A pixel uniformity analysis method for a nested pixel display array panel

Abstract

The present invention provides a pixel uniformity analysis method for a nested pixel display array panel, which relates to the field of panel pixel analysis, including: obtaining product information of multiple batches of panels of the same model, and selecting panel test samples corresponding to the multiple batches of product information; combining preset performance test indicators and using preset test equipment to perform performance tests on the panel test samples, and outputting performance test data; using a preset analysis model to perform pixel uniformity analysis on the performance test data, and outputting a first result; combining a preset result-strategy-method mapping table, obtaining a calibration strategy and calibration method that match the first result, and improving and adjusting the production strategy and display strategy of the corresponding panel based on the calibration strategy and calibration method. The present invention can timely discover problems in the production process of the panel, and improve the production strategy and display strategy through the calibration strategy and calibration method, thereby improving the pixel uniformity and overall quality of the display panel.

Description

Technical Field

[0001] The present invention relates to the technical field of panel pixel analysis, and in particular to a pixel uniformity analysis method for a nested pixel display array panel. Background Art

[0002] The nested pixel display array panel is a special display technology that is usually used to improve the resolution and display effect of the display screen. In traditional display panels, pixels are usually arranged in a regular matrix, while in the nested pixel display array panel, the arrangement of pixels is more complicated. In the nested pixel display array panel, the arrangement of pixels will present a nested and staggered structure to improve the pixel density and display effect. This arrangement can make the spacing between adjacent pixels smaller, thereby improving the clarity and detail of the display.

[0003] Although the nested pixel display array panel has many advantages, its structure is more complicated than that of the traditional display panel. This complex pixel arrangement may lead to problems such as poor pixel uniformity, which will affect the display effect of the panel. The current pixel uniformity analysis method suitable for traditional panels has a large error between the analysis results and the actual results when facing the nested pixel display array panel, which makes it difficult to meet the detection and analysis needs of the nested pixel display array panel.

[0004] Therefore, the present invention provides a pixel uniformity analysis method for a nested pixel display array panel. Summary of the invention

[0005] The present invention provides a pixel uniformity analysis method for a nested pixel display array panel, which is used to improve the accuracy of pixel uniformity analysis on the nested pixel display array panel, timely and accurately check the pixel uniformity problems in the panel, and improve and optimize the pixel uniformity of the panel from both production strategy and display strategy aspects, thereby improving the display effect of the panel.

[0006] The present invention provides a pixel uniformity analysis method for a nested pixel display array panel, comprising:

[0007] Step 1: obtaining product information of multiple batches of panels of the same model, and selecting panel test samples corresponding to the product information of the multiple batches;

[0008] Step 2: Performing a performance test on the panel test sample in combination with preset performance test indicators and using preset test equipment, and outputting performance test data;

[0009] Step 3: Perform pixel uniformity analysis on the performance test data using a preset analysis model, and output a first result;

[0010] Step 4: Acquire a calibration strategy and calibration method matching the first result in combination with a preset result-strategy-method mapping table, and improve and adjust the production strategy and display strategy of the corresponding panel based on the calibration strategy and calibration method;

[0011] Wherein, step 4 includes:

[0012] Acquire standard control parameter data matching the first result, and compare and analyze the standard control parameter data with the first result, and output the comparison and analysis result;

[0013] Obtain theoretical pixel uniformity limit parameters of the current panel, determine the index to be calibrated, and determine the calibration target value under the corresponding index;

[0014] Based on the calibration target value under each indicator, the calibration strategy and calibration method corresponding to the comparative analysis result are obtained in the preset result-strategy-method mapping table;

[0015] At the same time, the production strategy and display strategy of the current model panel are obtained, and the production strategy and display strategy of the panel are adjusted and improved based on the calibration strategy and calibration method. At the same time, the pixel uniformity data of the panel after adjusting and improving the production strategy and display strategy is obtained, and compared with the calibration target value and theoretical pixel uniformity limit parameters to obtain feedback data.

[0016] Preferably, before obtaining product information of multiple batches of panels of the same model, the method includes:

[0017] The detection requirements of the user are obtained, the user requirement information is output, and the equipment information and production batch information of the panel to be detected are determined based on the user requirement information.

[0018] Preferably, step 1 includes:

[0019] Based on the equipment information and production batch information, and in combination with a preset sampling strategy, determine product batch data of the panel to be tested and product information under the corresponding batch, and output multiple batches of product information;

[0020] Based on the information of the multiple batches of products, panel samples to be tested are selected from the corresponding batches of panel products to obtain panel test samples.

[0021] Preferably, step 2 includes:

[0022] Based on the user demand information, an indicator for performing a performance test on the panel test sample is obtained from a preset indicator pool, and outputted as a preset performance test indicator;

[0023] Based on the preset performance test indicators and in combination with the preset indicator-equipment comparison table, the corresponding preset test equipment is obtained, and the preset test equipment is used to perform performance testing on the panel test sample to obtain test data corresponding to each of the preset performance test indicators, and the performance test data is summarized.

[0024] Preferably, before the aggregating to obtain the performance test data, the method further includes:

[0025] A preset preprocessing method is selected in a method database, and test data corresponding to each preset performance test indicator is preprocessed based on the preprocessing method to obtain first data, and the first data that meets the first preset condition is output as data to be summarized.

[0026] Preferably, step 3 includes:

[0027] Selecting a data analysis model that meets a preset screening condition from a model database and outputting it as a preset analysis model;

[0028] Perform pixel uniformity analysis on the performance test data based on the preset analysis model and output a first result.

[0029] Preferably, step 3 further includes:

[0030] Identify the types of the performance test data, collect statistics on the type identification results, and output a test data type statistics table;

[0031] Obtaining the user's data analysis needs, outputting analysis demand data, and performing feature extraction on the analysis demand data, and obtaining a demand feature set based on the extracted features;

[0032] In combination with the test data type statistical table, a first model matching the required feature set and a second model for comparison with the first model are selected from the model database;

[0033] At the same time, historical performance test data matching the current panel is selected from the historical database in combination with the preset performance test index, the data to be divided is output, and the data to be divided is divided based on the preset data division method to obtain a training set, a validation set and a test set for training and optimizing the first model and the second model;

[0034] Training and optimizing the first model and the second model based on the training set, the validation set and the test set, and outputting the first model and the second model that meet the preset screening conditions as the first analysis model and the second analysis model respectively;

[0035] At the same time, pixel evaluation indicators matching the data analysis requirements are selected from the indicator database, and an evaluation indicator set is output;

[0036] In combination with the evaluation index set, the performance test data is respectively input into a first analysis model and a second analysis model for pixel analysis, RGB three-primary color pixel data of the panel at each pixel point is obtained, and a first analysis result corresponding to the first analysis model and a second analysis result corresponding to the second analysis model are obtained by summarizing;

[0037] Based on the evaluation index set, the pixel uniformity of each batch of panels in the first analysis result is analyzed by a first analysis model to obtain a first pixel analysis result;

[0038] At the same time, the pixel uniformity of each batch of panels in the second analysis result is analyzed by a second analysis model to obtain a second pixel analysis result;

[0039] The first pixel analysis result and the second pixel analysis result are classified and divided according to the production batch of the panel, and the uniform pixel analysis results of the same model panel in each batch are obtained and output as the first result.

[0040] Preferably, the algorithm for determining the calibration target value under the corresponding indicator includes:

[0041] ;

[0042] in, represents the calibration target value corresponding to the i-th indicator; It represents the complex correlation coefficient between the ith indicator and the other n-1 indicators among all indicators to be calibrated, with a total number of n; represents the real-time analysis data value corresponding to the i-th indicator in the first result; represents the standard control parameter value corresponding to the i-th indicator at the panel production stage; represents the standard control parameter value corresponding to the ith indicator during the panel usage phase; represents the adjustable space margin value corresponding to the i-th indicator at the panel production stage; represents the adjustable space margin value corresponding to the ith indicator during the panel usage phase; represents the calibration weight coefficient corresponding to the i-th indicator under the panel production stage; Represents the calibration weight coefficient corresponding to the ith indicator under the panel usage stage.

[0043] The present invention provides a pixel uniformity analysis method for a nested pixel display array panel. The present invention can timely discover problems in the production process by performing performance tests and pixel uniformity analysis on multiple batches of products of the same model of panels, and improve the production and display strategies through calibration strategies and calibration methods, thereby improving the pixel uniformity and overall quality of the display panel. The present invention can help panel manufacturers continuously optimize the production process of panels, improve the quality of panel products, and provide users with a better visual experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] 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.

[0045] Figure 1 The present invention is a flowchart of a pixel uniformity analysis method for a nested pixel display array panel provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] 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.

[0047] like Figure 1 As shown, an embodiment of the present invention provides a pixel uniformity analysis method for a nested pixel display array panel, comprising:

[0048] Step 1: Obtain product information of multiple batches of panels of the same model, and select panel test samples corresponding to the product information of the multiple batches;

[0049] Step 2: Perform performance test on the panel test sample in combination with preset performance test indicators and using preset test equipment, and output performance test data;

[0050] Step 3: Perform pixel uniformity analysis on the performance test data using a preset analysis model and output a first result;

[0051] Step 4: Acquire a calibration strategy and calibration method matching the first result in combination with a preset result-strategy-method mapping table, and improve and adjust the production strategy and display strategy of the corresponding panel based on the calibration strategy and calibration method;

[0052] Wherein, step 4 includes:

[0053] Acquire standard control parameter data matching the first result, and compare and analyze the standard control parameter data with the first result, and output the comparison and analysis result;

[0054] Obtain theoretical pixel uniformity limit parameters of the current panel, determine the index to be calibrated, and determine the calibration target value under the corresponding index;

[0055] Based on the calibration target value under each indicator, the calibration strategy and calibration method corresponding to the comparative analysis result are obtained in the preset result-strategy-method mapping table;

[0056] At the same time, the production strategy and display strategy of the current model panel are obtained, and the production strategy and display strategy of the panel are adjusted and improved based on the calibration strategy and calibration method. At the same time, the pixel uniformity data of the panel after adjusting and improving the production strategy and display strategy is obtained, and compared with the calibration target value and theoretical pixel uniformity limit parameters to obtain feedback data.

[0057] In this embodiment, the multi-batch product information refers to the production information of multiple batches of the same model of panels, including production date, production parameters, production process, etc. For example, one batch may contain 1,000 display panels;

[0058] In this embodiment, panel test samples are panel samples selected from multiple batches of products for performance testing, and these samples represent the quality status of the entire batch;

[0059] In this embodiment, the preset performance test index: a pre-set index for evaluating panel performance, such as brightness uniformity, color accuracy, grayscale uniformity, etc.;

[0060] In this embodiment, the preset test equipment: equipment for performing performance tests on panel test samples, such as a photometer, a color analyzer, etc.;

[0061] In this embodiment, performance test data: data obtained by testing a panel test sample using a preset test device, including brightness data, color data, etc.;

[0062] In this embodiment, the preset analysis model: a model used to analyze performance test data, which may be a statistical model, a machine learning model, etc.;

[0063] In this embodiment, pixel uniformity analysis: analyzing the performance test data to evaluate the degree of pixel uniformity of the panel;

[0064] In this embodiment, the first result: i.e., the result of the pixel uniformity analysis, may be in the form of a numerical value, a chart, etc.;

[0065] In this embodiment, the preset result-strategy-method mapping table: a table or database containing mapping relationships between the first result and the calibration strategy and the calibration method, used to obtain the corresponding calibration strategy and calibration method according to the input first result;

[0066] In this embodiment, the calibration strategy and calibration method: a method and strategy for improving and adjusting the production strategy and display strategy of the panel formulated according to the first result, for example, according to the location and type of abnormal pixels, a corresponding uniformity improvement strategy is formulated, which may be to adjust the pixel driving voltage, calibrate the pixel color or take other correction measures;

[0067] In this embodiment, production strategy refers to the management strategy in the panel production process, such as adjusting production line parameters, optimizing production processes, etc.;

[0068] In this embodiment, the display strategy refers to the display management strategy of the panel in actual use, such as adjusting display settings, optimizing display effects, etc.;

[0069] In this embodiment, standard control parameter data: standard parameter data used for comparative analysis, usually a pre-set reference value or standard value, for example, a standard value of a certain pixel color is set to (100, 150, 200) for comparison with actual test data;

[0070] In this embodiment, the comparative analysis result: the result obtained by comparative analysis of the standard control parameter data and the first result is used to evaluate the difference between the actual data and the standard;

[0071] In this embodiment, the theoretical pixel uniformity limit parameter refers to the limit parameter of the pixel uniformity of the panel under ideal conditions, which is used to set the target value of calibration. It is assumed that under ideal conditions, the pixel uniformity of the panel should reach a certain value range, and this range is the theoretical pixel uniformity limit parameter;

[0072] In this embodiment, the index to be calibrated: the specific index or parameter to be calibrated, which may include indexes such as pixel brightness, color accuracy, and contrast;

[0073] In this embodiment, calibration target value: a calibration target value calculated based on actual data and standard control parameter data, used to guide the calibration process;

[0074] In this embodiment, pixel uniformity data: data obtained by re-evaluating the pixel uniformity of the calibrated panel;

[0075] In this embodiment, feedback data: data obtained after adjusting the production strategy and the display strategy, used to evaluate the effect of the adjustment and provide feedback information.

[0076] Implementation principle and beneficial effects of this embodiment: The present invention can timely discover problems in the production process by performing performance tests and pixel uniformity analysis on multiple batches of products of the same model of panels, and improve the production and display strategies through calibration strategies and calibration methods, thereby improving the pixel uniformity and overall quality of the display panel. The present invention can help panel manufacturers continuously optimize the production process of panels, improve the quality of panel products, and provide users with a better visual experience.

[0077] A pixel uniformity analysis method for a nested pixel display array panel provided by an embodiment of the present invention includes: before obtaining product information of multiple batches of panels of the same model, the method comprises:

[0078] Obtain the user's testing requirements, output user requirement information, and determine the equipment information and production batch information of the panel to be tested based on the user requirement information.

[0079] In this embodiment, detection requirements: user requirements and expectations for panel performance and quality, including brightness uniformity, color accuracy, grayscale performance, etc. For example, the manufacturer requires the brightness uniformity of the panel to reach more than 95%, and the color accuracy to meet the sRGB standard;

[0080] In this embodiment, user demand information: specific requirements and expectations provided by the user regarding panel performance and quality, which may be quantitative indicators or qualitative descriptions;

[0081] In this embodiment, the panel to be tested: the panel samples to be tested are determined according to user requirements, and these panel samples will be used for subsequent performance testing and pixel uniformity analysis;

[0082] In this embodiment, the device information: i.e., the device parameters of the panel to be detected, including the model and parameters of the panel;

[0083] In this embodiment, the production batch information refers to information about the production batch of the panel to be tested, including production date, production parameters, production process, etc. This information helps to track the production process and quality of the panel.

[0084] Implementation principle and beneficial effects of this embodiment: By acquiring the user's detection requirements and requirement information, the present invention can clarify the user's expectations, help determine the device information and production batch information of the panel to be detected, and thus carry out performance testing and pixel uniformity analysis in a targeted manner. The present invention can ensure that the detection process meets the user's requirements and improve the accuracy and effectiveness of the detection.

[0085] An embodiment of the present invention provides a pixel uniformity analysis method for a nested pixel display array panel, comprising: step 1, comprising:

[0086] Based on the equipment information and production batch information, and in combination with the preset sampling strategy, the product batch data of the panel to be tested and the product information under the corresponding batch are determined, and multiple batches of product information are output;

[0087] Based on the information of multiple batches of products, panel samples to be tested are selected from corresponding batches of panel products to obtain panel test samples.

[0088] In this embodiment, the preset sampling strategy: the strategy set in advance for selecting the panel samples to be tested may be random sampling, stratified sampling, etc. For example, according to the production batch information, a stratified sampling strategy is adopted to select 5% of the panels in each production batch as samples for testing;

[0089] In this embodiment, product batch data: data of the product batch to which the panel to be tested belongs, including production date, production parameters, quality records, etc. For example, the production date of the first batch of products is March 2023, and the production parameters include brightness adjustment range, color temperature, etc.;

[0090] In this embodiment, product information: specific product information associated with product batch data, which may include product model, production process, quality index, etc.;

[0091] In this embodiment, panel samples: panel samples for testing are selected from multiple batches of products, representing the quality status of the products of the corresponding batches. For example, 5 panels are selected from the first batch of products as samples to be tested, and these samples represent the quality level of the first batch of products.

[0092] Implementation principle and beneficial effects of this embodiment: The present invention determines the product batch data and panel samples of the panel to be tested based on equipment information, production batch information and preset sampling strategy, which can ensure the representativeness of the test samples and the comprehensiveness of multi-batch product information. The present invention is helpful to conduct comprehensive performance testing and pixel uniformity analysis on the same model of panels, discover differences between production batches, and further improve production strategies and product quality.

[0093] An embodiment of the present invention provides a pixel uniformity analysis method for a nested pixel display array panel, wherein step 2 includes:

[0094] Based on the user demand information, an indicator for performing a performance test on the panel test sample is obtained from a preset indicator pool and output as a preset performance test indicator;

[0095] Based on the preset performance test indicators and in combination with the preset indicator-equipment comparison table, the corresponding preset test equipment is obtained, and the preset test equipment is used to perform performance tests on the panel test samples to obtain test data corresponding to each preset performance test indicator, and the performance test data is summarized.

[0096] In this embodiment, the preset index pool includes various indicators for performance testing of panel test samples, which may cover aspects such as brightness, color accuracy, and contrast;

[0097] In this embodiment, the preset indicator-equipment comparison table: the specific test equipment required for each preset performance test indicator, so that the appropriate test equipment can be selected according to the test indicator, for example, the photometer equipment required for the brightness uniformity test and the color analyzer equipment required for the color accuracy test.

[0098] The implementation principle and beneficial effects of this embodiment: The present invention can ensure comprehensive performance testing of panel test samples by determining preset performance test indicators according to user demand information and selecting corresponding test equipment in combination with the preset indicator-equipment comparison table. The present invention helps to evaluate the performance of the panel under various indicators, discover potential problems and make timely improvements, and ultimately improve the pixel uniformity and overall performance of the panel to meet the needs and expectations of users.

[0099] A pixel uniformity analysis method for a nested pixel display array panel provided by an embodiment of the present invention further includes, before aggregating performance test data:

[0100] A preset preprocessing method is selected in a method database, and test data corresponding to each preset performance test indicator is preprocessed based on the preprocessing method to obtain first data, and the first data satisfying the first preset condition is output as data to be summarized.

[0101] In this embodiment, the method database: a database storing various preprocessing methods, including data smoothing, data standardization, outlier processing and other methods;

[0102] In this embodiment, the preset preprocessing method: a preprocessing method selected from a method database for processing test data to ensure data accuracy and consistency;

[0103] In this embodiment, the first data: data obtained after being processed by the preprocessing method, that is, the processed test data;

[0104] In this embodiment, the first preset condition is a requirement or condition restriction on the first data, and the data satisfying the condition will be selected as the data to be summarized;

[0105] In this embodiment, the data to be aggregated are pre-processed data that meets the first preset condition and is prepared for aggregation of the final performance test data.

[0106] The implementation principle and beneficial effects of this embodiment: The present invention processes the test data through a preprocessing method, which can eliminate noise and outliers in the data and ensure the accuracy and reliability of the data. The present invention can filter out data that meets specific conditions as data to be summarized, which helps to improve the quality and accuracy of performance test data and provide a reliable basis for subsequent data summary and analysis.

[0107] An embodiment of the present invention provides a pixel uniformity analysis method for a nested pixel display array panel, wherein step 3 includes:

[0108] Selecting a data analysis model that meets a preset screening condition from a model database and outputting it as a preset analysis model;

[0109] Perform pixel uniformity analysis on the performance test data based on a preset analysis model and output a first result.

[0110] In this embodiment, the model database: a database storing various data analysis models, including statistical models, machine learning models, deep learning models, etc., specifically, such as linear regression models, support vector machine models, neural network models, etc.;

[0111] In this embodiment, the screening conditions are preset: conditions or standards for screening data analysis models to ensure that the selected model meets actual needs;

[0112] In this embodiment, the data analysis model is a model used to analyze and process performance test data, which can help understand data characteristics, discover patterns or make predictions.

[0113] Implementation principle and beneficial effects of this embodiment: The present invention can more deeply understand the distribution and uniformity performance of panel pixels by selecting an applicable data analysis model and performing pixel uniformity analysis based on the model. The present invention can discover potential pixel deviation or non-uniformity problems and provide directions for further improvement. At the same time, by adopting a suitable data analysis model, the accuracy and efficiency of the analysis can be improved, providing useful support for optimizing panel performance and improving user experience.

[0114] The pixel uniformity analysis method of a nested pixel display array panel provided by an embodiment of the present invention further includes, in step 3:

[0115] Identify the types of performance test data, collect statistics on the type identification results, and output a statistical table of test data types;

[0116] Obtain the user's data analysis needs, output the analysis demand data, extract features from the analysis demand data, and obtain the demand feature set based on the extracted features;

[0117] In combination with the test data type statistical table, a first model matching the required feature set and a second model for comparison with the first model are selected from the model database;

[0118] At the same time, historical performance test data matching the current panel is selected from the historical database in combination with the preset performance test indicators, the data to be divided is output, and the data to be divided is divided based on the preset data division method to obtain a training set, a validation set and a test set for training and optimizing the first model and the second model;

[0119] Training and optimizing the first model and the second model based on the training set, the validation set, and the test set, and outputting the first model and the second model that meet the preset screening conditions as the first analysis model and the second analysis model, respectively;

[0120] At the same time, pixel evaluation indicators matching the data analysis requirements are selected from the indicator database, and an evaluation indicator set is output;

[0121] In combination with the evaluation index set, the performance test data is input into the first analysis model and the second analysis model for pixel analysis, the RGB three primary color pixel data of the panel at each pixel point is obtained, and the first analysis result corresponding to the first analysis model and the second analysis result corresponding to the second analysis model are obtained by summarizing;

[0122] Based on the evaluation index set, the pixel uniformity of each batch of panels in the first analysis result is analyzed by a first analysis model to obtain a first pixel analysis result;

[0123] At the same time, the pixel uniformity of each batch of panels in the second analysis result is analyzed by a second analysis model to obtain a second pixel analysis result;

[0124] The first pixel analysis result and the second pixel analysis result are classified and divided according to the production batch of the panel, and the uniform pixel analysis results of the same model panel in each batch are obtained and output as the first result.

[0125] In this embodiment, category identification: classifying or identifying the performance test data to determine the type or attribute of the data, which is helpful for subsequent data processing and analysis, for example, the performance test data is identified and the data is divided into brightness data, chromaticity data and contrast data;

[0126] In this embodiment, type identification result: the result of identifying the data type or attribute, which is used to guide subsequent analysis and processing;

[0127] In this embodiment, the test data type statistics table: a table of statistical results of various data types in the performance test data, used to understand the composition and distribution of the data, for example, statistics on the proportion of brightness data, chromaticity data and contrast data in the performance test data;

[0128] In this embodiment, data analysis requirements: the user's specific requirements or needs for data analysis, including the information to be understood, the purpose of analysis, etc. For example, the user needs to analyze the pixel uniformity of the panel and understand the RGB three primary color data of each pixel;

[0129] In this embodiment, analysis demand data: a data set for data analysis extracted according to user demand;

[0130] In this embodiment, demand feature set: a feature set extracted from the analysis demand data, used for screening and building a data analysis model;

[0131] In this embodiment, the first model: a model used for data analysis and processing, which may be a machine learning model, a statistical model, etc.;

[0132] In this embodiment, the second model: a backup data analysis model, used to perform a comprehensive analysis of pixel uniformity in combination with the first model, which can improve the accuracy of the analysis result, for example, a support vector machine is selected as the first model and a linear regression model is selected as the second model;

[0133] In this embodiment, historical database: a database storing past performance test data for comparison or reference with current data;

[0134] In this embodiment, historical performance test data: test data on panel performance collected in the past, used for analysis and comparison;

[0135] In this embodiment, the data to be divided: data selected from the historical database, to be divided into training, verification and test data sets;

[0136] In this embodiment, the preset data partitioning method: a pre-set data set partitioning method is used to divide the data to be divided into a training set, a validation set and a test set;

[0137] In this embodiment, the training set, validation set and test set are data sets used to train, validate and test the first model and the second model;

[0138] In this embodiment, the first analysis model: an output model that is obtained after the first model is trained and optimized and satisfies the preset screening conditions;

[0139] In this embodiment, the second analysis model: the output model after the second model is trained and optimized and satisfies the preset screening conditions corresponds to the first analysis model;

[0140] In this embodiment, the index database: a database storing various pixel evaluation indexes for evaluating the performance of panel pixels;

[0141] In this embodiment, pixel evaluation index: an index used to evaluate pixel performance, such as color accuracy, brightness uniformity, etc.;

[0142] In this embodiment, the evaluation index set: a set of indicators selected for evaluating pixel performance;

[0143] In this embodiment, RGB three-primary color pixel data: pixel value data of each pixel on the three basic color channels of red, green and blue, used to describe the color information of each pixel. For example, the RGB three-primary color data of a pixel is (120, 50, 200), which means that the value of the pixel on the red channel is 120, the value on the green channel is 50, and the value on the blue channel is 200;

[0144] In this embodiment, the first analysis result: a pixel analysis result obtained by the first analysis model;

[0145] In this embodiment, the second analysis result: a pixel analysis result obtained by the second analysis model;

[0146] In this embodiment, the first pixel analysis result is: a result obtained by analyzing the pixel uniformity of each batch of panels using a first analysis model;

[0147] In this embodiment, the second pixel analysis result is: a result obtained by analyzing the pixel uniformity of each batch of panels using the second analysis model;

[0148] In this embodiment, classification and division: the process of classifying and dividing panel results of different batches according to specific standards;

[0149] In this embodiment, result consensus refers to a consistent conclusion or consensus obtained by comparing and synthesizing the analysis results obtained from different models;

[0150] In this embodiment, pixel uniformity analysis result: the final analysis result on the uniformity of panel pixels can help improve production quality and product performance.

[0151] Implementation principle and beneficial effects of this embodiment: The present invention can comprehensively analyze the pixel uniformity of the panel and improve production quality and product performance through steps such as category identification, model selection, data division, training optimization and pixel analysis. The present invention combines evaluation indicators and historical performance test data to more accurately evaluate the performance of the panel. The final pixel uniformity analysis results can help manufacturers better understand the changing trend of panel performance, optimize production processes, improve product quality, and enhance user experience.

[0152] An embodiment of the present invention provides a pixel uniformity analysis method for a nested pixel display array panel, and an algorithm for determining a calibration target value under corresponding indicators, including:

[0153] ;

[0154] in, represents the calibration target value corresponding to the i-th indicator; It represents the complex correlation coefficient between the ith indicator and the other n-1 indicators among all indicators to be calibrated, with a total number of n; represents the real-time analysis data value corresponding to the i-th indicator in the first result; represents the standard control parameter value corresponding to the i-th indicator at the panel production stage; represents the standard control parameter value corresponding to the ith indicator during the panel usage phase; represents the adjustable space margin value corresponding to the i-th indicator at the panel production stage; represents the adjustable space margin value corresponding to the ith indicator during the panel usage phase; represents the calibration weight coefficient corresponding to the i-th indicator under the panel production stage; Represents the calibration weight coefficient corresponding to the ith indicator under the panel usage stage.

[0155] Implementation principle and beneficial effects of this embodiment: The present invention can improve the pixel uniformity of the panel and ensure that the produced panel meets the standard requirements by calculating the calibration target value, selecting the calibration strategy and method, and adjusting the production strategy and display strategy. At the same time, by collecting and analyzing feedback data, the calibration process can be continuously optimized, the production process can be improved, and the product quality and performance can be improved. The present invention can help manufacturers better control and optimize the panel production process, thereby improving product competitiveness and user satisfaction.

[0156] 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 pixel uniformity analysis method for a nested pixel display array panel, characterized in that: include: Step 1: obtaining product information of multiple batches of panels of the same model, and selecting panel test samples corresponding to the product information of the multiple batches; Step 2: Performing a performance test on the panel test sample in combination with preset performance test indicators and using preset test equipment, and outputting performance test data; Step 3: Perform pixel uniformity analysis on the performance test data using a preset analysis model, and output a first result; Step 4: Acquire a calibration strategy and calibration method matching the first result in combination with a preset result-strategy-method mapping table, and improve and adjust the production strategy and display strategy of the corresponding panel based on the calibration strategy and calibration method; Wherein, step 3 includes: Selecting a data analysis model that meets a preset screening condition from a model database and outputting it as a preset analysis model; Performing pixel uniformity analysis on the performance test data based on the preset analysis model, and outputting a first result; Wherein, step 3 also includes: Identify the types of the performance test data, collect statistics on the type identification results, and output a test data type statistics table; Obtaining the user's data analysis needs, outputting analysis demand data, and performing feature extraction on the analysis demand data, and obtaining a demand feature set based on the extracted features; In combination with the test data type statistical table, a first model matching the required feature set and a second model for comparison with the first model are selected from the model database; At the same time, historical performance test data matching the current panel is selected from the historical database in combination with the preset performance test index, the data to be divided is output, and the data to be divided is divided based on the preset data division method to obtain a training set, a validation set and a test set for training and optimizing the first model and the second model; Training and optimizing the first model and the second model based on the training set, the validation set and the test set, and outputting the first model and the second model that meet the preset screening conditions as the first analysis model and the second analysis model respectively; At the same time, pixel evaluation indicators matching the data analysis requirements are selected from the indicator database, and an evaluation indicator set is output; In combination with the evaluation index set, the performance test data is input into the first analysis model and the second analysis model respectively for pixel analysis, the RGB three primary color pixel data of the panel at each pixel point is obtained, and a first analysis result corresponding to the first analysis model and a second analysis result corresponding to the second analysis model are obtained by summarizing; Based on the evaluation index set, the pixel uniformity of each batch of panels in the first analysis result is analyzed by a first analysis model to obtain a first pixel analysis result; At the same time, the pixel uniformity of each batch of panels in the second analysis result is analyzed by a second analysis model to obtain a second pixel analysis result; Classifying and dividing the first pixel analysis result and the second pixel analysis result according to the production batches of the panels and reaching consensus on the results, obtaining uniform pixel analysis results of the same model of panels in each batch, and outputting them as the first results; Preset result-strategy-method mapping table: a table or database containing the mapping relationship between the first result and the calibration strategy and calibration method, used to obtain the corresponding calibration strategy and calibration method according to the input first result; Calibration strategies and calibration methods: methods and strategies for improving and adjusting the production strategy and display strategy of the panel based on the first results; Standard control parameter data: standard parameter data used for comparative analysis, usually pre-set reference values ​​or standard values; Theoretical pixel uniformity limit parameter: refers to the limit parameter of the pixel uniformity of the panel under ideal conditions, which is used to set the target value for calibration; Calibration target value: The calibration target value calculated based on actual data and standard reference parameter data, used to guide the calibration process; First pixel analysis result: the result obtained by analyzing the pixel uniformity of each batch of panels using the first analysis model; Second pixel analysis result: the result obtained by analyzing the pixel uniformity of each batch of panels using the second analysis model; Pixel uniformity analysis results: The final analysis results of panel pixel uniformity can help improve production quality and product performance; Wherein, step 4 includes: Acquire standard control parameter data matching the first result, and compare and analyze the standard control parameter data with the first result, and output the comparison and analysis result; Obtain theoretical pixel uniformity limit parameters of the current panel, determine the index to be calibrated, and determine the calibration target value under the corresponding index; Based on the calibration target value under each indicator, the calibration strategy and calibration method corresponding to the comparative analysis result are obtained in the preset result-strategy-method mapping table; At the same time, the production strategy and display strategy of the current model panel are obtained, and the production strategy and display strategy of the panel are adjusted and improved based on the calibration strategy and calibration method. At the same time, the pixel uniformity data of the panel after the production strategy and display strategy are adjusted and improved is obtained, and compared with the calibration target value and the theoretical pixel uniformity limit parameter to obtain feedback data; Wherein, the algorithm for determining the calibration target value under the corresponding indicator includes: ； in, represents the calibration target value corresponding to the i-th indicator; It represents the complex correlation coefficient between the ith indicator and the other n-1 indicators among all indicators to be calibrated, with a total number of n; represents the real-time analysis data value corresponding to the i-th indicator in the first result; represents the standard control parameter value corresponding to the i-th indicator at the panel production stage; represents the standard control parameter value corresponding to the ith indicator during the panel usage phase; represents the adjustable space margin value corresponding to the i-th indicator at the panel production stage; represents the adjustable space margin value corresponding to the ith indicator during the panel usage phase; represents the calibration weight coefficient corresponding to the i-th indicator under the panel production stage; Represents the calibration weight coefficient corresponding to the ith indicator under the panel usage stage.

2. The pixel uniformity analysis method for a nested pixel display array panel according to claim 1, characterized in that: Before obtaining product information of multiple batches of panels of the same model, the following steps are included: The detection requirements of the user are obtained, the user requirement information is output, and the equipment information and production batch information of the panel to be detected are determined based on the user requirement information.

3. The pixel uniformity analysis method for a nested pixel display array panel according to claim 2, characterized in that: Step 1 includes: Based on the equipment information and production batch information, and in combination with a preset sampling strategy, determine product batch data of the panel to be tested and product information under the corresponding batch, and output multiple batches of product information; Based on the information of the multiple batches of products, panel samples to be tested are selected from the corresponding batches of panel products to obtain panel test samples.

4. The pixel uniformity analysis method for a nested pixel display array panel according to claim 2, characterized in that: Step 2 includes: Based on the user demand information, an indicator for performing a performance test on the panel test sample is obtained from a preset indicator pool, and outputted as a preset performance test indicator; Based on the preset performance test indicators and in combination with the preset indicator-equipment comparison table, the corresponding preset test equipment is obtained, and the preset test equipment is used to perform performance testing on the panel test sample to obtain test data corresponding to each of the preset performance test indicators, and the performance test data is summarized.

5. The pixel uniformity analysis method for a nested pixel display array panel according to claim 4, characterized in that: Before the performance test data is obtained by aggregating, the following steps are also included: A preset preprocessing method is selected in a method database, and test data corresponding to each preset performance test indicator is preprocessed based on the preprocessing method to obtain first data, and the first data that meets the first preset condition is output as data to be summarized.

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