A component bias-based textbook printing diagnosis method and system

Abstract

The application discloses a textbook printing diagnosis method and system based on component deviation, and the method comprises the following steps: determining a chroma detection reference based on the brightness average value, the red-green color average value and the yellow-blue color average value of N textbook samples in a target printing batch; referring to the chroma detection reference, determining that the comprehensive color difference value of a first textbook sample is greater than a third color difference threshold value based on the brightness difference value, the red-green color difference value and the yellow-blue color difference value of each textbook sample; comparing the first brightness difference value, the first red-green color difference value and the first yellow-blue color difference value of the first textbook sample with corresponding brightness difference threshold values, red-green color difference threshold values and yellow-blue color difference threshold values respectively to determine the deviation mode of the first textbook sample; and matching and outputting a printing adjustment suggestion for the first textbook sample from a preset process adjustment rule library according to the deviation mode.

Description

Technical Field

[0001] This invention relates to the field of printing quality inspection technology, and relates to, but is not limited to, a diagnostic method and system for textbook printing based on component deviation. Background Technology

[0002] The printing quality of primary and secondary school textbooks directly affects the reading experience and visual health of teenagers. Color consistency, as one of the core indicators for evaluating printing quality, is highly valued by textbook publishing quality inspection agencies.

[0003] National Standard GB / T 34053.4-2017, "Inspection Specifications for Printing Quality of Paper Printed Products Part 4: Primary and Secondary School Textbooks," clearly stipulates the testing requirements for color difference in the same batch and position of textbook printing. This standard specifies that color difference testing in the same batch and position should be conducted using a spectrophotometer conforming to GB / T 18722, following the method specified in 6.6 of GB / T 7705-2008. Specifically, the average value of the L, a, and b values ​​in the CIEL*a*b*uniform color space of the tested batch of samples should be used as the benchmark. The L value represents lightness (brightness), ranging from 0 (pure black) to 100 (pure white), with a higher value indicating a brighter color. The a value represents the chromaticity along the red-green axis, with positive values ​​leaning towards red and negative values ​​leaning towards green, ranging from approximately -128 to 127. The b value represents the chromaticity along the yellow-blue axis, with positive values ​​leaning towards yellow and negative values ​​leaning towards blue, ranging from approximately -128 to 127.

[0004] However, in the actual inspection of textbook printing quality, the following technical problems exist: some testing operations do not strictly follow the mean benchmark method, and the statistical representativeness of the test results is insufficient; the existing color difference judgment lacks refined graded management methods, which cannot effectively distinguish samples with different degrees of deviation, and is not conducive to the refined evaluation of printing quality. More critically, existing color difference detection methods and systems (including online detection schemes based on comparison of individual sample images and automatic sorting schemes based on color difference judgment) remain at a binary "pass / fail" level. When excessive color difference is detected, existing technologies either directly determine it as a defective product, physically sort it, or send simple adjustment instructions to the printing equipment. None of these methods can answer the most core questions in printing quality control: what causes the color difference; which specific color dimension has a deviation and to what extent; and what targeted measures should be taken to correct it. This leads to a serious information gap between quality inspection and process improvement, leaving operators unsure of where to begin when faced with excessive color difference alarms and forced to rely on personal experience for blind adjustments.

[0005] Therefore, there is an urgent need for a textbook printing colorimetric testing method that strictly adheres to national standard average benchmark specifications, possesses refined grading capabilities, and can achieve intelligent upgrades from "judgment" to "diagnosis". Summary of the Invention

[0006] In view of this, embodiments of the present invention provide a textbook printing diagnostic method based on component deviation, which addresses the technical gap in the prior art that cannot perform refined deviation diagnosis on samples with excessive color difference and provide executable process adjustment suggestions. This method is designed to quickly screen abnormal samples, accurately identify deviation patterns, and output printing adjustment suggestions.

[0007] The specific technical solutions of this invention are as follows: This invention provides a textbook printing diagnostic method based on component deviation, comprising: The colorimetric standard for the target printing batch is determined based on the average brightness, average red-green hue, and average yellow-blue hue of N textbook samples in the target printing batch, where N is an integer greater than or equal to 5. Using the colorimetric detection benchmark as a reference, based on the brightness difference, red-green difference, and yellow-blue difference of each textbook sample, it is determined that the comprehensive color difference value of the first textbook sample is greater than the third color difference threshold. Here, the brightness difference is the difference between the brightness value and the average brightness value, the red-green difference is the difference between the red-green brightness value and the average red-green brightness value, and the yellow-blue difference is the difference between the yellow-blue brightness value and the average yellow-blue brightness value. The first brightness difference, the first red-green difference, and the first yellow-blue difference of the first textbook sample are compared with the corresponding brightness difference threshold, red-green difference threshold, and yellow-blue difference threshold, respectively, to determine the deviation pattern of the first textbook sample. For the aforementioned deviation pattern, matching and outputting printing adjustment suggestions for the first textbook sample from a preset process adjustment rule library.

[0008] This invention provides a textbook printing diagnostic system based on component deviation, comprising: Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0009] In this embodiment of the invention, the average value of multiple samples from the same batch is used as the colorimetric benchmark, avoiding misjudgments caused by inherent differences in paper and ink from different batches. The detection benchmark aligns with actual production conditions, significantly improving the accuracy of judgment. Abnormal samples are quickly screened by comprehensively analyzing color difference values, eliminating the need for in-depth testing of each entire batch, thus improving the efficiency of color difference screening for batch textbooks and meeting the online quality inspection needs of large-scale printing. Through independent threshold comparison of the three components, single-component / multi-component composite deviations can be accurately distinguished, and deviation patterns can be accurately identified. Only for warning-level samples (the first textbook sample) with excessive color difference, the deviation patterns of the three independent dimensions—brightness difference (DL), red-green difference (Da), and yellow-blue difference (Db)—are automatically analyzed to identify specific and interpretable printing fault patterns. Graded and executable maintenance suggestions are then matched and output from a preset process adjustment rule library. This technical solution upgrades printing color difference detection from "simple judgment" to "intelligent diagnosis," filling a gap in existing technology. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. 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, wherein: Figure 1 This is a schematic flowchart of a textbook printing diagnostic method based on component deviation, provided as an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0013] It should be noted that the terms "first, second, and third" used in the embodiments of the present invention are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0014] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which these embodiments of the invention pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0015] Figure 1 This is a schematic flowchart of a textbook printing diagnosis method based on component deviation, provided by an embodiment of the present invention. Figure 1 As shown, the method includes at least the following steps: Step S110: Determine the colorimetric detection benchmark of the target printing batch based on the average brightness, average red-green hue, and average yellow-blue hue of the N textbook samples in the target printing batch, where N is an integer greater than or equal to 5; During the implementation process, N≥5 qualified textbook samples from the same batch are selected, and the average values ​​of the three components of lightness L*, red-green hue a*, and yellow-blue hue b* are calculated respectively. These values ​​are used as the exclusive colorimetric test benchmark for that batch to avoid judgment deviations caused by inherent differences in paper and ink between different batches.

[0016] Preferably, a spectrophotometer conforming to GB / T 18722 is used for measurement. The illumination / observation geometry is 45° / 0° or 0° / 45°, the light source type is D50, the observer angle is 10°, the minimum measuring aperture is 2mm, and the measurement point deviation is controlled within 0.5mm. Each textbook sample is measured repeatedly at the same location at least three times, and the average value is taken. The "preset detection location" refers to the solid color block detection area specified in GB / T 7705-2008 or GB / T 34053.4-2017, preferably a solid color block area with uniform color on the cover or a designated page of the main text. Preferably, N≥20 to meet statistical requirements.

[0017] Calculate the arithmetic mean of the L*, a*, and b* values ​​of the N samples, and use it as the colorimetric detection benchmark (L_avg, a_avg, b_avg) for this batch.

[0018] Step S120: Using the colorimetric detection benchmark as a reference, based on the brightness difference, red-green difference, and yellow-blue difference of each textbook sample, determine that the comprehensive color difference value of the first textbook sample is greater than the third color difference threshold. The brightness difference is the difference between the brightness value and the average brightness value, the red-green difference is the difference between the red-green brightness value and the average red-green brightness value, and the yellow-blue difference is the difference between the yellow-blue brightness value and the average yellow-blue brightness value. During implementation, the lightness difference (DL), red-green difference (Da), and yellow-blue difference (Db) are calculated using the colorimetric testing benchmark as a reference. Based on these values, the overall color difference value (ΔE) is then calculated. This approach uses the batch statistical mean (colorimetric testing benchmark) as an objective reference, avoiding the random bias introduced by using a single sample as the standard, and strictly adhering to the requirements of GB / T 7705-2008.

[0019] In some embodiments, the CIEDE2000 color difference formula ΔE* can be preferentially used. 00 The comprehensive color difference value ΔE is calculated using a formula published by the International Commission on Illumination (CIE) (CIE 142-2001) and adopted as the international standard ISO / CIE 11664-6:2014. This formula offers better consistency with human visual perception across different lightness and hue regions. Employing the CIEDE2000 color difference formula ensures a high degree of consistency between the calculated comprehensive color difference and human visual perception, laying a reliable data foundation for subsequent accurate pattern diagnosis.

[0020] Meanwhile, to ensure compatibility with traditional standards, ΔE* can also be selected. ab The formula is preset by the user according to actual needs.

[0021] When ΔE exceeds the preset third color difference threshold, the textbook sample is marked as the first textbook sample to be diagnosed, quickly identifying the abnormal product. That is, for the warning-level sample (first textbook sample) whose comprehensive color difference value ΔE exceeds the third color difference threshold T3, the following deviation mode diagnosis step S130 is automatically executed.

[0022] Step S130: Compare the first brightness difference, the first red-green difference, and the first yellow-blue difference of the first textbook sample with the corresponding brightness difference threshold, red-green difference threshold, and yellow-blue difference threshold, respectively, to determine the deviation pattern of the first textbook sample; During implementation, the presence of a brightness deviation pattern is determined by comparing the first brightness difference value DL of the first textbook sample with the first component threshold V_DL (brightness difference threshold).

[0023] Based on the comparison of the red-green difference value Da and sign of the first textbook sample with the second component threshold V_Da (red-green difference threshold), it is determined whether there is a red-green deviation pattern.

[0024] Based on the comparison of the yellow-blue difference value Db and sign of the first textbook sample with the third component threshold V_Db (yellow-blue difference threshold), it is determined whether a yellow-blue deviation pattern exists.

[0025] Step S140: For the deviation pattern, match and output printing adjustment suggestions for the first teaching material sample from the preset process adjustment rule library.

[0026] During implementation, based on the results of deviation mode diagnosis, specific printing process adjustment suggestions for the brightness deviation mode, red-green deviation mode and / or yellow-blue deviation mode can be matched and output from the preset process adjustment rule library.

[0027] In this embodiment of the invention, the average value of multiple samples from the same batch is used as the colorimetric benchmark, avoiding misjudgments caused by inherent differences in paper and ink from different batches. The detection benchmark aligns with actual production conditions, significantly improving the accuracy of judgment. Abnormal samples are quickly screened by comprehensively analyzing color difference values, eliminating the need for in-depth testing of each entire batch, thus improving the efficiency of color difference screening for batch textbooks and meeting the online quality inspection needs of large-batch printing. Through independent threshold comparison of the three components, single-component / multi-component composite deviations can be accurately distinguished, and deviation patterns can be accurately identified. Only for warning-level samples (the first textbook sample) with excessive color difference, the deviation patterns of the three independent dimensions—brightness difference (DL), red-green difference (Da), and yellow-blue difference (Db)—are automatically analyzed to identify specific and interpretable printing fault patterns. Graded and executable maintenance suggestions are then matched and output from a preset process adjustment rule library. This technical solution upgrades printing color difference detection from "simple judgment" to "intelligent diagnosis," filling a gap in existing technology.

[0028] In some embodiments, before performing step S110, this application embodiment also provides a method for outlier detection and determining the average brightness, average red-green brightness, and average yellow-blue brightness, which can be implemented through the following steps: Step A: Obtain the brightness, red-green saturation, and yellow-blue saturation values ​​of the N textbook samples at the preset detection positions; During implementation, the CIEL*a*b* chromaticity values ​​(L*, a*, b*) of N textbook samples from the same printing batch at preset detection positions are obtained. Preferably, a spectrophotometer conforming to GB / T 18722 is used for measurement, with illumination / observation geometry of 45° / 0° or 0° / 45°, light source type of D50, observer angle of 10°, minimum measurement aperture of 2mm, and measurement point deviation controlled within 0.5mm. Each sample is measured repeatedly at the same position at no less than 3 times and the average value is taken.

[0029] Step B: Perform Grubbs outlier detection on the brightness value, the red-green value, and the yellow-blue value to remove abnormal data and obtain the target brightness value, the target red-green value, and the target yellow-blue value. Before calculating the arithmetic mean, Grubbs outlier testing was performed on the collected L*, a*, and b* datasets, and data points identified as outliers were removed to ensure the statistical robustness of the colorimetric detection benchmark and avoid interference from individual extreme values ​​on the mean benchmark. Grubbs outlier testing prevents extreme samples from interfering with the mean benchmark, making the judgment benchmark more consistent with the overall true level of the batch.

[0030] Step C: Determine the average value of the target brightness as the average brightness; determine the average value of the target red-green hue as the average red-green hue; determine the average value of the target yellow-blue hue as the average yellow-blue hue.

[0031] In this embodiment of the invention, before calculating the mean, the three components L*, a*, and b* are independently subjected to Grubbs' test to eliminate outliers caused by detection errors and sample contamination, thus preventing dirty data from skewing the batch colorimetric reference. The component mean is calculated based on the validated valid dataset, resulting in a batch colorimetric reference that more closely matches the true color characteristics of the printed batch. This forms a complete link with subsequent color difference calculation and deviation diagnosis steps, preventing subsequent misjudgments caused by reference distortion from the source, and making the output results of the entire printing deviation diagnosis method more stable and reliable.

[0032] In some embodiments, the step S120 above, "using the colorimetric detection benchmark as a reference, determining that there exists a comprehensive color difference value of the first textbook sample that is greater than the third color difference threshold based on the brightness difference, red-green difference, and yellow-blue difference of each textbook sample," can be achieved through the following steps: Step 121: Using the colorimetric detection benchmark as a reference, determine the corresponding lightness difference, red-green difference, and yellow-blue difference for each of the textbook samples; During implementation, the following formulas (1), (2), and (3) can be used to determine the lightness difference, red-green difference, and yellow-blue difference: DL=L*-L_avg (1; Da = a * - a_avg (2); Db=b*-b_avg (3; Wherein, DL is the lightness difference, L* is the lightness value, and L_avg is the average lightness value; Da is the red-green difference, a* is the red-green lightness value, and a_avg is the average red-green lightness value; Db is the yellow-blue difference, b* is the yellow-blue lightness value, and b_avg is the average yellow-blue lightness value.

[0033] Step 122: Square the values ​​of lightness difference, red-green difference, and yellow-blue difference, and then sum them to obtain a sum of squares. Step 123: Take the arithmetic square root of the sum of the squares to obtain the comprehensive color difference value of each of the teaching material samples; During implementation, the color difference value ΔE* can be determined using the following formula (4). ab : ΔE* ab = (4); Where L*-L_avg yields the brightness difference. Obtain the red-green difference. The difference between yellow and blue is obtained.

[0034] Step 124: Determine that the overall color difference value of the first textbook sample is greater than the third color difference threshold, and classify the first textbook sample as a warning level.

[0035] During implementation, determine ΔE* ab Under the condition of T3 (third color difference threshold), the first textbook sample is judged as "warning" level, the color deviation exceeds the qualified limit, and process adjustment is required.

[0036] Among them, T3 is not greater than the acceptable limit for color difference in the same batch and position as specified in GB / T 34053.4-2017. The third color difference threshold T3 is strictly limited to the acceptable limit for color difference in the same batch and position for printing primary and secondary school textbooks in GB / T 34053.4-2017. The warning line is triggered in advance of the national standard acceptance line, which will not cause the problem of qualified samples being mistakenly judged as abnormal and will also avoid missing potential deviation samples that are close to the acceptance edge, thus ensuring the color compliance of the final textbooks from the source.

[0037] In this embodiment of the invention, L_avg, a_avg, and b_avg of samples from the same batch are directly used as the benchmark for difference calculation, rather than universal standard color values, completely avoiding systematic deviations caused by differences in whiteness between different batches of paper and ink. The comprehensive color difference value is obtained through the standard process of "summing the squares of the components and taking the square root," matching internationally accepted color measurement rules. The first textbook sample is directly marked as "warning level," skipping the invalid in-depth diagnostic process for a large number of qualified samples. In batch quality inspection scenarios involving tens of thousands of textbooks, this improves the efficiency of initial screening of abnormal samples and accurately identifies deviation samples requiring process adjustments, avoiding the waste of production capacity caused by a complete shutdown for adjustments.

[0038] In some embodiments, the present invention also provides a method for determining the level of teaching material samples, which can be implemented through the following steps: Step A: When it is determined that the comprehensive color difference value of the second textbook sample is less than or equal to the first color difference threshold, the second textbook sample is defined as an excellent grade; Step B: When it is determined that the comprehensive color difference value of the third textbook sample is greater than the first color difference threshold and less than or equal to the second color difference threshold, the third textbook sample is defined as a good grade; Step C: When it is determined that the comprehensive color difference value of the fourth textbook sample is greater than the second color difference threshold and less than or equal to the third color difference threshold, the fourth textbook sample is defined as a qualified grade.

[0039] The comprehensive color difference value ΔE is compared with a preset multi-level threshold set, that is, the first color difference threshold T1, the second color difference threshold T2, and the third color difference threshold T3 (0<T1<T2<T3), to determine the grade of each sample. The grading threshold system is as follows: ΔE≤T1, judged as excellent grade, with excellent color control, which is basically imperceptible to the human eye; T1<ΔE≤T2, judged as good grade, with good color control, which is slightly perceptible but fully meets the quality requirements; T2<ΔE≤T3, judged as qualified grade, the color meets the standard requirements, and it is recommended to pay attention to the trend of the printing process; ΔE>T3, judged as warning grade, the color deviation exceeds the qualified limit, and process adjustment is required.

[0040] In the embodiment of the present invention, the extensive logic of traditional printing that only uses the binary judgment of "qualified / unqualified" is broken. A four-grade grading system is built through the three progressive thresholds of T1<T2<T3, which completely matches the perception gradient of the human eye to printing color difference: within T1, the human eye can hardly perceive, in the T1-T2 interval, only professional quality inspectors can capture slight differences, and the T2-T3 interval fully meets the national standard requirements, with a high fitting degree of layered judgment.

[0041] In some embodiments, the lightness difference threshold includes a first lightness difference threshold and a second lightness difference threshold, where the first lightness difference threshold is smaller than the second lightness difference threshold; comparing the first lightness difference value with the lightness difference threshold in step S130 above to determine the deviation mode of the first textbook sample, and step S140 "for the deviation mode, match and output the printing adjustment suggestion for the first textbook sample from the preset process adjustment rule base" can be realized through the following judgment and suggestion output steps: Step A1: When it is determined that the absolute value of the first lightness difference value is greater than the second lightness difference threshold, the deviation mode is determined as a severe lightness deviation mode; for the severe lightness deviation mode, the output printing adjustment suggestion includes: adjusting the ink output of the ink fountain or the printing pressure; During implementation, when |DL|>V_DL2 (the second brightness difference threshold), it is determined to be a severe brightness deviation mode.

[0042] For severe brightness deviation mode, the output suggestion is to "adjust the ink flow from the ink fountain or the printing pressure".

[0043] Step B1: If the absolute value of the first brightness difference is greater than the first brightness difference threshold and less than the second brightness difference threshold, the deviation mode is determined to be a slight brightness drift mode; for the slight brightness drift mode, the printing adjustment suggestions output include: pay attention to ink supply stability.

[0044] During implementation, when V_DL1 (first brightness difference threshold) <|DL|≤V_DL2, it is determined to be a slight brightness drift mode.

[0045] In the slight brightness drift mode, the output suggestion is "Pay attention to ink supply stability"; when |DL|≤V_DL1, the brightness is considered normal.

[0046] In this embodiment of the invention, a dual-threshold stratification of lightness difference thresholds (first and second) is used to perform two-level gradient judgments on the lightness component. This allows for the precise differentiation between slight lightness drift that is almost imperceptible to the human eye and severe lightness deviation that has already significantly affected visual effects. Differentiated adjustment schemes are output for different levels of lightness deviation: severe deviations directly trigger directional hard parameter adjustments to the ink fountain output and printing pressure, quickly correcting large deviations; slight drifts only prompt attention to ink supply stability, without the need to rashly change already stable machine parameters. Textbook printing often involves large-batch continuous paper feeding, and the ink supply system is prone to slow ink drift during long-term operation. This dual-threshold lightness judgment rule avoids frequent invalid machine adjustments due to minor normal fluctuations and intervenes in time before the drift expands, ensuring the lightness consistency of the entire batch of textbooks and meeting the mandatory requirement for ink uniformity within the same batch. The excessive overall color difference is deconstructed into specific process problems such as "darker lightness" and "slight lightness drift," bridging the information gap between quality inspection and printing process adjustment. This is a fundamental improvement over the traditional "alarm-shutdown" mode.

[0047] In some embodiments, the red-green difference threshold includes a first red-green difference threshold and a second red-green difference threshold, wherein the first red-green difference threshold is less than the second red-green difference threshold; in step S130 above, comparing the first brightness difference value with the brightness difference threshold to determine the deviation pattern of the first teaching material sample, and step S140 "matching and outputting printing adjustment suggestions for the first teaching material sample from a preset process adjustment rule library for the deviation pattern" can be achieved through the following judgment and suggestion output steps: Step A2: If the first red-green difference value is greater than 0 and the absolute value of the first red-green difference value is greater than the second red-green difference threshold, then the deviation mode is determined to be the magenta over-dark mode; for the magenta over-dark mode, the printing adjustment suggestion is output, which includes reducing the amount of magenta ink. During implementation, when Da>0 and |Da|>V_Da2 (the second red-green difference threshold), it is determined to be a magenta over-dark mode.

[0048] For the magenta mode that is too dark, the output suggestion is to "reduce the amount of magenta ink".

[0049] Step B2: If the first red-green difference is less than 0 and the absolute value of the first red-green difference is greater than the second red-green difference threshold, then the deviation mode is determined to be the cyan too dark mode; for the cyan too dark mode, the printing adjustment suggestion is output, which includes reducing the amount of cyan ink. During implementation, when Da < 0 and |Da| > V_Da2, it is determined to be an overly dark cyan mode.

[0050] For the mode where cyan is too dark, the output suggestion is to "reduce the amount of cyan ink".

[0051] Step C2: If the absolute value of the first red-green difference is greater than the first red-green difference threshold and less than or equal to the second red-green difference threshold, the deviation mode is determined to be the red-cyan balance drift mode; for the red-cyan balance drift mode, the printing adjustment suggestion is output, which includes paying attention to the balance of magenta and cyan ink.

[0052] During implementation, when V_Da1 (first red-green difference threshold) <|Da|≤V_Da2, regardless of the sign, it is determined to be a red-green balance drift mode.

[0053] In the red-cyan balance drift mode, the output suggestion is to "pay attention to the balance of magenta / cyan ink volume".

[0054] When |Da|≤V_Da1, the red-green color is considered normal.

[0055] In this embodiment of the invention, a positive Da value points to red (corresponding to a higher proportion of magenta ink in printing) and a negative Da value points to green (corresponding to a higher proportion of cyan ink in printing). This completely avoids the blind spot of traditional methods that rely on comprehensive color difference to distinguish between red and green biases. Without the need for additional color measuring equipment, it can accurately pinpoint whether there is an excess of magenta or cyan ink simply by determining the sign of a single component and adding a threshold. The root cause identification accuracy is far higher than that of general color difference diagnostic solutions. For severe deviations exceeding the second red-green difference threshold, it directly outputs a reduction command for the corresponding single ink color, skipping the traditional debugging process of "repeated trial printing - comparison with sample artwork." A single machine adjustment can bring the a* component back to the acceptable range. The red-cyan balance drift captured by the first red-green difference threshold corresponds precisely to the slight asynchronous shift in the ink supply of magenta and cyan ink paths after the printing press has been running for a long time. This type of shift will not immediately trigger a non-compliance warning for overall color difference, but it will gradually amplify as the number of prints accumulates. It provides early warning to pay attention to the ink path balance before the color deviation is detected by the human eye, thus preventing batch quality problems such as reddish / greenish tints in the entire batch of teaching materials from the source. By establishing a mapping between deviation patterns and process adjustment rule bases, the system can output precise and graded (distinguishing between "adjust" and "pay attention") operation suggestions such as "reduce magenta ink amount" and "pay attention to magenta / cyan ink balance". Even inexperienced operators can quickly locate the root cause of the problem, greatly improving troubleshooting efficiency and solidifying expert diagnostic knowledge into the system.

[0056] In some embodiments, the yellow-blue difference threshold includes a first yellow-blue difference threshold and a second yellow-blue difference threshold, wherein the first yellow-blue difference threshold is less than the second yellow-blue difference threshold; in step S130 above, comparing the first brightness difference value with the brightness difference threshold to determine the deviation pattern of the first teaching material sample, and step S140 "matching and outputting printing adjustment suggestions for the first teaching material sample from a preset process adjustment rule library for the deviation pattern" can be achieved through the following judgment and suggestion output steps: Step A3: If the first yellow-blue difference value is greater than 0 and the absolute value of the first yellow-blue difference value is greater than the second yellow-blue difference threshold, then the deviation mode is determined to be the yellow-too-dark mode; for the yellow-too-dark mode, the printing adjustment suggestion is output, which includes reducing the amount of yellow ink. During implementation, when Db>0 and |Db|>V_Db2 (the second yellow-blue difference threshold), it is determined to be a yellow-too-dark mode.

[0057] For the mode where the yellow color is too dark, the output suggestion is to "reduce the amount of yellow ink".

[0058] Step B3: If the first yellow-blue difference is less than 0 and the absolute value of the first yellow-blue difference is greater than the second yellow-blue difference threshold, then the deviation mode is determined to be the blue-to-dark mode; for the blue-to-dark mode, the printing adjustment suggestion is output, which includes checking the amount of cyan ink and magenta ink. During implementation, when Db < 0 and |Db| > V_Db2, it is determined to be an overly dark blue mode.

[0059] In the mode where blue is too dark, the output suggestion is "Check the amount of cyan and magenta ink".

[0060] Step C3: If the absolute value of the first yellow-blue difference is greater than the first yellow-blue difference threshold and less than or equal to the second yellow-blue difference threshold, the deviation mode is determined to be the yellow-blue balance drift mode; for the yellow-blue balance drift mode, the printing adjustment suggestion is output, which includes paying attention to the balance of yellow and cyan ink amounts.

[0061] During implementation, when V_Db1 (the first yellow-blue difference threshold) <|Db|≤V_Db2, it is determined to be a yellow-blue balance drift mode.

[0062] Yellow-blue balance drift mode, output suggestion: "Pay attention to the balance of yellow / cyan ink".

[0063] When |Db|≤V_Db1, the yellow-blue hue is considered normal.

[0064] In this embodiment of the invention, Db>0 corresponds to a high proportion of yellow, and Db<0 corresponds to a high proportion of blue. This directly establishes the correspondence between component differences and printing ink paths, accurately pinpointing the root cause of deviations without additional color conversion. For severe yellow deviations with positive Db exceeding the limit, a clear instruction to reduce the amount of yellow ink is directly output, correcting common problems in textbook printing such as yellowing of paper and yellowing of background colors in images and text. For severe blue deviations with negative Db exceeding the limit, the abnormal coordination between cyan and magenta ink paths is precisely pointed to, avoiding secondary color imbalances caused by blindly adjusting ink paths in one direction. The yellow-blue balance drift captured by the first yellow-blue difference threshold corresponds precisely to the slight asynchronous shift in the supply of yellow and cyan inks in large-batch continuous printing. This type of deviation will not immediately trigger a comprehensive color difference failure, but it will gradually accumulate and amplify with the number of prints. It provides early warning of ink path balance before the human eye can perceive the color deviation, preventing batch quality accidents such as yellowing background colors and bluish images and text in the entire batch of textbooks from the source.

[0065] The multi-level thresholds T1, T2, T3 and component thresholds V_DL, V_Da, V_Db can be preset according to the substrate type, wherein T3 shall not exceed the acceptable limit for color difference in the same position of the same batch as specified in GB / T 34053.4-2017 or GB / T 7705-2008.

[0066] For example, for coated paper, T1=0.50, T2=1.00, T3=1.50; V_DL1=0.5, V_DL2=1.0; V_Da1=0.5, V_Da2=1.0; V_Db1=0.5, V_Db2=1.0; For offset paper, T1=0.80, T2=1.50, T3=2.00; V_DL1=1.0, V_DL2=1.8; V_Da1=1.0, V_Da2=1.8; V_Db1=1.0, V_Db2=1.8.

[0067] In this invention, the "preset process adjustment rule library" is a pre-established and stored set of condition-action rules, and each rule has the structure "IF deviation mode condition THEN printing adjustment suggested action". The rule library includes, but is not limited to, the following rule entries:

[0068] The matching logic of the rule base is as follows: once the deviation pattern of the first textbook sample is determined, the system matches rules sequentially from high to low priority; if there are multiple component composite deviations (e.g., both magenta and yellow are too dark), multiple adjustment suggestions are output and executed sequentially by the operator according to the priority order of R09. The thresholds (V_DL1, V_DL2, V_Da1, V_Da2, V_Db1, V_Db2) in the rule base can be preset according to the substrate type or defined by the user.

[0069] In some embodiments, the present invention also provides a method for determining the existence of intra-batch color difference drift risk, which can be achieved through the following steps: In step S150, during the process of generating a sequence map of comprehensive color difference values ​​based on the detection order of the textbook samples, a monotonicity detection algorithm based on a sliding window is started. Step S160: When the monotonicity detection algorithm determines that the comprehensive color difference value of M consecutive samples in the sequence image is monotonically increasing or decreasing, the output prompt information includes the risk of intra-batch color difference drift of the textbook sample, where M is an integer greater than or equal to 3.

[0070] During implementation, a sequence map of the comprehensive color difference value ΔE is generated according to the sample detection order; a monotonicity detection algorithm based on a sliding window is started, and when the ΔE value of M consecutive samples shows a monotonically increasing or decreasing trend, an "intra-batch color difference drift risk warning" is output, where M is an integer greater than or equal to 3.

[0071] In this embodiment of the invention, the static judgment logic of whether the ΔE of a single sample exceeds the standard is bypassed. By capturing the monotonic change trend of color difference of three or more consecutive samples through a sliding window, the hidden drift of continuous increase / decrease in color difference can be identified in advance when a single sample is still in the qualified range. This is earlier than the traditional single-sample threshold warning and is suitable for production scenarios with continuous paper feeding of large batches of textbooks.

[0072] In some embodiments, the present invention also provides a method for determining anomalies in the printing process of textbook samples, which can be achieved through the following steps: In step S170, during the process of generating a sequence diagram of comprehensive color difference values ​​based on the detection order of the textbook samples, a cumulative sum control diagram is generated based on the sequence diagram; Step S180: If the cumulative offset value of the textbook sample exceeds the preset offset threshold based on the cumulative control chart, output a prompt message including an abnormal warning of the printing process stability of the textbook sample.

[0073] During implementation, the cumulative sum (CUSUM) control chart of the ΔE sequence is calculated. When the cumulative offset value exceeds the preset control limit, a "process stability anomaly warning" is output. This trend analysis function assists quality inspection agencies in judging the changing trends of batch printing consistency. Even if the ΔE of all samples remains within the acceptable range, the system can detect potential "slow drift" risks through cumulative offset, achieving predictive quality monitoring. This function does not involve real-time equipment control.

[0074] In this embodiment, the Cumulative Sum Control Chart (CUSUM) amplifies subtle color shifts that persist throughout the textbook printing process into clear trend signals by accumulating the deviations of historical samples relative to a baseline. This allows for earlier detection of hidden process anomalies such as slow degradation of the ink supply system and gradual changes in paper ink absorption, significantly improving the sensitivity to early quality risk identification. By introducing the CUSUM control chart, not only can sudden deviations in individual samples be detected, but early warnings can also be provided for trend risks of "overall acceptable but continuous deviations," extending the scope from post-inspection to process monitoring.

[0075] In some embodiments, the colorimetric data, mean baseline, individual color difference values, judgment grades, deviation pattern diagnostic results, and corresponding process adjustment suggestions of all samples are summarized to generate a batch colorimetric consistency evaluation report. The report content includes, but is not limited to: total number of samples, batch mean baseline, ΔE and grade of each sample, batch ΔE mean and standard deviation, number and proportion of samples at each grade, color difference distribution histogram, overall batch consistency judgment conclusion, ΔE sequence diagram, and trend analysis results.

[0076] This approach integrates data across the entire supply chain, from original colorimetric sampling, baseline mean calculation, single-sample component differences, grading determination, deviation diagnosis, and process recommendations. All indicators can be traced back to the original color measurement records, meeting the quality compliance archiving requirements of the textbook printing industry. Through multi-dimensional statistics of batch ΔE mean, standard deviation, and the proportion of samples at each grade, it moves beyond the traditional, crude method of judging batch quality solely based on single-sample pass rates, accurately quantifying the color dispersion of the entire batch of textbooks. Accompanying color difference distribution histograms and ΔE sequence trend charts visually present the distribution pattern and temporal variation of color differences. Combined with previous sliding window monotonicity detection, CUSUM cumulative sum control chart analysis results, it can directly pinpoint the starting position of color difference drift within a batch, precisely corresponding to the printing press's operating period.

[0077] This invention can be embedded into the spectrophotometer software of educational material inspection institutions or into independent quality inspection systems to achieve automated and standardized batch color difference grading evaluation and intelligent diagnosis. It is applicable to the quality inspection of printed materials such as primary and secondary school textbooks, supplementary teaching materials, and extracurricular reading materials. The grading data and diagnostic results can be provided to the quality management system of printing companies through data interfaces (such as XML, JSON, or CIP4 / JDF formats) for offline traceability or process analysis.

[0078] The above-mentioned textbook printing diagnosis method based on component deviation will be described below with reference to specific embodiments. However, it should be noted that the specific embodiments are only for better illustrating the present invention and do not constitute an improper limitation of the present invention.

[0079] Example 1 (Cover paper): Taking the covers of a batch of primary and secondary school textbooks (printed on coated paper) as an example, N=20 copies were sampled, and the chromaticity values ​​of solid color blocks with uniform color on the covers were measured. The measuring equipment used was a 45° / 0° annular illumination spectrophotometer conforming to GB / T 18722. Each sample was measured three times at the same location, and the average value was taken. The deviation of the measurement points for all samples was controlled within 0.5mm.

[0080] Step S1: Obtain the L*, a*, and b* values ​​of 20 samples. No outliers were found after Grubbs spectroscopy. The calculated colorimetric benchmark for this batch is: L_avg=45.2, a_avg=38.7, b_avg=25.1.

[0081] Step S2: Calculate the overall color difference value ΔE and components DL, Da, and Db of each sample relative to the reference using the CIEDE2000 color difference formula ΔE*00.

[0082] Step S3: Using the coated paper threshold system, T1=0.50, T2=1.00, T3=1.50. The grading results are: 12 excellent, 5 good, 2 acceptable, and 1 warning.

[0083] For the unique warning-level sample (Volume 15), its ΔE = 1.82, DL = -1.20, Da = 0.35, and Db = -0.15. The system automatically performs deviation mode diagnosis: Lightness component: |DL|=1.20>V_DL2(1.0), judged as "severe lightness deviation mode"; Red-green component: |Da|=0.35≤V_Da1(0.5), judged as "normal red-green color"; Yellow-blue component: |Db|=0.15≤V_Db1(0.5), judged as "normal yellow-blue saturation".

[0084] Step S4: The system matches the rule base and outputs a graded maintenance suggestion: "The brightness is too dark. It is recommended to adjust the ink flow of the ink fountain or the printing pressure."

[0085] Example 2 (Text on offset paper): Taking a batch of textbooks (offset paper) as an example, N=25 books were sampled, and the specified solid color areas were measured. The ΔE*ab formula (compatible mode) was used, and the thresholds were based on the offset paper system: T1=0.80, T2=1.50, T3=2.00. The grading results were: 10 excellent books, 7 good books, 2 qualified books, and 1 warning book (the 8th book).

[0086] The warning sample has ΔE=2.45, DL=-1.65, Da=1.82, and Db=-0.12.

[0087] Deviation pattern diagnosis: Lightness component: |DL|=1.65, V_DL1(1.0)<1.65≤V_DL2(1.8), judged as "slight lightness drift mode"; Red-green components: Da=1.82>0 and |Da|=1.82>V_Da2(1.8), which is determined to be "magenta too dark mode"; Yellow-blue component: |Db|=0.12≤V_Db1(1.0), judged as "normal yellow-blue saturation".

[0088] The graded maintenance suggestions output in step S4 are: "Brightness drift, it is recommended to pay attention to ink supply stability"; "Magenta is too dark, it is recommended to reduce magenta ink amount".

[0089] Step S5: Generate the ΔE sequence plot. After sliding window monotonicity detection, no monotonic drift of 3 or more consecutive samples was found, and the CUSUM control chart did not trigger any abnormal warnings.

[0090] This invention uses the statistical mean of CIEL*, a*, and b* chromaticity values ​​of batch samples as a benchmark, calculates the comprehensive color difference value ΔE using the CIEDE2000 formula, and achieves a fine classification of excellent, good, acceptable, and warning levels through multi-level thresholds. Its core lies in automatically analyzing the component deviation patterns of the brightness difference DL, red-green difference Da, and yellow-blue difference Db for warning-level samples. For example, it identifies specific patterns such as "excessively dark magenta" or "severely excessive brightness," and then matches and outputs precise classification maintenance suggestions such as "reduce magenta ink content" from the process adjustment rule library, thus establishing an information loop from quality judgment to process diagnosis. Simultaneously, the robustness of the benchmark is ensured through Grubbs testing, and CUSUM control charts can optionally be used for statistical early warning of color difference drift within a batch. This invention upgrades color difference detection in teaching materials from simple judgment to intelligent diagnosis, significantly improving the refinement and intelligence of printing quality control. This invention not only achieves fine-grained classification of color difference levels through multi-level thresholds, but its core innovation lies in automatically analyzing the deviation patterns of three independent dimensions—lightness difference (DL), red-green difference (Da), and yellow-blue difference (Db)—only for warning-level samples with excessive color difference. Based on the absolute value and sign direction of each component, it identifies specific and interpretable printing fault patterns (such as "magenta too dark" or "severe lightness deviation"), and matches and outputs graded, actionable maintenance suggestions from a preset process adjustment rule library. This technical solution upgrades printing color difference detection from "simple judgment" to "intelligent diagnosis," filling a gap in existing technology.

[0091] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of the invention, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the invention. The sequence numbers of the above-described embodiments of the invention are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0092] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0093] In the several embodiments provided by this invention, it should be understood that the disclosed methods can be implemented in other ways. The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments. The features disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0094] The above description is merely an embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A textbook printing diagnostic method based on component deviation, characterized in that, include: The colorimetric standard for the target printing batch is determined based on the average brightness, average red-green hue, and average yellow-blue hue of N textbook samples in the target printing batch, where N is an integer greater than or equal to 5. Using the colorimetric detection benchmark as a reference, based on the brightness difference, red-green difference, and yellow-blue difference of each textbook sample, it is determined that the comprehensive color difference value of the first textbook sample is greater than the third color difference threshold. Here, the brightness difference is the difference between the brightness value and the average brightness value, the red-green difference is the difference between the red-green brightness value and the average red-green brightness value, and the yellow-blue difference is the difference between the yellow-blue brightness value and the average yellow-blue brightness value. The first brightness difference, the first red-green difference, and the first yellow-blue difference of the first textbook sample are compared with the corresponding brightness difference threshold, red-green difference threshold, and yellow-blue difference threshold, respectively, to determine the deviation pattern of the first textbook sample. For the aforementioned deviation pattern, matching and outputting printing adjustment suggestions for the first textbook sample from a preset process adjustment rule library.

2. The method as described in claim 1, characterized in that, The method further includes: Obtain the brightness, red-green saturation, and yellow-blue saturation values ​​of the N textbook samples at preset detection positions; Grubbs outlier detection is performed on the brightness value, the red-green value, and the yellow-blue value to remove outlier data and obtain the target brightness value, target red-green value, and target yellow-blue value. The average value of the target brightness is determined as the average brightness; the average value of the target red-green hue is determined as the average red-green hue; the average value of the target yellow-blue hue is determined as the average yellow-blue hue.

3. The method as described in claim 1, characterized in that, The step of determining, based on the brightness difference, red-green difference, and yellow-blue difference of each textbook sample and using the colorimetric detection benchmark as a reference, that the overall color difference value of the first textbook sample is greater than the third color difference threshold includes: Using the aforementioned colorimetric detection benchmark as a reference, the corresponding brightness difference, red-green difference, and yellow-blue difference are determined for each of the aforementioned textbook samples; The squares of the brightness difference, the red-green difference, and the yellow-blue difference are respectively summed to obtain a square sum value; The arithmetic square root of the sum of the squares is taken to obtain the comprehensive color difference value of each of the textbook samples; If it is determined that the overall color difference value of the first textbook sample is greater than the third color difference threshold, the first textbook sample is classified as a warning level.

4. The method as described in claim 3, characterized in that, The method further includes: If it is determined that the overall color difference value of the second textbook sample is less than or equal to the first color difference threshold, the second textbook sample is determined to be of the superior level; If it is determined that the overall color difference value of a third teaching material sample is greater than the first color difference threshold and less than or equal to the second color difference threshold, the third teaching material sample is determined to be of good quality. If it is determined that the overall color difference value of the fourth textbook sample is greater than the second color difference threshold and less than or equal to the third color difference threshold, the fourth textbook sample is determined to be of the qualified level.

5. The method as described in claim 1, characterized in that, The brightness difference threshold includes a first brightness difference threshold and a second brightness difference threshold, wherein the first brightness difference threshold is smaller than the second brightness difference threshold; comparing the first brightness difference value with the brightness difference threshold to determine the deviation pattern of the first teaching material sample includes: If the absolute value of the first brightness difference is greater than the second brightness difference threshold, the deviation mode is determined to be a severe brightness deviation mode. Correspondingly, the step of matching and outputting printing adjustment suggestions for the first textbook sample from a preset process adjustment rule library for the deviation pattern includes: For the aforementioned severe brightness deviation mode, the printing adjustment suggestions include: adjusting the ink flow rate of the ink fountain or the printing pressure; If the absolute value of the first brightness difference is greater than the first brightness difference threshold and less than the second brightness difference threshold, the deviation mode is determined to be a slight brightness drift mode. Correspondingly, the step of matching and outputting printing adjustment suggestions for the first textbook sample from a preset process adjustment rule library for the deviation pattern includes: For the aforementioned slight brightness drift pattern, the printing adjustment suggestions include: paying attention to ink supply stability.

6. The method as described in claim 1, characterized in that, The red-green difference threshold includes a first red-green difference threshold and a second red-green difference threshold, wherein the first red-green difference threshold is smaller than the second red-green difference threshold; comparing the first red-green difference value with the red-green difference threshold to determine the deviation pattern of the first textbook sample includes: If the first red-green difference value is greater than 0 and the absolute value of the first red-green difference value is greater than the second red-green difference threshold, then the deviation mode is determined to be the magenta over-dark mode. Correspondingly, the step of matching and outputting printing adjustment suggestions for the first textbook sample from a preset process adjustment rule library for the deviation pattern includes: For the aforementioned magenta over-dark mode, the printing adjustment suggestion output includes reducing the amount of magenta ink; If the first red-green difference is less than 0 and the absolute value of the first red-green difference is greater than the second red-green difference threshold, then the deviation mode is determined to be the cyan-too-dark mode. Correspondingly, the step of matching and outputting printing adjustment suggestions for the first textbook sample from a preset process adjustment rule library for the deviation pattern includes: For the aforementioned excessively dark cyan mode, the output printing adjustment suggestion includes reducing the amount of cyan ink; If the absolute value of the first red-green difference is greater than the first red-green difference threshold and less than or equal to the second red-green difference threshold, the deviation mode is determined to be the red-green balance drift mode. Correspondingly, the step of matching and outputting printing adjustment suggestions for the first textbook sample from a preset process adjustment rule library for the deviation pattern includes: For the aforementioned red-cyan balance drift mode, the output printing adjustment suggestions include paying attention to the balance of magenta and cyan ink amounts.

7. The method as described in claim 1, characterized in that, The yellow-blue difference threshold includes a first yellow-blue difference threshold and a second yellow-blue difference threshold, wherein the first yellow-blue difference threshold is smaller than the second yellow-blue difference threshold; comparing the first yellow-blue difference value with the yellow-blue difference threshold to determine the deviation pattern of the first teaching material sample includes: If the first yellow-blue difference is greater than 0 and the absolute value of the first yellow-blue difference is greater than the second yellow-blue difference threshold, then the deviation mode is determined to be the yellow-too-dark mode. Correspondingly, the step of matching and outputting printing adjustment suggestions for the first textbook sample from a preset process adjustment rule library for the deviation pattern includes: For the aforementioned excessively dark yellow pattern, the printing adjustment suggestions include reducing the amount of yellow ink. If the first yellow-blue difference is less than 0 and the absolute value of the first yellow-blue difference is greater than the second yellow-blue difference threshold, then the deviation mode is determined to be the blue-too-dark mode. Correspondingly, the step of matching and outputting printing adjustment suggestions for the first textbook sample from a preset process adjustment rule library for the deviation pattern includes: For the aforementioned blue-to-too-dark mode, the output printing adjustment suggestions include checking the amount of cyan ink and magenta ink; If the absolute value of the first yellow-blue difference is greater than the first yellow-blue difference threshold and less than or equal to the second yellow-blue difference threshold, the deviation mode is determined to be the yellow-blue balance drift mode. Correspondingly, the step of matching and outputting printing adjustment suggestions for the first textbook sample from a preset process adjustment rule library for the deviation pattern includes: For the aforementioned yellow-blue balance drift mode, the output printing adjustment suggestions include paying attention to the balance of yellow and cyan ink amounts.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: During the process of generating a sequence map of comprehensive color difference values ​​based on the detection order of the textbook samples, a monotonicity detection algorithm based on a sliding window is initiated. When the monotonicity detection algorithm determines that the overall color difference value of M consecutive samples in the sequence image is monotonically increasing or decreasing, the output prompt information includes the risk of intra-batch color difference drift of the textbook samples, where M is an integer greater than or equal to 3.

9. The method according to any one of claims 1 to 7, characterized in that, The method further includes: During the process of generating a sequence diagram of comprehensive color difference values ​​based on the detection order of the textbook samples, a cumulative sum control diagram is generated based on the sequence diagram. If the cumulative offset value of the textbook sample exceeds the preset offset threshold based on the cumulative control chart, the output prompt information includes an abnormal warning about the stability of the printing process of the textbook sample.

10. A textbook printing diagnostic system based on component deviation, characterized in that, The system includes: Memory, used to store one or more programs; When the one or more programs are executed by one or more processors, the one or more processors implement the method as described in any one of claims 1 to 9.

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