Process for improving the production of light emitting display materials

Abstract

The application relates to the technical field of data processing of light-emitting display material manufacturing, and provides a light-emitting display material manufacturing process, which is a brand-new solution for solving the problem that the prior art cannot effectively process and analyze data in the manufacturing process to reduce errors, receives a manufacturing process task and generates corresponding control parameters, collects data generated by equipment execution parameters in real time, processes abnormal values and fluctuation data by using an optimization algorithm, adjusts the control parameters and verifies the effect, constructs a digital twin model if the error exceeds a preset range, optimizes the simulation model by using a genetic algorithm, and further adjusts the control parameters, so that the manufacturing error of the light-emitting display material is effectively reduced, the accuracy of the manufacturing process is significantly improved, and the application is suitable for light-emitting display material manufacturing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology for the fabrication of light-emitting display materials, and more particularly to an improvement in the fabrication process of light-emitting display materials. Background Technology

[0002] The fabrication process of light-emitting display materials mainly includes substrate preparation, thin film deposition, and encapsulation. In the substrate preparation stage, ITO glass is typically used as the substrate. Rigorous cleaning and surface pretreatment processes ensure the cleanliness and flatness of the substrate, providing a good foundation for subsequent thin film deposition. Thin film deposition is the core of the light-emitting display material fabrication process, primarily including vacuum evaporation and inkjet printing. Vacuum evaporation involves heating organic materials in a high-vacuum environment to evaporate and deposit them onto the substrate to form a thin film. Inkjet printing, on the other hand, uses inkjet equipment to precisely deposit a solution of organic materials onto the substrate. Finally, in the encapsulation stage, adhesives and covering materials are used to isolate the organic and metal thin films from external water and air to protect the device's performance and extend its lifespan. The entire fabrication process must be carried out in a clean environment to ensure product quality and stability.

[0003] In the manufacturing process of light-emitting display materials, the following technical challenges exist in reducing material errors through data processing.

[0004] Different luminescent materials have different luminous efficiency, lifespan and other characteristics, which directly affect the display quality of images. This effect is particularly pronounced in the manufacturing process of large-size displays. Current technology cannot reduce the manufacturing error of luminescent display materials by processing and analyzing the data generated during the manufacturing process. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an improved manufacturing process for luminescent display materials, solving the problem that existing technologies cannot reduce manufacturing errors by processing and analyzing data generated during the manufacturing process of luminescent display materials.

[0006] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0007] This invention provides an improved manufacturing process for light-emitting display materials, comprising:

[0008] Step S101: Receive the light-emitting display material manufacturing process task, send the light-emitting display material manufacturing process task to the preset light-emitting display material manufacturing process control model, output the light-emitting display material manufacturing process control parameters, send the light-emitting display material manufacturing process control parameters to the light-emitting display material manufacturing equipment, collect the data generated by the light-emitting display material manufacturing equipment in executing the light-emitting display material manufacturing process control parameters, and obtain the basic data for light-emitting display material manufacturing.

[0009] Step S102: Process the collected basic data for manufacturing luminescent display materials, identify outliers and fluctuations in the basic data for manufacturing luminescent display materials, obtain the outlier and fluctuation data in the basic data for manufacturing luminescent display materials, and use a preset optimization algorithm to optimize the outlier and fluctuation data in the basic data for manufacturing luminescent display materials to generate optimized data for manufacturing luminescent display materials.

[0010] Step S103: Based on the optimized basic data for manufacturing light-emitting display materials, adjust the process control parameters for manufacturing light-emitting display materials to obtain the adjusted process control parameters. Send the adjusted process control parameters to the manufacturing equipment and collect the manufacturing operation data of the equipment executing the adjusted process control parameters to obtain the manufacturing operation data.

[0011] Step S104: Substitute the adjusted light-emitting display material manufacturing process control parameters into the preset light-emitting display material manufacturing simulation model, output the light-emitting display material manufacturing simulation results, compare the light-emitting display material manufacturing operation data with the light-emitting display material manufacturing simulation results, obtain the light-emitting display material manufacturing error data, and compare the light-emitting display material manufacturing error data with the preset error range value.

[0012] Step S105: If the manufacturing error data of the light-emitting display material exceeds the preset error range, the data generated during the data processing in steps S101, S102, S103, and S104 is used to obtain a dataset to be processed. A digital twin model of the manufacturing process of the light-emitting display material is constructed based on the dataset to be processed. A mirror model of the manufacturing process of the light-emitting display material is established to obtain a mirror model group of the manufacturing process of the light-emitting display material. The manufacturing error data of the light-emitting display material and the preset simulation model of the manufacturing process of the light-emitting display material are sequentially configured into the mirror model group of the manufacturing process of the light-emitting display material. A genetic algorithm is used to optimize the preset simulation model of the manufacturing process of the light-emitting display material in each mirror model group of the manufacturing process of the light-emitting display material to obtain an optimized simulation model of the manufacturing process of the light-emitting display material. The optimized simulation model of the manufacturing process of the light-emitting display material is used to process the adjusted control parameters of the manufacturing process of the light-emitting display material to obtain optimized control parameters of the manufacturing process of the light-emitting display material. The optimized control parameters of the manufacturing process of the light-emitting display material are sent to the manufacturing equipment of the light-emitting display material.

[0013] Furthermore, in the improved light-emitting display material manufacturing process of the present invention, step S101 includes:

[0014] The task of fabricating light-emitting display materials includes information on the required material types, specifications, quantities, and expected performance indicators;

[0015] The process control model for the fabrication of light-emitting display materials is built based on historical data, empirical formulas, and machine learning algorithms. It is used to generate corresponding process control parameters according to task requirements. The process control model outputs process control parameters for the fabrication of light-emitting display materials based on the input process task. The process control parameters for the fabrication of light-emitting display materials include temperature, pressure, time, and material ratio.

[0016] During the execution of process control parameters for the fabrication of light-emitting display materials in the equipment, data generated by the equipment in real time are collected, including temperature, pressure, time, current, voltage, and material state change data, to obtain basic data for the fabrication of light-emitting display materials.

[0017] Furthermore, in the improved light-emitting display material manufacturing process of the present invention, step S102 includes:

[0018] Perform time series analysis on the data to identify fluctuation patterns. For the identified outliers, select to delete, replace, or retain them and mark them.

[0019] For fluctuating data, filtering, smoothing, or prediction methods are used to reduce fluctuations and improve data stability. Preset optimization algorithms are selected, including genetic algorithms, particle swarm optimization, and simulated annealing, to optimize the processed data. Through the processing of optimization algorithms, optimized basic data for the fabrication of light-emitting display materials are obtained.

[0020] Furthermore, in the improved process for manufacturing light-emitting display materials described in this invention, step S103 includes:

[0021] Based on the optimized data on the basic data for manufacturing luminescent display materials, determine the direction and extent to which the process control parameters for manufacturing luminescent display materials need to be adjusted.

[0022] In the process control model for the fabrication of light-emitting display materials, the process control parameters for the fabrication of light-emitting display materials are adjusted according to the determined adjustment direction so that the adjusted parameter values ​​are within the acceptable range of the equipment and meet the requirements of the production process.

[0023] The adjusted process control parameters for the manufacturing of light-emitting display materials are compiled into a parameter set and sent to the light-emitting display material manufacturing equipment via a communication interface.

[0024] Furthermore, in the improved light-emitting display material manufacturing process of the present invention, step S104 includes:

[0025] Start the preset simulation model for the production of light-emitting display materials, perform simulation calculations based on the input and adjusted process control parameters for the production of light-emitting display materials, and output the simulation results of the production of light-emitting display materials after the simulation model is completed.

[0026] The simulation results of luminescent display material fabrication include the effects of product quality indicators, process parameters, and changes during the luminescent display material fabrication process;

[0027] The simulation results of luminescent display material manufacturing are compared with the actual and simulated values ​​of luminescent display material manufacturing operation data. The comparison includes the differences between the actual and simulated values ​​of product quality indicators and process parameters. The comparison results of the simulation results and the actual and simulated values ​​of luminescent display material manufacturing operation data are obtained.

[0028] Based on the comparison between the simulation results of the light-emitting display material manufacturing and the operational data of the light-emitting display material manufacturing, the error data of the light-emitting display material manufacturing is calculated, and the calculated manufacturing error data is compared with the preset error range value.

[0029] If the error data is within the preset error range, the adjustment of the light-emitting display material manufacturing process control parameters is effective, and production can continue using the adjusted light-emitting display material manufacturing process control parameters.

[0030] Furthermore, in the improved light-emitting display material manufacturing process of the present invention, step S105 includes:

[0031] Within the framework of the digital twin model, a mirror model of the manufacturing process of luminescent display materials is established based on the dataset to be processed; according to different parts or stages of the manufacturing process, the mirror model is subdivided into multiple sub-models to form a mirror model group;

[0032] The error data of light-emitting display material manufacturing and the preset simulation model of light-emitting display material manufacturing are sequentially configured into each mirror model group. The genetic algorithm is used to optimize the preset simulation model of light-emitting display material manufacturing in each mirror model group. The optimized simulation model is verified. If the verification result is satisfactory, the optimized simulation model of light-emitting display material manufacturing is used for subsequent data processing.

[0033] Furthermore, in the improved light-emitting display material manufacturing process of the present invention, step S105 includes:

[0034] The specific steps for configuring the light-emitting display material manufacturing error data and the preset light-emitting display material manufacturing simulation model into the light-emitting display material manufacturing process mirror model group, and then using a genetic algorithm for optimization, to finally obtain the optimized light-emitting display material manufacturing process control parameters are as follows:

[0035] Import the manufacturing error data of the light-emitting display material into the mirror model group of the manufacturing process of the light-emitting display material, and also load the preset simulation model of the manufacturing process of the light-emitting display material into the mirror model group of the manufacturing process of the light-emitting display material.

[0036] Initialize the genetic algorithm and determine its objective function, which is the error between the simulation results of the light-emitting display material fabrication and the actual fabrication data.

[0037] The parameters in the preset simulation model for manufacturing light-emitting display materials are encoded into a gene form that can be processed by a genetic algorithm. Each gene represents a parameter in the simulation model for manufacturing light-emitting display materials, and the combination of genes represents the parameter set of the entire simulation model for manufacturing light-emitting display materials.

[0038] An initial population is generated by using a random or specific method to generate an initial set of parameters for the simulation model of luminescent display materials, which serves as the initial population for the genetic algorithm. Each parameter set corresponds to a simulation model of luminescent display materials, which is used for subsequent evaluation and optimization.

[0039] Assessing population fitness:

[0040] Each parameter set (i.e., each individual) in the initial population is evaluated, and its fitness value is calculated.

[0041] The fitness value is based on the degree of matching between the simulation results of luminescent display material fabrication generated using a parameter set in the simulation model and the actual fabrication data of the luminescent display material.

[0042] The process involves evaluation, selection, crossover, and mutation until the preset number of generations or the desired optimization result is achieved. After the genetic algorithm completes its iterations, the individual with the highest fitness value is selected from the final population as the optimized simulation model for manufacturing luminescent display materials. The adjusted process control parameters for manufacturing luminescent display materials are then input into the optimized simulation model.

[0043] The optimized simulation model for the fabrication of light-emitting display materials is used to perform simulation calculations based on the adjusted process control parameters for the fabrication of light-emitting display materials, and the predicted results of the optimized process control parameters are obtained.

[0044] The beneficial effects of this invention are:

[0045] This invention, through data processing and optimization algorithms, reduces errors in the manufacturing process of luminescent display materials, thereby improving manufacturing precision and enhancing product quality stability. Utilizing the optimization capabilities of simulation models and genetic algorithms, this invention continuously adjusts and optimizes manufacturing process control parameters, making the manufacturing process more refined and efficient. By optimizing the manufacturing process, this invention reduces material waste and defect rates, thereby lowering production costs and improving economic benefits.

[0046] This invention reduces manual intervention through automated and intelligent data processing and analysis, thereby improving production efficiency and shortening production cycles. Providing a high-quality, high-efficiency manufacturing process for light-emitting display materials, this invention helps enterprises gain a competitive edge and enhance their market competitiveness in a fiercely competitive market.

[0047] This invention applies advanced technologies such as data processing, optimization algorithms, simulation models, and genetic algorithms to the manufacturing process of light-emitting display materials, driving technological progress and innovation in this field. By improving product quality and stability, this invention meets customer expectations and needs, thereby enhancing customer satisfaction and trust in the product. Furthermore, by optimizing the manufacturing process and reducing material waste, this invention reduces environmental impact, aligning with the principles and requirements of sustainable development and contributing to the green and sustainable development of the industry.

[0048] In summary, this invention has significant beneficial effects on improving the manufacturing process of light-emitting display materials. It not only enhances product quality and production efficiency but also promotes technological progress and sustainable development, bringing substantial benefits to enterprises and customers. Attached Figure Description

[0049] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the accompanying drawings without creative effort.

[0050] Figure 1 This is a schematic flowchart illustrating the improved manufacturing process of light-emitting display materials according to an embodiment of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings. To better understand the objectives of this invention, it will be described in further detail below.

[0052] This invention provides an improved manufacturing process for light-emitting display materials, comprising:

[0053] Step S101: Receive the light-emitting display material manufacturing process task, send the light-emitting display material manufacturing process task to the preset light-emitting display material manufacturing process control model, output the light-emitting display material manufacturing process control parameters, send the light-emitting display material manufacturing process control parameters to the light-emitting display material manufacturing equipment, collect the data generated by the light-emitting display material manufacturing equipment in executing the light-emitting display material manufacturing process control parameters, and obtain the basic data for light-emitting display material manufacturing.

[0054] Step S101: Receive the task of manufacturing process for light-emitting display materials, and generate and execute the corresponding manufacturing process control parameters.

[0055] Receiving Task Information: The system receives tasks related to the fabrication process of luminescent display materials through a user interface or automation interface. This task information includes details such as the required material type (e.g., organic luminescent materials, inorganic luminescent materials, etc.), specifications (e.g., dimensions, thickness, etc.), quantity, and expected performance indicators (e.g., luminous efficiency, lifespan, color purity, etc.).

[0056] Task Analysis: The received task information is analyzed in detail to extract key parameters and requirements, providing precise guidance for subsequent steps.

[0057] Construction and application of a process control model for the fabrication of luminescent display materials;

[0058] The manufacturing process control model for luminescent display materials is built upon rich historical data, empirical formulas, and advanced machine learning algorithms. This model can accurately generate corresponding manufacturing process control parameters based on the input task requirements. These parameters cover multiple aspects, including temperature, pressure, time, and material ratios. Extensive data collection from historical production tasks, including both successful and failed cases, provides comprehensive samples for model training. Industry-accumulated empirical formulas are effectively integrated into the model, further enhancing its practicality and accuracy.

[0059] Advanced machine learning algorithms such as neural networks and support vector machines are used to train and optimize the model, enabling it to automatically generate optimal manufacturing process control parameters based on task requirements. The parsed task information is accurately input into the luminescent display material manufacturing process control model, which then generates the corresponding manufacturing process control parameters based on this information.

[0060] This invention sends the generated manufacturing process control parameters to the light-emitting display material fabrication equipment via a communication interface (such as TCP / IP, serial communication, etc.). Upon receiving the control parameters, the light-emitting display material fabrication equipment strictly executes the corresponding manufacturing process steps according to the parameter requirements. These steps may include substrate processing, thin film deposition (such as vacuum evaporation and inkjet printing), and encapsulation.

[0061] During the fabrication process control parameters of the light-emitting display material manufacturing equipment, various data generated by the equipment are collected in real time. This data includes temperature, pressure, time, current, voltage, and material state changes (such as evaporation rate and deposition thickness). The collected data is properly stored in a database to facilitate subsequent data processing and analysis.

[0062] To continuously improve the model's accuracy and practicality, the following sub-technical solutions are employed for training and optimization: cross-validation is used for rigorous training to enhance the model's generalization ability. Grid search and random search methods are used to fine-tune the model's hyperparameters to find the optimal parameter combination. New data is continuously collected during production, and this data is used for online model learning, enabling the model to flexibly adapt to new production tasks and requirements.

[0063] To improve the accuracy and completeness of the data, the following lower-level technical solutions are adopted to optimize data acquisition and monitoring:

[0064] By effectively combining data from different sensors and devices, the overall accuracy of the data is improved. Machine learning algorithms are used to detect anomalies in the collected data, and an alarm mechanism is immediately triggered upon detection of abnormal data for timely handling.

[0065] Before data storage, the collected data undergoes comprehensive cleaning and preprocessing, such as noise removal and missing value filling, to improve the accuracy of subsequent data analysis.

[0066] Step S102: Process the collected basic data for manufacturing luminescent display materials, identify outliers and fluctuations in the basic data for manufacturing luminescent display materials, obtain the outlier and fluctuation data in the basic data for manufacturing luminescent display materials, and use a preset optimization algorithm to optimize the outlier and fluctuation data in the basic data for manufacturing luminescent display materials to generate optimized data for manufacturing luminescent display materials.

[0067] Identifying outliers: Use statistical methods (such as Z-score, IQR, etc.) or machine learning algorithms (such as Isolation Forest) to effectively identify outliers in the data.

[0068] Handling outliers:

[0069] Deletion: For obvious errors or outliers, delete them directly.

[0070] Replacement: Use the mean, median, or interpolation method for appropriate replacement.

[0071] Labeling: Outliers are labeled for further analysis later.

[0072] Data cleaning involves thoroughly removing duplicate data. Missing values ​​can be filled using methods such as interpolation, mean imputation, or predictive imputation based on machine learning models.

[0073] In-depth time series analysis is conducted on the data to accurately identify trends, seasonality, and cyclical components. Methods such as moving averages and exponential smoothing are used to effectively remove random fluctuations and improve data stability.

[0074] Low-pass filtering: Removes high-frequency noise and retains low-frequency signals.

[0075] Bandpass filtering: retains signals within a specific frequency range while removing noise at other frequencies.

[0076] Kalman filtering: It is particularly suitable for smoothing data in dynamic systems, combining predicted and measured data to obtain the optimal estimate.

[0077] Fluctuation identification accurately identifies fluctuation patterns in data through time series decomposition or statistical methods.

[0078] Reduced volatility;

[0079] Smoothing techniques, such as moving averages and Savitzky-Golay filters, are used to effectively reduce short-term fluctuations in data.

[0080] Forecasting methods: Use time series forecasting models (such as ARIMA, LSTM, etc.) to accurately predict future data and reduce the uncertainty caused by random fluctuations.

[0081] Carefully select appropriate optimization algorithms based on data characteristics and optimization objectives, such as genetic algorithms, particle swarm optimization (PSO), and simulated annealing (SA).

[0082] Genetic Algorithm:

[0083] Encoding: Encoding data or parameters into gene form.

[0084] Initialize the population: Randomly generate a set of initial solutions as the population.

[0085] Fitness assessment: The fitness of each individual is accurately calculated based on the optimization objective function.

[0086] Selection, crossover, and mutation: Generate a new generation of population through selection, crossover, and mutation operations.

[0087] Iteration: Repeat the above process until the preset number of generations is reached or the optimization objective is met.

[0088] Particle Swarm Optimization (PSO):

[0089] Initialize the particle swarm: Randomly generate a set of particles, each particle representing a solution.

[0090] Velocity update: Update the particle velocity in a timely manner based on the individual optimal solution and the global optimal solution.

[0091] Position update: Update the particle's position based on its velocity.

[0092] Iteration: Repeat the velocity update and position update until the stopping condition is met.

[0093] Simulated Annealing (SA):

[0094] Initial solution: A random initial solution is generated.

[0095] Neighborhood search: Generate a new solution within the neighborhood of the current solution.

[0096] Acceptance Criterion: Decide whether to accept a new solution based on the Metropolis criterion.

[0097] Cooling down: Gradually reduce the temperature parameters.

[0098] Iteration: Repeat the neighborhood search, acceptance criteria, and cooling process until the stopping condition is met.

[0099] After processing with the optimized algorithm, the final optimized basic data for manufacturing luminescent display materials is obtained.

[0100] Step S103: Based on the optimized basic data for manufacturing light-emitting display materials, adjust the process control parameters for manufacturing light-emitting display materials to obtain the adjusted process control parameters. Send the adjusted process control parameters to the manufacturing equipment and collect the manufacturing operation data of the equipment executing the adjusted process control parameters to obtain the manufacturing operation data.

[0101] Based on the optimized data for manufacturing luminescent display materials, the effectiveness of the current manufacturing process control parameters is accurately evaluated, and the direction and extent of adjustments are determined.

[0102] Establish evaluation indicators: Based on the characteristics of light-emitting display materials, establish a series of evaluation indicators, such as material deposition rate, thin film uniformity, and luminous efficiency. These indicators can comprehensively reflect the effect of the manufacturing process.

[0103] Comparative analysis: The optimized basic data is compared with the preset standard data in detail. Through statistical methods and visualization tools, the differences between each evaluation indicator and the standard value are identified, providing a basis for subsequent parameter adjustments.

[0104] Based on the evaluation results, specific adjustment strategies for the process control parameters of the light-emitting display material manufacturing process were determined to improve the manufacturing process.

[0105] Rule base establishment: Based on historical data and expert experience, establish a library containing adjustment rules. Each rule should clearly describe how to adjust the corresponding manufacturing process control parameters, such as the adjustment range and direction, when a certain evaluation indicator deviates from the standard value.

[0106] Rule matching and execution: Based on the evaluation results, the corresponding adjustment rules are matched in the rule base, and the adjusted manufacturing process control parameters are generated. This step can be automated to improve the efficiency and accuracy of the adjustment.

[0107] Provided that the adjusted parameter values ​​are within the acceptable range of the equipment and meet the requirements of the production process, the manufacturing process control parameters are finely adjusted to further improve the manufacturing quality.

[0108] Before adjusting the parameters, first verify that the adjusted parameter values ​​are within the acceptable range of the device. If they are outside the range, appropriate adjustments are needed, such as through linear interpolation or limiting the maximum / minimum values, to ensure that the device can operate normally.

[0109] For key parameters, a multi-round iterative optimization approach can be adopted. In each iteration, based on the production effect under the current parameters, the parameter values ​​are further fine-tuned, and steps S101 to S104 are repeated until the optimal production effect is achieved. During this process, machine learning algorithms or optimization algorithms can be used to assist in parameter adjustment to improve optimization efficiency.

[0110] The adjusted manufacturing process control parameters are compiled into a parameter set and sent to the light-emitting display material manufacturing equipment through a communication interface to achieve real-time parameter updates and application.

[0111] The adjusted manufacturing process control parameters are then organized into a parameter set according to the equipment's required format. This step requires ensuring that the parameter set format matches the equipment's interface specifications to avoid communication errors.

[0112] Proper configuration of the communication interface, including communication protocol and transmission rate, is essential to ensure that parameter sets are sent accurately and promptly to the manufacturing equipment. Additionally, an error handling mechanism needs to be established to address any anomalies that may occur during communication.

[0113] Step S104: Substitute the adjusted light-emitting display material manufacturing process control parameters into the preset light-emitting display material manufacturing simulation model, output the light-emitting display material manufacturing simulation results, compare the light-emitting display material manufacturing operation data with the light-emitting display material manufacturing simulation results, obtain the light-emitting display material manufacturing error data, and compare the light-emitting display material manufacturing error data with the preset error range value.

[0114] First, a pre-built and rigorously validated simulation model for the fabrication of luminescent display materials is initiated. This simulation model must be capable of simulating various physical and chemical changes during the fabrication process, and also reflect the impact of different process parameters on the quality of the final product.

[0115] The simulation model for fabricating light-emitting display materials employs a physics-based modeling approach, closely aligned with the working principles of organic light-emitting diodes (OLEDs). The model must consider key factors such as material evaporation rate, deposition thickness, and current density distribution to establish a high-precision mathematical model.

[0116] The adjusted process control parameters for the light-emitting display material manufacturing process (including temperature, pressure, time, material ratio, etc.) from step S103 are accurately input into the simulation model. These parameters are transmitted in a structured data format (such as JSON, XML, etc.) through the model's standardized input interface to ensure that the simulation model can correctly parse and utilize these parameters for subsequent simulation calculations.

[0117] The simulation model for manufacturing luminescent display materials performs a series of complex calculations and simulations based on the input process control parameters, ultimately outputting the simulation results of luminescent display material manufacturing. These results should comprehensively reflect product quality indicators (such as luminous efficiency, color purity, and lifespan), the influence of process parameters, and various changes during the manufacturing process.

[0118] To improve computational speed and accuracy, simulations can employ high-performance computing techniques such as parallel computing and distributed computing. Simulation results should be output in an intuitive format (such as charts, reports, etc.) to facilitate subsequent analysis and comparison.

[0119] When the equipment for manufacturing light-emitting display materials is running according to the adjusted process control parameters, it is necessary to collect the actual operating data of the equipment in real time. This data should cover key information such as temperature, pressure, time, current, voltage, and changes in the state of the material.

[0120] High-precision sensors and data acquisition systems should be used to ensure data accuracy. Simultaneously, data caching and preprocessing mechanisms should be established to perform preliminary processing on the acquired data (such as noise reduction and filtering) to improve data reliability and effectiveness.

[0121] A comprehensive and detailed comparison is made between the simulation results and actual operating data, including the differences between actual and simulated values ​​of product quality indicators and process parameters. Data comparison and statistical analysis methods (such as mean square error (MSE) and root mean square error (RMSE)) are used to quantify the differences between the simulation results and actual operating data. Simultaneously, comparative charts are created to visually demonstrate the differences and trends, providing strong support for subsequent analysis.

[0122] Based on the comparison results, calculate the error data for the fabrication of the luminescent display material. This error data should accurately reflect the degree of deviation between the simulation results and the actual operating data. Clearly define the error calculation formulas and indicators (such as absolute error, relative error, etc.). Obtain specific error data values ​​by calculating these indicators. Simultaneously, classify and statistically analyze the error data (e.g., by process parameters, equipment status, etc.) for subsequent analysis and improvement.

[0123] The calculated error data is compared with the preset error range value to determine whether the error is within an acceptable range.

[0124] The preset error range should be reasonably determined based on actual application requirements and process standards. When making comparisons, a clear threshold or tolerance range should be set. If the error data exceeds this range, the error is considered unacceptable and further adjustments and optimizations are required.

[0125] Step S105: If the manufacturing error data of the light-emitting display material exceeds the preset error range, the data generated during the data processing in steps S101, S102, S103, and S104 is used to obtain a dataset to be processed. A digital twin model of the manufacturing process of the light-emitting display material is constructed based on the dataset to be processed. A mirror model of the manufacturing process of the light-emitting display material is established to obtain a mirror model group of the manufacturing process of the light-emitting display material. The manufacturing error data of the light-emitting display material and the preset simulation model of the manufacturing process of the light-emitting display material are sequentially configured into the mirror model group of the manufacturing process of the light-emitting display material. A genetic algorithm is used to optimize the preset simulation model of the manufacturing process of the light-emitting display material in each mirror model group of the manufacturing process of the light-emitting display material to obtain an optimized simulation model of the manufacturing process of the light-emitting display material. The optimized simulation model of the manufacturing process of the light-emitting display material is used to process the adjusted control parameters of the manufacturing process of the light-emitting display material to obtain optimized control parameters of the manufacturing process of the light-emitting display material. The optimized control parameters of the manufacturing process of the light-emitting display material are sent to the manufacturing equipment of the light-emitting display material.

[0126] Collect all relevant data generated in steps S101 to S104. This data includes, but is not limited to, basic data on the fabrication of light-emitting display materials, outlier and fluctuation data, optimized data, adjusted manufacturing process control parameters, fabrication operation data of light-emitting display materials, and manufacturing error data.

[0127] The collected data undergoes rigorous cleaning to remove duplicate, redundant, or erroneous data points, ensuring accuracy, consistency, and reliability. The cleaned data is then integrated into a unified dataset for further processing, providing a solid data foundation for the subsequent construction of the digital twin model.

[0128] Based on the concept of digital twins, a mirror model framework for the fabrication process of luminescent display materials is designed using the dataset to be processed. This framework should be able to comprehensively and accurately reflect each step and stage of the actual fabrication process.

[0129] The sub-model is further subdivided according to different parts or stages of the manufacturing process, such as substrate processing, thin film deposition, and packaging. Each sub-model is responsible for simulating the process and data changes of the corresponding stage, improving the model's precision and accuracy.

[0130] The various sub-models are organically combined to form a complete mirror model group. This model group should be able to comprehensively and systematically reflect the entire process and data flow of the light-emitting display material manufacturing process, providing strong support for subsequent optimization.

[0131] In genetic algorithms, the error between the simulated results of luminescent display material fabrication and the actual fabrication data is explicitly used as the objective function. The optimization goal is to minimize this error, thereby improving the accuracy and reliability of the simulation results.

[0132] Parameter encoding: The parameters in the pre-defined simulation model of luminescent display material fabrication are encoded into a gene format that can be processed by the genetic algorithm. Each gene represents a parameter in the simulation model, and the combination of genes represents the parameter set of the entire simulation model, providing the basis for the operation of the genetic algorithm.

[0133] Initial Population Generation: An initial set of parameters for luminescent display material fabrication simulation models is generated using random or specific methods, serving as the initial population for the genetic algorithm. Each parameter set corresponds to a possible luminescent display material fabrication simulation model, providing diverse starting points for subsequent optimization.

[0134] Fitness assessment: Fitness is assessed for each parameter set (i.e., each individual) in the initial population, and its fitness value is calculated. The fitness value is based on the degree of matching between the simulation results generated by the simulation model using the parameter set and the operational data of the light-emitting display material fabrication, reflecting the individual's quality.

[0135] Genetic operations: Performing genetic operations such as selection, crossover, and mutation to progressively optimize individuals in the population. Selection retains individuals with high fitness, crossover generates new combinations of individuals, and mutation introduces new genetic variations, increasing population diversity and optimization potential.

[0136] Iterative optimization: Repeatedly execute genetic operations until the preset number of generations is reached or the optimization requirements are met (e.g., the error is less than a preset threshold). In each iteration, the population is updated based on the new fitness value, driving the population to evolve in a better direction.

[0137] Optimization result verification: After the genetic algorithm completes its iterations, the individual with the highest fitness value is selected from the final population as the optimized luminescent display material simulation model. The optimized model undergoes rigorous verification to ensure it accurately simulates the actual manufacturing process and significantly reduces manufacturing errors.

[0138] Application of optimized manufacturing process control parameters

[0139] Parameter Input and Simulation: The adjusted process control parameters for the luminescent display material manufacturing are input into the optimized simulation model for luminescent display material manufacturing, and simulation calculations are performed. This step is crucial for verifying the optimization effect.

[0140] Results Output and Analysis: The simulation model outputs predicted results of optimized manufacturing process control parameters, including product quality indicators and the impact of process parameters. The predicted results are analyzed in depth to ensure they meet practical application requirements and process standards.

[0141] Parameter sending and execution: If the prediction results are satisfactory, the optimized manufacturing process control parameters are sent to the light-emitting display material manufacturing equipment to execute the new manufacturing process.

[0142] Specifically, the improved light-emitting display material manufacturing process of the present invention includes step S101, which comprises:

[0143] The task of fabricating light-emitting display materials includes information on the required material types, specifications, quantities, and expected performance indicators;

[0144] The process control model for the fabrication of light-emitting display materials is built based on historical data, empirical formulas, and machine learning algorithms. It is used to generate corresponding process control parameters according to task requirements. The process control model outputs process control parameters for the fabrication of light-emitting display materials based on the input process task. The process control parameters for the fabrication of light-emitting display materials include temperature, pressure, time, and material ratio.

[0145] During the execution of process control parameters for the fabrication of light-emitting display materials in the equipment, data generated by the equipment in real time are collected, including temperature, pressure, time, current, voltage, and material state change data, to obtain basic data for the fabrication of light-emitting display materials.

[0146] Task Reception: The system receives luminescent display material fabrication process tasks from users or upstream systems. These tasks are sent in structured data formats (such as JSON, XML, etc.), which include information on the required material type, specifications, quantity, and expected performance indicators.

[0147] The extracted key information is fed into the process control model for the fabrication of light-emitting display materials. This model is built upon historical data, empirical formulas, and machine learning algorithms (such as neural networks and decision trees), and can generate corresponding process control parameters according to task requirements. These parameters include temperature, pressure, time, and material ratios, all of which are crucial to the quality and efficiency of light-emitting display material fabrication.

[0148] During the execution of control parameters in the light-emitting display material fabrication equipment, the system collects data generated by the equipment in real time. This real-time data includes temperature, pressure, time, current, voltage, and material state changes, which are used for subsequent data processing and analysis. During data acquisition, the system ensures the integrity and accuracy of the data to avoid deviations in the manufacturing process due to data errors.

[0149] Specifically, the improved light-emitting display material manufacturing process of the present invention, step S102 includes:

[0150] Perform time series analysis on the data to identify fluctuation patterns. For the identified outliers, select to delete, replace, or retain them and mark them.

[0151] For fluctuating data, filtering, smoothing, or prediction methods are used to reduce fluctuations and improve data stability. Preset optimization algorithms are selected, including genetic algorithms, particle swarm optimization, and simulated annealing, to optimize the processed data. Through the processing of optimization algorithms, optimized basic data for the fabrication of light-emitting display materials are obtained.

[0152] Time series analysis: Time series analysis is performed on the collected basic data of luminescent display material production to identify fluctuation patterns and outliers in the data. Time series analysis uses methods such as sliding windows or autoregressive models to capture the trend and periodic fluctuations of the data over time.

[0153] Outlier Handling: For identified outliers, appropriate handling methods are selected based on their impact on the overall data and the requirements of the manufacturing process. If an outlier is caused by equipment failure or operational error and has a significant impact on subsequent analysis, the data point is deleted. If an outlier is representative or reflects a specific process condition, it is replaced with the average or median of adjacent data, or retained but marked for consideration in subsequent analysis.

[0154] Fluctuating data processing: For fluctuating data, filtering, smoothing, or forecasting methods are used to reduce volatility and improve data stability. Filtering methods include mean filtering, median filtering, or Kalman filtering to smooth data and remove noise; smoothing methods use moving averages or exponential smoothing to smooth data trends; forecasting methods utilize time series forecasting models, such as ARIMA models or LSTM neural networks, to predict and correct fluctuating data.

[0155] Optimization Algorithm Application: Select a preset optimization algorithm, such as genetic algorithm, particle swarm optimization, or simulated annealing, to optimize the processed data. The optimization algorithm uses key indicators in the fabrication process of luminescent display materials (such as luminous efficiency, uniformity, and stability) as the objective function, and finds the optimal solution through iterative calculation. During the optimization process, adjust the algorithm parameters (such as crossover probability and mutation probability in genetic algorithms) according to the data characteristics and algorithm performance to ensure the optimization effect.

[0156] Specifically, the improved light-emitting display material manufacturing process of the present invention, step S103 includes:

[0157] Based on the optimized data on the basic data for manufacturing luminescent display materials, determine the direction and extent to which the process control parameters for manufacturing luminescent display materials need to be adjusted.

[0158] In the process control model for the fabrication of light-emitting display materials, the process control parameters for the fabrication of light-emitting display materials are adjusted according to the determined adjustment direction so that the adjusted parameter values ​​are within the acceptable range of the equipment and meet the requirements of the production process.

[0159] The adjusted process control parameters for the manufacturing of light-emitting display materials are compiled into a parameter set and sent to the light-emitting display material manufacturing equipment via a communication interface.

[0160] Control parameter adjustment strategy: Based on the optimized basic data for luminescent display material production, determine the direction and magnitude of control parameter adjustments. This involves fine-tuning key parameters such as temperature, pressure, time, and material ratios. The adjustment strategy is based on data analysis and process requirements, ensuring that the adjusted parameters improve the production quality and efficiency of the luminescent display material.

[0161] Equipment compatibility considerations: When adjusting parameters in the control model, the acceptable range of the manufacturing equipment and the requirements of the production process must be fully considered. It is essential to ensure that the adjusted parameter values ​​not only comply with process standards but also achieve optimal performance while maintaining stable equipment operation. This requires a thorough understanding of the equipment's performance and parameter adjustments based on its specifications and limitations.

[0162] Parameter Set Compilation and Transmission: The adjusted process control parameters for the luminescent display material manufacturing process are compiled into a parameter set, ensuring the completeness and accuracy of the parameters. This parameter set is then transmitted to the luminescent display material manufacturing equipment via a communication interface. The communication interface must support efficient and stable data transmission to ensure that the parameters are accurately and promptly conveyed to the equipment.

[0163] Operational data acquisition and monitoring: After the equipment implements new parameters, operational data is collected in real time, including temperature, pressure, time, current, voltage, and material state changes. This data is used for subsequent data analysis, simulation comparison, and error calculation to verify whether the adjusted parameters achieve the expected results. Simultaneously, operational data is monitored to promptly detect and address any anomalies.

[0164] Specifically, the improved light-emitting display material manufacturing process of the present invention, step S104 includes:

[0165] Start the preset simulation model for the production of light-emitting display materials, perform simulation calculations based on the input and adjusted process control parameters for the production of light-emitting display materials, and output the simulation results of the production of light-emitting display materials after the simulation model is completed.

[0166] The simulation results of luminescent display material fabrication include the effects of product quality indicators, process parameters, and changes during the luminescent display material fabrication process;

[0167] The simulation results of luminescent display material manufacturing are compared with the actual and simulated values ​​of luminescent display material manufacturing operation data. The comparison includes the differences between the actual and simulated values ​​of product quality indicators and process parameters. The comparison results of the simulation results and the actual and simulated values ​​of luminescent display material manufacturing operation data are obtained.

[0168] Based on the comparison between the simulation results of the light-emitting display material manufacturing and the operational data of the light-emitting display material manufacturing, the error data of the light-emitting display material manufacturing is calculated, and the calculated manufacturing error data is compared with the preset error range value.

[0169] If the error data is within the preset error range, the adjustment of the light-emitting display material manufacturing process control parameters is effective, and production can continue using the adjusted light-emitting display material manufacturing process control parameters.

[0170] The pre-set simulation model for the fabrication of luminescent display materials is launched. This model, built on advanced simulation technology, accurately reflects the physical and chemical changes during the fabrication process. The adjusted process control parameters for luminescent display material fabrication are input into the simulation model for calculation. After the simulation is complete, the simulation results for luminescent display material fabrication are output, including product quality indicators, the impact of process parameters, and changes during the fabrication process.

[0171] The simulated results of luminescent display material fabrication were compared with the actual data from the fabrication process. The comparison covered product quality indicators (such as luminous efficiency, uniformity, and stability) and the differences between actual and simulated values ​​of process parameters. Through comparative analysis, the impact of adjusted parameters on the fabrication effect of the luminescent display material can be clearly understood, providing a foundation for subsequent error analysis.

[0172] Based on the comparison results, error data in the fabrication of the luminescent display material is calculated. This error data reflects the degree of deviation between the simulation results and the actual operational data. The calculated fabrication error data is compared with a preset error range to determine if the error is within acceptable limits. If the error data is within the preset error range, it indicates that the adjustment of the luminescent display material fabrication process control parameters is effective, and production can continue using the adjusted parameters. If the error exceeds the range, further parameter adjustments or optimization of the simulation model are needed to reduce the error.

[0173] Based on the error analysis results, optimization decisions are made. If the error is within an acceptable range, the current parameters are maintained and production continues. If the error is large, the process control parameters for the luminescent display material manufacturing process need to be adjusted or the simulation model improved, depending on the specific error data, to enhance the accuracy of the simulation and the precision of the manufacturing. Simultaneously, the results of the optimization decisions are fed back to relevant departments to allow for timely adjustments to the production plan and process flow.

[0174] Specifically, the improved light-emitting display material manufacturing process of the present invention, step S105 includes:

[0175] Within the framework of the digital twin model, a mirror model of the manufacturing process of luminescent display materials is established based on the dataset to be processed; according to different parts or stages of the manufacturing process, the mirror model is subdivided into multiple sub-models to form a mirror model group;

[0176] The error data of light-emitting display material manufacturing and the preset simulation model of light-emitting display material manufacturing are sequentially configured into each mirror model group. The genetic algorithm is used to optimize the preset simulation model of light-emitting display material manufacturing in each mirror model group. The optimized simulation model is verified. If the verification result is satisfactory, the optimized simulation model of light-emitting display material manufacturing is used for subsequent data processing.

[0177] Within the framework of the digital twin model, a digital twin model of the luminescent display material manufacturing process is constructed based on the dataset to be processed (including all data generated in steps S101 to S104). This model is a virtual representation of the manufacturing process, capable of reflecting the state and changes of the process in real time. According to different parts or stages of the manufacturing process, the digital twin model is subdivided into multiple sub-models, forming a mirror model group. Each sub-model corresponds to a specific part or stage of the manufacturing process, allowing for more refined analysis and optimization.

[0178] Error data on the fabrication of light-emitting display materials and preset simulation models for the fabrication process are sequentially configured into each mirror model group. A genetic algorithm is then used to optimize the preset simulation models within each mirror model group for the fabrication process of light-emitting display materials. The genetic algorithm is an optimization algorithm based on the principles of biological evolution. Through operations such as selection, crossover, and mutation, iteratively optimizes model parameters to minimize the error between the simulation results and the actual data. The optimization process includes initializing the genetic algorithm, determining the objective function (i.e., the error between the simulation results and the actual data), parameter encoding, generating an initial population, evaluating population fitness, and performing selection, crossover, and mutation operations until the preset number of generations is reached or the optimization requirements are met.

[0179] The optimized simulation model performs calculations based on the adjusted manufacturing process control parameters to obtain more accurate prediction results. These predictions are then compared with actual operating data to verify the model's accuracy and reliability. If the verification results are satisfactory, the optimized luminescent display material is used to create a simulation model for subsequent data processing and production guidance. This includes sending the optimized manufacturing process control parameters to the luminescent display material manufacturing equipment for a new round of production and verification to ensure the stability of the manufacturing process and product quality.

[0180] In the construction and optimization of digital twin models, error data is fully utilized to create luminescent display materials. This data reflects the actual deviations and fluctuations in the manufacturing process, providing an important basis for model optimization. By configuring the error data into the mirror model group and using a genetic algorithm for optimization, model parameters can be continuously adjusted to make the simulation results closer to actual operating conditions. Simultaneously, the error data can also be used to evaluate the accuracy and reliability of the model, providing a reference for subsequent model improvement and optimization.

[0181] Specifically, the improved light-emitting display material manufacturing process of the present invention, step S105 includes:

[0182] The specific steps for configuring the light-emitting display material manufacturing error data and the preset light-emitting display material manufacturing simulation model into the light-emitting display material manufacturing process mirror model group, and then using a genetic algorithm for optimization, to finally obtain the optimized light-emitting display material manufacturing process control parameters are as follows:

[0183] Import the manufacturing error data of the light-emitting display material into the mirror model group of the manufacturing process of the light-emitting display material, and also load the preset simulation model of the manufacturing process of the light-emitting display material into the mirror model group of the manufacturing process of the light-emitting display material.

[0184] Initialize the genetic algorithm and determine its objective function, which is the error between the simulation results of the light-emitting display material fabrication and the actual fabrication data.

[0185] The parameters in the preset simulation model for manufacturing light-emitting display materials are encoded into a gene form that can be processed by a genetic algorithm. Each gene represents a parameter in the simulation model for manufacturing light-emitting display materials, and the combination of genes represents the parameter set of the entire simulation model for manufacturing light-emitting display materials.

[0186] An initial population is generated by using a random or specific method to generate an initial set of parameters for the simulation model of luminescent display materials, which serves as the initial population for the genetic algorithm. Each parameter set corresponds to a simulation model of luminescent display materials, which is used for subsequent evaluation and optimization.

[0187] Assessing population fitness:

[0188] Each parameter set (i.e., each individual) in the initial population is evaluated, and its fitness value is calculated.

[0189] The fitness value is based on the degree of matching between the simulation results of luminescent display material fabrication generated using a parameter set in the simulation model and the actual fabrication data of the luminescent display material.

[0190] The process involves evaluation, selection, crossover, and mutation until the preset number of generations or the desired optimization result is achieved. After the genetic algorithm completes its iterations, the individual with the highest fitness value is selected from the final population as the optimized simulation model for manufacturing luminescent display materials. The adjusted process control parameters for manufacturing luminescent display materials are then input into the optimized simulation model.

[0191] The optimized simulation model for the fabrication of light-emitting display materials is used to perform simulation calculations based on the adjusted process control parameters for the fabrication of light-emitting display materials, and the predicted results of the optimized process control parameters are obtained.

[0192] Error data in the fabrication of luminescent display materials were imported into the mirror model group of the luminescent display material fabrication process. This data reflects the actual deviations and fluctuations in the fabrication process, providing crucial information for model optimization. Simultaneously, a pre-defined simulation model of luminescent display material fabrication was also loaded into the mirror model group for subsequent optimization.

[0193] Genetic Algorithm Initialization: Initialize the genetic algorithm and determine its objective function. The objective function is the error between the simulation results of the luminescent display material fabrication and the actual fabrication data; the goal of optimization is to minimize this error.

[0194] Set parameters for the genetic algorithm, such as population size, number of generations, crossover probability, and mutation probability, to ensure the effectiveness and stability of the algorithm.

[0195] The parameters in the pre-defined simulation model for manufacturing luminescent display materials are encoded into a gene format that can be processed by a genetic algorithm. Each gene represents a parameter in the simulation model, and the combination of genes represents the entire parameter set of the simulation model.

[0196] An initial set of parameters for luminescent display material fabrication simulation models is generated using a random or specific method, serving as the initial population for the genetic algorithm. Each parameter set corresponds to a luminescent display material fabrication simulation model, used for subsequent evaluation and optimization.

[0197] Fitness assessment: Each parameter set (i.e., each individual) in the initial population is evaluated, and its fitness value is calculated. The fitness value is based on the degree of match between the simulation results generated by the parameter set used in the simulation model for fabricating luminescent display materials and the operational data for fabricating luminescent display materials. The higher the degree of match, the greater the fitness value.

[0198] Selection, crossover, and mutation operations: Based on the fitness value, select individuals with higher fitness as parents, perform crossover and mutation operations, and generate new offspring individuals.

[0199] Crossover operations generate offspring with new characteristics by exchanging some genes from parent individuals.

[0200] Mutation operations increase population diversity by randomly altering certain genes in offspring individuals, thus preventing the algorithm from falling into local lag.

[0201] Repeat the selection, crossover, and mutation steps until the preset number of generations is reached or the optimization requirements are met.

[0202] Optimization result selection: After the genetic algorithm completes its iterations, the individual with the highest fitness value is selected from the final population as the optimized simulation model for luminescent display material fabrication. This model has the optimal parameter set and can produce simulation results that best match the operational data of luminescent display material fabrication.

[0203] Simulation calculation and prediction results: The adjusted process control parameters for the fabrication of luminescent display materials are input into the optimized simulation model for the fabrication of luminescent display materials.

[0204] The optimized simulation model performs calculations based on the adjusted parameters to obtain predicted results for the optimized manufacturing process control parameters. These predictions can be used to guide subsequent production practices, reduce manufacturing errors, and improve product quality.

[0205] This invention provides a technical solution to improve the manufacturing process of light-emitting display materials, aiming to solve the problem that existing technologies cannot reduce manufacturing errors of light-emitting display materials by processing and analyzing the data generated during the manufacturing process.

[0206] The system receives a task related to the fabrication process of light-emitting display materials, including information such as material type, specifications, quantity, and expected performance indicators. This task information is then sent to a pre-defined process control model. Based on historical data, empirical formulas, and machine learning algorithms, the process control model generates control parameters for the fabrication process, such as temperature, pressure, time, and material ratios.

[0207] The process control parameters for the luminescent display material manufacturing process are sent to the manufacturing equipment, and data generated during the process, such as temperature, pressure, time, current, voltage, and material state change data, are collected as the basic data for manufacturing.

[0208] Time series analysis is performed on the collected basic production data to identify outliers and fluctuations. Pre-defined optimization algorithms, such as genetic algorithms, particle swarm optimization, and simulated annealing, are then used to optimize these outliers and fluctuations, generating optimized data. This step aims to improve the accuracy and reliability of the data, providing a foundation for subsequent control parameter adjustments.

[0209] Based on the optimized data, the manufacturing process control parameters are adjusted to ensure that these parameter values ​​are within the acceptable range of the equipment and meet the production process requirements. The adjusted control parameters are then sent back to the manufacturing equipment, and the execution data is collected. These execution data are compared with the simulation results of a preset simulation model for luminescent display material manufacturing to calculate the manufacturing error data. This step is used to verify whether the adjusted control parameters have effectively reduced manufacturing errors.

[0210] If the error data exceeds the preset range, it indicates that the current manufacturing process and control parameters still need further optimization. At this point, a digital twin model of the luminescent display material manufacturing process is constructed, and a mirror model group is established. The error data and simulation model are configured into the mirror model group, and a genetic algorithm is used to optimize the simulation model. The genetic algorithm searches for and finds the optimal or near-optimal solution by simulating natural selection and genetic mechanisms, thereby optimizing the accuracy and predictive ability of the simulation model. The optimized simulation model is then used to process the adjusted control parameters to obtain new optimized control parameters. These parameters can more accurately reflect the actual needs of the manufacturing process and reduce manufacturing errors. Finally, the new optimized control parameters are sent to the manufacturing equipment to guide the actual luminescent display material manufacturing process.

[0211] Through the above steps, the present invention can achieve comprehensive and in-depth processing and analysis of data generated during the manufacturing process of light-emitting display materials, thereby effectively reducing manufacturing errors and improving the accuracy of the manufacturing process.

Claims

1. An improved manufacturing process for light-emitting display materials, characterized in that, include: Step S101: Receive the light-emitting display material manufacturing process task, send the light-emitting display material manufacturing process task to the preset light-emitting display material manufacturing process control model, output the light-emitting display material manufacturing process control parameters, send the light-emitting display material manufacturing process control parameters to the light-emitting display material manufacturing equipment, collect the data generated by the light-emitting display material manufacturing equipment in executing the light-emitting display material manufacturing process control parameters, and obtain the basic data for light-emitting display material manufacturing. Step S102: Process the collected basic data for manufacturing luminescent display materials, identify outliers and fluctuations in the basic data for manufacturing luminescent display materials, obtain the outlier and fluctuation data in the basic data for manufacturing luminescent display materials, and use a preset optimization algorithm to optimize the outlier and fluctuation data in the basic data for manufacturing luminescent display materials to generate optimized data for manufacturing luminescent display materials. Step S103: Based on the optimized basic data for manufacturing light-emitting display materials, adjust the process control parameters for manufacturing light-emitting display materials to obtain the adjusted process control parameters. Send the adjusted process control parameters to the manufacturing equipment and collect the manufacturing operation data of the equipment executing the adjusted process control parameters to obtain the manufacturing operation data. Step S104: Substitute the adjusted light-emitting display material manufacturing process control parameters into the preset light-emitting display material manufacturing simulation model, output the light-emitting display material manufacturing simulation results, compare the light-emitting display material manufacturing operation data with the light-emitting display material manufacturing simulation results, obtain the light-emitting display material manufacturing error data, and compare the light-emitting display material manufacturing error data with the preset error range value. Step S105: If the manufacturing error data of the light-emitting display material exceeds the preset error range, the data generated during the data processing in steps S101, S102, S103, and S104 is used to obtain a dataset to be processed. A digital twin model of the manufacturing process of the light-emitting display material is constructed based on the dataset to be processed. A mirror model of the manufacturing process of the light-emitting display material is established to obtain a mirror model group of the manufacturing process of the light-emitting display material. The manufacturing error data of the light-emitting display material and the preset simulation model of the manufacturing process of the light-emitting display material are sequentially configured into the mirror model group of the manufacturing process of the light-emitting display material. A genetic algorithm is used to optimize the preset simulation model of the manufacturing process of the light-emitting display material in each mirror model group of the manufacturing process of the light-emitting display material to obtain an optimized simulation model of the manufacturing process of the light-emitting display material. The optimized simulation model of the manufacturing process of the light-emitting display material is used to process the adjusted control parameters of the manufacturing process of the light-emitting display material to obtain optimized control parameters of the manufacturing process of the light-emitting display material. The optimized control parameters of the manufacturing process of the light-emitting display material are sent to the manufacturing equipment of the light-emitting display material.

2. The improved manufacturing process for light-emitting display materials as described in claim 1, characterized in that, Step S101 includes: The task of fabricating light-emitting display materials includes information on the required material types, specifications, quantities, and expected performance indicators; The process control model for the fabrication of light-emitting display materials is built based on historical data, empirical formulas, and machine learning algorithms. It is used to generate corresponding process control parameters according to task requirements. The process control model outputs process control parameters for the fabrication of light-emitting display materials based on the input process task. The process control parameters for the fabrication of light-emitting display materials include temperature, pressure, time, and material ratio. During the execution of process control parameters for the fabrication of light-emitting display materials in the equipment, data generated by the equipment in real time are collected, including temperature, pressure, time, current, voltage, and material state change data, to obtain basic data for the fabrication of light-emitting display materials.

3. The improved manufacturing process for light-emitting display materials as described in claim 1, characterized in that, Step S102 includes: Perform time series analysis on the data to identify fluctuation patterns. For the identified outliers, select to delete, replace, or retain them and mark them. For fluctuating data, filtering, smoothing, or prediction methods are used to reduce fluctuations and improve data stability. Preset optimization algorithms are selected, including genetic algorithms, particle swarm optimization, and simulated annealing, to optimize the processed data. Through the processing of optimization algorithms, optimized basic data for the fabrication of light-emitting display materials are obtained.

4. The improved manufacturing process for light-emitting display materials as described in claim 1, characterized in that, Step S103 includes: Based on the optimized data on the basic data for manufacturing luminescent display materials, determine the direction and extent to which the process control parameters for manufacturing luminescent display materials need to be adjusted. In the process control model for the fabrication of light-emitting display materials, the process control parameters for the fabrication of light-emitting display materials are adjusted according to the determined adjustment direction so that the adjusted parameter values ​​are within the range acceptable to the equipment and meet the requirements of the production process. The adjusted process control parameters for the manufacturing of light-emitting display materials are compiled into a parameter set and sent to the light-emitting display material manufacturing equipment via a communication interface.

5. The improved manufacturing process for light-emitting display materials as described in claim 1, characterized in that, Step S104 includes: Start the preset simulation model for the production of light-emitting display materials, perform simulation calculations based on the input and adjusted process control parameters for the production of light-emitting display materials, and output the simulation results of the production of light-emitting display materials after the simulation model is completed. The simulation results of luminescent display material fabrication include the effects of product quality indicators, process parameters, and changes during the luminescent display material fabrication process; The simulation results of luminescent display material manufacturing are compared with the actual and simulated values ​​of luminescent display material manufacturing operation data. The comparison includes the differences between the actual and simulated values ​​of product quality indicators and process parameters. The comparison results of the simulation results and the actual and simulated values ​​of luminescent display material manufacturing operation data are obtained. Based on the comparison between the simulation results of the light-emitting display material manufacturing and the operational data of the light-emitting display material manufacturing, the error data of the light-emitting display material manufacturing is calculated, and the calculated manufacturing error data is compared with the preset error range value. If the error data is within the preset error range, the adjustment of the light-emitting display material manufacturing process control parameters is effective, and production can continue using the adjusted light-emitting display material manufacturing process control parameters.

6. The improved manufacturing process for light-emitting display materials as described in claim 1, characterized in that, Step S105 includes: Within the framework of the digital twin model, a mirror model of the manufacturing process of luminescent display materials is established based on the dataset to be processed; according to different parts or stages of the manufacturing process, the mirror model is subdivided into multiple sub-models to form a mirror model group; The error data of light-emitting display material manufacturing and the preset simulation model of light-emitting display material manufacturing are sequentially configured into each mirror model group. The genetic algorithm is used to optimize the preset simulation model of light-emitting display material manufacturing in each mirror model group. The optimized simulation model is verified. If the verification result is satisfactory, the optimized simulation model of light-emitting display material manufacturing is used for subsequent data processing.

7. The improved manufacturing process for light-emitting display materials as described in claim 6, characterized in that, Step S105 includes: The specific steps for configuring the light-emitting display material manufacturing error data and the preset light-emitting display material manufacturing simulation model into the light-emitting display material manufacturing process mirror model group, and then using a genetic algorithm for optimization, to finally obtain the optimized light-emitting display material manufacturing process control parameters are as follows: Import the manufacturing error data of the light-emitting display material into the mirror model group of the manufacturing process of the light-emitting display material, and also load the preset simulation model of the manufacturing process of the light-emitting display material into the mirror model group of the manufacturing process of the light-emitting display material. Initialize the genetic algorithm and determine its objective function, which is the error between the simulation results of the light-emitting display material fabrication and the actual fabrication data. The parameters in the preset simulation model for manufacturing light-emitting display materials are encoded into a gene form that can be processed by a genetic algorithm. Each gene represents a parameter in the simulation model for manufacturing light-emitting display materials, and the combination of genes represents the parameter set of the entire simulation model for manufacturing light-emitting display materials. An initial population is generated by randomly generating a set of initial parameters for the simulation model of luminescent display materials, which serves as the initial population for the genetic algorithm. Each parameter set corresponds to a simulation model of luminescent display materials, which is used for subsequent evaluation and optimization. Assessing population fitness: For each parameter set, i.e. each individual, in the initial population, evaluate and calculate its fitness value; The fitness value is based on the degree of matching between the simulation results of luminescent display material fabrication generated using a parameter set in the simulation model and the actual fabrication data of the luminescent display material. The process involves evaluation, selection, crossover, and mutation until the preset number of generations or the desired optimization result is achieved. After the genetic algorithm completes its iterations, the individual with the highest fitness value is selected from the final population as the optimized simulation model for manufacturing luminescent display materials. The adjusted process control parameters for manufacturing luminescent display materials are then input into the optimized simulation model. The optimized simulation model for the fabrication of light-emitting display materials is used to perform simulation calculations based on the adjusted process control parameters for the fabrication of light-emitting display materials, and the predicted results of the optimized process control parameters are obtained.

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