A flexible transparent display material manufacturing process method

Abstract

The present application relates to the technical field of flexible transparent display screen material manufacturing data processing, and provides a flexible transparent display screen material manufacturing process method, which is particularly optimized for possible instability problems in the manufacturing process. Through real-time monitoring of the manufacturing process, once instability is detected, relevant data is retrieved and a suitable real-time optimization algorithm is matched in the optimization algorithm knowledge base. Genetic algorithm, particle swarm optimization algorithm or simulated annealing algorithm, etc. are used to comprehensively optimize and adjust the process parameters, equipment settings and material formulations. This process ensures the stability of the manufacturing process, improves product quality, and reduces material waste and energy consumption. The implementation of the present application enhances the intelligent decision-making capability of manufacturing and improves the competitiveness of products, providing an innovative technical solution for the manufacturing process of flexible transparent display screen materials.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flexible transparent display screen material manufacturing data processing, and particularly relates to a flexible transparent display screen material manufacturing process. BACKGROUND

[0002] The flexible substrate material of the flexible transparent display screen material manufacturing process includes polyimide (PI) or polyester film (PET), which has good flexibility and transparency. Thin film transistors, organic light emitting diodes and other electronic devices are prepared on the flexible substrate, which is usually completed through processes such as photolithography and evaporation. At the same time, a driving circuit that meets the deformation requirements of the flexible screen is designed and connected with the display device. The prepared thin film electronic devices, driving circuits and other elements are assembled together, packaged and fixed to form a complete flexible display module.

[0003] In the manufacturing process of the flexible transparent display screen, the existing flexible transparent display screen material manufacturing process is based on the manufacturing of the flexible transparent display screen material. The prior art cannot monitor the flexible transparent display screen material manufacturing process and adaptively adjust the flexible transparent display screen material manufacturing process according to the real-time data generated by the flexible transparent display screen material manufacturing. Therefore, the present application provides a flexible transparent display screen material manufacturing process. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a flexible transparent display screen material manufacturing process, which solves the problem that the prior art cannot monitor the flexible transparent display screen material manufacturing process and adaptively adjust the flexible transparent display screen material manufacturing process according to the real-time data generated by the flexible transparent display screen material manufacturing.

[0005] To solve the above technical problems, the specific technical solutions of the present application are as follows:

[0006] The present application provides a flexible transparent display screen material manufacturing process, which comprises the following steps:

[0007] Step S101, obtaining substrate material data, thin film transistor preparation data, organic light emitting diode preparation data, driving circuit design data, manufacturing process environment data, real-time monitoring data and adjustment data;

[0008] Step S102, the substrate material data, thin film transistor preparation data, organic light emitting diode preparation data, driving circuit design data, manufacturing process environment data, real-time monitoring data and adjustment data are integrated to form a flexible transparent display screen material manufacturing data set, the flexible transparent display screen material manufacturing data set is stored to a database, the flexible transparent display screen material manufacturing data set is divided into substrate material data, flexible transparent display screen material preparation data and flexible transparent display screen material preparation data, and an identifier and metadata are assigned to each category;

[0009] Step S103, the substrate material data, flexible transparent display screen material preparation data and flexible transparent display screen material preparation data are statistically analyzed by using a statistical method, the regular relationship between the substrate material data, flexible transparent display screen material preparation data and flexible transparent display screen material preparation data is obtained, a deep learning algorithm is applied to mine the substrate material data, flexible transparent display screen material preparation data and flexible transparent display screen material preparation data, and a data mining result is obtained;

[0010] Step S104, the manufacturing process is monitored in real time by using a real-time monitoring system, a monitoring threshold is set, when the data exceeds the normal range, a warning signal is sent in time, when the warning signal is received, the data corresponding to the warning signal is substituted into a preset warning analysis model, and a warning signal analysis result is obtained;

[0011] Step S105, the manufacturing process is monitored in real time by using a real-time monitoring system, real-time monitoring data is obtained, and process parameters, equipment settings and material formulations are adjusted according to the real-time monitoring data and the warning analysis result.

[0012] Further, the flexible transparent display screen material manufacturing process method provided by the present application, the step S101, comprising:

[0013] The substrate material data includes the physical properties and chemical properties of the substrate material;

[0014] The thin film transistor preparation data includes the deposition rate, thickness uniformity and electrical performance parameters of each layer of material in the TFT preparation process, and the process parameters in the TFT preparation process;

[0015] The organic light emitting diode preparation data includes the deposition rate, thickness uniformity and light emitting performance of the organic material;

[0016] The driving circuit design data includes the design drawing, layout and wiring information of the driving circuit, and the steps and parameters in the manufacturing process of the driving circuit;

[0017] The manufacturing process environment data includes temperature in the production workshop, humidity in the production workshop, and cleanliness environment parameters in the production workshop.

[0018] The real-time monitoring data acquisition includes equipment in the manufacturing process, process parameters in the manufacturing process, and product quality in the manufacturing process.

[0019] The adjustment data acquisition includes process parameter adjustment, equipment setting adjustment, and material formula adjustment.

[0020] Further, the flexible transparent display screen material manufacturing process method comprises the following steps:

[0021] The flexible transparent display screen material manufacturing data set is imported into a database, data indexing is established, the flexible transparent display screen material manufacturing data set is classified, and it is divided into base material data, flexible transparent display screen material preparation data including flexible transparent display screen material manufacturing data, driving circuit design data, and flexible transparent display screen material manufacturing process environment data.

[0022] An identifier is assigned to each data category, the identifier is a data table name, a field name, or a code, and metadata is added to each data category.

[0023] Further, the flexible transparent display screen material manufacturing process method comprises the following steps:

[0024] The base material data is analyzed by using a statistical method, and the physical and chemical properties of the base material and the distribution thereof are obtained.

[0025] The flexible transparent display screen material preparation data is statistically analyzed, and the relationship between process parameters, equipment settings, material formulas, and product quality in the preparation process is obtained.

[0026] The influence of the driving circuit design data and the manufacturing process environment data on the flexible transparent display screen material preparation process is analyzed, and the regular relationship between the base material data, the flexible transparent display screen material preparation data, and the preparation data is obtained through statistical analysis.

[0027] Further, the flexible transparent display screen material manufacturing process method comprises the following steps:

[0028] According to the results of statistical analysis, the product quality is predicted, the process parameters are optimized, and the abnormal mode is identified, and a deep learning algorithm is selected for data mining.

[0029] The preprocessed data is trained by using a deep learning algorithm, and a preset early warning analysis model is obtained.

[0030] The early warning signal analysis result includes a prediction value, a classification label and an abnormal pattern;

[0031] The data mining result is applied to monitoring and adjustment of the flexible transparent display screen material manufacturing process, process parameters are adjusted according to the prediction result, an abnormal pattern is identified according to the classification label, and a material formula is optimized according to the mining result.

[0032] Further, the flexible transparent display screen material manufacturing process method provided by the present application, the step S104 comprises:

[0033] A monitoring point is set, and the monitoring point comprises a device state, a process parameter, an environmental condition and product quality;

[0034] A monitoring threshold is set for each monitoring point, and the set monitoring threshold is input into a real-time monitoring system as a basis for data judgment;

[0035] The real-time monitoring system is started, and real-time monitoring of the manufacturing process is started, and the real-time monitoring system collects data of each monitoring point according to a set frequency and time interval;

[0036] The collected data is stored in a database in real time, and the real-time monitoring system judges the collected data in real time according to the set monitoring threshold;

[0037] When the data exceeds the normal range, the real-time monitoring system timely sends an early warning signal;

[0038] When the early warning signal is received, the real-time monitoring system automatically extracts data corresponding to the early warning signal;

[0039] The extracted data corresponding to the early warning signal is substituted into a preset early warning analysis model, and the early warning analysis model performs data analysis, pattern recognition and abnormality detection processing according to the input data.

[0040] Further, the flexible transparent display screen material manufacturing process method provided by the present application, the step S105 comprises:

[0041] The real-time monitoring system is used to monitor the flexible transparent display screen material manufacturing process, and data of the flexible transparent display screen material manufacturing process parameters, device state, environmental condition and product quality monitoring point is collected in real time;

[0042] According to the real-time monitoring data and the early warning analysis result, it is judged whether the current process parameter is in the best state;

[0043] If it is found that the process parameter deviates from the normal range, the process parameter is adjusted to the set value;

[0044] During the adjustment process, the device state and operating parameters are checked, and the device settings are adjusted according to the early warning analysis results, including device operating speed parameters, temperature parameters, and pressure parameters.

[0045] Further, the flexible transparent display screen material manufacturing process method of the present application, the step S105, comprises:

[0046] If the device fails, determine whether the current material formula matches according to real-time monitoring data and early warning analysis results;

[0047] If the material formula does not match, adjust the material formula and optimize the material combination and ratio, and verify the adjusted process parameters, device settings and material formula, and monitor the stability of the adjusted manufacturing process through real-time monitoring data;

[0048] If the adjusted manufacturing process is unstable, optimize the adjustment process.

[0049] Further, the flexible transparent display screen material manufacturing process method of the present application, the step S105, comprises:

[0050] If the adjusted manufacturing process is unstable, retrieve the corresponding data and match the corresponding data in the optimization algorithm knowledge base to obtain real-time optimization algorithm, and the optimization algorithm knowledge base includes genetic algorithm, particle swarm optimization algorithm and simulated annealing algorithm;

[0051] Set the parameters of the optimization algorithm, such as population size, iteration number, crossover probability and mutation probability;

[0052] Run the optimization algorithm to optimize and adjust the process parameters, device settings and material formula.

[0053] The beneficial effects of the present application are:

[0054] The present application monitors the manufacturing process through real-time monitoring data, and retrieves relevant data in time when instability occurs, and uses algorithms in the optimization algorithm knowledge base for optimization adjustment, effectively improving the stability of the manufacturing process. This helps to reduce production process failures and downtime, and improves production efficiency.

[0055] The present application uses optimization algorithms to comprehensively optimize process parameters, device settings and material formula, ensuring their optimal matching. This not only improves product quality, but also reduces material waste and energy consumption, and reduces production costs. By matching real-time optimization algorithms, the present application realizes data-driven intelligent decision-making. This decision-making method is more accurate and efficient, and can quickly respond to changes in the manufacturing process, improving production flexibility and adaptability.

[0056] In summary, the present application effectively improves the manufacturing process stability and product quality of flexible transparent display screen materials, reduces production costs, enhances intelligent decision-making capabilities, improves product competitiveness, and promotes technological innovation and industrial upgrading through real-time monitoring, optimization algorithms, and knowledge base matching and other technical means. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, for those skilled in the art, other drawings can also be obtained from the drawings without creative labor.

[0058] Figure 1 The flowchart of the flexible transparent display screen material manufacturing process method provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present application clearer, the following will combine the specific embodiments of the present application and the corresponding drawings to clearly and completely describe the technical solutions of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. The following describes the technical solutions provided by the embodiments of the present application in combination with the drawings. In order to better understand the purpose of the present application, the present application will be further described below.

[0060] The present application provides a flexible transparent display screen material manufacturing process method, comprising:

[0061] Step S101, obtaining substrate material data, thin film transistor preparation data, organic light emitting diode preparation data, driving circuit design data, manufacturing process environment data, real-time monitoring data and adjustment data;

[0062] Substrate material data: This is the basis of flexible transparent display screen, including the physical and chemical properties of the substrate material. These data are crucial for understanding the characteristics of the substrate material, selecting the appropriate substrate material and predicting its performance in the manufacturing process.

[0063] Thin film transistor (TFT) preparation data: TFT is one of the core components in flexible transparent display screen. Obtaining data during TFT preparation process, such as deposition rate of each layer material, thickness uniformity, electrical performance parameters and process parameters, etc., helps to monitor and optimize the TFT preparation process, to ensure the performance and quality of TFT.

[0064] Organic light-emitting diode preparation data: is the light-emitting element of flexible transparent display screen. Obtain data during preparation, such as deposition rate, thickness uniformity and light-emitting performance of organic materials, which is of great significance to improve light-emitting efficiency and stability.

[0065] Drive circuit design data: the drive circuit is responsible for controlling the display content of the flexible transparent display screen. Obtain the design drawing, layout and wiring information of the drive circuit, as well as the step and parameter data in the manufacturing process, which helps to ensure the rationality of the design of the drive circuit and the controllability of the manufacturing process.

[0066] Manufacturing process environment data: the temperature, humidity and cleanliness of the production workshop have important influence on the manufacturing process of flexible transparent display screen. Obtaining these environmental data helps to monitor and adjust the manufacturing environment to ensure the stability of the manufacturing process and product quality.

[0067] Real-time monitoring data: in the manufacturing process, real-time monitoring of equipment status, process parameters and product quality is carried out, and obtaining these data helps to find and solve problems in time to ensure the smooth progress of the manufacturing process.

[0068] Adjustment data: in the manufacturing process, according to the real-time monitoring data and early warning analysis results, the process parameters, equipment settings and material formula are adjusted. Obtaining these adjustment data helps to evaluate the adjustment effect and further optimize the manufacturing process.

[0069] The data acquisition in step S101 is carried out according to the logical sequence of the manufacturing process of flexible transparent display screen materials. First, the substrate material data is obtained, which is the basis of the manufacturing process; then, the TFT and OLED preparation data is obtained, which is the core component of the flexible transparent display screen; then, the drive circuit design data is obtained, which is the key to control the display content; at the same time, the manufacturing process environment data is obtained to ensure the suitability of the manufacturing environment; in the manufacturing process, real-time monitoring is carried out and relevant data is obtained; finally, according to the real-time monitoring data and early warning analysis results, adjustment data is obtained and corresponding adjustment is carried out. This logical and clear data acquisition sequence helps to ensure the smooth progress of the manufacturing process of flexible transparent display screen materials and the improvement of product quality.

[0070] In step S102, the substrate material data, thin film transistor preparation data, organic light-emitting diode preparation data, drive circuit design data, manufacturing process environment data, real-time monitoring data and adjustment data are integrated to form the flexible transparent display screen material manufacturing data set, the flexible transparent display screen material manufacturing data set is stored in the database, the flexible transparent display screen material manufacturing data set is divided into substrate material data, flexible transparent display screen material preparation data and flexible transparent display screen material preparation data, and an identifier and metadata are assigned to each category.

[0071] Data Integration: This step integrates various types of data obtained in step S101 (substrate material data, thin film transistor preparation data, organic light-emitting diode preparation data, drive circuit design data, manufacturing process environment data, real-time monitoring data, and adjustment data) to form a complete flexible transparent display material manufacturing data set. This integration enables all data to be managed and analyzed on the same platform, improving data processing efficiency and accuracy.

[0072] Data Storage: The integrated data set is stored in a database. As an efficient data management tool, the database can provide functions such as fast retrieval, update, and deletion of data, providing convenience for subsequent data analysis and application.

[0073] Data Classification: In the database, the flexible transparent display material manufacturing data set is further subdivided into multiple categories, such as substrate material data, flexible transparent display material preparation data (including thin film transistor preparation data, organic light-emitting diode preparation data, and drive circuit design data), and flexible transparent display material manufacturing process data (including manufacturing process environment data and real-time monitoring data). This classification helps better understand and analyze data, providing a foundation for subsequent data mining and application.

[0074] Identifier Assignment: Assign an identifier (such as a data table name, field name, or code) and metadata (such as data description, unit, range, etc.) to each data category. The identifier enables the data to be uniquely identified and quickly retrieved in the database, while the metadata provides detailed information about the data, helping to understand and use the data.

[0075] Systematic Data Management: Through data integration and storage, the technical solution of the present invention realizes the systematic management of various data generated during the manufacturing process of flexible transparent display materials. This management method makes the relationship between data clearer, providing strong support for subsequent data analysis and application.

[0076] Convenience of Data Use: Through data classification and identifier assignment, the technical solution of the present invention makes data retrieval and use in the database more convenient. Users can quickly find the required data according to their needs and perform corresponding analysis and processing.

[0077] Step S103, statistically analyze the substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material preparation data using statistical methods, obtain the regular relationship between the substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material preparation data, apply deep learning algorithm to mine the substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material preparation data, and obtain the data mining result;

[0078] Statistically analyze the substrate material data, flexible transparent display screen material preparation data (including thin film transistor preparation data, organic light emitting diode preparation data, and driving circuit design data), and flexible transparent display screen material manufacturing process data (although the expression is slightly repetitive here, it should be understood as environmental data related to the preparation process, etc.) using statistical methods.

[0079] Analyze the substrate material data to understand the distribution of its physical and chemical properties, providing a basis for material selection and use.

[0080] Statistically analyze the flexible transparent display screen material preparation data to reveal the relationship between process parameters, equipment settings, material formulations, and product quality in the preparation process, and identify key factors affecting product quality.

[0081] Analyze the influence of driving circuit design data and manufacturing process environmental data on the flexible transparent display screen material preparation process, and comprehensively understand various factors and their interactions in the manufacturing process.

[0082] Results: Through statistical analysis, the regular relationship between the substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material manufacturing process data is obtained, providing a basis for subsequent data mining and manufacturing process adjustment.

[0083] Deep learning mining: obtain the data mining result, apply deep learning algorithm to further mine the data obtained by statistical analysis, to obtain deeper information and knowledge. Choose appropriate deep learning algorithms such as neural networks, convolutional neural networks, etc., determine the mining target (such as predicting product quality, optimizing process parameters, identifying abnormal patterns, etc.) according to the results of statistical analysis. Train the preprocessed data to establish prediction models or classification models, etc. Use the trained model to predict or classify new data to obtain the data mining result. The data mining result includes predicted values (such as predicted values of product quality), classification labels (such as classification labels of abnormal patterns), etc., which can be used for monitoring and adjusting the flexible transparent display screen material manufacturing process.

[0084] Progressive data analysis: Step S103 first reveals the regular relationship between data through statistical analysis, and then mines deeper information and knowledge through deep learning. This progressive data analysis method helps to comprehensively and deeply understand the manufacturing process of flexible transparent display screen materials.

[0085] Targeted data mining: The goal of deep learning mining is determined based on the results of statistical analysis, with clear targeting and practicality. This helps to ensure the effectiveness and accuracy of data mining results.

[0086] Scientific data application: The data mining results obtained through step S103 can be applied to the monitoring and adjustment of the manufacturing process of flexible transparent display screen materials, realizing scientific decision-making and optimization based on data.

[0087] Step S104, using a real-time monitoring system to monitor the manufacturing process in real time, setting a monitoring threshold, and sending an early warning signal when the data exceeds the normal range. When receiving the early warning signal, the data corresponding to the early warning signal is substituted into the pre-set early warning analysis model to obtain the early warning signal analysis result.

[0088] Use the real-time monitoring system to comprehensively and real-time monitor the manufacturing process of flexible transparent display screen materials. The monitoring system covers key links in the manufacturing process, such as substrate material processing, thin film transistor preparation, organic light-emitting diode preparation, drive circuit design, and environmental parameters. Real-time collection and processing of these data ensures the accuracy and timeliness of the data.

[0089] Setting of monitoring threshold: In order to timely discover data anomalies, set reasonable monitoring threshold. According to the statistical analysis results, historical data and the requirements of the manufacturing process, set the normal range or threshold for each monitoring index. When the monitoring data exceeds the set threshold, the system can automatically identify and trigger the early warning mechanism.

[0090] Early warning signal: When data is abnormal, send an early warning signal for quick response. When the monitoring system detects that the data exceeds the threshold, an early warning signal is immediately sent. The early warning signal can be conveyed to relevant personnel through various ways such as sound and light alarm, SMS notification, email reminder, etc.

[0091] Application of early warning analysis model: In-depth analysis of the early warning signal to find out the abnormal reasons and provide decision support for adjustment.

[0092] Pre-set early warning analysis model, which is based on historical data, statistical analysis results and domain knowledge. When receiving the early warning signal, substitute the data corresponding to the early warning signal into the early warning analysis model. The model analyzes the input data and outputs the early warning signal analysis result, including abnormal type, possible reason, suggested measures, etc.

[0093] Step S105, using the real-time monitoring system, real-time monitoring of the manufacturing process, get real-time monitoring data, according to the real-time monitoring data and early warning analysis results, flexible transparent display screen material manufacturing process adjustment process parameters, equipment settings and material formula.

[0094] Continuous application of real-time monitoring system: continuously use the real-time monitoring system to monitor the manufacturing process of flexible transparent display screen material. The monitoring system collects various data in the manufacturing process in real time, such as process parameters, equipment status, material usage, etc.

[0095] Real-time monitoring data acquisition: acquire real-time monitoring data to provide basis for subsequent adjustment. Through the monitoring system, real-time acquisition of key data in the manufacturing process, which reflects the current state of the manufacturing process.

[0096] Use of early warning analysis results: combined with the early warning analysis results, the manufacturing process is adjusted. Early warning analysis results provide information such as abnormal type, possible cause and recommended measures.

[0097] Manufacturing process adjustment: according to the real-time monitoring data and early warning analysis results, the manufacturing process is adjusted to optimize the process, improve product quality and production efficiency.

[0098] According to the real-time monitoring data and early warning analysis results, adjust the process parameters in the manufacturing process, such as temperature, pressure, time, etc., to ensure process stability. According to the equipment status and early warning information, adjust the settings of the equipment, such as speed, position, mode, etc., to ensure the normal operation of the equipment. According to the material usage and early warning analysis results, adjust the material formula, such as ingredient ratio, additive type, etc., to optimize the material performance.

[0099] The adjustment of the manufacturing process is based on real-time monitoring data and early warning analysis results, through the real-time monitoring system, the abnormalities in the manufacturing process can be found in time and adjusted quickly, reducing the production loss. Adjustment covers process parameters, equipment settings and material formula, etc., to ensure the overall optimization of the manufacturing process. From real-time monitoring to data acquisition, to the use of early warning analysis results and the adjustment of the manufacturing process, the whole process is coherent and orderly, which reflects the logical clarity of the technical scheme of the invention.

[0100] Specifically, the flexible transparent display screen material manufacturing process method described in the present application, the step S101 comprises:

[0101] The substrate material data includes the physical and chemical properties of the substrate material;

[0102] Thin-film transistor fabrication data includes deposition rate, thickness uniformity, and electrical performance parameters of each layer of material during TFT fabrication, as well as process parameters during TFT fabrication.

[0103] Organic light-emitting diode fabrication data includes deposition rate, thickness uniformity, and light-emitting performance of organic materials.

[0104] Driving circuit design data includes design drawings, layout, and routing information of the driving circuit, as well as steps and parameters during the manufacturing process of the driving circuit.

[0105] Manufacturing process environment data includes temperature, humidity, and cleanliness parameters in the production workshop.

[0106] Real-time monitoring data acquisition includes equipment, process parameters, and product quality during the manufacturing process.

[0107] Adjustment data acquisition includes process parameter adjustment, equipment setting adjustment, and material formula adjustment.

[0108] Substrate material data includes physical and chemical properties of the substrate material. The substrate material is the foundation of the flexible transparent display screen, and its physical properties (such as thickness, hardness, flexibility) and chemical properties (such as corrosion resistance, compatibility with subsequent materials) directly affect the performance and lifespan of the display screen. Therefore, accurate acquisition of these data is crucial for selecting the appropriate substrate material.

[0109] Thin-film transistor (TFT) fabrication data includes deposition rate, thickness uniformity, and electrical performance parameters of each layer of material, using sensors and measuring instruments for real-time monitoring.

[0110] Process parameters during TFT fabrication. TFT is one of the key components of the flexible transparent display screen, and its performance directly affects the resolution, response speed, and power consumption of the display screen. By monitoring and recording these data in real time, we can ensure the stability and controllability of the TFT fabrication process, thereby improving the quality of the display screen.

[0111] Organic light-emitting diode fabrication data includes deposition rate, thickness uniformity, and light-emitting performance of organic materials, which is another core component of the flexible transparent display screen, and its light-emitting performance directly determines the color performance and brightness of the display screen. By monitoring the deposition rate and thickness uniformity of organic materials, we can ensure that the fabrication process meets the design requirements, thereby improving the display effect of the display screen.

[0112] The driving circuit design data includes the design drawings, layout and routing information of the driving circuit, the steps and parameters in the manufacturing process of the driving circuit. The driving circuit is a key part of controlling the working of the flexible transparent display screen, and its design rationality and manufacturing precision directly affect the stability and reliability of the display screen. By recording the design drawings, layout and routing information, and the steps and parameters in the manufacturing process in detail, it can be ensured that the manufacturing process of the driving circuit meets the design requirements, thereby improving the stability of the display screen.

[0113] The manufacturing process environment data includes environmental parameters such as temperature, humidity and cleanliness in the production workshop. The environmental parameters in the manufacturing process have an important influence on the quality and performance of the flexible transparent display screen. By monitoring and recording these environmental parameters in real time, adverse environmental factors can be found and adjusted in time, so as to ensure that the manufacturing process of the display screen is carried out in a suitable environment.

[0114] Real-time monitoring data acquisition includes equipment status, process parameters and product quality in the manufacturing process. Real-time monitoring data is an important means to ensure the stability and controllability of the manufacturing process. By monitoring the equipment status, process parameters and product quality in real time, abnormal situations can be found and handled in time, so as to ensure the smooth progress of the manufacturing process of the display screen.

[0115] Adjustment data acquisition includes process parameter adjustment, equipment setting adjustment and material formula adjustment. In the manufacturing process, it may be necessary to adjust the process parameters, equipment settings and material formulas according to the real-time monitoring data and early warning analysis results. By recording these adjustment data, strong support can be provided for subsequent optimization and improvement.

[0116] Specifically, the flexible transparent display screen material manufacturing process method disclosed by the present application comprises the following steps:

[0117] The flexible transparent display screen material manufacturing data set is imported into the database, the data index is established, the flexible transparent display screen material manufacturing data set is classified, and it is divided into substrate material data, flexible transparent display screen material preparation data including flexible transparent display screen material manufacturing data, driving circuit design data and flexible transparent display screen material manufacturing process environment data.

[0118] An identifier is assigned to each data category, the identifier is a data table name, a field name or a code, and metadata is added to each data category.

[0119] Data is imported into the database, and the flexible transparent display screen material manufacturing data set is imported into the database. The database is an effective tool for storing and managing a large amount of data. Importing the manufacturing data set into the database can facilitate the query, analysis and processing of data, and provides a basis for subsequent data management and application.

[0120] Establishing data index, establishing data index in database. Data index is an important means to improve data query efficiency. By establishing index, the required data can be quickly located, the query time is reduced, and the efficiency of data processing is improved.

[0121] Data classification: classify the flexible transparent display material manufacturing data set. It is divided into substrate material data, flexible transparent display material preparation data (herein "flexible transparent display material manufacturing data" should be understood as specific data in the preparation process, to avoid confusion, it can be understood as a subset or detailed classification of preparation data), driving circuit design data and flexible transparent display material manufacturing process environment data. Data classification is an important part of data management and organization. Through classification, similar data can be merged together, which is convenient for subsequent data analysis and processing. At the same time, classification is also helpful to better understand the source and purpose of data.

[0122] Assigning identifiers: assigning identifiers to each data category. Identifier can use data table name, field name or code.

[0123] The identifier is the unique identifier of the data, which can ensure the accuracy and consistency of the data. By assigning an identifier to each data category, data reference and association can be facilitated, and data management efficiency can be improved.

[0124] Adding metadata: adding metadata to each data category. Metadata is data that describes data, providing detailed information about data, such as data source, format, purpose, etc. By adding metadata, data can be better understood and managed, and the value of data can be improved.

[0125] Specifically, the flexible transparent display material manufacturing process method of the present application, the step S103 comprises:

[0126] Statistical analysis of substrate material data is used to analyze the physical and chemical properties of the substrate material and its distribution;

[0127] Statistical analysis of flexible transparent display material preparation data is used to analyze the relationship between process parameters, equipment settings, material formula and product quality in the preparation process;

[0128] Analysis of the influence of driving circuit design data and manufacturing process environment data on the preparation process of flexible transparent display material, through statistical analysis, the regular relationship between substrate material data, flexible transparent display material preparation data and preparation data is obtained.

[0129] Statistical analysis of substrate material data: statistical methods are used to analyze substrate material data. The physical and chemical properties of the substrate material and their distribution are obtained. The substrate material is the basis of the flexible transparent display screen, and its physical and chemical properties directly affect the performance and stability of the display screen. Through statistical analysis, the characteristics of the substrate material can be fully understood, providing a scientific basis for selecting the appropriate substrate material.

[0130] Statistical analysis of flexible transparent display screen material preparation data: statistical analysis of flexible transparent display screen material preparation data. The relationship between process parameters, equipment settings, material formulations and product quality in the preparation process is obtained. The preparation process is a key link in the manufacturing of flexible transparent display screens, and process parameters, equipment settings and material formulations directly affect the quality of the product. Through statistical analysis, the internal relationship between these factors and product quality can be revealed, providing data support for optimizing the preparation process.

[0131] Analysis of driving circuit design data and manufacturing process environment data: analyze the influence of driving circuit design data and manufacturing process environment data on the flexible transparent display screen material preparation process. Through statistical analysis, the regular relationship between substrate material data, flexible transparent display screen material preparation data and preparation data (here "preparation data" may refer to specific data in the preparation process or the overall preparation data mentioned earlier) is obtained. Driving circuit design and manufacturing process environment are important factors affecting the preparation process of flexible transparent display screen materials. Through statistical analysis, the complex relationship between these factors and the preparation process and product quality can be revealed, providing more comprehensive data support for the overall optimization of the preparation process.

[0132] Data-driven analysis method: step S103 uses statistical methods to analyze various types of data to ensure the objectivity and accuracy of the analysis results.

[0133] Comprehensive and in-depth analysis content: analysis covers multiple aspects such as substrate material, preparation process, driving circuit design and manufacturing process environment, ensuring a comprehensive understanding of the flexible transparent display screen material manufacturing process.

[0134] Clear analysis purpose and result: each step of analysis has a clear purpose and expected result, such as revealing the characteristics of the substrate material, the relationship between the preparation process and the product quality, etc., making the analysis process targeted and practical.

[0135] Correlation analysis between data: not only the characteristics of a single data class are analyzed, but also the correlation and regularity between data classes, such as the relationship between substrate material data and preparation data, providing a more comprehensive perspective for the overall optimization of the manufacturing process.

[0136] Specifically, the flexible transparent display screen material manufacturing process described in the present invention, step S103, includes:

[0137] According to the results of statistical analysis, determine the prediction of product quality, optimization of process parameters and identification of abnormal patterns, select deep learning algorithms for data mining;

[0138] Using deep learning algorithms to train the preprocessed data, get the preset early warning analysis model;

[0139] Early warning signal analysis results include predicted values, classification labels and abnormal patterns;

[0140] Apply the results of data mining to the monitoring and adjustment of the flexible transparent display screen material manufacturing process, adjust the process parameters according to the prediction results, identify abnormal patterns according to the classification labels, and optimize the material formula according to the mining results.

[0141] Select deep learning algorithms according to statistical analysis results: according to the results of statistical analysis, determine the goal of predicting product quality, optimizing process parameters and identifying abnormal patterns, select suitable deep learning algorithms for data mining. Statistical analysis provides a preliminary understanding of the data, while deep learning algorithms can handle complex data relationships and mine hidden patterns and rules. Selecting the right algorithm is the key to ensuring the accuracy and effectiveness of the mining results.

[0142] Using deep learning algorithms to train early warning analysis models: using the selected deep learning algorithms to train the preprocessed data, get the preset early warning analysis model. Data preprocessing is an important step to ensure data quality and consistency, including data cleaning, normalization, feature extraction, etc. The training process is to optimize the algorithm parameters through iteration, so that the model can accurately predict, classify and identify. Early warning analysis model is the core achievement of data mining, which can provide prediction values, classification labels and abnormal patterns according to input data, providing decision support for monitoring and adjustment of manufacturing process.

[0143] Early warning signal analysis results: early warning signal analysis results include predicted values, classification labels and abnormal patterns.

[0144] Predicted value: used to predict the future trend of product quality or process parameters.

[0145] Classification label: used to identify the category or pattern to which the data belongs, such as normal pattern, abnormal pattern, etc.

[0146] Abnormal pattern: refers to the pattern in the data that is significantly different from the normal pattern, which may indicate problems or potential risks in the manufacturing process.

[0147] Applying data mining results to manufacturing process: applying data mining results to monitoring and adjusting of flexible transparent display material manufacturing process. Adjusting process parameters according to prediction results: ensuring product quality meets expected requirements. Identifying abnormal patterns according to classification labels: timely discovering and handling problems in manufacturing process. Optimizing material formula according to mining results: improving material performance and stability. Application of data mining results realizes intelligentization and automation of manufacturing process, improves production efficiency and product quality, reduces production cost and risk.

[0148] Step S103 aims to predict product quality, optimize process parameters and identify abnormal patterns, ensuring the direction and focus of data mining. Selecting appropriate deep learning algorithms for data mining and training with preprocessed data ensures the accuracy and effectiveness of mining results. Early warning signal analysis results include predicted values, classification labels and abnormal patterns, providing comprehensive monitoring of the manufacturing process. Applying data mining results to monitoring and adjusting of manufacturing process realizes intelligentization and automation of manufacturing process, improves production efficiency and product quality.

[0149] Specifically, the flexible transparent display material manufacturing process method of the present application, step S104, comprises:

[0150] Setting monitoring points, including equipment status, process parameters, environmental conditions and product quality;

[0151] Setting monitoring thresholds for each monitoring point, inputting the set monitoring thresholds into the real-time monitoring system as the basis for data judgment;

[0152] Starting the real-time monitoring system to start real-time monitoring of the manufacturing process, the real-time monitoring system collects data from each monitoring point at a set frequency and time interval;

[0153] Storing the collected data in real time to the database, the real-time monitoring system judges the collected data in real time according to the set monitoring thresholds;

[0154] When the data exceeds the normal range, the real-time monitoring system sends out early warning signals in time;

[0155] When receiving the early warning signal, the real-time monitoring system automatically extracts the data corresponding to the early warning signal;

[0156] Substitute the extracted data corresponding to the early warning signal into the preset early warning analysis model, the early warning analysis model analyzes the data, identifies the pattern and detects the abnormality according to the input data.

[0157] Setting monitoring points: Determine the key points that need to be monitored, including equipment status, process parameters, environmental conditions, and product quality. These monitoring points are the key factors that affect product quality and stability in the manufacturing process. By monitoring these points, we can fully understand the real-time status of the manufacturing process.

[0158] Setting monitoring thresholds: Set reasonable monitoring thresholds for each monitoring point. Based on historical data and experience of the manufacturing process, determine the normal range of each monitoring point and set the monitoring threshold. The monitoring threshold is an important basis for judging whether the data is normal, providing a data judgment standard for the real-time monitoring system.

[0159] Starting the real-time monitoring system: Input the set monitoring threshold into the real-time monitoring system and start the system. The real-time monitoring system automatically collects data from each monitoring point according to the set frequency and time interval. The start of the real-time monitoring system ensures the continuous monitoring of the manufacturing process, enabling timely discovery and handling of abnormal situations.

[0160] The collected data is stored in the database in real time, and the data is judged in real time according to the set monitoring threshold. The real-time monitoring system continuously receives new data, stores it in the database, and simultaneously compares it with the monitoring threshold to determine whether the data is within the normal range. Data storage provides a basis for subsequent data analysis and traceability, while real-time judgment ensures timely discovery of abnormal situations.

[0161] When the data exceeds the normal range, the real-time monitoring system sends out a warning signal in a timely manner. The issuance of the warning signal reminds the operator to pay attention to the abnormal situation, providing the possibility of timely measures.

[0162] When receiving the warning signal, the real-time monitoring system automatically extracts the data corresponding to the warning signal. The extracted data is the basis for subsequent analysis of the cause of the abnormality and the adoption of measures, helping to quickly locate the problem and solve it.

[0163] Substitute the warning analysis model: Substitute the extracted data corresponding to the warning signal into the preset warning analysis model. The warning analysis model performs data analysis, pattern recognition, and abnormality detection based on the input data.

[0164] The warning analysis model can deeply mine the hidden information and rules in the data, providing scientific basis for accurate judgment and effective handling of abnormal situations.

[0165] Step S104 starts from setting monitoring points, to the start of the real-time monitoring system, data storage and judgment, the issuance of the warning signal, data extraction, and the application of the warning analysis model, forming a complete monitoring system operation process. Each step has clear content and meaning, and the steps are logically close and mutually supportive, ensuring the effective operation of the monitoring system.

[0166] Through real-time monitoring of the system settings and operation, abnormal conditions in the manufacturing process can be found and handled in time, improving product quality and production efficiency.

[0167] Specifically, the flexible transparent display screen material manufacturing process method comprises the following steps:

[0168] The real-time monitoring system is used to monitor the flexible transparent display screen material manufacturing process, and data of the flexible transparent display screen material manufacturing process parameters, equipment state, environmental conditions, and product quality monitoring points are collected in real time.

[0169] According to the real-time monitoring data and early warning analysis results, it is determined whether the current process parameters are in the best state.

[0170] If the process parameters deviate from the normal range, the process parameters are adjusted to the set value.

[0171] During the adjustment process, the equipment state and operating parameters are checked, and the equipment settings are adjusted according to the early warning analysis results, including the equipment operating speed parameters, temperature parameters, and pressure parameters.

[0172] Real-time monitoring and data collection: The real-time monitoring system is used to comprehensively monitor the flexible transparent display screen material manufacturing process, and data of the manufacturing process parameters, equipment state, environmental conditions, product quality, and other key monitoring points are collected in real time. Real-time monitoring ensures the transparency and traceability of the manufacturing process, providing accurate and timely data basis for subsequent data analysis and decision-making.

[0173] Determine the process parameter state: According to the real-time monitoring data and early warning analysis results, it is determined whether the current process parameters are in the best state. Compare the real-time monitoring data with the preset best process parameter range, combine the early warning analysis results, and evaluate the rationality of the process parameters. Timely determine the state of the process parameters, which helps to find potential manufacturing problems and ensure product quality and production efficiency.

[0174] Adjust the process parameters: If the process parameters deviate from the normal range, immediately adjust the process parameters to the set value. According to the judgment result, adjust the process parameters deviating from the normal range to make them return to the best state or within the set range. Timely adjustment of process parameters can correct the deviation in the manufacturing process and ensure the stability and consistency of product quality.

[0175] Check and adjust equipment settings: In the process of adjusting process parameters, check the equipment state and operating parameters at the same time, and adjust the equipment settings according to the early warning analysis results. The equipment settings include equipment running speed parameters, temperature parameters, and pressure parameters, etc. According to the early warning analysis results and actual needs, these parameters are adjusted accordingly. The good equipment state and accurate operating parameters are the key to ensure the smooth progress of the manufacturing process. By adjusting the equipment settings, the manufacturing process can be further optimized, and the production efficiency and product quality can be improved.

[0176] Step S105 first starts from real-time monitoring and data acquisition, ensuring the data basis for subsequent analysis and decision-making. After collecting data, the process parameter state is judged and adjusted if necessary, reflecting real-time control and optimization of the manufacturing process. While adjusting the process parameters, the equipment state and operating parameters are not forgotten, and adjustments are made as needed to ensure comprehensive optimization of the manufacturing process. Throughout the process, the early warning analysis results serve as an important basis for decision-making, reflecting the important role of the early warning system in the manufacturing process.

[0177] Specifically, the flexible transparent display screen material manufacturing process method disclosed by the present application, the step S105 comprises:

[0178] If the equipment fails, determine whether the current material formula matches according to the real-time monitoring data and the early warning analysis results;

[0179] If the material formula does not match, adjust the material formula, optimize the material combination and ratio, and verify the adjusted process parameters, equipment settings and material formula. Monitor the stability of the adjusted manufacturing process through real-time monitoring data;

[0180] If the adjusted manufacturing process is not stable, optimize the adjustment process.

[0181] Determine whether the material formula matches: If the equipment fails, determine whether the current material formula matches the equipment state and process requirements according to the real-time monitoring data and the early warning analysis results. Equipment failure may be related to the mismatch of the material formula. Timely judgment helps to quickly locate the problem cause.

[0182] Adjust the material formula: If the material formula is determined to be mismatched, adjust the material formula, including optimizing the material combination and ratio. By adjusting the material formula, the adaptability of the material to the equipment and process can be improved, which may help to solve the equipment failure or reduce the possibility of failure.

[0183] Verify the adjusted process parameters, equipment settings and material formula: Verify the adjusted process parameters, equipment settings and material formula. Monitor the adjusted manufacturing process through real-time monitoring data. Ensure that the adjusted manufacturing process is stable and reliable, and verify the effectiveness of the adjustment measures.

[0184] Monitor manufacturing process stability: continuously monitor whether the adjusted manufacturing process is stable by real-time monitoring data. Stability is an important indicator of manufacturing process, ensuring the stability of manufacturing process helps to ensure product quality and production efficiency.

[0185] Optimize the adjustment process: if the adjusted manufacturing process is not stable. Optimize the adjustment process, which may include further adjustment of material formula, process parameters or equipment settings. By optimizing the adjustment process, solve the problem of unstable manufacturing process, ensure the smooth progress of manufacturing process.

[0186] First of all, from the equipment failure, judge whether the material formula is matched, accurately locate the possible problem reason. If the material formula is not matched, the measure of adjusting the material formula is proposed, and the material combination and ratio are optimized. The adjusted process parameters, equipment settings and material formula are verified to ensure the effectiveness of the adjustment measures. In the verification process, continuously monitor the stability of the manufacturing process, and optimize in time when it is not stable, which embodies the comprehensive control and continuous optimization of the manufacturing process.

[0187] Specifically, the flexible transparent display screen material manufacturing process method disclosed by the present application, the step S105 comprises:

[0188] If the adjusted manufacturing process is not stable, the corresponding data is retrieved and matched in the optimization algorithm knowledge base to obtain real-time optimization algorithm. The optimization algorithm knowledge base includes genetic algorithm, particle swarm optimization algorithm and simulated annealing algorithm.

[0189] Set the parameters of the optimization algorithm, such as population size, iteration number, crossover probability and mutation probability.

[0190] Run the optimization algorithm to optimize and adjust the process parameters, equipment settings and material formula.

[0191] Retrieve corresponding data: if the adjusted manufacturing process is not stable, retrieve the data related to the instability of the manufacturing process, which may include real-time monitoring data, early warning analysis results, previous adjustment records, etc. Accurate data is the basis for optimization and adjustment, which helps to match the appropriate optimization algorithm in the optimization algorithm knowledge base.

[0192] Match real-time optimization algorithm: match the retrieved data in the optimization algorithm knowledge base to obtain real-time optimization algorithm suitable for the current situation.

[0193] Optimization algorithm knowledge base: including genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm and other optimization algorithms. Different optimization algorithms are suitable for different optimization problems and scenarios. By matching the real-time optimization algorithm, the pertinence and effectiveness of optimization and adjustment can be improved.

[0194] Setting optimization algorithm parameters: setting parameters for the selected optimization algorithm, such as population size, iteration number, crossover probability, and mutation probability, etc. These parameters are important factors affecting the performance and results of the optimization algorithm, and by setting reasonable parameters, the stability and convergence of the optimization algorithm can be ensured.

[0195] Running the optimization algorithm: running the set optimization algorithm to optimize and adjust the process parameters, equipment settings, and material formulations. The optimization algorithm will find the optimal or near-optimal solution according to the set target and constraint conditions through iterative search. By running the optimization algorithm, the process parameters, equipment settings, and material formulations in the manufacturing process can be comprehensively optimized, thereby solving the problem of unstable manufacturing process and improving product quality and production efficiency.

[0196] Firstly, the problem of unstable manufacturing process is clarified, which provides a clear target for subsequent optimization and adjustment. By retrieving relevant data and matching in the optimization algorithm knowledge base, the real-time optimization algorithm suitable for the current situation is obtained, which embodies the idea of data-driven and intelligent decision-making. Reasonable parameters are set for the selected optimization algorithm to ensure the stability and convergence of the optimization algorithm. Through the running of the optimization algorithm, the process parameters, equipment settings, and material formulations are comprehensively optimized, which embodies the comprehensive control and continuous optimization of the manufacturing process.

[0197] The technical scheme of the present application solves the problem that the prior art cannot monitor the manufacturing process of flexible transparent display screen materials, and according to the data generated in real time during the manufacturing of flexible transparent display screen materials, the manufacturing process of flexible transparent display screen materials is adaptively adjusted.

[0198] Firstly, in step S101, a plurality of data related to the manufacturing of flexible transparent display screen materials is obtained, including substrate material data, thin film transistor preparation data, organic light emitting diode preparation data, driving circuit design data, manufacturing process environment data, real-time monitoring data, and adjustment data. These data cover comprehensive information from raw materials to manufacturing process to product quality.

[0199] Then, in step S102, these data are integrated to form a flexible transparent display screen material manufacturing data set and stored in a database. The data set is subdivided into substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material manufacturing process data, and an identifier and metadata are assigned to each category for subsequent data processing and analysis.

[0200] Then, in step S103, statistical methods are used to statistically analyze various types of data to reveal the regular relationship between them. At the same time, deep learning algorithms are applied to mine data to discover deeper potential information. These analysis results and mining results can provide strong support for subsequent manufacturing process monitoring and adjustment.

[0201] In step S104, a real-time monitoring system is set up to monitor the manufacturing process in real time, and a monitoring threshold is set. When the data exceeds the normal range, the system timely issues a warning signal. After receiving the warning signal, the data corresponding to the warning signal is substituted into the pre-set warning analysis model for analysis to determine the nature and severity of the problem.

[0202] Finally, in step S105, based on real-time monitoring data and warning analysis results, the flexible transparent display screen material manufacturing process is adjusted adaptively. The adjustment content includes process parameters, equipment settings, and material formulations. Through adjustment, the manufacturing process can be more stable, efficient, and the product quality can be improved.

[0203] Specifically, the present application integrates and analyzes various data in the manufacturing process to reveal the regular relationship between the data and apply deep learning algorithms to mine the data. At the same time, a real-time monitoring system is used to monitor and analyze the manufacturing process in real time to timely discover problems and take appropriate adjustment measures. These measures include adjusting process parameters, equipment settings, and material formulations to ensure the stability and efficiency of the manufacturing process.

Claims

1. A manufacturing process for a flexible transparent display screen material, characterized in that, include: Step S101: Acquire substrate material data, thin film transistor fabrication data, organic light-emitting diode fabrication data, driving circuit design data, manufacturing process environment data, real-time monitoring data, and adjustment data; Step S102: Integrate the substrate material data, thin film transistor fabrication data, organic light-emitting diode fabrication data, driving circuit design data, manufacturing process environment data, real-time monitoring data, and adjustment data to form a flexible transparent display screen material manufacturing dataset. Store the flexible transparent display screen material manufacturing dataset in a database. Divide the flexible transparent display screen material manufacturing dataset into substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material preparation data, and assign an identifier and metadata to each category. Step S103: Statistical analysis is performed on the substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material preparation data using statistical methods. Based on the results of the statistical analysis, the predicted product quality, optimized process parameters, and anomaly patterns are determined. A deep learning algorithm is selected for data mining to obtain the regular relationships between the substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material preparation data. The deep learning algorithm is then applied to mine the substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material preparation data to obtain data mining results, including predicted values ​​and classification labels. Step S104: Use a real-time monitoring system to monitor the manufacturing process in real time, set monitoring thresholds, and issue a warning signal in a timely manner when the data exceeds the normal range. When a warning signal is received, substitute the data corresponding to the warning signal into the preset warning analysis model to obtain the warning signal analysis result. Step S105: Use a real-time monitoring system to monitor the manufacturing process in real time, obtain real-time monitoring data, and adjust the process parameters, equipment settings and material formulas for the manufacturing process of flexible transparent display screen materials based on the real-time monitoring data and early warning analysis results. Step S105 further includes: If the adjusted manufacturing process is unstable, the corresponding data is retrieved and matched in the optimization algorithm knowledge base to obtain the real-time optimization algorithm. The optimization algorithm knowledge base includes genetic algorithm, particle swarm optimization algorithm and simulated annealing algorithm. Set the parameters of the optimization algorithm, such as population size, number of iterations, crossover probability, and mutation probability; The optimization algorithm is run to optimize and adjust process parameters, equipment settings, and material formulations.

2. The manufacturing process of the flexible transparent display screen material as described in claim 1, characterized in that, Step S101 includes: Substrate material data includes the physical and chemical properties of the substrate material; Thin-film transistor fabrication data includes real-time monitoring of the deposition rate, thickness uniformity, and electrical performance parameters of each layer of material during TFT fabrication using sensors and measuring instruments, as well as process parameters during TFT fabrication. Data on organic light-emitting diode fabrication includes the deposition rate, thickness uniformity, and luminescent properties of organic materials; The driver circuit design data includes the driver circuit design drawings, layout and routing information, as well as the steps and parameters in the driver circuit manufacturing process; Manufacturing process environmental data includes environmental parameters such as temperature, humidity, and cleanliness within the production workshop. Real-time monitoring data acquisition includes equipment used in the manufacturing process, process parameters used in the manufacturing process, and product quality used in the manufacturing process; Adjusting data acquisition includes adjusting process parameters, equipment settings, and material formulations.

3. The manufacturing process of the flexible transparent display screen material as described in claim 1, characterized in that, Step S102 includes: Import the flexible transparent display material manufacturing dataset into the database, establish a data index, and classify the flexible transparent display material manufacturing dataset into substrate material data, flexible transparent display material preparation data including flexible transparent display material manufacturing data, drive circuit design data, and flexible transparent display material manufacturing process environmental data. Assign an identifier to each data category, using the data table name, field name, or code, and add metadata to each data category.

4. The manufacturing process of the flexible transparent display screen material as described in claim 1, characterized in that, Step S103 includes: Statistical methods were used to analyze the substrate material data to obtain the physical and chemical properties and distribution of the substrate material. Statistical analysis was performed on the data of flexible transparent display material preparation to obtain the relationship between process parameters, equipment settings, material formulation and product quality during the preparation process; The influence of driving circuit design data and manufacturing process environment data on the preparation process of flexible transparent display materials was analyzed. Through statistical analysis, the regular relationship between substrate material data, flexible transparent display material preparation data, and preparation data was obtained.

5. The manufacturing process for flexible transparent display screen materials as described in claim 4, characterized in that, Step S103 includes: The preprocessed data is trained using deep learning algorithms to obtain a pre-defined early warning analysis model; The results of the early warning signal analysis include predicted values, classification labels, and anomaly patterns; The data mining results are applied to the monitoring and adjustment of the manufacturing process of flexible transparent display materials. Process parameters are adjusted based on the prediction results, abnormal patterns are identified based on classification tags, and material formulations are optimized based on the mining results.

6. The manufacturing process of the flexible transparent display screen material as described in claim 1, characterized in that, Step S104 includes: Set up monitoring points, which include equipment status, process parameters, environmental conditions, and product quality; Set monitoring thresholds for each monitoring point and input the set monitoring thresholds into the real-time monitoring system as the basis for data judgment; The real-time monitoring system is activated to begin real-time monitoring of the manufacturing process. The real-time monitoring system collects data from each monitoring point according to the set frequency and time interval. The collected data is stored in the database in real time, and the real-time monitoring system makes real-time judgments on the collected data based on the set monitoring thresholds. When the data exceeds the normal range, the real-time monitoring system will issue an early warning signal in a timely manner; When an early warning signal is received, the real-time monitoring system automatically extracts the data corresponding to the triggering early warning signal; The extracted early warning signal data is substituted into a preset early warning analysis model. The early warning analysis model performs data analysis, pattern recognition, and anomaly detection processing based on the input data.

7. The manufacturing process of the flexible transparent display screen material as described in claim 1, characterized in that, Step S105 includes: The manufacturing process of flexible transparent display screen materials is monitored using a real-time monitoring system, which collects data on manufacturing process parameters, equipment status, environmental conditions, and product quality monitoring points in real time. Based on real-time monitoring data and early warning analysis results, determine whether the current process parameters are in the optimal state; If the process parameters are found to deviate from the normal range, adjust the process parameters to the set values; During the adjustment process, check the equipment status and operating parameters, and adjust the equipment settings based on the early warning analysis results. The equipment settings adjustment includes the equipment operating speed parameters, temperature parameters, and pressure parameters.

8. The manufacturing process of the flexible transparent display screen material as described in claim 7, characterized in that, Step S105 includes: If equipment malfunctions, the system will determine whether the current material formulation is suitable based on real-time monitoring data and early warning analysis results. If the material formula is mismatched, adjust the material formula and optimize the material combination and ratio. Verify the adjusted process parameters, equipment settings and material formula, and monitor whether the adjusted manufacturing process is stable through real-time monitoring data. If the adjusted manufacturing process is unstable, then the adjusted process should be optimized.

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