A method and system for high-precision determination of aluminum content in zinc liquid components

Abstract

The present application relates to the technical field of aluminum content determination, and specifically to a high-precision determination method and system for aluminum content in zinc liquid components, which specifically includes: analyzing the data fluctuation degree of each element near the corresponding wavelength in the spectral data, determining the weight of each spectral data according to the similarity of the data fluctuation degree of the aluminum element in the zinc liquid to be measured and other elements, and the peak characteristics of the characteristic peak corresponding to the characteristic spectral line of the aluminum element in the zinc liquid to be measured in the LIBS spectral data, and then performing weighted averaging on all the spectral data to obtain the spectral intensity at each wavelength, calculating the spectral intensity at each wavelength corresponding to a series of standard zinc liquid samples with known aluminum content, constructing a calibration curve, and determining the aluminum content in the zinc liquid to be measured, thereby reducing the possibility of increased errors when using the same weight to process all the collected LIBS spectral data in the traditional method, and improving the accuracy of the determination result of the aluminum content in the zinc liquid to be measured.

Description

Technical Field

[0001] The present application relates to the technical field of aluminum content determination, and in particular to a method and system for high-precision determination of aluminum content in zinc liquid components. Background Art

[0002] In the field of continuous hot-dip galvanizing production technology for strip steel, maintaining precise control over the composition of the zinc liquid during the production process is crucial to ensuring product quality and process stability. Therefore, the aluminum content in the zinc liquid must be accurately managed to form a high-quality coating on the surface of the strip steel. The online zinc liquid composition measurement equipment based on LIBS (Laser-induced breakdown spectroscopy) technology does not require special pretreatment of the sample, has a fast quantitative analysis speed, and can monitor the aluminum content in the zinc liquid in real time.

[0003] Since the sampling and acquisition time of LIBS spectral data is very short, generally not more than 1 millisecond in total, the fluctuations of the laser energy, delay time and distance from the lens to the sample surface of the data acquisition device often cause errors in the spectrum generation and signal acquisition, which in turn causes the spectral intensity in the LIBS spectral data of the zinc liquid to fluctuate and deviate from the actual value, making it difficult to accurately obtain the aluminum content in the zinc liquid. Therefore, the method of averaging the LIBS spectral data collected multiple times is usually adopted to reduce the influence of the interference errors caused by these fluctuations in a single measurement and improve the stability and reliability of the measurement results. However, this method usually uses the same weight to process all the collected LIBS spectral data, without considering the different influences of such fluctuations of the data acquisition device on the spectral intensity of the aluminum element in the zinc liquid in each collected LIBS spectral data, which results in the LIBS spectral data obtained by averaging being insufficient to accurately reflect the true content of aluminum in the zinc liquid. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and system for high-precision determination of aluminum content in zinc liquid components. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for high-precision determination of aluminum content in zinc liquid components, the method comprising the following steps:

[0006] Collect LIBS spectrum data of the zinc solution to be tested at each time, and record the set consisting of all spectrum data as spectrum data set;

[0007] Acquire the characteristic spectral line wavelength of any element in the zinc liquid to be tested, and for any spectral data in the spectral data set, construct the data fluctuation degree of any element in any spectral data based on the spectral intensity change near the characteristic spectral line wavelength in any spectral data;

[0008] In the spectral data at adjacent moments of any spectral data, a data fluctuation sequence of any element for any spectral data is constructed based on the data fluctuation degree in all spectral data; based on the similarity between the data fluctuation sequence of the aluminum element and the other elements in the zinc liquid to be tested, a fluctuation confidence of the aluminum element in any spectral data is constructed, and in combination with the data fluctuation degree, a fluctuation influence degree of the aluminum element in any spectral data is constructed;

[0009] Recording a characteristic peak formed by the aluminum element in any of the spectral data as a first characteristic peak, analyzing the peak protrusion of the first characteristic peak based on the wave peak characteristics of the first characteristic peak, determining the self-absorption influence of the aluminum element in any of the spectral data, and constructing the spectral data weight of the aluminum element in any of the spectral data in combination with the wave peak influence;

[0010] The spectral intensity at any wavelength of the average spectral data is constructed based on each spectral data in the spectral data set and the corresponding spectral data weight; a calibration curve of aluminum content is constructed based on the spectral intensity at any wavelength corresponding to a series of standard zinc liquid samples with known aluminum content, and the aluminum content in the zinc liquid to be tested is determined in combination with the spectral intensity at any wavelength of the average spectral data.

[0011] In one embodiment, the process of obtaining the data fluctuation degree is:

[0012] Spectral intensity data within a wavelength interval of a preset length centered on the characteristic spectral line wavelength of any element is obtained in any of the spectral data, all spectral intensity data within the wavelength interval are fit by a fitting algorithm, and the sum of the residuals in the fitting process is recorded as the data fluctuation degree of any of the elements in any of the spectral data.

[0013] In one embodiment, the process of acquiring the data fluctuation sequence is as follows:

[0014] Acquire a preset number of spectral data closest to the collection time of any spectral data from the spectral data set, and record the composed set as the local spectral data set of any spectral data;

[0015] A sequence consisting of the data fluctuation degrees of the any element in all spectral data in the local spectral data set is recorded as a data fluctuation sequence of the any element for the any spectral data.

[0016] In one embodiment, the process of obtaining the fluctuation confidence of the aluminum element in any spectral data is as follows:

[0017] The Pearson correlation coefficient between the data fluctuation series of the aluminum element and any of the spectral data of the other elements in the zinc liquid to be tested is calculated, and the normalized result of the mean value of all the Pearson correlation coefficients of the aluminum element is recorded as the fluctuation confidence of the aluminum element in any of the spectral data.

[0018] In one embodiment, the fluctuation influence degree of the aluminum element in any of the spectral data is: the product of the data fluctuation degree of the aluminum element in any of the spectral data and the fluctuation confidence.

[0019] In one embodiment, the process of obtaining the self-absorption influence of the aluminum element in any spectral data is as follows:

[0020] The peak width, peak value and wavelength corresponding to the peak of each characteristic peak in any of the spectral data are extracted using a characteristic peak recognition algorithm, and the characteristic peak with the smallest wavelength difference between the peak wavelength and the wavelength of the characteristic spectrum line of the aluminum element is taken as the first characteristic peak;

[0021] In the i-th spectral data, a neighborhood window of a preset size corresponding to the wavelength of the first characteristic peak is obtained, and the range of the spectral intensity in the neighborhood window is recorded as the peak protrusion degree of the first characteristic peak ;

[0022] The self-absorption influence of aluminum element in the i-th spectrum data is recorded as , The expression is:

[0023] , where represents the peak width of the first characteristic peak; Preset positive numbers for humans; is the Min-Max normalization function.

[0024] In one embodiment, the process of obtaining the spectral data weight is:

[0025] The average of the fluctuation influence degree and self-absorption influence degree of the aluminum element in the i-th spectrum data is recorded as the spectral comprehensive influence degree of the aluminum element in the i-th spectrum data ; The spectral data weight of the i-th spectral data about the aluminum element is recorded as , The expression is: , where Preset positive numbers for humans; is the Min-Max normalization function.

[0026] In one embodiment, the process of acquiring the spectral intensity at any wavelength of the average spectral data is:

[0027] The spectral intensity at the wavelength j of the average spectral data is recorded as , The expression is:

[0028] ,Mode middle, represents the spectral intensity of the i-th spectral data at wavelength j in the spectral data set; represents the spectral data weight of the i-th spectral data with respect to the aluminum element; Q represents the sum of the spectral data weights of all spectral data in the spectral data set; N represents the number of spectral data in the spectral data set;

[0029] The spectrum intensities at all wavelengths of the average spectrum data are arranged according to the arrangement order of the wavelengths in any spectrum data, and the constructed spectrum data is recorded as the average spectrum data.

[0030] In one embodiment, the process of determining the aluminum content in the zinc solution to be tested is:

[0031] The characteristic peak formed by the aluminum element in the average spectrum data is recorded as the second characteristic peak; the spectrum intensity corresponding to the peak value of the second characteristic peak is used as the comprehensive spectrum intensity of the characteristic spectrum line of the aluminum element in the zinc liquid to be tested during the data collection period;

[0032] Based on the LIBS spectral data of a series of standard zinc liquid samples with known aluminum content, the same acquisition method as that of the comprehensive spectral intensity of the aluminum element in the zinc liquid to be tested is adopted to respectively obtain the comprehensive spectral intensity of the aluminum element in each standard zinc liquid sample in the series of standard zinc liquid samples, and a coordinate system is constructed with the aluminum content of the series of standard zinc liquid samples and the comprehensive spectral intensity of the aluminum element as the horizontal and vertical coordinates, and curve fitting is performed on the data points in the coordinate system to obtain a calibration curve of the aluminum content; the aluminum content corresponding to the comprehensive spectral intensity of the aluminum element in the zinc liquid to be tested in the calibration curve is obtained as the aluminum content in the zinc liquid to be tested.

[0033] In a second aspect, an embodiment of the present application further provides a high-precision determination system for aluminum content in zinc liquid components, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0034] The embodiments of the present application have at least the following beneficial effects:

[0035] The present application collects LIBS spectral data of aluminum liquid to be tested, analyzes the data fluctuation degree of each element near the corresponding wavelength in the spectral data, and determines the fluctuation influence degree of aluminum element in any spectral data according to the similarity of the data fluctuation degree of aluminum element and other elements in the zinc liquid to be tested. The spectral data collection equipment is affected by laser energy, delay time, distance from lens to sample surface, etc. on the spectral data collection of aluminum element in the zinc liquid to be tested. The influence degree of spectral data can be more accurately evaluated by calculating the fluctuation influence degree. For the emission spectrum self-absorption phenomenon caused by the large variety of elements and large concentration differences contained in the zinc liquid to be tested, the characteristic peaks corresponding to the characteristic spectral lines of aluminum element in the zinc liquid to be tested in the LIBS spectral data are analyzed to construct the self-absorption influence degree, and more accurately evaluate the different influence degrees of the above-mentioned self-absorption phenomenon on the spectral intensity of aluminum element in each collected spectral data. The weight of each spectral data is determined according to the influence of the laser energy, delay time and self-absorption, and then all the spectral data are weighted and averaged to obtain the spectral intensity at each wavelength. The fluctuations of the laser energy, delay time and distance from the lens to the sample surface of the data acquisition equipment, as well as the self-absorption phenomenon of the emission spectrum caused by the large variety of elements and large concentration differences in the zinc liquid are taken into account. The different influences of the spectral intensity of the aluminum element in the zinc liquid to be tested in each LIBS spectral data collected are taken into account, which reduces the possibility of increased errors when all the collected LIBS spectral data are processed with the same weight in the traditional method, so that the LIBS spectral data obtained by weighted average can more accurately reflect the true content of aluminum in the zinc liquid; the spectral intensity at each wavelength corresponding to a series of standard zinc liquid samples with known aluminum content is further calculated, and a calibration curve is constructed to determine the aluminum content in the zinc liquid to be tested, thereby improving the accuracy of the determination result of the aluminum content in the zinc liquid to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 A flowchart of a method for high-precision determination of aluminum content in zinc liquid components provided in one embodiment of the present application;

[0038] Figure 2 Schematic diagram of the process of obtaining data fluctuation sequence;

[0039] Figure 3 Schematic diagram of the process for determining the aluminum content in the zinc liquid to be tested. DETAILED DESCRIPTION

[0040] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the method and system for high-precision determination of aluminum content in zinc liquid components proposed in the present application, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0041] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0042] The specific scheme of a high-precision determination method and system for aluminum content in zinc liquid provided by the present application is described in detail below with reference to the accompanying drawings.

[0043] See also Figure 1 , which shows a flow chart of the steps of a method for high-precision determination of aluminum content in zinc liquid components provided by an embodiment of the present application, the method comprising the following steps:

[0044] Step S1, collecting LIBS spectrum data of the zinc liquid to be tested at each time, and recording a set consisting of all spectrum data as a spectrum data set.

[0045] An online zinc liquid composition measurement device based on LIBS laser induced breakdown spectroscopy technology is used to collect LIBS spectrum data of the zinc liquid to be tested. The device is mainly composed of a probe, a sampling tube, a laser, and a spectrometer. The sampling tube of the device is inserted into the zinc liquid to be tested to collect LIBS spectrum data, wherein the sampling delay time of the spectrometer in the device is set to 1 , the detector integration time is 1.1ms, the laser energy is 65mJ, the repetition frequency is 20Hz, and the number of LIBS spectral data collected is 100. It should be noted that the settings of the sampling delay time, the detector integration time, the laser energy, the repetition frequency and the number of data collected can be set by the implementer according to the actual situation, and this application does not make specific restrictions. Among them, each spectral data collected is a spectral curve.

[0046] In the data processing unit, each collected LIBS spectral data is subjected to baseline correction processing to eliminate the interference of background noise, and a spectral data set P consisting of all spectral data after baseline correction processing is obtained, wherein baseline correction is a well-known technology, and the specific process is not repeated. To facilitate subsequent processing, the data collection time of the LIBS spectral data corresponding to the spectral data in the spectral data set P is used as the data collection time of the spectral data in the spectral data set P.

[0047] Step S2, obtaining the characteristic spectral line wavelength of any element in the zinc liquid to be tested, and for any spectral data in the spectral data set, constructing the data fluctuation degree of any element in any spectral data based on the spectral intensity change near the characteristic spectral line wavelength in any spectral data.

[0048] During the LIBS spectral data collection process, since the content of each component in the zinc liquid in the sampling tube usually does not change, if the spectral intensity of the aluminum element in a certain LIBS spectral data fluctuates due to the fluctuation of the laser energy, delay time and the distance from the lens to the sample surface of the data acquisition device, then this fluctuation will also affect the spectral intensity of the remaining elements in the zinc liquid in the LIBS spectral data. This is because the LIBS technology is based on the spectrum emitted by the plasma generated after the sample is irradiated by a high-energy laser pulse to perform elemental analysis. The plasma contains electrons, ions, atoms, molecules, etc., and the characteristic spectral lines emitted are consistent with the proportion of each element in the sample, so that any factors that affect the plasma characteristics, such as laser energy, delay time, and the distance from the lens to the sample surface, will affect the spectral intensity of all elements. Therefore, in order to accurately evaluate the different degrees of influence of this fluctuation of the data acquisition equipment on the spectral intensity of the aluminum element in the zinc liquid to be tested in each spectral data collected, the following processing is performed.

[0049] Specifically, the characteristic spectral line wavelengths of each element in the zinc liquid to be tested are obtained, taking the characteristic spectral line wavelength Al of the aluminum element in the zinc liquid to be tested as an example, and taking the i-th spectral data in the spectral data set P as For example, from the spectral data The spectrum data with the characteristic line wavelength Al as the center and the wavelength interval length of 3nm is intercepted , used for subsequent evaluation of the spectral data of aluminum in the zinc solution to be tested The degree of data fluctuation caused by the spectral intensity, wherein the wavelength interval length can be set by the implementer and is not specifically limited in this application.

[0050] The polynomial curve fitting algorithm based on the least squares method is used to fit all the spectral intensity data within the wavelength range. The sum of the residuals of the spectral intensities of all wavelengths in the fitting process is recorded as the data fluctuation degree of the aluminum element in the i-th spectral data. , which is used to characterize the data fluctuation degree of the spectral intensity of the aluminum element in the zinc liquid to be tested in the i-th spectral data, wherein the polynomial curve fitting algorithm based on the least squares method is a well-known technology, and the specific process is not repeated here. It should be noted that for the fitting of the spectral intensity data within the wavelength range, this application only provides a fitting method, and there are many existing fitting methods. The implementer can also use other fitting algorithms to fit the spectral intensity data within the wavelength range.

[0051] Step S3, in the spectral data at adjacent moments of any spectral data, construct a data fluctuation sequence of any element for any spectral data based on the data fluctuation degrees in all spectral data; construct a fluctuation confidence of the aluminum element in any spectral data based on the similarity between the data fluctuation sequences of the aluminum element and the remaining elements in the zinc liquid to be tested, and construct the fluctuation influence degree of the aluminum element in any spectral data in combination with the data fluctuation degree.

[0052] Get the spectral data from the spectral data set P The M spectral data adjacent to the data collection time are the most recent, wherein the M spectral data adjacent to the most recent include spectral data ; The obtained set of M spectral data is recorded as spectral data Local spectral data set , used to characterize the spectral data The set of all spectral data collected in the time period of the data collection moment is composed of, wherein, preferably, in one embodiment of the present application, the value of M is set to 10. As other embodiments of the present application, the implementer can set the value of M according to the actual situation.

[0053] Based on local spectral data collection Each spectral data is measured using the data fluctuation degree The same acquisition method is used to calculate the aluminum element in the local spectrum data set The degree of data fluctuation in each spectral data in The data fluctuation degree of all spectral data in the spectral data is arranged in ascending order according to the data collection time of the spectral data, and the data fluctuation sequence of the aluminum element for the i-th spectral data is obtained. , recorded as the i-th data fluctuation sequence of aluminum element, which is used to characterize the aluminum element in the zinc liquid to be tested in the spectral data The data fluctuation degree of the spectral intensity in the spectral data collected during the time period of the data collection moment changes with time.

[0054] Based on the characteristic spectral line wavelength of each element in the zinc solution to be tested and the spectral data set, the i-th data fluctuation sequence of the aluminum element is The same acquisition method is used to obtain the i-th data fluctuation sequence of each element in the zinc solution to be tested, where the characteristic spectral line wavelength Al is replaced by the characteristic spectral line wavelength of each element; the data fluctuation sequence is calculated respectively The Pearson correlation coefficient between the ith data fluctuation sequence of each element except aluminum in the zinc liquid to be tested is obtained. The Pearson correlation coefficient is a well-known technology and the specific process is not repeated here. The mean normalization result of all the obtained Pearson correlation coefficients is recorded as the aluminum element in the spectral data The volatility confidence in , used to characterize the aluminum element in the zinc solution to be tested in the spectral data The degree of data fluctuation in the spectral intensity of the spectral data collected at the time of data collection is the possibility of data fluctuation caused by fluctuations in the laser energy, delay time and distance from the lens to the sample surface of the data acquisition device. The aluminum element in the zinc liquid to be tested and the other elements in the spectral data The more similar the change trend of the data fluctuations of the spectral intensity in the spectral data collected within the time period of the data collection time, that is, the larger the mean value, the greater the possibility, that is, the fluctuation confidence The bigger.

[0055] Preferably, in the embodiment of the present application, the normalization method for the mean of all Pearson correlation coefficients is: calculate the calculation result of an exponential function with a natural constant as the base and the opposite number of the mean of all Pearson correlation coefficients as the exponent, and use the difference between the natural number 1 and the calculation result as the normalized value of the mean of all Pearson correlation coefficients. It should be noted that the present application only provides one normalization method, there are many existing normalization methods, and the implementer may also use other normalization methods for normalization, and the present application does not make specific restrictions.

[0056] The data fluctuation and volatility confidence The product of is recorded as the aluminum element in the spectral data The degree of impact of fluctuations in , which is used to characterize the fluctuation of laser energy, delay time and distance from lens to sample surface of data acquisition equipment on the spectral data of aluminum element in zinc liquid to be tested. The greater the product, the greater the influence, that is, the fluctuation influence The bigger.

[0057] Step S4, recording the characteristic peak formed by the aluminum element in any of the spectral data as the first characteristic peak, analyzing the peak protrusion of the first characteristic peak based on the peak characteristics of the first characteristic peak, determining the self-absorption influence of the aluminum element in any of the spectral data, and constructing the spectral data weight of the aluminum element in any of the spectral data in combination with the fluctuation influence degree.

[0058] Secondly, when the characteristic line of the aluminum element in the zinc liquid to be tested has a self-absorption phenomenon in the corresponding characteristic peak in the LIBS spectrum data, it usually causes the peak top of the characteristic peak to decrease and the peak width of the characteristic peak to increase, and the more serious the self-absorption phenomenon is, the greater the degree of decrease in the peak top of the characteristic peak and the increase in the peak width. Therefore, in order to accurately evaluate the different degrees of influence of the emission spectrum self-absorption phenomenon caused by the large variety of elements and large concentration differences contained in the zinc liquid to be tested on the spectrum intensity of the aluminum element in each collected spectrum data, the following processing is performed.

[0059] Specifically, the aluminum element and spectral data in the zinc solution to be tested As an example, the characteristic peak recognition algorithm is used to extract spectral data The peak width of each characteristic peak in the image and the spectral intensity and wavelength corresponding to the peak value of the characteristic peak are used to select the characteristic peak with the smallest wavelength difference between the peak wavelength and the characteristic spectral line wavelength Al. , used to characterize the characteristic spectral line of aluminum in the zinc liquid to be tested in the spectral data The characteristic peak formed in the process is recorded as the first characteristic peak, wherein the characteristic peak recognition algorithm is a well-known technology, and the specific process will not be repeated here.

[0060] It should be noted that, with respect to the acquisition of the characteristic peak of the aluminum element, the present application only provides a method for acquiring the characteristic peak of the aluminum element. There are many existing methods for acquiring the characteristic peak of the aluminum element, and the implementer may also adopt other methods to acquire the characteristic peak of the aluminum element, and the present application does not make any specific restrictions.

[0061] In the spectral data In the characteristic peak The wavelength corresponding to the peak value is taken as the center, and a wavelength window with a length of 1 nm is set. The length can be set by the implementer. The extreme difference of the spectral intensity in the wavelength window is recorded as the characteristic peak. The peak protrusion , used to characterize characteristic peaks The greater the range, the greater the degree to which the peak protrudes upward, that is, the peak protrusion degree The larger the value is, the larger the characteristic spectrum line of aluminum in the zinc liquid to be tested is in the spectrum data. The smaller the degree to which the peak top of the characteristic peak formed in the medium appears to be reduced.

[0062] Furthermore, the spectral data of aluminum element The self-absorption influence It is used to characterize the self-absorption phenomenon of emission spectrum caused by the large variety of elements and large concentration differences in the zinc solution to be tested, which affects the aluminum element in the spectral data. The influence of spectral intensity in:

[0063] , where The spectral data for aluminum The degree of self-absorption influence in , Characteristic peaks The peak width and peak protrusion; It is a positive number that is preset artificially to prevent the denominator from being 0; is the Min-Max normalization function. Preferably, in the embodiment of the present application, The value of is set to 0.01. As other embodiments of the present application, the implementer can set it according to the actual situation. The value of .

[0064] The characteristic spectral line of aluminum element in the zinc liquid to be tested is in the spectral data The greater the degree to which the peak top of the characteristic peak formed in the The larger the characteristic peak, the larger the peak width. The larger the value, the more serious the self-absorption phenomenon of the characteristic peak. The emission spectrum self-absorption phenomenon caused by the large variety of elements and large concentration differences contained in the zinc liquid to be tested has a great impact on the aluminum element in the spectral data. The greater the influence of the spectral intensity in the The bigger.

[0065] Furthermore, the impact of fluctuations Influence of self-absorption ) is recorded as the mean value of aluminum in the spectral data The comprehensive influence of the spectrum in It is used to characterize the fluctuation of laser energy, delay time and distance from lens to sample surface of data acquisition equipment, as well as the self-absorption of emission spectrum caused by the large variety and concentration difference of elements contained in zinc liquid to be tested, and the self-absorption of aluminum element in zinc liquid to be tested in spectral data. The comprehensive influence degree of the spectral intensity in the spectrum, the larger the mean value, the greater the comprehensive influence degree, that is, the comprehensive influence degree of the spectrum The bigger.

[0066] Furthermore, the spectral data Spectral data weight , used to characterize the spectral data in the spectral data set P when the spectral data are averaged. The weight coefficient is expressed as:

[0067] , where is the spectral data weight of the i-th spectral data about the aluminum element; Indicates the aluminum element in the spectral data The comprehensive influence of the spectrum in ; Preset positive numbers for humans; is the Min-Max normalization function.

[0068] In order to reduce the fluctuation of laser energy, delay time and distance from lens to sample surface of data acquisition equipment, as well as the self-absorption of emission spectrum caused by many kinds of elements and large concentration differences in zinc liquid to be tested, the spectral data with a greater influence on the spectral intensity of the spectral data in the spectral data set P of aluminum element in zinc liquid to be tested have a greater influence on the spectral data obtained by averaging the final spectral data. The comprehensive influence of the spectrum in The larger the value is, the smaller the spectral data is when the spectral data in the spectral data set P is averaged. The weight coefficient should be smaller, that is, the spectral data weight The smaller.

[0069] Step S5, constructing the spectral intensity at any wavelength of the average spectral data based on each spectral data in the spectral data set and the corresponding spectral data weight; constructing a calibration curve of aluminum content based on the spectral intensity at any wavelength corresponding to a series of standard zinc liquid samples with known aluminum content, and determining the aluminum content in the zinc liquid to be tested in combination with the spectral intensity at any wavelength of the average spectral data.

[0070] Based on each spectral data in the spectral data set P, the Spectral data weight The same acquisition method is used to calculate the spectral data weight of each spectral data in the spectral data set P, and the obtained spectral data weight is used as the weight coefficient of the spectral intensity of each spectral data to obtain the spectral intensity at the wavelength j of the average spectral data. The expression is:

[0071] , where represents the spectral intensity at wavelength j of the average spectral data, Represents the i-th spectral data in the spectral data set P Spectral intensity at wavelength j; represents the spectral data weight of the i-th spectral data about the aluminum element; Q represents the sum of the spectral data weights of all spectral data in the spectral data set P; N represents the number of spectral data in the spectral data set P. The spectral intensities at all wavelengths of the average spectral data are arranged according to the arrangement order of the wavelengths in any spectral data, and the resulting spectral data is recorded as the average spectral data PA.

[0072] The same acquisition method as the first characteristic peak is used to obtain the characteristic peak formed by the aluminum element in the average spectral data, which is recorded as the second characteristic peak; the spectral intensity corresponding to the peak value of the second characteristic peak is used as the comprehensive spectral intensity G of the characteristic spectral line of the aluminum element in the zinc liquid to be tested in the LIBS spectral data collected during the data acquisition period.

[0073] Based on the LIBS spectral data of a series of standard zinc liquid samples with known aluminum content, the same acquisition method as the comprehensive spectral intensity of the aluminum element in the zinc liquid to be tested is adopted to obtain the comprehensive spectral intensity of the aluminum element in each standard zinc liquid sample in the series of standard zinc liquid samples. Preferably, in the embodiment of the present application, the number of standard zinc liquid samples is set to 100. As other embodiments of the present application, the implementer can set the number of standard zinc liquid samples according to actual conditions. A coordinate system is constructed with the aluminum content and the comprehensive spectral intensity of the aluminum element of the series of standard zinc liquid samples as the horizontal and vertical coordinates, and a curve fitting is performed on the data points in the coordinate system using a fitting algorithm based on the least squares method to obtain a calibration curve for determining the aluminum content in the zinc liquid to be tested, wherein the curve fitting algorithm based on the least squares method is a well-known technology, and the specific process is not repeated here. The aluminum content corresponding to the obtained comprehensive spectral intensity G in the calibration curve is obtained as the aluminum content in the zinc liquid to be tested, and the high-precision determination of the aluminum content in the zinc liquid components is completed.

[0074] The schematic diagram of the data fluctuation sequence acquisition process is as follows: Figure 2 The schematic diagram of the process for determining the aluminum content in the zinc solution to be tested is shown in Figure 3 shown.

[0075] Based on the same inventive concept as the above method, an embodiment of the present application also provides a high-precision determination system for aluminum content in zinc liquid components, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods for high-precision determination of aluminum content in zinc liquid components when executing the computer program.

[0076] In summary, the embodiment of the present application provides a high-precision determination method for aluminum content in zinc liquid components, by collecting LIBS spectral data of the aluminum liquid to be tested, analyzing the data fluctuation degree of each element near the corresponding wavelength in the spectral data, and determining the fluctuation influence degree of the aluminum element in any spectral data according to the similarity between the data fluctuation degree of the aluminum element in the zinc liquid to be tested and other elements. The spectral data acquisition equipment is affected by the laser energy, delay time, distance from the lens to the sample surface, etc. on the spectral data acquisition of the aluminum element in the zinc liquid to be tested. The degree of influence of the spectral data can be more accurately evaluated by calculating the fluctuation influence degree. For the emission spectrum self-absorption phenomenon caused by the large variety of elements and large concentration differences contained in the zinc liquid to be tested, the characteristic peaks corresponding to the characteristic spectral lines of the aluminum element in the zinc liquid to be tested in the LIBS spectral data are analyzed to construct the self-absorption influence degree, and the above-mentioned self-absorption phenomenon on the spectral intensity of the aluminum element in each collected spectral data is more accurately evaluated. Different degrees of influence; the weight of each spectral data is determined by the fluctuation influence degree and the self-absorption influence degree, and then all the spectral data are weighted and averaged to obtain the spectral intensity at each wavelength, taking into account the fluctuations of the laser energy, delay time and distance from the lens to the sample surface of the data acquisition equipment, as well as the self-absorption phenomenon of the emission spectrum caused by the large variety of elements and large concentration differences contained in the zinc liquid, and the different degrees of influence of the spectral intensity of the aluminum element in the zinc liquid to be tested in each LIBS spectral data collected, reducing the possibility of increased errors when the traditional method uses the same weight to process all the collected LIBS spectral data, so that the LIBS spectral data obtained by weighted average can more accurately reflect the true content of aluminum in the zinc liquid; further calculate the spectral intensity at each wavelength corresponding to a series of standard zinc liquid samples with known aluminum content, construct a calibration curve, determine the aluminum content in the zinc liquid to be tested, and improve the accuracy of the determination result of the aluminum content in the zinc liquid to be tested.

[0077] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0079] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for high-precision determination of aluminum content in zinc liquid components, characterized in that: The method comprises the following steps: Collect LIBS spectrum data of the zinc solution to be tested at each time, and record the set consisting of all spectrum data as spectrum data set; Acquire the characteristic spectral line wavelength of any element in the zinc liquid to be tested, and for any spectral data in the spectral data set, construct the data fluctuation degree of any element in any spectral data based on the spectral intensity change near the characteristic spectral line wavelength in any spectral data; In the spectral data at adjacent moments of any spectral data, a data fluctuation sequence of any element for any spectral data is constructed based on the data fluctuation degree in all spectral data; based on the similarity between the data fluctuation sequence of the aluminum element and the other elements in the zinc liquid to be tested, a fluctuation confidence of the aluminum element in any spectral data is constructed, and in combination with the data fluctuation degree, a fluctuation influence degree of the aluminum element in any spectral data is constructed; Recording a characteristic peak formed by the aluminum element in any of the spectral data as a first characteristic peak, analyzing the peak protrusion of the first characteristic peak based on the wave peak characteristics of the first characteristic peak, determining the self-absorption influence of the aluminum element in any of the spectral data, and constructing the spectral data weight of the aluminum element in any of the spectral data in combination with the wave peak influence; The spectral intensity at any wavelength of the average spectral data is constructed based on each spectral data in the spectral data set and the corresponding spectral data weight; a calibration curve of aluminum content is constructed based on the spectral intensity at any wavelength corresponding to a series of standard zinc liquid samples with known aluminum content, and the aluminum content in the zinc liquid to be tested is determined in combination with the spectral intensity at any wavelength of the average spectral data.

2. A method for high-precision determination of aluminum content in zinc liquid components according to claim 1, characterized in that: The process of obtaining the data fluctuation degree is as follows: Spectral intensity data within a wavelength interval of a preset length centered on the characteristic spectral line wavelength of any element is obtained in any of the spectral data, all spectral intensity data within the wavelength interval are fit by a fitting algorithm, and the sum of the residuals in the fitting process is recorded as the data fluctuation degree of any of the elements in any of the spectral data.

3. The method for high-precision determination of aluminum content in zinc liquid components according to claim 1, characterized in that: The acquisition process of the data fluctuation sequence is as follows: Acquire a preset number of spectral data closest to the collection time of any spectral data from the spectral data set, and record the composed set as the local spectral data set of any spectral data; A sequence consisting of the data fluctuation degrees of the any element in all spectral data in the local spectral data set is recorded as a data fluctuation sequence of the any element for the any spectral data.

4. The method for high-precision determination of aluminum content in zinc liquid components according to claim 1, characterized in that: The process of obtaining the fluctuation confidence of the aluminum element in any spectral data is as follows: The Pearson correlation coefficient between the data fluctuation series of the aluminum element and any of the spectral data of the other elements in the zinc liquid to be tested is calculated, and the normalized result of the mean value of all the Pearson correlation coefficients of the aluminum element is recorded as the fluctuation confidence of the aluminum element in any of the spectral data.

5. The method for high-precision determination of aluminum content in zinc liquid components according to claim 1, characterized in that: The degree of influence of the fluctuation of the aluminum element in any of the spectral data is: the product of the data fluctuation degree of the aluminum element in any of the spectral data and the fluctuation confidence.

6. A method for high-precision determination of aluminum content in zinc liquid components as claimed in claim 1, characterized in that: The process of obtaining the self-absorption influence of the aluminum element in any of the spectral data is as follows: The peak width, peak value and wavelength corresponding to the peak of each characteristic peak in any of the spectral data are extracted using a characteristic peak recognition algorithm, and the characteristic peak with the smallest wavelength difference between the peak wavelength and the wavelength of the characteristic spectrum line of the aluminum element is taken as the first characteristic peak; In the i-th spectral data, a neighborhood window of a preset size corresponding to the wavelength of the first characteristic peak is obtained, and the range of the spectral intensity in the neighborhood window is recorded as the peak protrusion degree of the first characteristic peak ; The self-absorption influence of aluminum element in the i-th spectrum data is recorded as , The expression is: , where represents the peak width of the first characteristic peak; Preset positive numbers for humans; is the Min-Max normalization function.

7. A method for high-precision determination of aluminum content in zinc liquid components as claimed in claim 1, characterized in that: The process of obtaining the spectral data weight is as follows: The average of the fluctuation influence degree and self-absorption influence degree of the aluminum element in the i-th spectrum data is recorded as the spectral comprehensive influence degree of the aluminum element in the i-th spectrum data ; The spectral data weight of the i-th spectral data about the aluminum element is recorded as , The expression is: , where Preset positive numbers for humans; is the Min-Max normalization function.

8. A method for high-precision determination of aluminum content in zinc liquid components as claimed in claim 1, characterized in that: The process of obtaining the spectral intensity at any wavelength of the average spectral data is as follows: The spectral intensity at the wavelength j of the average spectral data is recorded as , The expression is: , where represents the spectral intensity of the i-th spectral data at wavelength j in the spectral data set; represents the spectrum data weight of the i-th spectrum data about the aluminum element; Q represents the sum of the spectral data weights of all spectral data in the spectral data set; N represents the number of spectral data in the spectral data set; The spectrum intensities at all wavelengths of the average spectrum data are arranged according to the arrangement order of the wavelengths in any spectrum data, and the constructed spectrum data is recorded as the average spectrum data.

9. A method for high-precision determination of aluminum content in zinc liquid components as claimed in claim 1, characterized in that: The determination process of the aluminum content in the zinc liquid to be measured is: The characteristic peak formed by the aluminum element in the average spectrum data is recorded as the second characteristic peak; the spectrum intensity corresponding to the peak value of the second characteristic peak is used as the comprehensive spectrum intensity of the characteristic spectrum line of the aluminum element in the zinc liquid to be tested during the data collection period; Based on the LIBS spectral data of a series of standard zinc liquid samples with known aluminum content, the same acquisition method as that of the comprehensive spectral intensity of the aluminum element in the zinc liquid to be tested is adopted to respectively obtain the comprehensive spectral intensity of the aluminum element in each standard zinc liquid sample in the series of standard zinc liquid samples, and a coordinate system is constructed with the aluminum content of the series of standard zinc liquid samples and the comprehensive spectral intensity of the aluminum element as the horizontal and vertical coordinates, and curve fitting is performed on the data points in the coordinate system to obtain a calibration curve of the aluminum content; the aluminum content corresponding to the comprehensive spectral intensity of the aluminum element in the zinc liquid to be tested in the calibration curve is obtained as the aluminum content in the zinc liquid to be tested.

10. A high-precision determination system for aluminum content in zinc liquid, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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