Data detection method, apparatus, system, and storage medium

Abstract

The application discloses a data detection method, device and system, and a storage medium. The method comprises the following steps: obtaining a metallographic phase diagram corresponding to photographing data, wherein the photographing data is obtained by photographing a test picture of a display module; and detecting the photographing data according to the metallographic phase diagram to obtain a detection result. The application can provide a means for detecting photographing data anomalies, thereby quickly checking the photographing data collected by a camera or the anomalies in the brightness compensation process of the display module.

Description

Technical Field

[0001] This application belongs to the field of display technology, and in particular relates to a data detection method, apparatus, detection system and computer-readable storage medium. Background Technology

[0002] OLED (Organic Light Emitting Diode) display panels suffer from uneven brightness, resulting in various display artifacts, a phenomenon known as the Mura effect.

[0003] To mitigate the Mura phenomenon and improve display quality, brightness compensation is necessary. To obtain brightness compensation data, relevant technologies typically display a test image on the display module and determine the compensation data based on data captured by a high-definition camera. However, this brightness compensation process can sometimes result in suboptimal compensation. In such cases, it's necessary to rule out whether the poor brightness compensation is caused by incorrect data provided by the high-definition camera. Summary of the Invention

[0004] This application provides a data detection method, apparatus, system, and storage medium, aiming to provide a means of detecting anomalies in photographed data.

[0005] On one hand, embodiments of this application provide a data detection method, the method comprising:

[0006] Obtain the metallographic image corresponding to the photographed data, which is the photographed data obtained by shooting the test screen of the display module;

[0007] Based on the metallographic image, the photographic data is examined to obtain the test results.

[0008] Optionally, the photographed data can be inspected based on the metallographic image to obtain inspection results, including:

[0009] The metallographic image is compared with the images before and after brightness compensation to obtain the detection results;

[0010] Preferably, the display module exhibits uneven display after brightness compensation.

[0011] Optionally, the metallographic image is compared with the image before and after brightness compensation to obtain the detection results, including:

[0012] If uneven display exists in the first region of the metallographic image, and uneven display does not exist in the second region of the image before brightness compensation, the detection result indicates that there is an anomaly in the photographed data. Both the first and second regions correspond to the third region, which is the region in the image after brightness compensation where uneven display exists.

[0013] Optionally, the metallographic image is compared with the image before and after brightness compensation to obtain the detection results, including:

[0014] There is no uneven display in the first area of ​​the metallographic image, and the test result indicates that there is no abnormality in the photographed data. The first area corresponds to the third area, which is the area in the image after brightness compensation where there is uneven display.

[0015] Preferably, the detection result indicates an anomaly in the algorithm involved in brightness compensation.

[0016] Optionally, the metallographic image is compared with the image before and after brightness compensation to obtain the detection results, including:

[0017] Calculate the first similarity between the metallographic image and the image before brightness compensation;

[0018] If the first similarity is lower than the first threshold, the second similarity between the metallographic image and the brightness-compensated image is obtained.

[0019] Based on the first and second similarity scores, the unevenness in display between the metallographic image and the image before and after brightness compensation is determined, and the detection results are obtained.

[0020] Preferably, the first threshold is lower than the second threshold.

[0021] Optionally, a second similarity is obtained between the metallographic image and the brightness-compensated image, including:

[0022] Alternately display metallographic images and brightness-compensated images;

[0023] Take pictures of the displayed metallographic image and the brightness-compensated image to obtain the photo test data;

[0024] The second similarity between the metallographic image and the brightness-compensated image was determined by taking photos of the test data.

[0025] Optionally, the metallographic image corresponding to the photographed data can be obtained, including:

[0026] Multiple test images corresponding to different emitted light colors are generated based on the photographic data;

[0027] Multiple test images are composited, and a metallographic image corresponding to the photographed data is generated based on the composite test images.

[0028] Optionally, before obtaining the metallographic image corresponding to the photographic data, the process also includes:

[0029] If uneven display is detected in the same area of ​​multiple brightness-compensated display modules, the following steps are performed: Obtain the metallographic image corresponding to the photographed data.

[0030] On the other hand, embodiments of this application provide a data detection device, the device comprising:

[0031] The acquisition module is used to acquire the metallographic image corresponding to the photographed data. The photographed data is the photographed data obtained by shooting the test screen of the display module.

[0032] The detection module is used to detect the photographed data based on the metallographic image and obtain the detection results.

[0033] Furthermore, embodiments of this application provide a detection system, which includes:

[0034] Processor and memory storing computer program instructions;

[0035] The data detection methods described above are implemented when the processor executes computer program instructions.

[0036] In another aspect, embodiments of this application provide a computer storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the data detection method described above is implemented.

[0037] In another aspect, embodiments of this application provide a computer program product, which includes a computer program that, when executed by a processor, implements the data detection method described above.

[0038] The data detection method, apparatus, system, and storage medium of this application embodiment can acquire metallographic images corresponding to photographed data. The photographed data is obtained by capturing test images of a display module. The photographed data is then detected based on the metallographic images to obtain detection results. Thus, the detection of photographed data is achieved using the metallographic images corresponding to the photographed data. The resulting detection results can reflect whether there are errors in the captured data during brightness compensation, providing a means to detect anomalies in photographed data. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic flowchart of a data detection method provided in one embodiment of this application;

[0041] Figure 2 This is an optional schematic diagram of the metallographic image and the brightness-compensated image in a data detection method provided in one embodiment of this application;

[0042] Figure 3 This is a schematic diagram of the structure of a data detection device provided in another embodiment of this application;

[0043] Figure 4 This is a schematic diagram of the detection system provided in another embodiment of this application. Detailed Implementation

[0044] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0045] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0046] OLED display panels, due to the inconsistency between the characteristics of TFTs (Thin Film Transistors) and OLEDs (Organic Light-Emitting Diodes), are prone to exhibiting inconsistent brightness under the same grayscale and data voltage. This phenomenon is known as the Mura effect. Existing technologies for Mura compensation are mainly divided into two categories: internal compensation and external compensation. Internal compensation refers to compensation performed within the pixel using pixel sub-circuits constructed with TFTs, while external compensation refers to compensation methods that sense the electrical or optical characteristics of the pixel through external driving circuits or devices.

[0047] External compensation methods include optical extraction technology, also known as Demura, which specifically uses subpixel optical imaging technology and software algorithms to process display modules with Mura defects, thereby eliminating the Mura phenomenon. Currently, Demura is widely used due to its simple structure and flexible approach.

[0048] During the Demura process, a camera first captures the brightness of each pixel in the display module. The camera generates image data, and the display driver chip then generates brightness compensation data based on this data. For example, this brightness compensation data can be a bin format file. Alternatively, the brightness compensation data can be burned into a storage module.

[0049] However, in some cases, the display module's Demura effect may be poor. Since the camera that generates the image data and the display driver chip that generates the brightness compensation data are produced by two different manufacturers during the actual production process of the display module, if a Demura problem occurs, it is necessary to investigate whether the problem lies with the image data captured by the camera or with the display driver chip during the brightness compensation process.

[0050] Therefore, embodiments of this application provide a data detection method, apparatus, system, and storage medium. The data detection method provided in the embodiments of this application will be described first.

[0051] Figure 1 A flowchart illustrating a data detection method provided in one embodiment of this application is shown. Figure 1 As shown, the method may include the following steps:

[0052] S110, acquire the metallographic image corresponding to the photographed data.

[0053] Among them, the photo data refers to the photo data obtained by taking pictures of the test screen of the display module.

[0054] S120, based on the metallographic image, performs testing on the photographed data to obtain the test results.

[0055] This application embodiment acquires a metallographic image corresponding to the photographed data, which is the photographed data obtained from capturing the test screen of the display module. Then, based on the metallographic image, the photographed data is inspected to obtain the inspection result. Thus, the inspection of the photographed data is achieved using the metallographic image corresponding to the photographed data. The resulting inspection result can reflect whether there are errors in the photographed data during brightness compensation, providing a means to detect anomalies in photographed data. This allows for quick identification of whether the problem lies in the photographed data acquired by the camera or a problem in the display driver chip during the brightness compensation process.

[0056] In some optional examples of S110, the above-described data detection method can be applied to a detection system, which may include a computer, a high-definition camera, and a device for illuminating the display module. For example, the high-definition camera may be a charge-coupled device (CCD) camera.

[0057] In addition, the detection system may include a platform for supporting the display module, which can be set horizontally and can lay the display module flat so that the image capture height remains horizontal.

[0058] The detection system may also include at least one of the following: a positioning sensor, a robotic arm capable of lightly wiping the surface of the display module, and a sliding support capable of height adjustment. The robotic arm can be used to wipe dust from the module surface, and the positioning sensor can locate the position of the display module.

[0059] Understandably, the relevant equipment in the testing system can illuminate the display module. Once illuminated, the computer can output a specific image, allowing the display module to show the test screen. At this point, the high-definition camera can capture the test screen and obtain photographic data.

[0060] For example, the image data can be a Comma-Separated Values ​​(CVS) file. This image data is the raw brightness data used for Demura brightness compensation.

[0061] For example, the above-mentioned display module is a display module with poor brightness compensation. These display modules still have areas of uneven display after brightness compensation, resulting in poor brightness compensation effect of the display module.

[0062] For example, the above-mentioned display module still has uneven display after brightness compensation. These uneven display areas are numerous or obvious, resulting in poor brightness compensation effect of the display module.

[0063] To detect anomalies in the photographed data, instead of generating and burning a bin file from the photographed data, a corresponding metallographic image can be obtained using the photographed data. This metallographic image, also known as a Golden simulation image, is a compensation (offset) image generated based on the photographed data of each pixel, eliminating interference such as moiré noise.

[0064] In some optional examples, if uneven display is detected in the same area of ​​multiple brightness-compensated display modules, step S110 may be performed to obtain the metallographic image corresponding to the photographed data.

[0065] For example, when the inspector finds that uneven display occurs in the same area of ​​multiple display modules after passing through Demura, the data detection method can be triggered, that is, the metallographic image corresponding to the photographed data can be acquired.

[0066] For example, after brightness compensation is performed on the display modules, an Automated Optical Inspection (AOI) function can be added. When the AOI continuously detects uneven display across multiple display modules in the same fixed area, it triggers the execution of a data detection method.

[0067] These examples provide multiple scenarios for triggering the execution of the data detection method, which can be triggered when a display module brightness compensation defect is detected, thereby quickly identifying the anomaly.

[0068] In some alternative examples, S110 above may include:

[0069] The S210 generates multiple test images corresponding to different emitted light colors based on the captured image data.

[0070] The S220 performs composite processing on multiple test images and generates a metallographic image corresponding to the photographed data based on the composite test images.

[0071] In this example, image data can be extracted from a high-definition camera, and then multiple test images can be formed by distinguishing different light-emitting colors. For example, test images of red, green and blue can be generated. Then, the test images corresponding to different light-emitting colors are composited according to a consistent grayscale, and each pixel is compensated according to the composited test image to form a metallographic image that emits white light and eliminates interference such as moiré noise.

[0072] It should also be noted that when forming the test image, test images under different typical gray levels can be obtained. These typical gray levels can be, for example, 32 gray levels, 64 gray levels, 128 gray levels, and 224 gray levels. Through the test images under different typical gray levels, metallographic images under different typical gray levels can also be formed in the end.

[0073] In some optional examples of S120, the obtained metallographic image can be used to detect the photographic data, and then the detection result can be used to determine whether the photographic data is abnormal. Thus, by using the metallographic image corresponding to the photographic data, the detection of photographic data is achieved, which can reflect whether there are errors in the captured data during brightness compensation, thereby providing a means to detect abnormalities in photographic data.

[0074] In some optional examples, the process of detecting the photographic data based on the metallographic image and obtaining the detection result may include: comparing the metallographic image with the image before and after brightness compensation to obtain the detection result.

[0075] The aforementioned brightness compensation involves using the captured image data as the raw brightness data, combining it with an algorithm to generate brightness compensation data, and then burning this brightness compensation data into the storage module.

[0076] The brightness-compensated image can be the image captured by a high-definition camera when the control display module displays the image according to the brightness compensation data.

[0077] The image before brightness compensation can be the image captured by a high-definition camera when the control display module displays the image according to the original brightness data.

[0078] In this example, by using the generated metallographic image as a reference and comparing it with the images before and after brightness compensation, it is possible to quickly rule out whether there is any abnormality in the high-definition camera based on the obtained detection results.

[0079] Furthermore, in some alternative examples, the above-mentioned comparison of the metallographic image with the image before and after brightness compensation to obtain the detection results includes:

[0080] S310, calculate the first similarity between the metallographic image and the image before brightness compensation.

[0081] S320, if the first similarity is lower than the first threshold, obtain the second similarity between the metallographic image and the brightness-compensated image.

[0082] S330, based on the first similarity and the second similarity, determine the display unevenness in the metallographic image and the image before and after brightness compensation, and obtain the detection result.

[0083] The first and second similarities mentioned above can be cosine similarity, which characterizes the similarity between the metallographic image and the images before and after brightness compensation in terms of the Mura trend. This cosine similarity can be between 0 and 1. The larger the cosine similarity value, the higher the similarity between the two images for which cosine similarity is calculated; conversely, a smaller value indicates a greater difference.

[0084] The first threshold can be lower than the second threshold. For example, the first threshold can be 0.8 and the second threshold can be 0.9. In other examples, the first threshold can also be equal to or greater than the second threshold.

[0085] The first similarity score can be calculated using a histogram to determine the structural similarity between the metallographic image and the image before brightness compensation. If the first similarity score is below a first threshold, it indicates a significant difference between the metallographic image and the image before brightness compensation, with some areas of one of them exhibiting uneven brightness.

[0086] The second similarity score between the metallographic image and the brightness-compensated image can be obtained. By combining the first and second similarity scores, it is possible to determine which aspects of the metallographic image and the images before and after brightness compensation exhibit uneven brightness.

[0087] For example, the process of obtaining the second similarity between the metallographic image and the brightness-compensated image may include:

[0088] The metallographic image and the brightness-compensated image are displayed alternately; the displayed metallographic image and the brightness-compensated image are photographed to obtain photographic test data; the second similarity between the metallographic image and the brightness-compensated image is determined based on the photographic test data.

[0089] The device can illuminate the display module and control it to alternately display a metallographic image and a brightness-compensated image. A high-definition camera can capture images of both the brightness-compensated image and the metallographic image displayed on the display module. The detection system can then use the captured test data to calculate a second similarity between the metallographic image and the brightness-compensated image.

[0090] When the second similarity exceeds the second threshold, the metallographic image and the brightness-compensated image can be considered identical. However, due to poor brightness compensation in the display module, the brightness-compensated image exhibits the Mura phenomenon. Therefore, it can be assumed that corresponding areas in both the metallographic image and the brightness-compensated image show uneven display.

[0091] For example, if there is uneven display in the first region of the metallographic image, but no uneven display in the second region of the image before brightness compensation, both the first and second regions correspond to the third region. The third region is the region in the image after brightness compensation where uneven display exists. In this case, the above detection result indicates that the photographed data is abnormal.

[0092] For example, please see Figure 2 , Figure 2 The left side shows a metallographic image generated based on the photographic data. Figure 2 The image on the right shows the image after brightness compensation. A Mura region exists in the upper left of the metallographic image; this region is the first region. A Mura phenomenon also exists in the same location in the upper left of the image after brightness compensation; this Mura phenomenon is the third region.

[0093] If the Mura phenomenon is not present in the image before brightness compensation, but it exists in the same area in both the metallographic image and the image after brightness compensation, it indicates that the brightness compensation algorithm cannot eliminate the uneven brightness phenomenon through brightness compensation, and the problem actually lies in the camera's image data.

[0094] When the second similarity does not exceed the second threshold, the metallographic image and the brightness-compensated image can be considered not similar. Due to poor brightness compensation in the display module involved, the brightness-compensated image exhibits the Mura phenomenon. Therefore, it can be considered that the metallographic image does not have display unevenness, but the corresponding area in the brightness-compensated image does have display unevenness.

[0095] For example, there may be no unevenness in the first region of the metallographic image. The first region corresponds to the third region, which is the region in the image after brightness compensation where unevenness exists. In this case, the above detection result indicates that there is no abnormality in the photographed data.

[0096] by Figure 2 The diagram on the right illustrates this. Suppose that the Mura phenomenon exists in the third region on the upper left side of the image after brightness compensation, but the metallographic image corresponding to the photographic data does not contain the Mura region, then it means that the photographic data is not abnormal.

[0097] If the Mura phenomenon exists in the image after brightness compensation, but the corresponding area in the metallographic image does not exhibit the Mura phenomenon, it indicates that the brightness compensation algorithm is ineffective, the algorithm involved in brightness compensation is abnormal, and the actual camera does not have any abnormal shooting data.

[0098] It should also be noted that the Mura phenomenon exists in the image before brightness compensation, but the Mura trend of the metallographic image and the image after brightness compensation is consistent and highly similar. This indicates that the shooting data of the high-definition camera is correct and the algorithm involved in brightness compensation has also achieved normal compensation.

[0099] These examples demonstrate the process of comparing metallographic images with images before and after brightness compensation under different conditions. Based on the test results, it is possible to quickly identify abnormalities in the photographic data or the brightness compensation algorithm, thereby improving the troubleshooting of anomalies in the display module production process and helping to locate problems as early as possible.

[0100] Figure 3 A schematic diagram of the hardware structure of the data detection device provided in an embodiment of this application is shown. Figure 3 The data detection device includes:

[0101] The acquisition module 310 is used to acquire the metallographic image corresponding to the photographed data, which is the photographed data obtained by shooting the test screen of the display module;

[0102] The detection module 320 is used to detect the photographed data based on the metallographic image and obtain the detection result.

[0103] Optionally, the detection module 320 includes:

[0104] The comparison unit is used to compare the metallographic image with the image before and after brightness compensation to obtain the detection result;

[0105] Preferably, the display module has poor brightness compensation.

[0106] Optionally, the comparison unit is also used to indicate that there is an abnormality in the photographed data when there is uneven display in the first region of the metallographic image and there is no uneven display in the second region of the image before brightness compensation. Both the first region and the second region correspond to the third region, which is the region in the image after brightness compensation where there is uneven display.

[0107] Optionally, the comparison unit is also used to ensure that there is no display unevenness in the first region of the metallographic image, and the detection result indicates that there is no abnormality in the photographed data. The first region corresponds to the third region, and the third region is the region in the image after brightness compensation where there is display unevenness.

[0108] Preferably, the detection result indicates an anomaly in the algorithm involved in brightness compensation.

[0109] Optionally, the comparison unit is also used to calculate the first similarity between the metallographic image and the image before brightness compensation; if the first similarity is lower than the first threshold, to obtain the second similarity between the metallographic image and the image after brightness compensation; and to determine the display unevenness in the metallographic image and the image before and after brightness compensation based on the first similarity and the second similarity, and to obtain the detection result.

[0110] Preferably, the first threshold is lower than the second threshold.

[0111] Optionally, the data inspection system also includes:

[0112] The display module is used to alternately display metallographic images and brightness-compensated images;

[0113] The camera module is used to take pictures of the displayed metallographic image and the brightness-compensated image to obtain camera test data;

[0114] The comparison unit is also used to determine the second similarity between the metallographic image and the brightness-compensated image by taking photographic test data.

[0115] Optionally, the acquisition module 310 includes:

[0116] The generation unit is used to generate multiple test images corresponding to different emitted light colors based on the photographed data.

[0117] The processing unit is used to synthesize multiple test images and generate metallographic images corresponding to the photographed data based on the synthesized test images.

[0118] Preferably, the data detection system further includes:

[0119] The detection module is used to trigger the acquisition module 310 to acquire the metallographic image corresponding to the photographed data when uneven display is detected in the same area of ​​multiple brightness-compensated display modules.

[0120] Figure 4 A schematic diagram of the hardware structure of the detection system provided in an embodiment of this application is shown. The detection system includes a processor 401 and a memory 402 storing computer program instructions.

[0121] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0122] Memory 402 may include a mass storage device for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where suitable, memory 402 may include removable or non-removable (or fixed) media. Where suitable, memory 402 may be internal or external to the detection system. In a particular embodiment, memory 402 is a non-volatile solid-state memory.

[0123] Memory 402 may include read-only memory (ROM), flash memory devices, random access memory (RAM), disk storage media devices, optical storage media devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory 402 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods described above according to the foregoing aspects of this disclosure.

[0124] The processor 401 implements any of the data detection methods described in the above embodiments by reading and executing computer program instructions stored in the memory 402.

[0125] In one example, the detection system may also include a communication interface 403 and a bus 410. For example, Figure 4As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0126] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0127] Bus 410 includes hardware, software, or both, that couples components of the detection system together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0128] This detection system can be based on data detection methods, thereby achieving a combination of... Figures 1 to 3 The data detection method and apparatus described.

[0129] Furthermore, in conjunction with the data detection methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the data detection methods in the above embodiments.

[0130] In addition, this application also provides a computer program product, including a computer program, which, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0131] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0132] It should be understood that in the embodiments of this application, "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean that B is determined solely based on A; B can also be determined based on A and / or other information.

[0133] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data detection method, characterized in that, include: Multiple test images corresponding to different emitted light colors are generated based on the photographic data; Multiple test images are composited to obtain a composite test image. Each pixel is compensated according to the composite test image to obtain a metallographic image that emits white light and eliminates moiré noise interference. The photographic data refers to the photographic data obtained by capturing the test screen of the display module; The metallographic image is compared with the image before and after brightness compensation to obtain the detection result, which indicates that the algorithm involved in the brightness compensation is abnormal. The display module exhibits uneven display after brightness compensation.

2. The method according to claim 1, characterized in that, The step of comparing the metallographic image with the image before and after brightness compensation to obtain the detection result includes: If there is uneven display in the first region of the metallographic image, and there is no uneven display in the second region of the image before brightness compensation, the detection result indicates that the photographed data is abnormal. Both the first region and the second region correspond to the third region, which is the region in the image after brightness compensation where there is uneven display.

3. The method according to claim 1, characterized in that, The step of comparing the metallographic image with the image before and after brightness compensation to obtain the detection result includes: There is no uneven display in the first region of the metallographic image, and the detection result indicates that there is no abnormality in the photographed data. The first region corresponds to the third region, which is the region in the image after brightness compensation where there is uneven display.

4. The method according to claim 1, characterized in that, The step of comparing the metallographic image with the image before and after brightness compensation to obtain the detection result includes: Calculate the first similarity between the metallographic image and the image before brightness compensation; If the first similarity is lower than the first threshold, a second similarity is obtained between the metallographic image and the brightness-compensated image. Based on the first similarity and the second similarity, the uneven display phenomenon in the metallographic image and the image before and after brightness compensation is determined, and the detection result is obtained.

5. The method according to claim 4, characterized in that, The step of obtaining the second similarity between the metallographic image and the brightness-compensated image includes: Alternately display the metallographic image and the brightness-compensated screen image; Take pictures of the displayed metallographic image and the brightness-compensated image to obtain photographic test data; The second similarity between the metallographic image and the brightness-compensated image is determined using the photographic test data.

6. The method according to claim 1, characterized in that, Also includes: If uneven display is detected in the same area of ​​multiple brightness-compensated display modules, the following steps are performed: Obtain the metallographic image corresponding to the photographed data.

7. A data detection device, characterized in that, The device includes: The acquisition module is used to generate multiple test images corresponding to different emitted light colors based on the captured image data; Multiple test images are composited to obtain a composite test image. Each pixel is compensated according to the composite test image to obtain a metallographic image that emits white light and eliminates moiré noise interference. The photographic data refers to the photographic data obtained by capturing the test screen of the display module; The detection module is used to compare the metallographic image with the image before and after brightness compensation to obtain a detection result, which indicates an anomaly in the algorithm involved in the brightness compensation. The display module exhibits uneven display after brightness compensation.

8. A detection system, characterized in that, The detection system includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the data detection method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the data detection method as described in any one of claims 1 to 6.

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