Maintenance and detection system for liquid crystal module of display screen

Abstract

The invention discloses a maintenance and detection system for a liquid crystal module of a display screen, which relates to the technical field of maintenance and detection and comprises a fault phenomenon test module, a fault reason pre-selection module, an actual data acquisition module, a pre-selected fault confirmation module, a fault phenomenon test module and a fault reason pre-selection module, according to the method, fault phenomena of the display screen liquid crystal module are tested, a highly customized reason diagnosis model is combined, possible fault reasons of the display screen liquid crystal module are preselected, an actual data acquisition module and a preselected fault confirmation module are arranged, and the possible fault reasons of the display screen liquid crystal module are specifically and orderly detected; according to the method, key fault reasons of the display screen liquid crystal module are confirmed preferentially, detection and confirmation of the fault reasons are completed through combination of two modes of deep learning and analogue simulation, and efficient and accurate maintenance and detection work of the display screen liquid crystal module is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of maintenance and detection, and more specifically, to a display screen liquid crystal module maintenance and detection system. Background Art

[0002] With the rapid development of display technology, the LCD module of the display screen is an important component of electronic equipment. Its performance stability and reliability are directly related to the user experience and service life of the entire device. However, in actual applications, the LCD module of the display screen will inevitably have various faults due to the influence of various factors such as the use environment, operation mode, and material aging. These faults not only affect the normal use of the equipment, but may also have an adverse effect on the user's visual experience.

[0003] The traditional maintenance and inspection method of LCD modules for display screens mainly relies on the experience and intuition of maintenance personnel to determine the cause of the fault through observation and testing. However, this method has many shortcomings. First, the experience levels of maintenance personnel vary, which may lead to misjudgment or low maintenance efficiency. Secondly, traditional maintenance and inspection methods often lack scientific data support and accurate diagnostic methods, making it difficult to accurately determine the specific cause and severity of the fault. In addition, with the continuous advancement of LCD module technology for display screens, the types of faults are becoming increasingly complex and diverse, and traditional maintenance and inspection methods can no longer meet actual needs.

[0004] In order to overcome the shortcomings of traditional maintenance and detection methods and improve the efficiency and accuracy of display LCD module maintenance and detection, some maintenance and detection systems based on intelligent technology have gradually been applied to this field in recent years. These systems collect relevant data of display LCD modules and use advanced algorithms and models to perform fault analysis and diagnosis, thereby realizing rapid identification and location of the cause of the fault. However, the existing maintenance and detection systems still have some problems and limitations. For example, although some systems can achieve a preliminary judgment on the cause of the fault, the limited judgment depth can easily lead to inaccurate diagnosis results; although other systems can collect a large amount of data, they lack efficient methods in data processing and analysis, resulting in low detection efficiency.

[0005] Therefore, the present invention proposes a display screen liquid crystal module maintenance and detection system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention aims to provide a display screen liquid crystal module repair and detection system.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] Display LCD module maintenance and detection system, including fault phenomenon test module, fault cause pre-selection module, actual data acquisition module, pre-selected fault confirmation module;

[0009] The fault phenomenon test module is used to perform a fault phenomenon test on the LCD module of the display screen for a period of time t fault Fault phenomenon test, collect the LCD module of the display screen during the fault phenomenon test time t fault Fault phenomenon data within;

[0010] The fault cause preselection module is used to obtain cause diagnosis models of various fault causes, obtain cause diagnosis values ​​of various fault causes according to fault phenomenon data, and determine whether to mark the fault cause as a preselected fault cause based on a comparison result between the cause diagnosis value and the cause diagnosis threshold;

[0011] The actual data acquisition module is used to obtain all pre-selected fault causes of the display screen liquid crystal module, determine the detection data corresponding to each pre-selected fault cause, and perform fault cause detection time t on the display screen liquid crystal module. reason Maintenance inspection, collecting actual data corresponding to the inspection data of each pre-selected fault cause;

[0012] The preselected fault confirmation module is used to obtain the model of the display screen liquid crystal module, obtain the preselected detection value of each preselected fault cause in the display screen liquid crystal module of this model, sort all the preselected fault causes in order from large to small according to the numerical value of the preselected detection value, and obtain the preselected fault value of each preselected fault cause in order according to the sorting, and based on the comparison result of the preselected fault value and the preselected fault boundary value, determine whether to mark the preselected fault cause as a confirmed fault cause.

[0013] Furthermore, the cause diagnosis value of each fault cause is obtained according to the fault phenomenon data, specifically: the fault phenomenon data is preprocessed, the preprocessed fault phenomenon data is input into the cause diagnosis model of each fault cause respectively, and the cause diagnosis value of each fault cause is obtained by output.

[0014] Furthermore, the preselected detection value of the preselected fault cause is obtained by the following steps: obtain all preselected fault values ​​of a preselected fault cause in the LCD module of the display model obtained within k time periods before the current time of the system, mark the total number of preselected fault values ​​as Bray, sum up all preselected fault values ​​and take the average value to obtain the preselected fault mean value Ldst, and use the formula The preselected detection value Zsgy of the preselected fault cause is obtained, wherein h1 is the preselected fault quantity coefficient, and h2 is the preselected fault mean coefficient.

[0015] Furthermore, the preselected fault value of the preselected fault cause is obtained by the following steps: obtaining a first-order fault value Dcmt and a second-order fault value Pgbm of a preselected fault cause, and using the formula A preselected fault value Facp of the preselected fault cause is obtained.

[0016] Furthermore, a first-order fault value Dcmt of a preselected fault cause is obtained by the following steps: obtaining actual data of detection data corresponding to a preselected fault cause, preprocessing the actual data of the detection data and fusing them into a detection data set, obtaining a preselected fault model of the preselected fault cause, inputting the detection data set into the preselected fault model, and the preselected fault model outputs the first-order fault value Dcmt of the preselected fault cause.

[0017] Further, a second-order fault value Pgbm of a preselected fault cause is obtained by the following steps: obtaining ideal data of the detection data corresponding to the other preselected fault causes, building a display screen liquid crystal module simulation model according to the actual data of the detection data corresponding to the preselected fault cause and the ideal data of the detection data corresponding to the other preselected fault causes, and performing a fault phenomenon test on the display screen liquid crystal module simulation model for a duration t fault The fault phenomenon test, during the fault phenomenon test duration t fault The system is divided into multiple test periods, and the fault test value of each test period is obtained, and then the fault test mean Avz and the fault test turbulence mean Avm are obtained. The second-order fault value Pgbm of the pre-selected fault cause is obtained using the formula Pgbm=Avz*sa+Avm*sc, where sa is the fault test coefficient and sc is the test turbulence coefficient.

[0018] Furthermore, the fault test value of the test period is obtained through the following steps: collecting fault phenomenon data of the display screen LCD module simulation model during the test period, preprocessing the fault phenomenon data, inputting the preprocessed fault phenomenon data into the fault test model, and the fault test model outputs the fault test value of the test period.

[0019] Furthermore, the fault test mean value Avz is obtained by the following steps: the fault test values ​​of all test periods are summed up and averaged to obtain the fault test mean value Avz.

[0020] Furthermore, the fault test turbulence mean Avm is obtained through the following steps: all fault test values ​​are sorted in sequence according to the order of the test time periods, the difference between two adjacent fault test values ​​after sorting is calculated and the absolute value is taken to obtain the fault test turbulence value, all fault test turbulence values ​​are summed up and the average is taken to obtain the fault test turbulence mean Avm.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] A fault phenomenon testing module and a fault cause pre-selection module are set up to perform fault phenomenon testing on the LCD module of the display screen, and pre-select the possible fault causes of the LCD module of the display screen in combination with a highly customized cause diagnosis model. An actual data acquisition module and a pre-selected fault confirmation module are set up to conduct targeted and orderly detection of possible fault causes of the LCD module of the display screen, and give priority to confirming and processing the key fault causes of the LCD module of the display screen. The detection and confirmation of the fault causes are completed through the combination of deep learning and simulation, ensuring efficient and accurate maintenance and inspection of the LCD module of the display screen. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is the system module diagram of the display screen LCD module maintenance and detection system;

[0024] Figure 2 This is the operation flow chart of the display screen LCD module maintenance and detection system;

[0025] Figure 3 A method for obtaining the second-order fault value for the preselected fault cause. DETAILED DESCRIPTION

[0026] Reference Figures 1 to 3 The display screen LCD module maintenance and detection system includes a fault phenomenon testing module, a fault cause pre-selection module, an actual data acquisition module, and a pre-selected fault confirmation module.

[0027] Fault phenomenon test module: Set the fault phenomenon test duration to t fault (t fault The system preset time, which is adjusted according to the demand), the fault phenomenon test time of the LCD module of the display screen (to be repaired and tested) is t fault Fault phenomenon test (fault phenomenon test is to record the LCD module of the display screen during the fault phenomenon test time t fault The performance of the LCD module is collected during the fault phenomenon test time t fault The fault phenomenon data within (the fault phenomenon data includes black screen frequency, black screen duration, white screen frequency, white screen duration, flower screen frequency, flower screen duration, brightness data of each area of ​​the screen, signal interruption duration, color cast data, saturation data, etc.) are all fault phenomenon test duration t fault collected within).

[0028] Fault cause pre-selection module: obtain cause diagnosis models of various fault causes (types of fault causes include but are not limited to backlight module failure, liquid crystal layer failure, drive circuit failure, etc.), preprocess the fault phenomenon data (preprocessing methods include data cleaning, data normalization, etc.), input the preprocessed fault phenomenon data into the cause diagnosis model of each fault cause respectively, output the cause diagnosis value of each fault cause, set the cause diagnosis threshold (the cause diagnosis threshold is a system-defined preset value, and the size of the preset value is adjusted according to demand), when the cause diagnosis value of the fault cause is greater than or equal to the cause diagnosis threshold, mark the fault cause as a pre-selected fault cause, and when the cause diagnosis value of the fault cause is greater than or equal to the cause diagnosis threshold, no further processing is performed.

[0029] Each fault cause corresponds to a cause diagnosis model. For example, backlight module fault and liquid crystal layer fault each correspond to a cause diagnosis model. All cause diagnosis models are built based on deep learning models. Next, we will introduce the construction process of the cause diagnosis model of backlight module fault: collect multiple fault phenomenon data, each fault phenomenon data is collected during the fault phenomenon test time t fault The deep learning model is constructed, each fault phenomenon data is preprocessed, and the preprocessed fault phenomenon data is used as the training data of the deep learning model. A cause diagnosis value is assigned to each training data. The value range of the cause diagnosis value is (1.1-3.9). The closer the cause diagnosis value is to 3.9, the more likely the display screen LCD module is to have a backlight module fault. The closer the cause diagnosis value is to 1.1, the less likely the display screen LCD module is to have a backlight module fault (if it is a cause diagnosis model of liquid crystal layer fault, the closer the cause diagnosis value is to 3.9, the more likely the display screen LCD module is to have a liquid crystal layer fault. The closer the cause diagnosis value is to 1.1, the less likely the display screen LCD module is to have a liquid crystal layer fault). The training data is divided into a training set, a validation set, and a test set in a ratio of 2:2:1. The training set, the validation set, and the test set are trained. After the training is completed, a cause diagnosis model for the backlight module fault is constructed.

[0030] Actual data acquisition module: obtain all pre-selected fault causes of the LCD module of the display screen, and set the fault cause detection time to t reason, determine the detection data corresponding to each pre-selected fault cause (for different pre-selected fault causes, the corresponding detection data are also different, for example, for the pre-selected fault cause is a backlight module fault, the corresponding detection data includes backlight module current and voltage data, backlight module brightness data, backlight module temperature data, etc., for example, for the pre-selected fault cause is a liquid crystal layer fault, the corresponding detection data includes liquid crystal layer brightness data, liquid crystal layer contrast data, liquid crystal layer color performance data, liquid crystal layer response data, for example, for the pre-selected fault cause is a drive circuit fault, the corresponding detection data includes drive circuit current and voltage data, drive circuit signal data, drive circuit temperature data), and perform fault cause detection time t on the display screen liquid crystal module reason Maintenance inspection, collect the actual data of the inspection data corresponding to each pre-selected fault cause.

[0031] Preselected fault confirmation module: obtain the model of the display screen LCD module, obtain the preselected detection value of each preselected fault cause in the display screen LCD module of this model, sort all the preselected fault causes in order from large to small according to the numerical value of the preselected detection value, and obtain the preselected fault value of each preselected fault cause in order, and set the preselected fault boundary value each time the preselected fault value of a preselected fault cause is obtained (the preselected fault boundary value is a system-defined preset value, and the size of the preset value is adjusted according to demand). When the preselected fault value of the preselected fault cause is greater than or equal to the preselected fault boundary value, the preselected fault cause is marked as a confirmed fault cause (maintenance and inspection personnel can perform maintenance on the display screen LCD module according to the confirmed fault cause). When the preselected fault value of the preselected fault cause is less than the preselected fault boundary value, no further processing is performed.

[0032] The preselected detection value of the preselected fault cause is obtained by the following steps: obtain all preselected fault values ​​of a preselected fault cause in the LCD module of the display model within k time periods before the current system time, mark the total number of preselected fault values ​​as Bray, sum up all preselected fault values ​​and take the average value to obtain the preselected fault mean value Ldst, and use the formula The preselected detection value Zsgy of the preselected fault cause is obtained, where h1 is the preselected fault quantity coefficient, h2 is the preselected fault mean coefficient, the value of h1 is 0.58, and the value of h2 is 0.49.

[0033] The preselected fault value of the preselected fault cause is obtained by the following steps: obtaining actual data of detection data corresponding to a preselected fault cause, preprocessing the actual data of the detection data and fusing them into a detection data set, obtaining a preselected fault model of the preselected fault cause, inputting the detection data set into the preselected fault model, the preselected fault model outputting the first-order fault value Dcmt of the preselected fault cause, obtaining ideal data of the detection data corresponding to the remaining preselected fault causes (for preselected fault causes other than the selected fault cause, the ideal data is system preset data, and the performance data of the detection data corresponding to the preselected fault cause under the normal working state of the display screen liquid crystal module), constructing a display screen liquid crystal module simulation model according to the actual data of the detection data corresponding to the preselected fault cause and the ideal data of the detection data corresponding to the remaining preselected fault causes, and performing a fault phenomenon test on the display screen liquid crystal module simulation model for a duration t fault The fault phenomenon test, during the fault phenomenon test duration t fault The test period is divided into multiple test periods, and the fault test value of each test period is obtained. The fault test values ​​of all test periods are summed and averaged to obtain the fault test mean Avz. All fault test values ​​are sorted in sequence according to the order of the test periods. The difference between two adjacent fault test values ​​after sorting is calculated and the absolute value is taken to obtain the fault test turbulence value. All fault test turbulence values ​​are summed and averaged to obtain the fault test turbulence mean Avm. The second-order fault value Pgbm of the pre-selected fault cause is obtained by using the formula Pgbm=Avz*sa+Avm*sc, where sa is the fault test coefficient, sc is the test turbulence coefficient, the value of sa is 1.38, the value of sc is 0.41, and the formula is used. A preselected fault value Facp of the preselected fault cause is obtained.

[0034] The fault test value of the test period is obtained by the following steps: collecting fault phenomenon data of the display screen liquid crystal module simulation model during the test period, preprocessing the fault phenomenon data, inputting the preprocessed fault phenomenon data into the fault test model, and the fault test model outputs the fault test value of the test period;

[0035] The fault test model is constructed as follows: collect multiple groups of fault phenomenon data, build a deep learning model, use the fault phenomenon data as training data for the deep learning model, assign a fault test value to each training data, and the fault test value range is (1-2). The closer the fault test value is to 2, the more serious the fault phenomenon of the LCD module of the display screen is, and the closer the fault test value is to 1, the less serious the fault phenomenon of the LCD module of the display screen is. The training data is divided into a training set, a validation set, and a test set in a ratio of 3:2:1, and the training set, validation set, and test set are trained. After the training is completed, a fault test model is constructed.

[0036] The construction method of the display screen LCD module simulation model is as follows: select simulation software such as MATLAB Simulink and NI Multisim, create a new model in the device simulation software, add corresponding entities and set corresponding parameters in the model according to the actual data of the detection data corresponding to the pre-selected fault cause and the ideal data of the detection data corresponding to the other pre-selected fault causes, and then construct the display screen LCD module simulation model.

[0037] The actual data of all detection data are preprocessed and merged into a detection data set. Specifically, the actual data of the detection data are cleaned and normalized, and then the actual data of the detection data are integrated into a unified data set to obtain the detection data set.

[0038] Each fault cause corresponds to a pre-selected fault model, and all pre-selected fault models are constructed based on the deep learning model. Next, the construction process of the pre-selected fault model for the backlight module fault is introduced: multiple detection data sets for the backlight module fault are collected (each detection data set includes backlight module current and voltage data, backlight module brightness data, backlight module temperature data and other data), and a deep learning model is constructed. The detection data set of the backlight module fault is used as the training data of the deep learning model, and a first-order fault value is assigned to each training data. The value range of the first-order fault value is (0.1-0.9). The closer the value of the first-order fault value is to 0.9, the more serious the backlight module fault is, and the closer the value of the first-order fault value is to 0.1, the less serious the backlight module fault is. The training data is divided into a training set, a validation set and a test set in a ratio of 3:1:1, and the training set, validation set and test set are trained. After the training is completed, a pre-selected fault model for the backlight module fault is constructed.

[0039] A fault phenomenon testing module and a fault cause pre-selection module are set up to perform fault phenomenon testing on the LCD module of the display screen, and pre-select the possible fault causes of the LCD module of the display screen in combination with a highly customized cause diagnosis model. An actual data acquisition module and a pre-selected fault confirmation module are set up to conduct targeted and orderly detection of possible fault causes of the LCD module of the display screen, and give priority to confirming and processing the key fault causes of the LCD module of the display screen. The detection and confirmation of the fault causes are completed through the combination of deep learning and simulation, ensuring efficient and accurate maintenance and inspection of the LCD module of the display screen.

[0040] The above formulas are all dimensionless and numerical calculations are performed, and the preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0041] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0042] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0043] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0044] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0045] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0046] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0047] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. Display screen LCD module maintenance and detection system, characterized in that: It includes fault phenomenon testing module, fault cause pre-selection module, actual data acquisition module and pre-selected fault confirmation module; The fault phenomenon test module is used to perform a fault phenomenon test on the LCD module of the display screen for a period of time t fault Fault phenomenon test, collect the LCD module of the display screen during the fault phenomenon test time t fault Fault phenomenon data within; The fault cause preselection module is used to obtain cause diagnosis models of various fault causes, obtain cause diagnosis values ​​of various fault causes according to fault phenomenon data, and determine whether to mark the fault cause as a preselected fault cause based on a comparison result between the cause diagnosis value and the cause diagnosis threshold; The actual data acquisition module is used to obtain all pre-selected fault causes of the display screen liquid crystal module, determine the detection data corresponding to each pre-selected fault cause, and perform fault cause detection time t on the display screen liquid crystal module. reason Maintenance inspection, collecting actual data corresponding to the inspection data of each pre-selected fault cause; The preselected fault confirmation module is used to obtain the model of the display screen liquid crystal module, obtain the preselected detection value of each preselected fault cause in the display screen liquid crystal module of this model, sort all the preselected fault causes in order from large to small according to the numerical value of the preselected detection value, and obtain the preselected fault value of each preselected fault cause in order according to the sorting, and based on the comparison result of the preselected fault value and the preselected fault boundary value, determine whether to mark the preselected fault cause as a confirmed fault cause.

2. The display screen liquid crystal module maintenance and detection system according to claim 1, characterized in that: The cause diagnosis value of each fault cause is obtained according to the fault phenomenon data, specifically: the fault phenomenon data is preprocessed, the preprocessed fault phenomenon data is input into the cause diagnosis model of each fault cause respectively, and the cause diagnosis value of each fault cause is obtained by output.

3. The display screen liquid crystal module maintenance and detection system according to claim 1, characterized in that: The preselected detection value of the preselected fault cause is obtained by the following steps: obtain all preselected fault values ​​of a preselected fault cause in the LCD module of the display model within k time periods before the current system time, mark the total number of preselected fault values ​​as Bray, sum up all preselected fault values ​​and take the average value to obtain the preselected fault mean value Ldst, and use the formula The preselected detection value Zsgy of the preselected fault cause is obtained, wherein h1 is the preselected fault quantity coefficient, and h2 is the preselected fault mean coefficient.

4. The display screen liquid crystal module maintenance and detection system according to claim 1, characterized in that: The preselected fault value of the preselected fault cause is obtained by the following steps: obtain a first-order fault value Dcmt and a second-order fault value Pgbm of a preselected fault cause, and use the formula A preselected fault value Facp of the preselected fault cause is obtained.

5. The display screen liquid crystal module maintenance and detection system according to claim 1, characterized in that: A first-order fault value Dcmt of a preselected fault cause is obtained by the following steps: obtaining actual data of detection data corresponding to a preselected fault cause, preprocessing the actual data of the detection data and fusing them into a detection data set, obtaining a preselected fault model of the preselected fault cause, inputting the detection data set into the preselected fault model, and the preselected fault model outputs the first-order fault value Dcmt of the preselected fault cause.

6. The display screen liquid crystal module maintenance and detection system according to claim 1, characterized in that: The second-order fault value Pgbm of a preselected fault cause is obtained by the following steps: obtaining the ideal data of the detection data corresponding to the other preselected fault causes, building a display screen liquid crystal module simulation model according to the actual data of the detection data corresponding to the preselected fault cause and the ideal data of the detection data corresponding to the other preselected fault causes, and performing a fault phenomenon test on the display screen liquid crystal module simulation model for a duration t fault The fault phenomenon test, during the fault phenomenon test duration t fault The system is divided into multiple test periods, and the fault test value of each test period is obtained, and then the fault test mean Avz and the fault test turbulence mean Avm are obtained. The second-order fault value Pgbm of the pre-selected fault cause is obtained using the formula Pgbm=Avz*sa+Avm*sc, where sa is the fault test coefficient and sc is the test turbulence coefficient.

7. The display screen liquid crystal module maintenance and detection system according to claim 6, characterized in that: The fault test value of the test period is obtained through the following steps: collecting fault phenomenon data of the display screen LCD module simulation model during the test period, preprocessing the fault phenomenon data, inputting the preprocessed fault phenomenon data into the fault test model, and the fault test model outputs the fault test value of the test period.

8. The display screen liquid crystal module maintenance and detection system according to claim 6, characterized in that: The fault test mean value Avz is obtained by the following steps: the fault test values ​​of all test periods are summed up and averaged to obtain the fault test mean value Avz.

9. The display screen liquid crystal module maintenance and detection system according to claim 6, characterized in that: The fault test turbulence mean Avm is obtained by the following steps: sort all fault test values ​​in the order of the test time periods, calculate the difference between two adjacent fault test values ​​after sorting and take the absolute value to obtain the fault test turbulence value, sum all fault test turbulence values ​​and take the average to obtain the fault test turbulence mean Avm.

Images