A method for detecting light elements

Abstract

The application discloses a light element detection method, comprising: acquiring a characteristic spectral line of a light element in a sample; searching for a characteristic peak of the light element from the characteristic spectral line, and performing element matching to obtain a first detection result of the light element; superimposing a spectral line model of a light element to be estimated, and outputting a second detection result of the light element when the superimposition result matches the characteristic spectral line; and calculating a final detection result of the light element according to the first detection result and the second detection result. The light element detection method reduces the detection limit of the light element, reduces the hardware cost, and improves the accuracy of light element detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of detection, in particular, the present application relates to a light element detection method. BACKGROUND

[0002] X-ray fluorescence spectroscopy (XRF) is an effective tool for elemental analysis, and its detection capability for different elements is different. Because the spectral line energy of the characteristic X-ray of heavy metal element is large, the difference with the background noise is large, so that the XRF technology can accurately analyze the heavy metal element qualitatively and quantitatively. Compared with heavy metal elements, the spectral line energy of the characteristic X-ray of light elements is small, which is easy to be submerged in the background noise, resulting in that the analysis of light elements has always been the short board of XRF technology.

[0003] For accurate detection of heavy elements, researchers in the prior art have made a series of improvements. In 2015, Li Fang et al. used wavelet transform method to denoise and smooth the characteristic spectral line of low content heavy metal element in XRF fluorescence spectrum, selected signal-to-noise ratio, mean square error and information entropy three indicators to evaluate the denoising effect, selected appropriate wavelet basis and decomposition layer, obtained an approximate background curve, and removed the background according to the principle of peak stripping, to realize the rapid stripping of spectral peak. It is shown that the wavelet transform (WT) method has certain feasibility for XRF fluorescence spectrum denoising and baseline correction processing.

[0004] In February 2018, Yixiang Xin et al. designed an X-ray fluorescence energy spectrum (EDXRF) spectral analysis software, selected EDXRF spectrometer with 60kV, tube flow 130mA and W filter to detect cadmium element in rice powder sample, and the measurement time was 600s. In the spectral analysis, the advantages and disadvantages of a variety of algorithms were compared horizontally, which showed that wavelet transform, data fitting, derivative analysis and area calculation were important methods for EDXRF spectral analysis.

[0005] In February 2022, Chen Wei et al. aimed at the problem of extracting quantitative information of trace nuclide characteristic peak affected by high background, based on the research on digital filter, proposed a multiple digital filter spectral processing technology. The technology uses the independently developed EDXRF type X fluorescence analyzer to measure the spectral line of 0.19mg·kg -1 of cadmium rice standard sample, and respectively does multiple Sallen-Key (Multiple Sallen-Key, MSK) smoothing and multiple Gaussian smoothing on the spectral line. The results show that under the same smoothing effect, the MSK smoothing frequency is less, and the code execution efficiency is higher. By comparing with the cadmium element content in the rice standard sample measured by graphite furnace atomic absorption spectrometry, the relative error between the cadmium element content after MSK smoothing processing and the standard content value is reduced, and the measurement precision of the sample content value is improved.

[0006] In January 2022, Wang Xueyuan et al. studied an X-ray fluorescence overlapping peak decomposition method based on a multi-adaptive quantum genetic algorithm to solve the problem of X-ray fluorescence overlapping peaks. The traditional quantum genetic algorithm and the improved quantum genetic algorithm were used to analyze the experimental spectrum of a lead brass standard sample (Cu: 62.83%, Zn: 33.56%, Pb: 2.30%, Fe: 0.16%, Sn: 0.33%, Ni: 0.31%, Mn: 0.12%). The results showed that the improved quantum genetic algorithm was better than the traditional quantum genetic algorithm in decomposing overlapping peaks, and this method was suitable for the decomposition of severely overlapping spectral peaks.

[0007] However, in terms of improving the detection accuracy of light elements, many current technologies and researches focus on improving the hardware aspects such as instrument design and performance. High-power, large-current X-ray generators, end-window, ultra-sharp, ultra-Be window X-ray tubes, artificial thin film spectral devices, and ultra-thin detector windows have been developed to improve the excitation and detection performance of light elements. The improvement of hardware can reduce the detection limit of specific elements, but also increases the detection cost and has certain limitations.

[0008] With the rapid development of signal processing theory, it is possible to use filtering and fusion methods to solve the shortcomings of XRF technology in light element analysis. However, the characteristics of light elements and the constraints of detectors make the XRF spectrum have a large number of spectral line overlaps in the light element part, and it is easy to be submerged in the background and noise. There are difficulties in finding characteristic peaks, and it is difficult to quantitatively analyze. SUMMARY

[0009] The present application provides a light element detection method to solve the problem of difficulty in finding characteristic peaks and difficulty in quantitative analysis during light element detection.

[0010] In order to at least solve one or more of the above-mentioned technical problems, the present application provides the following technical solution: a light element detection method, comprising:

[0011] Obtaining the characteristic spectral line of the light element in the sample;

[0012] Finding the characteristic peak of the light element from the characteristic spectral line, and performing element matching to obtain the first detection result of the light element;

[0013] Superimposing the spectral line model of the estimated light element, and outputting the second detection result of the light element when the superimposition result matches the characteristic spectral line;

[0014] Calculating the final detection result of the light element according to the first detection result and the second detection result.

[0015] In one embodiment, the characteristic peaks of light elements are searched from the characteristic spectrum, and element matching is performed to obtain a first detection result of the light elements, including: searching for characteristic peak spectrum of light elements from the characteristic spectrum, the characteristic peak spectrum including a plurality of characteristic peaks; performing characteristic peak fitting on each of the found characteristic peaks, and performing element matching on the fitted characteristic peaks to obtain the first detection result of the light elements.

[0016] In one embodiment, the characteristic peak fitting is performed on each of the found characteristic peaks, and element matching is performed on the fitted characteristic peaks to obtain the first detection result of the light elements, including: searching for a maximum peak in the characteristic peak spectrum; performing characteristic peak fitting on the found maximum peak, and performing element matching according to the fitting result to obtain a first sub-detection result of the light element corresponding to the maximum peak; subtracting the fitting result from the characteristic peak spectrum to obtain a subtracted characteristic peak spectrum, and performing the step of searching for a maximum peak in the characteristic peak spectrum on the subtracted characteristic peak spectrum, and the step is executed in a loop until there is no characteristic peak in the subtracted characteristic peak spectrum, and all the obtained first sub-detection results are taken as the first detection result.

[0017] In one embodiment, when the subtracted characteristic peak spectrum does not conform to a Gaussian distribution, the subtracted characteristic peak spectrum is supplemented to obtain a characteristic peak spectrum conforming to the Gaussian distribution.

[0018] In one embodiment, the characteristic peak fitting is performed on the found maximum peak, and element matching is performed according to the fitting result to obtain a first sub-detection result of the light element corresponding to the maximum peak, including: searching for a maximum peak in the characteristic peak spectrum; performing characteristic peak fitting on the found maximum peak; when the fitting result includes Gaussian distribution spectrums of a plurality of light elements, taking the kind and content of the light element corresponding to the Gaussian distribution spectrum with the largest peak value as the first sub-detection result of the light element corresponding to the maximum peak; and subtracting the fitting result from the characteristic peak spectrum, including: subtracting the Gaussian distribution spectrum with the largest peak value from the characteristic peak spectrum.

[0019] In one embodiment, the method adopted for searching for the characteristic peaks of light elements from the characteristic spectrum is a second derivative peak searching method.

[0020] In one embodiment, the model of the spectrum of the light element is superimposed, and when the superimposed result matches the characteristic spectrum, a second detection result of the light element is output, including: estimating the kind of the light element; obtaining a light element spectrum model corresponding to the estimated kind of the light element from an element characteristic spectrum database, superimposing the obtained light element spectrum model by using an optimization algorithm, and outputting the second detection result of the light element when the superimposed model matches the characteristic spectrum.

[0021] In one embodiment, the spectrum line model of the estimated light element is superimposed, and when the superimposed result matches the characteristic spectrum line, the second detection result of the light element is output. Further comprising: when the superimposed model cannot match the characteristic spectrum line, adjusting the type of the estimated light element, and continuing to perform the steps of obtaining the light element spectrum line model corresponding to the type of the adjusted estimated light element from the element characteristic spectrum database, and using the optimization algorithm to perform model superposition on the obtained light element spectrum line model, until the superimposed model matches the characteristic spectrum line, and outputting the second detection result of the light element.

[0022] In one embodiment, the optimization algorithm is a genetic algorithm.

[0023] In one embodiment, the final detection result of the light element is calculated according to the first detection result and the second detection result, including: comparing the first detection result and the second detection result, and determining the final detection result of the light element according to the comparison result.

[0024] In one embodiment, when the comparison result shows that the first detection result and the second detection result have an intersection, the intersection content is taken as the final detection result of the light element.

[0025] In one embodiment, when the comparison result shows that the first detection result and the second detection result have no intersection, the first detection result is taken as the final detection result of the light element.

[0026] In one embodiment, the characteristic spectrum line of the light element in the sample is obtained, including: obtaining the original spectrum line of the sample; denoising the original spectrum line to filter out external noise in the original spectrum line to obtain the sample spectrum line of the sample itself; and subtracting the background signal from the sample spectrum line to obtain the characteristic spectrum line of the light element.

[0027] In one embodiment, the original spectrum line is denoised to filter out external noise in the original spectrum line to obtain the sample spectrum line of the sample itself, including: wavelet decomposing the original spectrum line to project the original spectrum line to a subspace at different frequencies to obtain detail coefficients and approximation coefficients at each decomposition scale; thresholding the detail coefficients in each subspace, and wavelet reconstructing according to the approximation coefficients and the thresholded detail coefficients to obtain the sample spectrum line of the sample itself.

[0028] In one embodiment, the background signal is subtracted from the sample spectrum line to obtain the characteristic spectrum line of the light element, including:

[0029] determining the mother wavelet, the decomposition scale, and the iteration number, and setting the initial value of the iteration counter to 1;

[0030] set the sample spectrum line as an input signal;

[0031] perform N-layer wavelet decomposition on the input signal based on the mother wavelet and the decomposition scale, and select the approximation coefficient of the Nth layer for thresholding processing;

[0032] reconstruct the thresholded approximation coefficient of the Nth layer to obtain a background estimation signal consistent with the signal length of the sample spectrum line, and the value of the iteration counter is +1;

[0033] set the background estimation signal as an input signal, perform the step of performing N-layer wavelet decomposition on the input signal, and select the approximation coefficient of the Nth layer for thresholding processing, and perform the step in a loop until the value of the iteration counter is equal to the iteration number;

[0034] subtract the last obtained background estimation signal from the sample spectrum line signal to obtain a characteristic spectrum line of light elements.

[0035] Through the light element detection method provided above, the characteristic spectrum line of the light element in the sample is first obtained. On the one hand, the characteristic peak of the light element is searched from the characteristic spectrum line, and element matching is performed to obtain a first detection result of the light element. On the other hand, the spectrum line model of the estimated light element is superimposed, and when the superimposed result matches the characteristic spectrum line, a second detection result of the light element is output. The detection results obtained based on the two aspects are further calculated in the present application, and the results of the types and contents of the light elements are more accurate, the detection limit of the light elements is reduced, and the hardware cost is also reduced. BRIEF DESCRIPTION OF DRAWINGS

[0036] The above and other objects, features and advantages of the example embodiments of the present application will be more apparent from the following detailed description read in conjunction with the accompanying drawings, in which several embodiments of the present application are shown by way of example, and wherein the same or corresponding elements are referred to by the same or corresponding reference numerals, in which:

[0037] Figure 1 a flowchart of a light element detection method of one embodiment of the present application is shown;

[0038] Figure 2 a flowchart of a light element detection method of another embodiment of the present application is shown;

[0039] Figure 3 a flowchart of a light element detection method of another embodiment of the present application is shown; Figure 2 a flowchart of a light element detection method of another embodiment of the present application is shown;

[0040] Figure 4 a flowchart of a light element detection method of another embodiment of the present application is shown; Figure 2 a flowchart of a light element detection method of another embodiment of the present application is shown. Detailed Implementation

[0041] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] It should be understood that the terms "comprising" and "including" used in the specification and claims of this application indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0043] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0044] The specific embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0045] Figure 1 A schematic flowchart illustrating the light element detection method 100 applicable to this application is shown. Figure 1 As shown, in step S110, characteristic spectral lines of light elements in the sample are acquired. In one embodiment, the aforementioned characteristic spectral lines can be acquired using an XRF fluorescence spectrometer, and the light elements are elements with atomic numbers less than 20 in the periodic table. Specifically, the aforementioned sample can be soil.

[0046] Based on the aforementioned characteristic spectral lines, in step S120, characteristic peaks of light elements are searched from the aforementioned characteristic spectral lines, and elemental matching is performed to obtain the first detection result of the light elements. This first detection result is used to characterize the type and content of light elements in the sample. That is, the first detection result is the first type and first content of the obtained light elements.

[0047] In step S130, the spectral line models of the estimated light elements are superimposed. When the superposition result matches the aforementioned characteristic spectral lines, a second detection result of the light elements is output. This second detection result is used to characterize the type and content of light elements in the sample. In other words, the second detection result represents the second type and second content of the obtained light elements.

[0048] In step S140, the final detection result of the light elements is calculated based on the aforementioned first detection result and the aforementioned second detection result. That is, the final type and final content of the light elements are obtained based on the first type and first content, second type and second content of the light elements obtained above. This application calculates the type and content of light elements in the sample from two aspects, reducing the error of traditional light element characteristic peak fitting and extraction, and improving the accuracy of XRF detection of light elements.

[0049] Figure 2 A schematic flowchart of another method 200 for detecting light elements according to this application is shown. It should be noted that... Figure 2 Steps S210 to S230 in method 200 shown can be understood as a combination of the preceding text. Figure 1 An example of an application of step S110 in the described method 100, Figure 2 Step S240 in method 200 shown can be understood as a combination of the preceding text. Figure 1 An example of an application of step S120 in the described method 100, Figure 2 Step S250 in method 200 shown can be understood as a combination of the preceding text. Figure 1 An example of an application of step S130 in the described method 100. Figure 2 Steps S260 to S270 in method 200 shown can be understood as a combination of the preceding text. Figure 1 An application example of step S140 in the described method 100. Therefore, in conjunction with the preceding text... Figure 1 The description of the method for detecting light elements also applies to the following text.

[0050] like Figure 2 As shown, in step S210, sample data is read to obtain the original spectral lines of the sample. In one application scenario, when the sample is soil, the original spectral lines of the sample include the spectral lines corresponding to all elements in the soil. In step S220, the original spectral lines are denoised to remove external noise and obtain the sample spectral lines of the sample itself.

[0051] In one implementation method Figure 3 Schematic illustration Figure 2 The flowchart of the denoising process in the detection method shown is illustrated. Figure 3The method 300 shown can be understood as an application of the foregoing Figure 2 An application example of step S220 in the method 200 described. In one application scenario, the original spectrum is denoised to filter out external noise in the original spectrum to obtain the sample spectrum of the sample itself, such as Figure 3 As shown, it includes: at step S310, wavelet decomposition is performed on the original spectrum of the sample, i.e., the original signal f(t), to project the foregoing original spectrum into a subspace at different frequencies, which includes an approximation space and a detail space, to obtain detail coefficients and approximation coefficients at each decomposition scale. Specifically, in the wavelet decomposition, the wavelet basis is preferably haar, and the decomposition layer is three. At step S320, the detail coefficients Wf(u, s) at each of the foregoing subspaces are thresholded to obtain the thresholded detail coefficients Wf'(u, s). The wavelet basis and the threshold are selected according to the number of data burrs, whether the processed data will exist peak distortion, peak shift, and whether the fluorescence technology will change greatly. Preferably, the thresholding process adopts a soft threshold. At step S330, wavelet reconstruction is performed according to the approximation coefficients and the thresholded detail coefficients to obtain the sample spectrum of the sample itself, achieving the purpose of denoising, i.e., obtaining the denoised signal f'(t). In one application scenario, the noise in the original spectrum can be device noise or environmental noise.

[0052] Based on the denoised sample spectrum of the sample itself obtained in the foregoing S220, at step S230, the foregoing sample spectrum of the sample itself is subjected to background signal subtraction to obtain the characteristic spectrum of the light element in the sample.

[0053] In one embodiment, Figure 4 The detection method is schematically shown Figure 2 The flowchart of the background signal subtraction process in the detection method is shown, Figure 4 The method 400 shown can be understood as an application of the foregoing Figure 2 An application example of step S230 in the method 200 described. In one application scenario, because the actually collected spectrum signal is superimposed on a certain background signal, the fluorescence count of each channel is increased. The background signal is relatively smoother and flatter than the true spectrum signal. According to the principle of wavelet multi-resolution analysis, the background signal corresponds to the approximation component at a higher scale level in the wavelet domain. Direct reconstruction of the approximation component at a higher scale level can obtain the background estimation signal. Subtracting the background estimation signal from the original signal can achieve the subtraction of the background signal. Specifically, as shown Figure 4 As shown, the background subtraction is performed on the foregoing sample spectrum to obtain the characteristic spectrum of the light element, including the following steps:

[0054] Step S410 parameter setting: determine the mother wavelet, the decomposition scale, the iteration number Num, and set the initial value of the iteration counter Count as 1; specifically, the optimal wavelet basis is selected according to the maximum peak background ratio of the background-subtracted spectrum, and preferably the wavelet basis is selected as sym4 and Meyer.

[0055] Step S420 obtaining the denoised signal: setting the sample spectrum as the input signal, that is, assigning the denoised signal S_DeNoised to the input signal S_In. That is, the denoised signal f'(t) is taken as the input signal.

[0056] Step S430 wavelet decomposition: based on the mother wavelet and the decomposition scale, the input signal S_In is subjected to N-layer wavelet decomposition.

[0057] Step S440 thresholding processing: selecting the approximation coefficients of the Nth layer for thresholding processing.

[0058] Step S450 signal reconstruction: reconstructing the approximation coefficients of the Nth layer after thresholding processing to obtain a background estimation signal S_BackGround consistent with the signal length of the sample spectrum, and the value of the iteration counter is +1. Specifically, reconstruction refers to the wavelet reconstruction process, and each scale coefficient is processed according to the threshold selection rule. Since the background signal corresponds to the approximation component of a high level scale, the Nth layer coefficient is generally set to 0, and then each processed coefficient is reconstructed according to the decomposition rule to synthesize the original signal.

[0059] Step S460 judgment execution: setting the background estimation signal as the input signal, executing the steps of N-layer wavelet decomposition on the input signal, selecting the approximation coefficients of the Nth layer for thresholding processing, and cyclically executing until the value of the iteration counter Count is equal to the iteration number Num; specifically, it is judged whether the value of the iteration counter Count is equal to the iteration number Num. If they are equal, the process ends and goes to step S470. Otherwise, S_BackGround is assigned to S_In, Count is incremented by 1, and the process returns to step S430.

[0060] S470 background estimation signal: obtaining the background estimation signal, and judging whether it conforms to the distribution of the signal in the real environment according to experience, and subtracting the last obtained background estimation signal from the sample spectrum signal;

[0061] S480: obtaining the characteristic spectrum of light elements.

[0062] Based on the characteristic spectrum of the light element obtained at the foregoing step S230, at step S240, a characteristic peak of the light element is searched from the characteristic spectrum of the light element in the sample, and element matching is performed to obtain a first detection result of the light element. Step S240 is a kind of element type and content analysis according to the characteristic spectrum actually detected, which can also be called a forward calculation process.

[0063] In one embodiment, step S240 can include searching for a characteristic peak spectrum of the light element from the characteristic spectrum of the light element in the foregoing sample, the characteristic peak spectrum including a plurality of characteristic peaks; performing characteristic peak fitting on each characteristic peak found, and performing element matching on the fitted characteristic peak to obtain a first detection result of the light element, which is used to characterize a first type and a first content of the light element in the sample. According to theoretical analysis or experience, the element type is determined according to the position of the characteristic peak, and the element content is determined by using the characteristic peak area to establish a relationship curve with the light element content in the known sample. Therefore, the corresponding position of the fitted characteristic peak can determine the element type, and the characteristic peak area can determine the element content. In a specific application scenario, for example, when the sample is soil, the target light element can include multiple elements, such as C, N, and O. A plurality of characteristic peak spectra corresponding to the target light elements C, N, and O are searched from the characteristic spectrum, each element corresponding characteristic peak is fitted, for example, Gaussian fitting, element matching and characteristic peak area extraction are performed on the fitted characteristic peak to obtain the first detection result, and it can be determined whether the soil contains the light elements C, N, and O and the content thereof.

[0064] In another embodiment, step S240 can include searching for a characteristic peak spectrum of the light element from the characteristic spectrum at step S241, and the characteristic peak spectrum includes a plurality of characteristic peaks. The method of searching for the characteristic peak of the light element from the characteristic spectrum can be a second derivative peak search method. The characteristic peak positioning mainly solves the problems of peak overlap, weak peak, escape peak and interference peak, etc. Since the spectrum energy of the light element is low and similar, the weak peak and the peak overlap phenomenon are serious, and the second derivative peak search method has strong peak resolution ability, weak peak search ability and false peak suppression ability.

[0065] At step S242, the maximum peak in the characteristic peak spectrum is found. Then step S243 performs characteristic peak fitting on the found maximum peak. Step S244 performs characteristic peak matching on the found maximum peak, and according to the fitting result, the first sub-detection result of the light element corresponding to the maximum peak is obtained. When the fitting result contains Gaussian distribution spectrum lines of multiple light elements, the kind and content of the light element corresponding to the Gaussian distribution spectrum line with the maximum peak value are taken as the first sub-detection result of the light element corresponding to the maximum peak. In theory, the characteristic X-ray spectrum emitted by an element is a linear spectrum with a certain energy value. Due to the existence of a certain width of the energy level itself and the limited resolution of the detector, the characteristic spectrum of the element is a Gaussian distribution spectrum peak with a certain width. The Gaussian fitting method can realize the calculation of the characteristic peak area, that is, the content of the corresponding element is obtained.

[0066] At step S245, the fitting result is subtracted from the characteristic peak spectrum to obtain the subtracted characteristic peak spectrum. Further, the Gaussian distribution spectrum line with the maximum peak value is subtracted from the characteristic peak spectrum. When the subtracted characteristic peak spectrum does not conform to the Gaussian distribution, the subtracted characteristic peak spectrum is supplemented to obtain the characteristic peak spectrum conforming to the Gaussian distribution. At step S246, the step of finding the maximum peak in the characteristic peak spectrum is performed on the subtracted characteristic peak spectrum, and the step is repeatedly performed until there is no characteristic peak in the subtracted characteristic peak spectrum. At step S247, all the first sub-detection results obtained as described above are fitted and output as the first detection result.

[0067] As shown in Figure 2 The detection method of the light element further includes step S250 of superimposing the spectrum line model of the estimated light element, and outputting the second detection result of the light element when the superimposed result matches the characteristic spectrum. Step S250 is the estimation of the kind and content of the light element that may exist in the sample by using an artificial algorithm system. The process of obtaining the kind and content of the light element by using the artificial algorithm system can include modeling the estimated light element, spectrum line approximation of the element model and the characteristic spectrum, etc. This process can be called a reverse calculation process.

[0068] In one embodiment, step S250 can comprise: at step S251, inverse modeling, obtaining the light element spectral line model corresponding to the estimated light element category from the element characteristic spectrum database. Step S252 models each estimated light element. Step S253 performs element estimation, estimates the light element category contained in the sample, such as soil, and the estimated light element can be multiple. It can be understood that the category of multiple light elements contained in the sample, such as soil, can also be estimated first, and then the light element spectral line model corresponding to the estimated light element category is obtained from the element characteristic spectrum database, and each estimated light element is modeled. At step S254, the obtained light element spectral line model is model superimposed using an optimization algorithm, and preferably the optimization algorithm can be a genetic algorithm. Step S255 performs spectral line approximation of the superimposed model and the characteristic spectrum, and the process can also include adjusting the content of each element to adjust the peak shape. Specifically, the superimposed model is approximated with the characteristic spectrum of the light element obtained in step S230. Step S256 judges whether the superimposed model matches the characteristic spectrum, and when the superimposed model matches the characteristic spectrum, step S257 outputs the second detection result of the light element, i.e., the second category and the second content of the light element. When the superimposed model cannot match the characteristic spectrum, the category of the estimated light element is adjusted, and the steps of obtaining the light element spectral line model corresponding to the adjusted estimated light element category from the element characteristic spectrum database, and superimposing the obtained light element spectral line model using the optimization algorithm are continued until the superimposed model matches the characteristic spectrum, and the second detection result of the light element is output.

[0069] In one specific embodiment, for example, the Kα1 characteristic peak position of Ca is at 3.691 KeV, and the sample with known Ca content is measured multiple times, and the Kα1 characteristic peak value corresponding to each Ca content can be obtained. Based on this, the model of Ca is established, and repeating this step can obtain the models of multiple elements. The models of the aforementioned multiple elements are superimposed, the content of each element is adjusted to control the peak shape, and approximation is performed with the characteristic spectrum, and the matching condition can be, for example, a preset threshold. When the peak intensity difference of the corresponding elements is less than the preset threshold, it can be considered that the element spectrum obtained by the inverse calculation process matches the characteristic spectrum obtained after step S230, and the result of the inverse calculation process, i.e., the second detection result, is output.

[0070] Based on the first detection result of the light element obtained in the foregoing step S240 and the second detection result of the light element obtained in step S250, bidirectional fusion calculation is performed at step S260, specifically, the first detection result and the second detection result can be compared, and a final detection result of the light element is determined according to the comparison result at step S270. When the comparison result shows that the first detection result and the second detection result have an intersection, the intersection content is taken as the final detection result of the light element. When the comparison result shows that the first detection result and the second detection result have no intersection, the first detection result is taken as the final detection result of the light element. It can be understood that according to the foregoing process, the output first element type, first element content, second element type and second element content will be different from the actual element content in the sample. The bidirectional fusion calculation process can also be that after multiple tests, multiple sets of data values of the first element content, the second element content and the actual element content are obtained, fitting processing is performed on the multiple sets of data values to obtain the relationship among the three, such as a weighted average value or a function relationship, and the final detection result, i.e., the type and content of the final light element, is further determined according to the relationship among the three.

[0071] Compared with the traditional XRF fluorescence analysis technology, two directions of XRF spectral line decoupling methods are designed according to the characteristics of low characteristic ability and low fluorescence yield of light elements, and the results obtained by the two methods are compared and analyzed, which greatly reduces the error of traditional characteristic peak fitting and extraction, and also reduces the distortion of algorithm model prediction, solves the problem of XRF fluorescence analysis technology in light element analysis, effectively improves the detection ability of XRF for light elements, improves the detection precision and reduces the detection error.

[0072] Although the embodiments of the present application have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided only by way of example. Many modifications, changes and alternatives can be made to the embodiments of the present application without departing from the spirit and scope of the present application. It should be understood that various alternatives to the embodiments of the present application described herein can be employed in practicing the present application. The appended claims are intended to define the scope of protection of the present application and thus cover equivalent or alternative solutions within the scope of the claims.

Claims

1. A method of detecting a light element, characterized by, The method comprises the following steps: acquiring characteristic spectrum lines of light elements in a sample, the sample being soil, the light elements being elements with atomic numbers less than 20 in the periodic table, and the characteristic spectrum lines being collected by an XRF fluorescence spectrum analyzer; finding characteristic peaks of the light elements from the characteristic spectrum lines and performing element matching to obtain a first detection result of the light elements; superimposing a spectrum line model of a predicted light element, and outputting a second detection result of the light element when the superimposed result matches the characteristic spectrum lines; calculating a final detection result of the light element according to the first detection result and the second detection result; wherein the superimposing of the spectrum line model of the predicted light element and the outputting of the second detection result of the light element when the superimposed result matches the characteristic spectrum lines comprise the following steps: estimating the type of the light element; acquiring a light element spectrum line model corresponding to the estimated type of the light element from an element characteristic spectrum database, superimposing the acquired light element spectrum line model by using a genetic algorithm, and outputting the second detection result of the light element when the superimposed model matches the characteristic spectrum lines; wherein the superimposing of the spectrum line model of the predicted light element and the outputting of the second detection result of the light element when the superimposed result matches the characteristic spectrum lines further comprise the following steps: when the superimposed model cannot match the characteristic spectrum lines, adjusting the type of the predicted light element, continuing the steps of acquiring a light element spectrum line model corresponding to the adjusted type of the predicted light element from the element characteristic spectrum database, and superimposing the acquired light element spectrum line model by using the genetic algorithm until the superimposed model matches the characteristic spectrum lines, and outputting the second detection result of the light element; wherein the calculating of the final detection result of the light element according to the first detection result and the second detection result comprises the following steps: comparing the first detection result and the second detection result, and determining the final detection result of the light element according to the comparison result; wherein when the comparison result shows that the first detection result and the second detection result have an intersection, the intersection content is taken as the final detection result of the light element; and wherein when the comparison result shows that the first detection result and the second detection result have no intersection, the first detection result is taken as the final detection result of the light element.

2. The light element detection method according to claim 1, wherein The finding of the characteristic peaks of the light elements from the characteristic spectrum lines and the performing of the element matching to obtain the first detection result of the light elements comprise the following steps: finding characteristic peak spectrum lines of the light elements from the characteristic spectrum lines, the characteristic peak spectrum lines comprising a plurality of characteristic peaks; performing characteristic peak fitting on each found characteristic peak, and performing element matching on the fitted characteristic peak to obtain the first detection result of the light elements.

3. The light element detection method of claim 2, wherein, The performing of the characteristic peak fitting on each found characteristic peak and the performing of the element matching on the fitted characteristic peak to obtain the first detection result of the light elements comprise the following steps: finding a maximum peak in the characteristic peak spectrum lines; performing characteristic peak fitting on the found maximum peak, and performing element matching according to the fitting result to obtain a first sub-detection result of the light element corresponding to the maximum peak; Subtract the fitting result from the characteristic peak spectrum line to obtain a subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum 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spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the subtracted characteristic peak spectrum line, and perform the step of finding the maximum peak in the 4. The light element detection method according to claim 3, wherein ​ 5. The light element detection method of claim 3, wherein ​ ​ ​ ​ ​ ​ 6. The light element detection method of claim 2, wherein, ​ 7. The light element detection method of claim 1, wherein ​ ​ ​ ​ 8. The light element detection method according to claim 7, wherein ​ ​ 9. The light element detection method according to claim 7, wherein ​ ​ ​ ​ ​ ​ ​

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