An on-line detection management method for liquid crystal display

Abstract

The application provides an online detection management method for a liquid crystal display screen, and relates to the technical field of data processing.The method comprises the following steps: dividing a verified reference axis at equal intervals to generate a plurality of continuous detection analysis sections, and calculating a dynamic compensation coefficient according to the dispersion degree of all the detection analysis sections; correcting a judgment standard value in a detection parameter set according to the dynamic compensation coefficient to obtain a corrected judgment standard value; comparing and analyzing an initial detection data set with the corrected judgment standard value to determine whether each detection item is qualified, and obtaining an identification result of a bad type to which an unqualified item belongs; generating a final quality judgment result based on the bad type to which the unqualified item belongs and a defect grade corresponding to each unqualified item; and performing a corresponding management action according to the final quality judgment result.The application improves the detection judgment accuracy and rationality, and effectively balances the detection precision and production control efficiency.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an online detection and management method for liquid crystal displays. Background Technology

[0002] In the manufacturing process of LCD screens, online inspection is a crucial step in ensuring product quality. Currently, many inspection solutions rely on preset fixed judgment standards (such as brightness uniformity thresholds and color tolerances), which are used to scan the effective display area and compare it with fixed standard values ​​to determine the product's qualification. However, during large-scale mass production, factors such as batch differences in raw materials (e.g., slight differences in the light transmittance of different batches of ITO glass), minor changes in the state of production equipment (e.g., slight deviations in the control of adhesive layer thickness by the coating machine), and short-term fluctuations in the workshop environment (e.g., the impact of small changes in temperature and humidity on liquid crystal alignment) can cause even the same model of LCD screen to exhibit slight fluctuations in its basic display characteristics.

[0003] Existing detection methods that rely on fixed thresholds have relatively limited adaptability to these subtle overall drifts. In some cases, they may struggle to accurately distinguish between overall baseline drift and genuine local defects, potentially leading to over- or under-detection. For example, a batch of TN LCD displays used in smart meters may have slightly lower overall brightness than usual due to batch variations in ITO glass raw materials. However, the brightness uniformity across different areas of the screen is good, and there are no dead pixels, foreign objects, or other appearance issues, fully meeting actual usage requirements. Yet, it may be deemed unqualified because the fixed brightness threshold does not adapt to such subtle drifts. Similarly, a batch of TFT-LCM modules used in automotive applications may have fluctuating backlight LED chip temperature stability. While the overall chromaticity may not exceed the fixed threshold, localized chromaticity shifts in certain areas of the screen may exceed usage requirements. This may be overlooked because the fixed threshold only considers overall data. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an online inspection and management method for liquid crystal displays, which improves the accuracy and rationality of inspection and judgment, and effectively ensures a balance between inspection accuracy and production control efficiency.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, an online detection and management method for liquid crystal displays (LCDs), the method comprising: Step 1: Set the set of detection parameters according to the product type of the LCD screen; Step 2: Based on the set of detection parameters, perform detection operations on the LCD screens on the production line to generate an initial set of detection data; Step 3: Based on the initial detection data set, establish a first reference detection area and a second reference detection area within the effective display area of ​​the LCD screen; determine a reference axis line that runs through the two areas according to the first and second reference detection areas, and verify it using a line segment intersection judgment algorithm to obtain the verified reference axis line. Step 4: Divide the calibrated reference axis into equal intervals to generate multiple continuous detection and analysis segments, and calculate the dynamic compensation coefficient based on the dispersion of all detection and analysis segments. Step 5: Based on the dynamic compensation coefficient, correct the judgment standard values ​​in the detection parameter set to obtain the corrected judgment standard values; Step 6: Compare and analyze the initial test data set with the corrected judgment standard value to determine whether each test item is qualified and obtain the identification result of the defect type to which the unqualified item belongs; Step 7: Generate the final quality assessment result based on the defect type of the nonconformity and the defect level of each nonconformity; and execute the corresponding management actions based on the final quality assessment result.

[0006] In a second aspect, a computing device includes: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0007] Thirdly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0008] The above-described solution of the present invention has at least the following beneficial effects: By establishing a benchmark detection area, generating a calibration benchmark axis, calculating dynamic compensation coefficients, and correcting judgment standard values, the judgment threshold can be dynamically adjusted based on the actual display characteristics of the current product. This avoids misjudging qualified products due to minor overall drifts, such as TN screens with slightly lower overall brightness but good uniformity not being judged as unqualified. It also identifies overall local defects, such as local color deviations in TFT-LCM modules not being ignored, thus improving the accuracy and rationality of detection judgments. Through dynamic compensation mechanisms and targeted management actions, it can stably cope with characteristic fluctuations in mass production, avoiding batch misjudgments caused by fixed threshold detection, such as a batch being judged as unqualified due to overall brightness shifts caused by differences in ITO glass. It also reduces unnecessary inspection rework time through precise control. Attached Figure Description

[0009] Figure 1This is a schematic flowchart of an online testing and management method for a liquid crystal display screen provided by an embodiment of the present invention.

[0010] Figure 2 This is a flowchart illustrating the process of performing detection operations on a liquid crystal display screen on a production line and generating an initial detection data set based on a set of detection parameters, according to an embodiment of the present invention. Detailed Implementation

[0011] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0012] like Figures 1-2 As shown, an embodiment of the present invention proposes an online testing and management method for a liquid crystal display screen, the method comprising the following steps: Step 1: Set the set of detection parameters according to the product type of the LCD screen; Step 2: Based on the set of detection parameters, perform detection operations on the LCD screens on the production line to generate an initial set of detection data; Step 3: Based on the initial detection data set, establish a first reference detection area and a second reference detection area within the effective display area of ​​the LCD screen; determine a reference axis line that runs through the two areas according to the first and second reference detection areas, and verify it using a line segment intersection judgment algorithm to obtain the verified reference axis line. Step 4: Divide the calibrated reference axis into equal intervals to generate multiple continuous detection and analysis segments, and calculate the dynamic compensation coefficient based on the dispersion of all detection and analysis segments. Step 5: Based on the dynamic compensation coefficient, correct the judgment standard values ​​in the detection parameter set to obtain the corrected judgment standard values; Step 6: Compare and analyze the initial test data set with the corrected judgment standard value to determine whether each test item is qualified and obtain the identification result of the defect type to which the unqualified item belongs; Step 7: Generate the final quality assessment result based on the defect type of the nonconformity and the defect level of each nonconformity; and execute the corresponding management actions based on the final quality assessment result.

[0013] In this embodiment of the invention, by establishing a benchmark detection area, generating a calibration benchmark axis, calculating dynamic compensation coefficients, and correcting the judgment standard value, the judgment threshold can be dynamically adjusted based on the actual display characteristics of the current product. This avoids misjudging qualified products due to minor overall drift, such as TN screens with slightly lower overall brightness but good uniformity not being judged as unqualified. It also identifies overall local defects, such as local color deviations in TFT-LCM modules not being ignored, thus improving the accuracy and rationality of detection and judgment. Through the dynamic compensation mechanism and targeted management actions, it can stably cope with characteristic fluctuations in mass production, avoiding batch misjudgments caused by fixed threshold detection, such as a batch being judged as unqualified due to overall brightness shift caused by differences in ITO glass. It also reduces unnecessary inspection rework time through precise control.

[0014] In a preferred embodiment of the present invention, step 1, setting a set of detection parameters according to the product type of the liquid crystal display screen, may include: Step 101: For each product type, core testing items need to be determined. Taking a TN-type LCD screen used in smart meters as an example, it needs to stably display numbers inside the meter box for a long time. Core testing items include brightness uniformity, color deviation, and the number of dead pixels. Brightness uniformity affects the recognizability of numbers, color deviation affects long-term visual consistency, and the number of dead pixels affects display integrity. For TFT-LCM modules used in vehicle equipment, navigation and vehicle status information need to be clearly displayed in strong light and vibration environments. In addition to brightness uniformity, color deviation, and the number of dead pixels, response speed and temperature adaptability-related testing items also need to be added to ensure display performance in complex environments. Set the judgment threshold for each testing item. The threshold setting needs to be combined with the product's historical stable batch data, industry quality standards, and the tolerance of application scenarios. Taking a TN-type smart meter screen as an example, collect brightness uniformity data of stable qualified batches in the past 12 months and calculate the brightness of these batches. The average fluctuation value for uniformity is 0.35. Combining this with the industry standard's maximum allowable fluctuation value of 0.45, the midpoint between the two is taken and appropriately tightened to allow for a quality margin, setting the brightness uniformity judgment threshold to 0.4. Regarding color deviation, smart meter screens operate in environments with minimal light variations, and users have a high tolerance for minor color differences. Referring to the historical batch's maximum color deviation of 0.9, the color deviation judgment threshold is set to 1.0. Regarding the number of dead pixels, because the display area of ​​the meter screen is small, a single dead pixel can affect digit recognition; therefore, the dead pixel number judgment threshold is set to 0. For automotive TFT-LCM modules, uneven brightness in strong light environments can easily lead to unclear information; referring to industry automotive screen standards, the brightness uniformity judgment threshold is set to 0.3. Color deviation needs to be adapted to the driver's visual fatigue threshold, and is set to 0.8. Regarding the number of dead pixels, considering the large display area of ​​automotive screens, allowing for a single, scattered dead pixel, the threshold is set to 1.

[0015] A quality acceptance standard is established, which specifies the limit on the number of non-conforming items for each defect level. For TN-type smart meter screens, the quality acceptance standard is that the number of serious non-conforming items (such as brightness uniformity exceeding 0.6, number of dead pixels ≥1) must be 0, the number of moderate non-conforming items (such as brightness uniformity 0.5 to 0.6, color deviation 1.2 to 1.5) should not exceed 3, and the number of minor non-conforming items (such as brightness uniformity 0.4 to 0.5, color deviation 1.0 to 1.2) should not exceed 8. The quality acceptance standard for automotive TFT-LCM modules is more stringent, with the number of serious non-conforming items being 0, the number of moderate non-conforming items not exceeding 2, and the number of minor non-conforming items not exceeding 5. The test items, judgment thresholds, and quality acceptance standards corresponding to each product type are integrated to form a set of test parameters.

[0016] This embodiment solves the problem of difficulty in adapting to different types of LCD screens; it ensures that the test parameters cover the core requirements of electrical performance, appearance and reliability in the disclosure document, while avoiding insufficient test accuracy due to one-size-fits-all parameter settings, and reducing misjudgments caused by mismatch between parameters and product type.

[0017] In a preferred embodiment of the present invention, step 2, which involves performing a detection operation on the liquid crystal display screen on the production line based on the detection parameter set to generate an initial detection data set, may include: Step 201: Based on the set of detection parameters, control the detection probe of the online detection device to move to a preset starting position in the effective display area of ​​the LCD screen to obtain the probe positioning result. Specifically, this includes: activating the drive control function of the online detection device; retrieving the coordinate information of the preset starting position according to the effective display area parameters corresponding to the model of the LCD screen to be tested. This coordinate is set based on the physical fixed corner of the screen, for example, using the lower left corner of the screen as the reference point, and moving the preset starting position 8 mm to the right and 8 mm upward from the reference point; driving the detection probe through a mechanical transmission mechanism according to the above coordinates. During the movement, the optical positioning component on the probe captures the edge contour of the effective display area of ​​the screen in real time and calculates the deviation values ​​of the actual moving position from the preset starting position in the X and Y axis directions. When the X-axis deviation value and the Y-axis deviation value are both less than 0.02 mm, the mechanical transmission mechanism stops moving and records the coordinates of the detection probe at this time as the probe positioning result. If the deviation value in either direction is greater than or equal to 0.02 mm, the moving direction of the mechanical transmission mechanism is adjusted according to the sign of the deviation value, and the movement continues and the comparison process is repeated until the deviation values ​​in both directions are less than 0.02 mm, and the probe positioning result is finally determined.

[0018] Step 202: Based on the probe positioning result, control the detection probe to perform a scanning operation on the effective display area along a preset path to obtain raw detection data. Specifically, this includes: determining the starting point of the scan based on the probe positioning result, planning the scanning trajectory according to the preset path rules, which is a serpentine path covering the entire effective display area. That is, moving from the starting point along the positive X-axis, reaching the edge of the effective display area along the X-axis, moving 2 mm along the positive Y-axis, and then moving along the negative X-axis, repeating this process until the entire area of ​​the effective display area is covered; driving the detection probe to move along the above trajectory. During the movement, the probe triggers a data acquisition every 0.3 mm. The acquired content includes the brightness value, chromaticity value, conduction status of the pixel, and the physical size data of the display screen corresponding to the current position; each acquired raw data is associated with the unique production number of the display screen to be tested in real time and stored in the local cache of the online testing equipment to form a raw detection data set.

[0019] Step 203: Preprocess the raw detection data to obtain valid detection data; classify and integrate the valid detection data according to the detection item type to generate an initial detection data set, specifically including: extracting the raw detection data set from the local cache, and processing the data for each detection item separately; for luminance value data, calculate the average of all luminance values, and then calculate the difference between each luminance value and the average value. If the difference between a luminance value and the average value is greater than three times the standard deviation of luminance values, then the luminance value is judged as an outlier and removed; for chrominance value data, use the same method to calculate the average and standard deviation of chrominance values, and remove chrominance values ​​whose difference from the average value is greater than three times the standard deviation. For pixel conduction status data, duplicate abnormal records of five consecutive identical positions caused by poor probe contact are removed. Based on the boundary coordinates of the effective display area, all detection data exceeding the boundary (including brightness, chromaticity, size, etc.) are filtered out, and only the detection data within the effective display area is retained to obtain effective detection data. The effective detection data are classified according to the detection item type: all brightness-related data are classified into a brightness detection item dataset, all chromaticity-related data are classified into a chromaticity detection item dataset, all pixel conduction status data are classified into a pixel status detection item dataset, and all size data are classified into a size detection item dataset. These datasets together form the initial detection data set.

[0020] This embodiment effectively solves the problems of missed scanning areas and messy raw data interfering with the test results, which may be caused by probe positioning deviation; precise positioning ensures that the detection probe completely covers the effective display area, avoiding missed detections caused by incomplete scanning range; orderly scanning path and multi-dimensional data acquisition ensure that the raw test data can reflect the electrical and appearance characteristics of the product.

[0021] In a preferred embodiment of the present invention, step 3, based on the initial detection data set, establishes a first reference detection area and a second reference detection area within the effective display area of ​​the liquid crystal display screen; according to the first reference detection area and the second reference detection area, determines a reference axis line passing through the two areas, and verifies it using a line segment intersection judgment algorithm to obtain the verified reference axis line, which may include: Step 301: Based on the initial test data set, calculate the display stability index of each sub-region within the effective display area, and select the two sub-regions with larger display stability index values. These two sub-regions are respectively identified as the first reference test area located in the upper left part of the effective display area and the second reference test area located in the lower right part of the effective display area. The geometric center coordinates of the first and second reference test areas are calculated respectively. Specifically, this includes: dividing the effective display area of ​​the LCD screen into sub-regions of fixed size, with each sub-region having a length and width of 10 mm. That is, dividing the effective display area horizontally and vertically at 10 mm intervals to form multiple 10 mm × 10 mm rectangular sub-regions; extracting the brightness data of all test points within each sub-region from the initial test data set; calculating the standard deviation of the brightness data for each sub-region; and then dividing 1 by the standard deviation to obtain the display stability index of the sub-region (this index reflects the degree to which the sub-region is affected by batch differences in raw materials and minor changes in equipment status; the larger the index value, the smaller the fluctuation in display characteristics within the sub-region and the stronger the stability).

[0022] By comparing the display stability index of all sub-regions, the two sub-regions with the largest values ​​are selected. The positions of these two sub-regions within the effective display area are observed. The sub-region located in the upper left quarter of the effective display area is identified as the first reference detection area, and the sub-region located in the lower right quarter of the effective display area is identified as the second reference detection area. The coordinates of the four vertices of the first reference detection area are obtained, for example, the coordinates of the four vertices are (x1, y1), (x2, y1), (x2, y2), and (x1, y2). The X-coordinate of the first vertex is added to the X-coordinate of the second vertex, and then divided by 2 to obtain the X-coordinate of the geometric center point of the first reference detection area. The Y-coordinate of the first vertex is added to the Y-coordinate of the fourth vertex, and then divided by 2 to obtain the Y-coordinate of the geometric center point of the first reference detection area. The same method is used to obtain the coordinates of the four vertices of the second reference detection area. The X and Y coordinates of the geometric center point of the second reference detection area are calculated by adding the vertex coordinates and dividing by 2.

[0023] Step 302: Based on the coordinates of the geometric center point of the first reference detection area and the geometric center point of the second reference detection area, calculate the equation of the straight line connecting the two geometric center points to generate a reference axis that runs through the first and second reference detection areas. Specifically, this includes: recording the coordinates of the geometric center point of the first reference detection area as (xa, ya) and the coordinates of the geometric center point of the second reference detection area as (xb, yb); calculating the horizontal distance between the two center points, i.e., subtracting the X coordinate of the first center point from the X coordinate of the second center point to obtain the horizontal distance value; calculating the vertical distance between the two center points, i.e., subtracting the Y coordinate of the first center point from the Y coordinate of the second center point to obtain the vertical distance value; and determining a straight line connecting (xa, ya) and (xb, yb) based on the coordinates of the two center points and the calculated horizontal and vertical distances. This straight line will pass through the interior of the first and second reference detection areas, and this straight line will be used as the reference axis that runs through the two reference detection areas.

[0024] Step 303a: Extract the start and end coordinates of the reference axis and read the preset set of vertex coordinates of the boundary polygon of the effective display area of ​​the LCD screen; based on the coordinates of the reference axis and the set of vertex coordinates of the boundary polygon, calculate the intersection of the reference axis and the boundary line segment of the effective display area to obtain the intersection coordinate data. Specifically, this includes: determining the start and end points from the generated reference axis; selecting the endpoint closest to the center point (xa, ya) of the first reference detection area as the start point and recording the coordinates of the start point as (x_start, y_start); selecting the endpoint closest to the center point (x_b, y_b) of the second reference detection area as the end point and recording the coordinates of the end point as (x_end, y_end); and reading the preset set of vertex coordinates of the boundary polygon of the effective display area, which is set in advance according to the model parameters of the LCD screen to be tested, for example, for a TN type LCD screen used in smart meters. Its effective display area is a rectangle with a preset width W of 40 mm and a preset height H of 30 mm. Therefore, the set of vertex coordinates of the boundary polygon contains four vertex coordinates, namely the lower left vertex (0, 0), the lower right vertex (40, 0), the upper right vertex (40, 30), and the upper left vertex (0, 30) of the effective display area. The reference axis is regarded as a continuous line segment extending from (x-start, y-start) to (x-end, y-end). The boundary of the effective display area is then divided into four independent boundary line segments, namely the lower line segment from the lower left vertex (0, 0) to the lower right vertex (40, 0), the right line segment from the lower right vertex (40, 0) to the upper right vertex (40, 30), the upper line segment from the upper right vertex (40, 30) to the upper left vertex (0, 30), and the left line segment from the upper left vertex (0, 30) to the lower left vertex (0, 0).

[0025] Intersection of each reference axis segment with one of the four boundary segments is determined. First, the coordinate range of the current boundary segment is determined, then the coordinates of the reference axis segment overlap with this range. Simultaneously, the direction of extension of the two segments is checked for intersection. Taking the judgment with the lower boundary segments (0, 0) and (40, 0) as an example, the Y-coordinate of the lower boundary segment is fixed at 0, and the X-coordinate ranges from 0 to 40. Then, it is checked whether there are positions on the reference axis segment where the Y-coordinate is equal to 0 or transitions from greater than 0 to less than 0. If so, the X-coordinate of that position is further confirmed. If the X-coordinate is between 0 and 40, and the X-coordinate also meets the condition, then it means that the two line segments intersect. Record the X-coordinate (i.e., the X value corresponding to Y=0 on the reference axis line segment) and Y-coordinate 0 at this time. If the Y-coordinate of the reference axis line segment is always greater than 0 or less than 0, or the X-coordinate is outside the range of 0 to 40, then it is determined that the two line segments do not intersect, and the coordinates are not recorded. In the same way, the intersection of the reference axis line segment with the right line segment, the upper line segment, and the left line segment is completed in sequence. All the determined intersection point coordinates are collected to form the intersection point coordinate data.

[0026] Step 303b: Verify the intersection point coordinate data. When the intersection point coordinate data indicates that the reference axis and the boundary polygon have only two intersection points, and the two intersection points are located near the start and end points of the reference axis, respectively, a verification result is obtained. Based on the verification result, a verified reference axis is generated. Specifically, this includes: counting the collected intersection point coordinate data, checking the specific value of each coordinate, eliminating duplicate coordinates (such as the case where the intersection points of two boundary line segments coincide), and counting the number of independent intersection points. If the counted number of intersection points is not two, the verification is directly judged as failing. If the number of intersection points is two, the distances between the two intersection points and the start and end points of the reference axis are calculated respectively.

[0027] To calculate the distance from the first intersection point to the starting point (x_start, y_start), first obtain the coordinates (x1, y1) of the first intersection point. Calculate the difference between x1 and x_start, square this difference, and then calculate the difference between y1 and y_start, square this difference as well. Add the results of the two squares, and then take the square root of the sum to obtain the final straight-line distance from the first intersection point to the starting point. Use the same method to obtain the coordinates (x2, y2) of the second intersection point. Calculate the difference between x2 and x_start, square this difference, and then calculate the difference between y2 and y_start, square this difference, add the results of the two squares, and take the square root to obtain the distance from the first intersection point to the starting point. The straight-line distance from the two intersection points to the endpoint is used to determine whether the distance meets the requirements. The distance standard is determined based on the size ratio of the effective display area. If the straight-line distance from the first intersection point to the starting point is less than 5 mm and the straight-line distance from the second intersection point to the endpoint is less than 5 mm, it means that the two intersection points are close to the starting point and the endpoint of the reference axis, respectively, and have not deviated from the key area of ​​the effective display area. At this time, the verification result is obtained. The two verified intersection points are used as new endpoints. The first intersection point (x1, y1) and the second intersection point (x2, y2) are connected to form a new line segment. This line segment is the verified reference axis.

[0028] This embodiment can effectively avoid areas with large fluctuations in display characteristics caused by batch differences in raw materials and minor changes in equipment status, reducing the interference of unstable areas on the establishment of the reference. At the same time, setting the reference detection areas in the upper left and lower right parts of the effective display area respectively can ensure that the reference covers the key range of the effective display area and avoid the limitations of a single area reference. Furthermore, accurately calculating the coordinates of the geometric center points of the two reference detection areas provides a reliable coordinate basis for the subsequent generation of the reference axis.

[0029] In a preferred embodiment of the present invention, step 4, which involves dividing the calibrated reference axis into equally spaced segments to generate multiple continuous detection and analysis segments, and calculating the dynamic compensation coefficient based on the dispersion of all detection and analysis segments, may include: Step 401: Based on the calibrated reference axis, divide it into equal intervals according to the preset spacing value to obtain multiple equal division point coordinates; according to the multiple equal division point coordinates, divide the reference axis into multiple continuous detection and analysis segments, and calculate the brightness value dispersion coefficient of each pixel in each detection and analysis segment to obtain a set of dispersion coefficients. Specifically, this includes: determining the preset spacing value, which needs to be comprehensively set in combination with the pixel density, product type and application scenario requirements of the LCD screen to be tested. For example, for TN LCD screens used in smart meters, the pixel density is about 100 PPI, and the content displayed on the meter screen is mainly digital, so the sensitivity to local brightness fluctuations is low. The preset spacing value is set to 2 mm, which can cover the key area while taking into account the detection efficiency; for TFT-LCM modules used in vehicles, the pixel density reaches 300 PPI, and the vehicle scenario needs to cope with complex environments such as strong light and vibration, so the display stability requirements are higher. The preset spacing value is set to 1 mm to capture more subtle brightness fluctuations.

[0030] To calculate the total length of the calibrated reference axis, first extract the specific coordinates of its two endpoints from the previously generated calibrated reference axis. For example, the coordinates of the starting endpoint (x1, y1) are (5, 5), and the coordinates of the ending endpoint (x2, y2) are (45, 35). Calculate the difference between x2 and x1, i.e., 45 minus 5 equals 40. Square this difference to get 1600. Calculate the difference between y2 and y1, i.e., 35 minus 5 equals 30. Square this difference to get 900. Add the two squared results, 1600 plus 900 equals 2500. Take the square root of 2500 to get 50 mm, which is the total length of the reference axis. Divide the total length by the preset spacing value. If it is a smart meter TN screen, 50 mm divided by 2 mm equals 24, which is the number of equal division points (excluding the two endpoints). Starting from the starting endpoint (5, 5) of the reference axis, determine the coordinates of each equal division point according to the preset spacing value: starting with the first equal division point... Taking the dividing point as an example, its cumulative distance on the axis is 2 mm. The X and Y coordinate increments are calculated according to the axis's directional proportions. The X coordinate increment is (x2 minus x1) multiplied by the cumulative distance and then divided by the total length, i.e., 40 multiplied by 2 and then divided by 50, resulting in 1.6. The Y coordinate increment is (y2 minus y1) multiplied by the cumulative distance and then divided by the total length, i.e., 30 multiplied by 2 and then divided by 50, resulting in 1.2. Adding the X coordinate increment 1.6 to the starting endpoint's X coordinate of 5 gives 6.6, and adding the Y coordinate increment 1.2 to the starting endpoint's Y coordinate of 5 gives 6.2. The coordinates of the first dividing point are (6.6, 6.2). Similarly, the cumulative distance of the second dividing point is 4 mm. The X increment is 40 multiplied by 4 and divided by 50, resulting in 3.2, and the Y increment is 30 multiplied by 4 and divided by 50, resulting in 2.4. The coordinates are (5 + 3.2, 5 + 2.4) = (8.2, 7.4). The coordinates of all 24 dividing points are calculated sequentially.

[0031] The detection and analysis segments are divided according to the coordinates of the dividing points. The axial portion between the starting endpoint (5, 5) and the first dividing point (6.6, 6.2) is divided into the first detection and analysis segment. The axial portion between the first dividing point and the second dividing point (8.2, 7.4) is divided into the second detection and analysis segment, and so on. The axial portion between the last dividing point and the ending endpoint (45, 35) is divided into the twenty-fifth detection and analysis segment, ensuring that the length of each detection and analysis segment is equal to the preset spacing value of 2 mm.

[0032] Calculate the dispersion coefficient of the brightness value of each pixel within each detection and analysis segment. Taking the first detection and analysis segment as an example, extract the brightness values ​​of all pixels within this segment from the initial detection data set. Assume this segment contains 5 pixels with brightness values ​​of 450 cd / m². 2 452cd / m 2 448cd / m 2 451cd / m 2 449cd / m 2 The total number of these brightness values ​​is 5. Add all the brightness values ​​together: 450 + 452 + 448 + 451 + 449 = 2250. Divide 2250 by the total number of values ​​(5) to get 450 cd / m². 2 This is the average value of the brightness value in this segment. The difference between each brightness value and the average value is calculated as follows: 450 - 450 = 0, 452 - 450 = 2, 448 - 450 = -2, 451 - 450 = 1, 449 - 450 = -1. Each difference is squared: 0 squared = 0, 2 squared = 4, -2 squared = 4, 1 squared = 1, -1 squared = 1. All squared results are summed: 0 + 4 + 4 + 1 + 1 = 10. 10 is divided by the total number 5 to get 2, which is the variance of the brightness value in this segment. The square root of the variance 2 is approximately 1.414, which is the standard deviation. Finally, the standard deviation 1.414 is divided by the average value 450 to get approximately 0.0031, which is the coefficient of variation of the brightness value in this detection and analysis segment. The coefficients of variation for the remaining 24 detection and analysis segments are calculated using the same method. All coefficients are collected sequentially to form a set of coefficients of variation.

[0033] Step 402: Based on the set of discrete coefficients, calculate the degree of variation of the discrete coefficients to obtain the dynamic compensation coefficients. Specifically, this includes: calculating the average value of the set of discrete coefficients. Assume that the set of discrete coefficients contains 25 coefficients, namely 0.0031, 0.0033, 0.0029, 0.0035, 0.0028, 0.0032, 0.0030, 0.0034, 0.0027, 0.0036, 0.0026, 0.0033, 0.0031, 0.0029, 0.0035, 0.0028, 0.0032, 0.0030, 0.0034, 0.0027, 0.0036, 0.0026, 0.0033, 0.0031, and 0.0029. Sum all the discrete coefficients in the set, the sum is 0.0775. Divide 0.0775 by the total number of discrete coefficients, 25, to get 0.0031, which is the average of the discrete coefficients. Next, calculate the difference between each discrete coefficient and this average: the first coefficient 0.0031 minus 0.0031 equals 0, the second 0.0033 minus 0.0031 equals 0.0002, the third 0.0029 minus 0.0031 equals -0.0002, and so on. Square each difference: 0 squared is 0, 0.0002 squared is 0.00000004, -0.0002 squared is 0.00000004, and so on. Sum all the squared results, the sum is 0.00000228. Divide 0.00000228 by the total number of discrete coefficients, 25, to get 0.00. 00000912, this is the variance of the coefficient of variation; taking the square root of the variance 0.0000000912 gives approximately 0.000302, which is the standard deviation of the coefficient of variation; dividing the standard deviation of the coefficient of variation 0.000302 by the average value of the coefficient of variation 0.0031 gives approximately 0.0974, which is the degree of variation of the coefficient of variation. The larger this value is, the more obvious the difference in brightness fluctuation between different detection and analysis sections caused by batch differences in raw materials (such as uneven light transmittance of ITO glass) and minor changes in equipment status (such as fluctuations in the thickness of the adhesive layer in the coating machine); finally, adding 1 to this degree of variation 0.093 gives 1.093, which is the dynamic compensation coefficient.

[0034] This embodiment, by setting reasonable intervals for dividing the detection and analysis segments according to product type, can accurately cover the key areas of the reference axis, avoiding omission of local fluctuations due to overly coarse division or increase of invalid calculations due to overly fine division.

[0035] In a preferred embodiment of the present invention, step 5, correcting the judgment standard value in the detection parameter set according to the dynamic compensation coefficient to obtain the corrected judgment standard value, may include: Step 501: Based on the dynamic compensation coefficient, extract the brightness uniformity judgment threshold and color deviation judgment threshold from the detection parameter set. Specifically, this includes: clarifying the source of the detection parameter set, which was pre-set in Step 1 according to the LCD screen product type. The set contains various detection judgment thresholds corresponding to different product types. These thresholds were set in conjunction with product application scenarios, industry standards, and historical stable batch data, and the threshold value range meets the quality requirements of the corresponding product. Specifically, for TN type LCD screens (such as those used in smart meters), the brightness uniformity judgment threshold is typically set between 0.3 and 0.5, and the color deviation judgment threshold is set between 0.8 and 1.2. For TFT-LCM modules (such as those used in automotive applications), due to higher display accuracy requirements, the brightness uniformity judgment threshold is set between 0.2 and 0.4, and the color deviation judgment threshold is set between 0. The values ​​are between 0.6 and 0.9. Taking specific products as examples, for TN-type LCD displays used in smart meters, which need to display numbers stably for a long time, the requirements for brightness uniformity are relatively high. Therefore, the preset brightness uniformity judgment threshold in the detection parameter set is 0.4 (in the middle to strict range of 0.3 to 0.5). Since the ambient light changes little (such as indoors or inside the meter box), the requirements for color stability are slightly lower, and the color deviation judgment threshold is 1.0 (in the middle range of 0.8 to 1.2). For TFT-LCM modules used in vehicles, since there are many strong light environments in vehicle scenarios (such as direct sunlight), it is necessary to ensure clear recognition by the driver. Therefore, the requirements for brightness uniformity and color stability are more stringent. Thus, the brightness uniformity judgment threshold is 0.3 (in the middle to strict range of 0.2 to 0.4), and the color deviation judgment threshold is 0.8 (in the middle to strict range of 0.6 to 0.9).

[0036] After defining the preset rules, extract the corresponding thresholds based on the specific type of the product to be tested. If testing a TN-type LCD screen of a smart meter, extract a brightness uniformity threshold of 0.4 and a color deviation threshold of 1.0. If testing a vehicle-mounted TFT-LCM module, extract a brightness uniformity threshold of 0.3 and a color deviation threshold of 0.8. During the extraction process, the dynamic compensation coefficient calculated in step 402 needs to be considered. This coefficient is calculated based on the degree of variation of the discrete coefficient. Since the fluctuations in raw materials and equipment during production are usually small, the dynamic compensation coefficient is stable between 1.05 and 1.2 (e.g., 1.093 for a TN screen of a smart meter and 1.12 for a vehicle-mounted TFT-LCM module, both within this range). At the same time, it is clear that the extracted thresholds will be corrected in conjunction with this coefficient to avoid the thresholds from being disconnected from the compensation logic.

[0037] Step 502: Multiply the brightness uniformity judgment threshold by the dynamic compensation coefficient to obtain the corrected brightness uniformity judgment threshold; multiply the chromaticity deviation judgment threshold by the dynamic compensation coefficient to obtain the corrected chromaticity deviation judgment threshold. Specifically, taking the TN-type LCD screen of the currently tested smart meter as an example, the brightness uniformity judgment threshold of 0.4 extracted in step 501 (within the reasonable range of 0.3-0.5 for TN screens) is multiplied by the dynamic compensation coefficient of 1.093 calculated in step 402 (within the normal fluctuation range of 1.05 to 1.2), i.e., 0.4 multiplied by... From 1.093, we get 0.4372. This value is within the reasonable range (0.315 to 0.6) after the correction of the TN screen brightness uniformity judgment threshold. It can adapt to the slight overall brightness drift caused by batch differences in ITO glass raw materials (transmittance fluctuation is usually within ±5%), and avoid the original fixed threshold of 0.4 from misjudging qualified products as unqualified due to slightly lower batch brightness (such as 2% to 3%). At the same time, the color deviation judgment threshold of 1.0 extracted in step 501 (which is within the reasonable range of 0.8 to 1.2 for TN screens) is multiplied by the dynamic compensation coefficient of 1.093. That is, 1.0 multiplied by 1.093 equals 1.093. This value is within the reasonable range (0.84 to 1.44) after the TN screen color deviation judgment threshold correction. It can handle local color fluctuations caused by small changes in the thickness of the adhesive layer in the coating machine (the deviation is usually within ±0.01mm), ensuring that the judgment standard matches the actual display characteristics of the current batch. If the current test is of an automotive TFT-LCM module, the brightness uniformity judgment threshold of 0.3 extracted in step 501 (which is within the reasonable range of 0.2 to 0.4 for TFT-LCM modules) is consistent with the dynamic compensation coefficient of 1.1. Multiplying by 2 yields 0.336, a value within the reasonable range (0.21 to 0.48) after brightness uniformity correction for automotive modules. The extracted chromaticity deviation judgment threshold of 0.8 (within the reasonable range of 0.6 to 0.9 for TFT-LCM modules) is multiplied by the dynamic compensation coefficient of 1.12 to obtain 0.896, a value within the reasonable range (0.63 to 1.08) after chromaticity deviation correction for automotive modules. This value can adapt to the changes in display characteristics of automotive modules caused by temperature stability fluctuations of backlight LED beads (brightness fluctuations of ±3% when temperature changes by ±5℃).

[0038] Step 503: Generate corrected judgment standard values ​​based on the corrected brightness uniformity judgment threshold and the corrected chromaticity deviation judgment threshold. Specifically, the corrected judgment standard values ​​must cover the core display characteristic judgment items of the current tested product, namely brightness uniformity and chromaticity deviation. These two characteristics directly affect the user experience of the product. For example, uneven brightness in a smart meter screen will lead to decreased digital recognizability, and excessive chromaticity deviation in a vehicle screen will affect the driver's judgment of the displayed information. Furthermore, the corrected thresholds must always be within the acceptable quality range for the corresponding product. For example, the corrected brightness threshold for a TN screen cannot exceed 0.6, and the chromaticity threshold cannot exceed 1.44; the corrected brightness threshold for a vehicle module cannot exceed 0.48, and the chromaticity threshold cannot exceed 1.44. The brightness uniformity threshold cannot exceed 1.08; the two corrected thresholds calculated in step 502 are integrated to form the corrected judgment standard value for the current batch of products: if the product is a smart meter TN type LCD display, the corrected judgment standard value includes the corrected brightness uniformity judgment threshold of 0.4372 (in the range of 0.315 to 0.6) and the corrected color deviation judgment threshold of 1.093 (in the range of 0.84 to 1.44); if the product is an automotive TFT-LCM module, the corrected judgment standard value includes the corrected brightness uniformity judgment threshold of 0.336 (in the range of 0.21 to 0.48) and the corrected color deviation judgment threshold of 0.896 (in the range of 0.63 to 1.08).

[0039] This embodiment multiplies the threshold by a dynamic compensation coefficient, ensuring that the corrected threshold always remains within the acceptable range for product quality. This adapts to batch-specific fluctuations in display characteristics without compromising quality requirements, effectively solving the problem of over- or under-inspection caused by fixed thresholds. By integrating core judgment items, associated product unique identifiers, and record correction basis, it ensures that the corrected judgment standard value is clear and traceable.

[0040] In a preferred embodiment of the present invention, step 6, comparing and analyzing the initial test data set with the corrected judgment standard value to determine whether each test item is qualified, and obtaining the identification result of the defect type to which the unqualified item belongs, may include: Step 601: Based on the corrected judgment criteria value, extract the brightness uniformity data and color deviation data of each detection point from the initial detection data set. Specifically, this includes: clarifying that the corrected judgment criteria value includes the corrected brightness uniformity judgment threshold and the corrected color deviation judgment threshold. These two thresholds are associated with the unique identifier of the product to be tested. When extracting data, the initial detection data set of the corresponding product must first be located through this unique identifier. In the initial detection data set, each detection point records coordinate information (e.g., 10 mm on the X-axis and 8 mm on the Y-axis), brightness uniformity data, color deviation data, and pixel conduction status data. All data are stored in the order of the detection path (e.g., the path of the previous serpentine scan). Based on the detection dimensions corresponding to the revised judgment criteria values, data is extracted point by point: For TN-type LCD displays in smart meters, the brightness uniformity data (such as the brightness uniformity value of each detection point) and color deviation data (such as the color deviation value of each detection point) of all detection points of the product are found from the initial data; For vehicle-mounted TFT-LCM modules, the corresponding brightness uniformity data and color deviation data are extracted in the order of detection points. During the extraction process, it is necessary to ensure that the coordinate information of each detection point is extracted synchronously with the data to avoid data being disconnected from the detection position. At the same time, the detection data of non-effective display areas (such as the data outside the boundary that has been filtered in the previous steps) are excluded, and only the target data of the detection points within the effective display area are retained.

[0041] Step 602: Compare the brightness uniformity data with the corrected brightness uniformity judgment threshold, and compare the color deviation data with the corrected color deviation judgment threshold to obtain the pass / fail judgment results for each test data. Specifically, this includes: determining the correction threshold corresponding to the current product—if it is a TN-type LCD display for a smart meter, the corrected brightness uniformity judgment threshold is 0.4372, and the corrected color deviation judgment threshold is 1.093; if it is a vehicle-mounted TFT-LCM module, the corrected brightness uniformity judgment threshold is 0.336, and the corrected color deviation judgment threshold is 0.896; comparing each test point individually. Taking a certain test point of a TN screen for a smart meter as an example, the brightness uniformity data of this test point is 0.45. Compare it with the corrected brightness uniformity judgment threshold of 0.4372. Since 0.45 is greater than 0.4372, the test point is deemed to be pass / fail. The brightness uniformity data at the measuring point is unqualified; if the brightness uniformity data at another measuring point is 0.42, which is less than 0.4372, then the data is considered qualified. For color deviation data, taking the TN screen of a smart meter as an example, if the color deviation data at a measuring point is 1.12, which is greater than the corrected color deviation threshold of 1.093, then the data is considered unqualified; if the color deviation data at a measuring point is 1.05, which is less than 1.093, then the data is considered qualified. The comparison logic for the measuring points of the vehicle-mounted TFT-LCM module is the same. For example, if the color deviation data at a measuring point is 0.91, which is greater than the corrected threshold of 0.896, then the data is considered unqualified; if the data is 0.87, then the data is considered qualified. The comparison result of each measuring point must be associated with its coordinate information and the product's unique identifier to form a qualified judgment result containing the measuring point coordinates, the measuring item, the data value, and the qualified status.

[0042] Step 603: Based on the pass / fail judgment results, classify and process the non-compliant data. Data with brightness uniformity exceeding the threshold is judged as brightness unevenness defect, data with color deviation exceeding the threshold is judged as color deviation defect, and detected abnormal pixels are judged as bad pixels defect. The classification and processing results are obtained, specifically including: First, filtering out all non-compliant records in the pass / fail judgment results and splitting them according to the type of test item: Records marked as having non-compliant brightness uniformity data are grouped into one category. This type of data all have brightness uniformity values ​​greater than the corrected brightness uniformity judgment threshold and are judged as brightness unevenness defect. This type of defect is mostly caused by batch differences in raw materials, such as local unevenness of light transmittance of ITO glass or slight fluctuations in equipment (such as uneven light refraction caused by deviation in the thickness of the glue layer in the glue coating machine). The corresponding test point coordinates need to be marked to facilitate subsequent tracing of the problem area; the marked color deviation data... Non-compliant records are categorized into another type. This type of data consists of color deviation values ​​exceeding the corrected color deviation judgment threshold, and is judged as color deviation defects. These defects are often caused by temperature stability fluctuations of backlight LED beads (such as localized temperature rise of automotive module beads) or polarizer attachment deviations, and also need to be associated with the detection point coordinates. In addition, abnormal records are extracted from the pixel status dataset of the initial detection data set: if the conduction status data of a certain pixel shows that it is not conducting or the conduction status frequently switches after three consecutive tests, after ruling out poor probe contact, it is judged as a bad pixel defect. This type of defect is mostly caused by scratches on the glass substrate or impurities in the liquid crystal, and the specific coordinates and abnormal status description of the pixel need to be recorded. The three types of defect records are sorted separately, and each defect record includes a unique product identifier, detection point coordinates, defect type and corresponding data value, forming a classification processing result.

[0043] Step 604: Based on the classification results, summarize the identification results of the defect types to which the non-conforming items belong. Specifically, this includes: for TN-type LCD screens of smart meters, counting the number of detection points for uneven brightness defects, the number of detection points for color deviation defects, and the number of dead pixels; for automotive TFT-LCM modules, similarly counting the number of the three types of defects; associating the detection point coordinates of defect records, marking the distribution of each type of defect in the effective display area, such as uneven brightness defects of TN screens of smart meters being concentrated in the lower right part of the effective display area, color deviation defects being concentrated in the upper left part, and dead pixels being scattered in the central area; integrating all information to form an identification result that includes the number of each defect type, the distribution of defect areas, and the corresponding defect data value range, all based on the product's unique identifier.

[0044] This embodiment extracts target data by associating it with the unique identifier of the product, ensuring that the data accurately corresponds to the product and the detection location, avoiding data confusion, and excluding data from invalid areas to reduce interference from invalid information. By comparing each item with the corrected threshold, the pass / fail judgment result can be adapted to the actual display characteristics of the current batch of products, solving the problem of over-detection or under-detection caused by fixed thresholds, and ensuring the rationality of the judgment.

[0045] In a preferred embodiment of the present invention, step 7, based on the defect type of the nonconforming item and the defect level corresponding to each nonconforming item, generates a final quality judgment result; according to the final quality judgment result, the corresponding management action is performed, which may include: Step 701: Based on the identification results of the defect type to which the non-conforming item belongs, compare the detection data of each non-conforming item with the corresponding defect level classification threshold in the corrected judgment standard value to obtain the comparison results. Specifically, this includes: clarifying that the corrected judgment standard value contains defect level classification thresholds for each defect type. These thresholds are set according to the degree of impact of the defect on the use of the product. For example, for the TN type LCD display of a smart meter, the defect level of uneven brightness is divided into three levels: slight, moderate, and severe. The corresponding classification thresholds are 1.2 times (0.5246) and 1.5 times (0.6558) of the corrected brightness uniformity judgment threshold of 0.4372, respectively; the classification threshold for color deviation is 1.2 times (1.3116) and 1.5 times (1.6395) of the corrected color deviation judgment threshold of 1.093; the classification threshold for dead pixels is set according to the number: a single dead pixel is slight, two to three consecutive dead pixels are moderate, and four or more dead pixels are severe.

[0046] From the defect type identification results of each nonconformity, extract the specific test data and defect type for each nonconformity: for example, if it is a brightness unevenness defect, extract its brightness uniformity data, such as 0.5; if it is a color deviation defect, extract its color deviation data, such as 1.4; if it is a dead pixel defect, count its number of consecutive or scattered defects, such as 2 consecutive dead pixels; compare the test data of each nonconformity with the defect level classification threshold of the corresponding defect type: for example, the test data of a brightness unevenness defect is 0.5, which is compared with the minor level threshold of 0.43. Comparing values ​​from 72 to 0.5246, 0.5 falls within this range and is classified as a minor defect. A color deviation defect with a measured value of 1.4 is compared to the moderate defect threshold of 1.3116 to 1.6395, and 1.4 falls within this range, thus classifying it as a moderate defect. A defect consisting of two consecutive defects is compared to the moderate defect threshold of two to three defects, and is classified as a moderate defect. Each comparison result is associated with the product's unique identifier and the coordinates of the detection point for the non-conforming item, forming a comparison result that includes the defect type, detection data, defect level range, and preliminary level.

[0047] Step 702: Determine the defect level of each nonconforming item based on the comparison results, and count the number of nonconforming items at each defect level to obtain statistical results; compare the statistical results with the quality acceptance standards in the set of detection parameters to generate the final quality judgment results, specifically including: based on the comparison results of step 701, clarify the final defect level of each nonconforming item. For example, if the brightness unevenness defect is initially judged to be of the minor level, confirm that its data does not exceed the upper limit of the minor level of 0.5246, it is finally determined to be of the minor level; if the color deviation defect is initially judged to be of the moderate level, confirm that its data does not exceed the upper limit of the moderate level of 1.6395, it is finally determined to be of the moderate level.

[0048] Then, count the number of defects by defect level: For TN-type LCD displays in smart meters, count the number of minor defects (e.g., 3 for uneven brightness, 2 for color deviation), the number of moderate defects (e.g., 1 for uneven brightness, 1 for dead pixels), and the number of severe defects (e.g., none). Obtain the total number for each level. Extract the corresponding product quality acceptance standard from the set of detection parameters. This standard is set according to the product application scenario. For example, the quality acceptance standard for TN screens in smart meters is that the number of severe defects must be 0, the number of moderate defects cannot exceed 3, and the number of minor defects cannot exceed 1. The number of minor non-conformities is limited to no more than 8. The statistical results are compared with the quality acceptance standards. If the number of severe non-conformities is 0, the number of moderate non-conformities is 2 and less than 3, and the number of minor non-conformities is 5 and less than 8, the quality acceptance standards are met and the final quality judgment result is qualified. If the number of moderate non-conformities is 4 and more than 3, the work needs to be reworked. If a severe non-conformity occurs, the work is scrapped. The quality acceptance standards for automotive TFT-LCM modules are more stringent, for example, no more than 2 moderate non-conformities. The comparison logic is the same, and the final judgment result is qualified, requires rework, or is scrapped.

[0049] Step 703: Based on the final quality judgment result, determine the management actions to be performed according to the preset correspondence between quality levels and processing actions. Specifically, this includes: clarifying the preset correspondence, which is set according to the production process and quality control requirements. When the final quality judgment result is qualified, the corresponding management action is release into the warehouse, that is, allowing the product to enter the next production process or be stored in the finished product warehouse, and recording the product's unique identifier and the basis for qualification judgment; when the judgment result is that rework is required, the corresponding management action is to mark the rework area and notify the production line, that is, according to the coordinates of the detection points of the non-conforming items, such as uneven brightness concentrated in the lower right area, mark the specific location of rework required on the product, and send the rework information, including the defect type, defect level, and quantity, to the production line terminal to prompt the operator to make targeted adjustments, such as recalibrating the backlight brightness in the lower right area; when the judgment result is scrap, the corresponding management action is to isolate and store the product and start the waste disposal process, that is, to transfer the product to the scrap isolation area, affix a scrap label containing the product's unique identifier and the reason for scrapping, and record the scrap information in the production system to trigger the waste recycling process, such as disassembling recyclable components.

[0050] This embodiment refines the defect level classification by combining the modified threshold, so that the severity of each non-conformity can be accurately defined, avoiding lax quality control or over-processing caused by general judgment.

[0051] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0052] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0053] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for online detection and management of liquid crystal displays, characterized in that, The method includes: Step 1: Set the set of detection parameters according to the product type of the LCD screen; Step 2: Based on the set of detection parameters, perform detection operations on the LCD screens on the production line to generate an initial set of detection data; Step 3: Based on the initial detection data set, establish a first reference detection area and a second reference detection area within the effective display area of ​​the LCD screen; determine a reference axis line running through the two areas based on the first and second reference detection areas, and verify it using a line segment intersection judgment algorithm to obtain the verified reference axis line. Specifically, this includes: based on the initial detection data set, calculating the display stability index of each sub-area within the effective display area, and selecting two sub-areas with larger display stability index values, identifying these two sub-areas as the first reference detection area located in the upper left part of the effective display area and the second reference detection area located in the lower right part of the effective display area, respectively, and calculating the geometric center point coordinates of the first and second reference detection areas; based on the geometric center point coordinates of the first and second reference detection areas, calculating the equation of the straight line connecting the two geometric center points to generate a reference axis line running through the first and second reference detection areas; verifying the reference axis line using a line segment intersection judgment algorithm to obtain the verified reference axis line. Step 4: Based on the verified reference axis, divide it into equal intervals according to the preset spacing value to obtain the coordinates of multiple equal division points; according to the coordinates of multiple equal division points, divide the reference axis into multiple continuous detection and analysis segments, and calculate the brightness value dispersion coefficient of each pixel in each detection and analysis segment to obtain the set of dispersion coefficients; according to the set of dispersion coefficients, calculate the degree of variation of the dispersion coefficients to obtain the dynamic compensation coefficient. Step 5: Based on the dynamic compensation coefficient, correct the judgment standard values ​​in the detection parameter set to obtain the corrected judgment standard values; Step 6: Compare and analyze the initial test data set with the corrected judgment standard value to determine whether each test item is qualified and obtain the identification result of the defect type to which the unqualified item belongs; Step 7: Generate the final quality assessment result based on the defect type of the nonconformity and the defect level of each nonconformity; and execute the corresponding management actions based on the final quality assessment result.

2. The online testing and management method for liquid crystal displays according to claim 1, characterized in that, Based on the set of detection parameters, a detection operation is performed on the LCD screens on the production line to generate an initial set of detection data, including: Based on the set of detection parameters, the detection probe of the online detection device is controlled to move to the preset starting position in the effective display area of ​​the LCD screen to obtain the probe positioning result; Based on the probe positioning results, the detection probe is controlled to perform a scanning operation on the effective display area along a preset path to obtain the raw detection data; The raw detection data is preprocessed to obtain valid detection data; the valid detection data is then classified and integrated according to the detection item type to generate an initial detection data set.

3. The online testing and management method for liquid crystal displays according to claim 2, characterized in that, The reference axis is verified using a line segment intersection judgment algorithm to obtain the verified reference axis, including: Extract the starting and ending coordinates of the reference axis and read the preset set of vertex coordinates of the boundary polygon of the effective display area of ​​the LCD screen; based on the coordinates of the reference axis and the set of vertex coordinates of the boundary polygon, calculate the intersection of the reference axis and the boundary line segment of the effective display area to obtain the intersection point coordinate data. The intersection point coordinate data is verified and judged. When the intersection point coordinate data shows that there are only two intersection points between the reference axis and the boundary polygon, and the two intersection points are located near the start point and the end point of the reference axis, respectively, the verification result is obtained. Based on the verification result, the verified reference axis is generated.

4. The online testing and management method for liquid crystal displays according to claim 3, characterized in that, Based on the dynamic compensation coefficient, the judgment standard values ​​in the detection parameter set are corrected to obtain the corrected judgment standard values, including: Based on the dynamic compensation coefficient, the brightness uniformity judgment threshold and the color deviation judgment threshold are extracted from the detection parameter set. Multiply the brightness uniformity judgment threshold by the dynamic compensation coefficient to obtain the corrected brightness uniformity judgment threshold; multiply the chromaticity deviation judgment threshold by the dynamic compensation coefficient to obtain the corrected chromaticity deviation judgment threshold. The revised judgment standard value is generated based on the revised luminance uniformity judgment threshold and the revised chromaticity deviation judgment threshold.

5. The online testing and management method for liquid crystal displays according to claim 4, characterized in that, The initial test data set is compared and analyzed with the revised judgment criteria values ​​to determine whether each test item is qualified, and the identification results of the defect type to which the unqualified item belongs are obtained, including: Based on the revised judgment criteria, the brightness uniformity data and color deviation data of each detection point are extracted from the initial detection data set; The brightness uniformity data is compared with the corrected brightness uniformity judgment threshold, and the color deviation data is compared with the corrected color deviation judgment threshold to obtain the pass / fail judgment results of each test data. Based on the pass / fail judgment results, the non-passing data are classified and processed. Data with brightness uniformity exceeding the threshold is judged as brightness non-uniformity defect, data with color deviation exceeding the threshold is judged as color deviation defect, and abnormal pixels detected are judged as bad pixels defect, thus obtaining the classification and processing results. Based on the classification results, the identification results of the non-conforming items to which the defect types belong are summarized.

6. The online testing and management method for liquid crystal displays according to claim 5, characterized in that, Based on the type of nonconformity and the defect level corresponding to each nonconformity, the final quality judgment result is generated; Based on the final quality assessment results, corresponding management actions will be performed, including: Based on the identification results of the defect type to which the nonconformity belongs, the detection data of each nonconformity is compared with the corresponding defect level classification threshold in the corrected judgment standard value to obtain the comparison result. Based on the comparison results, the defect level of each nonconformity is determined, and the number of nonconformities at each defect level is counted to obtain statistical results. The statistical results are then compared with the quality acceptance criteria in the set of test parameters to generate the final quality judgment result. Based on the final quality assessment results, and according to the pre-defined correspondence between quality levels and processing actions, the management actions that need to be performed are determined.

7. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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