Detection system for preparation process of transparent flexible substrate

Abstract

The invention provides a detection system for a preparation process of a transparent flexible substrate, and relates to the technical field of substrate production detection, and the detection system comprises a process obtaining module which is used for obtaining preparation process data corresponding to a target substrate and outputting substrate-process contrast data; the data acquisition module is used for acquiring process data corresponding to each preparation flow of the target substrate and outputting the flow process data; the data analysis module is used for analyzing the substrate-flow contrast data and the flow process data in combination with a preset data analysis model and a preset evaluation index, and outputting a flow analysis result; and the optimization module is used for selecting an optimization strategy and an optimization method matched with the process analysis result from the strategy database, and optimizing the preparation process of the target substrate based on the optimization strategy and the optimization method. According to the invention, the detection efficiency and detection precision of the preparation process of the transparent flexible substrate can be improved, the process is continuously optimized, and the product quality of the substrate is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of substrate production detection, and in particular to a detection system for a preparation process of a transparent flexible substrate. Background Art

[0002] Transparent flexible substrates play an important role in many fields, such as electronic products, medical devices, etc. The transparent flexible substrate preparation process detection system can ensure that the quality of the substrate meets the standards and improve the reliability and stability of the product.

[0003] At present, the detection system for transparent flexible substrates involves multiple parameters and technologies, and needs to comprehensively consider many factors. The amount of detection data obtained is increasing, and these large amounts of data need to be effectively processed and analyzed to provide accurate feedback and improvement suggestions for subsequent production processes. However, the current substrate preparation process detection system has low detection efficiency and is difficult to meet the increasing data volume and detection needs.

[0004] Therefore, the present invention provides a detection system for a preparation process of a transparent flexible substrate. Summary of the invention

[0005] The present invention provides a detection system for a preparation process of a transparent flexible substrate, which is used to improve the detection efficiency and detection accuracy of the preparation process of the transparent flexible substrate, continuously optimize the process, and improve the product quality of the substrate.

[0006] The present invention provides a detection system for a preparation process of a transparent flexible substrate, comprising: A process acquisition module is used to acquire preparation process data corresponding to the target substrate in the process database and output substrate-process comparison data; A data acquisition module, configured to acquire process data corresponding to each preparation process of the target substrate in a historical database based on the substrate-process comparison data, and output the process data; A data analysis module, used to analyze the substrate-process comparison data and process data in combination with a preset data analysis model and preset evaluation indicators, and output a process analysis result; The optimization module is used to select an optimization strategy and an optimization method that match the process analysis result in the strategy database in combination with a preset result-strategy comparison table, and optimize the preparation process of the target substrate based on the optimization strategy and the optimization method.

[0007] Preferably, the process acquisition module includes: A substrate information acquisition submodule, used to identify and acquire substrate information of a target substrate in combination with a preset recognition method; The process data acquisition submodule is used to acquire the preparation process data corresponding to the substrate information of the target substrate in the process database, and output substrate-process comparison data.

[0008] Preferably, the process data acquisition submodule includes: A substrate information extraction unit, used for extracting features from the substrate information of the target substrate and constructing a substrate feature set based on the extracted features; The process data acquisition unit is used to select the preparation process data matching the substrate feature set from the process database, and output the substrate-process comparison data in combination with the mapping relationship between the target substrate and the preparation process data.

[0009] Preferably, the data acquisition module includes: A keyword matching submodule, used for performing keyword recognition and matching on the substrate-process comparison data, and outputting keyword matching results; The data matching submodule is used to select process data corresponding to the target substrate preparation process from the historical database based on the keyword matching result, and output the process data.

[0010] Preferably, the data acquisition module further includes: The checking submodule is used to verify and validate the substrate-process control data, and input the substrate-process control data that meets the preset verification conditions into the keyword matching submodule.

[0011] Preferably, the data analysis module includes: The model selection submodule is used to select an adapted preset data analysis model from the model database in combination with the user's detection requirements, and at the same time, select a corresponding preset evaluation indicator from the indicator database based on the detection requirements; The data analysis submodule is used to perform data analysis on the substrate-process comparison data and the process data based on the preset data analysis model and the preset evaluation index, and output the process analysis result.

[0012] Preferably, the data analysis module further includes: A process analysis submodule, used to analyze the substrate-process comparison data and process data, and obtain process analysis data based on the analysis content; The abnormality identification submodule is used to compare and analyze the process analysis data with the preset standard process control data, identify and extract abnormal process data in the process analysis data, and output the abnormal process data; A process statistics submodule is used to count the occurrence frequency of abnormal data of each process in the abnormal process data and output a process abnormality frequency statistics table; The risk determination submodule is used to determine the risk type and corresponding risk index of each process based on the process abnormality frequency statistical table, and output the process-risk comparison result; The importance determination submodule is used to obtain the importance index of each process in the substrate preparation process and output the process-importance comparison result; The coefficient determination submodule is used to perform real-time analysis on the process-risk comparison results and the process-importance comparison results in combination with a preset coefficient determination algorithm, determine the type of detection data and the monitoring level coefficient corresponding to each process, and output the real-time analysis results; The project determination submodule is used to adjust the detection items of each process and the equipment detection parameters under each detection item in real time based on the real-time analysis results, and output a real-time comparison table of processes and detection items; A real-time data acquisition submodule, for calling the detection equipment corresponding to the detection items corresponding to each process in the detection system based on the process-detection item real-time comparison table, and acquiring the real-time data of each process based on the detection equipment, and outputting the real-time process data; The real-time analysis submodule is used to analyze the real-time process data based on the preset data analysis model and the preset evaluation index, output the real-time analysis results and summarize them into the process analysis results.

[0013] Preferably, the optimization module includes: A result analysis submodule is used to analyze the process analysis results, classify and analyze the analysis contents, and output result analysis data; A feature extraction submodule, used for extracting features from the result analysis data, and constructing a first feature set based on the extracted features; A screening factor determination submodule is used to obtain a strategy screening factor that matches each data feature in the first feature set in combination with a preset feature-factor comparison table, and summarize and output a screening factor library; A strategy selection submodule is used to select an optimization strategy that meets preset screening conditions and an optimization method under the corresponding optimization strategy from a strategy database based on the screening factor library; A process-strategy matching submodule, used to adapt the optimization strategy and optimization method to each process, and output a process-strategy-method matching table based on the adaptation result; An optimization processing submodule, for adjusting and optimizing each process based on the process-strategy-method matching table, and outputting process-substrate optimization process data based on process change data before and after adjustment and optimization and substrate quality parameter change data under each process; An indicator determination submodule, used to obtain the self-matching evaluation indicator obtained by the system according to the self-matching algorithm and the manual evaluation indicator input manually, and determine the optimization evaluation indicator set in combination with the execution priority corresponding to each indicator, and divide the optimization evaluation indicator set according to each process to obtain the evaluation indicator subset corresponding to each process, wherein the evaluation indicator subset includes a first indicator for evaluating the process change data and a second indicator for evaluating the substrate quality parameter change data; A process evaluation submodule, used for evaluating and analyzing the process-substrate optimization process data based on a subset of evaluation indicators corresponding to each process, and outputting a first evaluation result corresponding to each process; ; in, Indicates the first evaluation result corresponding to the lth process; represents the total number of first indicators corresponding to the lth process; represents the total number of second indicators corresponding to the lth process; It represents the change value of the process parameter corresponding to the i-th first indicator in the l-th process before and after optimization; represents the optimization evaluation coefficient corresponding to the i-th first indicator; Indicates that the lth process The error adjustment coefficient corresponding to the first indicator; represents the change value of the substrate quality parameter corresponding to the jth second index in the lth process before and after optimization; represents the optimization evaluation coefficient corresponding to the j-th second indicator; Indicates that the lth process The error adjustment coefficient corresponding to the second indicator; An overall evaluation submodule, used for performing an overall evaluation on the process-substrate optimization process data based on the optimization evaluation index set, and outputting a second evaluation result; The comprehensive analysis submodule is used to comprehensively analyze the first evaluation result and the second evaluation result, and output an optimized evaluation result.

[0014] The present invention provides a detection system for the preparation process of a transparent flexible substrate. The present invention acquires historical data, analyzes the quality and consistency of the current preparation process, and then optimizes the preparation process according to a preset optimization strategy and method to improve production efficiency and product quality. The present invention can realize automatic monitoring, analysis and optimization of the substrate preparation process, improve production efficiency, reduce costs, and ensure product quality and consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of a framework of a detection system for a preparation process of a transparent flexible substrate provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] 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 below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0018] like Figure 1 As shown, an embodiment of the present invention provides a detection system for a preparation process of a transparent flexible substrate, comprising: A process acquisition module is used to acquire preparation process data corresponding to the target substrate in the process database and output substrate-process comparison data; A data acquisition module, for acquiring process data corresponding to each preparation process of the target substrate in a historical database based on the substrate-process comparison data, and outputting the process data; A data analysis module is used to analyze substrate-process comparison data and process data in combination with a preset data analysis model and preset evaluation indicators, and output process analysis results; The optimization module is used to select the optimization strategy and optimization method that match the process analysis results in the strategy database in combination with the preset result-strategy comparison table, and optimize the preparation process of the target substrate based on the optimization strategy and optimization method.

[0019] In this embodiment, the process database: stores various preparation process data corresponding to the target substrate; In this embodiment, target substrate: a specific substrate for which preparation process detection and optimization is required; In this embodiment, the preparation process data: describes the detailed information of different preparation processes, such as parameter settings, process steps, etc.; In this embodiment, substrate-process comparison data: associates the target substrate with the corresponding preparation process data for subsequent analysis and optimization, for example, substrate A needs to be produced using preparation process X; In this embodiment, the historical database: stores data of past preparation processes for analyzing and optimizing the current preparation process; In this embodiment, process data: specific process data describing the historical preparation process, such as temperature, pressure, time, etc.; In this embodiment, a data analysis model is preset: for analyzing substrate-process control data and process data to evaluate the quality and consistency of the manufacturing process; In this embodiment, the preset evaluation index is an index used to evaluate the substrate preparation process, for example, substrate transparency, substrate surface flatness, dimensional accuracy, etc.; In this embodiment, the process analysis result: the preparation process analysis result obtained by using a preset data analysis model and preset evaluation indicators, for example, a certain preparation process of the flexible substrate has a problem of not meeting the transparency standard; In this embodiment, the preset result-strategy comparison table: a table containing the mapping relationship between the analysis results and the optimization strategies, which is preset; In this embodiment, the strategy database: stores various optimization strategies and optimization methods for improving the preparation process; In this embodiment, optimization strategy and optimization method: specific measures and methods for improving the preparation process according to the analysis results. For example, the optimization strategy may include adjusting process parameters, optimizing the production process, improving equipment settings, etc., and the optimization method may include using new materials, changing the order of process steps, adjusting the production temperature, etc.

[0020] Implementation principle and beneficial effects of this embodiment: The present invention acquires historical data, analyzes the quality and consistency of the current preparation process, and then optimizes the preparation process according to preset optimization strategies and methods to improve production efficiency and product quality. The present invention can realize automated monitoring, analysis and optimization of the substrate preparation process, improve production efficiency, reduce costs, and ensure product quality and consistency.

[0021] An embodiment of the present invention provides a detection system for a preparation process of a transparent flexible substrate, and a process acquisition module, comprising: A substrate information acquisition submodule, used to identify and acquire substrate information of a target substrate in combination with a preset recognition method; The process data acquisition submodule is used to acquire the preparation process data corresponding to the substrate information of the target substrate in the process database, and output the substrate-process comparison data.

[0022] In this embodiment, preset recognition method: a method or algorithm preset by the system for recognizing and acquiring substrate information of a target substrate. These methods may be based on technologies such as image recognition, sensor data analysis, and barcode scanning. The selection of the preset recognition method depends on the characteristics of the substrate information and the acquisition method. In this embodiment, substrate information refers to data on various attributes and characteristics of a target substrate, such as physical properties, dimensions, material composition, and production batch.

[0023] Implementation principle and beneficial effects of this embodiment: Through the synergistic effect of the substrate information acquisition submodule and the process data acquisition submodule, the system can select a suitable preparation process according to the characteristics of the target substrate to achieve customized production. The present invention can improve production efficiency, reduce production costs, ensure product quality and consistency, and provide more accurate control and monitoring for the production process.

[0024] An embodiment of the present invention provides a detection system for a preparation process of a transparent flexible substrate, and a process data acquisition submodule, including: A substrate information extraction unit, used for extracting features of substrate information of a target substrate and constructing a substrate feature set based on the extracted features; The process data acquisition unit is used to select the preparation process data matching the substrate feature set from the process database, and output the substrate-process comparison data in combination with the mapping relationship between the target substrate and the preparation process data.

[0025] In this embodiment, the substrate feature set is a set formed by extracting and describing the features of the target substrate, including various substrate properties and features, such as surface flatness, transparency, material composition, size features, etc.

[0026] The implementation principle and beneficial effects of this embodiment: The present invention realizes accurate comparison and matching between the substrate and the preparation process through the synergy of the substrate information extraction unit and the process data acquisition unit, and then through the construction of the substrate feature set and the matching of the process data. The present invention can automatically select a suitable preparation process according to the characteristics of the target substrate, thereby improving production efficiency and product quality. At the same time, by matching the process data based on the substrate feature set, human intervention can be reduced and the degree of automation of production can be improved.

[0027] An embodiment of the present invention provides a detection system for a preparation process of a transparent flexible substrate, and a data acquisition module, comprising: The keyword matching submodule is used to identify and match keywords for substrate-process comparison data and output keyword matching results; The data matching submodule is used to select process data corresponding to the target substrate preparation process from the historical database based on the keyword matching results, and output the process data.

[0028] In this embodiment, keyword identification and matching: a method for quickly and accurately identifying key information related to the target substrate preparation process from a large amount of data; In this embodiment, keyword matching results: the results obtained through keyword recognition and matching are used to indicate the relevance of the target substrate preparation process, which can be keywords describing substrate characteristics, preparation process, process parameters, etc., such as "transparency", "thin film coating", "temperature control", etc.

[0029] The implementation principle and beneficial effects of this embodiment: The present invention uses the synergy of the keyword matching submodule and the data matching submodule to quickly and accurately select historical data corresponding to the target substrate preparation process through keyword identification and matching, thereby achieving effective matching and extraction of data. The present invention can help users quickly obtain historical data related to the target substrate preparation process for analyzing, optimizing and improving the preparation process, thereby improving production efficiency, reducing production costs, and ensuring product quality and consistency. At the same time, through keyword matching, the system can perform data retrieval and analysis more intelligently.

[0030] An embodiment of the present invention provides a detection system for a preparation process of a transparent flexible substrate, and a data acquisition module, further comprising: The verification submodule is used to verify and validate the substrate-process comparison data, and input the substrate-process comparison data that meets the preset verification conditions into the keyword matching submodule.

[0031] In this embodiment, verification and validation: steps of checking and confirming substrate-process comparison data to ensure the accuracy, completeness and reliability of the data; In this embodiment, preset verification conditions: a set of conditions set in advance during the system design phase, which are used to verify and validate the accuracy and reliability of the substrate-process comparison data, are usually determined based on production requirements, technical specifications, and product standards to ensure that the selected data meets specific requirements, such as data integrity, ensuring that the substrate-process comparison data contains all necessary information, such as substrate characteristics, preparation process, key parameters, etc.

[0032] The implementation principle and beneficial effects of this embodiment: The verification submodule of the present invention verifies and validates the substrate-process control data through preset verification conditions to ensure that the selected data meets the system requirements, and then passes the qualified data to the keyword matching submodule for further processing. The present invention can exclude data that does not meet the requirements in the data acquisition stage, improve the accuracy and reliability of the data, help avoid the influence of erroneous data, ensure the stability and accuracy of the system operation, thereby effectively supporting the optimization and improvement of the preparation process, and improving production efficiency and product quality.

[0033] An embodiment of the present invention provides a detection system for a preparation process of a transparent flexible substrate, and a data analysis module, comprising: The model selection submodule is used to select an adaptive preset data analysis model from the model database in combination with the user's detection requirements, and at the same time, select the corresponding preset evaluation indicators from the indicator database based on the detection requirements; The data analysis submodule is used to perform data analysis on substrate-process comparison data and process data based on a preset data analysis model and preset evaluation indicators, and output the process analysis results.

[0034] In this embodiment, detection requirements: the requirements and requirements that the user needs to meet when performing transparent flexible substrate preparation process detection, including requirements in terms of preparation process parameters, product characteristics, production efficiency, etc. that the user is concerned about; In this embodiment, the model database: a database storing various data analysis models, such as machine learning models, statistical models, etc., for analyzing preparation process data; In this embodiment, the indicator database includes a database of various evaluation indicators, which are used to measure the quality, performance and other aspects of the preparation process.

[0035] The implementation principle and beneficial effects of this embodiment: The present invention can select appropriate data analysis models and evaluation indicators according to the user's detection needs through the model selection submodule, and then analyze the substrate-process control data and process data based on these models and indicators through the data analysis submodule, and output the process analysis results. The present invention can select the most suitable data analysis model and evaluation indicators according to user needs, so as to achieve a comprehensive analysis and evaluation of the preparation process. Furthermore, through data analysis, it can help users to deeply understand the characteristics and performance of the preparation process, find problems and optimize them, and improve production efficiency and product quality.

[0036] An embodiment of the present invention provides a detection system for a preparation process of a transparent flexible substrate, and a data analysis module, further comprising: The process analysis submodule is used to analyze the substrate-process comparison data and process data, and obtain process analysis data based on the analysis content; The abnormal identification submodule is used to compare and analyze the process analysis data with the preset standard process control data, identify and extract the abnormal process data in the process analysis data, and output the abnormal process data; The process statistics submodule is used to count the occurrence frequency of abnormal data of each process in the abnormal process data and output a process abnormality frequency statistics table; The risk determination submodule is used to determine the risk type and corresponding risk index of each process based on the process abnormality frequency statistics table, and output the process-risk comparison results; The importance determination submodule is used to obtain the importance index of each process in the substrate preparation process and output the process-importance comparison result; The coefficient determination submodule is used to perform real-time analysis on the process-risk comparison results and process-importance comparison results in combination with a preset coefficient determination algorithm, determine the type of detection data and monitoring level coefficient corresponding to each process, and output real-time analysis results; The project determination submodule is used to adjust the inspection items of each process and the equipment inspection parameters under each inspection item in real time based on the real-time analysis results, and output a real-time comparison table of processes and inspection items; The real-time data acquisition submodule is used to call the detection equipment corresponding to the detection items corresponding to each process in the detection system based on the process-detection item real-time comparison table, and acquire the real-time data of each process based on the detection equipment, and output the real-time process data; The real-time analysis submodule is used to analyze the real-time process data based on the preset data analysis model and preset evaluation indicators, output the real-time analysis results and summarize them into the process analysis results.

[0037] In this embodiment, process analysis data: data obtained by analyzing the substrate-process comparison data and the process data, including detailed analysis results of the preparation process, which can help users understand the execution status and characteristics of each process; In this embodiment, preset standard process comparison data: standard process data preset in the system, used for comparison and analysis with actual process analysis data, so as to find abnormalities and problems; In this embodiment, abnormal process data: abnormal data found after comparative analysis of process analysis data and preset standard process control data, indicating that there are abnormal or abnormal conditions in the preparation process; In this embodiment, the process abnormality frequency statistics table is used to count the frequency of abnormal data in each process, helping users understand which processes have more abnormal situations; In this embodiment, risk type: the risk type that may exist in each process; In this embodiment, the risk index is used to characterize the degree of risk. Generally speaking, the higher the index value, the higher the probability or possibility of the risk occurring. In this embodiment, the process-risk comparison result: each process is compared with its corresponding risk type and risk index to help users intuitively understand the risk status of each process; In this embodiment, the importance index: indicates the importance of each process in the substrate preparation process, which can help users determine which processes have the greatest impact on indicators such as product quality and production efficiency; In this embodiment, the process-importance comparison result: each process is compared with its corresponding importance index to help users intuitively understand the importance ranking of each process; In this embodiment, the preset coefficient determination algorithm: an algorithm for real-time analysis based on the process-risk comparison results and the process-importance comparison results, can determine the type of detection data and the monitoring level coefficient corresponding to each process according to the preset coefficient; In this embodiment, the detection data type: indicates the type of data that needs to be collected and analyzed during the detection process, such as temperature, pressure, humidity, etc.; In this embodiment, the monitoring level coefficient: a coefficient used to determine the monitoring level of each process, which helps users understand which processes require more intensive monitoring and adjustment; In this embodiment, real-time analysis results: the results obtained after analyzing the real-time process data can help users understand the status and problems of the current process in a timely manner; In this embodiment, the inspection items: represent specific inspection tasks to be performed on each process, such as surface flatness inspection, transparency inspection, flexibility inspection, and dimensional accuracy inspection; In this embodiment, equipment detection parameters: setting parameters of the detection equipment corresponding to each detection item, such as detection frequency, detection time, and number of re-tests, etc.; In this embodiment, the process-detection item real-time comparison table: a comparison table of detection items and equipment parameters adjusted according to the real-time analysis results, used to guide the real-time detection work; In this embodiment, detection equipment: equipment used to acquire and analyze real-time data of each process, such as sensors, monitoring instruments, etc.; In this embodiment, real-time process data refers to data on the status and parameters of each process collected during the real-time detection process.

[0038] The implementation principle and beneficial effects of this embodiment: The present invention comprehensively analyzes and evaluates the preparation process through the synergy of sub-modules such as process analysis, anomaly identification, and risk determination, thereby helping users to discover problems, identify risks, and provide optimization suggestions. The real-time data acquisition and real-time analysis sub-modules realize the monitoring and analysis of real-time process data, allowing users to adjust and optimize the process in a timely manner. The present invention can help users fully understand the abnormal situations and risks in the preparation process, provide real-time data analysis and monitoring, help users to adjust and optimize the process in a timely manner, improve production efficiency, reduce risks, and thus promote the continuous improvement and optimization of the preparation process.

[0039] An embodiment of the present invention provides a detection system and an optimization module for a preparation process of a transparent flexible substrate, comprising: The result analysis submodule is used to analyze the process analysis results, classify and analyze the analysis content, and output the result analysis data; A feature extraction submodule, used for extracting features from the result parsing data, and constructing a first feature set based on the extracted features; A screening factor determination submodule is used to obtain a strategy screening factor that matches each data feature in the first feature set in combination with a preset feature-factor comparison table, and summarize and output a screening factor library; The strategy selection submodule is used to select an optimization strategy that meets the preset screening conditions and an optimization method under the corresponding optimization strategy from the strategy database based on the screening factor library; The process-strategy matching submodule is used to adapt the optimization strategy and optimization method to each process, and output the process-strategy-method matching table based on the adaptation result; The optimization processing submodule is used to adjust and optimize each process based on the process-strategy-method matching table, and output the process-substrate optimization process data based on the process change data before and after the adjustment and optimization and the substrate quality parameter change data under each process; An indicator determination submodule, used to obtain the self-matching evaluation indicators obtained by the system according to the self-matching algorithm and the manual evaluation indicators input manually, and determine the optimization evaluation indicator set in combination with the execution priority corresponding to each indicator, and divide the optimization evaluation indicator set according to each process to obtain the evaluation indicator subset corresponding to each process, wherein the evaluation indicator subset includes a first indicator for evaluating the process change data and a second indicator for evaluating the substrate quality parameter change data; A process evaluation submodule, used to evaluate and analyze the process-substrate optimization process data based on the evaluation indicator subset corresponding to each process, and output a first evaluation result corresponding to each process; ; in, Indicates the first evaluation result corresponding to the lth process; represents the total number of first indicators corresponding to the lth process; represents the total number of second indicators corresponding to the lth process; It represents the change value of the process parameter corresponding to the i-th first indicator in the l-th process before and after optimization; represents the optimization evaluation coefficient corresponding to the i-th first indicator; Indicates that the lth process The error adjustment coefficient corresponding to the first indicator; represents the change value of the substrate quality parameter corresponding to the jth second index in the lth process before and after optimization; represents the optimization evaluation coefficient corresponding to the j-th second indicator; Indicates that the lth process The error adjustment coefficient corresponding to the second indicator; An overall evaluation submodule, used to perform an overall evaluation on the process-substrate optimization process data based on the optimization evaluation index set, and output a second evaluation result; The comprehensive analysis submodule is used to comprehensively analyze the first evaluation result and the second evaluation result, and output an optimized evaluation result.

[0040] In this embodiment, result analysis data: data obtained after parsing the process analysis results, used for further classification analysis and processing, can help users understand the results and trends of the process analysis; In this embodiment, the first feature set: a feature set extracted from the result parsing data by the feature extraction submodule, which is used for subsequent optimization strategy and method selection; In this embodiment, the preset feature-factor comparison table includes the correspondence between data features and screening factors, which is used as a reference when determining the optimization strategy; In this embodiment, the strategy screening factor: a screening factor obtained by matching the data features in the first feature set with a preset feature-factor comparison table; In this embodiment, the screening factor library is a collection of screening factors according to the strategy; In this embodiment, preset screening conditions: conditions set in advance by the user for screening optimization strategies, which may be some specific thresholds or requirements; In this embodiment, the process-strategy-method matching table: matches the optimization strategy and method with each process to determine the optimization solution for each process; In this embodiment, process change data: represents the change data of the process caused by before and after optimization; In this embodiment, substrate quality parameter change data: represents the change data of substrate quality parameters caused by before and after optimization; In this embodiment, the process-substrate optimization process data: that is, data obtained by summarizing the process change data and the substrate quality parameter change data; In this embodiment, the self-matching algorithm is a system-defined algorithm used to automatically select indicators for evaluating the optimization process; In this embodiment, the self-matching evaluation index: that is, the evaluation index obtained by the system according to the self-matching algorithm; In this embodiment, the manual evaluation index: that is, the evaluation index input manually, its execution priority is generally higher than the self-matching evaluation index of the system; In this embodiment, the optimization evaluation index set: a set of indicators determined according to the self-matching algorithm and the manually input evaluation index, used to evaluate the optimization effect of the process change data and the substrate quality parameter change data; In this embodiment, the evaluation indicator subset is a subset divided for each process in the optimization evaluation indicator set; In this embodiment, the first indicator: an indicator used to evaluate the process change data under each process, such as production efficiency, energy consumption, cost, process control capability, etc.; In this embodiment, the second index is an index used to evaluate the change data of substrate quality parameters under each process, such as surface roughness, transparency, tensile strength, flexural strength and dimensional stability; In this embodiment, the first evaluation result: a result obtained after evaluating and analyzing the process-substrate optimization process data based on the evaluation indicator subset corresponding to each process; In this embodiment, the second evaluation result: the result of overall evaluation of the process-substrate optimization process data based on the optimization evaluation indicator set; In this embodiment, the optimization evaluation result: the final evaluation result obtained by comprehensively considering the first evaluation result and the second evaluation result reflects the effect of the optimization of the entire process.

[0041] Implementation principle and beneficial effects of this embodiment: Through the synergy of various submodules, the system can achieve in-depth analysis and feature extraction of process analysis results, and then select appropriate optimization strategies and methods, and apply them to various processes, ultimately achieving process optimization and improvement of substrate quality parameters. The present invention can help users optimize and evaluate the preparation process from multiple perspectives, provide a comprehensive optimization scheme and evaluation indicators, and enable users to more effectively improve the production process, product quality and production efficiency.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A detection system for a preparation process of a transparent flexible substrate, characterized in that: include: A process acquisition module is used to acquire preparation process data corresponding to the target substrate in the process database and output substrate-process comparison data; A data acquisition module, configured to acquire process data corresponding to each preparation process of the target substrate in a historical database based on the substrate-process comparison data, and output the process data; A data analysis module, used to analyze the substrate-process comparison data and process data in combination with a preset data analysis model and preset evaluation indicators, and output a process analysis result; The optimization module is used to select an optimization strategy and an optimization method that match the process analysis result in the strategy database in combination with a preset result-strategy comparison table, and optimize the preparation process of the target substrate based on the optimization strategy and the optimization method.

2. A detection system for a preparation process of a transparent flexible substrate according to claim 1, characterized in that: The process acquisition module includes: A substrate information acquisition submodule, used to identify and acquire substrate information of a target substrate in combination with a preset recognition method; The process data acquisition submodule is used to acquire the preparation process data corresponding to the substrate information of the target substrate in the process database, and output substrate-process comparison data.

3. A detection system for a preparation process of a transparent flexible substrate according to claim 2, characterized in that: The process data acquisition submodule includes: A substrate information extraction unit, used for extracting features from the substrate information of the target substrate and constructing a substrate feature set based on the extracted features; The process data acquisition unit is used to select the preparation process data matching the substrate feature set from the process database, and output the substrate-process comparison data in combination with the mapping relationship between the target substrate and the preparation process data.

4. The detection system for the preparation process of a transparent flexible substrate according to claim 1, characterized in that: The data acquisition module comprises: A keyword matching submodule, used for performing keyword recognition and matching on the substrate-process comparison data, and outputting keyword matching results; The data matching submodule is used to select process data corresponding to the target substrate preparation process from the historical database based on the keyword matching result, and output the process data.

5. The detection system for the preparation process of a transparent flexible substrate according to claim 1, characterized in that: The data acquisition module further includes: The checking submodule is used to verify and validate the substrate-process control data, and input the substrate-process control data that meets the preset verification conditions into the keyword matching submodule.

6. The detection system for the preparation process of a transparent flexible substrate according to claim 1, characterized in that: The data analysis module comprises: The model selection submodule is used to select an adapted preset data analysis model from the model database in combination with the user's detection requirements, and at the same time, select a corresponding preset evaluation indicator from the indicator database based on the detection requirements; The data analysis submodule is used to perform data analysis on the substrate-process comparison data and the process data based on the preset data analysis model and the preset evaluation index, and output the process analysis result.

7. The detection system for the preparation process of a transparent flexible substrate according to claim 1, characterized in that: The data analysis module further includes: A process analysis submodule, used to analyze the substrate-process comparison data and process data, and obtain process analysis data based on the analysis content; The abnormality identification submodule is used to compare and analyze the process analysis data with the preset standard process control data, identify and extract abnormal process data in the process analysis data, and output the abnormal process data; A process statistics submodule is used to count the occurrence frequency of abnormal data of each process in the abnormal process data and output a process abnormality frequency statistics table; The risk determination submodule is used to determine the risk type and corresponding risk index of each process based on the process abnormality frequency statistical table, and output the process-risk comparison result; The importance determination submodule is used to obtain the importance index of each process in the substrate preparation process and output the process-importance comparison result; The coefficient determination submodule is used to perform real-time analysis on the process-risk comparison results and the process-importance comparison results in combination with a preset coefficient determination algorithm, determine the type of detection data and the monitoring level coefficient corresponding to each process, and output the real-time analysis results; The project determination submodule is used to adjust the detection items of each process and the equipment detection parameters under each detection item in real time based on the real-time analysis results, and output a real-time comparison table of processes and detection items; A real-time data acquisition submodule, for calling the detection equipment corresponding to the detection items corresponding to each process in the detection system based on the process-detection item real-time comparison table, and acquiring the real-time data of each process based on the detection equipment, and outputting the real-time process data; The real-time analysis submodule is used to analyze the real-time process data based on the preset data analysis model and the preset evaluation index, output the real-time analysis results and summarize them into the process analysis results.

8. The detection system for the preparation process of a transparent flexible substrate according to claim 1, characterized in that: The optimization module comprises: A result analysis submodule is used to analyze the process analysis results, classify and analyze the analysis contents, and output result analysis data; A feature extraction submodule, used for extracting features from the result analysis data, and constructing a first feature set based on the extracted features; A screening factor determination submodule is used to obtain a strategy screening factor that matches each data feature in the first feature set in combination with a preset feature-factor comparison table, and summarize and output a screening factor library; A strategy selection submodule is used to select an optimization strategy that meets preset screening conditions and an optimization method under the corresponding optimization strategy from a strategy database based on the screening factor library; A process-strategy matching submodule, used to adapt the optimization strategy and optimization method to each process, and output a process-strategy-method matching table based on the adaptation result; An optimization processing submodule, for adjusting and optimizing each process based on the process-strategy-method matching table, and outputting process-substrate optimization process data based on process change data before and after adjustment and optimization and substrate quality parameter change data under each process; An indicator determination submodule, used to obtain the self-matching evaluation indicator obtained by the system according to the self-matching algorithm and the manual evaluation indicator input manually, and determine the optimization evaluation indicator set in combination with the execution priority corresponding to each indicator, and divide the optimization evaluation indicator set according to each process to obtain the evaluation indicator subset corresponding to each process, wherein the evaluation indicator subset includes a first indicator for evaluating the process change data and a second indicator for evaluating the substrate quality parameter change data; A process evaluation submodule, used for evaluating and analyzing the process-substrate optimization process data based on a subset of evaluation indicators corresponding to each process, and outputting a first evaluation result corresponding to each process; ； in, Indicates the first evaluation result corresponding to the lth process; represents the total number of first indicators corresponding to the lth process; represents the total number of second indicators corresponding to the lth process; It represents the change value of the process parameter corresponding to the i-th first indicator in the l-th process before and after optimization; represents the optimization evaluation coefficient corresponding to the i-th first indicator; Indicates that the lth process The error adjustment coefficient corresponding to the first indicator; represents the change value of the substrate quality parameter corresponding to the jth second index in the lth process before and after optimization; represents the optimization evaluation coefficient corresponding to the j-th second indicator; Indicates that the lth process The error adjustment coefficient corresponding to the second indicator; An overall evaluation submodule, used for performing an overall evaluation on the process-substrate optimization process data based on the optimization evaluation index set, and outputting a second evaluation result; The comprehensive analysis submodule is used to comprehensively analyze the first evaluation result and the second evaluation result, and output an optimized evaluation result.

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