A copper-plated steel strip production quality management and control method

Abstract

The present application relates to the field of spectral analysis detection technology, disclose a kind of copper-plated steel strip production quality management and control method, including obtaining the XRF energy spectrum of copper-plated steel strip, carry out asymmetric peak shape fitting to Fe K alpha characteristic peak, obtain peak shape asymmetry parameter, calculate copper-iron complex intensity ratio;Construct substrate characteristic vector, substrate characteristic vector is transformed into substrate complex characteristic factor by principal component transformation matrix;Surface state index S is calculated;Calculate preliminary coating thickness;Preliminary coating thickness, substrate complex characteristic factor and surface state index S are input into nonlinear correction function, and coating thickness T is calculated;Nonlinear correction function includes a continuous weight function for adjusting the correction effect of surface state index S.The present application can carry out comprehensive nonlinear correction to various interference factors, improve the accuracy and environmental adaptability of XRF coating thickness detection, reduce the dependence on the consistency of substrate material and surface conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of spectral analysis detection technology, and particularly relates to a copper-plated steel strip production quality management method. BACKGROUND

[0002] X-ray fluorescence spectroscopy (XRF) has been widely used in industrial production lines due to its non-destructive, fast and non-contact sample advantages, especially in measuring the thickness of the surface coating of metal materials. Taking copper-plated steel strip as an example, the basic principle of X-ray fluorescence spectroscopy thickness measurement is to measure two characteristic X-ray signals from the coating and the substrate at the same time. As the copper coating becomes thicker, the copper signal will be enhanced; at the same time, the steel signal will be weakened because it is blocked by the copper layer. The intensity ratio of the two signals can very sensitively reflect the thickness change of the coating. By establishing the relationship between this ratio and the thickness, i.e. the calibration curve, the thickness of the coating can be accurately calculated. However, X-ray fluorescence spectroscopy thickness measurement relies on some idealized assumptions, such as the complete uniformity of the composition of the steel strip substrate and the perfect surface state. But in the actual production process, these ideal conditions are almost impossible to meet, which brings serious challenges to the accuracy and stability of the measurement.

[0003] In the real factory environment, the factors affecting the accuracy of X-ray fluorescence spectroscopy thickness measurement are complex. First of all, there is the problem of "matrix effect", that is, the steel strip as the substrate is not constant. The content of trace elements such as manganese and silicon in different batches of steel strip may be different. In addition, the physical properties of the steel strip such as cold rolling state and internal stress are also different. These changes will affect the absorption and scattering of X-rays by the steel strip, causing fluctuations in the iron signal as a measurement reference, which has nothing to do with the coating thickness, thus introducing systematic measurement errors. Secondly, the surface state of the steel strip is also an important influencing factor. The roughness of the surface, the residual rolling oil film, or a layer of passivation film will interfere with the propagation path and intensity of X-rays. This not only affects the characteristic signals from the elements, but also changes the background scattering signals, further affecting the accuracy of thickness calculation. The methods used in the prior art to compensate for these errors mostly still remain in simple linear correction, which is difficult to cope with the continuous and dynamic changes of the steel strip material and surface state in the production process, and the accuracy of the coating thickness detection is low. SUMMARY

[0004] The present application provides a copper-plated steel strip production quality management method to solve the problem of low accuracy of coating thickness detection in the prior art.

[0005] The copper-plated steel strip production quality management method of the present application comprises the following steps:

[0006] An XRF energy spectrum of the copper-plated steel strip is acquired, the XRF energy spectrum comprising Cu Kα and Cu Kβ characteristic peaks of a copper element, Fe Kα and Fe Kβ characteristic peaks of an iron element, characteristic peaks of at least two base trace elements, a coherent scattering peak and an incoherent scattering peak; an asymmetric peak shape fitting is performed on the Fe Kα characteristic peak to obtain a peak shape asymmetry parameter;

[0007] Based on the net intensities of the Cu Kα, Cu Kβ, Fe Kα and Fe Kβ characteristic peaks, the net intensities of the Cu Kβ and Fe Kβ characteristic peaks are weighted according to the signal-to-noise ratio of the Cu Kα characteristic peak and the Fe Kα characteristic peak, and a copper-iron composite intensity ratio is calculated; a base characteristic vector comprising an intensity ratio of the Fe Kα and base trace element characteristic peaks and the peak shape asymmetry parameter is constructed, and the base characteristic vector is transformed into a base composite characteristic factor through a principal component transformation matrix;

[0008] Based on the intensities of the coherent scattering peak and the incoherent scattering peak, a surface state index S is calculated; the copper-iron composite intensity ratio is used to calculate a preliminary coating thickness through a first calibration model; the preliminary coating thickness, the base composite characteristic factor and the surface state index S are input into a nonlinear correction function to calculate a coating thickness T; the nonlinear correction function comprises a continuous weight function for adjusting a correction effect of the surface state index S, and the continuous weight function monotonically changes within a preset effective threshold interval.

[0009] Preferably, the copper-iron composite intensity ratio calculated based on the net intensities of the Cu Kα, Cu Kβ, Fe Kα and Fe Kβ characteristic peaks and according to the signal-to-noise ratio of the Cu Kα characteristic peak and the Fe Kα characteristic peak includes: calculating the signal-to-noise ratios of the Cu Kα and Fe Kα characteristic peaks respectively and ; calculating the weighting coefficient of the Cu Kβ characteristic peak and the weighting coefficient of the Fe Kβ characteristic peak through the following formulas according to the signal-to-noise ratios of the Cu Kα characteristic peak and the Fe Kα characteristic peak and ;

[0010] ; ;

[0011] wherein, and are preset proportional constants, is the signal-to-noise ratio of the Cu Kα characteristic peak, is the signal-to-noise ratio of the Fe Kα characteristic peak;

[0012] The copper-iron composite intensity ratio is calculated through the following formula ;

[0013] ; wherein, is the net intensity of the Cu Kα characteristic peak, is the net intensity of the Cu Kβ characteristic peak, is the net intensity of the Fe Kα characteristic peak, is the net intensity of the Fe Kβ characteristic peak, is the weighting factor of the Cu Kβ characteristic peak, is the weighting factor of the Fe Kβ characteristic peak.

[0014] Preferably, the asymmetric peak fitting of the Fe Kα characteristic peak to obtain the peak asymmetry parameter comprises: fitting the Fe Kα characteristic peak by using a split Pearson VII function, the split Pearson VII function having a half-peak full width parameter and ; the peak asymmetry parameter is calculated by the following formula ;

[0015] ; wherein, is the half-peak full width parameter on the left side of the corresponding peak value, is the half-peak full width parameter on the right side of the corresponding peak value.

[0016] Preferably, the base trace elements are Mn and Cr.

[0017] Preferably, the base characteristic vector containing the intensity ratio of the Fe Kα characteristic peak to the base trace element characteristic peak and the peak asymmetry parameter is constructed by: calculating the intensity ratio of the Fe Kα characteristic peak to the Mn Kα and Cr Kα characteristic peaks, respectively and ;

[0018] The base characteristic vector is constructed ; wherein, is the peak asymmetry parameter.

[0019] Preferably, the base characteristic vector is transformed into a base composite characteristic factor by a principal component transformation matrix, comprising: multiplying the base characteristic vector V by a preset principal component transformation matrix to obtain the base composite characteristic factor .

[0020] Preferably, the surface state index S is calculated based on the intensity of the coherent scattering peak and the incoherent scattering peak, comprising: identifying the coherent scattering peak and the incoherent scattering peak of the X-ray tube target characteristic ray, calculating the net intensity of the coherent scattering peak and the net intensity of the incoherent scattering peak ; the surface state index S is calculated by the following formula: .

[0021] Preferably, the copper-iron composite strength ratio is used to calculate the preliminary coating thickness by a first calibration model, comprising: using the following exponential function as the first calibration model;

[0022] ; wherein a and b are model coefficients obtained by calibrating a plurality of standard samples with known thicknesses, is the preliminary coating thickness, is the copper-iron composite strength ratio.

[0023] Preferably, the preliminary coating thickness, the substrate composite characteristic factor and the surface state index S are input into a nonlinear correction function to calculate the coating thickness T, comprising: using the following formula as the nonlinear correction function:

[0024] ;

[0025] wherein, is the coating thickness, is the preliminary coating thickness, is the substrate composite characteristic factor, is a continuous weight function, , and is a preset correction coefficient;

[0026] ; wherein, is a function steepness coefficient, S is the surface state index, is a threshold center point.

[0027] Preferably, the XRF energy spectrum of the copper-plated steel strip is obtained, comprising: using an X-ray fluorescence spectrometer, irradiating the surface of the copper-plated steel strip with primary X-rays generated by an X-ray tube, receiving secondary fluorescent X-rays and scattered rays emitted by the copper-plated steel strip by a detector, and obtaining the XRF energy spectrum after processing by an analyzer.

[0028] The beneficial effects of the present application are: the present application overcomes the interference of the changes of the steel strip substrate chemical composition, physical state and surface roughness, oil film and other complex factors on the plating layer thickness measurement by comprehensively using various spectral information. By constructing a substrate characteristic vector containing the intensity ratio of trace elements of the substrate and the asymmetry parameter of the iron characteristic peak, and using principal component transformation to obtain a substrate composite characteristic factor, the compensation of the matrix effect is realized. At the same time, the surface state index is calculated by using coherent and incoherent scattering peaks to represent the surface state of the steel strip. The present application also obtains a more stable and reliable copper-iron composite intensity ratio by signal-to-noise ratio weighting of the Kα and Kβ spectral lines of copper and iron, which improves the robustness of the preliminary measurement. The preliminary thickness and the quantized substrate and surface influence factors are input into a nonlinear correction function with a built-in smoothing weight adjustment function, which can comprehensively correct various interference factors in a nonlinear manner, improve the accuracy and environmental adaptability of XRF plating layer thickness detection, and reduce the dependence on the consistency of the substrate material and surface conditions. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The flowchart of the copper-plated steel strip production quality control method provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0030] The embodiments of the present application will be described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0031] As shown in Figure 1 The copper-plated steel strip production quality control method provided by the embodiments of the present application specifically includes the following steps:

[0032] S1, obtaining the XRF energy spectrum of the copper-plated steel strip, the XRF energy spectrum containing Cu Kα and Cu Kβ characteristic peaks of copper element, Fe Kα and Fe Kβ characteristic peaks of iron element, characteristic peaks of at least two kinds of trace elements of the substrate, coherent scattering peaks and incoherent scattering peaks; performing asymmetric peak fitting on the Fe Kα characteristic peak to obtain the peak asymmetry parameter.

[0033] Specifically, using an X-ray fluorescence spectrometer, the surface of the copper-plated steel strip is irradiated by primary X-rays generated by an X-ray tube to excite the elements in the sample, and the secondary fluorescent X-rays and scattered rays emitted by the sample are received by a detector. After processing by a multichannel analyzer, an XRF energy spectrum with energy as the horizontal axis and count as the vertical axis is obtained. In the obtained XRF energy spectrum, the energy interval where the Fe Kα characteristic peak is located is selected, and an asymmetric Gaussian function or a function model containing an exponential decay tail is used to perform least squares fitting on the characteristic peak. The function model contains parameters such as peak position, peak height, and left and right half-peak width. After fitting, the parameter used to describe the asymmetry of the peak shape is extracted as the peak shape asymmetry parameter .

[0034] S2, based on the net intensities of Cu Kα, Cu Kβ, Fe Kα, and Fe Kβ characteristic peaks, the net intensities of Cu Kβ and Fe Kβ characteristic peaks are weighted according to the signal-to-noise ratio of Cu Kα characteristic peak and Fe Kα characteristic peak, and the copper-iron composite intensity ratio is calculated; a substrate characteristic vector containing the intensity ratio of Fe Kα and the characteristic peak of the trace element of the substrate and the peak shape asymmetry parameter is constructed, and the substrate characteristic vector is transformed into a substrate composite characteristic factor by a principal component transformation matrix.

[0035] Specifically, first, the Cu Kα, Cu Kβ, Fe Kα, and Fe Kβ characteristic peaks in the XRF energy spectrum are background-subtracted to obtain their respective net intensities 、 、 、 In an embodiment, background subtraction can be performed by selecting a background window on both sides of the peak, and then calculating and subtracting the background intensity under the peak by linear or nonlinear interpolation. The preset principal component transformation matrix is determined in advance by measuring a large number of bare steel substrate samples with different chemical compositions and physical states. The specific method is as follows: obtain the substrate characteristic vector of each sample, and then perform principal component analysis on the sample set composed of these vectors. The principal component transformation matrix selects the characteristic vector corresponding to the first principal component with the highest contribution rate.

[0036] S3, based on the intensities of the coherent scattering peak and the incoherent scattering peak, the surface state index S is calculated; and the copper-iron composite intensity ratio is used to calculate the preliminary coating thickness by a first calibration model.

[0037] Specifically, the positions of the coherent scattering peak and the incoherent scattering peak in the energy spectrum are determined, and their net intensities are calculated by background subtraction and .

[0038] S4, inputting the preliminary plating thickness, the substrate composite characteristic factor and the surface state index S into a non-linear correction function to calculate the plating thickness T; the non-linear correction function contains a continuous weight function for adjusting the correction effect of the surface state index S, and the continuous weight function monotonously changes within a preset effective threshold interval.

[0039] The design of the continuous weight function W(S) aims to dynamically adjust the correction strength according to the surface state of the copper-plated steel strip. When the surface state index S is within a preset threshold interval representing a good surface, the value of W(S) is very small, close to zero, which means that the correction effect can be ignored. However, when the S value exceeds this threshold interval, indicating an abnormal surface state, the value of W(S) monotonously increases, thereby smoothly enhancing the correction strength to ensure more accurate measurement results. The final non-linear correction function and all its parameters are determined by measuring a large number of samples covering different substrates, different surface states and different thicknesses, and performing multiple fitting.

[0040] In an optional embodiment, the copper-iron composite intensity ratio is calculated by weighting the net intensity of the Cu Kβ and Fe Kβ characteristic peaks according to the signal-to-noise ratio of the Cu Kα and Fe Kα characteristic peaks, including: calculating the signal-to-noise ratio of the Cu Kα and Fe Kα characteristic peaks respectively and ; calculating the weighting coefficient of the Cu Kβ characteristic peak and the weighting coefficient of the Fe Kβ characteristic peak according to the signal-to-noise ratio of the Cu Kα and Fe Kα characteristic peaks by the following formula

[0041] ; ;

[0042] wherein, and are preset proportional constants, is the signal-to-noise ratio of the Cu Kα characteristic peak, is the signal-to-noise ratio of the Fe Kα characteristic peak;

[0043] The copper-iron composite intensity ratio is calculated by the following formula ;

[0044] ; wherein, is the net intensity of the Cu Kα characteristic peak, is the net intensity of the Cu Kβ characteristic peak, is the net intensity of the Fe Kα characteristic peak, the net intensity of the Fe Kβ characteristic peak, the weighting coefficient of the Cu Kβ characteristic peak, the weighting coefficient of the Fe Kβ characteristic peak.

[0045] By introducing the signal-to-noise ratio of the Kα characteristic peak to dynamically weight the relatively weak Kβ characteristic peak, the stability and reliability of the measurement results are improved. When the signal quality of the main characteristic peak Cu Kα is good, that is, the signal-to-noise ratio is high, it means that the accompanying Cu Kβ signal is also relatively reliable, so a higher weight is given to make full use of the information; on the contrary, if the Cu Kα signal quality is poor, the weight of Cu Kβ is reduced to avoid introducing too much noise interference.

[0046] In order to measure the influence of the physical properties of the substrate material itself on the X-ray spectral line shape. In an optional embodiment, the Fe Kα characteristic peak is fitted with an asymmetric peak shape, and a peak shape asymmetry parameter is obtained, including: fitting the Fe Kα characteristic peak with a split Pearson VII function, the split Pearson VII function having a full width at half maximum parameter and ; the peak shape asymmetry parameter is calculated by the following formula ;

[0047] ; wherein, is the full width at half maximum parameter on the left side of the corresponding peak value, is the full width at half maximum parameter on the right side of the corresponding peak value.

[0048] The presence of multiple elements or different microstructures in alloy substrates such as steel can cause the characteristic peak of the main element Fe to be tailing or deformed, so that the characteristic peak is no longer a perfect symmetric peak. By using a split Pearson VII function that can describe the shape of both sides of the peak value, the asymmetry can be measured. The split Pearson VII function is an improvement based on the Pearson VII function, which divides the peak at the highest point into left and right halves, and each half is expressed by an independent Pearson VII function, allowing the width parameters and shape parameters on the left and right sides to be different.

[0049] For example, a steel plate substrate containing a specific alloy element is measured, and the Fe Kα characteristic peak shows a slight tailing phenomenon to the high energy side on the energy spectrum. After fitting by the spectral analysis software, the full width at half maximum parameter on the left side of the peak value is 0.15 kiloelectron-volt, and the full width at half maximum parameter on the right side is 0.18 kiloelectron-volt. According to the formula , the calculated peak shape asymmetry parameter is 1.2. The peak asymmetry parameter greater than 1 represents the degree of peak asymmetry characteristic of the substrate material.

[0050] In an alternative embodiment, the substrate characteristic vector comprising the intensity ratio of Fe Kα to the characteristic peak of the substrate trace element and the peak asymmetry parameter is transformed into a substrate composite characteristic factor by a principal component transformation matrix, including: the substrate trace element is Mn and Cr; the intensity ratio of the net intensity of Fe Kα characteristic peak to the net intensity of Mn Kα, Cr Kα characteristic peak is calculated respectively and ;

[0051] The substrate characteristic vector V is constructed ; wherein, is the peak asymmetry parameter;

[0052] The substrate characteristic vector V is multiplied by a preset principal component transformation matrix to obtain the substrate composite characteristic factor .

[0053] Combining multiple independent parameters reflecting the characteristics of the substrate into a single comprehensive factor can simplify the subsequent correction model. The content of trace elements manganese and chromium in the substrate, as well as the peak asymmetry caused by alloying effect, will affect the measurement accuracy of the coating thickness. Combining these information into a substrate characteristic vector, linear transformation by the transformation matrix obtained by principal component analysis can extract the most significant comprehensive variation information affecting the thickness measurement.

[0054] The substrate composite characteristic factor can comprehensively reflect the influence degree of the current steel strip substrate on the measurement. For example, assuming that in a measurement, the net intensity of Fe Kα is 120000 counts, the net intensity of Mn Kα is 2400 counts, the net intensity of Cr Kα is 3000 counts, and is 1.2. The intensity ratio is calculated to obtain is 50, is 40. Therefore, the constructed substrate characteristic vector V is [50, 40, 1.2]. If the preset principal component transformation matrix is the transpose matrix [0.6, 0.5, -0.8], then the substrate characteristic vector V is multiplied by the principal component transformation matrix to obtain the substrate composite characteristic factor is 49.04. represents the comprehensive influence degree of the current substrate on the measurement.

[0055] To measure the macroscopic physical state of a sample surface, such as roughness or the presence of low atomic number contaminants like oil, in an optional embodiment, the surface state index S is calculated based on the intensity of coherent and incoherent scattering peaks. This includes identifying the coherent and incoherent scattering peaks of the characteristic rays from the X-ray tube target and calculating the net intensity of the coherent scattering peaks. Net intensity of incoherent scattering peaks The surface condition index S is calculated using the following formula; .

[0056] The characteristic rays emitted by the X-ray tube are scattered at the sample surface. The intensity of coherent scattering is mainly related to the material's density and surface smoothness, while incoherent scattering is more sensitive to surface roughness and the presence of light elements. By calculating the intensity ratio of these two, an index that effectively reflects the sample surface quality can be obtained.

[0057] For example, when measuring a sample using an instrument with a rhodium target X-ray tube, a coherent scattering peak generated by rhodium target Kα rays can be identified in the energy spectrum, and its net intensity... The count was set to 8000. Simultaneously, an incoherent scattering peak was found in the corresponding low-energy region, and its net intensity was measured. The count is set to 1200. Then, the surface condition index S is calculated to be 0.15 according to the formula. If the surface of another sample is very rough, the S value will increase significantly. Therefore, the S value provides a valid basis for assessing whether the sample surface meets the standard.

[0058] In an optional embodiment, the step of calculating the preliminary coating thickness using the copper-iron composite strength ratio through a first calibration model includes: using the following exponential function as the first calibration model;

[0059] Where a and b are model coefficients obtained by calibrating multiple standard samples of known thickness. This is the initial coating thickness. This represents the strength ratio of the copper-iron composite.

[0060] Copper-iron composite strength ratio This reflects the relative magnitudes of the X-ray signals from the copper plating and the steel substrate. This ratio increases with increasing copper thickness, but the relationship is non-linear, typically following an exponential or logarithmic law. The initial calibration model was established beforehand by measuring a series of standard samples with known different plating thicknesses. For each standard sample, the copper-iron composite strength ratio was calculated. ,Will The values are function fitted, such as polynomial fitting or theoretical formula fitting based on X-ray physical attenuation law, with the corresponding known thickness values to obtain a function relationship In actual measurement, the newly calculated preliminary coating thickness is substituted into the function to obtain the preliminary coating thickness .

[0061] In order to eliminate the measurement error caused by the difference in the substrate and the surface state, in an optional embodiment, the preliminary coating thickness, the substrate composite characteristic factor and the surface state index S are input into a nonlinear correction function to calculate the coating thickness T, which comprises: using the following formula as the nonlinear correction function:

[0062] ;

[0063] wherein, T is the coating thickness, T0 is the preliminary coating thickness, is the substrate composite characteristic factor, is a continuous weight function, , and is a preset correction coefficient;

[0064] ; wherein, is a function steepness coefficient, S is the surface state index, is a threshold center point.

[0065] The nonlinear correction function is composed of two parts: one part is a linear correction term related to the substrate composite characteristic factor , which is used to compensate the influence of different alloy substrates on X-ray absorption and enhancement effect; the other part is a correction term related to the surface state index S, which is used to compensate the signal attenuation caused by surface roughness or contamination.

[0066] For example, assuming that the preliminary coating thickness T0 is 5.47 microns, the substrate composite characteristic factor is 49.04, and the surface state index S is 0.15. The preset correction coefficient is equal to -0.002, is equal to 0.1, is equal to 0.05. The parameter of the continuous weight function is equal to 20, If S equals 0.2, then w(S) is approximately equal to 0.269. A smaller w(S) indicates a good surface state, and the correction is small. The calculated coating thickness T is approximately 5.47 microns. If the value of S changes to 0.25, then w(S) will increase to 0.731, and the correction term associated with the surface state index S will have a greater impact, ensuring accurate thickness measurement results under various complex actual working conditions.

[0067] The implementation principle of the copper-plated steel strip production quality control method of the embodiment of the present application is as follows: the present application overcomes the interference of complex factors such as the chemical composition, physical state of the steel strip substrate, and surface roughness, oil film, etc. on the coating thickness measurement by comprehensively using various XRF spectral information. First, it constructs a substrate characteristic vector containing the intensity ratio of trace elements of the substrate and the asymmetry parameter of the iron characteristic peak, and obtains a substrate composite characteristic factor by using principal component transformation, thereby effectively compensating for the "matrix effect". Second, by calculating the intensity of the coherent scattering peak and the incoherent scattering peak, a surface state index S is obtained, which can represent the surface state of the steel strip. In addition, the signal-to-noise ratio of the Kα and Kβ spectral lines of copper and iron is weighted, thereby obtaining a more stable and reliable copper-iron composite intensity ratio, improving the robustness of the preliminary measurement. Finally, the preliminary thickness and the quantified substrate and surface influence factors are input into a nonlinear correction function with a built-in smoothing weight adjustment function, which can comprehensively correct various interference factors in a nonlinear manner, greatly improving the accuracy and environmental adaptability of XRF coating thickness detection, and reducing the dependence on the consistency of the substrate material and surface conditions.

[0068] Although the embodiments of the present application have been shown and described above, it should be understood that the above-described embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present application.

Claims

1. A method for controlling the production quality of copper-plated steel strip, characterized in that, The method comprises the following steps: XRF energy spectrum of the copper-plated steel strip is acquired, the XRF energy spectrum comprising Cu Kα and Cu Kβ characteristic peaks of the copper element, Fe Kα and Fe Kβ characteristic peaks of the iron element, characteristic peaks of at least two base trace elements, coherent scattering peaks and incoherent scattering peaks of characteristic rays of a target material of an X-ray tube; the Fe Kα characteristic peak is subjected to asymmetric peak shape fitting to obtain a peak shape asymmetry parameter; Based on net intensities of the Cu Kα, Cu Kβ, Fe Kα and Fe Kβ characteristic peaks, the net intensities of the Cu Kβ and Fe Kβ characteristic peaks are weighted according to a signal-to-noise ratio of the Cu Kα characteristic peak and the Fe Kα characteristic peak, and a copper-iron composite intensity ratio is calculated; a base characteristic vector comprising an intensity ratio of the Fe Kα and base trace element characteristic peaks and the peak shape asymmetry parameter is constructed, and the base characteristic vector is transformed into a base composite characteristic factor through a principal component transformation matrix; Based on the intensity of coherent scattering peak and non-coherent scattering peak of X-ray tube target characteristic ray, a surface state index S is calculated; a copper-iron composite intensity ratio is used to calculate a preliminary coating thickness by a first calibration model, including: using an exponential function as the first calibration model; ; wherein a and b are model coefficients obtained by calibrating a plurality of standard samples with known thicknesses, is the preliminary coating thickness, is the copper-iron composite intensity ratio; the preliminary coating thickness, the base composite characteristic factor, and the surface state index S are input into a nonlinear correction function to calculate a coating thickness T, including: using the following formula as the nonlinear correction function: ; wherein, is the plating layer thickness, is the substrate complex characteristic factor, is the continuous weight function, , and is the preset correction coefficient; ; wherein, is a function steepness coefficient, S is a surface state index, is a threshold center point; the nonlinear correction function contains a continuous weight function that adjusts the correction effect of the surface state index S, and the continuous weight function monotonically changes within a preset effective threshold interval.

2. The copper plated steel strip production quality management method according to claim 1, characterized in that, The net intensity based on Cu Kα, Cu Kβ, Fe Kα, Fe Kβ characteristic peaks is weighted according to the signal-to-noise ratio of Cu Kα characteristic peak and Fe Kα characteristic peak to obtain the copper-iron composite intensity ratio, including: calculating the signal-to-noise ratio of Cu Kα and Fe Kα characteristic peaks respectively and ; the weighted coefficients of Cu Kβ characteristic peak and Fe Kβ characteristic peak are calculated according to the signal-to-noise ratio of Cu Kα characteristic peak and Fe Kα characteristic peak through the following formula and ; ； ； wherein and is a preset proportionality constant, is a signal-to-noise ratio of the Cu Kα characteristic peak, is a signal-to-noise ratio of the Fe Kα characteristic peak; The copper-iron composite strength ratio is calculated by the following equation ; ; wherein Ic is the net intensity of the Cu Kα characteristic peak, Ic is the net intensity of the Cu Kβ characteristic peak, Ic is the net intensity of the Fe Kα characteristic peak, Ic is the net intensity of the Fe Kβ characteristic peak, Ic is the weighting factor for the Cu Kβ characteristic peak, Ic is the weighting factor for the Fe Kβ characteristic peak.

3. The production quality management method of the copper plated steel strip according to claim 1, characterized by, The Fe Kα characteristic peak is subjected to asymmetric peak shape fitting to obtain the peak shape asymmetry parameter, comprising: The Fe K a characteristic peak is fitted by using a split Pearson VII function, the split Pearson VII function having a full width at half maximum parameter and ; The peak shape asymmetry parameter is calculated by the following equation ; ; wherein, is a full width at half maximum parameter on the left side of the corresponding peak, is a full width at half maximum parameter on the right side of the corresponding peak.

4. The production quality management method of the copper plated steel strip according to claim 1, characterized by, The base trace elements are Mn and Cr.

5. The copper plated steel strip production quality management method according to claim 4, characterized in that, The base characteristic vector comprising the intensity ratio of the Fe Kα and base trace element characteristic peaks and the peak shape asymmetry parameter is constructed, comprising: The ratio of the net intensity of the Fe Kα characteristic peak to the net intensity of the Mn Kα and Cr Kα characteristic peaks is calculated respectively and ; constructing the substrate property vector ; wherein, is a peak shape asymmetry parameter.

6. The production quality management method of the copper plated steel strip according to claim 5, characterized in that, The base characteristic vector is transformed into the base composite characteristic factor through the principal component transformation matrix, comprising: The substrate property vector V is multiplied by a preset The substrate property vector V is multiplied by a preset .

7. The production quality management method of the copper plated steel strip according to claim 1, characterized by, The surface state index S is calculated based on intensities of the coherent scattering peaks and the incoherent scattering peaks, comprising: identifying coherent and incoherent scattering peaks of characteristic x-ray tube target material; calculating net intensity of coherent scattering peaks and net intensity of incoherent scattering peaks ; The surface state index S is calculated through the following formula: 。 8. The production quality management method of the copper plated steel strip according to claim 1, characterized by, The XRF energy spectrum of the copper-plated steel strip is acquired by using an X-ray fluorescence spectrometer, primary X-rays generated by an X-ray tube irradiate a surface of the copper-plated steel strip, secondary fluorescent X-rays and scattered rays emitted by the copper-plated steel strip are received by a detector, and the XRF energy spectrum is obtained after processing by an analyzer.

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