Display screen fault detection and repair method based on Mini LED array

Abstract

The invention discloses a display screen fault detection and repair method based on a Mini LED array, and the method comprises the steps: carrying out the real-time fault detection: carrying out the optical detection of each pixel of a Mini LED array display screen through a high-resolution multispectral sensor and a deep learning algorithm, obtaining the brightness, color temperature, color difference and spectrum information of the display screen in real time, carrying out the comprehensive fault monitoring of the display screen, and carrying out the real-time fault detection. Comprising defects of bright spots, dead points, uneven brightness and the like, and deep learning fault analysis: carrying out automatic processing on pixel data collected in real time by adopting deep learning algorithms such as a convolutional neural network (CNN) and the like. According to the display screen fault detection and repair method based on the Mini LED array, through adoption of a high-resolution multispectral sensor, sub-nanometer level detection can be realized, any tiny defect in the Mini LED array can be accurately captured, and the sensitivity and the accuracy of fault detection are remarkably improved. Through the assistance of the deep learning algorithm, the fault pixel type can be intelligently identified, the identification accuracy is improved, and false alarm and missing report are reduced.

Description

Technical Field

[0001] The present invention relates to the field of Mini LED display technology, and specifically to a display screen fault detection and repair method based on a Mini LED array. Background Art

[0002] This display screen fault detection and repair method based on a Mini LED array uses high-precision sensors and intelligent algorithms to detect faults in the Mini LED array in the display in real time, including bright spots, dead spots, uneven brightness, and other defects. This method uses image processing technology to monitor each pixel of the display, automatically identifying and analyzing the faulty area. For detected faults, the intelligent repair system adjusts the local current or drive signal of the faulty pixel to restore normal display performance. In addition, the system can dynamically adjust display parameters based on environmental changes (such as temperature and humidity), ensuring the stability of the display under different environmental conditions, thereby improving display quality and extending the service life of the display.

[0003] In the existing technology, the fault detection and repair methods of display screens based on Mini LED arrays have the following shortcomings: most existing fault detection methods rely on traditional sensors or simple image processing algorithms, which may not be able to accurately identify subtle pixel problems, especially in high-resolution or large-size displays, where it is difficult to accurately detect tiny bright spots, dead spots or uneven brightness problems; current repair methods mainly rely on current regulation or simple signal correction. For severely damaged pixels, the repair effect is limited and may not be able to fully restore the display effect, resulting in the faulty area still significantly affecting the overall visual experience; some repair methods require adjusting or reconfiguring the drive signal pixel by pixel, which is not only time-consuming but also increases the processing delay of the display, affecting the screen's immediate response performance, especially in dynamic displays or real-time applications. Existing repair methods generally lack dynamic adaptability to different environmental changes (such as temperature, humidity, etc.) and are easily affected by external environmental factors, resulting in unstable repair results, affecting the display effect after long-term use; most existing technologies rely on manual or simple automated detection and repair processes, lacking intelligent and self-learning capabilities. The lack of a dynamic, adaptive repair system tailored to specific display conditions means that all potential issues may not be detected during the repair process, especially when the display is used for a long time or has aged. Existing technologies require complex hardware and expensive sensors, increasing production costs. Furthermore, the extensive computational and data processing involved in the detection and repair process can lead to low system efficiency, making it unsuitable for large-scale production or real-time applications.

[0004] To this end, we propose a display screen fault detection and repair method based on Mini LED array. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution: a display screen fault detection and repair method based on a Mini LED array, comprising the following steps:

[0006] S1: Real-time fault detection: Utilizes high-resolution multispectral sensors and deep learning algorithms to perform optical inspections on each pixel of the Mini LED array display, acquiring real-time information on the display's brightness, color temperature, color difference, and spectrum. This allows for comprehensive fault monitoring of the display, including bright spots, dead spots, uneven brightness, and other defects.

[0007] S2: Deep Learning Fault Analysis: This uses deep learning algorithms such as convolutional neural networks (CNNs) to automatically process real-time pixel data, identify different types of display faults, locate and classify the faulty areas, assess their severity, and generate a priority list for fault repair.

[0008] S3: Automatic repair and compensation: For identified faulty pixels, automatic repair and compensation are performed through the current regulation module, drive signal optimization module, and image compensation algorithm to ensure that the display effect of the faulty area is restored to the optimal state;

[0009] S4: Environmental adaptability calibration: Real-time monitoring of external environmental parameters such as temperature, humidity, and light intensity, and automatic adjustment of the display's operating parameters such as driving current, brightness, and color temperature based on monitored environmental changes to adapt to different environmental conditions and ensure a stable display effect;

[0010] S5: Intelligent self-diagnosis and self-repair function: Through the integrated intelligent diagnosis module, it continuously monitors the pixel status of the display, automatically detects and repairs potential faults, and provides early warning and repairs before faults occur, ensuring long-term stability of the display effect;

[0011] S6: Fault warning and reporting function: Through fault trend analysis and prediction, potential faults of the display screen can be identified and warned in advance, fault reports can be generated and pushed to operation and maintenance personnel, and detailed fault data analysis can be provided.

[0012] Preferably, the deep learning algorithm uses a pre-trained convolutional neural network (CNN) model to continuously optimize network parameters through training data sets to improve the accuracy of fault detection and repair effects.

[0013] Preferably, the multispectral sensor can simultaneously capture spectral information from visible light to near infrared, has nanometer-level optical resolution, and can perform high-precision detection on each pixel on the display screen.

[0014] Preferably, the environmental adaptability calibration monitors environmental variables in real time through temperature and humidity sensors, light intensity sensors, and air quality sensors, and automatically adjusts the operating parameters of the display screen to ensure consistency in display quality.

[0015] Preferably, the intelligent self-diagnosis and self-repair function dynamically identifies and repairs faults through algorithms, and can perform self-learning and optimization based on feedback data during long-term operation.

[0016] Preferably, the automatic repair is carried out by the following modules working together: the current regulation module repairs pixels with uneven brightness, the drive signal optimization module is used to adjust the drive current of the failed pixels, and the image compensation module is used to deal with the dead point problem and perform compensation display of surrounding pixels.

[0017] Compared with the prior art, the present invention provides a display screen fault detection and repair method based on a Mini LED array, which has the following beneficial effects:

[0018] 1. This Mini LED array-based display screen fault detection and repair method utilizes a high-resolution multispectral sensor to achieve sub-nanometer detection, accurately capturing any tiny defects in the Mini LED array and significantly improving the sensitivity and accuracy of fault detection. Assisted by a deep learning algorithm, it can intelligently identify the type of faulty pixel, improving recognition accuracy and reducing false positives and missed detections.

[0019] 2. This Mini LED array-based display fault detection and repair method utilizes deep learning technology for fault analysis and automated repair mechanisms. Upon detecting a display fault, it automatically adjusts the drive current, optimizes the signal and image, and rapidly restores the display. Compared to traditional manual repair methods, this solution is not only faster but also capable of handling complex display faults without compromising display quality.

[0020] 3. This Mini LED array-based display screen fault detection and repair method uses environmental adaptability calibration technology to enable the display screen to self-adjust under varying environmental conditions, such as temperature and humidity, thereby ensuring consistent display quality. The display screen maintains stable performance under varying external environmental conditions without manual intervention, enhancing the system's intelligence and adaptability.

[0021] 4. This Mini LED array-based display screen fault detection and repair method, through built-in intelligent diagnostic and prediction mechanisms, can identify potential problems before they occur and provide early warnings, thereby reducing the probability of sudden failures. At the same time, the system can automatically generate fault reports for operation and maintenance personnel to carry out timely repairs, avoiding the serious impact caused by undetected faults.

[0022] 5. This Mini LED array-based display screen fault detection and repair method, through automatic repair and long-term self-learning capabilities, can significantly improve display screen stability, reduce the occurrence of long-term display screen failures, and extend its service life. Because the system can adapt to environmental changes in real time, it can maintain efficient repair results even in extreme environments such as high load, high temperature, or humidity.

[0023] 6. This Mini LED array-based display screen fault detection and repair method significantly reduces the time and cost of manual intervention and manual inspection, as most fault detection and repair processes can be automated. Furthermore, the intelligent fault warning system can identify potential faults in advance, reducing the frequency of maintenance during system operation and further reducing repair costs. DETAILED DESCRIPTION

[0024] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0025] Example

[0026] An embodiment of a display screen fault detection and repair method based on Mini LED array

[0027] A method for detecting and repairing display screen faults based on a Mini LED array includes the following steps:

[0028] S1: Real-time fault detection: Utilizes high-resolution multispectral sensors and deep learning algorithms to perform optical inspections on each pixel of the Mini LED array display, acquiring real-time information on the display's brightness, color temperature, color difference, and spectrum. This allows for comprehensive fault monitoring of the display, including bright spots, dead spots, uneven brightness, and other defects.

[0029] S2: Deep Learning Fault Analysis: This uses deep learning algorithms such as convolutional neural networks (CNNs) to automatically process real-time pixel data, identify different types of display faults, locate and classify the faulty areas, assess their severity, and generate a priority list for fault repair.

[0030] S3: Automatic repair and compensation: For identified faulty pixels, automatic repair and compensation are performed through the current regulation module, drive signal optimization module, and image compensation algorithm to ensure that the display effect of the faulty area is restored to the optimal state;

[0031] S4: Environmental adaptability calibration: Real-time monitoring of external environmental parameters such as temperature, humidity, and light intensity, and automatic adjustment of the display's operating parameters such as driving current, brightness, and color temperature based on monitored environmental changes to adapt to different environmental conditions and ensure a stable display effect;

[0032] S5: Intelligent self-diagnosis and self-repair function: Through the integrated intelligent diagnosis module, it continuously monitors the pixel status of the display, automatically detects and repairs potential faults, and provides early warning and repairs before faults occur, ensuring long-term stability of the display effect;

[0033] S6: Fault warning and reporting function: Through fault trend analysis and prediction, potential faults of the display screen can be identified and warned in advance, fault reports can be generated and pushed to operation and maintenance personnel, and detailed fault data analysis can be provided.

[0034] Specifically, the deep learning algorithm uses a pre-trained convolutional neural network (CNN) model to continuously optimize network parameters through training data sets to improve the accuracy of fault detection and repair effects.

[0035] Specifically, the multispectral sensor can simultaneously capture spectral information from visible light to near-infrared, has nanometer-level optical resolution, and can perform high-precision detection on each pixel on the display screen.

[0036] Specifically, environmental adaptability calibration monitors environmental variables in real time through temperature and humidity sensors, light intensity sensors, and air quality sensors, and automatically adjusts the operating parameters of the display to ensure consistency in display quality.

[0037] Specifically, the intelligent self-diagnosis and self-repair functions dynamically identify and repair faults through algorithms, and can self-learn and optimize based on feedback data during long-term operation.

[0038] Specifically, the automatic repair is achieved through the collaborative work of the following modules: the current regulation module repairs pixels with uneven brightness, the drive signal optimization module is used to adjust the drive current of failed pixels, and the image compensation module is used to deal with dead point problems and compensate for the display of surrounding pixels.

[0039] Through the above technical solution, the present invention, by using a high-resolution multispectral sensor, can achieve sub-nanometer-level detection, accurately capture any tiny defects in the Mini LED array, and significantly improve the sensitivity and accuracy of fault detection. With the assistance of deep learning algorithms, it can intelligently identify the type of faulty pixels, improve recognition accuracy, and reduce false positives and missed reports. The fault analysis and automatic repair mechanism based on deep learning technology can automatically adjust the drive current, optimize the signal and image for repair after detecting a display screen fault, and quickly restore the display effect. Compared with traditional manual repair methods, this solution is not only fast in repair speed, but also can handle complex display faults and avoid affecting display quality. The environmental adaptability calibration technology enables the display screen to self-adjust under different environmental conditions such as temperature and humidity, thereby ensuring the consistency of display quality. Under different external environmental conditions, the display screen can maintain stable performance without manual intervention, which improves the intelligence and adaptability of the system. Through the built-in intelligent diagnosis and prediction mechanism, potential problems can be identified and early warnings can be issued before the fault occurs, thereby reducing the probability of sudden failures. At the same time, the system can automatically generate fault reports for maintenance personnel to carry out timely repairs, avoiding the serious impact caused by undetected faults. Through automatic repair and long-term self-learning functions, this method can greatly improve the stability of the display screen, reduce the occurrence of faults during long-term use, and extend the service life. Because the system can adapt to environmental changes in real time, it can still maintain efficient repair results in extreme environments such as high load, high temperature, or humidity. Because most fault detection and repair processes can be automated, this method can significantly reduce the time cost of manual intervention and manual inspection. In addition, the intelligent fault warning system can identify potential faults in advance, reducing the frequency of maintenance during system use and further reducing maintenance costs.

[0040] Fault detection and analysis:

[0041] On the production line, each pixel of the display is optically scanned by a high-resolution multispectral sensor. The sensor collects real-time data on the display's brightness, color temperature, and color difference, transmitting this data to a deep learning algorithm for analysis. A convolutional neural network (CNN) model analyzes the spectral characteristics of each pixel to determine whether it is faulty and classify the fault type (e.g., bright spot, dead spot, uneven brightness, etc.).

[0042] Troubleshooting process:

[0043] Once a fault is detected, the system automatically selects a repair strategy. For pixels with uneven brightness, the current regulation module adjusts the current in the corresponding area based on the severity of the fault to balance the brightness. For dead pixels, the image compensation module uses the values ​​of adjacent pixels to repair and restore the display quality. The drive signal optimization module adjusts the current driving the faulty pixel to achieve a more uniform overall display effect.

[0044] Environmental adaptability calibration:

[0045] The system integrates multiple environmental sensors to monitor the display's operating environment in real time. For example, when the temperature rises, the system automatically adjusts the current output to reduce the screen's workload. In high humidity environments, the system adjusts brightness and color temperature to compensate for brightness loss and color shift.

[0046] Intelligent diagnosis and self-repair:

[0047] During normal operation, the system continuously monitors the status of each pixel and uses intelligent diagnostics to identify any new faults. All detected faults are prioritized, automatically repaired, and a repair report is generated and sent to maintenance personnel to ensure a timely response.

[0048] Fault warning and reporting:

[0049] The system stores all fault data and analyzes trends to predict the types of future display screen failures. Early warning information is promptly pushed to the operation and maintenance system, helping operators take preventive measures. The system also generates detailed fault reports, analyzing the causes of the problems and providing repair recommendations.

[0050] The present invention proposes a fully automatic, high-precision, and environmentally adaptable Mini LED display screen fault detection and repair method through multispectral sensors, deep learning algorithms, intelligent fault repair systems, and environmental adaptability adjustments. Through automatic detection, repair, and adaptive adjustment, the display quality is significantly improved, the service life of the display screen is extended, while maintenance costs are reduced and production efficiency is improved. This technology has broad application prospects in the fields of high-end display screens, televisions, mobile phones, and billboards. Although the embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A display screen fault detection and repair method based on a Mini LED array, characterized by: The following steps are involved: S1: Real-time fault detection: Utilizes high-resolution multispectral sensors and deep learning algorithms to perform optical inspections on each pixel of the Mini LED array display, acquiring real-time information on the display's brightness, color temperature, color difference, and spectrum. This allows for comprehensive fault monitoring of the display, including bright spots, dead spots, uneven brightness, and other defects. S2: Deep Learning Fault Analysis: This uses deep learning algorithms such as convolutional neural networks (CNNs) to automatically process real-time pixel data, identify different types of display faults, locate and classify the faulty areas, assess their severity, and generate a priority list for fault repair. S3: Automatic repair and compensation: For identified faulty pixels, automatic repair and compensation are performed through the current regulation module, drive signal optimization module, and image compensation algorithm to ensure that the display effect of the faulty area is restored to the optimal state; S4: Environmental adaptability calibration: Real-time monitoring of external environmental parameters such as temperature, humidity, and light intensity, and automatic adjustment of the display's operating parameters such as driving current, brightness, and color temperature based on monitored environmental changes to adapt to different environmental conditions and ensure a stable display effect; S5: Intelligent self-diagnosis and self-repair function: Through the integrated intelligent diagnosis module, it continuously monitors the pixel status of the display, automatically detects and repairs potential faults, and provides early warning and repairs before faults occur, ensuring long-term stability of the display effect; S6: Fault warning and reporting function: Through fault trend analysis and prediction, potential faults of the display screen can be identified and warned in advance, fault reports can be generated and pushed to operation and maintenance personnel, and detailed fault data analysis can be provided.

2. The method for detecting and repairing display screen faults based on a Mini LED array according to claim 1, wherein: The deep learning algorithm uses a pre-trained convolutional neural network (CNN) model to continuously optimize network parameters through training data sets to improve the accuracy of fault detection and repair effects.

3. The method for detecting and repairing display screen faults based on a Mini LED array according to claim 1, wherein: The multispectral sensor can simultaneously capture spectral information from visible light to near-infrared, has nanometer-level optical resolution, and can perform high-precision detection on each pixel on the display screen.

4. The method for detecting and repairing display screen faults based on a Mini LED array according to claim 1, wherein: The environmental adaptability calibration monitors environmental variables in real time through temperature and humidity sensors, light intensity sensors, and air quality sensors, and automatically adjusts the operating parameters of the display screen to ensure consistency in display quality.

5. The method for detecting and repairing display screen faults based on a Mini LED array according to claim 1, wherein: The intelligent self-diagnosis and self-repair function dynamically identifies and repairs faults through algorithms, and can self-learn and optimize based on feedback data during long-term operation.

6. The method for detecting and repairing display screen faults based on a Mini LED array according to claim 1, wherein: The automatic repair is achieved through the collaborative work of the following modules: a current regulation module to repair pixels with uneven brightness, a drive signal optimization module to adjust the drive current of failed pixels, and an image compensation module to address dead pixel problems and compensate for surrounding pixels.