Method for determining the area of the product chromaticity coordinates displayed by a colorimeter instrument under measurement

Abstract

The application discloses a kind of chromaticity instrument measurement display product chromaticity coordinate area determination method, including (1) chromaticity coordinate stability determination;(2) chromaticity target area optimization;(3) chromaticity target area attribution determination optimization;Traditional stability determination mode is single dimension, CIE1931 original chromaticity coordinate is provided in the form of discrete data with 1nm as wavelength interval, sampling interval is larger, precision is limited, traditional attribution determination is large in amount of calculation for chromaticity diagram of data point dense, according to covariance matrix construction minimum chromaticity ellipse, the spatial distribution characteristics of point set is determined by analysis chromaticity coordinate stability;Original chromaticity point is fitted using piecewise polynomial interpolation, and the coordinate precision is optimized to 0.01nm, two-stage rapid determination strategy is used, first based on polar angle realizes candidate sector preliminary screening, then in the local candidate sector obtained by preliminary screening, accurate determination is carried out using local ray method, and target area positioning of chromaticity coordinate is completed.

Description

Technical Field

[0001] This invention belongs to the field of colorimetric measurement and analysis technology, specifically relating to a method for determining the color coordinate region of a product under colorimetric instrument measurement. Background Technology

[0002] In the research and development and production of display products, the requirements for color quality control are increasingly stringent. High-precision spectroradiometers, spectral colorimeters, and other precision colorimetric instruments, as core equipment for quantitative detection of color parameters, play a crucial role in product quality control. To adapt to the modern display industry's demands for efficient, high-precision, and standardized testing, colorimetric testing technology has gradually evolved from traditional manual reading to automatic data acquisition, analysis, processing, and intelligent judgment. When using colorimetric instruments to automatically capture precise chromaticity coordinates at a specific moment as test data, it is susceptible to measurement ambiguity, data fluctuations, and outlier interference, leading to insufficient reliability of single sampling results. Therefore, by continuously acquiring chromaticity coordinate data, the stability of the sequence data can be evaluated, and representative valid values ​​can be selected as the final test results. Among these, data stability judgment is a key step in ensuring measurement accuracy and result reliability; commonly used methods include KPSS, ADF, PP, and DF-GLS testing. Furthermore, in high-precision testing of display products, chromaticity coordinate region determination is a core foundational step in scenarios such as screen uniformity testing, white balance calibration, and color gamut consistency verification. Color compliance and accuracy verification can be completed by mapping chromaticity data to the CIE 1931 chromaticity chart and determining whether it falls within the standard area. Relevant technical requirements are clearly stated in standards such as JT / T 597-2022 "LED Lane Control Signs", SJ / T 11746-2019 "Test Method for Display Performance of Ultra-High Definition Televisions", and SJ / T 11141-2025 "General Specification for Light Emitting Diode (LED) Displays". However, factors such as environmental temperature and humidity, changes in ambient light, instrument calibration deviations, measurement angle errors, and abnormal data transmission can easily cause the measured chromaticity points to fall outside the CIE 1931 chromaticity chart. Therefore, it is necessary to determine the inner and outer points of the irregular areas formed by the chromaticity contour curves of the CIE 1931 chromaticity chart. The commonly used algorithm is currently the ray casting method.

[0003] However, current color detection of display products based on colorimeters still has many shortcomings in terms of chromaticity coordinate stability analysis and automatic region determination: (1) Traditional stability determination methods have the limitation of separating and independently judging the stability of chromaticity coordinates U and V (where U and V correspond to the x-axis and y-axis of the chromaticity coordinates, respectively). Chromaticity coordinates are points in a two-dimensional chromaticity space, so they cannot accurately reflect the stability they exhibit as a whole in the two-dimensional chromaticity space; (2) The original chromaticity coordinates of CIE1931 are provided in the form of discrete data with a wavelength interval of 1nm. The sampling interval is large and the accuracy is limited, and the chromaticity contour curve formed has obvious tooth-like distortion. However, the actual color coordinates fall on the boundary and are often between discrete wavelengths, making it difficult to accurately match with the original data. At the same time, the measurement errors introduced by temperature and humidity, ambient light, instrument calibration, measurement angle and data transmission, etc., combined with the contour distortion, will make the determination of the boundary of the chromaticity target area significantly ambiguous. It is difficult to accurately distinguish the color points near the boundary, which cannot meet the high-precision analysis requirements such as nanoscale color feature recognition. Therefore, its accuracy must be optimized. (3) Global, ray-based attribution determination is required in complex and irregular chromatic areas such as the CIE 1931 chromaticity map. Especially in scenarios with dense data points or complex area division, the intersection relationship needs to be verified with each chromaticity coordinate point constituting the target area in turn, resulting in a huge amount of computation and a long time consumption. Summary of the Invention

[0004] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method for determining the color coordinate area of ​​a product under the measurement of a colorimeter.

[0005] This invention provides a new and scientific approach and method based on the stability of product chromaticity coordinates and the determination of target area attribution under colorimetric instrument measurement.

[0006] The above-mentioned objective of the present invention can be achieved by the following technical solution: a method for determining the chromaticity coordinate region of a product under colorimetric instrument measurement, comprising the following steps:

[0007] (1) Stability determination of chromaticity coordinates:

[0008] The minimum chromaticity ellipse is constructed by using the eigenvalues ​​of the covariance matrix to correspond to the square of the semi-axis length of the ellipse and the eigenvectors to correspond to the rotation direction of the ellipse. The chromaticity coordinates U and V (where U and V correspond to the x-axis and y-axis of the chromaticity coordinates, respectively) are regarded as a two-dimensional whole. The overall stability of the chromaticity coordinates is determined by analyzing the spatial distribution characteristics of the point set.

[0009] (2) Optimization of the chroma target area:

[0010] Piecewise polynomial interpolation is used to fit the original chromaticity points, optimizing the accuracy of the discrete original chromaticity point coordinates from 1nm to 0.01nm for high-density chromaticity points. Two-dimensional Euclidean distance is used to match the start and end points of the preset data points with the high-density chromaticity points.

[0011] (3) Optimization of color target area attribution determination:

[0012] An irregular polygon centroid calculation algorithm is used to calculate the center point of the chromaticity target area. Based on the center point, the chromaticity target area is divided into regular sectors and curved sectors. Then, the curved sectors are divided at equal intervals to form sub-sectors to complete the preprocessing. A two-level fast judgment strategy is adopted. First, the chromaticity coordinates to be tested are used to screen the sectors based on the polar angle. Then, the ray method is used to make accurate judgments within the local candidate sectors obtained from the initial screening.

[0013] In the method for determining the chromaticity coordinate region of a product under the above-mentioned colorimetric instrument measurement:

[0014] Preferably, the chromaticity coordinate stability determination in step (1) includes:

[0015] (1-1) Setting up the minimum chromaticity ellipse: Collect N sets of data, form a covariance matrix from the autocovariance and cross-covariance, integrate the fluctuations of the U and V coordinates in both single and two dimensions, map the eigenvalues ​​of the covariance matrix to the square of the semi-axis of the minimum chromaticity ellipse, and map the eigenvectors to the rotation direction of the ellipse, establishing a mapping relationship between the covariance matrix and the geometric parameters of the ellipse, further including:

[0016] (1-1-1) UV-related fluctuation settings: Collect N sets of data (U j V j Using the autocovariance of U and V coordinates s UU and s VV To detect the fluctuations in each of the U and V dimensions, cross-covariance is used. s UV , s VU To detect the correlation fluctuations between U and V, a covariance matrix is ​​constructed from the autocovariance and crosscovariance:

[0017] (Formula 1)

[0018] (1-1-2) Setting the minimum chromaticity ellipse: The eigenvalues ​​of the covariance matrix correspond to the square of the semi-axis of the minimum chromaticity ellipse, and the eigenvectors correspond to the rotation direction of the ellipse;

[0019] (1-1-3) Calculation of eigenvalues ​​of the covariance matrix: Set the characteristic equation for solving the covariance matrix as follows:

[0020] (Formula 2)

[0021] in, I It is the identity matrix. β These are the eigenvalues ​​of the covariance matrix; solving for them yields two eigenvalues. β 1、 β 2 The corresponding rotation angle is:

[0022] (Formula 3)

[0023] (1-2) Calculation of the area of ​​the minimum chromaticity ellipse: Introducing the chi-square distribution quantiles, setting the chromaticity confidence level, and combining the eigenvalues ​​of the covariance matrix to calculate the semi-axis length of the minimum chromaticity ellipse, and finally calculating the area of ​​the minimum chromaticity ellipse, further including:

[0024] (1-2-1) Setting the chromaticity confidence level: Introducing the chi-square distribution quantile, and setting the chromaticity confidence level based on the two-dimensional degrees of freedom of U and V. P 2 (2) ;

[0025] (1-2-2) Calculation of the semi-axis length of the minimum chromaticity ellipse: Let a be the major semi-axis and b be the minor semi-axis. Combining the eigenvalues ​​of the covariance matrix and the formula for calculating the chromaticity confidence level, the following formula is used:

[0026] (Formula 4)

[0027] (1-2-3) Calculation of the area of ​​the minimum chromaticity ellipse: Combining the semi-axis length of the minimum chromaticity ellipse, the area of ​​the minimum chromaticity ellipse is calculated as follows:

[0028] (Formula 5)

[0029] (1-3) Chromaticity coordinate stability determination: Stability is determined by comparing a threshold with the area of ​​the minimum chromaticity ellipse. If the area is less than the stability threshold, the chromaticity is stable. The center of the minimum chromaticity ellipse is selected as the final chromaticity coordinate point. This further includes:

[0030] (1-3-1) Calculation of the center of the minimum chromaticity ellipse: The two-dimensional mean point of N sets of data is taken as the center of the minimum chromaticity ellipse;

[0031] (1-3-2) Stability determination: Set a stability threshold and judge the stability by comparing the threshold with the area of ​​the minimum chromaticity ellipse. If it is less than the stability threshold, it is stable. Select the center of the minimum chromaticity ellipse as the final chromaticity coordinate point.

[0032] Preferably, the stability threshold in step (1-3-2) is 10. -6 .

[0033] In step (1), the traditional stability determination mode is a single dimension (U, V), while the chromaticity coordinates are points in a two-dimensional chromaticity space. Referring to the concept of the Macadam ellipse in colorimetry, which is the threshold of human eye colorimetry, the eigenvalues ​​of the covariance matrix correspond to the square of the semi-axis length of the ellipse, and the eigenvectors correspond to the rotation direction of the ellipse. The minimum chromaticity ellipse is constructed, and U and V are regarded as a two-dimensional whole, rather than two independent parameters. The overall stability of the chromaticity coordinates is determined by analyzing the spatial distribution characteristics of the point set.

[0034] Preferably, in step (2), piecewise polynomial interpolation is used to fit the original chromaticity points, optimizing the accuracy of the discrete original chromaticity point coordinates from 1 nm to 0.01 nm for high-density chromaticity points. Two-dimensional Euclidean distance is used to match the start and end points of the preset data points with the high-density chromaticity points. This further includes:

[0035] (2-1-1) Chromaticity curve fitting: Piecewise polynomial interpolation is used to fit the original chromaticity points. Each wavelength segment in the interval is set as an independent cubic polynomial. By ensuring the continuity of the first and second derivatives, the curve completely coincides with the original discrete points, thus adapting to the nonlinear variation characteristics of the chromaticity profile.

[0036] (2-1-2) Optimization of chromaticity point coordinate accuracy: Based on the results of chromaticity curve fitting, interpolation calculations are performed using independent cubic polynomials in each wavelength segment to optimize the accuracy to a high density of chromaticity points of 0.01nm, thereby improving curve smoothness and coordinate positioning accuracy.

[0037] (2-1-3) Chromaticity coordinate start and end anchor point positioning: High-density chromaticity points are discrete rather than continuous data with equal wavelength intervals. There is a deviation between the start and end points of the preset data points for constructing the chromaticity target area and the high-density chromaticity points. Two-dimensional Euclidean distance is used to match the start and end points of the preset data points with the high-density chromaticity points. The chromaticity point with the smallest distance is selected as the start and end anchor point, and the corresponding chromaticity coordinate sequence is extracted.

[0038] Considering the limited accuracy of the original chromaticity point coordinates, which leads to ambiguity in the judgment of regional boundaries and increased error in the classification, step (2-1-2) uses independent cubic polynomials to perform interpolation calculations in each wavelength segment based on the chromaticity curve fitting results, optimizing the accuracy to a high-density chromaticity point of 0.01nm, thereby improving the smoothness of the curve and the accuracy of coordinate positioning.

[0039] Considering that the original chromaticity point coordinates provided by CIE1931 are discrete data with a wavelength interval of 1nm, the interval is large and the coordinate accuracy is limited. The chromaticity contour curve formed by connecting them has obvious tooth-like distortion. In step (2), piecewise polynomial interpolation is used to fit the original chromaticity points, increasing the 1nm discrete data to a high-density coordinate of 0.01nm to eliminate tooth-like distortion. Then, the start and end anchor points are accurately located and the target curve segment is extracted by two-dimensional Euclidean distance, providing a high-precision geometric basis for subsequent region determination.

[0040] Preferably, in step (3), the centroid calculation algorithm for irregular polygons is used to calculate the center point of the chromaticity target region. Based on the center point, the chromaticity target region is divided into regular sectors and curved sectors. Then, the curved sectors are divided at equal intervals to form sub-sectors to complete the preprocessing. This further includes:

[0041] (3-1-1) Solving for the center point of the chromaticity target area: The irregular polygon centroid calculation algorithm is adopted to decompose the chromaticity target area into several triangles with the origin as the vertex, and the overall centroid is calculated as the center point by the area weighted average.

[0042] (3-1-2) Reconstruct the coordinate axis based on the center point of the chromaticity target area to facilitate the calculation of the polar angle;

[0043] (3-1-3) Chromaticity target area splitting: Based on the center point, the chromaticity target area is split into regular sectors and curved sectors. In view of the characteristics of dense and large number of chromaticity points in the curved sector, chromaticity points are collected at equal intervals √n to form sub-sectors. The target area generates a corresponding polar angle sequence, and the polar angle is uniformly converted to (0, 2π).

[0044] In step (3), for the dense chromaticity map data points with a precision of 0.01nm, the global ray method is used to determine the region's affiliation, which takes a long time. The irregular polygon centroid calculation algorithm is used to calculate the center point of the chromaticity target region. Based on the center point, the chromaticity target region is divided into regular sectors and curved sectors. Then, the curved sectors are divided at equal intervals to form sub-sectors to complete the preprocessing.

[0045] In step (3), the chromaticity target area includes the chromaticity curve, and the efficiency of the area classification is low. In the preprocessing stage, the irregular polygon centroid calculation algorithm is first used to calculate the center point of the area, and the local coordinate system is reconstructed based on this point. The entire chromaticity target area is divided into several sub-sectors according to the polar angle, which provides a geometric division basis for subsequent rapid initial screening and accurate positioning.

[0046] Preferably, step (3) employs a two-stage rapid determination strategy. First, the chromaticity coordinates to be tested are used to initially screen sectors based on polar angles. Then, within the local candidate sectors obtained from the initial screening, a ray-mapping method is used for precise determination. This further includes:

[0047] (3-2-1) Calculation of the polar angle of the chromatic point to be measured: Taking the center point as the pole, calculate the polar angle of the chromatic point to be measured and normalize it to (0, 2π);

[0048] (3-2-2) Setting floating-point precision tolerance: Since the floating-point precision of color points is too high and there are rounding errors in floating-point operations, numerical comparisons are prone to causing abnormal judgment logic. Set a very small floating-point precision tolerance to control the error range and smooth out calculation errors.

[0049] (3-2-3) Initial screening of candidate sectors: The polar angle can be used to quickly determine the angle sector to which the color point to be tested belongs. For the curve sector, there are several sub-sectors. Binary search is used to quickly locate the sub-sectors, so as to achieve rapid initial screening of candidate areas.

[0050] (3-2-4) Optimization of chromaticity target region attribution determination: After obtaining candidate sectors based on polar angle initial screening, the ray method is used in this local area to realize the region attribution of the chromaticity point to be tested, simplifying the global traversal to local fine judgment, effectively improving the efficiency of chromaticity point region attribution determination.

[0051] Preferably, the minimum floating-point precision tolerance in step (3-2-2) is (1~3)×10 -6 .

[0052] In step (3), to address the issue of low efficiency in determining region attribution, a two-level fast determination strategy is proposed. First, candidate sectors are initially screened based on polar angles, and complex sectors containing chromaticity curves are located using binary search. Then, within the local candidate sectors obtained from the initial screening, the ray casting method is used for precise determination. This method simplifies global traversal to local judgment, effectively improving the efficiency of determining the region attribution of chromaticity points.

[0053] Furthermore, it also includes step (4) verification of the color target area attribution: using the ray method to determine whether the point is located inside the color target area.

[0054] Step (4) Use the ray method to determine the target area. Based on the parity of the number of valid intersection points, determine whether the point is located inside the color target area.

[0055] Preferably, step (4) of using the ray method to determine whether the point is located inside the chromaticity target area further includes:

[0056] (4-1-1) Judgment of the intersection condition of ray and edge: Using the ray method, an infinitely long ray is emitted from the chromaticity point to the right horizontally and judged to intersect with all edges of the chromaticity target area. The intersection of ray and edge is judged by the two endpoints of the edge on the upper and lower sides of the ray.

[0057] (4-1-2) Determination of the chromaticity target area: Based on the intersection relationship between the edge and the ray, the x-coordinate of the intersection point is solved by linear interpolation and the valid intersection points in the positive direction of the ray are screened. The parity of the total number of valid intersection points Z is calculated to determine whether the chromaticity point to be tested is in the chromaticity target area. If Z is odd, the chromaticity point to be tested is inside the chromaticity target area. If Z is even, the chromaticity point to be tested is outside the chromaticity target area.

[0058] Compared with the prior art, the present invention has the following advantages:

[0059] (1) Treat the chromaticity coordinates as a two-dimensional whole rather than two independent parameters. Using the concept of the MacAdam ellipse model, construct the minimum chromaticity ellipse using the covariance matrix, and determine the overall stability of the chromaticity coordinates by analyzing the spatial distribution characteristics of the point set.

[0060] (2) Piecewise polynomial interpolation is used to fit the boundary of the CIE chromaticity target area more accurately, and the coordinate accuracy is optimized to 0.01nm. The accuracy of the original chromaticity target area definition is improved by curve fitting, and the ambiguity problem in the boundary assessment of the chromaticity target area is overcome.

[0061] (3) The centroid calculation algorithm of irregular polygons is used to calculate the center point. The curve sector is divided into sub-sectors by equal intervals. The chromaticity coordinates to be tested are initially screened based on the polar angle. Then, the ray method is used to make accurate judgments in the local candidate sectors obtained from the initial screening, simplifying the global judgment to the local judgment and improving the efficiency of the attribution judgment.

[0062] (4) By improving the accuracy of chromaticity coordinate stability determination and the efficiency of region attribution determination, this invention effectively solves the problems of measurement error and calculation time in complex environments in the prior art, and improves the applicability and reliability of colorimetric instruments in display product testing. Attached Figure Description

[0063] Figure 1 This is a flowchart of the method for determining the color coordinate region of a product under colorimetric instrument measurement in Embodiment 1 of the present invention;

[0064] Figure 2 These are schematic diagrams of two minimum chromaticity ellipses in Embodiment 1 of the present invention. 2.1 is a stable minimum chromaticity ellipse, and 2.2 is an unstable minimum chromaticity ellipse.

[0065] Figure 3 This is a chromaticity curve diagram composed of original chromaticity points provided by CIE1931 in Embodiment 1 of the present invention, wherein 3.1 is discrete original chromaticity points with a wavelength interval of 1nm, and 3.2 is the chromaticity target area composed of data points preset based on JT / T 597-2022 "LED Lane Control Signs";

[0066] Figure 4 This is the fitted chromaticity curve diagram after optimizing from a precision of 1nm to 0.01nm in Embodiment 1 of the present invention, where 4.1 represents the high-density chromaticity points after fitting;

[0067] Figure 5 In Embodiment 1 of the present invention, the color target area is divided into several triangles with the origin as the vertex, and each triangle is distinguished and identified by a different color;

[0068] Figure 6 This is a schematic diagram of the chromaticity target area and center point in Embodiment 1 of the present invention, wherein 6.1 is the light blue area, which is the chromaticity target area, and 6.2 is its center point;

[0069] Figure 7 This is a schematic diagram of the chromaticity target area segmentation in Embodiment 1 of the present invention, wherein 7.1 is a regular area filled with light green, 7.2 is an unclosed regular sector with the origin O as the vertex and OA and OD as the boundaries, and 7.3 represents one of the sub-sectors formed by dividing the curved sector at equal intervals (red dashed lines divide different sectors).

[0070] Figure 8 This is a schematic diagram of the local ray method in the local candidate sector obtained by the initial screening in Embodiment 1 of the present invention, where 8.1 is the local candidate sector obtained by screening, and 8.2 is the chromaticity coordinate point H to be tested (marked with a purple dot).

[0071] Figure 9 This is the application of the stability and target area determination method in Embodiment 1 of the present invention in JT / T 597-2022 "LED Lane Control Signs", where 9.1 is the chromaticity coordinate point H to be measured, which is within the chromaticity coordinate area;

[0072] Figure 10 This is the application of the stability and target area determination method in Embodiment 2 of the present invention in JT / T 597-2022 "LED Lane Control Signs", where 10.1 is the chromaticity coordinate point W to be measured, which is outside the chromaticity coordinate area; Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0074] like Figure 1 As shown in the figure, this embodiment provides a method for determining the chromaticity coordinate region of a product under colorimetric instrument measurement, which includes the following steps:

[0075] (1) Stability determination of chromaticity coordinates:

[0076] Traditional stability assessment methods are single-dimensional (U and V are separated, where U and V correspond to the x-axis and y-axis of chromaticity coordinates, respectively). Chromaticity coordinates are points in a two-dimensional chromaticity space. Referring to the application of the Macadam ellipse in colorimetry to the human eye's chromaticity resolution threshold, we construct a minimum chromaticity ellipse by using the eigenvalues ​​of the covariance matrix to correspond to the square of the semi-axis length of the ellipse and the eigenvectors to correspond to the rotation direction of the ellipse. U and V are treated as a two-dimensional whole, rather than two independent parameters. The overall stability of the chromaticity coordinates is determined by analyzing the spatial distribution characteristics of the point set.

[0077] The determination of chromaticity coordinate stability further includes:

[0078] (1-1) Setting up the minimum chromaticity ellipse; Collect N sets of data, form a covariance matrix from the autocovariance and crosscovariance, integrate the fluctuations of the U and V coordinates in both single and two dimensions, assign the eigenvalues ​​of the covariance matrix to the square of the semi-axis of the minimum chromaticity ellipse, and the eigenvectors to the rotation direction of the ellipse, connecting the key steps of the covariance matrix and the geometric parameters of the ellipse, specifically including:

[0079] (1-1-1) Construction of the covariance matrix of UV: N=15 sets of data were collected (U j V j (Using the autocovariance of U and V coordinates) s UU and s VV To detect the fluctuations in each of the U and V dimensions, cross-covariance is used. s UV , s VU Detect the correlation and fluctuation of U and V, such as whether V changes synchronously when U increases. s UV Greater than 0, the two are positively correlated; as U increases, V increases synchronously. s UV When the value is less than 0, the two are negatively correlated; as U increases, V decreases. s UV When the value is 0, U and V are uncorrelated, and the covariance matrix is ​​formed by combining the self-covariance and cross-covariance.

[0080] (Formula 1)

[0081] (1-1-2) Setting the minimum chromaticity ellipse: In order to establish the mapping relationship between the covariance matrix and the geometric parameters of the ellipse, the eigenvalues ​​of the covariance matrix are assigned to the square of the semi-axis length of the minimum chromaticity ellipse, and the eigenvectors are assigned to the rotation direction of the ellipse.

[0082] (1-1-3) Calculation of eigenvalues ​​of the covariance matrix: Set the characteristic equation for solving the covariance matrix as follows:

[0083] (Formula 2)

[0084] in, I It is the identity matrix. β These are the eigenvalues ​​of the covariance matrix, two eigenvalues. β 1、 β 2 The corresponding rotation angle is:

[0085] (Formula 3)

[0086] (1-2) Calculation of the area of ​​the minimum chromaticity ellipse: To ensure that the minimum chromaticity ellipse covers as much chromaticity data as possible, chi-square distribution quantiles are introduced, chromaticity confidence levels are set, and the semi-axis length of the minimum chromaticity ellipse is calculated using the eigenvalues ​​of the covariance matrix. Finally, the area of ​​the minimum chromaticity ellipse is calculated, specifically including:

[0087] (1-2-1) Chromaticity Confidence Setting: To ensure that the minimum chromaticity ellipse covers as much chromaticity data as possible and to make the generation of the minimum chromaticity ellipse more accurate, chi-square distribution quantiles are introduced. Based on the two-dimensional degrees of freedom U and V, the chromaticity confidence is set. P 2 (2) U and V have 2 degrees of freedom each. Using a chi-square value with a 95% confidence level, the value is... P 2 (2) =5.991, the smallest ellipse covering 95% of the measurement data, reflecting the two-dimensional fluctuation range of UV data;

[0088] (1-2-2) Calculation of the semi-axis length of the minimum chromaticity ellipse: Let a be the major semi-axis and b be the minor semi-axis. Combining the eigenvalues ​​of the covariance matrix and the formula for calculating the chromaticity confidence level, the following formula is used:

[0089] (Formula 4)

[0090] (1-2-3) Calculation of the area of ​​the minimum chromaticity ellipse: Combining the semi-axis length of the minimum chromaticity ellipse, the area of ​​the minimum chromaticity ellipse is calculated as follows:

[0091] (Formula 5)

[0092] (1-3) Stability determination of chromaticity coordinates: The stability is determined by comparing the threshold with the area of ​​the minimum chromaticity ellipse. If the area is less than the stability threshold, the chromaticity coordinates are stable, and the center of the minimum chromaticity ellipse is selected as the final chromaticity coordinate point.

[0093] (1-3-1) Calculation of the minimum chromaticity ellipse center: Collect N sets of data (U j V j The two-dimensional mean point is used as the center of the minimum chromaticity ellipse, and the calculation formula is:

[0094] (Formula 6)

[0095] (1-3-2) Stability determination: Set a stability threshold (1×10) -6 Stability is determined by comparing a threshold with the area of ​​the minimum chromaticity ellipse. A smaller ellipse area indicates higher clustering of U and V data, and better measurement stability. If the area is less than the stability threshold, the measurement is stable. The center of the minimum chromaticity ellipse is selected as the final chromaticity coordinate point. Two batches of data are used as examples, and the calculation results are shown in Table 1. The minimum chromaticity ellipses generated by data 1 and data 2 are shown in Table 1. Figure 2 As shown, the final stable chromaticity coordinate point obtained is point H (0.224513, 0.746760) obtained from data 1, which also corresponds to the coordinate axis (x, y).

[0096] Table 1. Stability determination of chromaticity coordinates

[0097]

[0098] (2) Optimization of the chroma target area:

[0099] Considering that the original chromaticity point coordinates provided by CIE1931 are discrete data with a wavelength interval of 1 nm, the interval is relatively large and the coordinate accuracy is limited. The resulting chromaticity contour curve exhibits obvious tooth-like distortion. Piecewise polynomial interpolation is used to fit the original chromaticity points, optimizing the coordinate accuracy to 0.01 nm. Further improvements include:

[0100] (2-1) Optimization of chromaticity point coordinate accuracy: Considering that the original chromaticity point coordinates provided by CIE1931 are discrete data with a wavelength interval of 1nm, such as Figure 3 As shown, the chromaticity contour curve formed by connecting the points exhibits significant tooth-like distortion. Piecewise polynomial interpolation is used to fit the original chromaticity points, ensuring the continuity of the first and second derivatives. The 1nm discrete data is increased to a 0.01nm high-density coordinate system to eliminate the tooth-like distortion. Then, the start and end anchor points are precisely located using two-dimensional Euclidean distance, and the target curve segment is extracted, providing a high-precision geometric basis for subsequent region determination. Specifically, this includes:

[0101] (2-1-1) Chromaticity Curve Fitting: To accurately reproduce the "horseshoe" contour of the CIE1931 chromaticity diagram, piecewise polynomial interpolation was used to fit the original chromaticity points. Each wavelength segment within the interval was set as an independent cubic polynomial. By ensuring the continuity of the first and second derivatives, the curve completely coincides with the original discrete points, thus adapting to the nonlinear variation characteristics of the chromaticity contour. The process is as follows:

[0102] Let the wavelength sequence corresponding to the original chromaticity points be: [λ0,λ1,…,λ m-1 (Wavelength step size is 1 nm);

[0103] The wavelength sequence is divided into m-1 sub-intervals [λ k ,λ k+1 ], where k = 0, 1, ..., m-2;

[0104] Chromaticity coordinates for each sub-interval x(λ) or y(λ) Performing a cubic polynomial S k (l) The fitting expression is:

[0105] (Formula 7)

[0106] in, l k It is the first k The wavelength at the left endpoint of each sub-interval, h k =λ k+1 - l k , where is the length of the subinterval, and λ is the input wavelength. S k (l) It is the first k Chromaticity coordinates within each sub-interval; a k , b k , c k , d k It is the first k The coefficients of the cubic polynomial in each subinterval, M k =S k " (l k ) yes S k (l) exist lk The second derivative at that point, M k+1 =S k+1 "(l k+1 ) yes S k+1 (l) exist l k+1 The second derivative at that point.

[0107] (2-1-2) Optimization of Chromaticity Point Coordinate Accuracy: Considering the limited accuracy of the original chromaticity point coordinates, which leads to ambiguity in region boundary judgment and increased classification error, failing to meet the requirements of high-precision color application scenarios, to achieve refined chromaticity coordinate representation, based on the aforementioned chromaticity curve fitting results, interpolation calculations are performed using independent cubic polynomials within each wavelength segment, optimizing the accuracy of the discrete original chromaticity coordinates from 1nm to a high-density chromaticity point of 0.01nm (mapped to coordinates as shown in the figure). Figure 4 As shown in the figure, a high-density, high-precision chromaticity coordinate sequence is constructed to improve curve smoothness and coordinate positioning accuracy.

[0108] Taking a wavelength of 520nm as an example, the sub-intervals are: l k =520、 l k+1 =521, and its original chromaticity point coordinates are:

[0109]

[0110] Substituting into Formula 7, the piecewise cubic spline formula is:

[0111] (Formula 8)

[0112] An example of the calculation process refined to 0.01 nm is as follows:

[0113]

[0114] The chromaticity coordinates of 520.01 with a precision of 0.01nm are (0.074379974407852, 0.833805963818843). The same method can be applied to 520.02, 520.03, ...

[0115] (2-1-3) Chromaticity Coordinate Start and End Anchor Point Positioning: Considering that the original chromaticity point coordinates are discrete rather than continuous data with equal wavelength intervals, preset data points, such as the preset data points in JT / T 597-2022 "LED Lane Control Standard" are A(0.305, 0.689), B(0.321, 0.493), C(0.228, 0.351), D(0.028, 0.385), constitute the chromaticity target area { V 0 ,V 1 ,....,V n} is the closed color gamut region formed by point D, the chromaticity curve segment from D to A back to point A, and then successively closed by line segments AB, BC, and CD (e.g. Figure 3 ); starting point D(x) start y start ) and the endpoint A(x) end y end ) and discrete high-density chromaticity point coordinates (x i ,y i ) There is a deviation. To accurately locate the target area, a two-dimensional Euclidean distance is used to match the preset points with the high-density chromaticity points, as shown in Formula 9. By calculating the distances between the preset points D and A and all the high-density chromaticity points respectively, the original point with the smallest distance is selected as the start and end anchor points, as shown in Formula 10. The subscripts of the coordinate sequence of the start and end anchor points in the high-density chromaticity points are calculated, and the coordinate sequence between the two anchor points is extracted.

[0116] (Formula 9)

[0117] (Formula 10)

[0118] (3) Optimization of color target area attribution determination:

[0119] To address the issue of time-consuming global ray tracing for determining chromaticity region affiliation in dense chromaticity map data with a precision of 0.01nm, this paper proposes an irregular polygon centroid calculation algorithm to calculate the center point of the chromaticity target region. Based on the center point, the chromaticity target region is divided into regular sectors and curved sectors. Further preprocessing is achieved by dividing the curved sectors at equal intervals to form sub-sectors. A two-level fast determination strategy is proposed: first, the chromaticity coordinates to be tested are used to initially screen sectors based on polar angles; then, within the locally selected candidate sectors, the ray tracing method is used for precise determination, simplifying global determination to local determination and improving efficiency. Further steps include:

[0120] (3-1) Preprocessing optimization for color target area attribution determination: The color target area contains color curves, and the efficiency of area attribution determination is low. In the preprocessing stage, the irregular polygon centroid calculation algorithm is first used to calculate the center point of the area, and the local coordinate system is reconstructed based on this point. The entire color target area is divided into several sub-sectors according to the polar angle, which provides a geometric division basis for subsequent rapid initial screening and accurate positioning.

[0121] (3-1-1) Solving for the center point of the chromaticity target region: An irregular polygon centroid calculation algorithm is used to decompose the chromaticity target region into several triangles with the origin as vertices, such as... Figure 5 As shown, the global centroid is calculated using an area-weighted average as the center point. O(x 0 ,y 0 ) like Figure 6 The complete process is as follows:

[0122] Arrange the vertices of the chromaticity target region in a clockwise order to ensure that the vertex sequence has no intersections;

[0123] The target color area is decomposed into several triangles with the origin as the vertex;

[0124] Calculate the area and centroid of each triangle;

[0125] Using the area of ​​the triangle as the weight, the overall centroid is calculated by area-weighted average, as shown in Formula 11:

[0126] (Formula 11)

[0127] in, G It is the total area of ​​the polygon, and the formula for calculation is:

[0128] (Formula 12)

[0129] Finally, the center point was calculated. O(x 0 ,y 0 ) The values ​​are (0.1506973649, 0.5843463246).

[0130] (3-1-2) Coordinate system reconstruction: The coordinate axes are reconstructed based on the center point of the chromaticity target area to facilitate the calculation of polar angles.

[0131] (3-1-3) Chromaticity Target Region Segmentation: Based on the center point, the chromaticity target region is divided into regular sectors (ODCBAO) and curved sectors (OD-(DA curve)-AO). Considering the dense and numerous chromaticity points in the curved sectors, they are divided into equal intervals. Collect chromaticity points to form sub-sectors; generate corresponding polar angle sequences for the target region. Thes =[ i D, i S1, i S2,..., i Sm, i A The polar angle is uniformly converted to (0, 2π), such as... Figure 7 As shown.

[0132] (3-2) Optimization of Chromaticity Target Region Attribution Determination: To address the low efficiency of region attribution determination, a two-level fast determination strategy is proposed. First, candidate sectors are initially screened based on polar angles, and complex sectors containing chromaticity curves are located using binary search. Then, within the locally selected candidate sectors, a ray casting method is used for precise determination. This method simplifies global traversal to local judgment, effectively improving the efficiency of chromaticity point region attribution determination. Further, it includes:

[0133] (3-2-1) Calculation of the polar angle of the chromatic point to be measured: Taking the center point as the pole, calculate the polar angle of the chromatic point to be measured. P(x p ,y p ) The polar angle is determined and normalized to (0, 2π). The formula for calculating the polar angle is:

[0134] (Formula 13)

[0135] The stable chromaticity coordinate point H is taken as the chromaticity point to be measured, and its relative center point is... O(x 0 ,y 0 ) Substituting the polar angle into Formula 13, the result is 65.63580190°.

[0136] (3-2-2) Setting the floating-point precision tolerance: Because the floating-point precision of the chroma point is too high, and there are rounding errors in floating-point operations, the numerical comparison is prone to causing abnormal judgment logic. Set the minimum floating-point precision tolerance ε = (1~3) × 10 -6 To control the error range and smooth out calculation errors.

[0137] (3-2-3) Initial Screening of Candidate Sectors: The polar angle can be used to quickly determine the angular sector to which the color point to be tested belongs. For curved sectors, which have several sub-sectors, binary search is used to quickly locate the sub-sectors, achieving rapid initial screening of candidate regions. The formula for initial screening of regular sectors is:

[0138] (Formula 14)

[0139] in, i D It is the polar angle of point D. i A It is the polar angle of point A.

[0140] The formula for quickly locating the sub-sector number using binary search in a curved sector is:

[0141] (Formula 15)

[0142] The formula for initial screening of curved sectors is:

[0143] (Formula 16)

[0144] Coordinate point H, after calculation, is in the polar angle sequence i S62 and i S63 Between these values ​​(corresponding polar angles of 65.425003134507236° and 67.438327807450804° respectively), the accuracy range of the corresponding sub-sector curves is 539.88nm to 540.62nm.

[0145] (3-2-4) Optimization of chromaticity target region attribution: After obtaining candidate sectors based on polar angle initial screening, the ray casting method is used within this local area to determine the region attribution of the chromaticity point H to be tested, simplifying the global traversal to a local (polar angle sequence) process. i S62 and i S63 The region between [65.425003134507236°, 67.438327807450804°], such as Figure 8 As shown in 8.1, the number of intersections in this region is 1, which is an odd number. Within the target region, as shown... Figure 8 As shown in 8.2.

[0146] Using the method described in this embodiment, based on the standard JT / T 597-2022 "LED Lane Control Signs", the chromaticity coordinates of point H (0.224513, 0.746760) (e.g.) Figure 9 (As shown in 9.1) Figure 9The area indicated in the text is the target color area, and it is an area constructed in green.

[0147] (4) Verification of the attribution of the chromaticity target area: After the optimization of the chromaticity target area in step (2), a global ray method is used to determine whether the point is located inside the chromaticity target area. A ray is emitted horizontally to the right from the point to be measured, and the effective intersection points are calculated by linear interpolation. Based on the parity of the number of effective intersection points, it is determined whether the point is located inside the chromaticity target area. This further includes:

[0148] (4-1-1) Determining the intersection condition of rays and edges: Using the ray method, start from the point of the color to be tested. P(x p ,y p ) A ray of infinite length is emitted horizontally to the right, and its interaction with all edges of the target color region is evaluated, passing through the two endpoints of each edge. V i (x i ,y i ) , V i+1 (x i+1 ,y i+1 ) To determine if a ray intersects an edge on either side of the ray (excluding collinear edges), the formula is:

[0149] (Equation 17).

[0150] (4-1-2) Global determination of the chromaticity target area: Based on the intersection relationship between the edge and the ray, the x-coordinate of the intersection point is solved by linear interpolation and the effective intersection points in the positive direction of the ray are screened (as shown in Formula 18). The parity of the total number of effective intersection points Z is calculated (if Z is odd, P is inside the polygon, and if it is even, it is outside). It is then determined whether the chromaticity point to be measured is within the chromaticity target area.

[0151] (Formula 18)

[0152] Its ε is the floating-point precision tolerance value.

[0153] After calculation, the number of times point H intersects with the target area is 1, which is an odd number. It is inside the target area, which is consistent with the result of the color target area attribution determination method optimized in step (3).

[0154] The original ray method (i.e. the method in step (4)) has a time complexity of O(n) (traversing all the edges formed by vertices). The optimized process (i.e. the method in step (3)) becomes a binary search of the curve sector with O(log(√n)) and a local ray method with O(√n), with a time complexity of O(log(√n)) + O(√n) and a total complexity of O(√n). The complexity is reduced to the O(√n) level, which can effectively improve the problem of low efficiency in assigning color point regions.

[0155] Therefore, the chromaticity map data points with a precision of 0.01nm are dense, and it takes a long time to determine the region affiliation using the global ray method (the method in step (4)). It is not as efficient as the chromaticity target region affiliation determination method optimized in step (3).

[0156] Example 2

[0157] Using the method in Example 1, the chromaticity coordinates of the display product were determined by measuring the chromaticity coordinates of point W under colorimetric instrument measurement, based on the standard JT / T 597-2022 "LED Lane Control Signs". First, based on the stability determination of the chromaticity coordinates, the finally obtained stable chromaticity coordinate point is point W (0.235602, 0.754690). Second, the chromaticity coordinates to be measured were initially screened into sectors based on the polar angle. The polar angle 63.54984794° falls within the range of [63.371506714799047°, 65.425117726066247°]. After local ray tracing, it was determined that the chromaticity coordinates are outside the target area, such as... Figure 10 As shown in 10.1.

[0158] The above embodiments are only used to illustrate the present invention, and the scope of protection of the present invention is not limited to the above embodiments. Those skilled in the art can achieve the purpose of the present invention based on the above disclosure. Any improvements and modifications made based on the concept of the present invention fall within the scope of protection of the present invention, and the specific scope of protection is determined by the claims.

Claims

1. A method for determining the chromaticity coordinate region of a product under colorimetric instrument measurement, characterized in that, Includes the following steps: (1) Stability determination of chromaticity coordinates: The minimum chromaticity ellipse is constructed by using the eigenvalues ​​of the covariance matrix to correspond to the square of the semi-axis length of the ellipse and the eigenvectors to correspond to the rotation direction of the ellipse. The chromaticity coordinates U and V are regarded as a two-dimensional whole, and the overall stability of the chromaticity coordinates is determined by analyzing the spatial distribution characteristics of the point set. (2) Optimization of the chroma target area: Piecewise polynomial interpolation is used to fit the original chromaticity points, optimizing the accuracy of the discrete original chromaticity point coordinates from 1nm to 0.01nm for high-density chromaticity points. Two-dimensional Euclidean distance is used to match the start and end points of the preset data points with the high-density chromaticity points. (3) Optimization of color target area attribution determination: An irregular polygon centroid calculation algorithm is used to calculate the center point of the chromaticity target area. Based on the center point, the chromaticity target area is divided into regular sectors and curved sectors. Then, the curved sectors are divided at equal intervals to form sub-sectors to complete the preprocessing. A two-level fast judgment strategy is adopted. First, the chromaticity coordinates to be tested are used to screen the sectors based on the polar angle. Then, the ray method is used to make accurate judgments within the local candidate sectors obtained from the initial screening. Step (3) employs a two-stage rapid determination strategy. First, the chromaticity coordinates to be tested are used to initially screen sectors based on polar angles. Then, within the local candidate sectors obtained from the initial screening, the ray casting method is used for precise determination, further including: (3-2-1) Calculation of the polar angle of the chromatic point to be measured: Taking the center point as the pole, calculate the polar angle of the chromatic point to be measured and normalize it to (0, 2π). (3-2-2) Setting floating-point precision tolerance: Since the floating-point precision of color points is too high and there are rounding errors in floating-point operations, numerical comparisons are prone to causing abnormal judgment logic. Set a very small floating-point precision tolerance to control the error range and smooth out calculation errors. (3-2-3) Initial screening of candidate sectors: The polar angle can be used to quickly determine the angle sector to which the color point to be tested belongs. For the curve sector, there are several sub-sectors. Binary search is used to quickly locate the sub-sectors, so as to achieve rapid initial screening of candidate areas. (3-2-4) Optimization of chromaticity target region attribution determination: After obtaining candidate sectors based on polar angle initial screening, the ray method is used in this local area to realize the region attribution of the chromaticity point to be tested, simplifying the global traversal to local fine judgment, effectively improving the efficiency of chromaticity point region attribution determination.

2. The method for determining the chromaticity coordinate region of a product under colorimetric instrument measurement according to claim 1, characterized in that, The determination of chromaticity coordinate stability in step (1) includes: (1-1) Setting up the minimum chromaticity ellipse: Collect N sets of data, form a covariance matrix from the autocovariance and cross-covariance, integrate the fluctuations of the U and V coordinates in both single and two dimensions, map the eigenvalues ​​of the covariance matrix to the square of the semi-axis of the minimum chromaticity ellipse, and map the eigenvectors to the rotation direction of the ellipse, establishing a mapping relationship between the covariance matrix and the geometric parameters of the ellipse, further including: (1-1-1) UV-related fluctuation settings: Collect N sets of data (U j V j Using the autocovariance of U and V coordinates σ UU and σ VV To detect the fluctuations in each of the U and V dimensions, cross-covariance is used. σ UV , σ VU To detect the correlation fluctuations between U and V, a covariance matrix is ​​constructed from the autocovariance and crosscovariance: (Official 1) (1-1-2) Setting the minimum chromaticity ellipse: The eigenvalues ​​of the covariance matrix correspond to the square of the semi-axis of the minimum chromaticity ellipse, and the eigenvectors correspond to the rotation direction of the ellipse; (1-1-3) Calculation of eigenvalues ​​of the covariance matrix: Set the characteristic equation for solving the covariance matrix as follows: (Official 2) in, I It is the identity matrix. β These are the eigenvalues ​​of the covariance matrix; solving for them yields two eigenvalues. β 1、 β 2 and the corresponding rotation angle is (Official 3) (1-2) Calculation of the area of ​​the minimum chromaticity ellipse: Introducing the chi-square distribution quantiles, setting the chromaticity confidence level, and combining the eigenvalues ​​of the covariance matrix to calculate the semi-axis length of the minimum chromaticity ellipse, and finally calculating the area of ​​the minimum chromaticity ellipse, further including: (1-2-1) Setting the chromaticity confidence level: Introducing the chi-square distribution quantile, and setting the chromaticity confidence level based on the two-dimensional degrees of freedom of U and V. Ψ 2 (2) ; (1-2-2) Calculation of the semi-axis length of the minimum chromaticity ellipse: Let a be the major semi-axis and b be the minor semi-axis. Combining the eigenvalues ​​of the covariance matrix and the formula for calculating the chromaticity confidence level, the following formula is used: (Official 4) (1-2-3) Calculation of the area of ​​the minimum chromaticity ellipse: Combining the semi-axis length of the minimum chromaticity ellipse, the area of ​​the minimum chromaticity ellipse is calculated as follows: (Official 5) (1-3) Chromaticity coordinate stability determination: Stability is determined by comparing a threshold with the area of ​​the minimum chromaticity ellipse. If the area is less than the stability threshold, the chromaticity is stable. The center of the minimum chromaticity ellipse is selected as the final chromaticity coordinate point. This further includes: (1-3-1) Calculation of the center of the minimum chromaticity ellipse: The two-dimensional mean point of N sets of data is taken as the center of the minimum chromaticity ellipse; (1-3-2) Stability determination: Set a stability threshold and judge the stability by comparing the threshold with the area of ​​the minimum chromaticity ellipse. If it is less than the stability threshold, it is stable. Select the center of the minimum chromaticity ellipse as the final chromaticity coordinate point.

3. The method for determining the chromaticity coordinate region of a product under colorimetric instrument measurement according to claim 2, characterized in that, The stability threshold mentioned in step (1-3-2) is 10. -6 .

4. The method for determining the chromaticity coordinate region of a product under colorimetric instrument measurement according to claim 1, characterized in that, Step (2) uses piecewise polynomial interpolation to fit the original chromaticity points, optimizing the accuracy of the discrete original chromaticity point coordinates from 1 nm to 0.01 nm for high-density chromaticity points. Two-dimensional Euclidean distance is used to match the start and end points of the preset data points with the high-density chromaticity points. Further steps include: (2-1-1) Chromaticity curve fitting: Piecewise polynomial interpolation is used to fit the original chromaticity points. Each wavelength segment in the interval is set as an independent cubic polynomial. By ensuring the continuity of the first and second derivatives, the curve completely coincides with the original discrete points, thus adapting to the nonlinear variation characteristics of the chromaticity profile. (2-1-2) Optimization of chromaticity point coordinate accuracy: Based on the results of chromaticity curve fitting, interpolation calculations are performed using independent cubic polynomials in each wavelength segment to optimize the accuracy to a high density of chromaticity points of 0.01nm, thereby improving curve smoothness and coordinate positioning accuracy. (2-1-3) Chromaticity coordinate start and end anchor point positioning: High-density chromaticity points are discrete rather than continuous data with equal wavelength intervals. There is a deviation between the start and end points of the preset data points for constructing the chromaticity target area and the high-density chromaticity points. Two-dimensional Euclidean distance is used to match the start and end points of the preset data points with the high-density chromaticity points. The chromaticity point with the smallest distance is selected as the start and end anchor point, and the corresponding chromaticity coordinate sequence is extracted.

5. The method for determining the chromaticity coordinate region of a product under colorimetric instrument measurement according to claim 1, characterized in that, In step (3), the centroid calculation algorithm for irregular polygons is used to calculate the center point of the chromaticity target region. Based on the center point, the chromaticity target region is divided into regular sectors and curved sectors. Then, the curved sectors are divided at equal intervals to form sub-sectors to complete the preprocessing. This further includes: (3-1-1) Solving for the center point of the chromaticity target area: The irregular polygon centroid calculation algorithm is adopted to decompose the chromaticity target area into several triangles with the origin as the vertex, and the overall centroid is calculated as the center point by the area weighted average. (3-1-2) Reconstruct the coordinate axis based on the center point of the chromaticity target area to facilitate the calculation of the polar angle; (3-1-3) Chromaticity target area splitting: Based on the center point, the chromaticity target area is split into regular sectors and curved sectors. In view of the characteristics of dense and large number of chromaticity points in the curved sector, chromaticity points are collected at equal intervals √n to form sub-sectors. The target area generates a corresponding polar angle sequence, and the polar angle is uniformly converted to (0, 2π).

6. The method for determining the chromaticity coordinate region of a product under colorimetric instrument measurement according to claim 1, characterized in that, The minimum floating-point precision tolerance mentioned in step (3-2-2) is (1~3)×10 -6 .

7. The method for determining the chromaticity coordinate region of a product under colorimetric instrument measurement according to claim 1, characterized in that, It also includes step (4) verification of the color target area attribution: using the ray method to determine whether the point is located inside the color target area.

8. The method for determining the chromaticity coordinate region of a product under colorimetric instrument measurement according to claim 7, characterized in that, Step (4) uses the ray casting method to determine whether the point is located inside the chromaticity target area, and further includes: (4-1-1) Judgment of the intersection condition of ray and edge: Using the ray method, an infinitely long ray is emitted from the chromaticity point to the right horizontally and judged to intersect with all edges of the chromaticity target area. The intersection of ray and edge is judged by the two endpoints of the edge on the upper and lower sides of the ray. (4-1-2) Determination of the chromaticity target area: Based on the intersection relationship between the edge and the ray, the x-coordinate of the intersection point is solved by linear interpolation and the valid intersection points in the positive direction of the ray are screened. The parity of the total number of valid intersection points Z is calculated to determine whether the chromaticity point to be tested is in the chromaticity target area. If Z is odd, the chromaticity point to be tested is inside the chromaticity target area. If Z is even, the chromaticity point to be tested is outside the chromaticity target area.

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