Film spraying control optimization method and device

Abstract

The invention provides a thin film spraying control optimization method and device, and relates to the technical field of thin film spraying, and the method comprises the steps: obtaining spraying basic parameters and spraying control parameters of a target spraying gun, and obtaining to-be-sprayed solution parameters of a to-be-sprayed solution; the spraying basic parameters and the parameters of the solution to be sprayed are input into a spray gun-substrate distance comparison table for matching, and a first spraying distance is obtained; the first spraying distance is used as a constraint, the spraying control parameters are optimized, the optimal spraying control parameters are obtained, and the optimal spraying control parameters are the spraying control parameters with the highest coating quality coefficient; and according to the first spraying distance and the optimal spraying control parameters, spraying operation of the to-be-sprayed solution is conducted through the target spraying gun. Based on the scheme provided by the invention, the uniformity and stability of film spraying can be improved, so that the photoelectric conversion efficiency of the solar cell is improved, and the technical problems of relatively low uniformity and stability of film spraying in the prior art can be solved.

Description

Technical Field

[0001] The present application relates to the field of thin film spraying technology, and in particular to a thin film spraying control optimization method and device. Background Art

[0002] With the rapid growth of global energy demand, increasingly serious supply and demand and environmental problems have become bottlenecks restricting economic and social development. The development of clean and renewable new energy has become an urgent need for the long-term sustainable development of society.

[0003] However, the traditional spraying process of silicon oxide thin films used in solar cells cannot guarantee the uniformity and stability of the thin film spraying, thereby affecting the photoelectric conversion efficiency. Summary of the Invention

[0004] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the first purpose of this application is to propose a thin film spraying control optimization method to solve the technical problems of low uniformity and stability of thin film spraying in the prior art.

[0006] The second purpose of this application is to provide a thin film spraying control optimization device.

[0007] The third objective of this application is to provide an electronic device.

[0008] The fourth object of this application is to provide a computer-readable storage medium.

[0009] To achieve the above objectives, the first embodiment of the present application proposes a thin film spraying control optimization method, comprising:

[0010] Obtaining basic spraying parameters and spraying control parameters of a target spray gun, and obtaining parameters of a solution to be sprayed;

[0011] Inputting the basic spraying parameters and the parameters of the solution to be sprayed into a spray gun-substrate distance comparison table for matching to obtain a first spraying distance;

[0012] Taking the first spraying distance as a constraint, optimizing the spraying control parameters to obtain optimal spraying control parameters, wherein the optimal spraying control parameters are spraying control parameters with the highest coating quality coefficient;

[0013] Based on the first spraying distance and the optimal spraying control parameter, a spraying operation of the solution to be sprayed is performed through the target spray gun.

[0014] Optionally, obtaining parameters of the solution to be sprayed includes:

[0015] Acquiring a plurality of sample spray solution parameters and a plurality of sample spray quality coefficients, wherein the sample spray solution parameters and the sample spray quality coefficients have a corresponding relationship;

[0016] screening the plurality of sample spray solution parameters according to a preset spray quality threshold, marking the sample spray solution parameters that meet the preset spray quality threshold as better sample spray solution parameters, and obtaining a plurality of better sample spray solution parameters;

[0017] The average value of the spray solution parameters of the plurality of better samples is calculated, and the average value calculation result is used as the parameter of the solution to be sprayed.

[0018] Optionally, the process of obtaining the spray gun-substrate distance comparison table includes:

[0019] Obtaining sample spraying basic parameters of a plurality of spray gun samples and sample spraying solution parameters of a plurality of solution samples to be sprayed;

[0020] Selecting a first sample spray basic parameter from the plurality of sample spray basic parameters, and selecting a first sample spray solution parameter from the plurality of sample spray solution parameters, wherein the selection criteria for the first sample spray basic parameter and the first sample spray solution parameter are to select representative parameters;

[0021] Obtaining a spraying distance threshold, and randomly expanding within the spraying distance threshold according to a preset step size to obtain multiple sample spraying distances;

[0022] According to the first sample spraying basic parameters and the first sample spraying solution parameters, a plurality of spraying tests are sequentially performed at a plurality of sample spraying distances to obtain a plurality of sample spraying test results;

[0023] Determine a plurality of sample spraying quality evaluation coefficients according to the plurality of sample spraying test results, and use the sample spraying distance corresponding to the largest sample spraying quality evaluation coefficient as the first optimal spraying distance;

[0024] The spray gun-substrate distance comparison table is constructed based on a mapping relationship among the first sample spray basic parameters, the first sample spray solution parameters, and the first optimal spray distance.

[0025] Optionally, the spraying control parameters include a spray gun movement speed, a nozzle flow rate, a spraying angle, and a number of spraying times. The spraying control parameters are optimized with the first spraying distance as a constraint to obtain optimal spraying control parameters. The optimal spraying control parameters are spraying control parameters with the highest coating quality coefficient, including:

[0026] Acquiring a spray control parameter adjustment threshold of the target spray gun, and randomly generating a plurality of spray control parameter combinations within the spray control parameter adjustment threshold;

[0027] Taking the first spraying distance as a constraint, multiple spraying control parameter combinations are optimized to obtain optimal spraying control parameters.

[0028] Optionally, the optimizing a plurality of spraying control parameter combinations based on the first spraying distance as a constraint to obtain the optimal spraying control parameters includes:

[0029] randomly selecting a first spraying control parameter combination and a second spraying control parameter combination from the plurality of spraying control parameter combinations;

[0030] Inputting the first spraying control parameter combination and the second spraying control parameter combination into a spraying twin model to simulate a spraying operation, thereby obtaining a first simulated coating and a second simulated coating;

[0031] Performing spraying quality evaluation on the first simulated coating and the second simulated coating respectively to obtain a first coating quality coefficient and a second coating quality coefficient;

[0032] When the first coating quality coefficient is greater than the second coating quality coefficient, the first control parameter combination is set as the current optimal spraying control parameter; otherwise, the second control parameter combination is set as the current optimal spraying control parameter;

[0033] The iterative optimization is continuously performed until the preset optimization constraints are met, and the optimization is stopped, and the current optimal spraying control parameters are output as the optimal spraying control parameters.

[0034] Optionally, the performing spraying quality assessment on the first simulated coating and the second simulated coating respectively to obtain a first coating quality coefficient and a second coating quality coefficient includes:

[0035] Dividing the first simulated coating and the second simulated coating into grid areas to obtain a first coating grid area and a second coating grid area;

[0036] performing deviation calculation on pixel values of a plurality of grid areas in the first coating grid area and the second coating grid area according to preset spraying pixel values, to obtain a first pixel deviation set and a second pixel deviation set;

[0037] Performing variance calculation on the first pixel deviation set and the second pixel deviation set to obtain a first variance calculation result and a second variance calculation result;

[0038] The inverse of the first variance calculation result is set as the first coating quality coefficient, and the inverse of the second variance calculation result is set as the second coating quality coefficient.

[0039] Optionally, after performing the spraying operation of the solution to be sprayed by the target spray gun according to the first spraying distance and the optimal spraying control parameter, the method further includes:

[0040] Acquiring spraying operation results, performing coating quality analysis on the spraying operation results, and obtaining a spraying operation coating quality coefficient;

[0041] Determining whether the coating quality coefficient of the spraying operation meets the expected quality index;

[0042] If not, a parameter update instruction is generated, and the optimal spraying control parameter is updated based on the parameter update instruction.

[0043] To achieve the above objectives, the second embodiment of the present application proposes a thin film spraying control optimization device, comprising:

[0044] A parameter acquisition module is used to obtain basic spraying parameters and spraying control parameters of a target spray gun, and to obtain parameters of a solution to be sprayed;

[0045] a comparison and matching module, configured to input the basic spraying parameters and the parameters of the solution to be sprayed into a spray gun-substrate distance comparison table for matching, thereby obtaining a first spraying distance;

[0046] an optimization module, configured to optimize the spraying control parameters with the first spraying distance as a constraint to obtain optimal spraying control parameters, wherein the optimal spraying control parameters are spraying control parameters with the highest coating quality coefficient;

[0047] A spraying operation module is used to perform a spraying operation of the solution to be sprayed through the target spray gun based on the first spraying distance and the optimal spraying control parameter.

[0048] To achieve the above-mentioned purpose, a third embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0049] The memory stores computer-executable instructions;

[0050] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects above.

[0051] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the above-mentioned first aspects.

[0052] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0053] By obtaining the basic spraying parameters and spraying control parameters of the target spray gun, and obtaining the parameters of the solution to be sprayed of the solution to be sprayed; inputting the basic spraying parameters and the parameters of the solution to be sprayed into the spray gun-substrate distance comparison table for matching, a first spraying distance is obtained; with the first spraying distance as a constraint, the spraying control parameters are optimized to obtain the optimal spraying control parameters, which are the spraying control parameters with the highest coating quality coefficient; according to the first spraying distance and the optimal spraying control parameters, the solution to be sprayed is sprayed by the target spray gun. Based on the solution proposed in this application, the uniformity and stability of thin film spraying can be improved, thereby improving the photoelectric conversion efficiency of solar cells, and can solve the technical problem of low uniformity and stability of thin film spraying in the prior art.

[0054] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0056] Figure 1 is a flow chart of a thin film spraying control optimization method according to an embodiment of the present application;

[0057] Figure 2 This is a flow chart of obtaining parameters of a solution to be sprayed in a thin film spraying control optimization method according to an embodiment of the present application;

[0058] Figure 3 This is a flow chart of obtaining a spray gun-substrate distance comparison table in a thin film spray control optimization method according to an embodiment of the present application;

[0059] Figure 4 This is a flow chart for optimizing spray control parameters in a thin film spray control optimization method according to an embodiment of the present application;

[0060] Figure 5 This is a flow chart of spray quality evaluation of a simulated coating in a thin film spray control optimization method according to an embodiment of the present application;

[0061] Figure 6 is a flow chart of a thin film spraying control optimization device according to an embodiment of the present application;

[0062] Figure 7 It is a block diagram of an electronic device. DETAILED DESCRIPTION

[0063] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0064] A thin film spraying control optimization method and device according to an embodiment of the present application will be described below with reference to the accompanying drawings.

[0065] Figure 1 is a flow chart of a thin film spraying control optimization method according to an embodiment of the present application, such as Figure 1 As shown, the method includes the following steps:

[0066] Step 101 : acquiring basic spraying parameters and spraying control parameters of a target spray gun, and acquiring parameters of a solution to be sprayed.

[0067] In the embodiments of the present application, the basic spraying parameters and spraying control parameters of the target spray gun are obtained by consulting the product manual, using professional measuring tools or making judgments based on experience, and the parameters of the solution to be sprayed, such as the viscosity and surface tension of the solution, are obtained by consulting the product manual.

[0068] The basic spraying parameters of the target spray gun include spray flow rate, spray pressure, and nozzle diameter. Spray flow rate is the amount of paint that can be sprayed per minute, usually ranging from 100 ml / min to 600 ml / min. Spray pressure determines the spray effect of the paint and is generally between 2.0 bar and 6.0 bar. Nozzle diameter is a key parameter that determines spray flow rate and spray shape, usually ranging from 0.5 mm to 2.5 mm.

[0069] Secondly, the spray control parameters of the target spray gun include the number of spray guns, spray speed, spray width, etc. The number of spray guns refers to the number of spray guns used in one operation, usually between 1 and 4; the spray speed refers to the movement speed of the spray gun during spraying, usually between 0.2 m / s and 1.0 m / s; the spray width refers to the width of the area covered by the paint, usually between 50 mm and 500 mm.

[0070] It is understandable that in actual applications, in addition to the basic spraying parameters and the parameters of the solution to be sprayed, other factors that affect the spraying distance must also be considered, such as the material, surface condition, ambient temperature and humidity of the substrate, etc. The spraying distance needs to be adjusted according to these factors. This application does not provide specific explanations and limitations here.

[0071] Step 102: input the basic spraying parameters and the parameters of the solution to be sprayed into a spray gun-substrate distance comparison table for matching to obtain a first spraying distance.

[0072] In an embodiment of the present application, the gun-substrate distance comparison table is a comparison table compiled according to different spraying conditions and requirements. The collected basic spraying parameters and parameters of the solution to be sprayed are input into the spray gun-substrate distance comparison table. Through a matching algorithm or chart search, a spraying distance that matches the current conditions is obtained. This spraying distance is the first spraying distance.

[0073] In step 103 , the spraying control parameters are optimized with the first spraying distance as a constraint to obtain the optimal spraying control parameters. The optimal spraying control parameters are the spraying control parameters with the highest coating quality coefficient.

[0074] It is understandable that spraying a coating requires a clear optimization goal, namely maximizing the coating quality factor. The coating quality factor is a comprehensive evaluation indicator that includes multiple aspects such as coating uniformity, density, and adhesion.

[0075] It should be noted that when optimizing the spraying control parameters, it is necessary to construct a parameter space. These parameters include the number of spray guns, spraying speed, spraying width, etc. This space includes all possible combinations of spraying control parameters.

[0076] The first spray distance is then used as a constraint to ensure that all parameter combinations satisfy this constraint during the optimization process. The optimal spray control parameters are searched for in the parameter space through experiments, simulations, or optimization algorithms. During the optimization process, each parameter combination is evaluated and its coating quality coefficient is compared. The parameter combination with the highest coating quality coefficient is selected as the optimal spray control parameter.

[0077] Step 104 : performing a spraying operation of the solution to be sprayed through the target spray gun based on the first spraying distance and the optimal spraying control parameters.

[0078] It is understood that before the spraying operation begins, the solution to be sprayed must be prepared to ensure that its composition and quality meet the requirements. The target spray gun must be thoroughly inspected to ensure that it is working properly and has no blockages or leaks. At the same time, other necessary spraying tools and auxiliary equipment must be prepared. The distance between the spray gun and the surface to be sprayed is adjusted according to the first spray distance, ensuring that this distance meets the constraints to ensure the best spraying effect. The number of spray guns, movement speed, spray width and other parameters are adjusted according to the optimal spray control parameter settings.

[0079] Then, after ensuring everything is ready, the coating solution is applied. During this process, the spray gun must be moved steadily to ensure even coverage of the target surface. During the spraying process, the coating layer is closely monitored, and any issues or deviations are promptly adjusted.

[0080] Through the above steps, the spraying operation of the solution to be sprayed can be performed according to the first spraying distance and the optimal spraying control parameters, thereby obtaining a high-quality coating.

[0081] It should be noted that after the spraying operation is completed, the completed coating needs to be inspected, and all relevant information and data of the spraying operation need to be collected and stored, including but not limited to coating thickness, coating uniformity, coating adhesion, etc. These data can be obtained through measuring equipment such as laser thickness gauges, surface roughness meters, adhesion testers, etc., to evaluate quality indicators such as appearance, uniformity and adhesion to ensure that they meet the expected requirements.

[0082] Specifically, after spraying is completed, the spraying operation results are obtained, and the coating quality analysis of the spraying operation results is performed to obtain the spraying operation coating quality coefficient, and it is judged whether the spraying operation coating quality coefficient meets the expected quality indicators. If not, a parameter update instruction is generated, and the optimal spraying control parameters are updated based on the parameter update instruction.

[0083] Understandably, if the coating quality coefficient does not meet the expected quality indicators, the system will generate parameter update instructions to guide the system to adjust the optimal spray control parameters to optimize the next spray operation. Parameter adjustments include spray gun movement speed, spray distance, paint flow rate, etc.

[0084] Figure 2 This is a flow chart of obtaining parameters of a solution to be sprayed in a thin film spraying control optimization method according to an embodiment of the present application.

[0085] like Figure 2 As shown, the method includes:

[0086] Step 201 : Acquire a plurality of sample spray solution parameters and a plurality of sample spray quality coefficients, wherein the sample spray solution parameters and the sample spray quality coefficients have a corresponding relationship.

[0087] In an embodiment of the present application, based on industrial big data, multiple sample spray solution parameters and multiple sample spray quality coefficients are collected. These data come from historical data, experimental data or real-time monitoring data in the actual production process. In order to ensure that the sample spray solution parameters and the sample spray quality coefficients have a corresponding relationship, these data need to be properly preprocessed and sorted to ensure the consistency and integrity of the data.

[0088] Step 202 : screening multiple sample spray solution parameters according to a preset spray quality threshold, marking the sample spray solution parameters that meet the preset spray quality threshold as better sample spray solution parameters, and obtaining multiple better sample spray solution parameters.

[0089] In the embodiment of the present application, a preset spraying quality threshold is set according to actual needs and industry standards.

[0090] It is understood that this spray quality threshold is used to screen out sample spray solution parameters that meet specific quality requirements. Based on the preset spray quality threshold, sample spray solution parameters that meet the threshold are screened out; the sample spray solution parameters that meet the preset spray quality threshold are marked as preferred sample spray solution parameters.

[0091] Step 203 : Calculate the mean of the spray solution parameters of the plurality of better samples, and use the mean calculation result as the spray solution parameter of the solution to be sprayed.

[0092] In the embodiments of the present application, the spray solution parameters of these better samples are averaged, and the average calculation can eliminate the influence of individual outliers and provide a set of more representative average parameter values. Finally, the result of the average calculation is used as the parameter of the solution to be sprayed.

[0093] Figure 3 It is a flow chart of obtaining a spray gun-substrate distance comparison table in a thin film spray control optimization method according to an embodiment of the present application.

[0094] like Figure 3 As shown, the method includes:

[0095] Step 301 : acquiring sample spraying basic parameters of a plurality of spray gun samples and sample spraying solution parameters of a plurality of solution samples to be sprayed.

[0096] In the embodiment of the present application, similar to step 201, basic spraying parameters and spraying solution parameters of multiple samples are obtained from industrial big data. These parameters include the model of the spray gun, nozzle diameter, spraying flow rate, spraying pressure, viscosity of the spraying solution, surface tension, etc.

[0097] Step 302 : Selecting first sample spray basic parameters from multiple sample spray basic parameters, and selecting first sample spray solution parameters from multiple sample spray solution parameters, wherein the selection criteria for the first sample spray basic parameters and the first sample spray solution parameters are to select representative parameters.

[0098] In the embodiment of the present application, a representative sample spraying basic parameter and sample spraying solution parameter are selected from the collected parameters as a first sample. This first sample can represent most situations so as to better evaluate the spraying effect.

[0099] As a possible implementation manner, the representative first sample spray basic parameters and first sample spray solution parameters may be found by calculating average values of multiple sample spray basic parameters and multiple sample spray solution parameters.

[0100] Step 303: Obtain a spraying distance threshold, and perform random expansion within the spraying distance threshold according to a preset step size to obtain multiple sample spraying distances.

[0101] In the embodiment of the present application, similar to step 202, a suitable spraying distance threshold is set according to the requirements and conditions of the actual spraying operation, and a plurality of sample spraying distances are randomly selected within the threshold range according to a preset step size.

[0102] Step 304 : performing multiple spraying tests in sequence at multiple sample spraying distances according to the first sample spraying basic parameters and the first sample spraying solution parameters to obtain multiple sample spraying test results.

[0103] Step 305 : Determine multiple sample spraying quality evaluation coefficients based on the multiple sample spraying test results, and use the sample spraying distance corresponding to the largest sample spraying quality evaluation coefficient as the first optimal spraying distance.

[0104] In the embodiment of the present application, the results of each test are evaluated according to the actual spraying effect, coating appearance, adhesion and other standards to obtain the corresponding multiple sample spraying quality evaluation coefficients. Among all the test results, the test with the largest spraying quality evaluation coefficient is found, and the corresponding sample spraying distance is used as the first optimal spraying distance.

[0105] It can be understood that the first optimal spraying distance is a spraying distance that can achieve the best spraying effect under given conditions.

[0106] Step 306 : constructing a spray gun-substrate distance comparison table based on the mapping relationship among the first sample spray basic parameters, the first sample spray solution parameters, and the first optimal spray distance.

[0107] In the embodiment of the present application, a spray gun-substrate distance comparison table is finally constructed based on the mapping relationship between the first sample spraying basic parameters, the first sample spraying solution parameters and the first optimal spraying distance.

[0108] This comparison table can be used to guide spraying operations in production, helping operators quickly find the optimal spraying distance, thereby improving production efficiency and product quality.

[0109] Figure 4 This is a flowchart for optimizing spray control parameters in a thin film spray control optimization method according to an embodiment of the present application.

[0110] like Figure 4 As shown, the method includes:

[0111] Step 401 : obtaining a spraying control parameter adjustment threshold of a target spray gun, and randomly generating a plurality of spraying control parameter combinations within the spraying control parameter adjustment threshold.

[0112] Spraying control parameters include spray gun movement speed, nozzle flow, spray angle and spraying times. Among them, the spray gun movement speed determines the coverage speed of the coating on the coated object, the nozzle flow affects the thickness and uniformity of the coating, the spray angle is related to the adhesion and uniformity of the coating, and the number of spraying times determines the total thickness of the coating and the uniformity of coverage.

[0113] In the embodiment of the present application, the adjustment ranges or thresholds of various spray control parameters of a target spray gun are obtained. These thresholds are derived based on equipment specifications, experience, or experimental data. Within the set spray control parameter adjustment thresholds, multiple different spray control parameter combinations are randomly generated.

[0114] Step 402 : Taking the first spraying distance as a constraint, randomly select a first spraying control parameter combination and a second spraying control parameter combination from a plurality of spraying control parameter combinations.

[0115] In an embodiment of the present application, two parameter combinations are randomly selected from multiple spray control parameters as the first spray control parameter combination and the second spray control parameter combination, respectively. Through this step, different parameter combinations can be explored and their effects on the spraying effect can be compared.

[0116] It is understandable that various optimization algorithms, such as genetic algorithms, simulated annealing algorithms, etc., can also be used in the optimization process, and this application does not make specific limitations here.

[0117] In step 403 , the first spraying control parameter combination and the second spraying control parameter combination are input into the spraying twin model to simulate the spraying operation, thereby obtaining a first simulated coating and a second simulated coating.

[0118] The spray twin model is a mathematical model that simulates the spraying process. This model can simulate the actual spraying process and generate a simulated coating based on the input spray control parameters, such as spray gun movement speed, nozzle flow, spray angle, and number of sprays. When establishing the spray twin model, it is necessary to collect parameters such as spray gun movement speed, nozzle flow, spray angle, spray distance, as well as results such as coating thickness, uniformity, and adhesion. Based on physical, chemical, and mathematical principles, a mathematical model is established to describe the spraying process. This process includes relevant models such as fluid dynamics, chemical reaction kinetics, and heat transfer to simulate the spraying, flow, diffusion, and curing processes of the coating. After the model is established, the collected data is used to identify and calibrate the model parameters. The model's predictive ability is then tested using independent data sets to ensure its reliability in practical applications.

[0119] It is understood that the model training process is a well-known method and will not be described in detail in this application.

[0120] Step 404 : Perform spraying quality evaluation on the first simulated coating and the second simulated coating respectively to obtain a first coating quality coefficient and a second coating quality coefficient.

[0121] In the embodiment of the present application, the quality of the first simulated coating and the second simulated coating are respectively evaluated according to the evaluation criteria, and the evaluation criteria include the uniformity, density, adhesion, etc. of the coating, to obtain the first coating quality coefficient and the second coating quality coefficient.

[0122] Step 405: When the first coating quality coefficient is greater than the second coating quality coefficient, the first control parameter combination is set as the current optimal spraying control parameter; otherwise, the second control parameter combination is set as the current optimal spraying control parameter.

[0123] In the embodiment of the present application, the first coating quality coefficient and the second coating quality coefficient are compared. If the first coating quality coefficient is greater than the second coating quality coefficient, the parameters in the first spray control parameter combination are set as the current optimal spray control parameters. Otherwise, the parameters in the second spray control parameter combination are set as the current optimal spray control parameters.

[0124] Step 406 , continuously iterate the optimization until the preset optimization constraints are met, then stop the optimization and output the current optimal spraying control parameters as the optimal spraying control parameters.

[0125] In the embodiment of the present application, the process of steps 402-405 is repeated to perform iterative optimization. In each iteration, a new first control parameter and a second control parameter are randomly selected to simulate the spraying operation and evaluate the coating quality. By comparing the coating quality coefficients under different parameter combinations, the optimal spraying control parameters are continuously updated.

[0126] When the preset optimization constraints such as the preset number of iterations is reached or the change in the optimal coating quality coefficient is less than a preset threshold are met, the iterative optimization is stopped and the current optimal spraying control parameters are output as the final optimal spraying control parameters.

[0127] It can be understood that the optimal spraying control parameters are the parameter combination that can achieve the best spraying effect under certain constraints.

[0128] Figure 5 This is a flow chart of spraying quality evaluation of a simulated coating in a thin film spraying control optimization method according to an embodiment of the present application.

[0129] like Figure 5 As shown, the method includes:

[0130] Step 501 : Divide the first simulated coating layer and the second simulated coating layer into grid regions to obtain a first coating grid region and a second coating grid region.

[0131] In the embodiment of the present application, in order to more conveniently process and analyze the structure and characteristics of the coating, the first simulated coating and the second simulated coating are divided into grid areas to obtain a first coating grid area and a second coating grid area.

[0132] Step 502 : performing deviation calculation on pixel values of a plurality of grid areas in the first coating grid area and the second coating grid area according to preset spraying pixel values, to obtain a first pixel deviation set and a second pixel deviation set.

[0133] In this embodiment, a deviation calculation is performed on the pixel values of each grid area based on a preset spray pixel value to obtain a first pixel deviation set and a second pixel deviation set. The deviation refers to the difference between the actual pixel value and the preset pixel value. This deviation calculation is used to determine whether the coating within each grid area meets the preset standard or requirement.

[0134] Step 503 : performing variance calculation on the first pixel deviation set and the second pixel deviation set to obtain a first variance calculation result and a second variance calculation result.

[0135] As you can understand, variance is used to assess the degree of dispersion of deviations, that is, the degree to which pixel values deviate from the preset value. A larger variance means that the distribution of pixel values is more dispersed, indicating poor coating quality; a smaller variance means that the pixel values are relatively concentrated, indicating good coating quality.

[0136] Step 504: Set the inverse of the first variance calculation result as the first coating quality coefficient, and set the inverse of the second variance calculation result as the second coating quality coefficient.

[0137] In the embodiment of the present application, the inverse of the variance calculation result is set as the coating quality coefficient in order to convert the variance into a quality coefficient that is easier to interpret and compare.

[0138] By taking the inverse of the variance, the non-uniformity can be converted into a relative quality coefficient. Thus, a larger coefficient indicates better coating quality, while a smaller coefficient indicates worse coating quality.

[0139] Through the above steps, the pixel deviations of the first simulated coating and the second simulated coating can be evaluated and converted into corresponding coating quality coefficients.

[0140] In summary, the embodiment of the present application obtains the basic spraying parameters and spraying control parameters of the target spray gun, and obtains the parameters of the solution to be sprayed of the solution to be sprayed; the basic spraying parameters and the parameters of the solution to be sprayed are input into the spray gun-substrate distance comparison table for matching to obtain the first spraying distance; with the first spraying distance as a constraint, the spraying control parameters are optimized to obtain the optimal spraying control parameters, which are the spraying control parameters with the highest coating quality coefficient; according to the first spraying distance and the optimal spraying control parameters, the solution to be sprayed is sprayed by the target spray gun. Based on the solution proposed in this application, the uniformity and stability of thin film spraying can be improved, thereby improving the photoelectric conversion efficiency of solar cells, and can solve the technical problem of low uniformity and stability of thin film spraying in the prior art.

[0141] Figure 6 : is a block diagram of a thin film spraying control optimization device according to an embodiment of the present application, comprising:

[0142] The parameter acquisition module 11 is used to obtain the spraying basic parameters and spraying control parameters of the target spray gun, and obtain the parameters of the solution to be sprayed;

[0143] A comparison and matching module 12 is used to input the basic spraying parameters and the parameters of the solution to be sprayed into the spray gun-substrate distance comparison table for matching to obtain a first spraying distance;

[0144] An optimization module 13 is configured to optimize the spraying control parameters with the first spraying distance as a constraint to obtain the optimal spraying control parameters, wherein the optimal spraying control parameters are spraying control parameters with the highest coating quality coefficient;

[0145] The spraying operation module 14 is configured to perform a spraying operation of the solution to be sprayed through the target spray gun based on the first spraying distance and the optimal spraying control parameters.

[0146] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0147] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0148] like Figure 7 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the device 700 can also be stored in the RAM 703. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0149] Various components in device 700 are connected to I / O interface 705, including an input unit 706, such as a keyboard, mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, optical disk, etc.; and a communication unit 709, such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0150] The computing unit 701 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as the voice command response method. For example, in some embodiments, the voice command response method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the voice command response method described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the voice command response method in any other appropriate manner (e.g., by means of firmware).

[0151] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0152] The program code for implementing the methods of the present application can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow charts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0153] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0154] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0155] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0156] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship is established by computer programs running on the respective computers, establishing a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or simply "VPS"). The server may also be a server in a distributed system or a server integrated with blockchain.

[0157] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

[0158] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A thin film spraying control optimization method, characterized in that: include: Obtaining basic spraying parameters and spraying control parameters of a target spray gun, and obtaining parameters of a solution to be sprayed; Inputting the basic spraying parameters and the parameters of the solution to be sprayed into a spray gun-substrate distance comparison table for matching to obtain a first spraying distance; Taking the first spraying distance as a constraint, optimizing the spraying control parameters to obtain optimal spraying control parameters, wherein the optimal spraying control parameters are spraying control parameters with the highest coating quality coefficient; Based on the first spraying distance and the optimal spraying control parameter, a spraying operation of the solution to be sprayed is performed through the target spray gun.

2. The method according to claim 1, characterized in that The step of obtaining parameters of the solution to be sprayed includes: Acquiring a plurality of sample spray solution parameters and a plurality of sample spray quality coefficients, wherein the sample spray solution parameters and the sample spray quality coefficients have a corresponding relationship; screening the plurality of sample spray solution parameters according to a preset spray quality threshold, marking the sample spray solution parameters that meet the preset spray quality threshold as better sample spray solution parameters, and obtaining a plurality of better sample spray solution parameters; The average value of the spray solution parameters of the plurality of better samples is calculated, and the average value calculation result is used as the parameter of the solution to be sprayed.

3. The method according to claim 1, characterized in that The process of obtaining the spray gun-substrate distance comparison table includes: Obtaining sample spraying basic parameters of a plurality of spray gun samples and sample spraying solution parameters of a plurality of solution samples to be sprayed; Selecting a first sample spray basic parameter from the plurality of sample spray basic parameters, and selecting a first sample spray solution parameter from the plurality of sample spray solution parameters, wherein the selection criteria for the first sample spray basic parameter and the first sample spray solution parameter are to select representative parameters; Obtaining a spraying distance threshold, and randomly expanding within the spraying distance threshold according to a preset step size to obtain multiple sample spraying distances; According to the first sample spraying basic parameters and the first sample spraying solution parameters, a plurality of spraying tests are sequentially performed at a plurality of sample spraying distances to obtain a plurality of sample spraying test results; Determine a plurality of sample spraying quality evaluation coefficients according to the plurality of sample spraying test results, and use the sample spraying distance corresponding to the largest sample spraying quality evaluation coefficient as the first optimal spraying distance; The spray gun-substrate distance comparison table is constructed based on a mapping relationship among the first sample spray basic parameters, the first sample spray solution parameters, and the first optimal spray distance.

4. The method according to claim 1, wherein The spraying control parameters include the spray gun movement speed, nozzle flow rate, spraying angle, and spraying times. The spraying control parameters are optimized with the first spraying distance as a constraint to obtain the optimal spraying control parameters. The optimal spraying control parameters are the spraying control parameters with the highest coating quality coefficient, including: Acquiring a spray control parameter adjustment threshold of the target spray gun, and randomly generating a plurality of spray control parameter combinations within the spray control parameter adjustment threshold; Taking the first spraying distance as a constraint, multiple spraying control parameter combinations are optimized to obtain optimal spraying control parameters.

5. The method according to claim 4, characterized in that The step of optimizing a plurality of spray control parameter combinations based on the first spray distance as a constraint to obtain optimal spray control parameters includes: Taking the first spraying distance as a constraint, randomly selecting a first spraying control parameter combination and a second spraying control parameter combination from the multiple spraying control parameter combinations; Inputting the first spraying control parameter combination and the second spraying control parameter combination into a spraying twin model to simulate a spraying operation, thereby obtaining a first simulated coating and a second simulated coating; Performing spraying quality evaluation on the first simulated coating and the second simulated coating respectively to obtain a first coating quality coefficient and a second coating quality coefficient; When the first coating quality coefficient is greater than the second coating quality coefficient, the first control parameter combination is set as the current optimal spraying control parameter; otherwise, the second control parameter combination is set as the current optimal spraying control parameter; The iterative optimization is continuously performed until the preset optimization constraints are met, and the optimization is stopped, and the current optimal spraying control parameters are output as the optimal spraying control parameters.

6. The method according to claim 5, characterized in that The spraying quality evaluation of the first simulated coating and the second simulated coating is performed respectively to obtain the first coating quality coefficient and the second coating quality coefficient, including: Dividing the first simulated coating and the second simulated coating into grid areas to obtain a first coating grid area and a second coating grid area; performing deviation calculation on pixel values of a plurality of grid areas in the first coating grid area and the second coating grid area according to preset spraying pixel values, to obtain a first pixel deviation set and a second pixel deviation set; Performing variance calculation on the first pixel deviation set and the second pixel deviation set to obtain a first variance calculation result and a second variance calculation result; The inverse of the first variance calculation result is set as the first coating quality coefficient, and the inverse of the second variance calculation result is set as the second coating quality coefficient.

7. The method according to claim 1, characterized in that After performing the spraying operation of the solution to be sprayed by the target spray gun according to the first spraying distance and the optimal spraying control parameter, the method further includes: Acquiring spraying operation results, performing coating quality analysis on the spraying operation results, and obtaining a spraying operation coating quality coefficient; Determining whether the coating quality coefficient of the spraying operation meets the expected quality index; If not, a parameter update instruction is generated, and the optimal spraying control parameter is updated based on the parameter update instruction.

8. A thin film spraying control optimization device, characterized in that: include: A parameter acquisition module is used to obtain basic spraying parameters and spraying control parameters of a target spray gun, and to obtain parameters of a solution to be sprayed; a comparison and matching module, configured to input the basic spraying parameters and the parameters of the solution to be sprayed into a spray gun-substrate distance comparison table for matching, thereby obtaining a first spraying distance; an optimization module, configured to optimize the spraying control parameters with the first spraying distance as a constraint to obtain optimal spraying control parameters, wherein the optimal spraying control parameters are spraying control parameters with the highest coating quality coefficient; A spraying operation module is used to perform a spraying operation of the solution to be sprayed through the target spray gun based on the first spraying distance and the optimal spraying control parameter.

9. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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