Mobile phone screen quality inspection method and system based on screen picture inspection partition

Abstract

The invention relates to the technical field of image processing, in particular to a mobile phone screen quality inspection method and system based on a screen picture inspection partition, and the method comprises the following steps: detecting partitions divided based on the surface of a mobile phone screen, recognizing a sudden change interval, analyzing the consistency of a gray gradient direction and an edge fitting vector, screening abnormal partitions, and extracting color and texture features. And evaluating deviation from a screen flatness curve, screening the subarea of which the response coverage area needs to be adjusted, and outputting a repairing and adjusting instruction set. According to the method, the image detection area is divided, and the abnormality is screened in combination with the gray slope and the edge deviation abrupt change, so that the recognition accuracy and the response speed are improved, the gradient direction and edge vector consistency are fused to enhance junction defect judgment, and the complex abnormality linkage screening is realized in combination with the color and texture features. The risk level is quantified based on the deviation flatness trend, and the functional diagram is linked to realize repair response scheduling, so that the detection reliability and decision pertinence are improved.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a mobile phone screen quality inspection method and system based on screen image inspection zoning. Background Art

[0002] The field of image processing technology involves image acquisition, analysis, recognition, segmentation, and enhancement to extract useful information or facilitate further application. The core of this technical field encompasses image acquisition, image preprocessing, feature extraction, and recognition analysis. Research and application are conducted in conjunction with fundamental theories such as computer vision and pattern recognition. Image processing is widely used in industrial inspection, medical imaging, traffic monitoring, safety protection, and other areas. It possesses a strong interdisciplinary nature and often prioritizes efficient and accurate image information extraction and analysis. In the field of industrial inspection, image processing is widely used for automated testing of product appearance quality. Through a variety of algorithms, it identifies and labels abnormal features such as flaws, cracks, and deformations in images, improving inspection efficiency and consistency.

[0003] Among them, the mobile phone screen quality inspection method refers to collecting images of the mobile phone screen and analyzing its surface features item by item to determine whether the screen has appearance defects such as scratches, bubbles, bright spots, black spots, color deviation, etc. during the production or assembly process. This method mainly covers two technical matters: image acquisition and image comparison. Image acquisition is completed by using a high-resolution industrial camera under specific lighting conditions. Image comparison uses grayscale contrast, edge fitting, color threshold setting and other methods to perform pixel-by-pixel analysis of the target image and the standard image. Judgment is made based on the grayscale distribution changes and color differences in the image to identify irregular changes or abnormal structures on the screen surface, ensuring that the image comparison is reproducible and consistent.

[0004] The existing technology relies on a single grayscale distribution or edge fitting strategy in the image comparison process, which is prone to misjudgment when facing complex or overlapping defects. In particular, in areas with dense multiple defects, the abnormal area is unclearly defined due to blurred grayscale transitions. In addition, no correlation judgment is made on the trend of grayscale changes between partitions, resulting in isolated and scattered abnormal signals, making it difficult to form an effective judgment basis. Although image acquisition has high-resolution conditions, a partition management mechanism that matches the risk level has not been established, making it impossible to present defect risks in a hierarchical manner in the spatial structure, resulting in a disconnect between image analysis results and subsequent repair responses, which in turn affects the consistency and controllability of the overall detection process. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a mobile phone screen quality inspection method and system based on screen inspection zoning.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a mobile phone screen quality inspection method based on screen image inspection partition, comprising the following steps: S1: Based on the detection partitions divided by the mobile phone screen surface, the grayscale distribution curve and edge fitting trajectory of the partition are extracted. The synchronous mutation intervals of the grayscale change slope and the edge fitting deviation value are identified on the time axis. The corresponding partition numbers and spatial coordinates are extracted to generate a candidate set of abnormal regions. S2: Calling the grayscale gradient direction and edge fitting vector of the partitions in the abnormal region candidate set, analyzing the direction consistency of the two at the partition boundary, combining the partition boundary connectivity, screening the partitions with connectivity and abnormal grayscale gradient, and forming a suspected defect partition layer; S3: Based on the suspected defect partition layer, extract the color distribution matrix and local texture features within the partition, analyze the color uniformity and texture consistency, screen and match the abnormal area, and obtain a defect linkage partition set; S4: According to the position number of the defect linkage partition set, analyze the screen stress distribution trend corresponding to the partition, evaluate the degree of deviation from the original flatness curve of the screen, calibrate the abnormality level of the partition according to the deviation amplitude, and output the screen partition risk level list.

[0007] As a further solution of the present invention, the abnormal area candidate set includes grayscale mutation partition numbers, edge fitting abnormal points, partition coordinate marks, and time series mutation identifiers; the suspected defect partition layer includes grayscale gradient abnormal partition identifiers, partition boundary connectivity units, partition junction consistency blocks, and abnormal grayscale distribution grids; the defect linkage partition set includes color abnormality continuous partitions, texture irregular areas, color spot mutation overlap areas, and linkage abnormal partition numbers; the screen partition risk level list includes risk level labels, partition response deviation values, local stress abnormality indicators, and flatness offset levels.

[0008] As a further solution of the present invention, the step of obtaining the abnormal region candidate set is specifically as follows: S111: Based on the detection partitions divided by the mobile phone screen surface, extract the grayscale distribution curve and edge fitting trajectory of the partitions, perform difference calculation on the two types of data in the same partition, and obtain the grayscale and edge fitting difference trend value; S112: Based on the grayscale and edge fitting difference trend values, identifying the deviation value of the moving slope value in the grayscale change curve and the edge fitting trajectory, superimposing the two types of values ​​over time, extracting the time intervals where the slope exceeds the baseline value and the deviation value exceeds the set threshold, and generating a high-frequency mutation interval time period set; S113: For the high-frequency mutation interval time period set, the corresponding partition number and spatial coordinate information are matched, the partition position where the signal occurs is extracted, and a candidate set of abnormal areas is generated.

[0009] As a further solution of the present invention, the steps for obtaining the suspected defect partition layer are specifically as follows: S211: calling the grayscale gradient direction and edge fitting vector of the partitions in the abnormal region candidate set, extracting the projection trajectory of the two at the partition boundary, identifying the distribution number and aggregation degree of the intersection points in the partition, and obtaining the partition boundary consistency map; S212: Based on the partition boundary consistency map, filter the boundary regions with a higher-than-average degree of aggregation, compare the spatial boundaries of the overall screen structure map, identify continuous boundary concentrated areas belonging to the same partition, and obtain grayscale gradient abnormality zoning within the screen partition; S213: Call the grayscale gradient abnormal zoning within the screen partition, perform integrated analysis on the partition boundary consistency, grayscale gradient discreteness, color distribution balance and edge fitting delay, calculate the defect high-sensitivity response coefficient, perform partition matching according to the response block in the layer, and form a suspected defect partition layer.

[0010] As a further solution of the present invention, the step of obtaining the defect linkage partition set is specifically as follows: S311: Based on the suspected defect partition layer, extract the color distribution matrix and local texture features of the numbered partitions in the layer, align the timestamps of the data in the partitions, identify the color intensity fluctuation value and texture consistency offset, and obtain the screen local defect response feature set; S312: Based on the local defect response feature set of the screen, jointly analyze the color uniformity and texture consistency within the partition, calculate the color-texture coupling feature value, select the partition units with the color-texture coupling degree in the layer, and establish a color-texture coordinated response spatial distribution map; S313: calling the color texture collaborative response space distribution map, clustering the partitions exceeding the collaborative identification benchmark in the coupled eigenvalue layer, marking the partition codes and coordinates corresponding to the continuous abnormal areas, and obtaining the defect linkage partition set.

[0011] As a further solution of the present invention, the steps for obtaining the screen partition risk level list are specifically as follows: S411: extracting the stress distribution curve of the partition with the specified number according to the position number of the defect linkage partition set, performing time uniform processing, identifying the stress change per unit time, and obtaining a partition stress abnormal change rate set; S412: Based on the set of abnormal stress change rates of the partitions, identifying the stress distribution curve of the original flatness stage, comparing the current stress change sequence with the reference curve, identifying the stress deviation level of the partitions, extracting and marking the partitions whose deviation levels exceed the warning upper limit, and obtaining a set of partitions with sudden deviation increases; S413: According to the deviation sudden increase partition set, the deviation level value of each partition is bound to the position number in the screen structure space diagram, sorted by risk level, and output a screen partition risk level list.

[0012] As a further embodiment of the present invention, the method further comprises step S5: S5: Calling the screen partition risk level list, identifying the corresponding number of the partition in the mobile phone screen function map, calling the repair response unit list, comparing the response level with the screen protection priority sequence, screening the partition number that needs to adjust the response coverage, and outputting the automatic linkage repair adjustment instruction set; The automatic linkage repair adjustment instruction set includes adjusting the target partition number, response level adjustment parameters, protection priority comparison items, and linkage response trigger type.

[0013] As a further solution of the present invention, the steps for obtaining the automatic linkage repair adjustment instruction set are specifically as follows: S511: Calling the screen partition risk level list, extracting the partition number in the mobile phone screen function map, mapping the partition risk level value with the regional coordinate boundary, identifying the partition information corresponding to the screen protection level, and generating a screen partition risk distribution map; S512: Based on the screen partition risk distribution map, extract the repair response unit numbers and response levels, match the partition risk levels with the repair response levels, identify the unit numbers with insufficient response coverage, and obtain a screen partition response risk disconnection list; S513: According to the screen partition response risk disconnection list, according to the level number in the screen protection priority sequence, extract the key partition number whose response coverage needs to be improved, output the adjustment control parameters linked with the original repair unit in sequence, and output the automatic linkage repair adjustment instruction set.

[0014] The mobile phone screen quality inspection system based on screen image inspection zoning is used to execute the mobile phone screen quality inspection method based on screen image inspection zoning, and the system includes: The grayscale monitoring module divides the detection zones based on the mobile phone screen surface, compares the grayscale change slope and edge fitting deviation value within the same time period, selects the synchronous mutation intervals between the two, extracts the zone number and spatial coordinates, summarizes the abnormal time periods and zone numbers, and generates a candidate set of abnormal areas; Based on the abnormal area candidate set, the partition positioning module identifies the consistency between the grayscale gradient direction and the edge fitting vector, calibrates the partition boundary number, matches the overall screen structure diagram, extracts the partition number range of the grayscale gradient abnormal area, and establishes a suspected defect partition layer; The defect linkage module retrieves the color distribution matrix and the continuous data sequence of the local texture features in the region based on the suspected defect partition layer, determines the connectivity of the color abnormality boundary and the texture consistency, marks the partition number that meets the linkage threshold of the two, and outputs the defect linkage partition set; The stress warning module analyzes the deviation degree between the stress distribution trend of the corresponding partition and the original flatness curve based on the partition number of the defect linkage partition set, extracts the partition number that deviates from the trend, completes the level identification according to the risk grading standard, and generates a screen partition stress abnormality grading list; The repair optimization module searches for the corresponding position number of the risk level partition in the mobile phone screen function diagram based on the screen partition stress anomaly classification list, retrieves the current repair response unit configuration list, compares the screen protection priority with the current response level, filters the partition number that needs to be updated, and outputs the automatic linkage repair adjustment instruction set.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the detection area is divided in the screen image and the grayscale change slope and the edge fitting deviation are combined to perform the initial screening of abnormalities, thereby improving the accuracy and response speed of abnormal area identification, and further enhancing the discrimination ability of fine-grained defects at the junction through the consistency analysis of the grayscale gradient direction and the edge vector. At the same time, the color distribution and local texture features are combined to realize the linkage screening of complex abnormalities. On this basis, the deviation trend of the abnormal area and the original flatness curve is analyzed, and the risk level of the screen structure abnormality is effectively quantified. Finally, the risk level and the functional diagram number are linked to achieve accurate scheduling of the repair response, thereby enhancing the closed-loop coordination of the defect detection and repair process, and significantly improving the detection reliability and the pertinence of the repair decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 This is a flowchart for obtaining the abnormal region candidate set in the present invention; Figure 3 This is a flowchart for obtaining a suspected defect partition layer in the present invention; Figure 4 This is a flow chart for obtaining a defect linkage partition set in the present invention; Figure 5 This is a flowchart for obtaining a risk level list of screen partitions in the present invention; Figure 6 This is a flow chart for obtaining the automatic linkage repair adjustment instruction set in the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0019] Example 1 See also Figure 1 The present invention provides a technical solution: a mobile phone screen quality inspection method based on screen inspection partitioning, comprising the following steps: S1: Based on the detection partitions divided by the mobile phone screen surface, the grayscale distribution curve and edge fitting trajectory of the partition are extracted. The synchronous mutation intervals of the grayscale change slope and the edge fitting deviation value are identified on the time axis. The corresponding partition numbers and spatial coordinates are extracted to generate a candidate set of abnormal regions. S2: Call the grayscale gradient direction and edge fitting vector of the partitions in the abnormal area candidate set, analyze the direction consistency of the two at the partition boundary, combine the partition boundary connectivity, and screen the partitions with connectivity and abnormal grayscale gradient to form a suspected defect partition layer; S3: Based on the suspected defect partition layer, the color distribution matrix and local texture features within the partition are extracted, the color uniformity and texture consistency are analyzed, and the abnormal areas are screened and matched to obtain the defect linkage partition set; S4: Based on the position number of the defect linkage partition set, analyze the screen stress distribution trend corresponding to the partition, evaluate the degree of deviation from the original screen flatness curve, calibrate the abnormality level of the partition according to the deviation amplitude, and output the screen partition risk level list; S5: Call the screen partition risk level list, identify the corresponding number of the partition in the mobile phone screen function map, call the repair response unit list, compare the response level with the screen protection priority sequence, screen the partition number that needs to adjust the response coverage, and output the automatic linkage repair adjustment instruction set.

[0020] The candidate set of abnormal areas includes grayscale mutation partition numbers, edge fitting abnormal points, partition coordinate marks, and time series mutation identifiers. The suspected defect partition layer includes grayscale gradient abnormal partition identifiers, partition boundary connectivity units, partition boundary consistency blocks, and abnormal grayscale distribution grids. The defect linkage partition set includes color abnormality continuous partitions, texture irregularity areas, color spot mutation overlap areas, and linkage abnormal partition numbers. The screen partition risk level list includes risk level labels, partition response deviation values, local stress abnormality indicators, and flatness offset levels. The automatic linkage repair adjustment instruction set includes adjusting the target partition number, response level adjustment parameters, protection priority comparison items, and linkage response trigger types.

[0021] See also Figure 2 , the specific steps for obtaining the abnormal region candidate set are: S111: Based on the detection partitions divided by the mobile phone screen surface, extract the grayscale distribution curve and edge fitting trajectory of the partitions, perform difference calculation on the two types of data in the same partition, and obtain the grayscale and edge fitting difference trend value; Based on the detection partitioning of the mobile phone screen surface, for example, a mobile phone screen with a resolution of 1920x1080 is divided into 180 10x10 detection partitions, totaling 18 rows and 10 columns. Each partition corresponds to a number (i, j), where i represents the row number and j represents the column number. The pixel grayscale values ​​in the partition are extracted, and the grayscale distribution curve of each partition is constructed. At the same time, based on the boundary pixel point data of the partition, the boundary pixel points are fitted, for example, by the least squares method, to construct the edge fitting trajectory of each partition. The difference calculation is performed between the sampling point values ​​on the grayscale distribution curve in the same partition and the corresponding sampling point values ​​on the edge fitting trajectory. For example, for a sampling point P in partition (5, 5), its grayscale value is 150, and the theoretical grayscale value of the point on the edge fitting trajectory is 145. The difference between the grayscale of the point and the edge fitting is 150-145=5. The difference calculation is performed on all sampling points or representative sampling points in the partition, and the difference results are arranged according to the sampling position or time series to obtain the trend value of the difference between the grayscale and edge fitting of the partition.

[0022] S112: Based on the grayscale and edge fitting difference trend values, identify the deviation between the moving slope value and the edge fitting trajectory in the grayscale change curve, overlap the two types of values ​​over time, extract the time intervals where the slope exceeds the baseline value and the deviation exceeds the set threshold, and generate a high-frequency mutation interval time period set; According to the trend value of the difference between grayscale and edge fitting, the trend value sequence of each partition is analyzed. For example, for the trend value sequence of partition (5, 5), its moving slope value is calculated. The difference method is used to calculate the ratio of the difference between the trend values ​​of adjacent time points or spatial position points and the corresponding interval. For example, the trend value is 5 at time point t and 8 at time point t+1, and the time interval is 1 unit. Then the moving slope is (8-5) / 1=3. The moving slope value in the grayscale change curve is identified, and the deviation value of the edge fitting trajectory is identified, such as the maximum distance of the edge fitting trajectory relative to the ideal straight line. The two types of values ​​are overlapped for time periods. For example, within a certain time period, if the moving slope value continues to be higher than the set slope reference value and the deviation value of the edge fitting trajectory continues to exceed the set deviation threshold, the time period is marked as a high-frequency mutation interval, and the slope reference value is set to, for example, the positive value of the grayscale difference change. For values ​​outside the normal fluctuation range, for example, by statistically analyzing a large amount of defect-free screen data, the mean and standard deviation of the moving slope are calculated, and the benchmark value is set to the mean plus 3 times the standard deviation. For example, if the statistical mean is 0.5 and the standard deviation is 0.8, then the base rate benchmark value is 0.5+3×0.8=2.9. A slope greater than 2.9 is considered to exceed the benchmark. The deviation threshold is set to, for example, the maximum allowable deviation between the edge fitting trajectory and the ideal boundary. For example, if the ideal boundary is a straight line and the maximum allowable deviation is 2 pixels, then the deviation threshold is 2. A deviation greater than 2 is considered to exceed the set threshold. The time interval in which the slope exceeds the benchmark value and the deviation value exceeds the set threshold is extracted. For example, it is found that the moving slope of partition (5, 5) is greater than 2.9 and the edge fitting deviation is greater than 2 in the time period [t1, t2]. Then the time period [t1, t2] is added to the high-frequency mutation interval time period set.

[0023] S113: For the high-frequency mutation interval time period set, the corresponding partition number and spatial coordinate information are matched, the partition location where the signal occurs is extracted, and a candidate set of abnormal regions is generated; For the high-frequency mutation interval time period set, for each high-frequency mutation interval in the set, match the number of the corresponding partition with the spatial coordinate information of the partition on the screen. For example, the high-frequency mutation interval [t1, t2] corresponds to partition (5, 5), the upper left corner coordinates of the partition on the screen are (500, 500) pixels, and the lower right corner coordinates are (600, 600) pixels. Record the partition number and its spatial coordinates, and extract the partition position where the signal occurs, that is, collect the numbers and spatial coordinate information of all partitions with high-frequency mutation intervals. For example, partition (5, 5), partition (8, 12), partition (15, 3), etc. are identified as having high-frequency mutation intervals, then record the numbers and corresponding spatial coordinate information of these three partitions, and generate an abnormal area candidate set, which contains the location information of the screen partitions with abnormalities.

[0024] See also Figure 3 , the specific steps for obtaining the suspected defect partition layer are: S211: Calling the grayscale gradient direction and edge fitting vector of the partition in the abnormal region candidate set, extracting the projection trajectory of the two at the partition boundary, identifying the distribution number and aggregation degree of the intersection points in the partition, and obtaining the partition boundary consistency map; Call the grayscale gradient direction and edge fitting vector of the partition in the abnormal area candidate set. For example, for the partition (5, 5) in the abnormal area candidate set, extract the grayscale gradient direction information of its internal pixels, and determine the direction of the gradient vector by calculating the grayscale difference of the pixel in the horizontal and vertical directions. For example, the grayscale gradient direction of a certain point points to the lower right, and at the same time extract the edge fitting vector of the partition. For example, the fitting vector of the right boundary of the partition points to the right, and extract the projection trajectory of the two at the junction of the partitions. For example, at the junction of partition (5, 5) and its right partition (5, 6), analyze the grayscale near the boundary of partition (5, 5). The projection of the gradient direction vector on the junction line and the projection of the right edge fitting vector of partition (5, 5) on the junction line are used to identify the distribution number and aggregation degree of the junction points in the partition. For example, on the junction line between partition (5, 5) and the adjacent partition (5, 6), the number of points whose grayscale gradient direction is consistent with the projection direction of the edge fitting vector is calculated, and the density of the consistent points on the junction line is analyzed. If the number of consistent points is large and the distribution is concentrated, it is considered that the consistency of the junction is high, and a partition junction consistency map is obtained. The map reflects the distribution of the consistency degree of the grayscale gradient direction and the edge fitting vector at the junction of each partition of the screen.

[0025] S212: Based on the partition boundary consistency map, filter the boundary areas with higher-than-average aggregation, compare the spatial boundaries of the overall screen structure map, identify continuous boundary concentrated areas belonging to the same partition, and obtain grayscale gradient abnormality zoning within the screen partition; According to the partition boundary consistency map, the boundary areas with a higher degree of aggregation than the average level are screened. For example, the average degree of aggregation of all boundary areas in the map is calculated, and the boundary areas with a higher degree of aggregation than this average value are screened. For example, if the average degree of aggregation is 0.7, boundary areas with degrees of aggregation of 0.8 and 0.9 are screened out. The spatial boundaries of the overall screen structure diagram are compared to identify the continuous boundary concentrated areas belonging to the same partition. For example, the high-polymerization boundary areas screened out form a continuous area on the screen, and the boundary of the continuous area coincides with or closely fits the boundary of a specific screen partition (for example, partition (5, 5)), and the grayscale gradient abnormal zoning within the screen partition is obtained. The zoning identifies the area range where the grayscale gradient direction within the screen partition is inconsistent with the edge fitting vector or an abnormality occurs.

[0026] S213: Calling the grayscale gradient abnormality zoning within the screen partition, performing integrated analysis on the partition boundary consistency, grayscale gradient dispersion, color distribution balance, and edge fitting delay, calculating the defect high-sensitivity response coefficient, and performing partition matching based on the response blocks in the layer to form a suspected defect partition layer; Call the grayscale gradient anomaly zoning within the screen partition, and perform an integrated analysis of the partition boundary consistency, grayscale gradient discreteness, color distribution balance and edge fitting delay. For example, for the grayscale gradient anomaly zoning within the partition (5, 5), analyze the partition boundary consistency value within the partition and the surrounding area, calculate the discreteness of the grayscale gradient direction within the partition, such as by calculating the variance or entropy value of the gradient direction to measure the discreteness, the larger the value, the more discrete, analyze the color distribution balance within the partition, such as calculating the uniformity of the pixel value distribution of the red, green and blue primary color channels, using indicators such as histogram balance, the lower the value, the worse the balance, and the delay degree of edge fitting within the partition, such as the edge fitting trajectory. The defect high-sensitivity response coefficient is calculated relative to the offset or lag of the actual edge. The coefficient is obtained by comprehensively considering the above indicators, for example, it is calculated by weighted summation. The weight setting is, for example, the partition boundary consistency weight is 0.3, the gray gradient discreteness weight is 0.2, the color distribution balance weight is 0.3, and the edge fitting delay weight is 0.2. The numerical values ​​of each indicator are normalized and multiplied by the corresponding weight and then summed to obtain the defect high-sensitivity response coefficient. Partition matching is performed according to the response block in the layer, and the calculated defect high-sensitivity response coefficient value is associated with the corresponding screen partition (for example, partition (5, 5)) to form a suspected defect partition layer, which marks the screen partition with a higher defect high-sensitivity response coefficient.

[0027] See also Figure 4 ,The specific steps for obtaining the defect linkage partition set are: S311: Based on the suspected defect partition layer, extract the color distribution matrix and local texture features of the numbered partitions in the layer, align the timestamps of the data in the partitions, identify the color intensity fluctuation value and texture consistency offset, and obtain the screen local defect response feature set; Based on the suspected defect partition layer, the color distribution matrix and local texture features of the numbered partitions in the layer are extracted. For example, for the partition (5, 5) in the suspected defect partition layer, the color values ​​of all pixels in the partition are extracted, and its color distribution matrix is ​​constructed. At the same time, the local texture features of the partition are extracted. For example, the texture descriptor is calculated using the gray level co-occurrence matrix, LBP (Local Binary Pattern) and other methods. The data in the partition is timestamp aligned. For example, it is ensured that the extracted color distribution matrix and local texture features are data from the same time point or time period. The color intensity fluctuation value and texture consistency offset are identified. For example, the variation amplitude of the pixel color intensity in the color distribution matrix of the partition with time or spatial position is calculated as the color intensity fluctuation value. The difference between the local texture feature of the partition and the normal texture feature template of the partition is calculated as the texture consistency offset. The local defect response feature set of the screen is obtained. The feature set contains the color intensity fluctuation value and texture consistency offset of each partition in the suspected defect partition layer.

[0028] S312: Based on the local defect response feature set of the screen, perform a joint analysis of the color uniformity and texture consistency within the partition using the formula: ; Calculate the color-texture coupling eigenvalue, screen the partition units of the color-texture coupling degree in the layer, and establish the color-texture synergistic response spatial distribution map; in, represents the color-texture coupling eigenvalue, Representative Texture feature values ​​in the region, represents the mean value of texture feature value, represents the standard deviation of texture eigenvalues, Representative The color feature value in the region, represents the mean of the color feature values, Represents the standard deviation of color feature values, Represents the total number of regional units involved in the calculation; According to the local defect response feature set of the screen, the color uniformity and texture consistency within the partition are jointly analyzed using the formula Calculate the color texture coupling eigenvalue, where Represents the color-texture coupling eigenvalue, reflecting the degree of correlation between color change and texture change. Representative Texture feature values ​​in the region, represents the mean value of texture feature value, Represents the standard deviation of texture eigenvalues, which is used to measure the degree of discreteness of texture eigenvalues. Calculate the normalized deviation of the texture eigenvalues, Representative The color feature value in the region, represents the mean of the color feature values, Represents the standard deviation of color feature values, which is used to measure the degree of dispersion of color feature values. Calculate the normalized deviation of the color feature values, Represents the total number of regional units involved in the calculation. For example, if a partition is divided into 4 sub-regions for calculation, n=4, and the summation symbol It represents the accumulation of the normalized squared deviations of n regional units. The logical idea of ​​the formula is to quantify the degree of common change of texture eigenvalues ​​and color eigenvalues ​​by calculating the square root of the normalized squared deviations of the two. The larger the value, the higher the coupling degree of color and texture, that is, the more consistent or relevant the change trend is. For example, for the partition (5, 5), it is divided into 4 sub-regions, and the texture eigenvalues ​​Ti of each sub-region are measured to be [0.5, 0.6, 0.7, 0.8], and the average is 0.65, the standard deviation F is calculated as 0.129 by the sample standard deviation formula, and the color feature values ​​Ci of each sub-region are measured to be [0.4, 0.5, 0.55, 0.6], and the mean is 0.5125, and the standard deviation E is calculated as 0.085 using the sample standard deviation formula; Then the color texture coupling eigenvalue calculation : ; ; ; ; ; By calculating the square root of the sum of the squares of the standardized deviations of the color and texture eigenvalues, the two different types of feature changes are coupled into one value to quantify the degree of coordinated change. This coupling eigenvalue can more comprehensively reflect the abnormal state of the local area of ​​the screen, and screen the partition units of the color-texture coupling degree in the layer. For example, a coupling threshold is set. For example, by performing statistical analysis on a large amount of normal screen data, the threshold is set to the mean plus 2 times the standard deviation. For example, if the mean is 1.2 and the standard deviation is 0.6, the threshold is 1.2+2×0.6=2.4, and partitions with coupling eigenvalues ​​greater than 2.4 are screened out. For example, if the calculated result 2.55 is greater than 2.4, then partition (5, 5) is screened out, and a color-texture coordinated response spatial distribution map is established, which reflects the distribution of partitions with high color-texture coupling on the screen.

[0029] S313: Calling the color-texture collaborative response spatial distribution map, clustering the partitions that exceed the collaborative identification benchmark in the coupled eigenvalue layer, marking the partition codes and coordinates corresponding to the continuous abnormal areas, and obtaining the defect linkage partition set; The color-texture collaborative response spatial distribution map is called to cluster the partitions in the coupling eigenvalue layer that exceed the collaborative identification benchmark. For example, the collaborative identification benchmark is set to the partition with a color-texture coupling eigenvalue greater than 2.4, and the partitions with high coupling are clustered in spatial position. The partitions with adjacent spatial positions and coupling eigenvalues ​​exceeding the benchmark are classified into one category. For example, the coupling eigenvalues ​​of partitions (5, 5), (5, 6), (6, 5), and (6, 6) are all greater than 2.4 and are spatially adjacent, so they are clustered into one category, and the partition codes and coordinates corresponding to the continuous abnormal areas are marked. For example, the above clustering result is marked as continuous abnormal area A, and the partition code set {(5, 5), (5, 6), (6, 5), (6, 6)} and the spatial coordinate range of the partition are recorded to obtain the defect linkage partition set, which contains the continuous abnormal partition areas identified after color-texture coupling analysis.

[0030] See also Figure 5 , the steps for obtaining the screen partition risk level list are as follows: S411: According to the position number of the defect linkage partition set, extract the partition stress distribution curve under the specified number and perform time uniform processing using the formula: ; Identify the stress change per unit time and obtain the abnormal stress change rate set of the partition; in, represents the stress change per unit time, represents the time interval, represents the number of time steps, represents the partition stress value at the kth moment, Representative The partition stress value at time , represents the maximum stress value during this period, represents the minimum stress value during this period, represents the average value of stress during the period; According to the position number of the defect linkage partition set, for example, for area A in the defect linkage partition set, which contains partitions (5, 5), (5, 6), (6, 5), (6, 6), extract the partition stress distribution curve under the specified number, for example, obtain the stress sensor data of partition (5, 5) within a period of time, form a stress distribution curve, and perform time uniform processing to ensure that the stress data of all partitions have the same time sampling rate and time range, using the formula Identify the stress change per unit time, where Represents the stress change per unit time, reflecting the total fluctuation intensity of stress per unit time. Represents the time interval, for example, set it to 1 second, Represents the number of time steps, from 1 to N, where N represents the total number of time steps. represents the partition stress value at the kth moment, Representative The partition stress value at time , Calculate the absolute change in stress between adjacent time steps, Calculate the absolute change in total stress, represents the maximum stress value during this period, represents the minimum stress value during this period, Calculate the stress fluctuation range, represents the average stress value within a period of time, It is a weighting factor. When the stress fluctuation range is larger than the average value, the factor increases, amplifying the stress change per unit time, and vice versa. The formula logic is to quantify the degree of stress instability by calculating the cumulative sum of the absolute stress changes per unit time and multiplying it by a weighting factor that reflects the relative magnitude of the stress fluctuation amplitude. The larger the value, the more drastic or unstable the stress change. For example, the stress data of partition (5, 5) within 10 seconds are as follows (sampling interval =1 second): [50, 52, 51, 55, 53, 60, 58, 62, 59, 65], =65, =50, =(50+52+51+55+53+60+58+62+59+65) / 10=56.5, N=9 (from k=1 to 9); calculate ; calculate : ; Obtain the partition stress abnormal change rate set, which contains the stress change per unit time of each partition in the defect linkage partition set.

[0031] S412: Based on the set of abnormal stress change rates of the partitions, the stress distribution curve of the original flatness stage is identified, the current stress change sequence is compared with the reference curve, the stress deviation level of the partitions is identified, and the partitions whose deviation levels exceed the warning upper limit are extracted and marked to obtain a set of partitions with sudden deviation increases; According to the set of abnormal stress change rates of the partitions, the stress distribution curve of the original flatness stage is identified. For example, the stress distribution data of the screen at the factory or after calibration is obtained as the stress distribution curve of the original flatness stage. The curve represents the stress change characteristics of the screen under normal conditions. The current stress change sequence is compared with the reference curve. For example, the unit time stress change sequence currently calculated for the partition (5, 5) is compared with the unit time stress change reference sequence of the partition in the original flatness stage to identify the stress deviation level of the partition. For example, a stress deviation level judgment standard is set. For example, if the current change rate exceeds 1 times the standard deviation of the reference sequence mean, it is a first-level deviation. A deviation exceeding 2 times the standard deviation is a second-level deviation, and a deviation exceeding 3 times the standard deviation is a third-level deviation. For example, the calculated result of the stress change rate per unit time of partition (5, 5) is 15.97, and the mean of the reference sequence in its original flatness stage is 5, and the standard deviation is 3. Then, the current change rate (15.97) exceeds the limit of the reference mean 5+3*3=14, reaching the third-level deviation. Extract and mark the partitions whose deviation levels exceed the upper warning limit. For example, set the upper warning limit as the second-level deviation, mark all partitions that reach the third-level deviation, for example, partition (5, 5) is marked, and obtain the deviation sudden increase partition set, which contains the screen partitions whose stress deviation levels reach or exceed the upper warning limit.

[0032] S413: Bind the deviation level value of each partition to the position number in the screen structure space map according to the deviation sudden increase partition set, sort them by risk level, and output a screen partition risk level list; According to the deviation sudden increase partition set, the deviation level value of each partition is bound to the position number in the screen structure space diagram. For example, the deviation sudden increase partition set includes partition (5, 5) and partition (8, 12). The deviation level of partition (5, 5) is level three, and the deviation level of partition (8, 12) is level two. The level three deviation is bound to the position number of partition (5, 5), and the level two deviation is bound to the position number of partition (8, 12). Sorting is performed according to the risk level, and the corresponding relationship between the risk level and the deviation level is set. For example, level three deviation corresponds to high risk, level two deviation corresponds to medium risk, and level one deviation corresponds to low risk. Then partition (5, 5) is high risk, and partition (8, 12) is medium risk. Sorting is performed in order from high risk to low risk, and a screen partition risk level list is output. The list lists the partition numbers, corresponding risk levels, and spatial position information in the deviation sudden increase partition set. For example: Partition (5, 5), risk level: high, coordinates: (500, 500)-(600, 600); Partition (8, 12), risk level: medium, coordinates: (800, 1200)-(900, 1300).

[0033] See also Figure 6The specific steps for obtaining the automatic linkage repair adjustment instruction set are as follows: S511: Calling the screen partition risk level list, extracting the partition number in the mobile phone screen function map, mapping the partition risk level value with the region coordinate boundary, identifying the partition information corresponding to the screen protection level, and generating a screen partition risk distribution map; Call the screen partition risk level list, for example, the list contains partition (5, 5) high risk and partition (8, 12) medium risk, extract the partition number in the mobile phone screen function map, the mobile phone screen function map identifies the function of each area of ​​the screen, for example, partition (5, 5) is located in the center area of ​​the screen, corresponding to the application operation area, its function number is F101, partition (8, 12) is located in the bottom area of ​​the screen, corresponding to the virtual key area, its function number is F305, map the partition risk level value with the area coordinate boundary, for example, map the high risk level of partition (5, 5) with the coordinate range (500 , 500)-(600, 600), and associate the medium risk level of partition (8, 12) with the coordinate range (800, 1200)-(900, 1300), and identify the partition information corresponding to the screen protection level. For example, the preset screen protection level, the first level protection corresponds to the high-risk area, the second level protection corresponds to the medium-risk area, and the third level protection corresponds to the low-risk area. It is identified that partition (5, 5) requires first-level protection and partition (8, 12) requires second-level protection, and a screen partition risk distribution map is generated. The map intuitively shows the risk levels corresponding to each functional partition of the screen.

[0034] S512: Based on the screen partition risk distribution map, the repair response unit numbers and response levels are extracted, the partition risk levels are matched with the repair response levels, the unit numbers with insufficient response coverage are identified, and a screen partition response risk disconnection list is obtained; Based on the risk distribution map of screen partitions, the repair response unit number and response level are extracted. The screen is equipped with multiple repair response units (for example, local micro-piezoelectric actuators or thermal elements). Each response unit has its corresponding number and available response level (for example, response unit R101 is located in partition (5, 5), with a response level of one, which can provide the maximum strength of local repair force; response unit R305 is located in partition (8, 12), with a response level of two, which can provide a medium strength of local repair force). Match the partition risk level with the repair response level. For example, match the high risk level of partition (5, 5) with the first-level response level of response unit R101. The medium risk level of partition (8, 12) is matched with the secondary response level of response unit R305 to identify the unit number with insufficient response coverage. If it is found during the matching process that a high-risk partition can only be covered by a unit with a medium response level, or a risk partition has no corresponding response unit coverage, then the response coverage of the unit number is considered to be insufficient. For example, if partition (5, 6) is a high-risk partition, but it is only covered by the response unit R102 with a secondary response level nearby, then the response coverage of response unit R102 is insufficient, and a screen partition response risk disconnect list is obtained, which lists the response unit numbers with insufficient response coverage and their corresponding risk partition information.

[0035] S513: Based on the screen partition response risk disconnection list and the level number in the screen protection priority sequence, the key partition numbers for which response coverage needs to be improved are extracted, and adjustment control parameters linked to the original repair unit are output in sequence, and an automatic linkage repair adjustment instruction set is output; According to the screen partition response risk disconnection list, according to the level number in the screen protection priority sequence, the screen protection priority sequence stipulates the repair priority of areas with different protection levels. For example, the first-level protection area takes priority over the second-level protection area for repair. The key partition numbers that need to improve the response coverage are extracted. For units with insufficient response coverage in the list, according to the protection priority level of their corresponding risk partitions, the key partitions with higher priority that need to improve the response coverage are identified. For example, the list shows that partitions (5, 6) are high risk and are covered by the second-level response unit R102. The screen protection priority sequence shows that the high-risk partition (first-level protection) has the highest priority, so partition (5, 6) is identified For the key partitions where the response coverage needs to be improved, the adjustment control parameters linked with the original repair unit are output in sequence. For the identified key partitions, such as partitions (5, 6), which were originally covered by the response unit R102, it is necessary to link with the adjacent high-response level units (such as the response unit R101) for collaborative repair. The adjustment control parameters are output, such as the output intensity, action range, action timing and other parameters of the response units R101 and R102. The parameters are used to guide the collaborative work of the repair units, and the automatic linkage repair adjustment instruction set is output. The instruction set contains the repair unit adjustment control parameters for the response risk disconnected partitions, which is used to drive the repair units to perform linkage repair operations.

[0036] The mobile phone screen quality inspection system based on screen image inspection zoning is used to execute the mobile phone screen quality inspection method based on screen image inspection zoning. The system includes: The grayscale monitoring module divides the detection zones based on the mobile phone screen surface, compares the grayscale change slope and edge fitting deviation value within the same time period, selects the synchronous mutation intervals between the two, extracts the zone number and spatial coordinates, summarizes the abnormal time periods and zone numbers, and generates a candidate set of abnormal areas; The partition positioning module identifies the consistency between the grayscale gradient direction and the edge fitting vector based on the abnormal area candidate set, calibrates the partition boundary number, matches the overall screen structure diagram, extracts the partition number range of the grayscale gradient abnormal area, and establishes the suspected defect partition layer; The defect linkage module retrieves the color distribution matrix and the continuous data sequence of local texture features in the region based on the suspected defect partition layer, determines the connectivity of the color anomaly boundary and the consistency of the texture, marks the partition numbers that meet the linkage threshold of the two, and outputs the defect linkage partition set; The stress warning module analyzes the deviation between the stress distribution trend of the corresponding partition and the original flatness curve based on the partition number of the defect linkage partition set, extracts the partition number that deviates from the trend, completes the level identification according to the risk grading standard, and generates a screen partition stress anomaly grading list; The repair optimization module is based on the screen partition stress anomaly classification list, searches for the corresponding position number of the risk level partition in the mobile phone screen function diagram, retrieves the current repair response unit configuration list, compares the screen protection priority with the current response level, filters the partition number that needs to be updated, and outputs the automatic linkage repair adjustment instruction set.

[0037] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A mobile phone screen quality inspection method based on screen inspection zoning, characterized in that: The following steps are involved: S1: Based on the detection partitions divided by the mobile phone screen surface, the grayscale distribution curve and edge fitting trajectory of the partition are extracted. The synchronous mutation intervals of the grayscale change slope and the edge fitting deviation value are identified on the time axis. The corresponding partition numbers and spatial coordinates are extracted to generate a candidate set of abnormal regions. S2: Calling the grayscale gradient direction and edge fitting vector of the partitions in the abnormal region candidate set, analyzing the direction consistency of the two at the partition boundary, combining the partition boundary connectivity, screening the partitions with connectivity and abnormal grayscale gradient, and forming a suspected defect partition layer; S3: Based on the suspected defect partition layer, extract the color distribution matrix and local texture features within the partition, analyze the color uniformity and texture consistency, screen and match the abnormal area, and obtain a defect linkage partition set; S4: According to the position number of the defect linkage partition set, analyze the screen stress distribution trend corresponding to the partition, evaluate the degree of deviation from the original flatness curve of the screen, calibrate the abnormality level of the partition according to the deviation amplitude, and output the screen partition risk level list.

2. The mobile phone screen quality inspection method based on screen image inspection and zoning according to claim 1 is characterized in that: The abnormal area candidate set includes grayscale mutation partition numbers, edge fitting abnormal points, partition coordinate marks, and time series mutation identifiers. The suspected defect partition layer includes grayscale gradient abnormal partition identifiers, partition boundary connectivity units, partition boundary consistency blocks, and abnormal grayscale distribution grids. The defect linkage partition set includes color abnormality continuous partitions, texture irregularity areas, color spot mutation overlap areas, and linkage abnormal partition numbers. The screen partition risk level list includes risk level labels, partition response deviation values, local stress abnormality indicators, and flatness offset levels.

3. The mobile phone screen quality inspection method based on screen image inspection and zoning according to claim 1 is characterized in that: The steps for obtaining the abnormal region candidate set are specifically as follows: S111: Based on the detection partitions divided by the mobile phone screen surface, extract the grayscale distribution curve and edge fitting trajectory of the partitions, perform difference calculation on the two types of data in the same partition, and obtain the grayscale and edge fitting difference trend value; S112: Based on the grayscale and edge fitting difference trend values, identifying the deviation value of the moving slope value in the grayscale change curve and the edge fitting trajectory, superimposing the two types of values ​​over time, extracting the time intervals where the slope exceeds the baseline value and the deviation value exceeds the set threshold, and generating a high-frequency mutation interval time period set; S113: For the high-frequency mutation interval time period set, the corresponding partition number and spatial coordinate information are matched, the partition position where the signal occurs is extracted, and a candidate set of abnormal areas is generated.

4. The mobile phone screen quality inspection method based on screen image inspection and zoning according to claim 3 is characterized in that: The specific steps for obtaining the suspected defect partition layer are as follows: S211: calling the grayscale gradient direction and edge fitting vector of the partitions in the abnormal region candidate set, extracting the projection trajectory of the two at the partition boundary, identifying the distribution number and aggregation degree of the intersection points in the partition, and obtaining the partition boundary consistency map; S212: Based on the partition boundary consistency map, filter the boundary regions with a higher-than-average degree of aggregation, compare the spatial boundaries of the overall screen structure map, identify continuous boundary concentrated areas belonging to the same partition, and obtain grayscale gradient abnormality zoning within the screen partition; S213: Call the grayscale gradient abnormal zoning within the screen partition, perform integrated analysis on the partition boundary consistency, grayscale gradient discreteness, color distribution balance and edge fitting delay, calculate the defect high-sensitivity response coefficient, perform partition matching according to the response block in the layer, and form a suspected defect partition layer.

5. The mobile phone screen quality inspection method based on screen image inspection and zoning according to claim 4 is characterized in that: The steps for obtaining the defect linkage partition set are specifically as follows: S311: Based on the suspected defect partition layer, extract the color distribution matrix and local texture features of the numbered partitions in the layer, align the timestamps of the data in the partitions, identify the color intensity fluctuation value and texture consistency offset, and obtain the screen local defect response feature set; S312: Based on the local defect response feature set of the screen, jointly analyze the color uniformity and texture consistency within the partition, calculate the color-texture coupling feature value, select the partition units with the color-texture coupling degree in the layer, and establish a color-texture coordinated response spatial distribution map; S313: calling the color texture collaborative response space distribution map, clustering the partitions exceeding the collaborative identification benchmark in the coupled eigenvalue layer, marking the partition codes and coordinates corresponding to the continuous abnormal areas, and obtaining the defect linkage partition set.

6. The mobile phone screen quality inspection method based on screen image inspection and zoning according to claim 5 is characterized in that: The steps for obtaining the screen partition risk level list are specifically as follows: S411: extracting the stress distribution curve of the partition with the specified number according to the position number of the defect linkage partition set, performing time uniform processing, identifying the stress change per unit time, and obtaining a partition stress abnormal change rate set; S412: Based on the set of abnormal stress change rates of the partitions, identifying the stress distribution curve of the original flatness stage, comparing the current stress change sequence with the reference curve, identifying the stress deviation level of the partitions, extracting and marking the partitions whose deviation levels exceed the warning upper limit, and obtaining a set of partitions with sudden deviation increases; S413: According to the deviation sudden increase partition set, the deviation level value of each partition is bound to the position number in the screen structure space diagram, sorted by risk level, and output a screen partition risk level list.

7. The mobile phone screen quality inspection method based on screen image inspection and zoning according to claim 1 is characterized in that: The method further comprises step S5: S5: Calling the screen partition risk level list, identifying the corresponding number of the partition in the mobile phone screen function map, calling the repair response unit list, comparing the response level with the screen protection priority sequence, screening the partition number that needs to adjust the response coverage, and outputting the automatic linkage repair adjustment instruction set; The automatic linkage repair adjustment instruction set includes adjusting the target partition number, response level adjustment parameters, protection priority comparison items, and linkage response trigger type.

8. The mobile phone screen quality inspection method based on screen image inspection and zoning according to claim 7 is characterized in that: The steps for obtaining the automatic linkage repair adjustment instruction set are specifically as follows: S511: Calling the screen partition risk level list, extracting the partition number in the mobile phone screen function map, mapping the partition risk level value with the regional coordinate boundary, identifying the partition information corresponding to the screen protection level, and generating a screen partition risk distribution map; S512: Based on the screen partition risk distribution map, extract the repair response unit numbers and response levels, match the partition risk levels with the repair response levels, identify the unit numbers with insufficient response coverage, and obtain a screen partition response risk disconnection list; S513: According to the screen partition response risk disconnection list, according to the level number in the screen protection priority sequence, extract the key partition number whose response coverage needs to be improved, output the adjustment control parameters linked with the original repair unit in sequence, and output the automatic linkage repair adjustment instruction set.

9. A mobile phone screen quality inspection system based on screen inspection zoning, characterized in that: The system is used to implement the mobile phone screen quality inspection method based on screen image inspection zoning according to any one of claims 1 to 8, and the system includes: The grayscale monitoring module divides the detection zones based on the mobile phone screen surface, compares the grayscale change slope and edge fitting deviation value within the same time period, selects the synchronous mutation intervals between the two, extracts the zone number and spatial coordinates, summarizes the abnormal time periods and zone numbers, and generates a candidate set of abnormal areas; Based on the abnormal area candidate set, the partition positioning module identifies the consistency between the grayscale gradient direction and the edge fitting vector, calibrates the partition boundary number, matches the overall screen structure diagram, extracts the partition number range of the grayscale gradient abnormal area, and establishes a suspected defect partition layer; The defect linkage module retrieves the color distribution matrix and the continuous data sequence of the local texture features in the region based on the suspected defect partition layer, determines the connectivity of the color abnormality boundary and the texture consistency, marks the partition number that meets the linkage threshold of the two, and outputs the defect linkage partition set; The stress warning module analyzes the deviation degree between the stress distribution trend of the corresponding partition and the original flatness curve based on the partition number of the defect linkage partition set, extracts the partition number that deviates from the trend, completes the level identification according to the risk grading standard, and generates a screen partition stress abnormality grading list; The repair optimization module searches for the corresponding position number of the risk level partition in the mobile phone screen function diagram based on the screen partition stress anomaly classification list, retrieves the current repair response unit configuration list, compares the screen protection priority with the current response level, filters the partition number that needs to be updated, and outputs the automatic linkage repair adjustment instruction set.

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