Aging degree detection system for organic electroluminescence display panel

Abstract

The invention provides an organic electroluminescent display panel aging degree detection system, and relates to the technical field of panel detection, and the system comprises a data monitoring module which is used for obtaining performance test data of organic electroluminescent display panels of the same model in real time through a preset sensor, and outputting initial data; the preprocessing module is used for preprocessing the initial data to obtain to-be-analyzed data; the data analysis module is used for analyzing the to-be-analyzed data by utilizing a preset data analysis model in combination with the market feedback data and a preset performance evaluation index, and outputting a panel aging analysis result; and the optimization module is used for performing optimization analysis on the panel aging analysis result, outputting an optimization analysis result, and obtaining a panel optimization strategy and a panel optimization method matched with the optimization analysis result in combination with a preset result-strategy comparison table. According to the invention, the aging detection efficiency and detection precision of the organic electroluminescent display panel can be improved, performance optimization is carried out, and the production quality of the panel is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of panel detection, and in particular to an organic electroluminescent display panel aging degree detection system. Background Art

[0002] Organic electroluminescent display panels have the advantages of high contrast, fast response time, wide viewing angle and self-luminescence, and thus have attracted wide attention. However, with the widespread use of organic electroluminescent display panels, their disadvantages have gradually been exposed.

[0003] The organic light-emitting materials in the display panel will gradually degrade over time, resulting in a relatively short life of the display. In addition, the organic light-emitting materials are sensitive to moisture and oxygen and are easily affected by oxidation, resulting in a decrease in display effect. Therefore, during the production process of the panel, it is particularly important to perform aging detection on the display panel. However, the current panel aging detection system has low detection accuracy and monitoring efficiency, which makes it difficult to meet the increasing detection needs.

[0004] Therefore, the present invention provides an organic electroluminescent display panel aging degree detection system. Summary of the invention

[0005] The present invention provides an organic electroluminescent display panel aging degree detection system, which is used to improve the aging detection efficiency and detection accuracy of the organic electroluminescent display panel, and to optimize the performance and improve the production quality of the panel.

[0006] The present invention provides an organic electroluminescent display panel aging degree detection system, comprising: A data monitoring module is used to obtain the performance test data of the same model of organic electroluminescent display panel in real time through a preset sensor and output the initial data; A preprocessing module, used to preprocess the initial data using a preset preprocessing method to obtain data to be analyzed; A data analysis module, used to combine market feedback data and preset performance evaluation indicators, and use a preset data analysis model to analyze the data to be analyzed, and output panel aging analysis results; The optimization module is used to optimize the panel aging analysis results in combination with preset optimization indicators, output the optimization analysis results, and obtain the panel optimization strategy and panel optimization method matching the optimization analysis results in combination with a preset result-strategy comparison table.

[0007] Preferably, the data monitoring module includes: The device selection submodule is used to obtain the user's device selection instruction, select the preset sensor information matching the user's selection instruction in the device database, and activate the corresponding preset sensor in the detection system; The data acquisition submodule is used to acquire the performance parameters of the selected organic electroluminescent display panel of the same model based on the preset sensor and output initial data.

[0008] Preferably, the data acquisition submodule includes: A demand acquisition unit, used to acquire the user's monitoring performance demand information and output monitoring demand data; The data acquisition unit is used to determine the model and batch information of the organic electroluminescent display panel to be inspected based on the monitoring demand information, output the panel information table to be inspected, and obtain the performance parameters of the corresponding panel in the panel information table to be inspected in combination with the preset sensor, and output the initial data.

[0009] Preferably, the preprocessing module comprises: A feature extraction submodule, used to extract features from the initial data and construct a first feature set; The preprocessing submodule is used to select a preset preprocessing method from a method database based on the first feature set and in combination with a preset feature-method matching table, and preprocess the initial data based on the preset preprocessing method to obtain data to be analyzed.

[0010] Preferably, the preprocessing module further includes: The denoising submodule is used to select a denoising method matching the first feature set from a method database in combination with the preset feature-method matching table, and to denoise the initial data before preprocessing based on the denoising method.

[0011] Preferably, the data analysis module includes: The market data acquisition submodule is used to obtain the market feedback information of the same model of organic electroluminescent display panel in real time and output the market feedback data; The data analysis submodule is used to analyze the market feedback data and the data to be analyzed by combining the preset performance evaluation index and using the preset data analysis model to obtain the panel aging analysis result.

[0012] Preferably, the data analysis submodule includes: An indicator matching unit, configured to select a matching performance evaluation indicator from an indicator database based on the first feature set and in combination with a preset feature-indicator mapping table, and construct an indicator candidate set; An instruction acquisition unit is used to acquire self-selected instructions automatically selected by the system according to a custom algorithm, and at the same time, acquire manually selected instructions input through a preset input port in real time, and output a selected instruction set in combination with the execution priorities corresponding to the self-selected instructions and the manually selected instructions; An indicator selection unit, configured to select a performance evaluation indicator matching the selection instruction set from the indicator candidate set, and output the selected performance evaluation indicator as a preset performance evaluation indicator set; A format acquisition unit, used to acquire format information of all data types in the data to be analyzed, and generate a data-format comparison table; A standardization processing unit, used for performing standardization processing on the data in the data-format comparison table based on a preset standardization method to obtain first data; A model matching unit, configured to select an adapted data analysis model from a model database based on the first feature set and a preset performance evaluation index set and in combination with a preset feature-index-model mapping table, and output a first model; A historical data acquisition unit, configured to select historical performance data and historical panel aging data from a historical database based on the first feature set; a data aggregation and division unit, configured to aggregate the first data, historical performance data, and historical panel aging data to obtain second data, and to divide the second data using a preset data division method to obtain a training data set, a verification data set, and a test data set; A model training unit, configured to train and test the first model based on the training data set, the validation data set, and the test data set, and output the first model that meets the first performance condition as a preset data analysis model; an aging analysis unit, configured to analyze the first data based on the preset data analysis model to obtain a model analysis result; The result output unit is used to adjust the model analysis result in combination with the market feedback data and output the panel aging analysis result.

[0013] Preferably, the optimization module includes: An indicator selection submodule, used for selecting preset optimization indicators for optimizing the panel aging analysis result from an indicator database, and constructing a first indicator set, wherein the preset optimization indicators include brightness attenuation, color drift, pixel uniformity, gray consistency, and life attenuation; A model selection submodule, used to select an optimization analysis model that matches each preset optimization index in the first index set from a model database; A data conversion submodule, used for acquiring format requirement information of the optimization analysis model, and converting the panel aging analysis result into a data format matching the format requirement information; A result analysis submodule, used for analyzing the panel aging analysis result after format conversion, outputting aging analysis data, and determining the panel index to be optimized based on the aging analysis data and the first index set, and outputting an optimized index set; An optimization analysis submodule, for inputting the optimization index set and the aging analysis data into the optimization analysis model for optimization analysis, combining the data corresponding to each index to be optimized in the optimization index set in the market feedback data to obtain optimization analysis data, and outputting the optimization analysis results; ; in, Indicates the optimization analysis data value; represents the exponential function; Indicates the total number of indicators to be optimized in the optimization indicator set; Represents the adjustment coefficient corresponding to n indicators to be optimized; Represents the parameter optimization space corresponding to the i-th indicator to be optimized; Represents the optimization calculation weight of the i-th indicator to be optimized during the optimization analysis process; represents the mean value of the real-time performance test parameter corresponding to the i-th indicator to be optimized; Represents the mean value of the market feedback parameter data corresponding to the i-th indicator to be optimized; represents the weight coefficient corresponding to the mean value of the real-time performance test parameter under the i-th indicator to be optimized; Represents the weight coefficient corresponding to the mean value of the market feedback parameter data under the i-th indicator to be optimized; A keyword matching submodule is used to identify and extract keywords from the optimization analysis results to construct a keyword set; A factor acquisition submodule, used to obtain a screening factor matching each keyword in the keyword set by combining a preset keyword-factor comparison table; A strategy matching submodule, for obtaining a panel optimization strategy and a panel optimization method matching the screening factor by combining a preset factor-strategy-method comparison table; An optimization submodule, used to adjust and optimize the design parameters of the panel based on the panel optimization strategy and the panel optimization method; A feedback data acquisition submodule is used to acquire performance test data of the optimized panel, output feedback data, and perform effect analysis on the feedback data using preset effect evaluation indicators, and output optimization effect analysis results; The strategy-method adjustment submodule is used to perform analysis based on the optimization effect analysis results, and adjust and update the panel optimization strategy and panel optimization method based on the analysis results.

[0014] The present invention provides an organic electroluminescent display panel aging degree detection system. The present invention uses real-time monitoring data, comprehensive market feedback and preset indicators, uses a data analysis model to evaluate the panel aging degree, and then performs further optimization analysis based on the optimization indicators to provide optimization strategies and methods, which can help users better understand the status of the display panel, extend its life, improve performance, and provide a better user experience. By comprehensively utilizing data and models, the present invention can more accurately evaluate the panel aging degree and provide an effective optimization solution, thereby improving the reliability and durability of the display device. 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 the framework of an organic electroluminescent display panel aging degree detection system 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 an organic electroluminescent display panel aging degree detection system, comprising: A data monitoring module is used to obtain the performance test data of the same model of organic electroluminescent display panel in real time through a preset sensor and output the initial data; A preprocessing module is used to preprocess the initial data using a preset preprocessing method to obtain data to be analyzed; The data analysis module is used to combine market feedback data and preset performance evaluation indicators, and use the preset data analysis model to analyze the data to be analyzed, and output the panel aging analysis results; The optimization module is used to optimize the panel aging analysis results in combination with preset optimization indicators, output the optimization analysis results, and obtain the panel optimization strategy and panel optimization method that match the optimization analysis results in combination with the preset result-strategy comparison table.

[0019] In this embodiment, the preset sensor: a sensor used to obtain the performance test data of the organic electroluminescent display panel in real time, which is usually a pre-set or selected sensor device, such as an optical sensor, a temperature sensor, a humidity sensor, etc.; In this embodiment, the performance test data: various performance data of the organic electroluminescent display panel acquired in real time by a preset sensor, which is used to evaluate the working state of the display panel, for example, data such as brightness, contrast, and color accuracy; In this embodiment, initial data: raw data obtained from the performance test data, serving as a starting point for analysis, including raw sensor readings or raw performance parameters; In this embodiment, the preset preprocessing method: a preset method for processing and cleaning the initial data to prepare the data for subsequent analysis, for example, data smoothing, filtering, correction, etc.; In this embodiment, the data to be analyzed: data processed by the preprocessing method, ready for subsequent data analysis, is more analyzable and accurate; In this embodiment, market feedback data: data from the market or user feedback, used for comprehensive analysis in combination with performance test data, such as user complaints, quality feedback, questionnaires, etc.; In this embodiment, the preset performance evaluation index: a preset index or standard for evaluating the performance of the organic electroluminescent display panel, for example, a brightness decay rate, a color accuracy standard, etc.; In this embodiment, the preset data analysis model: a preset model or algorithm for analyzing and evaluating the data to be analyzed, which may be a model constructed based on statistics, machine learning or other technologies; In this embodiment, panel aging analysis result: analysis result of the aging degree of the organic electroluminescent display panel output from the data analysis module, generally including aging degree assessment, prediction and suggestion; In this embodiment, preset optimization index: a preset standard or target for guiding optimization analysis, for example, extending the life of a display panel, improving display effects, etc.; In this embodiment, the optimization analysis result: the result of further optimizing the panel aging analysis result in combination with the preset optimization index, including improvement strategies, suggestions or measures; In this embodiment, the preset result-strategy comparison table: a comparison table containing the mapping relationship between the optimization analysis results and the panel optimization strategies, can help determine the best optimization solution; In this embodiment, the panel optimization strategy: an optimization strategy or solution obtained by matching the optimization analysis result with a preset comparison table, for example, adjusting display parameters, reducing brightness, etc.; In this embodiment, the panel optimization method refers to a specific method or step implemented according to the panel optimization strategy, such as regular calibration, adjustment of display settings, etc.

[0020] Implementation principles and beneficial effects of this embodiment: The present invention uses real-time monitoring data, comprehensive market feedback and preset indicators, uses data analysis models to evaluate the degree of panel aging, and then performs further optimization analysis based on optimization indicators to provide optimization strategies and methods, which can help users better understand the state of the display panel, extend its life, improve performance, and provide a better user experience. By comprehensively utilizing data and models, the present invention can more accurately evaluate the degree of panel aging and provide effective optimization solutions, thereby improving the reliability and durability of display devices.

[0021] An embodiment of the present invention provides an organic electroluminescent display panel aging degree detection system, a data monitoring module, including: The device selection submodule is used to obtain the user's device selection instruction, select the preset sensor information matching the user's selection instruction in the device database, and activate the corresponding preset sensor in the detection system; The data acquisition submodule is used to acquire the performance parameters of the selected organic electroluminescent display panel of the same model based on the preset sensor and output the initial data.

[0022] In this embodiment, the device selection instruction is an instruction issued by the user, which is used to select a specific device or sensor. The user can use this instruction to specify which sensors the system should use to monitor a specific organic electroluminescent display panel; In this embodiment, the device database: a database storing various device information, including available sensor information, device models, technical specifications, etc. The system can retrieve relevant information from the device database to satisfy the user selection instruction; In this embodiment, preset sensor information: information related to sensors preset by the system, used to monitor the performance parameters of the organic electroluminescent display panel, including sensor type, technical specifications, location, etc. For example, suppose a user wants to monitor the brightness and color accuracy of a specific model of organic electroluminescent display panel. The user sends a device selection instruction, and the system selects an optical sensor and a color sensor as preset sensors from the device database according to this instruction. The information of these sensors includes their technical specifications such as working range and sensitivity.

[0023] Implementation principle and beneficial effects of this embodiment: The present invention uses a device selection submodule and a data acquisition submodule, and the system can select a suitable sensor according to the user's device selection instruction and obtain the required performance parameter data, which can ensure that the system uses a suitable sensor for monitoring a specific model of organic electroluminescent display panel, thereby improving the accuracy and reliability of monitoring. The present invention can more effectively monitor the performance changes of the panel and detect aging signs in a timely manner.

[0024] An embodiment of the present invention provides an organic electroluminescent display panel aging degree detection system, a data acquisition submodule, including: A demand acquisition unit, used to acquire the user's monitoring performance demand information and output monitoring demand data; The data acquisition unit is used to determine the model and batch information of the organic electroluminescent display panel to be inspected based on the monitoring demand information, output the panel information table to be inspected, and obtain the performance parameters of the corresponding panel in the panel information table to be inspected in combination with the preset sensor, and output the initial data.

[0025] In this embodiment, monitoring performance requirement information: specific requirement information provided by the user on monitoring the performance of the organic electroluminescent display panel, the user can specify the parameters, frequency, accuracy and other requirements that he wants to monitor; In this embodiment, monitoring requirement data: data generated according to the user's monitoring performance requirement information, used to determine the model and batch information of the organic electroluminescent display panel to be inspected, so as to perform subsequent monitoring and data acquisition; In this embodiment, the organic electroluminescent display panels to be inspected are: organic electroluminescent display panels that need to be monitored as determined by the monitoring requirement data, and these panels may be products of specific models and batches; In this embodiment, model and batch information: the specific model and batch information of the organic electroluminescent display panel to be inspected, which is very important for distinguishing different products and batches to ensure accurate monitoring and data acquisition; In this embodiment, the panel information table to be inspected is a table or data structure containing detailed information of the panel to be inspected, which may include information such as the model, batch, production date, performance parameters, etc. of each organic electroluminescent display panel to be inspected. For example, suppose that the user requires monitoring the brightness and color accuracy of a batch of organic electroluminescent display panels of a specific model. The demand acquisition unit collects these monitoring performance demand information and generates monitoring demand data. The data acquisition unit determines that the model of the panel to be inspected is "Model A" and the batch information is "Batch 123" based on the monitoring demand information, and then, according to the preset sensor, obtains the performance parameters of the corresponding panel in the panel information table to be inspected, such as brightness, contrast and other data.

[0026] The implementation principle and beneficial effects of this embodiment: By obtaining the user's monitoring performance requirement information, the system can accurately determine the model and batch information of the organic electroluminescent display panel to be inspected, so as to facilitate subsequent monitoring and data acquisition, and help the system accurately monitor specific models and batches of panels according to user needs, thereby improving the pertinence and effectiveness of monitoring. The present invention can better meet the needs of users and provide customized monitoring services, thereby helping users to better understand the performance status of the display panel, discover problems in a timely manner and take corresponding measures, and improve the reliability and stability of the display panel.

[0027] An embodiment of the present invention provides an organic electroluminescent display panel aging degree detection system, a preprocessing module, including: A feature extraction submodule is used to extract features from the initial data and construct a first feature set; The preprocessing submodule is used to select a preset preprocessing method from a method database based on the first feature set and in combination with a preset feature-method matching table, and preprocess the initial data based on the preset preprocessing method to obtain data to be analyzed.

[0028] In this embodiment, the first feature set is a feature set extracted from the initial data by the feature extraction submodule, which are key attributes or significant features in the data and are used for subsequent analysis and processing; In this embodiment, the preset feature-method matching table: a table containing mapping relationships between data features and preset preprocessing methods, which is used for the system to select an appropriate preprocessing method to process data according to the type and attribute of the data features; In this embodiment, the method database: a database that stores various preprocessing methods. The system can select a suitable preprocessing method from the method database to process the initial data to obtain data that is more suitable for analysis. For example, assume that the feature extraction submodule extracts features such as brightness, color saturation and contrast from the initial data and constructs a first feature set. The preset feature-method matching table lists the preprocessing methods for these features, such as brightness correction method, color correction method, etc. Based on the first feature set and the preset feature-method matching table, the system selects the corresponding preprocessing method in the method database, preprocesses the initial data, and obtains the data to be analyzed.

[0029] Implementation principle and beneficial effects of this embodiment: Through the feature extraction submodule and the preprocessing submodule, the system can extract key features from the initial data, construct a first feature set, and select a suitable preprocessing method for data processing according to the preset feature-method matching table. This can make the data more accurate and reliable, and provide a better basis for subsequent analysis. The present invention can optimize the data, making the subsequent analysis more accurate and effective, and help the system better monitor the aging degree of the organic electroluminescent display panel, discover problems in time and take corresponding measures, and improve the performance and stability of the display panel.

[0030] An organic electroluminescent display panel aging degree detection system provided by an embodiment of the present invention, the preprocessing module further includes: The denoising submodule is used to select a denoising method matching the first feature set from the method database in combination with a preset feature-method matching table, and to denoise the initial data before preprocessing based on the denoising method.

[0031] In this embodiment, the noise reduction method is a processing method used to remove noise or interference in the data to improve the quality and accuracy of the data, including but not limited to filtering technology, smoothing, wavelet transform and other methods. For example, assume that there is some random noise in the data of the organic electroluminescent display panel, which may come from sensor errors or environmental interference. The noise reduction submodule selects a suitable noise reduction method in the method database according to the preset feature-method matching table, such as median filtering, Gaussian filtering, etc., and then applies these methods to perform noise reduction processing on the initial data to remove noise interference and obtain cleaner and more reliable data for subsequent analysis.

[0032] Implementation principle and beneficial effects of this embodiment: By adding a noise reduction submodule, the system can perform noise reduction processing on the data in the preprocessing stage, remove interference and noise, and improve the quality and accuracy of the data. This helps the system to more accurately evaluate the aging degree of the organic electroluminescent display panel and improve the accuracy and reliability of the detection. The present invention can reduce misjudgment and improve the accuracy of detection, thereby helping users to better understand the actual performance status of the display panel and perform maintenance and optimization in a timely manner.

[0033] An embodiment of the present invention provides an organic electroluminescent display panel aging degree detection system, a data analysis module, including: The market data acquisition submodule is used to obtain the market feedback information of the same model of organic electroluminescent display panel in real time and output the market feedback data; The data analysis submodule is used to combine preset performance evaluation indicators and analyze market feedback data and data to be analyzed through a preset data analysis model to obtain panel aging analysis results.

[0034] In this embodiment, market feedback information refers to real-time market information and user feedback from the same model of organic electroluminescent display panel. This information may include user evaluations, quality reports, sales data, maintenance records, etc., which are used to understand the performance and performance of the display panel in actual use. For example, assume that the market feedback information includes the user's experience, problem feedback, opinions and suggestions on a specific model of organic electroluminescent display panel, as well as the market demand shown by sales data. This information can help the system more comprehensively evaluate the performance and aging of the display panel, and provide more reference for subsequent data analysis.

[0035] Implementation principle and beneficial effects of this embodiment: The present invention can obtain market feedback information of the same model of organic electroluminescent display panel in real time by introducing market data acquisition submodule and data analysis submodule, and analyze this information in combination with preset performance evaluation indicators and data analysis models, so as to more comprehensively evaluate the aging degree and performance of the display panel and provide users with more accurate analysis results and suggestions. The present invention uses market feedback information to help the system better understand user needs and actual usage, timely adjust detection strategies and optimize display panel performance, and improve user satisfaction and product quality.

[0036] An embodiment of the present invention provides an organic electroluminescent display panel aging degree detection system, a data analysis submodule, including: An indicator matching unit, configured to select a matching performance evaluation indicator from an indicator database based on the first feature set and in combination with a preset feature-indicator mapping table, and construct an indicator candidate set; An instruction acquisition unit is used to acquire self-selected instructions automatically selected by the system according to a custom algorithm, and at the same time, acquire manually selected instructions input through a preset input port in real time, and output a selected instruction set in combination with the execution priorities corresponding to the self-selected instructions and the manually selected instructions; An indicator selection unit is used to select a performance evaluation indicator that matches the selected instruction set from the indicator candidate set, and output it as a preset performance evaluation indicator set; A format acquisition unit, used to acquire format information of all data types in the data to be analyzed, and generate a data-format comparison table; A standardization processing unit, used for performing standardization processing on the data in the data-format comparison table based on a preset standardization method to obtain first data; A model matching unit, configured to select an adapted data analysis model from a model database based on the first feature set and the preset performance evaluation index set and in combination with a preset feature-index-model mapping table, and output a first model; A historical data acquisition unit, configured to select historical performance data and historical panel aging data from a historical database based on the first feature set; A data aggregation and division unit, used to aggregate the first data, the historical performance data, and the historical panel aging data to obtain second data, and to divide the second data using a preset data division method to obtain a training data set, a verification data set, and a test data set; A model training unit, configured to train and test the first model based on the training data set, the validation data set, and the test data set, and output the first model that meets the first performance condition as a preset data analysis model; An aging analysis unit, configured to analyze the first data based on a preset data analysis model to obtain a model analysis result; The result output unit is used to adjust the model analysis results in combination with market feedback data and output the panel aging analysis results.

[0037] In this embodiment, the preset feature-indicator mapping table: a table containing the mapping relationship between data features and performance evaluation indicators. Through this table, the system can obtain the corresponding performance evaluation indicators according to the specific data features for subsequent data analysis and evaluation; In this embodiment, the candidate indicator set: a set of performance evaluation indicators obtained by matching a specific feature set in an indicator database, which is used for further indicator selection and analysis; In this embodiment, the custom algorithm refers to an algorithm designed within the system for automatically selecting instructions, which automatically generates instructions according to specific rules and logic; In this embodiment, self-selected instructions: instructions automatically generated by the system based on a custom algorithm to guide operations and processes during data analysis; In this embodiment, the preset input port: is an interface or channel reserved by the system for receiving external input data or instructions; In this embodiment, the manual selection instruction is an instruction or operation input by a user to intervene in or guide the data analysis process; In this embodiment, execution priority refers to the execution order or priority of different instructions or operations, which is used to determine the execution order of the system when processing multiple instructions; In this embodiment, the selection instruction set: an instruction set consisting of self-selected instructions and manually selected instructions, used to guide the data analysis process; In this embodiment, the preset performance evaluation index set is: an index set for evaluating the performance of the display panel selected from the index candidate set according to the selection instruction set; In this embodiment, the data-format comparison table is used to record the format information of various data types in the data to be analyzed, so that different types of data can be correctly identified and processed in the subsequent data processing process; In this embodiment, the preset standardization method refers to a method of standardizing data used in the data processing process, which is used to convert the data into a unified standard format for subsequent data analysis and processing; In this embodiment, the first data: data obtained after format acquisition and standardization processing, providing basic data for subsequent data analysis and model training; In this embodiment, the preset feature-indicator-model mapping table: a table containing the matching or mapping between data features, performance evaluation indicators and data analysis models, is preset; In this embodiment, the model database: a database storing various data analysis models, used to select an adaptive data analysis model during the model matching process; In this embodiment, the first model: a model for data analysis obtained by matching the first feature set with a preset performance evaluation indicator set; In this embodiment, the historical database: a database storing historical performance data and historical panel aging data, used to obtain historical data for analysis and comparison during the data analysis process; In this embodiment, the historical performance data and historical panel aging data are the display panel performance data and aging data recorded in the past, which are used to analyze the historical performance and aging of the display panel; In this embodiment, the second data is data obtained by aggregating and dividing the first data, historical performance data and historical panel aging data, and is used for generating a training data set, a validation data set and a test data set; In this embodiment, the preset data partitioning method: the method for partitioning the second data into a training data set, a validation data set, and a test data set is preset, for example, random partitioning, K-fold cross validation, stratified sampling, holdout method, etc.; In this embodiment, the training data set, the validation data set and the test data set are data sets used to train the data analysis model, validate the model effect and test the model performance; In this embodiment, the first performance condition refers to the performance requirement or condition that needs to be met when training and testing the first model; In this embodiment, the model analysis result: the result obtained after analyzing the first data according to the preset data analysis model, is used to evaluate the aging degree and performance of the display panel.

[0038] The implementation principle and beneficial effects of this embodiment: Through the combination and collaboration of these functional units, the system can select suitable performance evaluation indicators according to the preset feature-indicator mapping table, guide the data analysis process through custom algorithms and manually selected instructions, establish a data analysis model by combining historical data and model training, and finally perform aging analysis on the data to be analyzed and output the results. The present invention can improve the automation and accuracy of data analysis, help users more accurately evaluate the aging degree and performance of display panels, and thus perform maintenance and optimization in a timely manner.

[0039] An organic electroluminescent display panel aging degree detection system and an optimization module provided in an embodiment of the present invention include: An indicator selection submodule, used to select preset optimization indicators for optimizing panel aging analysis results from an indicator database, and construct a first indicator set, wherein the preset optimization indicators include brightness attenuation, color drift, pixel uniformity, gray consistency, and life attenuation; A model selection submodule is used to select an optimization analysis model that matches each preset optimization index in the first index set from the model database; A data conversion submodule is used to obtain the format requirement information of the optimization analysis model and convert the panel aging analysis results into a data format that matches the format requirement information; A result analysis submodule, used to analyze the panel aging analysis results after format conversion, output aging analysis data, determine the indicators to be optimized of the panel based on the aging analysis data and the first indicator set, and output the optimized indicator set; The optimization analysis submodule is used to input the optimization index set and the aging analysis data into the optimization analysis model for optimization analysis, and to obtain optimization analysis data by combining the data corresponding to each indicator to be optimized in the optimization index set in the market feedback data, and output the optimization analysis results; ; in, Indicates the optimization analysis data value; represents the exponential function; Indicates the total number of indicators to be optimized in the optimization indicator set; Represents the adjustment coefficient corresponding to n indicators to be optimized; Represents the parameter optimization space corresponding to the i-th indicator to be optimized; Represents the optimization calculation weight of the i-th indicator to be optimized during the optimization analysis process; represents the mean value of the real-time performance test parameter corresponding to the i-th indicator to be optimized; Represents the mean value of the market feedback parameter data corresponding to the i-th indicator to be optimized; represents the weight coefficient corresponding to the mean value of the real-time performance test parameter under the i-th indicator to be optimized; Represents the weight coefficient corresponding to the mean value of the market feedback parameter data under the i-th indicator to be optimized; The keyword matching submodule is used to identify and extract keywords from the optimization analysis results and construct a keyword set; The factor acquisition submodule is used to obtain the screening factors matching each keyword in the keyword set by combining the preset keyword-factor comparison table; The strategy matching submodule is used to obtain the panel optimization strategy and panel optimization method that match the screening factors by combining the preset factor-strategy-method comparison table; The optimization submodule is used to adjust and optimize the design parameters of the panel based on the panel optimization strategy and panel optimization method; The feedback data acquisition submodule is used to acquire the performance test data of the optimized panel, output the feedback data, and perform effect analysis on the feedback data using preset effect evaluation indicators, and output the optimization effect analysis results; The strategy-method adjustment submodule is used to analyze based on the optimization effect analysis results, and adjust and update the panel optimization strategy and panel optimization method based on the analysis results.

[0040] In this embodiment, the preset optimization index is a set of indexes for optimizing the panel aging analysis results, including brightness attenuation, color drift, pixel uniformity, grayscale consistency, and life attenuation; In this embodiment, the first indicator set: an indicator set constructed by preset optimization indicators selected by the indicator selection submodule; In this embodiment, the optimization analysis model: a model selected by the model selection submodule that matches each preset optimization index in the first index set; In this embodiment, the format requirement information is obtained by the data conversion submodule and is used to convert the panel aging analysis results into the data format required by the optimization analysis model; In this embodiment, aging analysis data: data obtained after being analyzed by the result analysis submodule, used to determine the index to be optimized of the panel; In this embodiment, the index to be optimized: the index to be optimized determined according to the aging analysis data and the first index set, for example, service life, brightness, etc.; In this embodiment, the optimization index set includes a set of indexes to be optimized; In this embodiment, optimization analysis data: optimization analysis results calculated based on the optimization indicator set and market feedback data; In this embodiment, optimization analysis result: the final optimization result obtained according to the optimization indicator set and market feedback data; For example, suppose that during the panel aging analysis process, it is found that brightness attenuation, color drift and life attenuation are indicators that need to be optimized, and these indicators constitute the first indicator set. The optimization analysis model is selected as a neural network model. The data conversion submodule converts the aging analysis results into the data format required by the neural network model. The result parsing submodule parses the indicators to be optimized, such as brightness attenuation and color drift, based on the converted data. The optimization analysis submodule inputs these indicators to be optimized into the neural network model for optimization analysis, combines the market feedback data to obtain the optimization analysis data, and finally outputs the optimization analysis results.

[0041] In this embodiment, keyword identification and extraction refers to the process of identifying and extracting keywords from the optimization analysis results; In this embodiment, keyword set: a set of keywords constructed by the keyword matching submodule; In this embodiment, the preset keyword-factor comparison table: a reference table containing the correspondence between keywords and screening factors, is preset; In this embodiment, the screening factor: the factor matching each keyword in the keyword set obtained by the factor obtaining submodule; In this embodiment, the preset factor-strategy-method comparison table: a reference table containing mapping or comparison relationships between screening factors and panel optimization strategies and panel optimization methods; In this embodiment, design parameters: a set of parameters used to describe the design characteristics of the panel, which can be adjusted and optimized according to the optimization strategy and method; In this embodiment, feedback data: optimized panel performance test data, used to evaluate the optimization effect; In this embodiment, the preset effect evaluation index: a set of indicators used to evaluate the optimization effect, which can be used to perform effect analysis on the feedback data; In this embodiment, the optimization effect analysis result: the optimization effect evaluation result obtained after analyzing the feedback data according to the effect evaluation index.

[0042] For example, suppose the keyword recognition and extraction submodule identifies a keyword set including "color accuracy" and "brightness uniformity". These keywords match the screening factors in the preset keyword-factor comparison table, and the factor acquisition submodule obtains the corresponding screening factors. The strategy matching submodule matches the corresponding panel optimization strategies and methods based on the screening factors, and the optimization submodule adjusts and optimizes the design parameters based on these strategies and methods. The feedback data acquisition submodule obtains the optimized panel performance test data, and uses the preset effect evaluation indicators to perform effect analysis to obtain the optimization effect analysis results.

[0043] Implementation principle and beneficial effects of this embodiment: Through the collaboration of these submodules, the system can perform panel optimization analysis according to preset optimization indicators and market feedback data, thereby adjusting panel design parameters and optimizing panel performance. The present invention can help users better understand the aging of panels, improve panel performance, and perform effect analysis and adjustment through feedback data obtained after optimization, thereby continuously optimizing the performance and quality of the panel.

[0044] 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. An organic electroluminescent display panel aging degree detection system, characterized in that: include: A data monitoring module is used to obtain the performance test data of the same model of organic electroluminescent display panel in real time through a preset sensor and output the initial data; A preprocessing module, used to preprocess the initial data using a preset preprocessing method to obtain data to be analyzed; A data analysis module, used to combine market feedback data and preset performance evaluation indicators, and use a preset data analysis model to analyze the data to be analyzed, and output panel aging analysis results; The optimization module is used to optimize the panel aging analysis results in combination with preset optimization indicators, output the optimization analysis results, and obtain the panel optimization strategy and panel optimization method matching the optimization analysis results in combination with a preset result-strategy comparison table.

2. The organic electroluminescent display panel aging degree detection system according to claim 1, characterized in that: The data monitoring module comprises: The device selection submodule is used to obtain the user's device selection instruction, select the preset sensor information matching the user's selection instruction in the device database, and activate the corresponding preset sensor in the detection system; The data acquisition submodule is used to acquire the performance parameters of the selected organic electroluminescent display panel of the same model based on the preset sensor and output initial data.

3. The organic electroluminescent display panel aging degree detection system according to claim 2, characterized in that: The data acquisition submodule includes: A demand acquisition unit, used to acquire the user's monitoring performance demand information and output monitoring demand data; The data acquisition unit is used to determine the model and batch information of the organic electroluminescent display panel to be inspected based on the monitoring demand information, output the panel information table to be inspected, and obtain the performance parameters of the corresponding panel in the panel information table to be inspected in combination with the preset sensor, and output the initial data.

4. The organic electroluminescent display panel aging degree detection system according to claim 1, characterized in that: The preprocessing module comprises: A feature extraction submodule, used to extract features from the initial data and construct a first feature set; The preprocessing submodule is used to select a preset preprocessing method from a method database based on the first feature set and in combination with a preset feature-method matching table, and preprocess the initial data based on the preset preprocessing method to obtain data to be analyzed.

5. The organic electroluminescent display panel aging degree detection system according to claim 4, characterized in that: The preprocessing module further includes: The denoising submodule is used to select a denoising method matching the first feature set from a method database in combination with the preset feature-method matching table, and to denoise the initial data before preprocessing based on the denoising method.

6. The organic electroluminescent display panel aging degree detection system according to claim 4, characterized in that: The data analysis module comprises: The market data acquisition submodule is used to obtain the market feedback information of the same model of organic electroluminescent display panel in real time and output the market feedback data; The data analysis submodule is used to analyze the market feedback data and the data to be analyzed by combining the preset performance evaluation index and using the preset data analysis model to obtain the panel aging analysis result.

7. The organic electroluminescent display panel aging degree detection system according to claim 6, characterized in that: The data analysis submodule comprises: An indicator matching unit, configured to select a matching performance evaluation indicator from an indicator database based on the first feature set and in combination with a preset feature-indicator mapping table, and construct an indicator candidate set; An instruction acquisition unit is used to acquire self-selected instructions automatically selected by the system according to a custom algorithm, and at the same time, acquire manually selected instructions input through a preset input port in real time, and output a selected instruction set in combination with the execution priorities corresponding to the self-selected instructions and the manually selected instructions; An indicator selection unit, configured to select a performance evaluation indicator matching the selection instruction set from the indicator candidate set, and output the selected performance evaluation indicator as a preset performance evaluation indicator set; A format acquisition unit, used to acquire format information of all data types in the data to be analyzed, and generate a data-format comparison table; A standardization processing unit, used for performing standardization processing on the data in the data-format comparison table based on a preset standardization method to obtain first data; A model matching unit, configured to select an adapted data analysis model from a model database based on the first feature set and a preset performance evaluation index set and in combination with a preset feature-index-model mapping table, and output a first model; A historical data acquisition unit, configured to select historical performance data and historical panel aging data from a historical database based on the first feature set; a data aggregation and division unit, configured to aggregate the first data, historical performance data, and historical panel aging data to obtain second data, and to divide the second data using a preset data division method to obtain a training data set, a verification data set, and a test data set; A model training unit, configured to train and test the first model based on the training data set, the validation data set, and the test data set, and output the first model that meets the first performance condition as a preset data analysis model; an aging analysis unit, configured to analyze the first data based on the preset data analysis model to obtain a model analysis result; The result output unit is used to adjust the model analysis result in combination with the market feedback data and output the panel aging analysis result.

8. The organic electroluminescent display panel aging degree detection system according to claim 1, characterized in that: The optimization module comprises: An indicator selection submodule, used for selecting preset optimization indicators for optimizing the panel aging analysis result from an indicator database, and constructing a first indicator set, wherein the preset optimization indicators include brightness attenuation, color drift, pixel uniformity, gray consistency, and life attenuation; A model selection submodule, used to select an optimization analysis model that matches each preset optimization index in the first index set from a model database; A data conversion submodule, used for acquiring format requirement information of the optimization analysis model, and converting the panel aging analysis result into a data format matching the format requirement information; A result analysis submodule, used for analyzing the panel aging analysis result after format conversion, outputting aging analysis data, determining the panel index to be optimized based on the aging analysis data and the first index set, and outputting an optimized index set; An optimization analysis submodule, for inputting the optimization index set and the aging analysis data into the optimization analysis model for optimization analysis, combining the data corresponding to each index to be optimized in the optimization index set in the market feedback data to obtain optimization analysis data, and outputting the optimization analysis results; ;in, Indicates the optimization analysis data value; represents the exponential function; Indicates the total number of indicators to be optimized in the optimization indicator set; Represents the adjustment coefficient corresponding to n indicators to be optimized; Represents the parameter optimization space corresponding to the i-th indicator to be optimized; Represents the optimization calculation weight of the i-th indicator to be optimized during the optimization analysis process; represents the mean value of the real-time performance test parameter corresponding to the i-th indicator to be optimized; Represents the mean value of the market feedback parameter data corresponding to the i-th indicator to be optimized; represents the weight coefficient corresponding to the mean value of the real-time performance test parameter under the i-th indicator to be optimized; Represents the weight coefficient corresponding to the mean value of the market feedback parameter data under the i-th indicator to be optimized; A keyword matching submodule is used to identify and extract keywords from the optimization analysis results to construct a keyword set; A factor acquisition submodule, used to obtain a screening factor matching each keyword in the keyword set by combining a preset keyword-factor comparison table; A strategy matching submodule, for obtaining a panel optimization strategy and a panel optimization method matching the screening factor by combining a preset factor-strategy-method comparison table; An optimization submodule, used to adjust and optimize the design parameters of the panel based on the panel optimization strategy and the panel optimization method; A feedback data acquisition submodule is used to acquire performance test data of the optimized panel, output feedback data, and perform effect analysis on the feedback data using preset effect evaluation indicators, and output optimization effect analysis results; The strategy-method adjustment submodule is used to perform analysis based on the optimization effect analysis results, and adjust and update the panel optimization strategy and panel optimization method based on the analysis results.

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