An adaptive adjustment system and method for coating conductive silver paste on touch screen

Abstract

The present invention discloses an adaptive adjustment system and method for coating conductive silver paste on a touch screen. The system collects and analyzes images corresponding to the touch screen of each size, including analysis of the boundary and thickness distribution of the coating layer, and obtains an ideal coating score based on the analysis data, which is used as the first basis for coating evaluation. Then, the actual physical parameters of the coating equipment, physical properties such as viscosity and surface tension of the conductive silver paste are obtained in real time through sensors on the coating equipment, and then input into a second coating score model to obtain an actual coating score, which is used as the first basis for coating evaluation. An absolute difference analysis is performed on the two scores to determine whether the touch screen has uneven coating. When uneven coating is detected, the speed and spray volume of the nozzle are adaptively adjusted based on the real-time monitored data to make the resistance distribution of the touch screen uniform, thereby ensuring the response performance and display effect of the touch screen.

Description

Technical Field

[0001] The invention belongs to the field of intelligent processing of touch screens, and in particular relates to an adaptive adjustment system and method for coating conductive silver paste on a touch screen. Background Art

[0002] With the development of science and technology, people's demand for wearable devices, smart homes and other products has increased rapidly. As an important part of wearable devices, the development and application of flexible touch panels have also attracted extensive attention from researchers. The materials for preparing flexible touch panels mainly include nano silver wire conductive film, metal grid conductive film, nano carbon tube and graphene conductive film. Among them, the preparation methods of nano carbon tube and graphene conductive film are still in the research and development stage, and the industrial mass production technology has not yet been perfected. Therefore, nano silver wire conductive film and metal grid conductive film are currently the most commonly used materials for preparing flexible touch panels. Metal grid conductive film has the advantages of low resistance, fast response speed and good flexibility, so it is particularly suitable for making medium and large-sized flexible touch panels.

[0003] At present, the conductive film coating method of the touch screen includes metal film photolithography, which first forms a metal film on a transparent substrate through a sputtering process or metal foil lamination, and then obtains a fine metal grid through steps such as photoresist coating, exposure, development, etching and film stripping; the above coating method often requires coating with conductive silver paste, then laser etching, and finally forming a conductive path. In the stage of coating the conductive silver paste, it is often necessary to use a nozzle to spray the silver paste. Since the touch screen sizes of different electronic devices are different, the fixed nozzle speed and spray volume will encounter the problem of coating uniformity. If no adaptive adjustment is made, the conductive silver paste coating will be uneven, affecting the resistance distribution of the touch screen, so the coating effect of the conductive silver paste directly affects the performance of the metal grid conductive film. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present application provides an adaptive adjustment system and method for coating conductive silver paste for a touch screen.

[0005] In a first aspect, the present application proposes an adaptive adjustment system for coating conductive silver paste of a touch screen, comprising an ideal coating image collection module, an image preprocessing module, an ideal coating score acquisition module, an actual coating score acquisition module, a score analysis module and a coating adjustment module;

[0006] The ideal coating image collection module is used to collect the ideal coating image corresponding to each size of touch screen during the conductive silver paste coating process;

[0007] The image preprocessing module is used to perform edge detection processing and grayscale analysis on the ideal coating image to obtain ideal coating image data;

[0008] The ideal coating score acquisition module is used to input the ideal coating image data into a pre-constructed first coating score model to obtain an ideal coating score;

[0009] The actual coating score acquisition module is used to collect actual coating data through a sensor and input the data into a pre-built second coating score model to obtain an actual coating score;

[0010] The score analysis module is used to combine and analyze the ideal coating score and the actual coating score. If the analysis result does not exceed the preset deviation threshold, it means that the coating is qualified. If the analysis result exceeds the preset deviation threshold, it means that the touch screen is unevenly coated.

[0011] The coating adjustment module is used to adaptively adjust the coating process parameters set for the coating equipment when uneven coating is detected.

[0012] In some embodiments, the image preprocessing module includes an edge detection unit and a grayscale analysis unit;

[0013] The edge detection unit is used to perform edge detection processing on the ideal coating image to obtain a boundary detection result;

[0014] The grayscale analysis unit is used to perform grayscale analysis on the ideal coating image, determine the coating layer thickness distribution, and obtain a grayscale analysis result.

[0015] In some embodiments, the ideal coating score acquisition module includes a first coating score model definition unit and an ideal coating score unit;

[0016] The first coating score model definition unit is used to define a first coating score model:

[0017] The ideal coating score unit is used to input the boundary detection result and the grayscale analysis result into the first coating score model to output the ideal coating score.

[0018] In some embodiments, the actual coating score acquisition module includes a preliminary evaluation model building unit, an actual coating parameter acquisition unit, a second coating score model building unit, and an actual coating score unit;

[0019] The preliminary evaluation model building unit is used to establish a preliminary evaluation model according to the physical properties of the coating and the equipment parameters:

[0020] The actual coating parameter acquisition unit is used to collect coating data in the actual coating process through sensors on the coating equipment, and the coating data includes viscosity, surface tension, nozzle speed and spray volume:

[0021] The second coating score model building unit is used to filter out data related to the coating uniformity score in the coating data through the Pearson correlation coefficient, and to construct the second coating score model through the multivariate linear regression function after preprocessing the filtered data:

[0022] The actual coating score unit is used to input the coating data into a pre-built coating uniformity actual score model to output an actual coating score.

[0023] In some embodiments, a second model verification unit is also included, and the second model verification unit is used to verify the accuracy of the second coating score model by calculating the determination coefficient, wherein, when the calculated determination coefficient is greater than 0.6 and less than or equal to 1, it means that the score predicted by the second coating score model is highly similar to the actual coating score, and there is no need to adjust the model; when the calculated determination coefficient is greater than 0 and less than or equal to 0.6, it means that the score predicted by the second coating score model is low similar to the actual coating score, and new physical properties need to be selected to adjust the model.

[0024] In some embodiments, the score analysis module includes a difference calculation unit and a comparison unit;

[0025] The difference calculation unit is used to perform deviation calculation on the ideal coating score and the actual coating score to obtain an absolute difference between the scores;

[0026] The comparison unit is used to compare the absolute difference with a preset deviation threshold. If the absolute difference does not exceed the preset deviation threshold, it means that the coating is qualified. If the absolute difference exceeds the preset deviation threshold, it means that the touch screen is unevenly coated.

[0027] In some embodiments, the coating adjustment module includes an original coating parameter acquisition unit, a parameter adjustment unit and a coating adjustment unit:

[0028] The original coating parameter acquisition unit is used to acquire the original nozzle speed and the original spray volume;

[0029] The parameter adjustment unit is used to determine the nozzle speed adjustment coefficient and the spray volume adjustment coefficient according to the adjustment coefficient function;

[0030] The coating adjustment unit is used to adjust the original nozzle speed and the original spray volume respectively according to the nozzle speed adjustment coefficient and the spray volume adjustment coefficient to obtain new coating process parameters.

[0031] In a second aspect, the present application proposes an adaptive adjustment method for coating a conductive silver paste for a touch screen, comprising the following steps:

[0032] Collect the ideal coating images corresponding to each size of touch screen during the conductive silver paste coating process;

[0033] Performing edge detection processing and grayscale analysis on the ideal coating image to obtain ideal coating image data;

[0034] Inputting the ideal coating image data into a pre-constructed first coating score model to obtain an ideal coating score;

[0035] Collecting actual coating data through a sensor and inputting the data into a pre-built second coating score model to obtain an actual coating score;

[0036] The ideal coating score and the actual coating score are combined and analyzed. If the analysis result does not exceed the preset deviation threshold, it means that the coating is qualified. If the analysis result exceeds the preset deviation threshold, it means that the touch screen is unevenly coated.

[0037] When uneven coating is detected, the coating process parameters set for the coating equipment are adaptively adjusted.

[0038] In a third aspect, the present application proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0039] In a fourth aspect, the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0040] Beneficial effects of the present invention:

[0041] By collecting and analyzing the images corresponding to the touch screen of each size, including the analysis of the boundary and thickness distribution of the coating layer, the ideal coating score is obtained according to the analysis data, which is used as the first basis for coating evaluation. Then, the actual physical parameters of the coating equipment, the viscosity of the conductive silver paste, the surface tension and other physical properties are obtained in real time through the sensors on the coating equipment, and then input into the second coating score model to obtain the actual coating score. This is used as the first basis for coating evaluation. The two types of scores are analyzed by absolute difference to determine whether there is uneven coating on the touch screen. When uneven coating is detected, the speed and spray volume of the nozzle are adaptively adjusted according to the real-time monitored data to make the resistance distribution of the touch screen uniform, thereby ensuring the response performance and display effect of the touch screen. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a system principle diagram of the present invention.

[0043] Figure 2It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0044] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein; on the contrary, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0045] In the first aspect, an adaptive adjustment system for coating conductive silver paste of a touch screen is characterized by comprising an ideal coating image collection module, an image preprocessing module, an ideal coating score acquisition module, an actual coating score acquisition module, a score analysis module and a coating adjustment module;

[0046] The ideal coating image collection module is used to collect the ideal coating image corresponding to each size of touch screen during the conductive silver paste coating process:

[0047] The image preprocessing module is used to perform edge detection processing and grayscale analysis on the ideal coating image to obtain ideal coating image data;

[0048] The image preprocessing module includes an edge detection unit and a grayscale analysis unit;

[0049] The edge detection unit is used to perform edge detection processing on the ideal coating image to obtain a boundary detection result:

[0050]

[0051] in, represents the boundary detection result, and Respectively represent the standard coating image in and Directional gradient;

[0052] The grayscale analysis unit is used to perform grayscale analysis on the ideal coating image, determine the coating layer thickness distribution, and obtain a grayscale analysis result:

[0053] Grayscale analysis is performed on the standard coating image data to determine the coating layer thickness distribution, wherein the coating layer thickness is linearly related to the grayscale value, which is expressed as:

[0054]

[0055] in, Indicates location The coating thickness, Represents the gray value at the same position, and Represents the linear coefficient.

[0056] Among them, monitoring probes are used to collect images of touch screens of different sizes at different angles and lighting conditions, and edge detection is used to determine the boundary of the coating layer. Common edge detection algorithms include Canny, Sobel, Prewitt, etc. In this embodiment, the Canny algorithm is used for edge detection. The Canny edge detection algorithm is a widely used edge detection algorithm that combines Gaussian filtering and edge detection technology. It can accurately detect edges in the image while reducing noise, and then determine the thickness distribution of the coating layer through grayscale analysis. Usually, the thickness of the coating layer is related to the grayscale value. Assume that in the image, the grayscale value of the boundary of the coating layer changes sharply, and the edge is detected by the Canny algorithm. At the same time, the average grayscale value of areas with different thicknesses is measured and obtained through calibration experiments. and , the coating thickness is calculated by obtaining the coefficient, and finally the edge detection result and the grayscale analysis result are combined to obtain the boundary and thickness distribution data of the coating layer.

[0057] The ideal coating score acquisition module is used to input the ideal coating image data into a pre-constructed first coating score model to obtain an ideal coating score;

[0058] In some embodiments, the ideal coating score acquisition module includes a first coating score model definition unit and an ideal coating score unit;

[0059] The first coating score model definition unit is used to define a first coating score model, and the first coating score model is a coating uniformity standard score model, which is expressed as:

[0060]

[0061] in, A standard score indicating coating uniformity, Represents the number of all pixels in the image. Indicates pixels, Indicates the average value of the coating thickness, represents the weight coefficient of boundary detection, Represents the weight coefficient of grayscale analysis;

[0062] The boundary detection result and the coating layer thickness distribution are input into the coating uniformity standard score model to output a standard uniform coating score.

[0063] Among them, the weight coefficient is used to adjust the importance of the boundary and thickness in the score. The weight coefficient can be determined by expert experience or machine learning algorithm. Assuming that the discontinuity of the boundary has a greater impact on the uniformity through actual coating results, the weight coefficient of the boundary detection is set. ; The influence of thickness change on uniformity is relatively small, so the weight coefficient of grayscale analysis is set , according to the above model, the calculated standard score It can be used to evaluate coating uniformity. Through step S100, automatic evaluation of coating uniformity can be achieved, and different coating scenarios can be adapted by adjusting weight coefficients and algorithm parameters.

[0064] The actual coating score acquisition module is used to collect actual coating data through a sensor and input the data into a pre-built second coating score model to obtain an actual coating score;

[0065] In some embodiments, the actual coating score acquisition module includes a preliminary evaluation model building unit, an actual coating parameter acquisition unit, a second coating score model and an actual coating score unit;

[0066] The preliminary evaluation model building unit is used to establish a preliminary evaluation model according to the physical properties of the coating and the equipment parameters:

[0067] Among them, according to the physical properties of the coating and device parameters Build a preliminary evaluation model:

[0068]

[0069] in, Indicates the actual coating score, represents the multiple linear regression function, Represents a vector of physical properties, including viscosity and surface tension , Represents the device parameter vector, including the nozzle speed and spray volume ;

[0070] The actual coating parameter acquisition unit is used to collect coating data in the actual coating process through sensors on the coating equipment, and the coating data includes viscosity, surface tension, nozzle speed and spray volume:

[0071] The coating data includes viscosity , surface tension , Nozzle speed and spray volume :

[0072] The second coating score model building unit is used to filter out data related to the coating uniformity score in the coating data through the Pearson correlation coefficient, and to construct the second coating score model through the multivariate linear regression function after preprocessing the filtered data:

[0073] Among them, the Pearson correlation coefficient is used to screen out the data related to the coating uniformity score in the coating data;

[0074]

[0075] in, Indicates physical properties, Representation and The correlation coefficient, represents the number of data points, Indicates data points, represents the average value of a physical property, represents the average of the actual coating scores;

[0076] After preprocessing the screened data, a second coating score model is constructed through the multivariate linear regression function. In this embodiment, the second coating score model is called the coating uniformity actual score model:

[0077]

[0078] in, represents the intercept, represents the regression coefficient calculated by the least squares method, represents the error term;

[0079] The coating data is input into the pre-built coating uniformity actual score model to obtain the actual coating score The score value of

[0080] Among them, it is necessary to collect enough data points, each of which contains physical characteristics and device parameters The measured value and the corresponding actual coating score Before building the model, the physical properties and equipment parameters that have the greatest impact on coating uniformity are selected through the Pearson correlation coefficient. These parameters will be included in the vector and In the process, since multivariate linear regression is sensitive to the scale of features, it is necessary to standardize the selected data to a similar scale. If there are missing values ​​in the data set, it is necessary to decide whether to delete these samples, fill in the missing values, or use some kind of interpolation to ensure data integrity. After the preprocessing is completed, the regression coefficient estimated by the least squares method is combined to build the actual coating uniformity score model. Finally, the selected coating data is detected by the sensor and input into the coating uniformity actual score model to obtain the actual coating score. The score value.

[0081] In some embodiments, a second model verification unit is further included, and the second model verification unit is used to verify the accuracy of the second coating score model by calculating a determination coefficient, wherein when the calculated determination coefficient is greater than 0.6 and less than or equal to 1, it indicates that the score predicted by the second coating score model is highly similar to the actual coating score, and no adjustment to the model is required; when the calculated determination coefficient is greater than 0 and less than or equal to 0.6, it indicates that the score predicted by the second coating score model is less similar to the actual coating score, and a new physical property needs to be selected to adjust the model. The calculation of the determination coefficient is specifically as follows:

[0082]

[0083] in, represents the coating uniformity score predicted by the model, Indicates the coefficient of determination. When the coefficient of determination is 0.6< ≤1, it means that the score predicted by the coating uniformity actual score model is highly similar to the actual coating score, and there is no need to adjust the model. When ≤0.6, it means that the score predicted by the coating uniformity actual score model is low in similarity to the actual coating score, and new physical properties need to be selected to adjust the model.

[0084] The score analysis module is used to combine and analyze the ideal coating score and the actual coating score. If the analysis result does not exceed the preset deviation threshold, it means that the coating is qualified. If the analysis result exceeds the preset deviation threshold, it means that the touch screen is unevenly coated.

[0085] In some embodiments, the score analysis module includes a difference calculation unit and a comparison unit;

[0086] The difference calculation unit is used to perform deviation calculation on the ideal coating score and the actual coating score to obtain an absolute difference between the scores:

[0087] The deviation between the standard uniform coating score and the actual coating score is calculated as follows:

[0088]

[0089] in, It represents the absolute difference between the actual coating score and the standard coating score. , then the actual coating is considered to be uneven. Indicates the preset deviation threshold.

[0090] The comparison unit is used to compare the absolute difference with a preset deviation threshold. If the absolute difference does not exceed the preset deviation threshold, it means that the coating is qualified. If the absolute difference exceeds the preset deviation threshold, it means that the touch screen is unevenly coated.

[0091] S400: When uneven coating is detected, coating process parameters are adjusted in real time according to the physical properties of the conductive silver paste.

[0092] In some embodiments, the coating adjustment module is used to adaptively adjust the coating process parameters set by the coating equipment when uneven coating is detected:

[0093] The coating adjustment module includes an original coating parameter acquisition unit, a parameter adjustment unit and a coating adjustment unit:

[0094] The original coating parameter acquisition unit is used to obtain the original nozzle speed and the original spray volume:

[0095] Among them, the nozzle speed adjustment formula and the spray volume adjustment formula are:

[0096]

[0097] in, and Indicates the new nozzle speed and spray volume. and Indicates the original speed and spray volume, and Indicates nozzle speed adjustment coefficient and spray volume adjustment coefficient;

[0098] The parameter adjustment unit is used to determine the nozzle speed adjustment coefficient and the spray volume adjustment coefficient according to the adjustment coefficient function, specifically:

[0099]

[0100] in, represents the score weight coefficient, Represents the weight coefficient of nozzle speed change rate, Indicates the weight coefficient of the injection volume change rate, represents the difference between the standard uniform coating score and the actual coating score, Indicates the rate of change of deviation with nozzle speed, It indicates the rate of change of deviation with spray volume, and is calculated through real-time data detected by the sensor. , and , thus obtaining the nozzle speed adjustment coefficient and the spray volume adjustment coefficient;

[0101] The coating adjustment unit is used to adjust the original nozzle speed and the original spray volume respectively according to the nozzle speed adjustment coefficient and the spray volume adjustment coefficient to obtain new coating process parameters.

[0102] 8. An adaptability adjustment method for coating conductive silver paste for a touch screen, characterized in that it comprises the following steps;

[0103] S100: collecting the ideal coating image corresponding to each size of touch screen during the conductive silver paste coating process;

[0104] S200: performing edge detection processing and grayscale analysis on the ideal coating image to obtain ideal coating image data;

[0105] S300: inputting the ideal coating image data into a pre-constructed first coating score model to obtain an ideal coating score;

[0106] S400: Collecting actual coating data through a sensor and inputting the data into a pre-built second coating score model to obtain an actual coating score;

[0107] S500: Analyze the ideal coating score and the actual coating score together. If the analysis result does not exceed the preset deviation threshold, it means that the coating is qualified. If the analysis result exceeds the preset deviation threshold, it means that the touch screen is unevenly coated.

[0108] S600: When uneven coating is detected, adaptively adjust the coating process parameters set for the coating equipment.

[0109] In a third aspect, the present application proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0110] In a fourth aspect, the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0111] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0112] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] Those of ordinary skill 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 to be beyond the scope of this disclosure.

[0114] In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus / computer equipment and methods can be implemented in other ways. For example, the apparatus / computer equipment embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of the apparatus or unit, which may be electrical, mechanical or other forms.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, each functional unit in each embodiment of the present disclosure 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0117] If the integrated module / unit 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 present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0118] The above are only preferred implementations of the present invention. It should be pointed out that a number of modifications and improved technical solutions made by those skilled in the art without departing from the technical solution should also be deemed to fall within the scope of protection required by the claims.

Claims

1. An adaptive adjustment system for coating conductive silver paste for touch screens, characterized in that: It includes an ideal coating image collection module, an image preprocessing module, an ideal coating score acquisition module, an actual coating score acquisition module, a score analysis module and a coating adjustment module; The ideal coating image collection module is used to collect the ideal coating image corresponding to each size of touch screen during the conductive silver paste coating process; The image preprocessing module is used to perform edge detection processing and grayscale analysis on the ideal coating image to obtain ideal coating image data; The ideal coating score acquisition module is used to input the ideal coating image data into a pre-constructed first coating score model to obtain an ideal coating score; The actual coating score acquisition module is used to collect actual coating data through a sensor and input the data into a pre-built second coating score model to obtain an actual coating score; The score analysis module is used to combine and analyze the ideal coating score and the actual coating score. If the analysis result does not exceed the preset deviation threshold, it means that the coating is qualified. If the analysis result exceeds the preset deviation threshold, it means that the touch screen is unevenly coated. The coating adjustment module is used to adaptively adjust the coating process parameters set for the coating equipment when uneven coating is detected.

2. The system according to claim 1, characterized in that: The image preprocessing module includes an edge detection unit and a grayscale analysis unit; The edge detection unit is used to perform edge detection processing on the ideal coating image to obtain a boundary detection result; The grayscale analysis unit is used to perform grayscale analysis on the ideal coating image, determine the coating layer thickness distribution, and obtain a grayscale analysis result.

3. The system according to claim 1, characterized in that: The ideal coating score acquisition module includes a first coating score model definition unit and an ideal coating score unit; The first coating score model definition unit is used to define a first coating score model: The ideal coating score unit is used to input the boundary detection result and the grayscale analysis result into the first coating score model to output the ideal coating score.

4. The system according to claim 3, characterized in that: The actual coating score acquisition module includes a preliminary evaluation model construction unit, an actual coating parameter acquisition unit, a second coating score model construction unit and an actual coating score unit; The preliminary evaluation model building unit is used to establish a preliminary evaluation model according to the physical properties of the coating and the equipment parameters: The actual coating parameter acquisition unit is used to collect coating data in the actual coating process through sensors on the coating equipment, and the coating data includes viscosity, surface tension, nozzle speed and spray volume: The second coating score model building unit is used to filter out data related to the coating uniformity score in the coating data through the Pearson correlation coefficient, and to construct the second coating score model through a multivariate linear regression function after preprocessing the filtered data: The actual coating score unit is used to input the coating data into a pre-built coating uniformity actual score model to output an actual coating score.

5. The system according to claim 4, characterized in that: It also includes a second model verification unit, which is used to verify the accuracy of the second coating score model by calculating the determination coefficient, wherein when the calculated determination coefficient is greater than 0.6 and less than or equal to 1, it means that the score predicted by the second coating score model is highly similar to the actual coating score, and there is no need to adjust the model; when the calculated determination coefficient is greater than 0 and less than or equal to 0.6, it means that the score predicted by the second coating score model is low similar to the actual coating score, and new physical properties need to be selected to adjust the model.

6. The system according to claim 5, characterized in that: The score analysis module includes a difference calculation unit and a comparison unit; The difference calculation unit is used to perform deviation calculation on the ideal coating score and the actual coating score to obtain an absolute difference between the scores; The comparison unit is used to compare the absolute difference with a preset deviation threshold. If the absolute difference does not exceed the preset deviation threshold, it means that the coating is qualified. If the absolute difference exceeds the preset deviation threshold, it means that the touch screen is unevenly coated.

7. The system according to claim 6, characterized in that: The coating adjustment module includes an original coating parameter acquisition unit, a parameter adjustment unit and a coating adjustment unit: The original coating parameter acquisition unit is used to acquire the original nozzle speed and the original spray volume; The parameter adjustment unit is used to determine the nozzle speed adjustment coefficient and the spray volume adjustment coefficient according to the adjustment coefficient function; The coating adjustment unit is used to adjust the original nozzle speed and the original spray volume respectively according to the nozzle speed adjustment coefficient and the spray volume adjustment coefficient to obtain new coating process parameters.

8. An adaptive adjustment method for coating conductive silver paste for touch screens, characterized in that: The steps include: Collect the ideal coating images corresponding to each size of touch screen during the conductive silver paste coating process; Performing edge detection processing and grayscale analysis on the ideal coating image to obtain ideal coating image data; Inputting the ideal coating image data into a pre-constructed first coating score model to obtain an ideal coating score; Collecting actual coating data through a sensor and inputting the data into a pre-built second coating score model to obtain an actual coating score; The ideal coating score and the actual coating score are combined and analyzed. If the analysis result does not exceed the preset deviation threshold, it means that the coating is qualified. If the analysis result exceeds the preset deviation threshold, it means that the touch screen is unevenly coated. When uneven coating is detected, the coating process parameters set for the coating equipment are adaptively adjusted.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claim 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 8 are implemented.

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