Bending test method, device and equipment of flexible OLED screen and storage medium

Abstract

The invention relates to a bending test method, system and device for a flexible OLED screen and a storage medium, and the method comprises the following steps: obtaining preset bending data of a target flexible OLED screen, and carrying out the dynamic bending test of the target flexible OLED screen based on the preset bending data; in the dynamic bending test process, performing non-contact detection on the target flexible OLED screen to obtain a detection image; judging whether a damage area exists in the detection image or not, and if yes, inputting the detection image into a preset damage model to determine the damage level of the target flexible OLED screen; and if the damage level exceeds a preset damage level, performing damage analysis on the damage area to obtain a damage analysis result, and formulating an OLED screen production management strategy based on the damage area and the damage analysis result. The technical problems that the damage risk in the testing process is increased, and the testing efficiency and accuracy are limited are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of OLED screens, and in particular to a bending test method, system, device and storage medium for a flexible OLED screen. Background Art

[0002] As electronic devices develop towards being thinner and more portable, flexible OLED screens have received widespread attention due to their unique flexibility and display effects. This type of screen can be bent, folded, and even curled, which greatly expands the design space of electronic products and meets consumers' demand for innovative electronic products. However, in the actual application of flexible OLED screens, bending durability has become one of the key factors restricting its development. The screen is prone to physical damage such as material fatigue and interlayer peeling during repeated bending. These problems not only affect the display effect of the screen, but also shorten the life of the screen. In severe cases, it may even cause the screen to fail, which undoubtedly brings challenges to the application of flexible OLED screens.

[0003] At present, the bending test method for flexible OLED screens mainly focuses on static bending tests, that is, evaluating the durability of the screen by setting a specific bending radius and number of times. Although this method can reflect the physical properties of the screen to a certain extent, due to the lack of real-time monitoring of the screen state changes during the bending process, it is impossible to accurately capture the specific location and degree of screen damage, resulting in a large deviation between the test results and actual usage. In addition, traditional bending tests often require contact measurement of the screen, which not only increases the risk of damage during the test process, but also limits the test efficiency and accuracy. Therefore, it is particularly important to develop a new test method that can effectively monitor screen damage and evaluate screen performance.

[0004] To solve the above problems, a dynamic bending test method for flexible OLED screens based on non-contact detection is proposed. This method can not only obtain the status information of the screen in real time during the bending process, accurately identify the location and degree of screen damage through image processing technology, but also use a pre-trained damage model to evaluate the damage level of the screen. Once it is found that the screen damage exceeds the preset standard, timely measures can be taken, such as adjusting the production process parameters or optimizing the screen structure design, thereby improving the quality and service life of the flexible OLED screen and promoting the further development of flexible display technology. Summary of the invention

[0005] The main purpose of the present invention is to provide a bending test method, system, equipment and storage medium for a flexible OLED screen, which solves the technical problem that traditional bending tests often require contact measurement of the screen, which not only increases the risk of damage during the test, but also limits the test efficiency and accuracy.

[0006] To achieve the above object, the present invention provides a bending test method for a flexible OLED screen, comprising the following steps: Acquire preset bending data of a target flexible OLED screen, and dynamically bend and test the target flexible OLED screen based on the preset bending data; During the dynamic bending test, non-contact detection is performed on the target flexible OLED screen to obtain a detection image; Determine whether there is a damaged area in the detection image, and if so, input the detection image into a preset damage model to determine the damage level of the target flexible OLED screen; If the damage level exceeds a preset damage level, a damage analysis is performed on the damaged area to obtain a damage analysis result, and a production OLED screen management strategy is formulated based on the damaged area and the damage analysis result.

[0007] Further, obtaining preset bending data of a target flexible OLED screen, and dynamically bending the target flexible OLED screen based on the preset bending data, comprises: Obtaining a model of a target flexible OLED screen, and obtaining preset bending data and basic physical parameters of the target flexible OLED screen from a preset database based on the model; wherein the preset bending data includes a bending direction and a bending path; The preset bending data is converted into a control instruction set, and the target flexible OLED screen is dynamically bent in multi-axis directions based on the preset bending data.

[0008] Furthermore, during the dynamic bending test, the target flexible OLED screen is subjected to non-contact detection to obtain a detection image; wherein the detection image is an image of microstructure changes of the internal material of the target flexible OLED screen, including: During the multi-axial dynamic bending test, the target flexible OLED screen is penetrated by terahertz time-domain spectroscopy technology to obtain an internal material image; Performing three-dimensional image reconstruction based on the internal material image to obtain a three-dimensional image of the internal material; Performing non-contact imaging on the target flexible OLED screen to obtain a surface topography image; Registering the internal material three-dimensional image with the surface topography image to form a registered detection image; Performing frequency domain analysis on the registered detection image to obtain an internal material microstructure image; The phase demodulation technology is used to detect the slight changes of the material in the internal material microstructure image to obtain a detection image.

[0009] Further, the determining whether there is a damaged area in the detection image, and if so, inputting the detection image into a preset damage model to determine the damage level of the target flexible OLED screen, includes: Performing pixel-level analysis on the detection image using a preset Gaussian mixture algorithm to determine whether there is a damaged area in the detection image; If it exists, binarization is performed on the damaged area to obtain a damaged area mask; Performing morphological operations on the damaged area based on the damaged area mask to obtain a damaged area boundary; Using a preset region growing algorithm, the damaged region boundary is positioned and calculated to obtain the damaged region boundary carrying coordinates; Based on the damaged area boundary carrying the coordinates, segmenting the damaged area from the detection image to obtain a segmented damaged area; Inputting the segmented damage area into a preset damage model to extract damage features to obtain damage extraction features; wherein the damage extraction features include damage depth and damage range; A multi-criteria decision analysis is performed on the damage extraction features to obtain the damage level of the target flexible OLED screen.

[0010] Furthermore, the use of a preset region growing algorithm to locate and calculate the damaged region boundary to obtain the damaged region boundary carrying coordinates includes: Determine the initial seed points of the damage area mask to obtain an initial seed point set; Using a preset region growing algorithm, the growing region is gradually expanded based on the initial seed point set until a preliminary damage region boundary is encountered and the preliminary damage region boundary is obtained; Performing boundary smoothing processing on the preliminary damaged area boundary to remove burrs and small protrusions on the preliminary damaged area boundary to obtain a smooth damaged area boundary; Performing sub-pixel positioning on the boundary of the smooth damaged area to obtain a positioned boundary of the damaged area; The located damage area boundary is converted into coordinate form to generate a damage area boundary carrying coordinates.

[0011] Furthermore, the damage extraction feature is subjected to a multi-criteria decision analysis to obtain the damage level of the target flexible OLED screen, including: Based on the damaged area boundary carrying the coordinates, obtaining the damaged area position; Performing importance assessment on the position of the damaged area to obtain an importance assessment result; Based on the importance evaluation result, the damage area is named according to its importance to obtain a damage area label; wherein the damage area label includes a key damage area, an important damage area and a general damage area; Constructing a decision matrix based on the damage feature data, with the damage area labels as rows and the damage extraction features as columns; The entropy weight method is used to evaluate the importance of the damage extraction features in the decision matrix to obtain corresponding weight values; Determine a positive ideal solution and a negative ideal solution in the decision matrix based on the weight value; wherein the positive ideal solution is the maximum weight of each damage extraction feature, and the negative ideal solution is the minimum weight of each damage extraction feature; Calculate the distance between the positive ideal solution and the negative ideal solution of each of the damaged areas based on the positive ideal solution and the negative ideal solution; Calculating the relative proximity of the damaged area based on the distance between the positive ideal solution and the negative ideal solution of the damaged area; Comparing the relative proximity of each of the damage extraction features with a preset damage level standard to determine the damage level corresponding to each of the damage extraction features; The damage level of the target flexible OLED screen is obtained based on the damage level corresponding to each of the damage extraction features.

[0012] Furthermore, a damage analysis is performed on the damaged area to obtain a damage analysis result, and a production OLED screen management strategy is formulated based on the damaged area and the damage analysis result, including: Searching for a damaged component in the internal image of the damaged area in the detection image, and performing image segmentation on the internal image of the damaged area based on the damaged component to obtain an internal damage map corresponding to the damaged component; Performing mechanical analysis on the internal damage map corresponding to the damaged component to obtain a stress distribution map of the damaged component; Performing damage type analysis on the stress distribution diagram of the damaged component to obtain a damage type analysis result, and determining a damage factor based on the damage type analysis result; A production management strategy for OLED screens is formulated based on the damage type analysis results, damage factors and stress distribution diagram of the damaged components.

[0013] The present invention also provides a bending test system for a flexible OLED screen, comprising: An acquisition module, used for acquiring preset bending data of a target flexible OLED screen, and dynamically bending and testing the target flexible OLED screen based on the preset bending data; A detection module, used to perform non-contact detection on the target flexible OLED screen during the dynamic bending test to obtain a detection image; A judgment module, used to judge whether there is a damaged area in the detection image, and if so, input the detection image into a preset damage model to determine the damage level of the target flexible OLED screen; The repair module is used to perform damage analysis on the damaged area if the damage level exceeds the preset damage level, obtain damage analysis results, and formulate a production OLED screen management strategy based on the damaged area and damage analysis results.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0016] The bending test method of the flexible OLED screen provided by the present invention comprises the following steps: obtaining preset bending data of the target flexible OLED screen, and dynamically bending and testing the target flexible OLED screen based on the preset bending data; in the process of dynamic bending test, performing non-contact detection on the target flexible OLED screen to obtain a detection image; judging whether there is a damaged area in the detection image, if so, inputting the detection image into a preset damage model to determine the damage level of the target flexible OLED screen; if the damage level exceeds the preset damage level, performing damage analysis on the damaged area to obtain a damage analysis result, and formulating a production OLED screen management strategy based on the damaged area and the damage analysis result. Through the above technical means, the technical problem that the traditional bending test often requires contact measurement of the screen is solved, which not only increases the risk of damage during the test process, but also limits the test efficiency and accuracy. It is realized that based on the damaged area and its analysis results, the enterprise can adjust the production process parameters or optimize the product design in a targeted manner to improve the production quality and service life of the flexible OLED screen. At the same time, for the screen that has been damaged, the method can help formulate an effective repair plan, reduce maintenance costs, and improve customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the steps of a bending test method for a flexible OLED screen in one embodiment of the present invention; Figure 2 It is a structural block diagram of a bending test system for a flexible OLED screen in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a bending test method for a flexible OLED screen in one embodiment of the present invention; In one embodiment of the present invention, a bending test method for a flexible OLED screen is provided, comprising the following steps: Step S1, obtaining preset bending data of a target flexible OLED screen, and dynamically bending and testing the target flexible OLED screen based on the preset bending data.

[0021] Specifically, obtaining the preset bending data of the target flexible OLED screen and dynamically bending and testing the target flexible OLED screen based on the preset bending data is one of the key steps of the test method. First, it is necessary to collect or formulate the preset bending data of the target flexible OLED screen, which generally includes but is not limited to the maximum allowable bending radius, minimum bending radius, number of bends, and speed of each bend. These preset bending data are determined according to the design specifications and expected use environment of the flexible OLED screen. For example, if a flexible OLED screen is designed for foldable mobile phones, then its preset bending data needs to take into account the various bending conditions encountered by the mobile phone in daily use by users, such as slight bending when held in one hand, large bending when folded and stored, etc. Next, based on these preset bending data, the tester will use special test equipment to perform dynamic bending tests on the target flexible OLED screen. In this process, the test equipment will simulate different bending scenarios and repeatedly bend the screen according to the preset data parameters to test the performance of the screen under various bending conditions to ensure that it can withstand the mechanical stress encountered in actual use without damage. For example, for a flexible OLED screen designed for foldable phones, the test process will simulate the situation of users frequently opening and closing the phone screen, and by setting a certain bending frequency and number, verify whether the screen can maintain good display performance and structural stability under long-term repeated bending. Such tests can not only help manufacturers evaluate the durability and reliability of flexible OLED screens, but also provide valuable data support for subsequent product optimization.

[0022] Step S2: During the dynamic bending test, non-contact detection is performed on the target flexible OLED screen to obtain a detection image.

[0023] Specifically, during the dynamic bending test, the target flexible OLED screen is non-contactly tested to obtain a test image. This step is to ensure that the state change of the screen can be monitored in real time while the screen is bent, and any damage that occurs can be discovered in time. Specifically, when the target flexible OLED screen is subjected to a dynamic bending test according to the preset bending data, a non-contact detection device, such as an optical imaging system or an infrared scanner, is used to continuously scan the screen surface. These detection devices can capture high-resolution images of the screen in different bending states without directly contacting the screen, thereby avoiding additional damage to the screen caused by traditional contact detection methods. For example, when testing a flexible OLED screen designed for foldable mobile phones, images of the screen during the opening and closing process can be captured in real time through multi-angle cameras installed around the test equipment. These images will be transmitted to the back-end image processing system, and the algorithm will be used to analyze whether there are abnormal phenomena such as cracks, color difference, uneven brightness, etc. on the screen surface. Once an abnormality is found, the system will automatically mark the damaged area and generate a detailed test report. In this way, testers can timely understand the actual performance of the screen during the dynamic bending process, and then evaluate the durability and reliability of the screen, providing an important reference for subsequent product design and manufacturing. In addition, the non-contact detection method not only improves the accuracy and efficiency of the test, but also greatly reduces the test cost, making the quality control of flexible OLED screens more efficient and economical.

[0024] Step S3, determining whether there is a damaged area in the detection image, and if so, inputting the detection image into a preset damage model to determine the damage level of the target flexible OLED screen.

[0025] Specifically, it is determined whether there is a damaged area in the test image. If so, the test image is input into the preset damage model to determine the damage level of the target flexible OLED screen. This is a crucial step in the entire test process. After completing the non-contact detection of the target flexible OLED screen and obtaining the test image, the next step is to analyze these images through image processing technology to determine whether there is a damaged area on the screen. This process usually involves the use of advanced image recognition algorithms, such as deep learning models, to automatically detect abnormal features in the image. For example, when testing a flexible OLED screen designed for foldable mobile phones, the system will use a pre-trained neural network model to automatically identify signs of damage such as cracks, pixel distortion, and uneven brightness that appear on the screen during the bending process. If the system detects the presence of these abnormal areas in the image, it will automatically input these test images into the preset damage model. The preset damage model is a model that has been trained with a large number of samples and can automatically evaluate and determine the damage level of the screen according to the type, scope and severity of the damage. The classification of damage levels can be based on international standards or industry specifications, or it can be set according to the quality control standards within the enterprise. For example, the damage level can be divided into three levels: slight damage, moderate damage and severe damage. The definition of each level clearly points out the specific manifestation of the damage and the maximum allowable range. In this way, testers can quickly obtain detailed evaluation results of screen damage, providing a scientific basis for subsequent production management and quality control. In addition, this process not only improves the accuracy and efficiency of detection, but also reduces the subjective error of human judgment, ensuring the consistency and reliability of test results.

[0026] Step S4: if the damage level exceeds a preset damage level, a damage analysis is performed on the damaged area to obtain a damage analysis result, and a production OLED screen management strategy is formulated based on the damaged area and the damage analysis result.

[0027] Specifically, if the damage level exceeds the preset damage level, the damage area is subjected to damage analysis to obtain damage analysis results, and a production OLED screen management strategy is formulated based on the damage area and the damage analysis results. This process is intended to ensure the reliability and durability of the flexible OLED screen in actual use. When the image acquired by non-contact detection is evaluated by a preset damage model, if it is found that the damage level exceeds the preset standard, the system will automatically trigger a further damage analysis procedure. For example, when testing a flexible OLED screen designed for foldable mobile phones, if it is detected that the screen has obvious cracks or pixel distortion in a certain area, and these damages meet the preset moderate or severe damage standards, the system will immediately perform a detailed analysis of the damaged area. This analysis usually includes magnified observation of the damaged area, precise measurement of the damaged edge, and evaluation of the impact of the damage on the overall performance of the screen. Through these detailed analyses, testers can obtain comprehensive information about the nature, scope and severity of the damage. For example, if fine cracks are detected at the bend of the screen, the analysis results show that these cracks are mainly concentrated in a specific area of ​​the screen, and the length and width of the cracks are within a certain range. Based on these analysis results, the impact of these damages on the screen display effect can be further evaluated, such as whether it will cause local brightness reduction or color deviation. Once the specific situation of the damage is clarified, the corresponding production OLED screen management strategy can be formulated. These strategies include adjusting the material formula used in the production process, optimizing the structural design of the screen, or improving the method and parameter setting of the bending test to reduce the probability of similar damage. For example, if the analysis results show that the screen is prone to cracks at a specific bending radius, the production team can consider increasing the flexibility of the screen or adding a protective layer to the bending part of the screen to improve its damage resistance. In addition, these damage analysis results can also be used to guide after-sales service and customer support. For example, if a batch of flexible OLED screens frequently have the same damage problem during use, the company can provide targeted maintenance suggestions based on the analysis results, or optimize the screen's display algorithm through software updates to reduce the impact of damage on user experience. In short, through in-depth analysis of the damaged area and the formulation of reasonable management strategies, not only can the quality and service life of the flexible OLED screen be improved, but also user satisfaction and market competitiveness can be enhanced.

[0028] In a specific embodiment, obtaining preset bending data of a target flexible OLED screen, and dynamically bending the target flexible OLED screen based on the preset bending data, includes: Obtaining a model of a target flexible OLED screen, and obtaining preset bending data and basic physical parameters of the target flexible OLED screen from a preset database based on the model; wherein the preset bending data includes a bending direction and a bending path; The preset bending data is converted into a control instruction set, and the target flexible OLED screen is dynamically bent in multi-axis directions based on the preset bending data.

[0029] Specifically, the preset bending data of the target flexible OLED screen is obtained, and the target flexible OLED screen is dynamically bent based on the preset bending data. This process is a key link to ensure the accuracy and reliability of the test results. Specifically, this process includes two main steps: first, the model of the target flexible OLED screen is obtained, and the preset bending data and basic physical parameters of the target flexible OLED screen are obtained from a preset database based on the model; second, the preset bending data is converted into a control instruction set, and the target flexible OLED screen is dynamically bent in multiple axes based on the preset bending data. These two steps are closely linked and together constitute a complete dynamic bending test process. First, obtaining the model of the target flexible OLED screen is the first step in the entire test process. Each flexible OLED screen has its own unique design and specifications, so it is crucial to accurately obtain the model information of the screen. For example, suppose a flexible OLED screen designed for foldable mobile phones is being tested, and the model of this screen is "FlexiScreen-123". After obtaining the model information, the tester needs to retrieve the preset bending data and basic physical parameters corresponding to the model from the preset database. The preset database usually contains detailed information of various models of flexible OLED screens, such as the maximum allowable bending radius, the minimum bending radius, the number of bends, the bending speed, etc. These data are determined according to the design specifications of the screen and the expected use environment. For example, for the "FlexiScreen-123" model screen, the preset bending data includes: the maximum allowable bending radius is 3 mm, the minimum bending radius is 1 mm, the number of bends is 100,000 times, and the bending speed is 1 time per second. In addition, the basic physical parameters include the thickness, material, elastic modulus, etc. of the screen, which are very important for understanding the mechanical properties of the screen and predicting its performance during the bending process. After obtaining the preset bending data and basic physical parameters, the next step is to convert these data into a control instruction set. The control instruction set is a command sequence used to instruct the test equipment on how to perform a specific bending action. For example, assume that the bending direction in the preset bending data is from left to right, and the bending path is an arc with a radius of 3 mm. The tester needs to convert these data into specific control instructions, such as "start left-to-right bending, set the bending radius to 3 mm, the bending speed to 1 time per second, and perform 100,000 bends". These control instructions will be compiled into codes that the test equipment can understand and execute, ensuring that the test equipment can accurately perform dynamic bending tests according to the preset bending data. Finally, the target flexible OLED screen is dynamically bent in multiple axes based on the preset bending data. Multi-axis dynamic bending means that the test equipment can bend the screen in multiple directions to simulate various bending conditions encountered by the screen in actual use. For example, for the "FlexiScreen-123" model screen, the test equipment will first bend from left to right, then from top to bottom, and finally bend in the diagonal direction.Each direction of bending will be strictly carried out according to the preset bending data to ensure the accuracy and consistency of the test. In this way, the performance of the screen in different bending directions and paths can be comprehensively evaluated to ensure that it can withstand various mechanical stresses without damage in actual use. For example, suppose a "FlexiScreen-123" model flexible OLED screen designed for foldable mobile phones is being tested. First, the tester obtains the preset bending data of the model screen from the preset database, including the maximum allowable bending radius of 3 mm, the minimum bending radius of 1 mm, the number of bends of 100,000 times, the bending speed of 1 time per second, and basic physical parameters such as the screen thickness of 0.1 mm and the material of polyimide. Then, these data are converted into a control instruction set, such as "start left-to-right bending, set the bending radius to 3 mm, the bending speed to 1 time per second, and perform 100,000 bends". After receiving these instructions, the test equipment will dynamically bend the screen in multiple directions, including from left to right, from top to bottom, and diagonally. Through this multi-axis dynamic bending test, the performance of the screen in different bending directions and paths can be comprehensively evaluated to ensure that it can withstand various mechanical stresses without damage in actual use. Ultimately, the test results will provide an important reference for production management and quality control, helping manufacturers optimize production processes and improve the quality and service life of flexible OLED screens.

[0030] In a specific embodiment, during the dynamic bending test, the target flexible OLED screen is subjected to non-contact detection to obtain a detection image; wherein the detection image is an image of microscopic structural changes of the internal material of the target flexible OLED screen, including: During the multi-axial dynamic bending test, the target flexible OLED screen is penetrated by terahertz time-domain spectroscopy technology to obtain an internal material image; Performing three-dimensional image reconstruction based on the internal material image to obtain a three-dimensional image of the internal material; Performing non-contact imaging on the target flexible OLED screen to obtain a surface topography image; Registering the internal material three-dimensional image with the surface topography image to form a registered detection image; Performing frequency domain analysis on the registered detection image to obtain an internal material microstructure image; The phase demodulation technology is used to detect the slight changes of the material in the internal material microstructure image to obtain a detection image.

[0031] Specifically, during the dynamic bending test, the target flexible OLED screen is non-contactly detected to obtain a detection image. This process is a key step to ensure the accuracy and reliability of the test results. Specifically, this process includes multiple links: first, during the multi-axial dynamic bending test, the target flexible OLED screen is penetrated by terahertz time-domain spectroscopy technology to obtain an internal material image; then, a three-dimensional image is reconstructed based on the internal material image to obtain a three-dimensional image of the internal material; then, the target flexible OLED screen is non-contactly imaged to obtain a surface morphology image; then, the internal material three-dimensional image is registered with the surface morphology image to form a registered detection image; then, the registered detection image is analyzed in the frequency domain to obtain an internal material microstructure image; finally, the internal material microstructure image is detected for subtle material changes using phase demodulation technology to obtain a detection image. These steps together constitute a comprehensive and detailed non-contact detection process. First, during the multi-axial dynamic bending test, the target flexible OLED screen is penetrated by terahertz time-domain spectroscopy technology to obtain an internal material image. Terahertz time-domain spectroscopy is a high-resolution non-destructive detection technology that can penetrate the multi-layer structure of a flexible OLED screen and obtain detailed information about its internal materials. For example, suppose a flexible OLED screen designed for a foldable mobile phone is being tested. When the screen is dynamically bent in multiple axes, the terahertz time-domain spectrometer will emit terahertz waves, penetrate different layers of the screen, and detect changes in its internal materials. These changes include changes in material density, interlayer separation, tiny cracks, etc. Through this penetration detection, images of the internal materials of the screen in different bending states can be obtained, providing basic data for subsequent analysis. Next, a three-dimensional image reconstruction is performed based on the internal material image to obtain a three-dimensional image of the internal material. Three-dimensional image reconstruction is the process of converting a two-dimensional internal material image into a three-dimensional image in order to more intuitively display the structural changes inside the screen. This process usually uses computer vision and image processing technology to fuse images of internal materials from multiple perspectives to generate a complete three-dimensional image. For example, through multi-angle terahertz time-domain spectroscopy detection, the changes in the internal materials of the screen in different directions can be obtained, and then these two-dimensional images are synthesized into a three-dimensional image using a three-dimensional reconstruction algorithm. This three-dimensional image can clearly show the distribution and changes of the internal materials of the screen, helping testers to more accurately assess the internal damage of the screen. Subsequently, the target flexible OLED screen is non-contact imaged to obtain a surface morphology image. Non-contact imaging technology usually includes optical imaging, laser scanning and other methods, which can obtain high-resolution surface morphology images of the screen without contacting the screen surface. For example, using a high-resolution optical microscope or laser scanner, subtle changes on the screen surface, such as cracks, scratches, color aberration, etc., can be captured.These surface morphology images can not only reflect the physical state of the screen surface, but also be compared with the internal material image to help testers understand the overall condition of the screen more comprehensively. Then, the internal material three-dimensional image is registered with the surface morphology image to form a registered detection image. Registration is the process of aligning images from different sources so that they can be compared in the same coordinate system. Through registration, the internal material three-dimensional image and the surface morphology image can be accurately aligned to ensure the correspondence between the two. For example, using an image registration algorithm, a specific area in the internal material three-dimensional image can be aligned with the corresponding area in the surface morphology image to generate a registered detection image that integrates internal and external information. This registered detection image can more comprehensively show the overall changes of the screen during the bending process, helping testers to more accurately evaluate the damage of the screen. Next, the registered detection image is subjected to frequency domain analysis to obtain an internal material microstructure image. Frequency domain analysis is to convert the image from the spatial domain to the frequency domain in order to better extract the high-frequency and low-frequency components in the image. Through frequency domain analysis, subtle changes in the image, such as tiny cracks in the material, density changes, etc., can be more clearly identified. For example, using frequency domain analysis techniques such as Fourier transform, the registration detection image can be converted to the frequency domain, the high-frequency components of the internal material can be extracted, and the internal material microstructure image can be generated. These microstructure images can show the subtle changes in the internal materials of the screen in more detail, helping testers to more accurately assess the degree of damage to the screen. Finally, the internal material microstructure image is subjected to material subtle changes detection using phase demodulation technology to obtain a detection image. Phase demodulation technology is a highly sensitive signal processing technology that can detect slight phase changes in materials, thereby revealing subtle changes in materials. For example, through phase demodulation technology, phase changes in the internal material microstructure image can be detected, and subtle damage such as tiny cracks and density changes in the material can be identified. These subtle damages are difficult to detect in conventional image analysis, but through phase demodulation technology, they can be clearly presented to generate the final detection image. These detection images can not only help testers more accurately assess the damage of the screen, but also provide important reference for subsequent production management and quality control. In summary, through the above series of non-contact detection steps, the damage of the flexible OLED screen during the dynamic bending test can be comprehensively and meticulously evaluated. These steps not only improve the accuracy and reliability of detection, but also provide a scientific basis for optimizing production processes and improving product quality. For example, for flexible OLED screens designed for foldable phones, this comprehensive non-contact detection can promptly detect minor damage to the screen during the bending process, so that corresponding measures can be taken, such as optimizing screen materials and improving production processes, to ensure the quality and reliability of the final product.

[0032] In a specific embodiment, the determining whether there is a damaged area in the detection image, and if so, inputting the detection image into a preset damage model to determine the damage level of the target flexible OLED screen, includes: Performing pixel-level analysis on the detection image using a preset Gaussian mixture algorithm to determine whether there is a damaged area in the detection image; If it exists, binarization is performed on the damaged area to obtain a damaged area mask; Performing morphological operations on the damaged area based on the damaged area mask to obtain a damaged area boundary; Using a preset region growing algorithm, the damaged region boundary is positioned and calculated to obtain the damaged region boundary carrying coordinates; Based on the damaged area boundary carrying the coordinates, segmenting the damaged area from the detection image to obtain a segmented damaged area; Inputting the segmented damage area into a preset damage model to extract damage features to obtain damage extraction features; wherein the damage extraction features include damage depth and damage range; A multi-criteria decision analysis is performed on the damage extraction features to obtain the damage level of the target flexible OLED screen.

[0033] Specifically, the determination of whether there is a damaged area in the detection image, if so, inputting the detection image into a preset damage model to determine the damage level of the target flexible OLED screen, this process is a key step to ensure the accuracy and reliability of the test results. Specifically, this process includes multiple links: first, the detection image is analyzed at the pixel level by a preset Gaussian mixture algorithm to determine whether there is a damaged area in the detection image; second, if there is a damaged area, the damaged area is binarized to obtain a damaged area mask; then, a morphological operation is performed on the damaged area based on the damaged area mask to obtain a damaged area boundary; then, the damaged area boundary is located and calculated using a preset region growing algorithm to obtain a damaged area boundary carrying coordinates; then, based on the damaged area boundary carrying coordinates, the damaged area is segmented from the detection image to obtain a segmented damaged area; finally, the segmented damaged area is input into a preset damage model to extract damage features to obtain damage extraction features, wherein the damage extraction features include damage depth and damage range; a multi-criteria decision analysis is performed on the damage extraction features to obtain the damage level of the target flexible OLED screen. These steps together constitute a comprehensive and detailed damage assessment process. First, the detection image is analyzed at the pixel level by a preset Gaussian mixture algorithm to determine whether there is a damaged area in the detection image. The Gaussian mixture algorithm is a commonly used image segmentation technology that can effectively separate different areas in the image. In this step, the Gaussian mixture algorithm analyzes each pixel in the detection image to determine whether it belongs to the damaged area. For example, suppose that a flexible OLED screen designed for foldable mobile phones is being tested. When the detection image is generated by terahertz time-domain spectroscopy technology and phase demodulation technology, the Gaussian mixture algorithm analyzes the image pixel by pixel to identify those pixels whose brightness, color or texture is significantly different from the normal area. These pixels are part of the damaged area. Through this pixel-level analysis, it can be preliminarily determined whether there is a damaged area in the detection image. Secondly, if there is a damaged area, the damaged area is binarized to obtain a damaged area mask. Binarization is the process of converting each pixel value in the image to 0 or 1 in order to display the damaged area more clearly. In this step, a threshold is set to distinguish the damaged area from the normal area in the detection image. For example, assuming that the Gaussian mixture algorithm has identified some pixels that are suspected to be damaged areas, a brightness threshold can be set, and pixels with brightness below the threshold are marked as 1 (indicating damaged areas), and pixels with brightness above the threshold are marked as 0 (indicating normal areas). In this way, a binary damaged area mask is obtained, which clearly shows the distribution of damaged areas. Then, a morphological operation is performed on the damaged area based on the damaged area mask to obtain the damaged area boundary.Morphological operation is an image processing technology that improves the shape characteristics of an image through operations such as expansion and erosion. In this step, the noise in the binary image can be removed by morphological operation to make the boundary of the damaged area clearer. For example, the binary image can be expanded first to expand the scope of the damaged area, and then the erosion operation can be performed to reduce the scope of the damaged area, thereby removing the noise at the edge and obtaining a clear boundary of the damaged area. Through this morphological operation, the scope of the damaged area can be more accurately defined, providing a basis for subsequent analysis. Then, the boundary of the damaged area is located and calculated using a preset region growing algorithm to obtain a boundary of the damaged area carrying coordinates. The region growing algorithm is an image segmentation technology based on seed points, which can start from a known boundary of the damaged area and gradually expand to the entire damaged area. In this step, several points on the boundary of the damaged area are selected as seed points, and gradually expanded through the region growing algorithm until the entire damaged area is covered. In this process, each expanded pixel will record its coordinate information, thereby obtaining a boundary of the damaged area carrying coordinates. For example, assuming that the morphological operation has obtained a clear damage area boundary, several key points on the boundary can be selected as seed points, and gradually expanded through the region growing algorithm to finally obtain a coordinate set containing all damage area boundaries. These coordinate information provides accurate positioning for subsequent damage area segmentation. Subsequently, based on the damage area boundary carrying the coordinates, the damage area is segmented from the detection image to obtain a segmented damage area. In this step, the damage area in the detection image can be accurately segmented using the damage area boundary carrying the coordinates. For example, assuming that the coordinate set of the damage area boundary has been obtained, the image area within these coordinate ranges can be extracted from the original detection image through image cropping technology to obtain a segmented damage area. These segmented damage area images will be used for subsequent damage feature extraction and evaluation. Finally, the segmented damage area is input into a preset damage model for damage feature extraction to obtain damage extraction features, wherein the damage extraction features include damage depth and damage range. The preset damage model is a machine learning model that has been trained with a large number of samples and can extract a variety of damage features based on the segmented damage area image. For example, the damage depth can be calculated by analyzing the change in the grayscale value of the damaged area, and the damage range can be determined by measuring the area of ​​the damaged area. These damage features can fully reflect the severity of the damage. For example, assuming that the segmented damaged area image shows an obvious crack, the damage model can calculate the damage depth and damage range by analyzing the depth and length of the crack. These features will be used for subsequent multi-criteria decision analysis. Multi-criteria decision analysis is performed on the damage extraction features to obtain the damage level of the target flexible OLED screen. Multi-criteria decision analysis is a method of making decisions based on multiple indicators, which can more comprehensively evaluate the severity of the damage.In this step, the characteristics such as damage depth and damage range are input into the multi-criteria decision model, and the damage level is comprehensively evaluated through the preset weights and rules. For example, assuming that the damage depth is 0.5 mm and the damage range is 1 square centimeter, the multi-criteria decision model can evaluate the damage as moderate damage based on these characteristics and the preset damage level standard. Through this multi-criteria decision analysis, the damage level of the flexible OLED screen can be determined more accurately, providing a scientific basis for subsequent production management and quality control. In summary, through the above series of steps, the damage of the flexible OLED screen during the dynamic bending test can be comprehensively and meticulously evaluated. These steps not only improve the accuracy and reliability of the detection, but also provide a scientific basis for optimizing the production process and improving product quality. For example, for a flexible OLED screen designed for foldable mobile phones, through this comprehensive damage assessment, the subtle damage of the screen during the bending process can be discovered in time, so that corresponding measures can be taken, such as optimizing the screen material, improving the production process, etc., to ensure the quality and reliability of the final product. Through these detailed damage assessments and analyses, manufacturers can better grasp the performance and durability of the screen and provide users with more high-quality and reliable products.

[0034] In a specific embodiment, the use of a preset region growing algorithm to locate and calculate the damaged region boundary to obtain the damaged region boundary carrying coordinates includes: Determine the initial seed points of the damage area mask to obtain an initial seed point set; Using a preset region growing algorithm, the growing region is gradually expanded based on the initial seed point set until a preliminary damage region boundary is encountered and the preliminary damage region boundary is obtained; Performing boundary smoothing processing on the preliminary damaged area boundary to remove burrs and small protrusions on the preliminary damaged area boundary to obtain a smooth damaged area boundary; Performing sub-pixel positioning on the boundary of the smooth damaged area to obtain a positioned boundary of the damaged area; The located damage area boundary is converted into coordinate form to generate a damage area boundary carrying coordinates.

[0035] Specifically, the preset region growing algorithm is used to locate and calculate the damage region boundary to obtain the damage region boundary carrying coordinates. This process is the basis for ensuring accurate positioning of the damage region and subsequent analysis. Specifically, this process includes multiple links: first, determine the initial seed point of the damage region mask to obtain an initial seed point set; second, use the preset region growing algorithm to gradually expand the growth region based on the initial seed point set until it stops at the initial damage region boundary to obtain the initial damage region boundary; then, perform boundary smoothing on the initial damage region boundary to remove burrs and small protrusions on the initial damage region boundary to obtain a smooth damage region boundary; then, perform sub-pixel level positioning on the smooth damage region boundary to obtain a located damage region boundary; finally, convert the located damage region boundary into a coordinate form to generate a damage region boundary carrying coordinates. These steps together constitute an accurate and reliable damage region boundary positioning process. First, determine the initial seed point of the damage region mask to obtain an initial seed point set. The selection of the initial seed point is the key to the success of the region growing algorithm. In this step, it is necessary to select some representative pixels from the damaged area mask after binarization as initial seed points. These seed points are usually the more obvious parts inside the damaged area, which can ensure that the region growing algorithm can accurately cover the entire damaged area when expanding from these points. For example, assuming that a clear damaged area mask has been obtained through binarization, the initial seed points can be determined by simple image processing techniques, such as finding the centroid of the connected area or selecting the point with the highest brightness value. These initial seed points will serve as the starting point of the region growing algorithm to ensure that the algorithm can gradually expand from these points. Secondly, using the preset region growing algorithm, the growth area is gradually expanded based on the initial seed point set until it stops at the initial damaged area boundary and obtains the initial damaged area boundary. The region growing algorithm is an image segmentation technology based on seed points. By gradually expanding the pixels around the seed points, similar pixels are classified into the same area. In this step, starting from the initial seed point, the growth area is gradually expanded until a boundary with a large difference in pixel attributes from the seed point is encountered. For example, assuming that the initial seed point is located at the center of the damage area, the region growing algorithm will start from these points and gradually expand outward, classifying pixels with brightness values ​​close to the seed point into the same area until a boundary with significantly different brightness values ​​is encountered, i.e., the preliminary damage area boundary. Through this gradual expansion method, the integrity and accuracy of the damage area can be ensured. Next, the preliminary damage area boundary is subjected to boundary smoothing to remove burrs and small protrusions on the preliminary damage area boundary to obtain a smooth damage area boundary. Boundary smoothing is to remove some small protrusions and burrs on the preliminary damage area boundary to make the boundary smoother and more continuous. In this step, some classic image processing techniques, such as median filtering, Gaussian filtering, etc., can be used to smooth the boundary.For example, assuming that there are some small burrs and protrusions on the boundary of the preliminary damage area, these small discontinuous parts can be removed by median filtering to make the boundary smoother. Through this smoothing process, the noise on the boundary can be reduced and the accuracy of subsequent analysis can be improved. Then, the smooth damage area boundary is positioned at the sub-pixel level to obtain the positioned damage area boundary. Sub-pixel level positioning is a technology to improve the accuracy of the boundary, which can further refine the position of the boundary on the basis of the pixel level. In this step, each pixel point on the boundary of the smooth damage area can be further subdivided into smaller units through the sub-pixel level positioning technology, so as to obtain a higher precision boundary position. For example, assuming that there is a pixel point on the boundary of the smooth damage area, this pixel point can be subdivided into multiple sub-pixel points through the sub-pixel level positioning technology, so as to more accurately determine the position of the boundary. Through this sub-pixel level positioning, the accuracy of the damage area boundary can be significantly improved, providing more accurate data for subsequent damage feature extraction. Finally, the positioned damage area boundary is converted into a coordinate form to generate a damage area boundary carrying coordinates. In this step, the damage area boundary after sub-pixel level positioning is converted into a specific coordinate form to generate a damage area boundary carrying coordinates. These coordinate information include not only the two-dimensional coordinates of each boundary point, but also other relevant information, such as the gray value of the boundary point, texture features, etc. For example, assuming that the boundary of the damaged area after sub-pixel level positioning is composed of a series of sub-pixel points, the coordinates of each sub-pixel point can be recorded to generate a list containing the coordinates of all boundary points. These coordinate information will be used for subsequent damage area segmentation and feature extraction to ensure the accuracy and reliability of damage assessment. In summary, through the above series of steps, the damaged area boundary of the flexible OLED screen during the dynamic bending test can be accurately located. These steps not only improve the accuracy of damage area positioning, but also provide a reliable basis for subsequent damage feature extraction and evaluation. For example, for a flexible OLED screen designed for foldable mobile phones, through this precise damage area boundary positioning, the subtle damage that occurs on the screen during the bending process can be more accurately identified and evaluated, so that corresponding measures can be taken, such as optimizing screen materials, improving production processes, etc., to ensure the quality and reliability of the final product. Through these detailed damage area boundary positioning and analysis, manufacturers can better grasp the performance and durability of the screen and provide users with more high-quality and reliable products.

[0036] In a specific embodiment, the multi-criteria decision analysis is performed on the damage extraction features to obtain the damage level of the target flexible OLED screen, including: Based on the damaged area boundary carrying the coordinates, obtaining the damaged area position; Performing importance assessment on the position of the damaged area to obtain an importance assessment result; Based on the importance evaluation result, the damage area is named according to its importance to obtain a damage area label; wherein the damage area label includes a key damage area, an important damage area and a general damage area; Constructing a decision matrix based on the damage feature data, with the damage area labels as rows and the damage extraction features as columns; The entropy weight method is used to evaluate the importance of the damage extraction features in the decision matrix to obtain corresponding weight values; Determine a positive ideal solution and a negative ideal solution in the decision matrix based on the weight value; wherein the positive ideal solution is the maximum weight of each damage extraction feature, and the negative ideal solution is the minimum weight of each damage extraction feature; Calculate the distance between the positive ideal solution and the negative ideal solution of each of the damaged areas based on the positive ideal solution and the negative ideal solution; Calculating the relative proximity of the damaged area based on the distance between the positive ideal solution and the negative ideal solution of the damaged area; Comparing the relative proximity of each of the damage extraction features with a preset damage level standard to determine the damage level corresponding to each of the damage extraction features; The damage level of the target flexible OLED screen is obtained based on the damage level corresponding to each of the damage extraction features.

[0037] Specifically, the damage extraction feature is subjected to a multi-criteria decision analysis to obtain the damage level of the target flexible OLED screen. This process is a key step to ensure the accuracy and reliability of the test results. Specifically, this process includes multiple steps: first, based on the damage area boundary carrying the coordinates, the damage area location is obtained; second, the damage area location is evaluated for importance to obtain an importance evaluation result; then, based on the importance evaluation result, the damage area is named according to its importance to obtain a damage area label, wherein the damage area label includes a critical damage area, an important damage area, and a general damage area; then, a decision matrix is ​​constructed based on the damage feature data, and the damage area labels are used as rows and the damage extraction features as columns; the entropy weight method is used to evaluate the importance of the damage extraction features in the decision matrix to obtain corresponding weight values; based on the The weight value determines the positive ideal solution and the negative ideal solution in the decision matrix, wherein the positive ideal solution is the maximum weight of each damage extraction feature, and the negative ideal solution is the minimum weight of each damage extraction feature; the distance between the positive ideal solution and the negative ideal solution of each damage area is calculated based on the positive ideal solution and the negative ideal solution; the relative proximity of the damage area is calculated based on the distance between the positive ideal solution and the negative ideal solution of the damage area; the relative proximity of each damage extraction feature is compared with the preset damage level standard to determine the damage level corresponding to each damage extraction feature; the damage level of the target flexible OLED screen is obtained based on the damage level corresponding to each damage extraction feature. These steps together constitute a comprehensive and detailed damage level assessment process. First, based on the damage area boundary carrying coordinates, the position of the damage area is obtained. In the previous stage, the damage area boundary carrying coordinates has been obtained through the region growing algorithm and sub-pixel level positioning. These coordinate information not only indicates the specific location of the damage area, but also provides the shape and size of the damage area. For example, assuming that the boundary coordinates of a damage area have been obtained, these coordinates can be used to determine the specific location of the damage area on the screen, such as the upper left corner, the center, the lower right corner, etc. These location information can provide a basis for subsequent importance and degree assessment. Secondly, the importance of the damaged area position is assessed to obtain an importance assessment result. Importance assessment is to judge the degree of impact of the damaged area on the screen performance based on its location. For example, if the damaged area is located in the central display area of ​​the screen, it has a greater impact on the display effect and is of higher importance; if the damaged area is located at the edge or non-display area of ​​the screen, it has a smaller impact on the display effect and is of lower importance. Through this importance assessment, it can be preliminarily determined which damaged areas need to be focused on. Next, based on the importance assessment result, the damaged areas are named according to their importance to obtain damaged area labels, wherein the damaged area labels include critical damaged areas, important damaged areas, and general damaged areas.According to the results of importance assessment, the damage area can be divided into different levels. For example, if the damage area is located in the center of the screen and has a great impact on the display effect, it can be named as a critical damage area; if the damage area is located at the edge of the screen and has a certain impact on the display effect, it can be named as an important damage area; if the damage area is located in the non-display area of ​​the screen and has a small impact on the display effect, it can be named as a general damage area. Through this naming, the importance of the damage area can be more intuitively reflected. Then, a decision matrix is ​​constructed based on the damage feature data, and the damage area label is used as a row and the damage extraction feature is used as a column. The decision matrix is ​​the basis of multi-criteria decision analysis and is used to comprehensively evaluate the damage features of each damage area. For example, assuming that features such as damage depth and damage range have been extracted from the damage area, these features can be used as columns, and the damage area labels (critical damage area, important damage area, general damage area) can be used as rows to construct a decision matrix. The value in each cell represents the specific value of the corresponding damage area on the feature. For example, the damage depth of the critical damage area A is 0.5 mm and the damage range is 1 square centimeter. These values ​​will be filled in the corresponding cells in the decision matrix. The entropy weight method is used to evaluate the importance of the damage extraction features in the decision matrix to obtain the corresponding weight values. The entropy weight method is an objective weighting method, which determines the weight of each feature by calculating the information entropy of each feature. The smaller the information entropy, the greater the amount of information of the feature and the higher the weight. For example, suppose there are two features in the decision matrix: damage depth and damage range. Through the entropy weight method, if the information entropy of the damage depth is small, it means that the damage depth has a greater impact on the damage assessment and its weight value is higher; conversely, if the information entropy of the damage range is large, it means that the damage range has a smaller impact on the damage assessment and its weight value is lower. Through this weight assignment, it can be ensured that the importance of each feature in the decision is reasonably reflected. Based on the weight value, the positive ideal solution and the negative ideal solution in the decision matrix are determined, wherein the positive ideal solution is the maximum weight of each damage extraction feature, and the negative ideal solution is the minimum weight of each damage extraction feature. The positive ideal solution and the negative ideal solution are two reference points in multi-criteria decision analysis, representing the optimal solution and the worst solution, respectively. For example, assuming that the weight value of the damage depth is 0.6 and the weight value of the damage range is 0.4, then the positive ideal solution is the combination of the largest damage depth and the largest damage range, and the negative ideal solution is the combination of the smallest damage depth and the smallest damage range. By determining the positive ideal solution and the negative ideal solution, a benchmark can be provided for subsequent distance calculations. The distance between the positive ideal solution and the negative ideal solution of each damage area is calculated based on the positive ideal solution and the negative ideal solution. Distance calculation is a key step in multi-criteria decision analysis, which is used to evaluate the proximity of each damage area to the optimal solution and the worst solution. For example, assuming that the damage depth of the key damage area A is 0.5 mm and the damage range is 1 square centimeter, the Euclidean distance between it and the positive ideal solution and the negative ideal solution can be calculated.These distance values ​​reflect the closeness of the damaged area to the optimal solution and the worst solution. The closer to the positive ideal solution, the more serious the damage; the closer to the negative ideal solution, the lighter the damage. The relative closeness of the damaged area is calculated based on the distance between the positive ideal solution and the negative ideal solution of the damaged area. The relative closeness is an indicator to measure the closeness of the damaged area to the optimal solution, usually using a formula. Calculate, where is the distance between the damaged area and the positive ideal solution, is the distance between the damaged area and the negative ideal solution. The value of relative proximity is between 0 and 1. The closer the value is to 1, the closer the damaged area is to the optimal solution and the more serious the damage is. The closer the value is to 0, the closer the damaged area is to the worst solution and the less serious the damage is. For example, assuming that the critical damaged area A is 0.8, is 0.2, then its relative closeness is 0.2 / (0.8 + 0.2) = 0.2, indicating that the damaged area is relatively light. The relative proximity of each of the damage extraction features is compared with the preset damage level standard to determine the damage level corresponding to each of the damage extraction features. The preset damage level standards generally include slight damage, moderate damage, and severe damage. For example, assume that the preset damage level standard is: a relative proximity of less than 0.3 is slight damage, 0.3 to 0.7 is moderate damage, and greater than 0.7 is severe damage. By comparing the relative proximity of each damaged area with these standards, its corresponding damage level can be determined. For example, the relative proximity of the key damaged area A is 0.2, and according to the preset standard, the damaged area is slightly damaged. The damage level of the target flexible OLED screen is obtained based on the damage level corresponding to each of the damage extraction features. After determining the damage level of each damaged area, the damage of the entire flexible OLED screen can be comprehensively evaluated. For example, assuming that the key damage area A is slightly damaged, the important damage area B is moderately damaged, and the general damage area C is slightly damaged, then the damage of the entire screen can be comprehensively evaluated based on these damage levels. If most of the damaged areas are slightly damaged, it can be considered that the overall damage of the screen is relatively light; if there are more moderate or severe damage areas, further measures need to be taken to repair or improve. Through this comprehensive evaluation, the accuracy and reliability of the test results can be ensured, providing a scientific basis for subsequent production management and quality control. In summary, through the above series of steps, the damage of the flexible OLED screen during the dynamic bending test can be comprehensively and meticulously evaluated. These steps not only improve the accuracy and reliability of damage assessment, but also provide a scientific basis for optimizing production processes and improving product quality. For example, for a flexible OLED screen designed for foldable mobile phones, through this multi-criteria decision analysis, the slight damage to the screen during the bending process can be discovered in time, so that corresponding measures can be taken, such as optimizing screen materials, improving production processes, etc., to ensure the quality and reliability of the final product. Through these detailed damage assessments and analyses, manufacturers can better grasp the performance and durability of the screen and provide users with more high-quality and reliable products.

[0038] In a specific embodiment, a damage analysis is performed on the damaged area to obtain a damage analysis result, and a production OLED screen management strategy is formulated based on the damaged area and the damage analysis result, including: Searching for a damaged component in the internal image of the damaged area in the detection image, and performing image segmentation on the internal image of the damaged area based on the damaged component to obtain an internal damage map corresponding to the damaged component; Performing mechanical analysis on the internal damage map corresponding to the damaged component to obtain a stress distribution map of the damaged component; Performing damage type analysis on the stress distribution diagram of the damaged component to obtain a damage type analysis result, and determining a damage factor based on the damage type analysis result; A production management strategy for OLED screens is formulated based on the damage type analysis results, damage factors and stress distribution diagram of the damaged components.

[0039] Specifically, the damage analysis is performed on the damaged area to obtain the damage analysis results, and the production OLED screen management strategy is formulated based on the damaged area and the damage analysis results. This process is a key step to ensure that the test results can effectively guide production and quality control. Specifically, this process includes multiple links: first, the damaged parts of the internal image of the damaged area are queried in the detection image, and the image of the internal image of the damaged area is segmented based on the damaged parts to obtain the internal damage map corresponding to the damaged parts; secondly, the internal damage map corresponding to the damaged parts is mechanically analyzed to obtain the stress distribution map of the damaged parts; then, the damage type analysis is performed on the stress distribution map of the damaged parts to obtain the damage type analysis results, and the damage factors are determined based on the damage type analysis results; finally, the production OLED screen management strategy is formulated based on the damage type analysis results, damage factors and the stress distribution map of the damaged parts. These steps together constitute a comprehensive and detailed damage analysis and management strategy formulation process. First, the damaged parts of the internal image of the damaged area are queried in the detection image, and the image of the internal image of the damaged area is segmented based on the damaged parts to obtain the internal damage map corresponding to the damaged parts. In the previous stage, a detailed image of the damaged area has been obtained through various technical means. In this step, it is necessary to further analyze these images to identify specific damaged parts. For example, assuming that the internal material microstructure image of the damaged area has been obtained by terahertz time-domain spectroscopy technology and phase demodulation technology, image segmentation technology, such as edge detection-based segmentation algorithm, can be used to separate different components in the damaged area. These components include the substrate, organic light-emitting layer, encapsulation layer, etc. of the screen. Through image segmentation, the internal damage map corresponding to each damaged component can be obtained, and these images are the basis for subsequent analysis. Secondly, the internal damage map corresponding to the damaged component is mechanically analyzed to obtain the stress distribution map of the damaged component. Mechanical analysis is an important means to evaluate the stress condition of the damaged component under different conditions. In this step, finite element analysis (FEA) and other methods can be used to perform mechanical modeling and simulation on the internal damage map of the damaged component. For example, assuming that the internal damage map of the substrate has been obtained, a finite element model of the substrate can be established to simulate its stress distribution under different bending conditions. Through this mechanical analysis, stress distribution maps of the damaged component at different positions can be obtained. These stress distribution maps can intuitively show which areas are stress concentrated, thereby helping to identify the main causes of the damage. Next, the damage type analysis is performed on the stress distribution map of the damaged component to obtain a damage type analysis result, and the damage factor is determined based on the damage type analysis result. The damage type analysis is to determine the specific type and cause of the damage based on the stress distribution map and other relevant data. In this step, the stress distribution map of the damaged component can be classified and analyzed through techniques such as pattern recognition and machine learning.For example, assuming that the stress distribution map shows stress concentration in certain areas, the material properties, structural design and other factors of these areas can be further analyzed to determine whether the damage type is material fatigue, interlayer delamination or other reasons. Through this damage type analysis, specific damage factors can be determined to provide a basis for the subsequent management strategy formulation. Finally, a production OLED screen management strategy is formulated based on the damage type analysis results, damage factors and the stress distribution map of the damaged component. The formulation of the management strategy is the last step in applying the damage analysis results to actual production. In this step, it is necessary to comprehensively consider the damage type, damage factors and stress distribution map to formulate a specific management strategy. For example, assuming that the damage type analysis results show that the damage in certain areas is mainly caused by material fatigue, the following measures can be taken: optimize material selection and select more fatigue-resistant materials; improve structural design to reduce stress concentration; adjust production process parameters, such as bending speed and bending times, to reduce material fatigue damage. Through these specific management strategies, the durability and reliability of the flexible OLED screen can be effectively improved. In summary, through the above series of steps, the damage of the flexible OLED screen during the dynamic bending test can be comprehensively and meticulously analyzed, and an effective production management strategy can be formulated. These steps not only improve the accuracy and reliability of damage analysis, but also provide a scientific basis for optimizing production processes and improving product quality. For example, for flexible OLED screens designed for foldable phones, through this detailed damage analysis and management strategy formulation, it is possible to promptly detect minor damage to the screen during the bending process, so that corresponding measures can be taken, such as optimizing screen materials and improving production processes, to ensure the quality and reliability of the final product. Through these detailed damage analysis and management strategy formulation, manufacturers can better understand the performance and durability of the screen and provide users with better quality and more reliable products.

[0040] The bending test method of the flexible OLED screen in the embodiment of the present invention is described above. The bending test system of the flexible OLED screen in the embodiment of the present invention is described below. Figure 2 In one embodiment of the present invention, a bending test system for a flexible OLED screen includes: An acquisition module 21 is used to acquire preset bending data of a target flexible OLED screen, and dynamically bend and test the target flexible OLED screen based on the preset bending data; A detection module 22 is used to perform non-contact detection on the target flexible OLED screen during the dynamic bending test to obtain a detection image; A judgment module 23 is used to judge whether there is a damaged area in the detection image. If so, the detection image is input into a preset damage model to determine the damage level of the target flexible OLED screen; The formulation module 24 is used to perform damage analysis on the damaged area if the damage level exceeds the preset damage level, obtain damage analysis results, and formulate a production OLED screen management strategy based on the damaged area and the damage analysis results.

[0041] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0042] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0043] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0044] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0045] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0046] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0047] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A bending test method for a flexible OLED screen, characterized in that: The following steps are involved: Acquire preset bending data of a target flexible OLED screen, and dynamically bend and test the target flexible OLED screen based on the preset bending data; During the dynamic bending test, non-contact detection is performed on the target flexible OLED screen to obtain a detection image; Determine whether there is a damaged area in the detection image, and if so, input the detection image into a preset damage model to determine the damage level of the target flexible OLED screen; If the damage level exceeds a preset damage level, a damage analysis is performed on the damaged area to obtain a damage analysis result, and a production OLED screen management strategy is formulated based on the damaged area and the damage analysis result.

2. The bending test method of the flexible OLED screen according to claim 1, characterized in that: Acquiring preset bending data of a target flexible OLED screen, and dynamically bending the target flexible OLED screen based on the preset bending data, including: Obtaining a model of a target flexible OLED screen, and obtaining preset bending data and basic physical parameters of the target flexible OLED screen from a preset database based on the model; wherein the preset bending data includes a bending direction and a bending path; The preset bending data is converted into a control instruction set, and the target flexible OLED screen is dynamically bent in multi-axis directions based on the preset bending data.

3. The bending test method of the flexible OLED screen according to claim 1, characterized in that: During the dynamic bending test, the target flexible OLED screen is non-contactly detected to obtain a detection image; wherein the detection image is an image of microscopic structural changes of the internal material of the target flexible OLED screen, including: During the multi-axial dynamic bending test, the target flexible OLED screen is penetrated by terahertz time-domain spectroscopy technology to obtain an internal material image; Performing three-dimensional image reconstruction based on the internal material image to obtain a three-dimensional image of the internal material; Performing non-contact imaging on the target flexible OLED screen to obtain a surface topography image; Registering the internal material three-dimensional image with the surface topography image to form a registered detection image; Performing frequency domain analysis on the registered detection image to obtain an internal material microstructure image; The phase demodulation technology is used to detect the slight changes of the material in the internal material microstructure image to obtain a detection image.

4. The bending test method of the flexible OLED screen according to claim 1, characterized in that: The determining whether there is a damaged area in the detection image, and if so, inputting the detection image into a preset damage model to determine the damage level of the target flexible OLED screen, includes: Performing pixel-level analysis on the detection image using a preset Gaussian mixture algorithm to determine whether there is a damaged area in the detection image; If it exists, binarization is performed on the damaged area to obtain a damaged area mask; Performing morphological operations on the damaged area based on the damaged area mask to obtain a damaged area boundary; Using a preset region growing algorithm, the damaged region boundary is positioned and calculated to obtain the damaged region boundary carrying coordinates; Based on the damaged area boundary carrying the coordinates, segmenting the damaged area from the detection image to obtain a segmented damaged area; Inputting the segmented damage area into a preset damage model to extract damage features to obtain damage extraction features; wherein the damage extraction features include damage depth and damage range; A multi-criteria decision analysis is performed on the damage extraction features to obtain the damage level of the target flexible OLED screen.

5. The bending test method of the flexible OLED screen according to claim 4, characterized in that: The method of using a preset region growing algorithm to locate and calculate the boundary of the damaged region to obtain the boundary of the damaged region carrying coordinates includes: Determine the initial seed points of the damage area mask to obtain an initial seed point set; Using a preset region growing algorithm, the growing region is gradually expanded based on the initial seed point set until a preliminary damage region boundary is encountered and the preliminary damage region boundary is obtained; Performing boundary smoothing processing on the preliminary damaged area boundary to remove burrs and small protrusions on the preliminary damaged area boundary to obtain a smooth damaged area boundary; Performing sub-pixel positioning on the boundary of the smooth damaged area to obtain a positioned boundary of the damaged area; The located damage area boundary is converted into coordinate form to generate a damage area boundary carrying coordinates.

6. The bending test method of the flexible OLED screen according to claim 5, characterized in that: The performing a multi-criteria decision analysis on the damage extraction features to obtain a damage level of the target flexible OLED screen includes: Based on the damaged area boundary carrying the coordinates, obtaining the damaged area position; Performing importance assessment on the position of the damaged area to obtain an importance assessment result; Based on the importance evaluation result, the damage area is named according to its importance to obtain a damage area label; wherein the damage area label includes a key damage area, an important damage area and a general damage area; Constructing a decision matrix based on the damage feature data, with the damage area labels as rows and the damage extraction features as columns; The entropy weight method is used to evaluate the importance of the damage extraction features in the decision matrix to obtain corresponding weight values; Determine a positive ideal solution and a negative ideal solution in the decision matrix based on the weight value; wherein the positive ideal solution is the maximum weight of each damage extraction feature, and the negative ideal solution is the minimum weight of each damage extraction feature; Calculate the distance between the positive ideal solution and the negative ideal solution of each of the damaged areas based on the positive ideal solution and the negative ideal solution; Calculating the relative proximity of the damaged area based on the distance between the positive ideal solution and the negative ideal solution of the damaged area; Comparing the relative proximity of each of the damage extraction features with a preset damage level standard to determine the damage level corresponding to each of the damage extraction features; The damage level of the target flexible OLED screen is obtained based on the damage level corresponding to each of the damage extraction features.

7. The bending test method of the flexible OLED screen according to claim 3, characterized in that: Perform damage analysis on the damaged area to obtain damage analysis results, and formulate a production OLED screen management strategy based on the damaged area and the damage analysis results, including: Searching for a damaged component in the internal image of the damaged area in the detection image, and performing image segmentation on the internal image of the damaged area based on the damaged component to obtain an internal damage map corresponding to the damaged component; Performing mechanical analysis on the internal damage map corresponding to the damaged component to obtain a stress distribution map of the damaged component; Performing damage type analysis on the stress distribution diagram of the damaged component to obtain a damage type analysis result, and determining a damage factor based on the damage type analysis result; A production management strategy for OLED screens is formulated based on the damage type analysis results, damage factors and stress distribution diagrams of damaged components.

8. A bending test system for a flexible OLED screen, characterized in that: include: An acquisition module, used for acquiring preset bending data of a target flexible OLED screen, and dynamically bending and testing the target flexible OLED screen based on the preset bending data; A detection module, used to perform non-contact detection on the target flexible OLED screen during the dynamic bending test to obtain a detection image; A judgment module, used to judge whether there is a damaged area in the detection image, and if so, input the detection image into a preset damage model to determine the damage level of the target flexible OLED screen; A formulation module is used to perform damage analysis on the damaged area if the damage level exceeds a preset damage level, obtain damage analysis results, and formulate a production OLED screen management strategy based on the damaged area and damage analysis results.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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