A display module integrated brightness and chroma uniformity synchronous detection automation system

Abstract

The application discloses a display module integrated brightness and chroma uniformity synchronous detection automation system, and relates to the technical field of display optical detection and analysis, which comprises the following steps: collecting optical response data of a display module under test driving, and performing spatial position mapping binding and time alignment on the optical response data to generate an optical response sequence; adopting a space-time interpolation method to perform spatial interpolation and continuous reconstruction on the brightness and chroma synchronous parameter sequence, to obtain brightness and chroma distribution data, combine the brightness and chroma distribution data with time changes, and continuously connect them to generate a brightness and chroma distribution field; performing regional division and unified evaluation on the brightness and chroma deviation sequence to obtain regional evaluation data, combining the regional evaluation data with spatial continuity, and performing hierarchical judgment to generate a brightness and chroma uniformity report. The application improves the continuity and accuracy of display module uniformity detection.

Description

Technical Field

[0001] This invention relates to the field of display optical testing and analysis technology, and in particular to an automated system for synchronous detection of brightness and color uniformity of integrated display modules. Background Technology

[0002] With the continuous improvement of display resolution and display quality requirements, brightness uniformity and color uniformity have gradually become important indicators for evaluating the performance of display modules. In the development of existing methods, the optical performance testing of display modules mainly relies on photometers, colorimeters, imaging brightness analyzers, and CCD / CMOS sensor-based area array acquisition methods. By sampling the display surface point by point or in sections, the brightness and color parameters are obtained. Some testing methods introduce standard light source calibration, spatial calibration matrices, and multi-point sampling interpolation algorithms to spatially reconstruct discrete sampling data, thereby obtaining more complete brightness and color distribution information.

[0003] Since optical response data often has both spatial distribution and temporal variation characteristics, there may be differences in time step and spatial sampling inconsistencies between different sampling nodes. Therefore, luminance and chromaticity information need to be aligned and reconstructed in multiple dimensions during fusion analysis. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an automated system for synchronous detection of brightness and chromaticity uniformity in display modules, which solves the problems of spatiotemporal alignment difficulties of optical response data and complex brightness and chromaticity fusion analysis.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides an automated system for synchronous detection of brightness and color uniformity of display modules, comprising: a light response sequence module, used to collect optical response data of the display module under test drive, and to perform spatial position mapping and binding and time alignment of the optical response data to generate an optical response sequence; The chromaticity analysis module is used to extract spectral response data from the optical response sequence. It analyzes the tristimulus values ​​of the spectral response data using the tristimulus analysis method to obtain the comprehensive chromaticity parameters. It then performs spatial alignment and temporal synchronization on the comprehensive chromaticity parameters to generate a luminance parameter sequence. The bright color reconstruction module is used to perform spatial interpolation and continuous reconstruction of the bright color synchronization parameter sequence using a spatiotemporal interpolation method to obtain bright color distribution data. The bright color distribution data is then combined with time changes and continuously connected to generate a bright color distribution field. The deviation analysis module is used to perform adjacent difference and cross-frame difference superposition calculations on the comprehensive chromaticity to obtain luminance deviation data. Based on the luminance deviation data, it performs luminance joint analysis and dominant decomposition to generate a luminance deviation sequence. The uniformity report module is used to divide and uniformly evaluate the bright color deviation sequence, obtain regional evaluation data, combine the regional evaluation data with spatial continuity and make hierarchical judgments, and generate a bright color uniformity report.

[0007] As a preferred embodiment of the automated system for synchronous detection of brightness and color uniformity of the integrated display module described in this invention, the specific steps for generating the optical response sequence are as follows: A test driver is applied to the display module and the light emission is excited point by point in the pixel scanning order. Optical response data is collected, and the optical response data is appended with the collection time mark and pixel position number to generate an optical response record. Pixel mapping tables are extracted from optical response records, and spatial mapping relationships are established based on the pixel mapping tables. Pixel position numbers are converted into corresponding spatial coordinate positions according to the spatial mapping relationships to generate spatial response data. The spatial response data is sorted in ascending time order according to the acquisition time marker to obtain the time-series response sequence. Then, a unified time base correction is performed based on the time-series response sequence to generate an aligned response sequence. The alignment response sequence is continuously organized and structured to generate an optical response sequence.

[0008] As a preferred embodiment of the automated system for synchronous detection of brightness and color uniformity of the integrated display module described in this invention, the specific steps for obtaining the comprehensive color parameters are as follows: The spectral response data corresponding to each spatial coordinate position at each acquisition time is read one by one from the optical response sequence, and the stable emission segment is screened and abnormal sampling is removed from the spectral response data to generate effective spectral data. Spectral correction benchmarks are extracted from effective spectral data. The spectral correction benchmarks are combined with spectral variations and neighborhood continuity constraints are constructed to generate corrected spectral data. Using the tristimulus analysis method, the corrected spectral data are expanded sequentially according to wavelength order to obtain wavelength positions, and then matched item by item with the first standard color matching response, the second standard color matching response and the third standard color matching response at the corresponding wavelength positions to generate three sets of basic stimulus components. The continuous changes of the three basic stimulus components between adjacent wavelength positions are subjected to mutation suppression and continuous accumulation to obtain the tristimulus quantities. The comprehensive chromaticity parameters are determined based on the proportional relationship between the tristimulus quantities.

[0009] As a preferred embodiment of the automated system for synchronous detection of brightness and chromaticity uniformity in the integrated display module described in this invention, the specific steps for spatial alignment and temporal synchronization of the comprehensive chromaticity parameters to generate a luminance parameter sequence are as follows: The time difference of the comprehensive chromaticity parameters is compared to obtain the time deviation sequence. The comprehensive chromaticity parameters with small deviation from the driving trigger time are selected from the time deviation sequence to generate time series filtering data. Time offset correction is performed on the comprehensive chromaticity parameters to obtain time correction data, and the spatial coordinate positions are aligned with the time axis according to a unified time reference to generate synchronized comprehensive chromaticity data. By combining time-series filtered data with synchronous integrated chromaticity data and organizing and arranging them in a continuous and structured manner, a luminance parameter sequence is generated.

[0010] As a preferred embodiment of the automated system for synchronous detection of brightness and color uniformity in the display module described in this invention, the specific steps for acquiring brightness and color distribution data are as follows: The comprehensive chromaticity parameters of each spatial coordinate position in the luminance parameter sequence are expanded frame by frame, and a spatial global grid is constructed based on the comprehensive chromaticity parameters; Extract the neighboring reference nodes of each node to be interpolated from the spatial global grid, and calculate the spatial distance, comprehensive chromaticity difference and neighborhood gradient change of the neighboring reference nodes to generate spatiotemporal constraint data; The spatiotemporal interpolation method is adopted. Based on the spatiotemporal constraint data, the spatiotemporal interpolation constraints corresponding to each node to be interpolated are determined. The spatiotemporal interpolation constraints are solved collaboratively to obtain the interpolation weight data. The interpolation weight data is then subjected to comprehensive chromaticity reconstruction to generate spatial interpolation data. The spatial interpolation data is integrated with the original node data to ensure that the spatial distribution is continuous and complete at each acquisition time, and to generate bright color distribution data.

[0011] As a preferred embodiment of the automated system for synchronous detection of brightness and color uniformity of display module integration described in this invention, the brightness and color distribution field refers to the extraction of comprehensive color parameters from brightness and color distribution data, the point-by-point comparison of the comprehensive color change trend to determine the direction and magnitude of change, and the continuous time acceptance and abnormal fluctuation correction of the direction and magnitude of change.

[0012] As a preferred embodiment of the automated system for synchronous detection of brightness and color uniformity in the display module described in this invention, the specific steps for performing adjacent difference and cross-frame difference superposition calculations on the comprehensive color to obtain brightness and color deviation data are as follows: Spatiotemporal variation data are extracted from the bright color distribution, and adjacent difference and cross-frame difference are jointly calculated on the spatiotemporal variation data to obtain single-point deviation data; The single-point deviation data is subjected to directional consistency discrimination and amplitude normalization to obtain deviation modulation data, and the deviation modulation data is spatially ordered to generate single-frame deviation data. The single-frame deviation data is continuously spliced ​​and verified to generate bright-color deviation data.

[0013] As a preferred embodiment of the automated system for synchronous detection of brightness and color uniformity in the display module of the present invention, the specific steps for generating the brightness-color deviation sequence are as follows: Based on the bright color deviation data, the degree of deviation of each spatial coordinate position is correlated with the dominant decomposition direction to obtain deviation mapping data, and the continuous deviation regions in the deviation mapping data are divided by connectivity to generate bright color deviation fragment data. The bright-color deviation data segments are joined together in time, and deviation segments that do not meet the continuity condition are marked as broken to generate a bright-color deviation sequence.

[0014] As a preferred embodiment of the automated system for synchronous detection of brightness and color uniformity in the display module of the present invention, the specific steps for dividing and uniformly evaluating the brightness and color deviation sequence to obtain regional evaluation data are as follows: The neighborhood deviation consistency is extracted from the bright color deviation sequence, the neighborhood deviation consistency is connected and merged to obtain the deviation region set, the regional deviation features are extracted from the deviation region set, the regional deviation features are merged, organized and correlated to generate basic data for regional assessment. The basic data for regional assessment are uniformly quantified and calculated to generate regional assessment data.

[0015] As a preferred embodiment of the automated system for synchronous detection of brightness and color uniformity in the display module described in this invention, the specific steps for generating the brightness and color uniformity report are as follows: Based on regional assessment data, the spatial boundaries of each deviation area are continuously detected and the fracture and expansion status are recorded to generate spatial continuity data. By combining regional assessment data with spatial continuity data and matching them accordingly, the grading basis data is obtained. The grading basis data is then used to make grading judgments, regional grading data is constructed, and the regional grading data is summarized and organized to generate a report on uniform brightness.

[0016] The beneficial effects of this invention are as follows: by spatially mapping and temporally aligning the optical response data of the display module, an optical response sequence with a unified spatiotemporal reference is constructed, enabling consistent expression of pixel distribution and sampling timing; and by combining spectral screening, continuous constraints, and tristimulus analysis to generate comprehensive chromaticity parameters, a stable conversion of optical information into visual quantification parameters is achieved; and by reconstructing the luminance distribution field through spatiotemporal interpolation and performing deviation calculations and region division evaluations, the grading determination of luminance and chromaticity uniformity is completed, thereby improving the continuity and accuracy of display module uniformity detection. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of the overall structure for synchronous detection of brightness and color uniformity in a display module.

[0019] Figure 2 This is a schematic diagram of the generation of optical response sequences and luminance parameters.

[0020] Figure 3 This is a schematic diagram illustrating the reconstruction of the bright color distribution and the analysis of the bright color deviation.

[0021] Figure 4 This is a comparison chart of sampling missing rate and reconstruction error.

[0022] Figure 5 This is a comparison chart of the percentage of the area deviating from the classification accuracy. Detailed Implementation

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0026] Reference Figures 1-5 As one embodiment of the present invention, this embodiment provides an automated system for synchronous detection of brightness and color uniformity in a display module, comprising the following steps: The optical response sequence module collects optical response data of the display module under test drive, and performs spatial location mapping and time alignment on the optical response data to generate an optical response sequence.

[0027] A test driver is applied to the display module, and light emission is excited point by point in the pixel scanning sequence. Optical response data is collected, and the optical response data is appended with the collection time mark and pixel position number to generate an optical response record.

[0028] It should be noted that after applying the test drive to the display module, the point-by-point excitation path is determined according to the pixel scanning order, and the corresponding driving electrical signal is applied to each pixel position in sequence so that the light emission behavior is triggered point by point in a predetermined order. During the point-by-point light emission process, optical response data acquisition is performed simultaneously. Each acquisition result is marked with an acquisition time identifier to record the time attribution, and a pixel position number is added to identify the spatial position source. The acquisition results with acquisition time identifier and pixel position number are continuously converged in the scanning order to generate an optical response record.

[0029] Pixel mapping tables are extracted from optical response records, and spatial mapping relationships are established based on the pixel mapping tables. Pixel position numbers are converted into corresponding spatial coordinate positions according to the spatial mapping relationships to generate spatial response data.

[0030] It should be noted that the process involves parsing pixel position numbers one by one from the optical response record, classifying and sorting the pixel position numbers for consistency, and extracting a pixel mapping table based on pixel arrangement rules. Based on the pixel mapping table, each pixel position number is matched with its spatial geometric arrangement, the row and column offset relationships between pixels are calculated, and corresponding spatial mapping relationships are established. According to the spatial mapping relationships, spatial coordinate transformation calculations are performed on each pixel position number in the optical response record, mapping each pixel position number to its corresponding spatial coordinate position, and the transformation results are verified for continuity and spatial consistency. After completing the transformation of all pixel position numbers to spatial coordinate positions, the corresponding optical response information for each spatial coordinate position is sequentially aggregated to generate spatial response data.

[0031] It should also be noted that the spatial geometric arrangement relationship originates from the physical layout structure of the pixel unit. The correspondence between the pixel position number and the spatial coordinate is established based on the pixel arrangement rules and row and column distribution method. This is used to describe the position and spacing characteristics of the pixel in two-dimensional space and to provide a unified basis for subsequent spatial coordinate transformation and position association calculation.

[0032] The expression for calculating the row and column offset relationship between pixels is: ; in, For the first The pixel position number and the first Normalized offset distance between pixel position numbers; For the first Each pixel position number corresponds to the horizontal coordinate value of its spatial coordinate position; For the first Each pixel position number corresponds to the horizontal coordinate value of its spatial coordinate position; For the first Each pixel position number corresponds to the vertical coordinate value of its spatial coordinate position; For the first Each pixel position number corresponds to the vertical coordinate value of its spatial coordinate position; The standard spacing between the centers of horizontally adjacent pixels; The standard spacing between the centers of vertically adjacent pixels; Number the index of the first pixel position involved in the calculation in the optical response record; Number the index of the second pixel position in the optical response record that is involved in the calculation; This represents the horizontal coordinate component of the pixel position in the spatial coordinate system; This represents the vertical coordinate component of the pixel position in the spatial coordinate system.

[0033] It should also be noted that the pixel arrangement rule is used to standardize and define the spatial distribution relationship of each pixel position in the display module. The incremental order of the pixel position number is determined according to the row and column direction, and the pixel position number is uniquely identified by the row-first or column-first scanning path constraint. At the same time, the spatial spacing relationship between adjacent pixel positions is fixedly mapped so that each pixel position number corresponds to a unique spatial coordinate position relationship, thereby providing a unified correspondence basis for the subsequent conversion between pixel position number and spatial coordinate position.

[0034] The spatial response data is sorted in ascending time order according to the acquisition time marker to obtain the time-series response sequence. Then, a unified time base correction is performed based on the time-series response sequence to generate an aligned response sequence.

[0035] It should be noted that, according to the acquisition time identifier, the spatial response data is read one by one and the time markers are extracted. The time markers are then parsed numerically and compared. The spatial response data corresponding to the earlier acquisition time is arranged first, and the spatial response data corresponding to the later acquisition time is arranged last, thus completing the ascending time order to form a time-series response sequence. Based on the time-series response sequence, the difference between each time marker and the unified time reference is extracted. The difference is then processed point by point, and the time offset is extracted. Based on the time offset, the time axis of each spatial response data in the time-series response sequence is corrected. The time offsets caused by different acquisition times are corrected one by one to the unified time reference position. The consistency of the corrected time is then checked for continuity to generate an aligned response sequence.

[0036] The alignment response sequence is continuously organized and structured to generate an optical response sequence.

[0037] It should be noted that the aligned response sequence is read line by line in both spatial coordinate position and temporal order. Taking spatial coordinate position as the first dimension, data within the same time slice is read in ascending order of rows and columns, and the difference between adjacent rows and columns is compared to determine spatial continuity or mark spatial breakpoints. Taking temporal order as the second dimension, data at the same spatial coordinate position at different times are read moment by moment, and the time difference is compared to determine temporal continuity or mark temporal anomalies and perform rearrangement. After completing the spatial continuity and temporal continuity verification, the data that meet the two-dimensional continuity conditions are jointly filtered and continuously spliced ​​and verified for consistency according to the spatial priority and temporal progression rules to generate the optical response sequence.

[0038] It should also be noted that the optical response sequence is used to organize the aligned response sequences at different spatial coordinate positions and acquisition times in a unified manner, so that the spatial response information and time changes form a continuous correspondence, thereby providing a structured data foundation for comprehensive colorimetric parameter extraction, spectral analysis and consistency analysis.

[0039] The spatial continuous increment rule originates from the row and column arrangement and scanning order of the pixels in the display module. It is established based on the incrementing relationship of the pixel position number in the row and column direction and is used to determine whether adjacent spatial coordinates are arranged continuously, thereby ensuring the consistency of the data order in the spatial dimension and avoiding misalignment and breakage.

[0040] The colorimetric analysis module extracts spectral response data from the optical response sequence, analyzes the tristimulus values ​​of the spectral response data using the tristimulus analysis method, obtains comprehensive colorimetric parameters, and performs spatial alignment and temporal synchronization of the comprehensive colorimetric parameters to generate a luminance parameter sequence.

[0041] The spectral response data corresponding to each spatial coordinate position at each acquisition time is read one by one from the optical response sequence, and the stable emission segment is screened and abnormal sampling is removed from the spectral response data to generate effective spectral data.

[0042] It should be noted that the corresponding spectral response data are read sequentially from the optical response sequence according to spatial coordinates and acquisition time. The difference between the spectral values ​​of adjacent sampling points is calculated, and the difference is divided by the acquisition time interval or sampling sequence interval between adjacent sampling points to obtain the rate of change. A sliding window of fixed length is constructed with the current sampling point as the center, and the mean value and fluctuation range of each rate of change within the window are calculated. When the rate of change of multiple consecutive sampling points falls within the convergence interval, it is determined to be a stable emission segment. When the rate of change of a certain sampling point deviates from the convergence interval and is inconsistent with the change trend of the previous and subsequent sampling points, it is determined to be an abnormal sampling and is removed. After the stable segment identification and abnormal sampling removal are completed, the remaining spectral response data are continuously spliced ​​and sequentially reassembled to generate valid spectral data.

[0043] The spectral correction benchmark is extracted from the effective spectral data, and the spectral correction benchmark is combined with the spectral variation and a neighborhood continuity constraint is constructed to generate the corrected spectral data.

[0044] It should be noted that the spectral values ​​of each wavelength are read point by point from the effective spectral data. Based on the global mean and the local mean and fluctuation amplitude of the sliding window, the continuous interval with the smallest deviation and fluctuation is determined as the spectral correction benchmark. The spectral correction benchmark is compared with the spectral change values ​​point by point, and a neighborhood continuity constraint is established based on the continuity of the change direction and amplitude between adjacent wavelengths. Under the neighborhood continuity constraint, the spectral changes of each wavelength are adjusted and smoothed for consistency. After eliminating abrupt shifts, they are continuously spliced ​​and uniformly arranged to generate the corrected spectral data.

[0045] It should also be noted that by comparing the spectral changes at each wavelength or adjacent spatial location point by point, analyzing the consistency between the direction and magnitude of change, identifying continuous change trends and filtering out abrupt and inconsistent parts, and constraining adjacent data that meet the continuity conditions, a neighborhood continuity constraint that maintains smooth transition and structural consistency is formed.

[0046] Using the tristimulus analysis method, the corrected spectral data are unfolded sequentially according to wavelength order to obtain wavelength positions. These positions are then matched one by one with the first, second, and third standard color matching responses at the corresponding wavelength positions to generate three sets of basic stimulus components.

[0047] It should be noted that the calibrated spectral data is expanded point by point in wavelength order, and the spectral values ​​corresponding to each wavelength position are extracted. Then, the corresponding first standard color matching response, second standard color matching response, and third standard color matching response are found in the standard response table using the wavelength position as an index. The first standard color matching response is used to characterize the red perception channel response, the second standard color matching response is used to characterize the green perception channel response, and the third standard color matching response is used to characterize the blue perception channel response. The spectral value at the current wavelength position is matched one by one with the three types of standard color matching responses at the same wavelength position, and the contribution of three single-point stimuli is calculated. After the continuity of the contribution of adjacent wavelength positions is verified, they are accumulated and converged in wavelength order to generate three sets of basic stimulus components.

[0048] It should also be noted that the tristimulus analysis method is based on the three-channel perception mechanism of the human eye. It expands the input spectrum point by point according to wavelength and matches it with three types of standard color matching responses to obtain the stimulus contribution of each wavelength. It also imposes continuity constraints on the changes of adjacent wavelengths to keep the results smooth and consistent, thus realizing the analytical mapping from spectrum to tristimulus components.

[0049] The continuous changes of the three basic stimulus components between adjacent wavelength positions are subjected to mutation suppression and continuous accumulation to obtain the tristimulus quantities. The comprehensive chromaticity parameters are determined based on the proportional relationship between the tristimulus quantities.

[0050] It should be noted that the three sets of basic stimulus components are read point by point in wavelength order, and the trend of change is obtained by difference and compared with the local fluctuation range. When the range is exceeded, mutation suppression is performed and replaced with the neighborhood mean. After the suppression is completed, the components at continuous wavelength positions are progressively accumulated to obtain the tristimulus quantity, and the proportional relationship between the tristimulus quantities is normalized to determine the comprehensive chromaticity parameter.

[0051] The time difference of the comprehensive chromaticity parameters is compared to obtain the time deviation sequence. The comprehensive chromaticity parameters with small deviation from the driving trigger time are then selected from the time deviation sequence to generate time series filtering data.

[0052] It should be noted that the acquisition time identifier of each comprehensive chromaticity parameter is read and compared with the driving trigger time point by point to obtain the corresponding time deviation value; the time deviation values ​​are sorted and statistically analyzed to form a time deviation sequence, and the parameters are filtered and marked according to the deviation range. Comprehensive chromaticity parameters with continuous and stable deviations and no sudden changes are retained first. Data that meet the time consistency condition are re-aggregated according to the original time sequence to generate time-series filtered data.

[0053] Time offset correction is performed on the comprehensive chromaticity parameters to obtain time correction data, and the time axis of each spatial coordinate position is aligned according to a unified time reference to generate synchronized comprehensive chromaticity data.

[0054] It should be noted that the acquisition time marker and spatial coordinate position of each integrated chromaticity parameter are read one by one to obtain the time offset relative to the unified time reference. The direction consistency of the time offset changes is judged to identify the early or late distribution. Time correction is performed based on the time offset to map each integrated chromaticity parameter to the corresponding position of the unified time reference and perform consistency verification. After the correction is completed, the data is rearranged on the time axis and continuously spliced ​​in combination with the spatial coordinate position to form a unified correspondence between different spatial positions and generate synchronous integrated chromaticity data.

[0055] By combining time-series filtered data with synchronous integrated chromaticity data and organizing and arranging them in a continuous and structured manner, a luminance parameter sequence is generated.

[0056] It should be noted that the time-series filtered data is read one by one, and the collection time marker and spatial coordinate position are read. The data is then matched point by point with the synchronous integrated colorimetric data and the consistency verification is performed to confirm the correspondence between parameters at the same spatial position and under a unified time reference. After the matching is completed, the data is sorted according to spatial coordinate priority and time sequence, and continuity checks and breakpoint filling are performed. After the continuous organization is completed, the data is integrated and sequentially aggregated to generate a bright color parameter sequence.

[0057] It should also be noted that the luminance parameter sequence is used to continuously organize the comprehensive chromaticity parameters of different spatial coordinate positions under a unified time base, so that luminance and chromaticity form a correspondence in time and space, thereby providing a structured data foundation for luminance distribution reconstruction, spatial interpolation and deviation analysis.

[0058] The bright color reconstruction module uses a spatiotemporal interpolation method to perform spatial interpolation and continuous reconstruction on the bright color synchronization parameter sequence to obtain bright color distribution data. It then combines the bright color distribution data with time changes and continuously connects them to generate a bright color distribution field.

[0059] like Figure 4 As shown, this figure illustrates the error changes between conventional and the present invention's processing methods during the luminance distribution data reconstruction stage under different sampling missing rates. As the sampling missing rate increases, the reconstruction error directly reflects the ability to approximate the true distribution when recovering luminance distribution data from the luminance parameter sequence. Therefore, this figure can intuitively demonstrate the role of reducing reconstruction error and improving spatial reconstruction accuracy through spatiotemporal interpolation constraints and comprehensive chromaticity reconstruction, thereby illustrating that the present invention has better continuous recovery capability and result reliability for display module uniformity detection under incomplete sampling conditions.

[0060] The conventional processing method refers to directly completing the spatial data or performing ordinary neighborhood interpolation based solely on the existing sampling points in the luminance parameter sequence during the luminance distribution data reconstruction stage. This method does not consider the continuous temporal changes or perform joint constraint analysis on spatial distance, comprehensive chromaticity differences, and neighborhood gradient changes. For missing sampling locations, estimation and reconstruction are typically performed based on the static distribution of adjacent pixels. While this method is relatively simple to implement, it is prone to problems such as local distortion, discontinuous boundary transitions, and increased overall reconstruction error when the sampling missing rate increases.

[0061] The comprehensive chromaticity parameters of each spatial coordinate position in the bright and chromaticity parameter sequence are expanded frame by frame, and a spatial global grid is constructed based on the comprehensive chromaticity parameters.

[0062] It should be noted that the comprehensive chromaticity parameters of each spatial coordinate position in the luminance parameter sequence are read sequentially frame by frame and decomposed into row coordinates and column coordinates; the row and column differences of adjacent spatial coordinates are compared point by point, and if they meet the increasing or decreasing pattern, they are judged as spatial continuity; otherwise, they are identified as spatial breakpoints; for breakpoints, consistency judgment is made by combining the neighborhood change trend and the similarity of comprehensive chromaticity parameters to determine the missing or abnormal positions; after the verification is completed, the verified spatial coordinates are mapped according to the two-dimensional row and column relationship and continuously stitched to construct a spatial global grid.

[0063] Extract the neighboring reference nodes of each node to be interpolated from the spatial global grid, and calculate the spatial distance, comprehensive chromaticity difference and neighborhood gradient change of the neighboring reference nodes to generate spatiotemporal constraint data.

[0064] It should be noted that the nodes to be interpolated are read from the spatial global grid and the neighboring reference nodes are filtered according to the neighborhood range to obtain the spatial distance and comprehensive chromaticity difference; the difference changes are compared along the row and column directions and the change trend is extracted in combination with the spatial spacing to form the neighborhood gradient change; on this basis, the spatial distance, comprehensive chromaticity difference and neighborhood gradient change are uniformly correlated and structured to generate spatiotemporal constrained data.

[0065] It should also be noted that the neighboring reference node refers to a set of adjacent spatial coordinate positions selected with the target position as the center in the spatial global grid. These nodes correspond to a set of nodes with known comprehensive chromaticity parameters. By calculating the spatial distance, comprehensive chromaticity difference, and trend of change between the node and the target position, the reference node provides a constraint basis for interpolation calculation and supports continuous reconstruction.

[0066] The spatiotemporal interpolation method is adopted. Based on the spatiotemporal constraint data, the spatiotemporal interpolation constraints corresponding to each node to be interpolated are determined. The spatiotemporal interpolation constraints are solved collaboratively to obtain the interpolation weight data. The interpolation weight data is then subjected to comprehensive chromaticity reconstruction to generate spatial interpolation data.

[0067] It should be noted that a spatiotemporal interpolation method is adopted. Spatial distance, comprehensive chromaticity difference, and neighborhood gradient changes are read line by line from the spatiotemporal constraint data, and an interpolation calculation process is constructed centered on the node to be interpolated. Spatial distance is normalized to its maximum and minimum values, mapping the distance to inverse attenuation weights to determine spatial weights. Comprehensive chromaticity differences are graded by amplitude and mapped proportionally, converting the difference amplitude into corresponding chromaticity weights. Neighborhood gradient changes are judged for directional consistency by comparing the gradient directions and trends of adjacent reference nodes, selecting constraint components with consistent directions to form change constraints. Spatial weights, chromaticity weights, and change constraints are weighted and combined according to a unified scale to construct spatiotemporal interpolation constraint relationships. Each constraint weight is dynamically adjusted through iterative calculations, gradually stabilizing and aligning the weights over multiple rounds of calculation. After weight convergence, the comprehensive chromaticity parameters of the neighborhood reference nodes are weighted and fused based on the final spatiotemporal interpolation constraints to obtain the comprehensive chromaticity estimate of each node to be interpolated, generating spatial interpolation data.

[0068] It should also be noted that the spatiotemporal interpolation method is based on spatial location relationships and temporal continuity. It performs comprehensive chromaticity reconstruction on missing or non-uniform sampling points, constructs interpolation weights by dividing spatial neighborhoods and combining spatial distance, temporal differences and comprehensive chromaticity changes, performs fusion estimation of neighborhood parameters, and ensures continuous and smooth results through neighborhood consistency verification.

[0069] The spatial interpolation data is integrated with the original node data to ensure that the spatial distribution is continuous and complete at each acquisition time, and to generate bright color distribution data.

[0070] It should be noted that the interpolated data of each spatial coordinate position is read one by one according to the spatial interpolation data, and matched and consistent with the corresponding original node data. When the difference is within the continuous range, the fusion process is performed. When it exceeds the range, the interpolated data is used for completion and correction. After fusion, the spatial coordinates are rearranged in two dimensions and the data is aggregated frame by frame according to the acquisition time. The continuity of adjacent positions is checked and the neighborhood is filled in for the broken positions to generate bright color distribution data.

[0071] The comprehensive chromaticity parameter is extracted from the luminous color distribution data. The trend of the comprehensive chromaticity change is compared point by point to determine the direction and magnitude of the change. The direction and magnitude of the change are then subjected to time continuity and abnormal fluctuation correction to generate the luminous color distribution field.

[0072] It should be explained that the comprehensive chromaticity parameters corresponding to each spatial coordinate position are read one by one from the bright color distribution data, and aligned point by point according to the spatial coordinate order and time order. Differential calculation and sliding window comparison are performed on adjacent comprehensive chromaticity parameters. The direction of change is determined by calculating the positive and negative changes in the difference of comprehensive chromaticity values ​​between adjacent points, and the magnitude of change is determined by comparing the absolute value of the difference with the average difference of the historical neighborhood. The continuity of the direction of change is checked along the time dimension. When the direction of change jumps between adjacent time points, the direction consistency correction is performed. Points with change magnitude exceeding the neighborhood statistical threshold are identified as having abnormal fluctuations, and weighted smoothing correction is performed in combination with the comprehensive chromaticity parameters of the preceding and following neighborhoods to ensure that the direction of change remains continuous in the time dimension and the magnitude of change remains gradual in the spatial dimension. The corrected direction of change and magnitude of change are remapped back to each spatial coordinate position to form a continuous evolution relationship expression, thereby generating the bright color distribution field.

[0073] It should also be noted that the luminance distribution field is used to express the continuous changes of the comprehensive chromaticity parameters at different spatial coordinate positions at each acquisition time. Through continuous spatial mapping and temporal evolution, the dispersed luminance information is transformed into an overall distribution pattern, thereby supporting the analysis of change trends and spatial gradients, and providing a continuous basis for subsequent evaluation and reconstruction.

[0074] The neighborhood statistical threshold is obtained based on the comprehensive color difference distribution statistics of the neighborhood reference nodes. The upper and lower bounds of the threshold are determined by calculating the mean and fluctuation range of the difference values ​​and combining them with the degree of dispersion. The threshold range consists of the mean and its upper and lower fluctuation ranges, which are used to characterize the normal range of change. Its basis comes from the statistical characteristics and spatial continuous change law of the neighborhood data.

[0075] The deviation analysis module performs adjacent difference and cross-frame difference superposition calculations on the comprehensive chromaticity to obtain luminance deviation data. Based on the luminance deviation data, it performs luminance joint analysis and dominant decomposition to generate a luminance deviation sequence.

[0076] Spatiotemporal variation data are extracted from the bright color distribution. Adjacent difference and cross-frame difference are jointly calculated on the spatiotemporal variation data to obtain single-point deviation data.

[0077] It should be noted that the comprehensive chromaticity parameters are read point by point from the luminous color distribution according to the spatial coordinate position and the acquisition time. The comprehensive chromaticity parameters of adjacent spatial coordinate positions at the same acquisition time are subtracted one by one to obtain the spatial difference variable. The comprehensive chromaticity parameters of the same spatial coordinate position at different acquisition times are subtracted one by one to obtain the time difference variable.

[0078] Spatial difference variables and temporal difference variables are matched according to the same spatial coordinate position and collection time. The direction of change is compared to see if they are consistent and the magnitude of change is continuous. The corresponding results are then superimposed and sorted to obtain single-point deviation data.

[0079] The single-point deviation data is subjected to directional consistency discrimination and amplitude normalization to obtain deviation modulation data. The deviation modulation data is then spatially ordered to generate single-frame deviation data.

[0080] It should be noted that the single-point deviation data is read item by item according to the spatial coordinate position, the change direction and change magnitude corresponding to each position are extracted, the change direction of adjacent spatial coordinate positions is compared item by item to determine whether the change direction is the same or maintains continuous transfer, and the single-point deviation data with the same direction is retained and marked. The variation amplitude corresponding to the retained marker is statistically analyzed in intervals, and the maximum and minimum amplitudes are extracted. Each variation amplitude is then mapped to a unified amplitude interval to obtain the deviation modulation data. Finally, the deviation modulation data is arranged point by point according to the row and column order of the spatial coordinate position to generate single-frame deviation data.

[0081] The single-frame deviation data is continuously spliced ​​and verified to generate bright-color deviation data.

[0082] It should be noted that the single-frame deviation data is read frame by frame in the order of acquisition time, and the deviation value and arrangement position corresponding to each spatial coordinate position are extracted. The deviation values ​​of the same or adjacent spatial coordinate positions in adjacent acquisition times are compared item by item to determine whether the deviation change is continuous, whether the deviation direction is consistent, and whether the spatial arrangement is connected. The single-frame deviation data that meets the continuity and continuation conditions are sequentially connected, and the positions that do not meet the conditions are marked as breakpoints and segmented and sorted to generate bright-colored deviation data.

[0083] Based on the bright color deviation data, the degree of deviation of each spatial coordinate position is correlated with the dominant decomposition direction to obtain deviation mapping data. Then, the continuous deviation regions in the deviation mapping data are divided by connectivity to generate bright color deviation segment data.

[0084] It should be explained that, based on the brightness deviation data, the deviation degree and dominant decomposition direction corresponding to each spatial coordinate position are read one by one. The deviation degree and dominant decomposition direction are matched point by point and a one-to-one correspondence is established. The matching results are mapped to the corresponding spatial coordinate positions to form deviation mapping data. On the basis of deviation mapping data, the difference of deviation degree between adjacent spatial coordinate positions is calculated, and the direction consistency judgment is performed on the dominant decomposition direction. When the difference of deviation degree between adjacent spatial coordinate positions is within a continuous range and the dominant decomposition direction is consistent, a spatial connectivity relationship is established. When the condition is not met, a breakpoint is marked. Based on the established spatial connectivity relationship, the deviation mapping data is expanded and aggregated. The continuously connected spatial regions are uniformly divided and the boundaries are determined to form multiple continuous deviation regions. Each continuous deviation region is fragmented according to spatial order to generate brightness deviation fragment data.

[0085] The bright-color deviation data segments are joined together in time, and deviation segments that do not meet the continuity condition are marked as broken to generate a bright-color deviation sequence.

[0086] It should be noted that the bright color deviation segment data is read frame by frame in the order of acquisition time, and the deviation segments corresponding to the spatial positions of adjacent time points are compared. The temporal continuity relationship is established by judging the consistency between the change in the degree of deviation and the dominant direction. When the degree of deviation changes abruptly or the direction reverses, a break mark is executed. After the continuity judgment is completed, the segments that meet the conditions are chained and sequentially extended, and the broken segments are segmented and sorted to generate a bright color deviation sequence with temporal continuity.

[0087] It should also be noted that the bright color deviation sequence is used to continuously organize the deviation changes of each spatial coordinate position in the time dimension. By connecting discrete segments in a chain through time succession, a traceable evolution trajectory is formed, thereby supporting the characterization of the deviation process, regional division, and uniformity analysis.

[0088] The continuity condition refers to the fact that between adjacent acquisition times or adjacent spatial locations, the deviation changes are consistent in direction, within a smooth transition range in amplitude, and without abrupt jumps. At the same time, the corresponding spatial locations can maintain the connection between the previous and subsequent times, thus determining that the deviation changes have continuous extension characteristics.

[0089] The uniformity reporting module divides and uniformly evaluates the bright color deviation sequence, obtains regional evaluation data, combines the regional evaluation data with spatial continuity and performs hierarchical judgment to generate a bright color uniformity report.

[0090] like Figure 5 As shown, this diagram illustrates the difference in grading and judgment capabilities between conventional and the present invention's methods in the output stages of regional assessment data and brightness uniformity reports under different deviation area proportions. The more complex the deviation area proportions, the more it examines the stability of the entire analysis chain from brightness deviation sequence to regional division, unified assessment, and grading judgment. Therefore, this diagram can intuitively illustrate that after performing connectivity merging, unified quantification, and grading judgment on the deviation areas in processes S5.1 to S5.4, the present invention can more accurately output regional level information, thereby better reflecting the accuracy and report usability of the present invention in brightness and color uniformity grading judgment.

[0091] The conventional approach refers to classifying and determining regions based solely on the degree of deviation at a single point in the bright color deviation sequence or simple regional statistical results during the regional assessment and classification stage. It fails to comprehensively analyze the spatial connectivity, neighborhood consistency, and temporal continuity between deviating regions. Typically, fixed thresholds or simple interval divisions are used to independently assess each region and directly assign a grade. This approach lacks a holistic characterization of the evolution process and spatial continuity of deviating regions. When the distribution of deviating regions is complex or their area proportions vary significantly, it is prone to problems such as unstable classification, inaccurate boundary delineation, and poor consistency in assessment results.

[0092] The neighborhood deviation consistency is extracted from the bright color deviation sequence, the neighborhood deviation consistency is connected and merged to obtain the deviation region set, the regional deviation features are extracted from the deviation region set, the regional deviation features are merged, organized and correlated to generate the basic data for regional assessment.

[0093] It should be noted that, from the bright color deviation sequence, the deviation degree and dominant decomposition direction of each spatial coordinate position at each acquisition time are read one by one. The deviation degree difference is calculated and the direction consistency is compared for adjacent spatial coordinate positions. Regions with continuous deviation degree differences and consistent dominant decomposition directions are identified as neighborhood deviation consistency. Connectivity merging is performed on all spatial regions that meet the neighborhood deviation consistency, and spatially continuous regions are aggregated and their boundaries are determined to obtain a set of deviation regions. Based on the set of deviation regions, the deviation degree distribution, spatial coverage, and temporal continuity of each deviation region are statistically analyzed and features are extracted. The extracted regional deviation features are then organized in a unified format and cross-regional correspondence is performed to structure the regional deviation features and generate basic data for regional assessment.

[0094] The basic data for regional assessment are uniformly quantified and calculated to generate regional assessment data.

[0095] It should be noted that the deviation degree distribution, spatial coverage, and temporal duration of each deviation area are read one by one from the basic data of the regional assessment. The mean and dispersion of the deviation degree distribution are calculated, the area and boundary length of the spatial coverage are calculated, and the duration and frequency of change of the temporal duration are calculated. The results of each calculation are normalized and uniformly mapped to the same dimension. The normalized results are weighted, summarized, and comprehensively calculated to form a comprehensive assessment value for each deviation area. The comprehensive assessment value is then matched and organized with the corresponding area to generate regional assessment data.

[0096] Based on regional assessment data, the continuity of spatial boundaries of each deviation area is detected and the fracture and expansion status are recorded to generate spatial continuity data.

[0097] It should be noted that, based on the regional assessment data, the spatial boundary information corresponding to each deviation area is read one by one. The spatial boundary is traversed point by point, and the distance change between adjacent boundary points is calculated. When the distance change between adjacent boundary points is within a continuous range, it is determined to be spatially continuous. When a jump change occurs, it is determined to be spatially broken, and the break location is recorded. At the same time, the spatial expansion of each deviation area at adjacent acquisition times is compared frame by frame. By jointly analyzing the regional area change and the boundary movement direction, the expansion trend and contraction trend are determined. The spatial continuity determination results, break conditions, and expansion status are uniformly recorded and structured to generate spatial continuity data.

[0098] It should also be noted that the breakage situation refers to the state in which the deviation changes are discontinuous, change direction abruptly or jump in amplitude between adjacent spatial locations or adjacent acquisition times, resulting in the interruption of the association; the expansion state refers to the deviation changes continuously extending outward in time or space, which is manifested as the deviation range expanding, the boundary shifting outward, and the change maintaining the same direction.

[0099] By combining regional assessment data with spatial continuity data and matching them accordingly, the grading basis data is obtained. The grading basis data is then used to perform grading determination, regional grading data is constructed, and the regional grading data is summarized and organized to generate a report on uniform brightness.

[0100] It should be explained that the regional assessment data and spatial continuity data are matched one by one with their spatial coordinates and collection times. Consistency checks are performed on the matching results, and the corresponding comprehensive assessment values ​​and spatial continuity indicators are extracted to form the grading basis data. Based on the grading basis data, the comprehensive assessment values ​​are divided into intervals and jointly judged in conjunction with the spatial continuity indicators. The grade category of each deviation area is determined by comparing the different interval ranges and continuity status item by item. The grade categories are mapped and organized with the corresponding areas to construct the regional grading data. After the regional grading data is constructed, all regional grading data are uniformly summarized according to spatial distribution and time order, and the grading results are presented in a structured and ordered manner to generate a bright and uniform report.

[0101] In summary, this invention improves the continuity and accuracy of display module uniformity detection by: spatially mapping and temporally aligning the optical response data of the display module to construct an optical response sequence with a unified spatiotemporal reference, thereby ensuring consistent expression of pixel distribution and sampling timing; combining spectral screening, continuous constraints, and tristimulus analysis to generate comprehensive chromaticity parameters, thus achieving a stable conversion of optical information into visual quantification parameters; and reconstructing the luminance distribution field through spatiotemporal interpolation, and performing deviation calculations and region segmentation evaluations to achieve graded determination of luminance and chromaticity uniformity.

[0102] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An automated system for synchronous detection of brightness and color uniformity in a display module, characterized in that, include: The optical response sequence module collects optical response data of the display module under test drive, and performs spatial position mapping and time alignment on the optical response data to generate an optical response sequence. The colorimetric analysis module extracts spectral response data from the optical response sequence, analyzes the tristimulus values ​​of the spectral response data using the tristimulus analysis method, obtains comprehensive colorimetric parameters, and performs spatial alignment and temporal synchronization of the comprehensive colorimetric parameters to generate a luminance parameter sequence. The bright color reconstruction module uses a spatiotemporal interpolation method to perform spatial interpolation and continuous reconstruction on the bright color parameter sequence to obtain bright color distribution data. It then combines the bright color distribution data with time changes and connects them continuously to generate a bright color distribution field. The deviation analysis module extracts spatiotemporal variation data from the bright color distribution field, performs joint calculation of adjacent difference and cross-frame difference on the spatiotemporal variation data, and obtains single-point deviation data. The single-point deviation data is subjected to directional consistency discrimination and amplitude normalization to obtain deviation modulation data, and the deviation modulation data is spatially ordered to generate single-frame deviation data. Continuous splicing and continuity verification are performed on single-frame deviation data to generate bright-color deviation data; Perform joint analysis and dominant decomposition of bright colors based on the bright color deviation data to generate a bright color deviation sequence; The uniformity reporting module divides and uniformly evaluates the bright color deviation sequence, obtains regional evaluation data, combines the regional evaluation data with spatial continuity and performs hierarchical judgment to generate a bright color uniformity report.

2. The automated system for synchronous detection of brightness and color uniformity of display modules as described in claim 1, characterized in that, The specific steps for generating the optical response sequence are as follows: A test driver is applied to the display module and the light emission is excited point by point in the pixel scanning order. Optical response data is collected, and the optical response data is appended with the collection time mark and pixel position number to generate an optical response record. Pixel mapping tables are extracted from optical response records, and spatial mapping relationships are established based on the pixel mapping tables. Pixel position numbers are converted into corresponding spatial coordinate positions according to the spatial mapping relationships to generate spatial response data. The spatial response data is sorted in ascending time order according to the acquisition time marker to obtain the time-series response sequence. Then, a unified time base correction is performed based on the time-series response sequence to generate an aligned response sequence. The alignment response sequence is continuously organized and structured to generate an optical response sequence.

3. The automated system for synchronous detection of brightness and color uniformity of display modules as described in claim 1, characterized in that, The specific steps for obtaining the comprehensive chromaticity parameters are as follows: The spectral response data corresponding to each spatial coordinate position at each acquisition time is read one by one from the optical response sequence, and the stable emission segment is screened and abnormal sampling is removed from the spectral response data to generate effective spectral data. Spectral correction benchmarks are extracted from effective spectral data. The spectral correction benchmarks are combined with spectral variations and neighborhood continuity constraints are constructed to generate corrected spectral data. Using the tristimulus analysis method, the corrected spectral data are expanded sequentially according to wavelength order to obtain wavelength positions, and then matched item by item with the first standard color matching response, the second standard color matching response and the third standard color matching response at the corresponding wavelength positions to generate three sets of basic stimulus components. The continuous changes of the three basic stimulus components between adjacent wavelength positions are subjected to mutation suppression and continuous accumulation to obtain the tristimulus quantities. The comprehensive chromaticity parameters are determined based on the proportional relationship between the tristimulus quantities.

4. The automated system for synchronous detection of brightness and color uniformity of display modules as described in claim 3, characterized in that, The specific steps for spatial alignment and temporal synchronization of the comprehensive chromaticity parameters to generate a luminance parameter sequence are as follows: The time difference of the comprehensive chromaticity parameters is compared to obtain the time deviation sequence. The comprehensive chromaticity parameters with small deviation from the driving trigger time are selected from the time deviation sequence to generate time series filtering data. Time offset correction is performed on the comprehensive chromaticity parameters to obtain time correction data, and the spatial coordinate positions are aligned with the time axis according to a unified time reference to generate synchronized comprehensive chromaticity data. By combining time-series filtered data with synchronous integrated chromaticity data and organizing and arranging them in a continuous and structured manner, a luminance parameter sequence is generated.

5. The automated system for synchronous detection of brightness and color uniformity of display modules as described in claim 1, characterized in that, The specific steps for obtaining the luminance distribution data are as follows: The comprehensive chromaticity parameters of each spatial coordinate position in the luminance parameter sequence are expanded frame by frame, and a spatial global grid is constructed based on the comprehensive chromaticity parameters; Extract the neighboring reference nodes of each node to be interpolated from the spatial global grid, and calculate the spatial distance, comprehensive chromaticity difference and neighborhood gradient change of the neighboring reference nodes to generate spatiotemporal constraint data; The spatiotemporal interpolation method is adopted. Based on the spatiotemporal constraint data, the spatiotemporal interpolation constraints corresponding to each node to be interpolated are determined. The spatiotemporal interpolation constraints are solved collaboratively to obtain the interpolation weight data. The interpolation weight data is then subjected to comprehensive chromaticity reconstruction to generate spatial interpolation data. The spatial interpolation data is integrated with the original node data to ensure that the spatial distribution is continuous and complete at each acquisition time, and to generate bright color distribution data.

6. The automated system for synchronous detection of brightness and color uniformity of display modules as described in claim 1, characterized in that, The bright color distribution field refers to the comprehensive color parameters extracted from the bright color distribution data, the point-by-point comparison of the comprehensive color change trend to determine the direction and magnitude of change, and the continuous time inheritance and abnormal fluctuation correction of the direction and magnitude of change.

7. The automated system for synchronous detection of brightness and color uniformity of display modules as described in claim 6, characterized in that, The specific steps for generating the bright color deviation sequence are as follows: Based on the bright color deviation data, the degree of deviation of each spatial coordinate position is correlated with the dominant decomposition direction to obtain deviation mapping data, and the continuous deviation regions in the deviation mapping data are divided by connectivity to generate bright color deviation fragment data. The bright-color deviation data segments are joined together in time, and deviation segments that do not meet the continuity condition are marked as broken to generate a bright-color deviation sequence.

8. The automated system for synchronous detection of brightness and color uniformity of display modules as described in claim 7, characterized in that, The specific steps for dividing and uniformly evaluating the bright color deviation sequence to obtain regional evaluation data are as follows: The neighborhood deviation consistency is extracted from the bright color deviation sequence, the neighborhood deviation consistency is connected and merged to obtain the deviation region set, the regional deviation features are extracted from the deviation region set, the regional deviation features are merged, organized and correlated to generate basic data for regional assessment. The basic data for regional assessment are uniformly quantified and calculated to generate regional assessment data.

9. The automated system for synchronous detection of brightness and color uniformity of display modules as described in claim 8, characterized in that, The specific steps for generating the uniform brightness report are as follows: Based on regional assessment data, the spatial boundaries of each deviation area are continuously detected and the fracture and expansion status are recorded to generate spatial continuity data. By combining regional assessment data with spatial continuity data and matching them accordingly, the grading basis data is obtained. The grading basis data is then used to perform grading determination, regional grading data is constructed, and the regional grading data is summarized and organized to generate a report on uniform brightness.

Images