Method for Optimizing Manufacturing Parameters of Photovoltaic Diodes Based on Twin Simulation

Abstract

The present invention provides a method for optimizing manufacturing parameters of photovoltaic diodes based on twin simulation, which relates to the field of data processing technology, including: obtaining diode characteristic data of a target photovoltaic diode; searching in a diode database to obtain a set of test data of the same family; constructing a diode test model based on digital twin technology; performing simulation tests on manufactured photovoltaic diode samples to generate simulation test results; judging multiple photoelectric conversion efficiencies to obtain N unqualified photoelectric conversion efficiencies that do not meet a photoelectric conversion efficiency threshold; optimizing the manufacturing parameters of the target photovoltaic diode based on the N unqualified photoelectric conversion efficiencies to obtain an optimal manufacturing parameter combination; and controlling the manufacturing of the target photovoltaic diode. The present invention solves the technical problem that traditional manufacturing parameter selection methods are often based on experience, lack systematicity and efficiency, and result in poor quality of photovoltaic diodes.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for optimizing photovoltaic diode manufacturing parameters based on twin simulation. Background Art

[0002] As an important component of clean energy, solar photovoltaic technology has been widely used in the energy field. However, there are still some technical problems in the manufacturing process of photovoltaic diodes. On the one hand, the photoelectric conversion efficiency of photovoltaic diodes is affected by many factors, and traditional manufacturing methods often make it difficult to ensure the quality stability of photovoltaic diodes under different production batches and working conditions; on the other hand, the optimization of manufacturing parameters is crucial to improving the performance of photovoltaic diodes, but traditional experience-based or trial-and-error methods are time-consuming and costly, and may not be able to find the global optimal solution, resulting in difficulties in optimizing manufacturing parameters. Summary of the invention

[0003] This application provides a photovoltaic diode manufacturing parameter optimization method based on twin simulation, aiming to solve the technical problem that traditional manufacturing parameter selection methods are often based on experience, lack systematicity and efficiency, and lead to poor quality of photovoltaic diodes.

[0004] In view of the above problems, the present application provides a photovoltaic diode manufacturing parameter optimization method based on twin simulation.

[0005] The first aspect disclosed in the present application provides a method for optimizing manufacturing parameters of photovoltaic diodes based on twin simulation, the method comprising: obtaining diode characteristic data of a target photovoltaic diode, wherein the diode characteristic data comprises material ratio characteristics and doping concentration characteristics; based on the diode characteristic data, searching in a diode database to obtain a set of test data of the same family, wherein the diode database is constructed based on a photoelectric conversion efficiency test of a diode; based on digital twin technology, constructing a diode test model based on the diode characteristic data and the set of test data of the same family; based on the diode test model, performing simulation tests on manufactured photovoltaic diode samples to generate simulation test results, wherein the simulation test results comprise multiple photoelectric conversion efficiencies under multiple working conditions; judging the multiple photoelectric conversion efficiencies to obtain N unqualified photoelectric conversion efficiencies that do not meet a photoelectric conversion efficiency threshold; based on the N unqualified photoelectric conversion efficiencies, optimizing the manufacturing parameters of the target photovoltaic diode to obtain an optimal manufacturing parameter combination; and based on the optimal manufacturing parameter combination, performing manufacturing control of the target photovoltaic diode.

[0006] The second aspect disclosed in the present application provides a photovoltaic diode manufacturing parameter optimization system based on twin simulation, and the system is used for the above-mentioned photovoltaic diode manufacturing parameter optimization method based on twin simulation, and the system includes: a feature data acquisition module, the feature data acquisition module is used to obtain the diode feature data of the target photovoltaic diode, wherein the diode feature data includes material ratio features and doping concentration features; a feature data retrieval module, the feature data retrieval module is used to search in a diode database based on the diode feature data to obtain a set of same-family test data, wherein the diode database is constructed based on the photoelectric conversion efficiency test of the diode; a test model construction module, the test model construction module is used to construct a test model based on the diode feature data and the same-family test data set based on the digital twin technology. A diode test model is constructed; a simulation test module is used to perform simulation tests on the manufactured photovoltaic diode samples based on the diode test model to generate simulation test results, wherein the simulation test results include multiple photoelectric conversion efficiencies under multiple working conditions; a conversion efficiency judgment module is used to judge the multiple photoelectric conversion efficiencies and obtain N unqualified photoelectric conversion efficiencies that do not meet the photoelectric conversion efficiency threshold; a manufacturing parameter optimization module is used to optimize the manufacturing parameters of the target photovoltaic diode based on the N unqualified photoelectric conversion efficiencies to obtain an optimal manufacturing parameter combination; a manufacturing control module is used to perform manufacturing control of the target photovoltaic diode based on the optimal manufacturing parameter combination.

[0007] According to a third aspect disclosed in the present application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any step of the first aspect disclosed in the present application is implemented.

[0008] The fourth aspect disclosed in the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the first aspect disclosed in the present application.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] Through digital twin technology, combined with actual diode characteristic data and the test data set of the same family, a diode test model is constructed, thus realizing the intelligent optimization of manufacturing parameters; by using the simulation test model, it is possible to quickly and efficiently conduct simulation tests on the manufactured photovoltaic diode samples, generate photoelectric conversion efficiency data under multiple working conditions, effectively improve the test efficiency, and avoid waste of resources; by judging the unqualified photoelectric conversion efficiency and optimizing the manufacturing parameters on this basis, the generation rate of unqualified products can be effectively reduced, thereby reducing the manufacturing cost; by adopting the optimized manufacturing parameters to control the manufacturing of the target photovoltaic diode, the performance and quality of the photovoltaic diode can be improved, and then the overall quality and performance of the product can be enhanced. In summary, the optimization method for manufacturing parameters of photovoltaic diodes based on twin simulation solves the problem of parameter optimization in the manufacturing of photovoltaic diodes through digital twin technology and big data analysis, and improves the manufacturing efficiency and product quality.

[0011] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented in accordance with the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are given below. Brief Description of the Drawings

[0012] Figure 1 It is a schematic flow chart of the optimization method for manufacturing parameters of photovoltaic diodes based on twin simulation provided by the embodiment of this application;

[0013] Figure 2 It is a schematic structural diagram of the optimization system for manufacturing parameters of photovoltaic diodes based on twin simulation provided by the embodiment of this application;

[0014] Figure 3 It is an internal structure diagram of the computer device provided by the embodiment of this application.

[0015] Description of the reference numerals: Feature data acquisition module 10, feature data retrieval module 20, test model construction module 30, simulation test module 40, conversion efficiency determination module 50, manufacturing parameter optimization module 60, manufacturing control module 70. Detailed Description of the Embodiments

[0016] By providing an optimization method for manufacturing parameters of photovoltaic diodes based on twin simulation in the embodiment of this application, the technical problem that the traditional method for selecting manufacturing parameters is often based on experience, lacks systematicness and efficiency, and results in poor quality of photovoltaic diodes is solved.

[0017] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] like Figure 1 As shown, the embodiment of the present application provides a method for optimizing photovoltaic diode manufacturing parameters based on twin simulation, the method comprising:

[0019] Acquiring diode characteristic data of a target photovoltaic diode, wherein the diode characteristic data includes material ratio characteristics and doping concentration characteristics;

[0020] Photovoltaic diodes are devices that convert light energy into electrical energy. Common materials include silicon, cadmium selenide, indium selenide, etc. A small amount of impurity elements, usually phosphorus or boron, are introduced into silicon crystals to change the electrical properties of silicon. This process is called doping, and the impurities introduced are called donors (n-type) or acceptors (p-type). The material proportion characteristics and doping concentration characteristics of the target photovoltaic diode can be obtained by retrieving the manufacturing records of the target photovoltaic diode, or by testing the target photovoltaic diode sample. The testing instruments include X-ray fluorescence spectrometers, etc., to determine which elements it is composed of, the proportion of each element, and the concentration of doping impurities.

[0021] Based on the diode characteristic data, searching in a diode database to obtain a set of test data of the same family, wherein the diode database is constructed based on a photoelectric conversion efficiency test of a diode;

[0022] Establish a database containing different types of photovoltaic diodes. These diodes can come from different manufacturers and have different material compositions and manufacturing parameters. Test each diode to determine its photoelectric conversion efficiency, such as exposing the diode to a specific light source, measuring its output current and light intensity, and then calculating its conversion efficiency. Use the characteristic data of the target photovoltaic diode as a search condition, that is, based on the material ratio characteristics and doping concentration characteristics of the target photovoltaic diode, search in the diode database, and obtain a set of test data of the same family of diodes with similar characteristics to the target photovoltaic diode from the database according to the search conditions. These test data sets of the same family include the test results of the photoelectric conversion efficiency of the same type of photovoltaic diodes under different working conditions.

[0023] Furthermore, based on the diode characteristic data, searching is performed in a diode database to obtain a set of test data of the same family, including:

[0024] Based on big data, multiple photoelectric conversion efficiency sets of multiple diode types under multiple working conditions are obtained to construct the diode database;

[0025] Randomly extracting a first diode type from the diode database, obtaining a first diode feature, and a first photoelectric conversion efficiency set;

[0026] Performing a similarity analysis on the diode characteristic data and the first diode characteristic to obtain a first characteristic similarity;

[0027] If the first feature similarity meets a preset similarity threshold, the first photoelectric conversion efficiency set is used as the same family test data set.

[0028] Based on big data, photoelectric conversion efficiency data of multiple diode types are collected. These data can come from different research institutions, manufacturers or laboratories. The data covers different types of diodes and test results under different working conditions, such as light intensity, temperature, etc. The collected data is cleaned, including processing missing data and outliers to ensure the accuracy and consistency of the data. A database is established to store data such as diode type, working conditions and photoelectric conversion efficiency. The cleaned data is imported into the database to establish a data set of the diode database, in which each data entry includes information such as diode type, working conditions, photoelectric conversion efficiency, etc.

[0029] A diode type is randomly selected from a diode database as a first type, and characteristic data, i.e., first diode characteristics, are extracted from the database according to the selected first diode type. A first photoelectric conversion efficiency set corresponding to the selected diode type is obtained, and the efficiency data includes multiple photoelectric conversion efficiency values of the diode under different working conditions.

[0030] The similarity calculation is performed on the selected first diode feature and the diode feature data of the target diode. Various similarity calculation methods, such as Euclidean distance, cosine similarity, etc., can be used to measure the similarity between the two features. After the similarity calculation, the first feature similarity is obtained, which is used to measure the similarity between the target diode and the selected first diode type.

[0031] A similarity threshold is preset to determine whether the first feature similarity meets the requirement. The setting of the similarity threshold can be determined according to specific circumstances and needs, and is usually based on actual experience or data analysis.

[0032] The calculated first feature similarity is judged to determine whether it meets the preset similarity threshold. If the first feature similarity reaches or exceeds the preset similarity threshold, the first photoelectric conversion efficiency set is used as the same family test data set, which means that the selected first diode type has sufficient similarity with the target diode. Therefore, the first photoelectric conversion efficiency set can be used as standard data to lay the foundation for subsequent model construction.

[0033] Based on the digital twin technology, a diode test model is constructed based on the diode characteristic data and the same family test data set;

[0034] Digital twin is a virtual simulation technology that simulates physical processes in the real world through digital modeling and simulation. This technology can conduct experiments and tests in a virtual environment to predict the behavior of real systems. Using digital twin technology, a diode test model is constructed based on the characteristic data of the target photovoltaic diode and a collection of test data of the same family. This model can be used to predict the performance of photovoltaic diodes under different working conditions, including the prediction of key performance parameters such as photoelectric conversion efficiency, to provide support for manufacturing parameter optimization and manufacturing control.

[0035] Furthermore, based on the digital twin technology, based on the diode characteristic data and the same family test data set, a diode test model is constructed, including:

[0036] Acquire basic equipment information of multiple main manufacturing equipment of the target photovoltaic diode, wherein the basic equipment information includes equipment serial information and equipment model information;

[0037] Based on the equipment sequence information and equipment model information and based on digital twin technology, the plurality of main manufacturing equipment are modeled to obtain a diode manufacturing model;

[0038] Mapping the diode characteristic data to the diode manufacturing model to generate a diode twin model;

[0039] Based on the diode twin model, a plurality of photoelectric conversion efficiency test channels are constructed, wherein the plurality of photoelectric conversion efficiency test channels correspond to a plurality of working conditions;

[0040] Based on the same family test data set, the multiple photoelectric conversion efficiency test channels are adjusted, and the adjusted multiple photoelectric conversion efficiency test channels are integrated to construct the diode test model.

[0041] Investigate the main manufacturing equipment in the factory and identify multiple main manufacturing equipment used for photovoltaic diode manufacturing. Identify each manufacturing equipment and record its unique equipment serial number as equipment serial information, which is used to distinguish different equipment for subsequent identification. Record the model information of each manufacturing equipment, including manufacturer, model, specifications, etc. The equipment model information can provide the basic technical parameters and performance characteristics of the equipment.

[0042] Based on the collected equipment information and digital twin technology, each major manufacturing equipment is modeled. The modeling process includes the structure, working principle, control system and other aspects of the manufacturing equipment. The model can be verified using actual operation data, and the model parameters can be adjusted and optimized. After modeling, calibrating and verifying all major manufacturing equipment, the resulting digital twin model set constitutes the diode manufacturing model, which can be used to simulate various parameters in the photovoltaic diode manufacturing process.

[0043] The diode characteristic data is input into the diode manufacturing model, the characteristic data is mapped and converted, and the actual diode characteristic data is coupled with the manufacturing model using digital twin technology to generate a corresponding diode twin model, which can be used to simulate and predict the target photovoltaic diode.

[0044] For the use scenarios of photovoltaic diodes, multiple working conditions involved are determined, such as different light intensity, temperature, voltage and other conditions. These working conditions can be determined according to the actual production situation or the expected use environment. For each working condition, a corresponding photoelectric conversion efficiency test channel is established. Each test channel includes the corresponding working condition parameter settings, simulation process and output results.

[0045] According to the photoelectric conversion efficiency data in the same family test data set, multiple photoelectric conversion efficiency test channels are adjusted. The adjustment includes adjusting the model parameters based on the same family test data set to make the output of the test channel closer to the data in the same family test data set. The multiple adjusted photoelectric conversion efficiency test channels are integrated to obtain a diode test model. This model can be used to predict the photoelectric conversion efficiency of the target diode under different working conditions and provide a reference for manufacturing parameter optimization and manufacturing control.

[0046] Based on the diode test model, a simulation test is performed on the manufactured photovoltaic diode sample to generate a simulation test result, wherein the simulation test result includes multiple photoelectric conversion efficiencies under multiple working conditions;

[0047] Prepare the finished photovoltaic diode samples, which are manufactured on the production line. Use the constructed diode test model to set the parameters and conditions of the simulation test, including determining the lighting conditions, temperature conditions, circuit connections, etc., to simulate different working conditions. Input the finished photovoltaic diode samples into the diode test model and perform simulation tests to simulate the electrical performance of the samples under different working conditions, including photoelectric conversion efficiency. Based on the results of the simulation test, record multiple photoelectric conversion efficiencies under multiple working conditions. These efficiency values correspond to the performance under different light intensity, temperature and other conditions.

[0048] Determine the multiple photoelectric conversion efficiencies, and obtain N unqualified photoelectric conversion efficiencies that do not meet the photoelectric conversion efficiency threshold;

[0049] Determine the photoelectric conversion efficiency threshold, which is used to judge which conversion efficiencies belong to the qualified range and which are unqualified. The photoelectric conversion efficiency threshold is set based on the actual situation and specific requirements. Judge each of the generated multiple photoelectric conversion efficiencies one by one. If a certain photoelectric conversion efficiency is lower than the set threshold, it is judged as unqualified. Record all the photoelectric conversion efficiencies lower than the threshold and count them. Finally, obtain N unqualified photoelectric conversion efficiencies. N is the number of unqualified photoelectric conversion efficiencies, and N is an integer greater than or equal to 1. When N is equal to 0, it means that no optimization is required.

[0050] Based on the N unqualified photoelectric conversion efficiencies, optimize the manufacturing parameters of the target photovoltaic diode to obtain the optimal manufacturing parameter combination;

[0051] Analyze the reasons for the unqualified photoelectric conversion efficiency. Possible reasons include inappropriate material ratio, inaccurate doping concentration, etc. According to the analysis results of the unqualified photoelectric conversion efficiency, design an optimization scheme for manufacturing parameters, including adjusting the material ratio and optimizing the doping concentration.

[0052] Use digital twin technology to conduct simulation tests on the designed optimization scheme for manufacturing parameters, that is, simulate the manufacturing process in a virtual environment and evaluate its impact on the photoelectric conversion efficiency. According to the simulation results, evaluate the effect of the optimization scheme for manufacturing parameters. If the simulation results show that the optimized manufacturing parameters can improve the photoelectric conversion efficiency and reduce the unqualified rate, it is considered that the optimization scheme is effective. During the process of parameter optimization, continuously adjust and optimize the manufacturing parameters until the optimal manufacturing parameter combination is obtained. The optimal combination is a set of parameters that can exhibit a high photoelectric conversion efficiency under various working conditions.

[0053] Based on the optimal manufacturing parameter combination, conduct the manufacturing control of the target photovoltaic diode.

[0054] Apply the determined optimal manufacturing parameter combination to actual production, that is, use the optimal manufacturing parameter combination to adjust the manufacturing equipment to ensure that various manufacturing parameters can be accurately controlled during the production process. Through this process, the manufacturing process of the target photovoltaic diode can be effectively controlled to ensure that the produced products have stable performance and excellent quality.

[0055] Furthermore, based on the N unqualified photoelectric conversion efficiencies, optimize the manufacturing parameters of the target photovoltaic diode to obtain the optimal manufacturing parameter combination, including:

[0056] Based on the N unqualified photoelectric conversion efficiencies, construct a material ratio optimization space and a doping concentration optimization space;

[0057] Randomly generate a first material ratio and a first doping concentration within the material ratio optimization space and the doping concentration optimization space as a first manufacturing parameter combination.

[0058] Use the first manufacturing parameter combination to perform a simulation manufacturing test in the diode test model to generate a first simulation manufacturing test result, where the first simulation manufacturing test result includes a set of simulated photoelectric conversion efficiencies and a simulated photoelectric conversion efficiency fluctuation coefficient.

[0059] Construct a diode quality evaluation function as follows:

[0060] ;

[0061] where FDQ is the diode quality evaluation coefficient, is the i-th simulated photoelectric conversion efficiency under the i-th condition, m is the number of conditions, is the weight of the i-th condition, is the simulated photoelectric conversion efficiency fluctuation coefficient, is the first weight, is the second weight, and , The sum of is 1;

[0062] According to the diode quality evaluation function, perform a quality evaluation on the first simulation manufacturing test result to obtain a first quality evaluation coefficient.

[0063] Continue to obtain a second manufacturing parameter combination and a second quality evaluation coefficient, and based on the second quality evaluation coefficient and the first quality evaluation coefficient, perform a comparison and screening of the manufacturing parameter combinations to obtain the current optimal manufacturing parameter combination.

[0064] Continue iterative optimization until the convergence condition is met to obtain the optimal manufacturing parameter combination.

[0065] For the material ratio, determine the variable range of each material. According to the existing unqualified photoelectric conversion efficiency data, analyze the material composition therein, determine the possible ratio range of each material, and construct a multi-dimensional space. Each dimension represents a material, and its value range is the possible ratio range of the material, thus forming a material ratio optimization space that contains all possible material ratio combinations.

[0066] For the doping concentration, a variable range is also determined. Based on the existing unqualified photovoltaic conversion efficiency data, the doping concentration situation is analyzed to determine the possible range of the doping concentration. A multi-dimensional space is constructed, where each dimension represents a doping type, and its value range is the possible concentration range of that doping type. In this way, an optimization space for the doping concentration is formed, which contains all possible combinations of doping concentrations.

[0067] Within the optimization space for the material ratio and the optimization space for the doping concentration, a first material ratio and a first doping concentration are randomly generated to obtain a first set of manufacturing parameters, providing initial parameters for subsequent simulated manufacturing tests.

[0068] The first set of manufacturing parameters is input into the diode test model as the parameter settings for simulated manufacturing. The diode test model is used to simulate the manufacturing process of the photovoltaic diode and test the manufacturing results. After the simulated manufacturing test is completed, a set of simulated photovoltaic conversion efficiencies is obtained, that is, the photovoltaic conversion efficiencies simulated under different working conditions. At the same time, the fluctuation coefficient of the simulated photovoltaic conversion efficiencies is calculated to evaluate the stability of the manufacturing process. The fluctuation coefficient is obtained by calculating the standard deviation of the set of simulated photovoltaic conversion efficiencies. The standard deviation is a measure of the average distance by which the data deviates from the mean, and the calculation formula is as follows:

[0069] ;

[0070] Where is the standard deviation of the data sequence, represents the i-th data point, represents the mean of the data, and n represents the total number of data points.

[0071] The diode quality evaluation function is constructed as follows:

[0072] ;

[0073] Where FDQ is the diode quality evaluation coefficient, used to represent the overall quality of the photovoltaic diode; is the i-th simulated photovoltaic conversion efficiency under the i-th working condition, representing the photovoltaic conversion efficiencies simulated under different working conditions; is the weight of the i-th working condition, used to weight the influence of different working conditions, and can be determined according to the importance or priority in actual applications. This evaluation function comprehensively evaluates the quality of the photovoltaic diode by performing a weighted sum of the photovoltaic conversion efficiencies and the simulated photovoltaic conversion efficiency fluctuation coefficient under different working conditions.

[0074] Substitute the set of simulated photoelectric conversion efficiencies and the simulated photoelectric conversion efficiency fluctuation coefficient of the first simulated manufacturing test results into the diode quality evaluation function. According to the weights and operating condition weights in the function, calculate the first quality evaluation coefficient. The first quality evaluation coefficient represents the quality of the photovoltaic diode represented by the first simulated manufacturing test results. The higher the value, the better the quality.

[0075] Similar to the process of obtaining the first manufacturing parameter combination before, randomly generate a second material ratio and a second doping concentration from the material ratio optimization space and the doping concentration optimization space as the second manufacturing parameter combination. Apply the second manufacturing parameter combination to the diode test model for simulated manufacturing tests to obtain the second simulated manufacturing test results, including the set of simulated photoelectric conversion efficiencies and the simulated photoelectric conversion efficiency fluctuation coefficient. For the second simulated manufacturing test results, calculate and obtain the second quality evaluation coefficient using the diode quality evaluation function.

[0076] Compare the first quality evaluation coefficient with the second quality evaluation coefficient. If the second quality evaluation coefficient is higher, then use the second manufacturing parameter combination as the current optimal manufacturing parameter combination; otherwise, use the second quality evaluation coefficient as the current optimal manufacturing parameter combination according to the probability.

[0077] Define a convergence condition. For example, set a difference threshold. When the difference between the optimal manufacturing parameter combinations in two iterations is less than this difference threshold, it is considered that the algorithm has converged. Or, set an iteration number threshold. When the iteration number threshold is reached, it is considered to have converged. In each round of iteration, check the difference between the current optimal manufacturing parameter combination and the optimal manufacturing parameter combination in the previous round, or the iteration number. If the convergence condition is reached, stop the iteration. At this time, the current optimal manufacturing parameter combination is the optimal manufacturing parameter combination.

[0078] Furthermore, based on the N unqualified photoelectric conversion efficiencies, construct a material ratio optimization space and a doping concentration optimization space, including:

[0079] Obtain the first operating condition of the first unqualified photoelectric conversion efficiency, and obtain the first record of photovoltaic diode manufacturing parameters that meet the photoelectric conversion efficiency threshold under the first operating condition;

[0080] Based on the first record of photovoltaic diode manufacturing parameters, extract the first set of material ratio information and the first set of doping concentration information;

[0081] Based on the first set of material ratio information and the first set of doping concentration information, obtain the first material ratio interval and the first doping concentration interval;

[0082] Successively obtain the second material ratio range, the third material ratio range until the Mth material ratio range, calculate the intersection of the first material ratio range, the second material ratio range, the third material ratio range until the Mth material ratio range, and obtain the target material ratio range;

[0083] Successively obtain the second doping concentration range, the third doping concentration range until the Mth doping concentration range, calculate the intersection of the first doping concentration range, the second doping concentration range, the third doping concentration range until the Mth doping concentration range, and obtain the target doping concentration range;

[0084] Establish a material ratio optimization space based on the target material ratio range, and establish a doping concentration optimization space based on the target doping concentration range.

[0085] The photovoltaic diode manufacturing parameter record includes photovoltaic diodes based on different process parameters and the photoelectric conversion efficiency of the photovoltaic diodes under different working conditions. Determine the first working condition corresponding to the first unqualified photoelectric conversion efficiency. For the determined first working condition, screen out the records that meet the photoelectric conversion efficiency threshold from the photovoltaic diode manufacturing parameter record as the first photovoltaic diode manufacturing parameter record, and the photoelectric conversion efficiency of the photovoltaic diodes corresponding to these records meets the requirements under this first working condition.

[0086] Extract photovoltaic diodes with multiple different manufacturing processes from the manufacturing parameter record of the first photovoltaic diode, and obtain their material ratios and doping concentrations, and obtain the first material ratio information set and the first doping concentration information set.

[0087] Based on the extracted first material ratio information set, determine the interval range of the material ratio, which can be the minimum value and the maximum value of each material ratio; similarly, based on the extracted first doping concentration information set, determine the interval range of the doping concentration, which can be the minimum value and the maximum value of the doping concentration.

[0088] In this way, the first material ratio range and the first doping concentration range are obtained, and these ranges will be used in the subsequent manufacturing parameter optimization process to ensure that the new manufacturing parameters are adjusted within a suitable range, thereby improving the quality and performance of the photovoltaic diode.

[0089] Similar to the steps of obtaining the first material ratio range, successively obtain the second, third until the Mth material ratio ranges, and these ranges are the value ranges of different material ratios. Calculate the intersection of the first material ratio range with the second to the Mth material ratio ranges one by one. The intersection of intervals refers to the common part of all intervals. The finally obtained target material ratio range is the intersection of all material ratio ranges, that is, the intersection part of all intervals.

[0090] Similar to the step of obtaining the first doping concentration range, the second, third, up to the Mth doping concentration ranges are sequentially obtained. These ranges are the value ranges of different doping concentrations. The first doping concentration range is calculated for the intersection with the second to the Mth doping concentration ranges one by one. The finally obtained target doping concentration range is the intersection of all doping concentration ranges, that is, the intersection part of all ranges.

[0091] Use the target material ratio range to determine the feasible range of the material ratio. These ranges may be the minimum and maximum values of each material ratio. Combine these ranges to form the optimization space of the material ratio; use the target doping concentration range to determine the feasible range of the doping concentration. These ranges may be the minimum and maximum values of the doping concentration. Combine these ranges to form the optimization space of the doping concentration.

[0092] Establishing these optimization spaces helps to determine the search range of manufacturing parameters for effective search in the subsequent manufacturing parameter optimization process. This can ensure that the new manufacturing parameters are adjusted within a suitable range, thereby improving the quality of the photovoltaic diode.

[0093] Furthermore, based on the second quality evaluation coefficient and the first quality evaluation coefficient, conduct a comparison and screening of manufacturing parameter combinations to obtain the current optimal manufacturing parameter combination, including:

[0094] Judge whether the second quality evaluation coefficient is greater than the first quality evaluation coefficient. If so, use the second manufacturing parameter combination as the current optimal manufacturing parameter combination;

[0095] If not, use the second manufacturing parameter combination as the current optimal manufacturing parameter combination according to a probability. The calculation formula of the probability is as follows:

[0096] ;

[0097] Where, is the natural logarithm, is the second quality evaluation coefficient, is the first quality evaluation coefficient, and C is the optimization rate factor.

[0098] Compare the magnitudes of the second quality evaluation coefficient and the first quality evaluation coefficient. If the second quality evaluation coefficient is greater than the first quality evaluation coefficient, it indicates that the quality of the second manufacturing parameter combination is better, and then set the second manufacturing parameter combination as the current optimal manufacturing parameter combination.

[0099] Set the probability calculation formula as follows:

[0100] ;

[0101] Among them, the probability is obtained by dividing the difference of the evaluation coefficients by the optimization rate factor and then transformed through the natural logarithm function. The purpose of setting this probability formula is to allow, to a certain extent, the acceptance of the manufacturing parameter combinations corresponding to the relatively poor quality evaluation coefficients, so as to jump out of the local optimal solution during the search process and thus better optimize the manufacturing parameters of the photovoltaic diode.

[0102] Determine whether to accept the second manufacturing parameter combination as the current optimal manufacturing parameter combination according to the calculated probability. Exemplarily, if the probability is greater than a randomly generated value, such as a random number between 0 and 1, then accept the second manufacturing parameter combination as the current optimal manufacturing parameter combination; otherwise, use the first manufacturing parameter combination as the current optimal manufacturing parameter combination.

[0103] Furthermore, adopting the first manufacturing parameter combination to conduct a simulated manufacturing test in the diode test model to generate a first simulated manufacturing test result, further includes:

[0104] Obtain constraint conditions according to the preset manufacturing requirements;

[0105] Obtain the first simulated manufacturing result in the diode manufacturing model based on the first manufacturing parameter combination;

[0106] Judge whether the first simulated manufacturing result meets the constraint conditions. If so, retain the first simulated manufacturing test result and continue the optimization;

[0107] If not, abandon the first manufacturing parameter combination and continue the optimization.

[0108] Define the preset manufacturing requirements, including but not limited to material requirements, process requirements, performance requirements, etc. According to the preset manufacturing requirements, derive the constraint conditions for various parameters and variables in the manufacturing process. These constraint conditions include limitation conditions in aspects such as material ratio, doping concentration, process flow, equipment use, and structural characteristics.

[0109] Apply the first manufacturing parameter combination to the diode manufacturing model. Using the diode manufacturing model, simulate the manufacturing process of the photovoltaic diode. During the manufacturing process, consider various manufacturing parameters, such as material ratio, doping concentration, etc., as well as the influence of manufacturing equipment, and obtain various data and indicators generated during the simulated manufacturing process, including but not limited to photoelectric conversion efficiency, material distribution, structural characteristics, etc.

[0110] Inspect the first simulated manufacturing result to judge whether the first simulated manufacturing result meets all the constraint conditions. If it meets all the constraint conditions, it means that this manufacturing parameter combination is acceptable. In this case, retain it as a reference for the subsequent optimization process and continue the optimization process of the manufacturing parameters to find a better manufacturing parameter combination.

[0111] If the first simulation manufacturing result does not meet the constraint condition, it means that the manufacturing parameter combination is unacceptable, and the first manufacturing parameter combination currently used is abandoned, and the manufacturing parameter optimization process is continued to find a new manufacturing parameter combination.

[0112] In summary, the photovoltaic diode manufacturing parameter optimization method based on twin simulation provided in the embodiment of the present application has the following technical effects:

[0113] 1. Through digital twin technology, the diode test model is constructed by combining the actual diode characteristic data and the same family test data set, thereby realizing the intelligent optimization of manufacturing parameters;

[0114] 2. The simulation test model can be used to quickly and efficiently perform simulation tests on the manufactured photovoltaic diode samples, generate photoelectric conversion efficiency data under multiple working conditions, effectively improve test efficiency, and avoid waste of resources;

[0115] 3. By determining the unqualified photoelectric conversion efficiency and optimizing the manufacturing parameters based on this, the generation rate of unqualified products can be effectively reduced, thereby reducing manufacturing costs;

[0116] 4. By using optimized manufacturing parameters to control the manufacturing of the target photovoltaic diode, the performance and quality of the photovoltaic diode can be improved, thereby improving the overall quality and performance of the product.

[0117] In summary, this photovoltaic diode manufacturing parameter optimization method based on twin simulation solves the parameter optimization problem in photovoltaic diode manufacturing through digital twin technology and big data analysis, and improves manufacturing efficiency and product quality.

[0118] Based on the same inventive concept as the photovoltaic diode manufacturing parameter optimization method based on twin simulation in the aforementioned embodiment, Figure 2 As shown, the present application provides a photovoltaic diode manufacturing parameter optimization system based on twin simulation, the system comprising:

[0119] A characteristic data acquisition module 10, wherein the characteristic data acquisition module 10 is used to acquire diode characteristic data of a target photovoltaic diode, wherein the diode characteristic data includes material ratio characteristics and doping concentration characteristics;

[0120] A characteristic data retrieval module 20, the characteristic data retrieval module 20 is used to search in a diode database based on the diode characteristic data to obtain a set of test data of the same family, wherein the diode database is constructed based on a photoelectric conversion efficiency test of the diode;

[0121] A test model construction module 30, wherein the test model construction module 30 is used to construct a diode test model based on the digital twin technology, the diode characteristic data, and the same family test data set;

[0122] A simulation test module 40, the simulation test module 40 is used to perform a simulation test on the manufactured photovoltaic diode sample based on the diode test model to generate a simulation test result, wherein the simulation test result includes a plurality of photoelectric conversion efficiencies under a plurality of working conditions;

[0123] A conversion efficiency determination module 50, wherein the conversion efficiency determination module 50 is used to determine the multiple photoelectric conversion efficiencies and obtain N unqualified photoelectric conversion efficiencies that do not meet the photoelectric conversion efficiency threshold;

[0124] A manufacturing parameter optimization module 60, wherein the manufacturing parameter optimization module 60 is used to optimize the manufacturing parameters of the target photovoltaic diode based on the N unqualified photoelectric conversion efficiencies to obtain an optimal manufacturing parameter combination;

[0125] A manufacturing control module 70 is used to perform manufacturing control of the target photovoltaic diode based on the optimal manufacturing parameter combination.

[0126] Furthermore, the system further includes a homogeneous test data set acquisition module to perform the following operation steps:

[0127] Based on big data, multiple photoelectric conversion efficiency sets of multiple diode types under multiple working conditions are obtained to construct the diode database;

[0128] Randomly extracting a first diode type from the diode database, obtaining a first diode feature, and a first photoelectric conversion efficiency set;

[0129] Performing a similarity analysis on the diode characteristic data and the first diode characteristic to obtain a first characteristic similarity;

[0130] If the first feature similarity meets a preset similarity threshold, the first photoelectric conversion efficiency set is used as the same family test data set.

[0131] Furthermore, the system further includes a diode test model building module to perform the following operation steps:

[0132] Acquire basic equipment information of multiple main manufacturing equipment of the target photovoltaic diode, wherein the basic equipment information includes equipment serial information and equipment model information;

[0133] Based on the equipment sequence information and equipment model information and based on digital twin technology, the plurality of main manufacturing equipment are modeled to obtain a diode manufacturing model;

[0134] Mapping the diode characteristic data to the diode manufacturing model to generate a diode twin model;

[0135] Based on the diode twin model, a plurality of photoelectric conversion efficiency test channels are constructed, wherein the plurality of photoelectric conversion efficiency test channels correspond to a plurality of working conditions;

[0136] Based on the same family test data set, the multiple photoelectric conversion efficiency test channels are adjusted, and the adjusted multiple photoelectric conversion efficiency test channels are integrated to construct the diode test model.

[0137] Furthermore, the system also includes an optimization module to perform the following operation steps:

[0138] Based on the N unqualified photoelectric conversion efficiencies, constructing a material ratio optimization space and a doping concentration optimization space;

[0139] In the material ratio optimization space and the doping concentration optimization space, randomly generating a first material ratio and a first doping concentration as a first manufacturing parameter combination;

[0140] Using the first manufacturing parameter combination, performing a simulated manufacturing test in the diode test model to generate a first simulated manufacturing test result, wherein the first simulated manufacturing test result includes a simulated photoelectric conversion efficiency set and a simulated photoelectric conversion efficiency fluctuation coefficient;

[0141] Construct a diode quality assessment function as follows:

[0142] ;

[0143] Where FDQ is the diode quality assessment factor, is the ith simulated photoelectric conversion efficiency under the ith working condition, m is the number of working conditions, is the weight of the i-th working condition, To simulate the fluctuation coefficient of photoelectric conversion efficiency, is the first weight, is the second weight, and , The sum of is 1;

[0144] Performing a quality assessment on the first simulation manufacturing test result according to the diode quality assessment function to obtain a first quality assessment coefficient;

[0145] Continue to obtain the second manufacturing parameter combination and the second quality evaluation coefficient, and based on the second quality evaluation coefficient and the first quality evaluation coefficient, compare and screen the manufacturing parameter combinations to obtain the current optimal manufacturing parameter combination;

[0146] Continue to iterate and optimize until the convergence condition is met to obtain the optimal manufacturing parameter combination.

[0147] Furthermore, the system further includes an optimization space construction module to perform the following operation steps:

[0148] Obtain the first working condition of the first unqualified photoelectric conversion efficiency, and obtain the manufacturing parameter record of the first photovoltaic diode that meets the photoelectric conversion efficiency threshold under the first working condition;

[0149] Based on the manufacturing parameter record of the first photovoltaic diode, extract the first material ratio information set and the first doping concentration information set;

[0150] Based on the first material ratio information set and the first doping concentration information set, obtain the first material ratio interval and the first doping concentration interval;

[0151] Sequentially obtain the second material ratio interval, the third material ratio interval until the Mth material ratio interval, calculate the interval intersection of the first material ratio interval, the second material ratio interval, the third material ratio interval until the Mth material ratio interval, and obtain the target material ratio interval;

[0152] Sequentially obtain the second doping concentration interval, the third doping concentration interval until the Mth doping concentration interval, calculate the interval intersection of the first doping concentration interval, the second doping concentration interval, the third doping concentration interval until the Mth doping concentration interval, and obtain the target doping concentration interval;

[0153] Based on the target material ratio interval, establish a material ratio optimization space, and based on the target doping concentration interval, establish a doping concentration optimization space.

[0154] Furthermore, the system further includes a current optimal manufacturing parameter combination acquisition module to perform the following operation steps:

[0155] Judge whether the second quality evaluation coefficient is greater than the first quality evaluation coefficient. If so, use the second manufacturing parameter combination as the current optimal manufacturing parameter combination;

[0156] If not, use the second manufacturing parameter combination as the current optimal manufacturing parameter combination according to a probability, where the calculation formula of the probability is as follows:

[0157] ;

[0158] Wherein, is the natural logarithm, is the second quality evaluation coefficient, is the first quality evaluation coefficient, and C is the optimization rate factor.

[0159] Furthermore, the system further includes a constraint condition setting module to perform the following operation steps:

[0160] Obtain constraint conditions according to preset manufacturing requirements;

[0161] Obtain the first simulated manufacturing result in the diode manufacturing model based on the first combination of manufacturing parameters;

[0162] Judge whether the first simulated manufacturing result meets the constraint conditions. If so, retain the first simulated manufacturing test result and continue the optimization;

[0163] If not, abandon the first combination of manufacturing parameters and continue the optimization.

[0164] Through the foregoing detailed description of the method for optimizing the manufacturing parameters of a photovoltaic diode based on twin simulation in this specification, those skilled in the art can clearly know the system for optimizing the manufacturing parameters of a photovoltaic diode based on twin simulation in this embodiment. Since it corresponds to the method disclosed in the embodiment, it is described relatively simply. For related parts, refer to the description in the method part.

[0165] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as Figure 3 shown. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities; the memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium; the network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the method for optimizing the manufacturing parameters of a photovoltaic diode based on twin simulation.

[0166] Those skilled in the art can understand that Figure 3 the structure shown in

[0167] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0168] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing manufacturing parameters of a photovoltaic diode based on twin simulation, characterized in that, The method comprises: Acquiring diode characteristic data of a target photovoltaic diode, wherein the diode characteristic data includes material ratio characteristics and doping concentration characteristics; Based on the diode characteristic data, searching in a diode database to obtain a set of test data of the same family, wherein the diode database is constructed based on a photoelectric conversion efficiency test of a diode; Based on the digital twin technology, a diode test model is constructed based on the diode characteristic data and the same family test data set; Based on the diode test model, a simulation test is performed on the manufactured photovoltaic diode sample to generate a simulation test result, wherein the simulation test result includes multiple photoelectric conversion efficiencies under multiple working conditions; Determining the plurality of photoelectric conversion efficiencies, and obtaining N unqualified photoelectric conversion efficiencies that do not meet a photoelectric conversion efficiency threshold; Based on the N unqualified photoelectric conversion efficiencies, optimizing the manufacturing parameters of the target photovoltaic diode to obtain an optimal manufacturing parameter combination; Based on the optimal manufacturing parameter combination, performing manufacturing control of the target photovoltaic diode; The method of optimizing the manufacturing parameters of the target photovoltaic diode based on the N unqualified photoelectric conversion efficiencies to obtain the optimal manufacturing parameter combination includes: Based on the N unqualified photoelectric conversion efficiencies, constructing a material ratio optimization space and a doping concentration optimization space; In the material ratio optimization space and the doping concentration optimization space, randomly generating a first material ratio and a first doping concentration as a first manufacturing parameter combination; Using the first manufacturing parameter combination, performing a simulated manufacturing test in the diode test model to generate a first simulated manufacturing test result, wherein the first simulated manufacturing test result includes a simulated photoelectric conversion efficiency set and a simulated photoelectric conversion efficiency fluctuation coefficient; Construct a diode quality assessment function as follows: ； wherein, FDQ is the diode quality evaluation coefficient, is the i-th simulated photoelectric conversion efficiency under the i-th working condition, m is the number of multiple working conditions, is the weight of the i-th working condition, is the simulated photoelectric conversion efficiency fluctuation coefficient, is the first weight, is the second weight, and and the sum of is 1; Performing a quality assessment on the first simulation manufacturing test result according to the diode quality assessment function to obtain a first quality assessment coefficient; Continue to obtain a second manufacturing parameter combination and a second quality assessment coefficient, and perform manufacturing parameter combination comparison and screening based on the second quality assessment coefficient and the first quality assessment coefficient to obtain a current optimal manufacturing parameter combination; Continue iterating and optimizing until the convergence condition is met, and obtain the optimal manufacturing parameter combination; Based on the second quality assessment coefficient and the first quality assessment coefficient, performing comparison and screening of manufacturing parameter combinations to obtain the current optimal manufacturing parameter combination includes: Determine whether the second quality assessment coefficient is greater than the first quality assessment coefficient, and if so, use the second manufacturing parameter combination as the current optimal manufacturing parameter combination; If not, the second manufacturing parameter combination is used as the current optimal manufacturing parameter combination according to probability, wherein the calculation formula of the probability is as follows: ； wherein, is the natural logarithm, is the second quality evaluation coefficient, is the first quality evaluation coefficient, and C is the optimization rate factor.

2. The method according to claim 1, wherein Based on the diode characteristic data, a search is performed in a diode database to obtain a set of test data of the same family, including: Based on big data, multiple photoelectric conversion efficiency sets of multiple diode types under multiple working conditions are obtained to construct the diode database; Randomly extracting a first diode type from the diode database, obtaining a first diode feature, and a first photoelectric conversion efficiency set; Performing a similarity analysis on the diode characteristic data and the first diode characteristic to obtain a first characteristic similarity; If the first feature similarity meets a preset similarity threshold, the first photoelectric conversion efficiency set is used as the same family test data set.

3. The method according to claim 1, characterized in that, Based on the digital twin technology, based on the diode characteristic data and the same family test data set, a diode test model is constructed, including: Acquire basic equipment information of multiple main manufacturing equipment of the target photovoltaic diode, wherein the basic equipment information includes equipment serial information and equipment model information; Based on the equipment sequence information and equipment model information and based on digital twin technology, the plurality of main manufacturing equipment are modeled to obtain a diode manufacturing model; Mapping the diode characteristic data to the diode manufacturing model to generate a diode twin model; Based on the diode twin model, a plurality of photoelectric conversion efficiency test channels are constructed, wherein the plurality of photoelectric conversion efficiency test channels correspond to a plurality of working conditions; Based on the same family test data set, the multiple photoelectric conversion efficiency test channels are adjusted, and the adjusted multiple photoelectric conversion efficiency test channels are integrated to construct the diode test model.

4. The method according to claim 1, wherein Based on the N unqualified photoelectric conversion efficiencies, a material ratio optimization space and a doping concentration optimization space are constructed, including: Acquire a first operating condition of a first unqualified photoelectric conversion efficiency, and acquire a first photovoltaic diode manufacturing parameter record that meets a photoelectric conversion efficiency threshold under the first operating condition; Based on the first photovoltaic diode manufacturing parameter record, extracting a first material ratio information set and a first doping concentration information set; Based on the first material ratio information set and the first doping concentration information set, obtaining a first material ratio interval and a first doping concentration interval; Sequentially obtain a second material ratio interval, a third material ratio interval, and an Mth material ratio interval, and calculate the interval intersection of the first material ratio interval, the second material ratio interval, the third material ratio interval, and the Mth material ratio interval to obtain a target material ratio interval; sequentially obtaining a second doping concentration interval, a third doping concentration interval, and an Mth doping concentration interval, and calculating the interval intersection of the first doping concentration interval, the second doping concentration interval, the third doping concentration interval, and the Mth doping concentration interval to obtain a target doping concentration interval; A material ratio optimization space is established based on the target material ratio interval, and a doping concentration optimization space is established based on the target doping concentration interval.

5. The method according to claim 1, wherein Using the first manufacturing parameter combination, performing a simulated manufacturing test in the diode test model to generate a first simulated manufacturing test result, further comprising: Obtain constraints based on preset manufacturing requirements; Acquire a first simulation manufacturing result based on the first manufacturing parameter combination in a diode manufacturing model; Determine whether the first simulation manufacturing result satisfies the constraint condition, and if so, retain the first simulation manufacturing test result and continue to search for the best result; If not, the first manufacturing parameter combination is abandoned and the optimization process continues.

6. A photovoltaic diode manufacturing parameter optimization system based on twin simulation, characterized in that, A system for implementing the photovoltaic diode manufacturing parameter optimization method based on twin simulation according to any one of claims 1 to 5, the system comprising: A characteristic data acquisition module, wherein the characteristic data acquisition module is used to acquire diode characteristic data of a target photovoltaic diode, wherein the diode characteristic data includes material ratio characteristics and doping concentration characteristics; A characteristic data retrieval module, the characteristic data retrieval module is used to search in a diode database based on the diode characteristic data to obtain a set of test data of the same family, wherein the diode database is constructed based on a photoelectric conversion efficiency test of the diode; A test model construction module, wherein the test model construction module is used to construct a diode test model based on the digital twin technology, the diode characteristic data, and the same family test data set; A simulation test module, the simulation test module is used to perform a simulation test on the manufactured photovoltaic diode sample based on the diode test model to generate a simulation test result, wherein the simulation test result includes a plurality of photoelectric conversion efficiencies under a plurality of working conditions; A conversion efficiency determination module, the conversion efficiency determination module is used to determine the multiple photoelectric conversion efficiencies and obtain N unqualified photoelectric conversion efficiencies that do not meet the photoelectric conversion efficiency threshold; A manufacturing parameter optimization module, wherein the manufacturing parameter optimization module is used to optimize the manufacturing parameters of the target photovoltaic diode based on the N unqualified photoelectric conversion efficiencies to obtain an optimal manufacturing parameter combination; A manufacturing control module, the manufacturing control module is used to perform manufacturing control of the target photovoltaic diode based on the optimal manufacturing parameter combination; The method of optimizing the manufacturing parameters of the target photovoltaic diode based on the N unqualified photoelectric conversion efficiencies to obtain the optimal manufacturing parameter combination includes: Based on the N unqualified photoelectric conversion efficiencies, constructing a material ratio optimization space and a doping concentration optimization space; In the material ratio optimization space and the doping concentration optimization space, randomly generating a first material ratio and a first doping concentration as a first manufacturing parameter combination; Using the first manufacturing parameter combination, performing a simulated manufacturing test in the diode test model to generate a first simulated manufacturing test result, wherein the first simulated manufacturing test result includes a simulated photoelectric conversion efficiency set and a simulated photoelectric conversion efficiency fluctuation coefficient; Construct a diode quality assessment function as follows: ； Among them, FDQ is the diode quality evaluation coefficient, is the i-th simulated photoelectric conversion efficiency under the i-th working condition, m is the number of multiple working conditions, is the weight of the i-th working condition, is the simulated photoelectric conversion efficiency fluctuation coefficient, is the first weight, is the second weight, and 、 The sum of is 1; Performing a quality assessment on the first simulation manufacturing test result according to the diode quality assessment function to obtain a first quality assessment coefficient; Continue to obtain a second manufacturing parameter combination and a second quality assessment coefficient, and perform manufacturing parameter combination comparison and screening based on the second quality assessment coefficient and the first quality assessment coefficient to obtain a current optimal manufacturing parameter combination; Continue iterating and optimizing until the convergence condition is met, and obtain the optimal manufacturing parameter combination.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the photovoltaic diode manufacturing parameter optimization method based on twin simulation described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method for optimizing photovoltaic diode manufacturing parameters based on twin simulation according to any one of claims 1 to 5.

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