Manufacturing process method of flexible transparent display screen material

Abstract

The invention relates to the technical field of flexible transparent display screen material manufacturing data processing, provides a flexible transparent display screen material manufacturing process method, and particularly optimizes the problem of instability possibly occurring in the manufacturing process. By monitoring the manufacturing process in real time, once an unstable condition is detected, relevant data are called, and a proper real-time optimization algorithm is matched in an optimization algorithm knowledge base. A genetic algorithm, a particle swarm optimization algorithm or a simulated annealing algorithm and the like are utilized to comprehensively optimize and adjust process parameters, equipment setting and a material formula. The process ensures the stability of the manufacturing process, improves the product quality, and reduces material waste and energy consumption at the same time. According to the implementation of the invention, the intelligent decision-making capability of manufacturing is enhanced, the competitiveness of the product is improved, and an innovative technical solution is provided for the manufacturing process of the flexible transparent display screen material.

Description

Technical Field

[0001] The invention relates to the technical field of data processing for manufacturing flexible transparent display screen materials, and in particular to a manufacturing process method for flexible transparent display screen materials. Background Art

[0002] The flexible substrate materials of the material manufacturing process of the flexible transparent display screen include polyimide (PI) or polyester film (PET). Polyimide (PI) or polyester film (PET) has good flexibility and transparency. Electronic devices such as thin film transistors and organic light-emitting diodes are prepared on the flexible substrate, usually 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 to the display device. The prepared thin-film electronic devices, driving circuits and other components 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 method is based on the manufacturing of the flexible transparent display screen material. The existing technology is unable to monitor the manufacturing process of the flexible transparent display screen material and adaptively adjust the manufacturing process of the flexible transparent display screen material according to the real-time data generated by the manufacturing of the flexible transparent display screen material. Therefore, the present invention provides a flexible transparent display screen material manufacturing process method. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a flexible transparent display screen material manufacturing process method to solve 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] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: The present invention provides a method for manufacturing a flexible transparent display screen material, comprising: Step S101, acquiring 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; Step S102, integrating 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 flexible transparent display screen material manufacturing data set, storing the flexible transparent display screen material manufacturing data set in a database, dividing the flexible transparent display screen material manufacturing data set into substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material preparation data, and assigning identifiers and metadata to each category; Step S103, using a statistical method to perform statistical analysis on the substrate material data, the flexible transparent display material preparation data, and the flexible transparent display material preparation data, to obtain a regular relationship between the substrate material data, the flexible transparent display material preparation data, and the flexible transparent display material preparation data, and applying a deep learning algorithm to mine the substrate material data, the flexible transparent display material preparation data, and the flexible transparent display material preparation data to obtain a data mining result; Step S104, using a real-time monitoring system to monitor the manufacturing process in real time, setting a monitoring threshold, and issuing a warning signal in a timely manner when the data exceeds a normal range. When a warning signal is received, the data corresponding to the warning signal is substituted into a preset warning analysis model to obtain a warning signal analysis result; Step S105, using a real-time monitoring system to monitor the manufacturing process in real time, obtain real-time monitoring data, and adjust process parameters, equipment settings and material formulas for the flexible transparent display material manufacturing process based on the real-time monitoring data and early warning analysis results.

[0006] Furthermore, the flexible transparent display screen material manufacturing process method of the present invention, the step S101 comprises: The base material data include the physical and chemical properties of the base material; Thin-film transistor preparation data includes the use of sensors and measuring instruments to monitor the deposition rate, thickness uniformity, and electrical performance parameters of each layer of materials in real time during the TFT preparation process, as well as the process parameters during the TFT preparation process; Organic light-emitting diode fabrication data including deposition rate, thickness uniformity and luminescence performance of organic materials; The drive circuit design data includes the design drawings, layout and routing information of the drive circuit, and the steps and parameters in the manufacturing process of the drive circuit; The manufacturing process environmental data includes the temperature, humidity and cleanliness parameters in the production workshop. Real-time monitoring data acquisition includes equipment in the manufacturing process, process parameters in the manufacturing process, and product quality in the manufacturing process; Adjustment data acquisition includes process parameter adjustment, equipment setting adjustment, and material recipe adjustment.

[0007] Furthermore, in the manufacturing process method of the flexible transparent display screen material of the present invention, the step S102 comprises: Importing the flexible transparent display material manufacturing data set into a database, establishing a data index, and classifying the flexible transparent display material manufacturing data set 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 environment data; Assign an identifier to each data category. The identifier uses the data table name, field name or code, and add metadata to each data category.

[0008] Furthermore, in the manufacturing process of the flexible transparent display screen material of the present invention, the step S103 comprises: Analyze the base material data using statistical methods to obtain the physical and chemical properties and distribution of the base material; Statistical analysis was performed on the preparation data of flexible transparent display screen materials to obtain the relationship between process parameters, equipment settings, material formula 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 is analyzed. Through statistical analysis, the substrate material data, flexible transparent display material preparation data and the regular relationship between the preparation data are obtained.

[0009] Furthermore, in the manufacturing process of the flexible transparent display screen material of the present invention, the step S103 comprises: Based on the results of statistical analysis, determine the prediction of product quality, optimize process parameters, identify abnormal patterns, and select deep learning algorithms for data mining; Use deep learning algorithms to train preprocessed data to obtain a preset early warning analysis model; The warning signal analysis results include predicted values, classification labels, and abnormal patterns; The data mining results are applied to the monitoring and adjustment of the manufacturing process of flexible transparent display materials, adjusting process parameters according to the prediction results, identifying abnormal patterns according to classification labels, and optimizing material formulations according to the mining results.

[0010] Furthermore, in the manufacturing process method of the flexible transparent display screen material of the present invention, the step S104 comprises: Set up monitoring points, including 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; Start 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 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 according to the set monitoring threshold; When the data exceeds the normal range, the real-time monitoring system will issue a warning signal in time; When a warning signal is received, the real-time monitoring system automatically extracts the data corresponding to the triggering warning signal; The extracted data corresponding to the warning signal is substituted into the preset warning analysis model, and the warning analysis model performs data analysis, pattern recognition, and anomaly detection processing based on the input data.

[0011] Furthermore, in the manufacturing process of the flexible transparent display screen material of the present invention, the step S105 comprises: Use the real-time monitoring system to monitor the manufacturing process of flexible transparent display screen materials, and collect data on manufacturing process parameters, equipment status, environmental conditions, and product quality monitoring points of flexible transparent display screen materials 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 adjusted equipment settings include equipment operating speed parameters, temperature parameters, and pressure parameters.

[0012] Furthermore, in the manufacturing process of the flexible transparent display screen material of the present invention, the step S105 comprises: If equipment fails, determine whether the current material formula matches based on real-time monitoring data and early warning analysis results; If the material formula does not match, adjust the material formula, 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, the adjusted process is optimized.

[0013] Furthermore, in the manufacturing process of the flexible transparent display screen material of the present invention, the step S105 comprises: If the adjusted manufacturing process is unstable, the corresponding data is retrieved and matched in the optimization algorithm knowledge base to obtain a real-time optimization algorithm. The optimization algorithm knowledge base includes a genetic algorithm, a particle swarm optimization algorithm, and a simulated annealing algorithm. Set the optimization algorithm parameters, such as population size, number of iterations, crossover probability, and mutation probability; Run optimization algorithms to optimize process parameters, equipment settings, and material recipes.

[0014] Beneficial effects of the present invention: The present invention monitors the manufacturing process through real-time monitoring data, and timely retrieves relevant data when instability occurs, and uses the algorithm in the optimization algorithm knowledge base to perform optimization adjustments, thereby effectively improving the stability of the manufacturing process. This helps to reduce failures and downtime in the production process and improve production efficiency.

[0015] The present invention uses an optimization algorithm to comprehensively optimize process parameters, equipment settings, and material formulations to ensure the best match between them. This can not only improve product quality, but also reduce material waste and energy consumption, and reduce production costs. By matching the real-time optimization algorithm, the present invention realizes data-driven intelligent decision-making. This decision-making method is more accurate and efficient, can quickly respond to changes in the manufacturing process, and improve production flexibility and adaptability.

[0016] In summary, the present invention effectively improves the manufacturing process stability and product quality of flexible transparent display materials through technical means such as real-time monitoring, optimization algorithm and knowledge base matching, reduces production costs, enhances intelligent decision-making capabilities, improves product competitiveness, and promotes technological innovation and industrial upgrading. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0018] Figure 1 A schematic diagram of a process for manufacturing a flexible transparent display screen material provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described below in conjunction with the drawings. In order to better understand the purpose of the present invention, the present invention is further described below.

[0020] The present invention provides a method for manufacturing a flexible transparent display screen material, comprising: Step S101, acquiring 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; Substrate material data: This is the basis of flexible transparent displays, including the physical and chemical properties of the substrate material. These data are critical to understanding the characteristics of the substrate material, selecting the appropriate substrate material, and predicting its performance during the manufacturing process.

[0021] Thin-film transistor (TFT) preparation data: TFT is one of the core components in flexible transparent displays. Obtaining data during the TFT preparation process, such as the deposition rate of each layer of material, thickness uniformity, electrical performance parameters, and process parameters, helps monitor and optimize the TFT preparation process and ensure TFT performance and quality.

[0022] Organic light-emitting diode preparation data: It is the light-emitting element of the flexible transparent display. Obtaining data during the preparation process, such as the deposition rate, thickness uniformity and luminescence performance of organic materials, is of great significance for improving luminous efficiency and stability.

[0023] Driving circuit design data: The driving circuit is responsible for controlling the display content of the flexible transparent display. Obtaining the design drawings, layout and routing information of the driving circuit, as well as the steps and parameters in the manufacturing process, will help ensure the rationality of the driving circuit design and the controllability of the manufacturing process.

[0024] Manufacturing process environmental data: Environmental parameters such as temperature, humidity and cleanliness in the production workshop have an important impact on the manufacturing process of flexible transparent displays. Obtaining these environmental data helps monitor and adjust the manufacturing environment to ensure the stability of the manufacturing process and product quality.

[0025] Real-time monitoring data: During the manufacturing process, real-time monitoring of equipment status, process parameters and product quality is carried out. Obtaining this data helps to promptly identify and solve problems and ensure the smooth progress of the manufacturing process.

[0026] Adjustment data: During the manufacturing process, process parameters, equipment settings, and material formulations are adjusted based on real-time monitoring data and early warning analysis results. Obtaining these adjustment data helps evaluate the adjustment effect and further optimize the manufacturing process.

[0027] The data acquisition in step S101 is carried out in the logical order of the manufacturing process of the flexible transparent display screen material. First, the substrate material data is obtained, which is the basis of the manufacturing process; then, the preparation data of TFT and 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 controlling the display content; at the same time, the manufacturing process environment data is obtained to ensure the suitability of the manufacturing environment; during 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, the adjustment data is obtained and corresponding adjustments are made. This logically clear data acquisition order helps to ensure the smooth progress of the flexible transparent display screen material manufacturing process and improve product quality.

[0028] Step S102, integrating 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 flexible transparent display screen material manufacturing data set, storing the flexible transparent display screen material manufacturing data set in a database, dividing the flexible transparent display screen material manufacturing data set into substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material preparation data, and assigning identifiers and metadata to each category; Data integration: This step integrates the various types of data (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) obtained in step S101 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 the efficiency and accuracy of data processing.

[0029] Data storage: The integrated data set is stored in the database. As an efficient data management tool, the database can provide functions such as fast retrieval, update and deletion of data, which facilitates subsequent data analysis and application.

[0030] 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), etc. This classification helps to better understand and analyze the data, and provides a basis for subsequent data mining and application.

[0031] Identifier assignment: Assign identifiers (such as data table names, field names or codes) and metadata (such as data description, units, range, etc.) to each data category. Identifiers enable data to be uniquely identified and quickly retrieved in the database, while metadata provides detailed information about the data, which helps to understand and use the data.

[0032] Systematic data management: Through data integration and storage, the technical solution of the present invention realizes systematic management of various data generated in the manufacturing process of flexible transparent display materials. This management method makes the relationship between data clearer and provides strong support for subsequent data analysis and application.

[0033] Convenience of data use: Through data classification and identifier allocation, the technical solution of the present invention makes it easier to retrieve and use data in the database. Users can quickly find the required data as needed and perform corresponding analysis and processing.

[0034] Step S103, using a statistical method to perform statistical analysis on the substrate material data, the flexible transparent display material preparation data, and the flexible transparent display material preparation data, to obtain a regular relationship between the substrate material data, the flexible transparent display material preparation data, and the flexible transparent display material preparation data, and applying a deep learning algorithm to mine the substrate material data, the flexible transparent display material preparation data, and the flexible transparent display material preparation data to obtain a data mining result; Statistical methods are used to perform statistical analysis on 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 (although the expression here is slightly repetitive, it should actually be understood as environmental data related to the preparation process, etc.).

[0035] Analyze the base material data to understand the distribution of its physical and chemical properties, and provide a basis for material selection and use.

[0036] Statistical analysis is conducted on the preparation data of flexible transparent display materials to reveal the relationship between process parameters, equipment settings, material formula and product quality during the preparation process, and to identify the key factors affecting product quality.

[0037] Analyze the impact of driving circuit design data and manufacturing process environment data on the preparation process of flexible transparent display materials, and fully understand the various factors in the manufacturing process and their interactions.

[0038] Results: Through statistical analysis, the regular relationship between substrate material data, flexible transparent display material preparation data and flexible transparent display material manufacturing process data was obtained, which provided a basis for subsequent data mining and manufacturing process adjustment.

[0039] Deep learning mining: Obtain data mining results, and apply deep learning algorithms to further mine the data obtained from statistical analysis to obtain deeper information and knowledge. Select appropriate deep learning algorithms, such as neural networks, convolutional neural networks, etc., and determine the mining target (such as predicting product quality, optimizing process parameters, identifying abnormal patterns, etc.) based on the results of statistical analysis. Train the preprocessed data to establish a prediction model or classification model. Use the trained model to predict or classify new data to obtain data mining results. Data mining results include prediction values ​​(such as prediction values ​​for product quality), classification labels (such as classification labels for abnormal patterns), etc. This information can be used to monitor and adjust the manufacturing process of flexible transparent display materials.

[0040] Progressive data analysis: Step S103 first reveals the regular relationship between data through statistical analysis, and then obtains deeper information and knowledge through deep learning. This progressive data analysis method helps to fully and deeply understand the manufacturing process of flexible transparent display materials.

[0041] Targeted data mining: The goal of deep learning mining is determined based on the results of statistical analysis, which is clearly targeted and practical. This helps ensure the effectiveness and accuracy of data mining results.

[0042] Scientificity of data application: The data mining results obtained in step S103 can be applied to the monitoring and adjustment of the manufacturing process of the flexible transparent display material, so as to achieve scientific decision-making and optimization based on data.

[0043] Step S104, using a real-time monitoring system to monitor the manufacturing process in real time, setting a monitoring threshold, and issuing a warning signal in a timely manner when the data exceeds a normal range. When a warning signal is received, the data corresponding to the warning signal is substituted into a preset warning analysis model to obtain a warning signal analysis result; The real-time monitoring system is used to conduct comprehensive and real-time monitoring of 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. The data is collected and processed in real time to ensure the accuracy and timeliness of the data.

[0044] Setting of monitoring thresholds: In order to detect data anomalies in a timely manner, set reasonable monitoring thresholds. According to statistical analysis results, historical data and manufacturing process requirements, set a normal range or threshold for each monitoring indicator. When the monitoring data exceeds the set threshold, the system can automatically identify and trigger the early warning mechanism.

[0045] Issuance of early warning signals: When data is abnormal, early warning signals are issued in time for quick response. When the monitoring system detects that the data exceeds the threshold, an early warning signal is immediately issued. The early warning signal can be conveyed to relevant personnel through various means such as sound and light alarms, SMS notifications, and email reminders.

[0046] Application of early warning analysis model: Conduct in-depth analysis of early warning signals, identify abnormal causes, and provide decision support for adjustments.

[0047] A pre-set early warning analysis model is built based on historical data, statistical analysis results, and domain knowledge. When an early warning signal is received, the data corresponding to the early warning signal is substituted into the early warning analysis model. The model analyzes the input data and outputs the early warning signal analysis results, including the abnormality type, possible causes, and recommended measures.

[0048] Step S105, using a real-time monitoring system to monitor the manufacturing process in real time, obtain real-time monitoring data, and adjust process parameters, equipment settings and material formulas for the flexible transparent display material manufacturing process based on the real-time monitoring data and early warning analysis results.

[0049] Continuous application of real-time monitoring system: Continuously use the real-time monitoring system to monitor the manufacturing process of flexible transparent display materials in real time. The monitoring system collects various data in the manufacturing process in real time, such as process parameters, equipment status, material usage, etc.

[0050] Acquisition of real-time monitoring data: Acquisition of real-time monitoring data provides a basis for subsequent adjustments. Through the monitoring system, key data in the manufacturing process can be obtained in real time, which reflects the current status of the manufacturing process.

[0051] Utilization of early warning analysis results: Combined with the early warning analysis results, targeted adjustments are made to the manufacturing process. The early warning analysis results provide information such as abnormality type, possible causes, and recommended measures.

[0052] Adjustment of manufacturing process: Based on real-time monitoring data and early warning analysis results, the manufacturing process is adjusted to optimize the process and improve product quality and production efficiency.

[0053] According to 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 equipment settings, 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 the proportion of ingredients, the type of additives, etc., to optimize the material performance.

[0054] The adjustment of the manufacturing process is based on real-time monitoring data and early warning analysis results. Through the real-time monitoring system, abnormalities in the manufacturing process can be discovered in time and adjustments can be made quickly, reducing production losses. The adjustment covers multiple aspects such as process parameters, equipment settings and material formulations, ensuring 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 entire process is coherent and orderly, reflecting the logical clarity of the technical solution of the present invention.

[0055] Specifically, the flexible transparent display screen material manufacturing process method of the present invention, step S101 includes: The base material data include the physical and chemical properties of the base material; Thin-film transistor preparation data includes the use of sensors and measuring instruments to monitor the deposition rate, thickness uniformity, and electrical performance parameters of each layer of materials in real time during the TFT preparation process, as well as the process parameters during the TFT preparation process; Organic light-emitting diode fabrication data including deposition rate, thickness uniformity and luminescence performance of organic materials; The drive circuit design data includes the design drawings, layout and routing information of the drive circuit, and the steps and parameters in the manufacturing process of the drive circuit; The manufacturing process environmental data includes the temperature, humidity and cleanliness parameters in the production workshop. Real-time monitoring data acquisition includes equipment in the manufacturing process, process parameters in the manufacturing process, and product quality in the manufacturing process; Adjustment data acquisition includes process parameter adjustment, equipment setting adjustment, and material recipe adjustment.

[0056] Substrate material data includes the physical and chemical properties of the substrate material. The substrate material is the basis of the flexible transparent display, 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 life of the display. Therefore, accurate acquisition of these data is crucial for selecting the appropriate substrate material.

[0057] Thin-film transistor (TFT) fabrication data, using sensors and measuring instruments to monitor the deposition rate, thickness uniformity, and electrical performance parameters of each layer of material in real time.

[0058] Process parameters in the TFT preparation process. TFT is one of the key components of flexible transparent displays, and its performance directly affects the resolution, response speed, and power consumption of the display. By monitoring and recording these data in real time, it is possible to ensure that the TFT preparation process is stable and controllable, thereby improving the quality of the display.

[0059] Organic light-emitting diode preparation data includes the deposition rate, thickness uniformity and luminous performance of organic materials. It is another core component of flexible transparent displays. Its luminous performance directly determines the color performance and brightness of the display. By monitoring the deposition rate and thickness uniformity of organic materials, it can be ensured that the preparation process meets the design requirements, thereby improving the display effect of the display.

[0060] 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 operation of the flexible transparent display screen. Its design rationality and manufacturing accuracy directly affect the stability and reliability of the display screen. By recording the design drawings, layout and routing information, as well as 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.

[0061] The manufacturing process environmental data includes environmental parameters such as temperature, humidity, and cleanliness in the production workshop. The environmental parameters in the manufacturing process have an important impact on the quality and performance of the flexible transparent display. By real-time monitoring and recording these environmental parameters, adverse environmental factors can be discovered and adjusted in a timely manner, thereby ensuring that the manufacturing process of the display is carried out in a suitable environment.

[0062] Real-time monitoring data acquisition includes equipment status, process parameters and product quality during the manufacturing process. Real-time monitoring data is an important means to ensure that the manufacturing process is stable and controllable. By real-time monitoring of equipment status, process parameters and product quality, abnormal situations can be discovered and handled in a timely manner, thereby ensuring the smooth manufacturing process of the display.

[0063] Adjustment data acquisition includes process parameter adjustment, equipment setting adjustment, and material formula adjustment. During the manufacturing process, it may be necessary to adjust process parameters, equipment settings, and material formulas based on real-time monitoring data and early warning analysis results. By recording these adjustment data, it can provide strong support for subsequent optimization and improvement.

[0064] Specifically, the flexible transparent display screen material manufacturing process method of the present invention, the step S102 comprises: Importing the flexible transparent display material manufacturing data set into a database, establishing a data index, and classifying the flexible transparent display material manufacturing data set 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 environment data; Assign an identifier to each data category. The identifier uses the data table name, field name, or code, and add metadata to each data category.

[0065] Import data into the database, import the manufacturing data set of the flexible transparent display material into the database. The database is an effective tool for storing and managing large amounts of data. Importing the manufacturing data set into the database can facilitate data query, analysis and processing, providing a basis for subsequent data management and application.

[0066] Establish data indexes in the database. Data indexes are an important means to improve data query efficiency. By establishing indexes, you can quickly locate the required data, reduce query time, and improve data processing efficiency.

[0067] Data classification: Classify the flexible transparent display material manufacturing data set. It is divided into base material data, flexible transparent display material preparation data (the "flexible transparent display material manufacturing data" in the original text here 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), drive 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 grouped together to facilitate subsequent data analysis and processing. At the same time, classification also helps to better understand the source and purpose of data.

[0068] Assign identifiers: Assign identifiers to each data category. The identifiers can use data table names, field names, or codes.

[0069] An identifier is a unique identifier for data, which can ensure the accuracy and consistency of data. By assigning an identifier to each data category, data can be easily referenced and associated, improving the efficiency of data management.

[0070] Add metadata Add metadata for each data category. Metadata is data that describes data and provides detailed information about the data, such as its source, format, and purpose. By adding metadata, you can better understand and manage data and improve the use value of data.

[0071] Specifically, the flexible transparent display screen material manufacturing process method of the present invention, step S103 includes: Analyze the base material data using statistical methods to obtain the physical and chemical properties and distribution of the base material; Statistical analysis was performed on the preparation data of flexible transparent display screen materials to obtain the relationship between process parameters, equipment settings, material formula 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 is analyzed. Through statistical analysis, the substrate material data, flexible transparent display material preparation data and the regular relationship between the preparation data are obtained.

[0072] Substrate material data analysis: Analyze the substrate material data using statistical methods. Obtain the physical and chemical properties and distribution of the substrate material. The substrate material is the basis of the flexible transparent display, and its physical and chemical properties directly affect the performance and stability of the display. Through statistical analysis, we can fully understand the characteristics of the substrate material and provide a scientific basis for selecting the appropriate substrate material.

[0073] Flexible transparent display material preparation data analysis: Statistical analysis of flexible transparent display material preparation data. Obtain the relationship between process parameters, equipment settings, material formula and product quality during the preparation process. The preparation process is a key link in the manufacture of flexible transparent displays. Process parameters, equipment settings and material formula directly affect product quality. Through statistical analysis, the intrinsic relationship between these factors and product quality can be revealed, providing data support for optimizing the preparation process.

[0074] Analysis of driving circuit design data and manufacturing process environment data: Analyze the impact of driving circuit design data and manufacturing process environment data on the preparation process of flexible transparent display screen materials. 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 above) 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 comprehensive optimization of the preparation process.

[0075] 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.

[0076] Comprehensive and in-depth analysis content: The analysis covers multiple aspects such as substrate materials, preparation process, drive circuit design and manufacturing process environment to ensure a comprehensive understanding of the manufacturing process methods of flexible transparent display materials.

[0077] Clear analysis objectives and results: Each step of the analysis has a clear purpose and expected results, such as revealing the characteristics of the substrate material, the relationship between the preparation process and product quality, etc., making the analysis process targeted and practical.

[0078] Correlation analysis between data: not only analyze the characteristics of a single data class, but also analyze the correlation and rules between data classes, such as the relationship between substrate material data and preparation data, to provide a more comprehensive perspective for comprehensive optimization of manufacturing processes.

[0079] Specifically, the flexible transparent display screen material manufacturing process method of the present invention, step S103 includes: Based on the results of statistical analysis, determine the prediction of product quality, optimize process parameters, identify abnormal patterns, and select deep learning algorithms for data mining; Use deep learning algorithms to train preprocessed data to obtain a preset early warning analysis model; The warning signal analysis results include predicted values, classification labels, and abnormal patterns; The data mining results are applied to the monitoring and adjustment of the manufacturing process of flexible transparent display materials, adjusting process parameters according to the prediction results, identifying abnormal patterns according to classification labels, and optimizing material formulations according to the mining results.

[0080] Select deep learning algorithms based on statistical analysis results: Based on the results of statistical analysis, determine the goals of predicting product quality, optimizing process parameters, and identifying abnormal patterns, and select appropriate 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 laws. Selecting the right algorithm is the key to ensuring the accuracy and effectiveness of mining results.

[0081] Using deep learning algorithms to train early warning analysis models: Use the selected deep learning algorithm to train the preprocessed data to obtain 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 iteratively optimize the algorithm parameters so that the model can accurately predict, classify and identify. The early warning analysis model is the core result of data mining. It can provide information such as predicted values, classification labels and abnormal patterns based on the input data, and provide decision support for monitoring and adjustment of the manufacturing process.

[0082] Warning signal analysis results: Warning signal analysis results include predicted values, classification labels, and abnormal patterns.

[0083] Predicted value: used to predict future trends in product quality or process parameters.

[0084] Classification label: used to identify the category or mode to which the data belongs, such as normal mode, abnormal mode, etc.

[0085] Abnormal patterns: These are patterns in the data that differ significantly from normal patterns and may indicate problems or potential risks in the manufacturing process.

[0086] Apply data mining results to the manufacturing process: Apply data mining results to the monitoring and adjustment of the manufacturing process of flexible transparent display materials. Adjust process parameters based on prediction results: Ensure that product quality meets expected requirements. Identify abnormal patterns based on classification labels: Timely discover and deal with problems in the manufacturing process. Optimize material formulations based on mining results: Improve material performance and stability. The application of data mining results realizes the intelligence and automation of the manufacturing process, improves production efficiency and product quality, and reduces production costs and risks.

[0087] Step S103 aims to predict product quality, optimize process parameters and identify abnormal patterns, ensuring the direction and focus of data mining. Selecting a suitable deep learning algorithm for data mining and using preprocessed data for training ensures the accuracy and effectiveness of the mining results. The early warning signal analysis results include predicted values, classification labels and abnormal patterns, providing comprehensive monitoring of the manufacturing process. Applying the data mining results to the monitoring and adjustment of the manufacturing process realizes the intelligence and automation of the manufacturing process, and improves production efficiency and product quality.

[0088] Specifically, the flexible transparent display screen material manufacturing process method of the present invention, the step S104 comprises: Set up monitoring points, including 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; Start 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 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 according to the set monitoring threshold; When the data exceeds the normal range, the real-time monitoring system will issue a warning signal in time; When a warning signal is received, the real-time monitoring system automatically extracts the data corresponding to the triggering warning signal; The extracted data corresponding to the warning signal is substituted into the preset warning analysis model, and the warning analysis model performs data analysis, pattern recognition, and anomaly detection processing based on the input data.

[0089] Set 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 key factors that affect product quality and stability during the manufacturing process. By monitoring these points, you can fully understand the real-time status of the manufacturing process.

[0090] Set monitoring thresholds: Set reasonable monitoring thresholds for each monitoring point. Determine the normal range of each monitoring point based on historical data and experience of the manufacturing process, and set the monitoring threshold. The monitoring threshold is an important basis for judging whether the data is normal, and provides a standard for data judgment for the real-time monitoring system.

[0091] Start 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 at the set frequency and time interval. The start of the real-time monitoring system ensures continuous monitoring of the manufacturing process and can detect and handle abnormal situations in a timely manner.

[0092] 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 compares it with the monitoring threshold to determine whether the data is within the normal range. Data storage provides the basis for subsequent data analysis and tracing, while real-time judgment ensures the timely discovery of abnormal situations.

[0093] When the data exceeds the normal range, the real-time monitoring system will issue a warning signal in time. The issuance of the warning signal reminds the operator to pay attention to the abnormal situation and makes it possible to take timely measures.

[0094] 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 data is the basis for subsequent analysis of abnormal causes and measures to be taken, which helps to quickly locate and solve problems.

[0095] Substitute into the early warning analysis model: Substitute the corresponding data of the extracted early warning signal into the preset early warning analysis model. The early warning analysis model performs data analysis, pattern recognition, and anomaly detection based on the input data.

[0096] The early warning analysis model can deeply explore the hidden information and patterns in the data, providing a scientific basis for the accurate judgment and effective handling of abnormal situations.

[0097] Step S104 starts from setting monitoring points, to starting the real-time monitoring system, data storage and judgment, issuing warning signals, extracting data, and then applying the warning analysis model, forming a complete monitoring system operation process. Each step has clear content and meaning, and the steps are closely logical and mutually supportive, ensuring the effective operation of the monitoring system.

[0098] Through the setting and operation of the real-time monitoring system, abnormal situations in the manufacturing process can be discovered and handled in a timely manner, thereby improving product quality and production efficiency.

[0099] Specifically, the flexible transparent display screen material manufacturing process method of the present invention, step S105 includes: Use the real-time monitoring system to monitor the manufacturing process of flexible transparent display screen materials, and collect data on manufacturing process parameters, equipment status, environmental conditions, and product quality monitoring points of flexible transparent display screen materials 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 adjusted equipment settings include equipment operating speed parameters, temperature parameters, and pressure parameters.

[0100] Real-time monitoring and data collection: The real-time monitoring system is used to comprehensively monitor the manufacturing process of flexible transparent display materials, and real-time data on key monitoring points such as manufacturing process parameters, equipment status, environmental conditions, and product quality are collected. Real-time monitoring ensures the transparency and traceability of the manufacturing process, and provides an accurate and timely data basis for subsequent data analysis and decision-making.

[0101] Determine the state of process parameters: Determine whether the current process parameters are in the optimal state based on real-time monitoring data and early warning analysis results. Compare the real-time monitoring data with the preset optimal process parameter range, and evaluate the rationality of the process parameters in combination with the early warning analysis results. Timely determination of the state of process parameters helps to discover potential manufacturing problems and ensure product quality and production efficiency.

[0102] Adjust process parameters: If process parameters are found to deviate from the normal range, adjust them to the set values ​​immediately. According to the judgment results, adjust the process parameters that deviate from the normal range to return them to the optimal state or set range. Timely adjustment of process parameters can correct deviations in the manufacturing process and ensure the stability and consistency of product quality.

[0103] Check and adjust equipment settings: In the process of adjusting process parameters, check the equipment status and operating parameters at the same time, and adjust the equipment settings according to the early warning analysis results. Equipment settings include equipment operating speed parameters, temperature parameters, and pressure parameters. These parameters are adjusted accordingly according to the early warning analysis results and actual needs. Good equipment status and accurate operating parameters are the key to ensuring the smooth progress of the manufacturing process. By adjusting equipment settings, the manufacturing process can be further optimized and production efficiency and product quality can be improved.

[0104] Step S105 first starts with real-time monitoring and data collection, ensuring the data basis for subsequent analysis and decision-making. After the data is collected, the process parameter status is immediately judged and adjusted when necessary, reflecting the real-time control and optimization of the manufacturing process. While adjusting the process parameters, do not forget to check the equipment status and operating parameters, and adjust them as needed to ensure the comprehensive optimization of the manufacturing process. In the whole 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.

[0105] Specifically, the flexible transparent display screen material manufacturing process method of the present invention, step S105 includes: If equipment fails, determine whether the current material formula matches based on real-time monitoring data and early warning analysis results; If the material formula does not match, adjust the material formula, 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, the adjusted process is optimized.

[0106] Determine whether the material formula matches: When equipment fails, determine whether the current material formula matches the equipment status and process requirements based on real-time monitoring data and early warning analysis results. Equipment failure may be related to material formula mismatch, and timely judgment helps to quickly locate the cause of the problem.

[0107] Adjust material formula: If the material formula is judged to be mismatched, adjust the material formula, including optimizing the material combination and ratio. By adjusting the material formula and improving the compatibility of materials with equipment and processes, it may help solve equipment failures or reduce the possibility of failures.

[0108] 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.

[0109] Monitoring manufacturing process stability: Real-time monitoring data is used to continuously monitor whether the adjusted manufacturing process is stable. Stability is an important indicator of the manufacturing process, and ensuring a stable manufacturing process helps to ensure product quality and production efficiency.

[0110] Optimize the adjustment process: If the adjusted manufacturing process is unstable, optimize the adjustment process, which may include further adjusting the material formula, process parameters or equipment settings. By optimizing the adjustment process, the problem of unstable manufacturing process can be solved to ensure the smooth progress of the manufacturing process.

[0111] First, we start from the equipment failure to determine whether the material formula matches and accurately locate the possible cause of the problem. If the material formula does not match, we clearly propose measures to adjust the material formula and optimize the material combination and ratio. We verify the adjusted process parameters, equipment settings and material formula to ensure the effectiveness of the adjustment measures. During the verification process, we continuously monitor the stability of the manufacturing process and optimize it in time when it is unstable, which reflects the comprehensive control and continuous optimization of the manufacturing process.

[0112] Specifically, the flexible transparent display screen material manufacturing process method of the present invention, step S105 includes: If the adjusted manufacturing process is unstable, the corresponding data is retrieved and matched in the optimization algorithm knowledge base to obtain a real-time optimization algorithm. The optimization algorithm knowledge base includes a genetic algorithm, a particle swarm optimization algorithm, and a simulated annealing algorithm. Set the optimization algorithm parameters, such as population size, number of iterations, crossover probability, and mutation probability; Run optimization algorithms to optimize process parameters, equipment settings, and material recipes.

[0113] Retrieve corresponding data: The adjusted manufacturing process is unstable. Retrieve 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, and helps to match the appropriate optimization algorithm in the optimization algorithm knowledge base later.

[0114] Matching real-time optimization algorithm: Match the retrieved data in the optimization algorithm knowledge base to obtain a real-time optimization algorithm suitable for the current situation.

[0115] 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 adjustment can be improved.

[0116] Set optimization algorithm parameters: Set parameters for the selected optimization algorithm, such as population size, number of iterations, crossover probability, and mutation probability. These parameters are important factors that affect the performance and results of the optimization algorithm. By setting the parameters reasonably, the stability and convergence of the optimization algorithm can be ensured.

[0117] Run the optimization algorithm: Run the set optimization algorithm to optimize and adjust process parameters, equipment settings, and material formulations. The optimization algorithm will find the optimal or near-optimal solution through iterative search according to the set goals and constraints. By running the optimization algorithm, the process parameters, equipment settings, and material formulations in the manufacturing process can be comprehensively optimized, thus solving the problem of unstable manufacturing process and improving product quality and production efficiency.

[0118] First, clarify the problem of unstable manufacturing process, which provides a clear goal for subsequent optimization and adjustment. By retrieving relevant data and matching it in the optimization algorithm knowledge base, a real-time optimization algorithm suitable for the current situation is obtained, reflecting 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. By running the optimization algorithm, the process parameters, equipment settings, and material formulations are comprehensively optimized, reflecting the comprehensive control and continuous optimization of the manufacturing process.

[0119] The technical solution of the present invention solves the problem that the prior art cannot monitor the manufacturing process of flexible transparent display screen materials and adaptively adjust the manufacturing process of flexible transparent display screen materials according to the data generated in real time during the manufacturing process of flexible transparent display screen materials through the following steps: First, in step S101, obtain various data related to the manufacturing of flexible transparent display screen materials, 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 all-round information from raw materials to the manufacturing process and then to product quality.

[0120] Next, in step S102, integrate these data to form a manufacturing dataset of flexible transparent display screen materials and store it in the database. The dataset is subdivided into categories such as substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material manufacturing process data, and identifiers and metadata are assigned to each category for subsequent data processing and analysis.

[0121] Then, in step S103, use statistical methods to statistically analyze various types of data to reveal the regular relationships between them. At the same time, apply deep learning algorithms to mine the data to discover deeper potential information. These analysis results and mining results can provide strong support for subsequent manufacturing process monitoring and adjustment.

[0122] 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 issues a warning signal in a timely manner. After receiving the warning signal, the data corresponding to the warning signal is substituted into a preset warning analysis model for analysis to determine the nature and severity of the problem.

[0123] Finally, in step S105, according to the real-time monitoring data and the warning analysis results, an adaptive adjustment is made to the manufacturing process of the flexible transparent display screen material. The adjustment content includes process parameters, equipment settings, and material formulations, etc. Through the adjustment, the manufacturing process can be made more stable and efficient, and the product quality can be improved.

[0124] Specifically, the present invention reveals the regular relationships between data by integrating and analyzing various data in the manufacturing process, and applies deep learning algorithms to mine the data. At the same time, a real-time monitoring system is used to monitor the manufacturing process in real time and conduct warning analysis, and problems are discovered in a timely manner and corresponding adjustment measures are taken. These measures include adjusting process parameters, equipment settings, and material formulations, etc., to ensure the stability and efficiency of the manufacturing process.

Claims

1. A method for manufacturing a flexible transparent display screen material, characterized in that: include: Step S101, acquiring 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; Step S102, integrating 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 flexible transparent display screen material manufacturing data set, storing the flexible transparent display screen material manufacturing data set in a database, dividing the flexible transparent display screen material manufacturing data set into substrate material data, flexible transparent display screen material preparation data, and flexible transparent display screen material preparation data, and assigning identifiers and metadata to each category; Step S103, using a statistical method to perform statistical analysis on the substrate material data, the flexible transparent display material preparation data, and the flexible transparent display material preparation data, to obtain a regular relationship between the substrate material data, the flexible transparent display material preparation data, and the flexible transparent display material preparation data, and applying a deep learning algorithm to mine the substrate material data, the flexible transparent display material preparation data, and the flexible transparent display material preparation data to obtain a data mining result; Step S104, using a real-time monitoring system to monitor the manufacturing process in real time, setting a monitoring threshold, and issuing a warning signal in a timely manner when the data exceeds a normal range. When a warning signal is received, the data corresponding to the warning signal is substituted into a preset warning analysis model to obtain a warning signal analysis result; Step S105, using a real-time monitoring system to monitor the manufacturing process in real time, obtain real-time monitoring data, and adjust process parameters, equipment settings and material formulas for the flexible transparent display material manufacturing process based on the real-time monitoring data and early warning analysis results.

2. The method for manufacturing a flexible transparent display screen material according to claim 1, characterized in that: The step S101 includes: The base material data include the physical and chemical properties of the base material; Thin-film transistor preparation data includes the use of sensors and measuring instruments to monitor the deposition rate, thickness uniformity, and electrical performance parameters of each layer of materials in real time during the TFT preparation process, as well as the process parameters during the TFT preparation process; Organic light-emitting diode fabrication data including deposition rate, thickness uniformity and luminescence performance of organic materials; The drive circuit design data includes the design drawings, layout and routing information of the drive circuit, and the steps and parameters in the manufacturing process of the drive circuit; The manufacturing process environmental data includes the temperature, humidity and cleanliness parameters in the production workshop. Real-time monitoring data acquisition includes equipment in the manufacturing process, process parameters in the manufacturing process, and product quality in the manufacturing process; Adjustment data acquisition includes process parameter adjustment, equipment setting adjustment, and material recipe adjustment.

3. The manufacturing process of the flexible transparent display screen material according to claim 1, characterized in that: The step S102 includes: Importing the flexible transparent display material manufacturing data set into a database, establishing a data index, and classifying the flexible transparent display material manufacturing data set 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 environment data; Assign an identifier to each data category. The identifier uses the data table name, field name or code, and add metadata to each data category.

4. The method for manufacturing a flexible transparent display screen material according to claim 1, characterized in that: The step S103 includes: Analyze the base material data using statistical methods to obtain the physical and chemical properties and distribution of the base material; Statistical analysis was performed on the preparation data of flexible transparent display screen materials to obtain the relationship between process parameters, equipment settings, material formula 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 is analyzed. Through statistical analysis, the substrate material data, flexible transparent display material preparation data and the regular relationship between the preparation data are obtained.

5. The method for manufacturing a flexible transparent display screen material according to claim 4, characterized in that: The step S103 includes: Based on the results of statistical analysis, determine the prediction of product quality, optimize process parameters, identify abnormal patterns, and select deep learning algorithms for data mining; Use deep learning algorithms to train preprocessed data to obtain a preset early warning analysis model; The warning signal analysis results include predicted values, classification labels, and abnormal patterns; The data mining results are applied to the monitoring and adjustment of the manufacturing process of flexible transparent display materials, adjusting process parameters according to the prediction results, identifying abnormal patterns according to classification labels, and optimizing material formulations according to the mining results.

6. The method for manufacturing a flexible transparent display screen material according to claim 1, characterized in that: The step S104 includes: Set up monitoring points, including 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; Start 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 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 according to the set monitoring threshold; When the data exceeds the normal range, the real-time monitoring system will issue a warning signal in time; When a warning signal is received, the real-time monitoring system automatically extracts the data corresponding to the triggering warning signal; The extracted data corresponding to the warning signal is substituted into the preset warning analysis model, and the warning analysis model performs data analysis, pattern recognition, and anomaly detection processing based on the input data.

7. The method for manufacturing a flexible transparent display screen material according to claim 1, characterized in that: The step S105 includes: Use the real-time monitoring system to monitor the manufacturing process of flexible transparent display screen materials, and collect data on manufacturing process parameters, equipment status, environmental conditions, and product quality monitoring points of flexible transparent display screen materials 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 adjusted equipment settings include equipment operating speed parameters, temperature parameters, and pressure parameters.

8. The method for manufacturing a flexible transparent display screen material according to claim 7, characterized in that: The step S105 includes: If equipment fails, determine whether the current material formula matches based on real-time monitoring data and early warning analysis results; If the material formula does not match, adjust the material formula, 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, the adjusted process is optimized.

9. The method for manufacturing a flexible transparent display screen material according to claim 8, characterized in that: The step S105 includes: If the adjusted manufacturing process is unstable, the corresponding data is retrieved and matched in the optimization algorithm knowledge base to obtain a real-time optimization algorithm. The optimization algorithm knowledge base includes a genetic algorithm, a particle swarm optimization algorithm, and a simulated annealing algorithm. Set the optimization algorithm parameters, such as population size, number of iterations, crossover probability, and mutation probability; Run optimization algorithms to optimize process parameters, equipment settings, and material recipes.

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