non-uniform area of ​​the coating device, so that the coating device can clean the non-uniform area and re-apply the nano coating.

[0072] In summary, by atomizing the surface of the nanocoating, the atomized water droplets can be distributed on the surface of the nanocoating. At this time, if the thickness of the nanocoating is uneven, the distribution of the atomized water droplets may also be uneven, and may be concentrated or sparse. In view of this feature, the nanocoating on the surface of the glass panel can be photographed to obtain a target image when the surface of the nanocoating is atomized. The control device then discretely divides the target image into K sub-image sets, and then analyzes the K sub-image sets separately through the AI / ML model to obtain K corresponding analysis results, and fuses the K analysis results to obtain the analysis result of the target image. This can reduce the amount of data processed, and processing with a single sub-image as the granularity can also improve processing accuracy, so that it can accurately indicate the area where the nanocoating is unevenly marked on the glass panel. Finally, the control device instructs the coating equipment to correct the nanocoating in the uneven area, thereby achieving low-cost and high-precision nanocoating thickness detection and control.

[0073] Combination of the above Figure 3The method provided by the embodiment of the present application is described in detail. The following describes a glass surface nano-coating thickness control system for executing the method provided by the embodiment of the present application, the system including a control device and a coating device, the control device being configured as follows: the control device acquires a target image, the surface of the glass panel is coated with a nano-coating, and the target image is obtained by photographing the nano-coating on the surface of the glass panel when the surface of the nano-coating is atomized; the control device discretely divides the target image into K sub-image sets, any two sub-image sets in the K sub-image sets contain different sub-images, K is an integer greater than 1, any two sub-images contained in the i-th sub-image set in the K sub-image sets are not adjacent in the target image, and any two sub-images contained in the i-th sub-image set are the same size, i is an integer from 1 to K; the control device analyzes at least two sub-image sets respectively through an AI / ML model to obtain at least two corresponding analysis results, and fuses the at least two analysis results to obtain an analysis result of the target image, the analysis result of the target image indicates an area of ​​non-uniform nano-coating in the glass panel; the control device instructs the coating device to correct the nano-coating in the non-uniform area.

[0074] The details of the system can also refer to the relevant introduction of the above method, which will not be repeated here.

[0075] The following combination Figure 4 The components of the electronic device 500 are described in detail.

[0076] The processor 501 is the control center of the electronic device 500 and can be a single processor or a collective term for multiple processing elements. For example, the processor 501 can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application, such as one or more microprocessors (digital signal processors, DSPs) or one or more field programmable gate arrays (FPGAs).

[0077] Optionally, the processor 501 can execute various functions of the electronic device 500 by running or executing the software program stored in the memory 502 and calling the data stored in the memory 502, as described above. Figure 2 Function in the method shown.

[0078] In a specific implementation, as an embodiment, the processor 501 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0079] In a specific implementation, as an embodiment, the electronic device 500 may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0080] Among them, the memory 502 is used to store the software program for executing the solution of the present application, and the execution is controlled by the processor 501. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0081] Alternatively, the memory 502 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 502 may be integrated with the processor 501 or exist independently and interface circuit ( Figure 4 (not shown) is coupled to the processor 501, which is not specifically limited in this embodiment of the present application.

[0082] The transceiver 503 is used for communicating with other devices. For example, if the multi-beam positioning device is a terminal, the transceiver 503 can be used to communicate with a network device or another terminal.

[0083] Optionally, the transceiver 503 may include a receiver and a transmitter ( Figure 4 (not shown separately in the figure). The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0084] Optionally, the transceiver 503 may be integrated with the processor 501 or may exist independently and communicate with the electronic device 500 through the interface circuit ( Figure 4 (not shown) is coupled to the processor 501, which is not specifically limited in this embodiment of the present application.

[0085] It should be noted that Figure 4 The structure of the electronic device 500 shown in the figure does not constitute a limitation on the device. The actual electronic device 500 may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0086] In addition, the technical effects based on the electronic device 500 can refer to the technical effects of the method in the above method embodiment, which will not be repeated here.

[0087] It should be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0088] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0089] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (such as infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (such as a floppy disk, hard disk, tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0090] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0091] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0092] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0093] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some feature fields can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0096] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0097] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0098] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program codes.

[0099] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for controlling the thickness of a glass surface nano-coating, characterized in that: The method is applied to a control device, and the method includes: The control device acquires a target image, wherein the surface of the glass panel is coated with a nano-coating, and the target image is obtained by photographing the nano-coating on the surface of the glass panel while the surface of the nano-coating is atomized; The control device discretely divides the target image into K sub-image sets, wherein any two sub-image sets in the K sub-image sets contain different sub-images, K is an integer greater than 1, any two sub-images contained in the i-th sub-image set in the K sub-image sets are not adjacent in position in the target image, and any two sub-images contained in the i-th sub-image set are of the same size, and i is an integer ranging from 1 to K; The control device analyzes the K sub-image sets respectively using an AI / ML model to obtain corresponding K analysis results, and fuses the K analysis results to obtain an analysis result of the target image, where the analysis result of the target image is an image marking an area of ​​the nanocoating non-uniformity on the glass panel; The control device instructs the coating device to correct the nano-coating in the non-uniform area; The control device analyzes the K sub-image sets respectively through the AI / ML model to obtain corresponding K analysis results, and fuses the K analysis results to obtain an analysis result of the target image, including: For the i-th sub-image set, where i traverses from 1 to K, the control device randomly combines the sub-images in the i-th sub-image set in pairs to obtain P sub-image groups, and analyzes the j-th sub-image group in the P sub-image groups using the AI / ML model to obtain an analysis result for the j-th sub-image group, where j is an integer traversing from 1 to P, and the analysis result of the j-th sub-image group is two sub-images with or without an area marked with a non-uniform nanocoating; the i-th analysis result in the K analysis results includes the analysis result of the j-th sub-image group; The control device splices the K analysis results in the order of the discrete segmentation to obtain an analysis result of the target image; The control device randomly combines sub-images in the i-th sub-image set in pairs to obtain P sub-image groups, and analyzes the j-th sub-image group in the P sub-image groups using the AI / ML model to obtain an analysis result for the j-th sub-image group, including: For the i-th sub-image set, i traverses from 1 to K: The control device randomly combines sub-images in the i-th sub-image set in pairs to obtain the P sub-image groups; the control device inputs the j-th sub-image group into the feature extraction layer of the AI / ML model, and obtains two feature sequences output by the feature extraction layer of the AI / ML model, which correspond one to one to two sub-images in the j-th sub-image group; the control device analyzes the two feature sequences through the feature processing layer of the AI / ML model to obtain an analysis result for the j-th sub-image group; The control device analyzes the two feature sequences through the feature processing layer of the AI / ML model to obtain an analysis result of the j-th sub-image group, including: The control device calculates the difference between each two characteristic elements at corresponding positions in the two characteristic sequences, and sets the value of each two characteristic elements in the two characteristic sequences whose difference is less than a preset threshold as a default value, thereby obtaining two modified characteristic sequences; The control device interleaves the characteristic elements of the two corrected feature sequences according to the energy level to obtain two interleaved feature sequences, and inputs the two interleaved feature sequences into the feature processing layer of the AI / ML model to obtain the analysis result of the j-th sub-image group output by the feature processing layer of the AI / ML model.

2. The method according to claim 1, characterized in that The control device discretely divides the target image into K sub-image sets, including: The control device discretely divides the target image into two sub-image sets according to the size of the target image being smaller than a threshold size.

3. The method according to claim 2, characterized in that The control device discretely divides the target image into two sub-image sets according to the size of the target image being smaller than a threshold size, including: The control device divides the target image into M1*N1 grids by gridding processing according to the size of the target image being smaller than the threshold size, where M1 is the number of grids contained in a row of the target image, and N1 is the number of grids contained in a column of the target image, and M1 and N1 are integers greater than or equal to 2; The control device determines, in the M1*N1 grids, a grid with an odd column index in each row with an odd row index as a sub-image belonging to the first sub-image set, and determines, in the M1*N1 grids, a grid with an even column index in each row with an even row index as a sub-image belonging to the first sub-image set; The control device determines, in the M1*N1 grids, a grid with an even column index in each row with an odd row index as a sub-image in the second sub-image set, and determines, in the M1*N1 grids, a grid with an odd column index in each row with an even row index as a sub-image in the second sub-image set; The two sub-image sets include the first sub-image set and the second sub-image set.

4. The method according to claim 1, wherein The control device discretely divides the target image into K sub-image sets, including: The control device discretely divides the target image into three sub-image sets according to the size of the target image being greater than or equal to a threshold size.

5. The method according to claim 4, characterized in that The control device discretely divides the target image into three sub-image sets according to a size of the target image being greater than or equal to a threshold size, including: The control device divides the target image into M2*N2 grids through gridding processing according to the size of the target image being greater than or equal to the threshold size, where M2 is the number of grids contained in a row of the target image, N2 is the number of grids contained in a column of the target image, and M2 and N2 are integers greater than or equal to 3; The control device determines, as a sub-image belonging to the first sub-image set, a grid with a starting column index of 1 and a step length of column index of 3 in a row of grids with a row index of 1 in the M2*N2 grids, a grid with a starting column index of 2 and a step length of column index of 3 in a row of grids with a row index of 2 in the M2*N2 grids, a grid with a starting column index of 3 and a step length of column index of 3 in a row of grids with a row index of 3 in the M2*N2 grids, and a grid with a starting column index of 1 and a step length of column index of 3 in a row of grids with a row index of 4 in the M2*N2 grids, and so on, until traversing to a row of grids with a row index of N2 in the M2*N2 grids; The control device determines, as a sub-image belonging to the second sub-image set, a grid with a starting column index of 2 and a step length of column index of 3 in a row of grids with a row index of 1 in the M2*N2 grids, a grid with a starting column index of 3 and a step length of column index of 3 in a row of grids with a row index of 2 in the M2*N2 grids, a grid with a starting column index of 4 and a step length of column index of 3 in a row of grids with a row index of 3 in the M2*N2 grids, and a grid with a starting column index of 2 and a step length of column index of 3 in a row of grids with a row index of 4 in the M2*N2 grids, and so on, until traversing to a row of grids with a row index of N2 in the M2*N2 grids; The control device determines, as a sub-image belonging to the second sub-image set, a grid with a starting column index of 3 and a step length of column index of 3 in a row of grids with a row index of 1 in the M2*N2 grids, a grid with a starting column index of 4 and a step length of column index of 3 in a row of grids with a row index of 2 in the M2*N2 grids, a grid with a starting column index of 5 and a step length of column index of 3 in a row of grids with a row index of 3 in the M2*N2 grids, and a grid with a starting column index of 3 and a step length of column index of 3 in a row of grids with a row index of 4 in the M2*N2 grids, and so on, until traversing to a row of grids with a row index of N2 in the M2*N2 grids; The three sub-image sets include the first sub-image set, the second sub-image set and the third sub-image set.

6. The method according to claim 1, characterized in that The control device interleaves the characteristic elements of the two modified characteristic sequences according to energy levels to obtain two interleaved characteristic sequences, including: For each of the two corrected feature sequences, if the value of a feature element in the corrected feature sequence is larger, the energy of the feature element is higher. On this basis, the control device interweaves the feature elements in the corrected feature sequence whose energies belong to the same energy level in the corrected feature sequence in order from high to low energy levels to obtain the two interleaved feature sequences.

7. A glass surface nano coating thickness control system, characterized in that: The system includes a control device and a coating device, wherein the control device is configured to: The control device acquires a target image, wherein the surface of the glass panel is coated with a nano-coating, and the target image is obtained by photographing the nano-coating on the surface of the glass panel while the surface of the nano-coating is atomized; The control device discretely divides the target image into K sub-image sets, wherein any two sub-image sets in the K sub-image sets contain different sub-images, K is an integer greater than 1, any two sub-images contained in the i-th sub-image set in the K sub-image sets are not adjacent in position in the target image, and any two sub-images contained in the i-th sub-image set are of the same size, and i is an integer ranging from 1 to K; The control device analyzes the at least two sub-image sets respectively using an AI / ML model to obtain at least two corresponding analysis results, and fuses the at least two analysis results to obtain an analysis result of the target image, wherein the analysis result of the target image indicates an area of ​​the nanocoating on the glass panel that is non-uniform; The control device instructs the coating device to correct the nano-coating in the non-uniform area; The control device analyzes the K sub-image sets respectively through the AI / ML model to obtain corresponding K analysis results, and fuses the K analysis results to obtain an analysis result of the target image, including: For the i-th sub-image set, where i traverses from 1 to K, the control device randomly combines the sub-images in the i-th sub-image set in pairs to obtain P sub-image groups, and analyzes the j-th sub-image group in the P sub-image groups using the AI / ML model to obtain an analysis result for the j-th sub-image group, where j is an integer traversing from 1 to P, and the analysis result of the j-th sub-image group is two sub-images with or without an area marked with a non-uniform nanocoating; the i-th analysis result in the K analysis results includes the analysis result of the j-th sub-image group; The control device splices the K analysis results in the order of the discrete segmentation to obtain an analysis result of the target image; The control device randomly combines sub-images in the i-th sub-image set in pairs to obtain P sub-image groups, and analyzes the j-th sub-image group in the P sub-image groups using the AI / ML model to obtain an analysis result for the j-th sub-image group, including: For the i-th sub-image set, i traverses from 1 to K: The control device randomly combines sub-images in the i-th sub-image set in pairs to obtain the P sub-image groups; the control device inputs the j-th sub-image group into the feature extraction layer of the AI / ML model, and obtains two feature sequences output by the feature extraction layer of the AI / ML model, which correspond one to one to two sub-images in the j-th sub-image group; the control device analyzes the two feature sequences through the feature processing layer of the AI / ML model to obtain an analysis result for the j-th sub-image group; The control device analyzes the two feature sequences through the feature processing layer of the AI / ML model to obtain an analysis result of the j-th sub-image group, including: The control device calculates the difference between each two characteristic elements at corresponding positions in the two characteristic sequences, and sets the value of each two characteristic elements in the two characteristic sequences whose difference is less than a preset threshold as a default value, thereby obtaining two modified characteristic sequences; The control device interleaves the characteristic elements of the two corrected feature sequences according to the energy level to obtain two interleaved feature sequences, and inputs the two interleaved feature sequences into the feature processing layer of the AI / ML model to obtain the analysis result of the j-th sub-image group output by the feature processing layer of the AI / ML model.

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