Quartz sand impurity detection method

Abstract

The present application relates to the technical field of quartz sand detection, and specifically to a method for detecting impurities in quartz sand, including: obtaining spectral data of a quartz sand sample, obtaining the degree of spectral interference of each characteristic peak based on the energy distribution of each characteristic peak of the spectral data; obtaining the degree of matrix interference of each characteristic peak based on the peak shape variation of each characteristic peak itself, and the offset between each characteristic peak and the characteristic peak of the matrix, and then obtaining the interference intensity of each characteristic peak in combination with the anti-interference degree of each characteristic peak, so as to optimize the chaos adjustment factor when the chaotic mapping population is initialized, and finally obtaining the characteristic band with the least interference in combination with the optimization algorithm, based on which the purity and impurity content of the quartz sand are detected. The present application can improve the accuracy of quartz sand impurity detection.

Description

Technical Field

[0001] The present application relates to the technical field of quartz sand detection, and in particular to a method for detecting impurities in quartz sand. Background Art

[0002] Quartz sand is a common non-metallic mineral, the main component of which is SiO2. It is a very important industrial mineral raw material. In the fields of photovoltaics, optical fibers and other electronic communications, there are high requirements for the purity of quartz sand, so it is necessary to detect impurities in the purified quartz sand.

[0003] At present, X-ray fluorescence spectrometer XRF is commonly used as a non-destructive testing method for quartz sand. XRF emits X-rays to the quartz sand sample to stimulate the electrons in the sample to undergo energy level transitions. The unstable excited electrons will emit corresponding radiation characteristic fluorescence to return to the excited state, thereby achieving quantitative and qualitative analysis of the elements in the sample to be tested based on the fluorescence wavelength and intensity.

[0004] The collected fluorescence spectrum contains thousands of data points, reflecting the information of each component in the quartz sand sample, but it is easily affected by matrix effects and spectral interference, resulting in a lot of redundant information in the original data. Therefore, the intelligent sensor module is required to have the ability to extract characteristic bands from the original data to improve the detection accuracy. Although there are some cases based on heuristic algorithms to extract characteristic bands of fluorescence spectra for quartz sand impurity detection, they are all based on the specific data settings of the heuristic algorithm, resulting in low convergence accuracy of the algorithm and easy to fall into local optimality. Summary of the invention

[0005] In order to solve the above technical problems, the present application provides a quartz sand impurity detection method to solve the existing problems.

[0006] The quartz sand impurity detection method of the present application adopts the following technical solution:

[0007] An embodiment of the present application provides a method for detecting impurities in quartz sand, comprising the following steps:

[0008] Obtain spectral data of quartz sand samples;

[0009] Extract each peak value of the quartz sand sample spectrum data to construct the characteristic peak of each peak value, and obtain the spectral line interference degree of each characteristic peak through the energy intensity distribution of each characteristic peak;

[0010] Select matrix characteristic peaks, analyze the offset of each characteristic peak relative to the matrix characteristic peak and the change of energy intensity of each characteristic peak, obtain the matrix interference degree of each characteristic peak, and combine the spectral line interference degree of each characteristic peak and the maximum energy intensity within the characteristic peak to obtain the interference intensity of each characteristic peak;

[0011] According to the interference intensity of each characteristic peak, the population is initialized through chaotic mapping, and the characteristic band with the least interference is obtained by using the optimization algorithm to obtain the purity and impurity content information of quartz sand.

[0012] Preferably, the extracting peak values ​​of the quartz sand sample spectrum data includes: performing peak detection on the quartz sand sample spectrum data using an automatic peak detection algorithm to obtain peak values ​​of the quartz sand sample spectrum data.

[0013] Preferably, the method for constructing the characteristic peaks of each peak is: extracting each baseline segment of the quartz sand sample spectrum data, if there is a peak point between two adjacent baseline segments, the spectrum data between the two adjacent baseline segments is regarded as a characteristic peak.

[0014] Preferably, the calculation method of the spectral line interference degree of each characteristic peak is:

[0015] ; In the formula, A represents the spectral interference degree of the current characteristic peak, represents an exponential function with a natural constant as base, Indicates the difference between the left and right boundary points of the current characteristic peak interval and the central peak point of the characteristic peak. and Indicates that the central peak point of the current feature peak divides the current feature peak into a left sequence and a right sequence. represents the Pearson correlation coefficient.

[0016] Preferably, the method for selecting the central peak point is:

[0017] When the number of peak points in a characteristic peak is 1, the peak point in the characteristic peak is taken as the central peak point; when the number of peak points in a characteristic peak is greater than 1, the largest peak point is taken as the central peak point of the characteristic peak.

[0018] Preferably, the matrix characteristic peak is a characteristic peak with the largest peak value.

[0019] Preferably, the calculation method of the matrix interference degree of each characteristic peak is:

[0020] ; In the formula, B represents the matrix interference degree of the current characteristic peak, F represents the fitting residual sequence of the current characteristic peak, and the fitting residual sequence is constructed by the deviation of the energy intensity within the characteristic peak. represents information entropy, represents a logarithmic function with base 2, N represents the number of peak points in the current feature peak, Indicates the distance between the center point of the current characteristic peak and the center point of the matrix characteristic peak.

[0021] Preferably, the method for constructing the fitting residual sequence is:

[0022] The energy intensity data within the characteristic peak is subjected to curve fitting, and the distance between each energy intensity data point and the fitting curve is counted. The distances between all energy intensity data points of the characteristic peak and the fitting curve constitute the fitting residual sequence of the characteristic peak.

[0023] Preferably, the calculation method of the interference intensity of each characteristic peak is:

[0024] ; In the formula, Indicates the interference intensity of the current characteristic peak, represents the normalization function, A represents the spectral interference degree of the current characteristic peak, B represents the matrix interference degree of the current characteristic peak, represents the logarithmic function with a natural constant as base, Indicates the maximum energy intensity corresponding to the peak point in the current characteristic peak.

[0025] Preferably, during the population initialization process, the interference intensity of each characteristic peak is used as a chaos adjustment factor of the chaotic mapping when each characteristic peak population is initialized.

[0026] This application has at least the following beneficial effects:

[0027] This application extracts high-purity quartz sand samples from the quartz sand purification line and uses intelligent sensors to obtain the X-ray fluorescence spectrum data of the samples. It mainly targets the characteristic bands that are easily affected by matrix effects and spectral line interference during the actual collection process, which leads to deviations in the extracted characteristic bands and affects the accuracy of the final impurity detection. In order to improve the extraction accuracy of characteristic bands, this application combines the energy distribution of a single characteristic peak in the fluorescence spectrum to obtain the degree of spectral line interference, reflecting the influence of spectral line interference on the characteristic peak. At the same time, based on the peak shape variation of the characteristic peak itself and the offset from the matrix, the degree of matrix interference is obtained to reflect the influence of the matrix effect. The interference intensity of a single characteristic peak is thus obtained, and the initialization of the characteristic peak population is guided based on the interference of the characteristic peak, thereby increasing the initialization exploration range of different characteristic peaks, thereby improving the population initialization quality of the optimization algorithm;

[0028] Compared with the traditional random initialization on the entire spectral subspace, the present application narrows the scope of initialization to the characteristic peak, and performs different initializations based on the interference degree of the characteristic peak, so as to improve the initialization quality of the population, improve the accuracy of the final characteristic band, and improve the detection accuracy of quartz sand impurities. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] 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.

[0030] Figure 1 A flow chart of the steps of the quartz sand impurity detection method provided in this application. DETAILED DESCRIPTION

[0031] 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 quartz sand impurity detection method proposed in the present application, its specific implementation, structure, characteristics 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.

[0032] Unless otherwise defined, terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such articles or devices. In the absence of further restrictions, an element defined by the statement "comprising a ..." does not exclude the presence of other identical elements in the article or device comprising the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present application belongs.

[0033] The specific scheme of the quartz sand impurity detection method provided by the present application is described in detail below with reference to the accompanying drawings.

[0034] The present application provides a method for detecting impurities in quartz sand. For details, please refer to Figure 1 , including the following steps:

[0035] Step 1: Obtain spectral data of quartz sand samples.

[0036] The main component of quartz sand is SiO2. Due to the influence of crystallization and structure, there is no pure quartz sand in nature. It contains a variety of impurity elements, such as P, Al, Fe, Ti, Na, K and Li, which exist in quartz sand crystals in different forms. For example, iron elements are attached to the surface of quartz sand particles in the form of Fe2O3 oxide film, and Al2O3 is mixed in the form of clay. Therefore, quartz ore needs to be purified through a certain purification process. This embodiment aims to detect impurities in purified quartz sand, without limiting the purification process, and at the same time provides a simple purification process embodiment.

[0037] The purification process in this embodiment is: washing, magnetic separation, flotation, acid leaching and high-temperature chlorination degassing to obtain high-purity quartz sand. This embodiment performs residual impurity detection on the high-purity quartz sand output by the purification process, and obtains a quartz sand sample every 2 hours. In order to facilitate XRF analysis, a direct tableting method or a melt tableting method can generally be used to obtain a quartz sand powder sample. In this embodiment, a melt tableting method is used to obtain a sample, wherein the mass ratio of the sample to the binder is 3:1.

[0038] Based on the obtained quartz sand sample, data is obtained through the intelligent sensor. Specifically, the sample is loaded into the X-ray fluorescence spectrometer, the test conditions of the X-ray fluorescence spectrometer are adjusted, and the standard sample is detected. In this embodiment, the test conditions are: the probe line is LB1, the crystal is LiF200, a scintillation detector is used, the debugging equipment increases the voltage to 60KV, the current is increased to 50mA, the voltage is increased first and then the current is increased, 10 units each time, the slit is set to 150μm, and the filter is selected to be aluminum 750μm.

[0039] Based on the above process, the fluorescence spectrum data of the purified quartz sand sample is obtained, wherein the serial number in the data, that is, the horizontal coordinate of the spectrum, represents the measured fluorescence wavelength. The wavelength corresponding to the electrons of different elements returning from the excited state to the ground state is different, thereby realizing the detection of impurities in the quartz sand. The data corresponding to the serial number, that is, the vertical coordinate of the spectrum, represents the fluorescence intensity of the corresponding wavelength, which is related to the content of the component in the substance. The greater the content, the greater the corresponding fluorescence intensity.

[0040] Step 2: Extract each peak value of the quartz sand sample spectrum data to construct the characteristic peak of each peak value, and obtain the spectral line interference degree of each characteristic peak through the energy intensity distribution of each characteristic peak.

[0041] Normally, when an X-ray fluorescence spectrometer is used to detect a quartz sand sample, a corresponding fluorescence spectrum is obtained, which contains the energy response of all components. The higher the content of the corresponding component, the greater the corresponding fluorescence intensity. Therefore, by comparing the fluorescence spectrum with that of the standard quartz sample, the content of the corresponding impurities and the purity of the quartz sand can be calculated.

[0042] However, in the actual test process, it is easily affected by matrix effects and spectral line interference. The basic principle is:

[0043] Matrix effect: The main component of quartz sand is silicon dioxide, which will produce different absorption and enhancement effects on the X-ray fluorescence of impurity elements. If the characteristic X-ray energy of the impurity element is close to the absorption edge of the matrix element (silicon dioxide), the ray energy of the impurity element will be selectively absorbed by the matrix, causing the fluorescence intensity of the impurity element to decrease; conversely, the matrix element can also enhance the fluorescence signal of the impurity element.

[0044] Spectral line interference: It mainly refers to the fact that the characteristic X-ray fluorescence lines of different elements may overlap in the measured spectrum, or the background interference caused by factors such as the instrument itself and sample scattering, resulting in the original data not being able to truly reflect the actual impurity content.

[0045] Due to the interference factors in measuring quartz sand impurities using XRF, the data volume in the fluorescence spectrum of quartz sand is large and the interference degree is different at different wavelengths. Therefore, it is necessary to extract the characteristic bands to accurately detect impurities. When the traditional Harris Eagle optimization algorithm extracts characteristic bands, the population is randomly initialized, which is a pseudo-random process based on random functions. In the fluorescence spectrum of quartz sand impurities, different bands have different influences and have certain band tendencies. Therefore, traditional random initialization easily leads to poor initial population quality, resulting in poor global exploration ability of the algorithm, and inability to effectively extract characteristic bands in the fluorescence spectrum, resulting in low precision in impurity detection.

[0046] In the measured fluorescence spectrum of the quartz sand sample, different elements are reflected in the form of characteristic peaks. The main reason is that different elements correspond to their unique atomic structures, so the fluorescence energy released by their inner electron transitions is different, which is manifested as characteristic peaks at different positions in the spectrum. By analyzing these characteristic peaks, different elements and the content of elements can be determined. Therefore, it is necessary to identify the different characteristic peaks in the fluorescence spectrum. In this embodiment, the peak automatic detection AMPD algorithm is selected to realize the peak detection of the fluorescence spectrum of the quartz sand sample, and the algorithm outputs all the peaks of the sample fluorescence spectrum. It should be noted that in order to reduce the influence of baseline drift during the detection process, in this embodiment, the weighted penalty least squares baseline correction method is used before peak detection to realize the correction of spectral data. Among them, the peak automatic detection AMPD algorithm and the weighted penalty least squares baseline correction algorithm are well-known technologies and will not be repeated in this embodiment.

[0047] During baseline correction, the baseline segments in the fluorescence spectrum can be obtained, and each baseline segment of the quartz sand sample spectrum data is extracted. If there is a peak point between two adjacent baseline segments, the spectrum data between the two adjacent baseline segments is taken as a characteristic peak. Each baseline segment and peak point in the spectrum is traversed to obtain all characteristic peaks in the quartz sand sample spectrum data.

[0048] For a single characteristic peak, ideally, the characteristic peak represents an element in the quartz sand, and its distribution is completely symmetrical. The main reason is that the photons excited from the excited state to the ground state of the element are not absolutely uniform, so the detector has a slight difference in the detection effect of the photons, and finally the response energy shows a basically centrally symmetrical distribution in the wavelength range corresponding to the characteristic peak. When the characteristic peak is disturbed by the spectral line, the symmetry of the characteristic peak may be broken, so that the energy distribution of the characteristic peak does not show central symmetry, and the spectral line does not show linear changes.

[0049] Therefore, taking the current characteristic peak as an example, based on the above analysis, the spectral line interference degree of each characteristic peak is obtained. In this embodiment, the calculation formula of the spectral line interference intensity of the current characteristic peak is:

[0050] ; In the formula, A represents the spectral interference degree of the current characteristic peak, represents an exponential function with a natural constant as base, Indicates the difference between the left and right boundary points of the current feature peak interval and the central peak point of the current feature peak. and Indicates that the central peak point of the current feature peak divides the current feature peak into a left sequence and a right sequence. It should be noted that the method for selecting the central peak point is: when the number of peak points in the characteristic peak is 1, the peak point in the characteristic peak is taken as the central peak point; when the number of peak points in the characteristic peak is greater than 1, the largest peak point is taken as the central peak point of the characteristic peak.

[0051] When not disturbed, the characteristic peak is normally symmetrical in the center, and the energy distribution increases linearly and then decreases linearly. Therefore, the distances from the left and right boundaries of the characteristic peak to the central peak are basically equal, and the similarity between the left and right sequences is high, and the final value of the spectral line interference is small. On the contrary, if the quartz sand fluorescence spectrum is greatly affected by the environment during the current detection, the interference of the spectral line is more serious, which will seriously affect the distribution of the characteristic peak, resulting in a large difference in the symmetrical distribution of the characteristic peak, and the value of the spectral line interference is larger.

[0052] Step 3: Select the matrix characteristic peaks, analyze the offset of each characteristic peak relative to the matrix characteristic peak and the change in the energy intensity of each characteristic peak, obtain the matrix interference degree of each characteristic peak, and combine the spectral line interference degree of each characteristic peak and the maximum energy intensity within the characteristic peak to obtain the interference intensity of each characteristic peak.

[0053] In the above process, the influence of matrix effect on characteristic peaks is mainly analyzed. At the same time, the influence of matrix effect also exists in the actual detection process. In the X-ray fluorescence spectrum of quartz sand, the most important matrix is ​​silicon dioxide, and the corresponding content is relatively high. Therefore, the characteristic peak with the largest peak value among the peak points is marked as the matrix characteristic peak. When the matrix effect interferes, the closer to the matrix characteristic peak, the greater the impact, which will cause the corresponding characteristic peak to appear peak shape classification, multiple peaks and peak front delay, resulting in peak shape variation and a large number of noise points in the characteristic peak.

[0054] Based on the above analysis, curve fitting is performed on the energy intensity data in each characteristic peak, and the distance between each energy intensity data point and the fitting curve is calculated. The sequence composed of the distances between all energy intensity data points of each characteristic peak and the fitting curve is used as the fitting residual sequence F of each characteristic peak.

[0055] Therefore, the matrix interference degree of each characteristic peak is obtained by the offset of each characteristic peak relative to the matrix characteristic peak and the change of the energy intensity of each characteristic peak. In this embodiment, the specific calculation formula is:

[0056] ; In the formula, B represents the matrix interference degree of the current characteristic peak, and F represents the fitting residual sequence of the current characteristic peak. represents information entropy, represents a logarithmic function with base 2, N represents the number of peak points in the current feature peak, Indicates the distance between the center point of the current characteristic peak and the center point of the matrix characteristic peak. It should be noted that if the current characteristic peak is a matrix characteristic peak, When set to 1, the center point of the characteristic peak is the midpoint of the characteristic peak interval.

[0057] Therefore, the closer the characteristic peak is to the matrix characteristic peak, the greater the interference degree of the matrix effect is. Specifically, the characteristic peak is prone to peak shape variation and more noise points appear, which may cause the characteristic peak to contain two or even more peak points. In addition, due to the influence of noise points, the distances from the characteristic peak data points to the fitting curve are uneven, resulting in a larger value of the information entropy of the fitting residual sequence, and ultimately a larger value of the matrix interference degree of the characteristic peak.

[0058] According to the performance characteristics of the matrix effect on the fluorescence spectrum of quartz sand, the matrix interference degree of each characteristic peak is obtained, and the interference of a single characteristic peak is comprehensively evaluated in combination with the spectral line interference degree. Through actual analysis, it can be observed that for characteristic peaks with larger peak energy, it means that the residual amount of the element in the quartz sand sample is larger, and the corresponding anti-interference ability is stronger, while for characteristic peaks with smaller peak energy, the anti-interference ability is weaker.

[0059] Based on the above analysis, according to the matrix interference degree of each characteristic peak, combined with the spectral interference degree of each characteristic peak and the maximum energy intensity within the characteristic peak, the interference intensity of each characteristic peak is obtained. Similarly, taking the current characteristic peak as an example, the calculation formula for the interference intensity is:

[0060] ; In the formula, Indicates the interference intensity of the current characteristic peak, represents the normalization function, A represents the spectral interference degree of the current characteristic peak, B represents the matrix interference degree of the current characteristic peak, represents the logarithmic function with a natural constant as base, Indicates the maximum energy intensity corresponding to the peak point in the current characteristic peak.

[0061] If the current characteristic peak is greatly affected by the spectral line interference and matrix effect, the peak shape of the characteristic peak will be greatly different, and the values ​​of the spectral line interference degree and the matrix interference degree will be larger. At the same time, the smaller the maximum energy intensity of the characteristic peak, the weaker the anti-interference ability, and the greater the degree of adjustment when initializing the characteristic peak. On the contrary, for characteristic peaks with less interference and stronger anti-interference ability, the adjustment intensity is greater.

[0062] Step 4: According to the interference intensity of each characteristic peak, the population is initialized through chaotic mapping, and the characteristic band with the least interference is obtained by using the optimization algorithm to obtain the purity and impurity content information of quartz sand.

[0063] The above analysis can be used to determine the influence of matrix effect and spectral interference on the characteristic peaks of fluorescence spectrum of quartz sand during X-ray fluorescence detection, and this influence can provide guidance for optimizing the algorithm to obtain characteristic bands.

[0064] In this embodiment, the Harris Hawk optimization algorithm is used to select the characteristic bands. First, the number of populations is set. In this embodiment, each characteristic peak is set to correspond to M populations, that is, the number of populations is M times the number of characteristic peaks, where M is set to 4.

[0065] Before the Harris Eagle optimization algorithm is optimized, the population needs to be initialized. The traditional method uses random initialization, that is, each individual searches in a subspace of the entire fluorescence spectrum. However, most of the fluorescence spectrum is a baseline segment, so the traditional initialization method affects the convergence speed of the algorithm and the accuracy of feature band extraction, and easily causes the model to fall into a local optimum.

[0066] In this embodiment, chaotic mapping is selected for population initialization. There are M populations corresponding to each characteristic peak, wherein the interference intensity of each characteristic peak is used as the chaos adjustment factor of the chaotic mapping when initializing each characteristic peak population. The larger the chaos adjustment factor, the greater the degree of interference to the corresponding characteristic peak. At this time, the larger the chaos adjustment factor is required to enhance the range of population initialization and improve the global exploration capability. On the contrary, for characteristic peaks with less interference, the smaller the chaos adjustment factor, the closer the initialized range is to the peak point, and the peak point can better represent the information of the characteristic peak.

[0067] Therefore, through the above process of this embodiment, the population initialization is achieved by using chaotic mapping.

[0068] Furthermore, other parameters in the Harris Hawk optimization algorithm are set. In this embodiment, the maximum number of iterations is set to 300, and the minimum RMSECV is used as the fitness function. After the search and predation stages of the Harris Hawk, the characteristic band with the least interference is finally output to obtain the characteristic band with the least interference in the entire quartz sand fluorescence spectrum.

[0069] At the same time, based on the annotated fluorescence spectrum of the standard quartz sand sample with known sample component content, the characteristic band corresponding to the fluorescence spectrum finally obtained by the optimization algorithm is selected from the annotated fluorescence spectrum, and the content information of each impurity component in the quartz sand sample is obtained by energy intensity conversion. Finally, the fluorescence spectrum, as well as the quartz sand composition and impurity content table are output.

[0070] It is understood that references to "one embodiment" or "some embodiments" etc. described in the specification of the present application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, if "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. appear in different places in this specification, they do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0071] It should be noted that the sequence of the above-mentioned embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous. At the same time, the size of the sequence number of each step in the embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments in this specification.

[0072] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for detecting impurities in quartz sand, characterized in that: The following steps are involved: Obtain spectral data of quartz sand samples; Extract each peak value of the quartz sand sample spectrum data to construct the characteristic peak of each peak value, and obtain the spectral line interference degree of each characteristic peak through the energy intensity distribution of each characteristic peak; Select matrix characteristic peaks, analyze the offset of each characteristic peak relative to the matrix characteristic peak and the change of energy intensity of each characteristic peak, obtain the matrix interference degree of each characteristic peak, and combine the spectral line interference degree of each characteristic peak and the maximum energy intensity within the characteristic peak to obtain the interference intensity of each characteristic peak; According to the interference intensity of each characteristic peak, the population is initialized through chaotic mapping, and the characteristic band with the least interference is obtained by using the optimization algorithm to obtain the purity and impurity content information of quartz sand.

2. The quartz sand impurity detection method according to claim 1, characterized in that: The extracting of peak values ​​of the quartz sand sample spectrum data comprises: performing peak detection on the quartz sand sample spectrum data by using a peak automatic detection algorithm to obtain peak values ​​of the quartz sand sample spectrum data.

3. The quartz sand impurity detection method according to claim 1, characterized in that: The method for constructing the characteristic peaks of each peak is: extracting each baseline segment of the quartz sand sample spectrum data, if there is a peak point between two adjacent baseline segments, the spectrum data between the two adjacent baseline segments is taken as a characteristic peak.

4. The method for detecting impurities in quartz sand according to claim 1, characterized in that: The calculation method of the spectral line interference degree of each characteristic peak is: ; In the formula, A represents the spectral interference degree of the current characteristic peak, represents an exponential function with a natural constant as base, Indicates the difference between the left and right boundary points of the current feature peak interval and the central peak point of the current feature peak. and Indicates that the central peak point of the current feature peak divides the current feature peak into a left sequence and a right sequence. represents the Pearson correlation coefficient.

5. The method for detecting impurities in quartz sand according to claim 4, characterized in that: The method for selecting the central peak point is: When the number of peak points in a characteristic peak is 1, the peak point in the characteristic peak is taken as the central peak point; when the number of peak points in a characteristic peak is greater than 1, the largest peak point is taken as the central peak point of the characteristic peak.

6. The method for detecting impurities in quartz sand according to claim 1, characterized in that: The matrix characteristic peak is a characteristic peak with the largest peak value.

7. The method for detecting impurities in quartz sand according to claim 1, characterized in that: The calculation method of the matrix interference degree of each characteristic peak is: ; In the formula, B represents the matrix interference degree of the current characteristic peak, F represents the fitting residual sequence of the current characteristic peak, and the fitting residual sequence is constructed by the deviation of the energy intensity within the characteristic peak. represents information entropy, represents a logarithmic function with base 2, N represents the number of peak points in the current feature peak, Indicates the distance between the center point of the current characteristic peak and the center point of the matrix characteristic peak.

8. The method for detecting impurities in quartz sand according to claim 7, characterized in that: The construction method of the fitting residual sequence is: The energy intensity data within the characteristic peak is subjected to curve fitting, and the distance between each energy intensity data point and the fitting curve is counted. The distances between all energy intensity data points of the characteristic peak and the fitting curve constitute the fitting residual sequence of the characteristic peak.

9. The method for detecting impurities in quartz sand according to claim 1, characterized in that: The calculation method of the interference intensity of each characteristic peak is: ; In the formula, Indicates the interference intensity of the current characteristic peak, represents the normalization function, A represents the spectral interference degree of the current characteristic peak, B represents the matrix interference degree of the current characteristic peak, represents the logarithmic function with a natural constant as the base, Indicates the maximum energy intensity corresponding to the peak point in the current characteristic peak.

10. The method for detecting impurities in quartz sand according to claim 1, characterized in that: In the population initialization process, the interference intensity of each characteristic peak is used as a chaos adjustment factor of the chaotic mapping when each characteristic peak population is initialized.

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