X-ray fluorescence spectroscopy method, system, medium and product for bauxite

Abstract

This invention relates to a method, system, medium, and product for X-ray fluorescence spectrometry detection of bauxite, belonging to the field of bauxite detection technology. The method first acquires fluorescence spectral data and mining area environmental fingerprint data of the sample to be tested, and determines the initial concentration by background noise stripping. Then, a mineral phase interference network is constructed, and the interfacial radiation attenuation rate and secondary fluorescence excitation probability are calculated based on mineral phase characteristics to generate an interphase coupling interference matrix. Next, a stress field model is constructed based on environmental parameters, and the interference matrix is ​​corrected using the lattice distortion coefficient. The coexisting mineral phase combination is input into the environmental adaptive interference model to determine the nonlinear matrix effect deviation value. Finally, iterative compensation correction is performed until the difference between adjacent correction values ​​is less than a threshold, outputting accurate grade data of the target metal element. Implementing the technical solution provided in this application can improve the accuracy of X-ray fluorescence spectrometry detection of bauxite.

Description

Technical Field

[0001] This application relates to the field of bauxite detection technology, specifically to an X-ray fluorescence spectrometry detection method, system, medium, and product for bauxite. Background Technology

[0002] With the metallurgical industry's increasing demands for raw material quality, accurate analysis of bauxite's chemical composition is crucial for optimizing production processes and improving product quality. As a key raw material for producing alumina and metallic aluminum, the accurate determination of bauxite's elemental composition directly impacts the performance and production efficiency of downstream products.

[0003] Currently, the compositional analysis of bauxite mainly relies on X-ray fluorescence spectroscopy, which uses standard sample working curves to quantitatively determine the elemental content. This working curve-based analytical method can rapidly obtain elemental composition information from samples and has been widely used in mineral quality testing.

[0004] However, with the aluminum industry's increasingly stringent requirements for raw material quality control, the accuracy requirements for bauxite analysis are constantly rising. In practical applications, due to the complex and variable phase structure of bauxite, samples from different producing areas under different environmental conditions, even with similar chemical compositions, exhibit significant differences in X-ray absorption and enhancement effects. In particular, when the phase composition of a sample deviates from that of a standard sample, the lack of an effective compensation mechanism can easily lead to systematic biases in the measurement results, thereby reducing the accuracy of X-ray fluorescence spectroscopy detection of bauxite. Summary of the Invention

[0005] This application provides a method, system, medium, and product for X-ray fluorescence spectroscopy detection of bauxite, which can improve the accuracy of X-ray fluorescence spectroscopy detection of bauxite.

[0006] The first aspect of this application provides a method for X-ray fluorescence spectrometry detection of bauxite, comprising:

[0007] Acquire the test samples and mining area environmental fingerprint data of the target production area;

[0008] The fluorescence spectral response data of the sample to be tested was obtained by X-ray fluorescence spectrometry, and the background noise was removed from the fluorescence spectral response data based on the environmental fingerprint data of the mining area to determine the initial concentration value of the target metal element.

[0009] A mineral phase interference network of the sample to be tested is constructed, and based on the elemental composition and density characteristics of each mineral phase in the mineral phase interference network, the interfacial radiation attenuation rate and secondary fluorescence excitation probability between each mineral phase are calculated to generate an interphase coupling interference matrix.

[0010] Based on the temperature, humidity and porosity parameters in the environmental fingerprint data of the mining area, an environmental stress field model is constructed, the lattice distortion coefficient of each mineral phase under different environmental stresses is calculated, and the interphase coupling interference matrix is ​​weighted and corrected using the lattice distortion coefficient to obtain the environmental interference model.

[0011] By inputting the coexisting mineral phase combination in the mineral phase interference network into the environmental disturbance model, the nonlinear matrix effect deviation value of the coexisting minerals relative to the target metal element is determined.

[0012] The initial concentration value is compensated and corrected by using the nonlinear matrix effect deviation value until the concentration difference between two adjacent corrections is less than the preset threshold, and the target grade data of the target metal element is output.

[0013] By adopting the above technical solution, firstly, the sample to be tested and the environmental fingerprint data of the mining area are obtained. Background noise is then removed by combining this with X-ray fluorescence spectral response data, effectively eliminating the interference of environmental factors on the measurement results. Secondly, by constructing a mineral phase interference network and calculating the interphase coupling interference matrix, the interfacial radiation attenuation and secondary fluorescence excitation effects between different mineral phases are accurately characterized. Thirdly, an environmental stress field model is constructed based on the mining area environmental fingerprint data, and the interphase coupling interference matrix is ​​weighted and corrected using the lattice distortion coefficient, achieving precise compensation for lattice structure changes caused by environmental stress. Finally, by inputting the combination of symbiotic mineral phases into the environmental adaptive interference model, the nonlinear matrix effect deviation value is calculated and iteratively corrected until the preset accuracy requirement is met. This overcomes the systematic errors caused by differences in sample phase structure in traditional X-ray fluorescence spectroscopy detection, improving the accuracy of determining the content of target metal elements in bauxite.

[0014] Optionally, atmospheric scattering index and dust concentration parameters are extracted from the environmental fingerprint data of the mining area to construct an environmental background noise benchmark spectral library; fluorescence spectral response data is spectrally matched with the environmental background noise benchmark spectral library to identify characteristic noise peak positions and calculate the noise contribution coefficient corresponding to the characteristic noise peak positions; based on the noise contribution coefficient, the spectral intensity of the corresponding peak positions in the fluorescence spectral response data is subtracted for background to obtain the net fluorescence spectral data after noise stripping; the characteristic fluorescence peak intensity of the target metal element is extracted from the net fluorescence spectral data, and the moisture absorption attenuation coefficient is calculated by combining it with the sample moisture content parameter in the environmental fingerprint data of the mining area; the absorption attenuation coefficient is used to correct the absorption of the characteristic fluorescence peak intensity, and the corrected characteristic fluorescence peak intensity is substituted into the standard sample concentration-intensity calibration curve to determine the initial concentration value of the target metal element.

[0015] Optionally, X-ray diffraction analysis is performed on the sample to obtain the mineral phase types and mass fractions of each mineral phase; anisotropic and isotropic mineral phases are identified among the mineral phase types, and the lattice structure evolution coefficients of the anisotropic mineral phases are determined based on the weathering parameters in the mining area environmental fingerprint data; the orientation interference intensity between the anisotropic and isotropic mineral phases is calculated based on the lattice structure evolution coefficients and the mass fractions of each mineral phase; an asymmetric interference correlation matrix between mineral phases is constructed based on the orientation interference intensity, and mineral phases with orientation interference intensity greater than a preset intensity threshold are marked as directional interference nodes to generate a mineral phase interference network.

[0016] Optionally, elemental composition data of each mineral phase is extracted from the mineral phase interference network, and the mass absorption coefficient of each mineral phase relative to incident X-rays and characteristic fluorescent X-rays is calculated based on the elemental composition data; based on the mass absorption coefficient and the density characteristics of each mineral phase, the X-ray penetration depth and fluorescence escape depth at the interface of adjacent mineral phases are calculated; the interfacial radiation attenuation rate between each mineral phase is determined according to the ratio of X-ray penetration depth to fluorescence escape depth; the fluorescence excitation energy transfer efficiency of elements with atomic numbers greater than or equal to a preset number in each mineral phase to elements with atomic numbers less than a preset number in adjacent mineral phases is calculated to determine the secondary fluorescence excitation probability between each mineral phase; the interfacial radiation attenuation rate is used as the attenuation weight and the secondary fluorescence excitation probability is used as the enhancement weight to construct an interphase coupling interference matrix reflecting the fluorescence signal transmission relationship between mineral phases.

[0017] Optionally, temperature, humidity, and porosity parameters are obtained from the environmental fingerprint data of the mining area. Based on the temperature and humidity parameters, the thermal expansion stress and hydration expansion stress values ​​inside the sample to be tested are determined, respectively. The pore distribution characteristics of the sample to be tested are determined according to the porosity parameters, and the stress concentration factor at the pore boundary is calculated based on the pore distribution characteristics. Based on the thermal expansion stress value, hydration expansion stress value, and stress concentration factor, an environmental stress field model describing the stress distribution state inside the sample is constructed. The thermal expansion coefficient and hydration sensitivity coefficient of each mineral phase in the mineral phase interference network are used as input parameters and substituted into the environmental stress field model to calculate the equivalent environmental stress on each mineral phase. Based on the elastic modulus and equivalent environmental stress of each mineral phase, the lattice distortion coefficient of each mineral phase under the current environmental stress is calculated.

[0018] Optionally, for each mineral phase, the elastic strain generated by the mineral phase under environmental stress is calculated based on the elastic modulus and equivalent environmental stress of the mineral phase, and the elastic strain is used as the lattice strain; the interplanar spacing of the mineral phase is corrected based on the lattice strain, and the deviation rate between the corrected interplanar spacing and the standard interplanar spacing is determined as the lattice distortion coefficient.

[0019] Optionally, based on the topology of the mineral phase interference network, the carrier mineral phase nodes carrying the target metal element and the associated perturbed phase nodes connected to the carrier mineral phase nodes are determined, generating the in-situ co-occurring topological cluster to be analyzed; the environmental-lattice coupling transport tensor of the carrier mineral phase nodes and each associated perturbed phase node under the current environmental stress is analyzed from the environmental interference model; combined with the mass fraction of each associated perturbed phase node, the fluorescence flux modulation calculation of the in-situ co-occurring topological cluster is performed using the environmental-lattice coupling transport tensor to quantify the matrix radiation index of the characteristic fluorescence of each associated perturbed phase node for the target metal element; based on the detection sensitivity of the X-ray fluorescence spectrometer, the matrix radiation index is mapped to the equivalent grade drift vector in the concentration dimension, and the modulus of the equivalent grade drift vector is determined as the nonlinear matrix effect deviation value.

[0020] In a second aspect, embodiments of this application provide an X-ray fluorescence spectrometry detection system for bauxite, the system comprising: one or more processors and a memory; the memory being coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors calling the computer instructions to cause the X-ray fluorescence spectrometry detection system for bauxite to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an X-ray fluorescence spectrometry detection system for bauxite, cause the X-ray fluorescence spectrometry detection system for bauxite to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an X-ray fluorescence spectrometry detection system for bauxite, cause the X-ray fluorescence spectrometry detection system for bauxite to perform the method described in the first aspect and any possible implementation thereof.

[0023] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages:

[0024] By adopting the above technical solution, firstly, the sample to be tested and the environmental fingerprint data of the mining area are obtained. Background noise is then removed by combining this with X-ray fluorescence spectral response data, effectively eliminating the interference of environmental factors on the measurement results. Secondly, by constructing a mineral phase interference network and calculating the interphase coupling interference matrix, the interfacial radiation attenuation and secondary fluorescence excitation effects between different mineral phases are accurately characterized. Thirdly, an environmental stress field model is constructed based on the mining area environmental fingerprint data, and the interphase coupling interference matrix is ​​weighted and corrected using the lattice distortion coefficient, achieving precise compensation for lattice structure changes caused by environmental stress. Finally, by inputting the combination of symbiotic mineral phases into the environmental adaptive interference model, the nonlinear matrix effect deviation value is calculated and iteratively corrected until the preset accuracy requirement is met. This overcomes the systematic errors caused by differences in sample phase structure in traditional X-ray fluorescence spectroscopy detection, improving the accuracy of determining the content of target metal elements in bauxite. Attached Figure Description

[0025] Figure 1 This is a schematic flowchart of an X-ray fluorescence spectrometry detection method for bauxite disclosed in an embodiment of this application;

[0026] Figure 2 This is another schematic flowchart of an X-ray fluorescence spectrometry detection method for bauxite disclosed in an embodiment of this application;

[0027] Figure 3 This is a schematic diagram of the structure of a system provided in an embodiment of this application.

[0028] Explanation of reference numerals in the attached drawings: 301, Central Processing Unit; 302, Read-Only Memory; 303, Random Access Memory; 304, Bus; 305, Input / Output Interface; 306, Input Section; 307, Output Section; 308, Storage Section; 309, Communication Section; 310, Driver; 311, Removable Media. Detailed Implementation

[0029] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0030] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0031] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0032] This application provides an X-ray fluorescence spectrometry detection method for bauxite, referring to... Figure 1 , Figure 1 This is a schematic flowchart of an X-ray fluorescence spectrometry detection method for bauxite provided in an embodiment of this application. The method is applied to a system, which refers to a hardware and software integrated platform capable of executing an X-ray fluorescence spectrometry detection program for bauxite. The system can execute an X-ray fluorescence spectrometry detection program for bauxite, and the method includes steps 101 to 106, as follows:

[0033] Step 101: Obtain the test samples and mining area environmental fingerprint data of the target production area.

[0034] The target production area refers to the specific mining location where bauxite composition analysis is required. This can be an open-pit mine, an underground mine, or an ore dump. The sample to be tested is a bauxite sample collected from the target production area for analysis, typically after pretreatment such as crushing, sieving, and drying. The mining area environmental fingerprint data contains a set of parameters reflecting the environmental characteristics of the mining area, specifically including: atmospheric scattering index (characterizing the intensity of X-ray scattering by particulate matter in the air, in m⁻¹), dust concentration in the mining area (characterizing the content of suspended particulate matter in the air, in mg / m³), temperature parameters (reflecting the temperature distribution in the mining area, in °C), humidity parameters (reflecting the relative humidity in the mining area, in %), porosity parameters (characterizing the internal porosity of the sample, dimensionless), and weathering parameters (quantitative indicators characterizing the degree of mineral weathering, dimensionless).

[0035] Specifically, an environmental monitoring array is first deployed in the target production area, with monitoring points spaced 10-50 meters apart. Temperature and humidity sensors, dust concentration detectors, and scattering spectrometers are installed at each monitoring point to collect 24-hour environmental data. The collected environmental data is then averaged over time and interpolated spatially to generate an environmental parameter distribution map covering the entire target production area. Simultaneously, bauxite samples are collected at the monitoring points, each weighing 100-500 grams. The collected samples are crushed to below 200 mesh, quartered, and then dried at 105℃. The porosity distribution of the samples is determined using mercury intrusion porosimetry, and the weathering degree is analyzed using X-ray diffraction. All environmental parameters and sample characteristic parameters are integrated to form a mining area environmental fingerprint database. The parameters in this database will be used for various correction calculations in subsequent X-ray fluorescence spectroscopy analysis to ensure the accuracy of the measurement results. The environmental fingerprint data is updated every 8 hours to dynamically track changes in the mining area environment.

[0036] Step 102: Obtain the fluorescence spectral response data of the sample to be tested using an X-ray fluorescence spectrometer, and remove background noise from the fluorescence spectral response data based on the environmental fingerprint data of the mining area to determine the initial concentration value of the target metal element.

[0037] X-ray fluorescence spectrometry (XRF) is an instrument that uses X-rays to excite samples to produce characteristic fluorescence and performs energy spectrum analysis. Typical operating voltages are 40-60 kV and operating currents are 30-50 mA. Fluorescence spectral response data refers to the characteristic fluorescence energy spectrum produced by the sample under X-ray excitation, including energy positions (in keV) and corresponding count intensities. Background noise includes non-characteristic X-ray signals generated by air scattering, instrument background, and matrix effects. Target metal elements refer to specific metal elements that require quantitative analysis; in bauxite analysis, this mainly includes Al, Fe, and Si. Initial concentration values ​​refer to the elemental content initially determined by the intensity of characteristic peaks after background subtraction.

[0038] Specifically, the sample to be tested, after being compressed, is first placed in the sample chamber of an X-ray fluorescence spectrometer and measured under operating conditions of 40kV and 30mA, with an acquisition time set to 100 seconds. The acquired fluorescence spectral response data contains energy spectrum information in the range of 0-40keV. Then, background noise is removed using environmental fingerprint data from the mining area: atmospheric scattering index and dust concentration parameters are extracted from the environmental fingerprint data to construct a background noise benchmark library reflecting the current measurement environment. The measured fluorescence spectrum is matched with the background noise benchmark library to identify characteristic noise peaks. For each characteristic noise peak, its contribution coefficient to the sample fluorescence spectrum is calculated. The contribution coefficient is calculated using the peak area comparison method: the standard peak area of ​​the corresponding peak in the background library is compared with the measured peak area of ​​the corresponding peak in the sample spectrum to obtain the noise contribution coefficient. The corresponding peak in the fluorescence spectrum is subtracted using the noise contribution coefficient to obtain the net fluorescence spectrum. The characteristic peak intensity of the target metal element is extracted from the net fluorescence spectrum, and the moisture absorption attenuation coefficient is calculated based on the sample moisture content parameter in the environmental fingerprint data. The moisture absorption attenuation coefficient is calculated using the Beer-Lambert law: exp(-μρd), where μ is the mass absorption coefficient of water, ρ is the density of water, and d is the equivalent thickness of the moisture layer in the sample. This attenuation coefficient is used to correct the characteristic peak intensity. Finally, the corrected peak intensity is substituted into a pre-established concentration-intensity calibration curve to determine the initial concentration value of the target metal element.

[0039] In one possible implementation, background noise is stripped from the fluorescence spectral response data based on the environmental fingerprint data of the mining area to determine the initial concentration value of the target metal element. Specifically, this includes steps 1021-1024, as follows:

[0040] Step 1021: Extract atmospheric scattering index and dust concentration parameters from the mining area environmental fingerprint data to construct an environmental background noise benchmark library.

[0041] The atmospheric scattering index is a quantitative parameter characterizing the ability of airborne particulate matter to scatter X-rays, measured in m⁻¹, with a typical range of 0.01–1.0 m⁻¹. Dust concentration parameters in mining areas reflect the content of suspended particulate matter in the air, measured in mg / m³, generally ranging from 0.1–10 mg / m³. The environmental background noise reference spectrum library is a dataset containing characteristic X-ray scattering spectra under different atmospheric scattering and dust concentration conditions; each spectrum includes energy position (keV) and corresponding scattering intensity (cps) information.

[0042] Specifically, the atmospheric scattering index and dust concentration time-series data for the most recent 8 hours are first extracted from the environmental fingerprint data. These data are then time-weightedly averaged to obtain the effective scattering index and dust concentration values ​​for the current measurement environment. Based on these two parameters, the energy distribution of air-scattered X-rays is calculated using Rayleigh scattering and Compton scattering theories. The intensity of scattered X-rays is calculated at 0.02 keV intervals within the range of 0-40 keV. For the Rayleigh scattering component, the intensity calculation formula is I_R = I_0·n·(e² / mc²)²·(1+cos²θ) / 2, where I_0 is the incident X-ray intensity, n is the scattering particle density, e is the electron charge, m is the electron mass, c is the speed of light, and θ is the scattering angle. For the Compton scattering component, the intensity calculation formula is I_C = I_0·n·(e² / mc²)²·(E' / E)²·(E' / E+E / E'-sin²θ), where E and E' are the X-ray energies before and after scattering, respectively. The calculated theoretical scattering spectrum is convolved with the standard air scattering measurement spectrum to obtain a background noise baseline spectrum reflecting the current environmental conditions. This baseline spectral library will be used for subsequent fluorescence spectral background subtraction.

[0043] Step 1022: Perform spectral matching between the fluorescence spectral response data and the environmental background noise reference spectral library, identify the characteristic noise peak positions and calculate the noise contribution coefficient corresponding to the characteristic noise peak positions; based on the noise contribution coefficient, perform background subtraction on the spectral intensity of the corresponding peak positions in the fluorescence spectral response data to obtain the net fluorescence spectral data after noise removal.

[0044] Spectral matching is the process of quantitatively comparing a measured spectrum with a reference spectrum. Characteristic noise peaks refer to non-sample characteristic X-ray peaks caused by environmental factors, mainly including air scattering peaks and instrument background peaks. The noise contribution coefficient represents the degree of influence of environmental noise on the measurement signal and is a dimensionless parameter. Net fluorescence spectral data refers to the true fluorescence signal of the sample after removing the influence of environmental background.

[0045] Specifically, the fluorescence spectral response data of the sample to be tested is first compared with the peak positions of a reference library of ambient background noise. A correlation coefficient method is used for spectral matching: the Pearson correlation coefficient between the measured spectrum and each background spectrum in the reference library is calculated, and the background spectrum with the highest correlation coefficient is selected as the matching result. Characteristic noise peaks are identified in the matched background spectra, and each noise peak is fitted using a Gaussian-Lorentz mixture function to determine its position and shape parameters. For each identified noise peak, its noise contribution coefficient is calculated: the ratio of the peak intensity in the background spectrum to the intensity of the corresponding position in the measured spectrum is used to obtain the noise contribution coefficient K_i. The calculation formula is: K_i = I_measured / I_background, where I_measured is the peak intensity in the measured spectrum, and I_background is the intensity of the corresponding peak in the background spectrum. Using the calculated noise contribution coefficient, background subtraction is performed on the corresponding peak in the measured spectrum: I_net = I_measured - K_i·I_background, where I_net is the net peak intensity after background subtraction. This subtraction process is performed on each of the identified noise peaks to obtain the net fluorescence spectrum data after noise removal.

[0046] Step 1023: Extract the characteristic fluorescence peak intensity of the target metal element from the net fluorescence spectral data, and calculate the moisture absorption attenuation coefficient by combining the sample moisture content parameter in the mining area environmental fingerprint data.

[0047] Net fluorescence spectroscopy data refers to the X-ray fluorescence spectrum of the sample after background noise stripping. Characteristic fluorescence peak intensity represents the emission intensity of characteristic X-rays of the target element, measured in cps (counts per second). Sample moisture content indicates the mass percentage of water in the sample, measured in %. Moisture absorption attenuation coefficient is a dimensionless physical quantity describing the degree of X-ray attenuation in a water-containing sample, typically ranging from 0.1 to 1.

[0048] Specifically, when extracting the characteristic fluorescence peaks of the target metal element from the net fluorescence spectroscopy data, a dynamic interval integration method is used: taking the theoretical energy position of the characteristic peak as the center, an integration interval of ±0.15 keV is selected, and the count values ​​within this interval are integrated by area. The trapezoidal rule is used for integration calculation, with an integration step size of 0.02 keV. After obtaining the characteristic peak area, it is divided by the integration time to obtain the characteristic fluorescence peak intensity I_0. Subsequently, the moisture absorption attenuation coefficient μ is calculated by combining the sample moisture content w from the environmental fingerprint data of the mining area. The calculation process is based on the Beer-Lambert law, and the specific steps are as follows: first, the mass absorption coefficient μm of water is obtained by looking up the table according to the characteristic X-ray energy E of the target element, and μm is multiplied by the density ρw of water to obtain the linear absorption coefficient μl. Considering the spatial distribution of moisture in the sample, the equivalent thickness of the moisture layer d = w·ρs·h / ρw is calculated, where ρs is the sample density and h is the sample thickness. The final formula for calculating the moisture absorption attenuation coefficient is: exp(-μl·d).

[0049] Step 1024: Use the moisture absorption attenuation coefficient to perform absorption correction on the characteristic fluorescence peak intensity, substitute the corrected characteristic fluorescence peak intensity into the standard sample concentration-intensity calibration curve, and determine the initial concentration value of the target metal element.

[0050] Absorption correction refers to the process of compensating for the influence of moisture in a sample on X-ray absorption. The standard sample concentration-intensity calibration curve is a quantitative curve reflecting the relationship between elemental content and its characteristic X-ray intensity. The initial concentration value refers to the preliminary elemental content data obtained after various corrections, expressed as a mass percentage (%).

[0051] Specifically, the characteristic fluorescence peak intensity is first corrected for moisture absorption using the formula: I_c = I_0 / exp(-μl·d), where I_c is the corrected intensity and I_0 is the original characteristic peak intensity. The corrected intensity value is then substituted into the standard sample concentration-intensity calibration curve for concentration calculation. The calibration curve is established as follows: 5-8 standard samples with known concentrations are selected, covering the expected concentration range of the sample to be tested. The characteristic fluorescence intensities of these standard samples are measured, and a polynomial fitting is used to establish the relationship between concentration C and intensity I: C = a0 + a1·I + a2·I², where a0, a1, and a2 are fitting coefficients. The least squares method is used for fitting, requiring a goodness of fit R² > 0.999. The corrected characteristic fluorescence peak intensity I_c is substituted into this polynomial equation to calculate the initial concentration value of the target metal element. This initial concentration value will be used for subsequent matrix effect correction calculations.

[0052] Step 103: Construct the mineral phase interference network of the sample to be tested, and calculate the interfacial radiation attenuation rate and secondary fluorescence excitation probability between each mineral phase based on the elemental composition and density characteristics of each mineral phase in the mineral phase interference network, and generate the interphase coupling interference matrix.

[0053] A mineral phase interference network (MTN) is a topological model describing the interactions between different mineral phases in a sample. Each node represents a mineral phase, and the edges represent the interference relationships between adjacent mineral phases. The interface radiation attenuation rate represents the proportion of X-ray energy loss at the interface between adjacent mineral phases, expressed as a percentage. The secondary fluorescence excitation probability refers to the likelihood that an element in one mineral phase will be excited by characteristic X-rays emitted from elements in other phases, ranging from 0 to 1. The interphase coupling interference matrix is ​​an n×n square matrix (n being the number of mineral phases), where the matrix elements represent the fluorescence signal transmission coefficients between mineral phases. Elemental composition refers to the chemical composition and mass fraction of each mineral phase. Density characteristics include the true density (g / cm³) and bulk density (g / cm³) of the mineral phases.

[0054] Specifically, X-ray diffraction analysis is first performed on the sample to obtain the types and mass fractions of mineral phases. Anisotropic and isotropic mineral phases are identified, and the lattice structure evolution coefficients of the anisotropic mineral phases are determined based on weathering parameters from the environmental fingerprint data. Based on the lattice structure evolution coefficients and the mass fractions of each mineral phase, the orientation interference intensity between the anisotropic and isotropic mineral phases is calculated. The orientation interference intensity is calculated using a modified March-Dollase function: R(α) = (r²·cos²α + sin²α / r)^(-3 / 2), where r is the orientation parameter and α is the angle between the crystal plane normal and the preferred orientation direction. An asymmetric interference correlation matrix is ​​constructed based on the calculated orientation interference intensity. Mineral phases with orientation interference intensities greater than a preset threshold (usually 0.1) are marked as directional interference nodes, generating a mineral phase interference network. Elemental composition data for each mineral phase is extracted from this network, and the mass absorption coefficients of the incident X-rays and characteristic fluorescent X-rays for each phase are calculated. The mass absorption coefficient μm is calculated using the formula: μm = Σ(wi·μi), where wi is the mass fraction of element i and μi is the mass absorption coefficient of element i. Based on the mass absorption coefficient and density characteristics, the X-ray penetration depth d = 1 / (μm·ρ) and fluorescence escape depth d' = 1 / (μm'·ρ) at the interface of adjacent mineral phases are calculated. The interface radiation attenuation rate η = 1 - exp(-d / d') is determined according to the ratio of penetration depth to escape depth. The fluorescence excitation energy transfer efficiency of elements with atomic number Z ≥ 20 in each mineral phase to elements with Z < 20 in adjacent phases is calculated to obtain the secondary fluorescence excitation probability P. The energy transfer efficiency is calculated using the Sherman equation: P = ω·(1 - 1 / J)·(μm / μm'), where ω is the fluorescence yield and J is the absorption edge transition ratio. Finally, the interface radiation attenuation rate is used as the attenuation weight, and the secondary fluorescence excitation probability is used as the enhancement weight to construct the interphase coupling interference matrix M, with matrix element Mij = (1 - ηij)·Pij.

[0055] In one possible implementation, a coded identifier is generated based on the cable well location information, channel type, and cable segment attributes, specifically including steps 1031-1033, as follows:

[0056] Step 1031: Perform X-ray diffraction analysis on the sample to be tested to obtain the mineral phase types and mass fractions of each mineral phase; identify the anisotropic and isotropic mineral phases among the mineral phase types, and determine the crystal structure evolution coefficients of the anisotropic mineral phases based on the weathering parameters in the mining area environmental fingerprint data.

[0057] X-ray diffraction analysis is a method for analyzing the structure of materials by utilizing the diffraction phenomenon of X-rays by crystals. The operating voltage is 40kV, the operating current is 40mA, and the scanning angle range is 5-70°. Mineral phases refer to the different types of minerals with different crystal structures present in the sample. Mass fraction represents the percentage of each mineral phase in the total mass of the sample. Anisotropic mineral phases refer to crystal structures with directional growth characteristics, such as platy and columnar minerals. Isotropic mineral phases refer to minerals whose crystal structures have the same properties in all directions. Weathering parameters are quantitative indicators characterizing the degree of weathering of minerals, with values ​​ranging from 0 to 1. Lattice structure evolution coefficients describe the degree of deformation of the mineral lattice under weathering, expressed as strain (%).

[0058] Specifically, the sample to be tested was first ground to below 200 mesh, and an X-ray diffraction sample was prepared using the back-mounting method. Testing was performed on an X-ray diffractometer with a scan step of 0.02° and a counting time of 2 seconds per step. After Ka2 removal, background subtraction, and smoothing, the obtained diffraction patterns were used for qualitative phase analysis using Jade software. The mineral phases in the sample were identified by comparison with a PDF card library. Quantitative analysis was performed using the RIR (Reference Intensity Ratio) method: corundum (Al2O3) was selected as the internal standard, with an addition amount of 10%. For each identified mineral phase i, its mass fraction wi was calculated using the formula: wi = (Ii / Ii,s)·(ki / ks)·ws, where Ii is the characteristic diffraction peak intensity of mineral phase i, Ii,s is the characteristic diffraction peak intensity of the internal standard, ki is the RIR value of mineral phase i, ks is the RIR value of the internal standard, and ws is the mass fraction of the internal standard. Based on crystal structure characteristics, the identified mineral phases are divided into anisotropic mineral phases (such as layered minerals like kaolinite and illite) and isotropic mineral phases (such as quartz and hematite). For anisotropic mineral phases, the lattice structure evolution coefficient ε is calculated using the weathering parameter f from the environmental fingerprint data. The calculation formula is: ε = ε0·[1 + k·ln(1-f)], where ε0 is the lattice strain under standard conditions, and k is the weathering sensitivity coefficient (usually taken as 1.5-2.5). By measuring the full width at half maximum (FWHM) of the diffraction peaks, the Williamson-Hall equation is used: βcosθ = Kλ / D + 4εsinθ, where β is the diffraction peak broadening, θ is the diffraction angle, K is the Scherrer constant (taken as 0.89), λ is the X-ray wavelength, and D is the grain size, to verify the calculated lattice structure evolution coefficient.

[0059] Step 1032: Based on the lattice structure evolution coefficient and the mass fraction of each mineral phase, calculate the orientation interference intensity between the anisotropic and isotropic mineral phases.

[0060] The lattice structure evolution coefficient represents the degree of deformation of the mineral lattice under weathering, expressed as strain (%). Mass fraction is the percentage of each mineral phase in the total sample mass. Anisotropic mineral phases are minerals with directional growth characteristics. Isotropic mineral phases are minerals whose crystal structures have the same properties in all directions. Orientation interference intensity describes the degree of mutual influence between the lattice orientations of different mineral phases, ranging from 0 to 1.

[0061] Specifically, based on the lattice structure evolution coefficient ε and the mass fraction w, the orientation interference intensity I between anisotropic and isotropic mineral phases is calculated. The calculation employs a modified March-Dollase function and a weighted superposition method. First, for each pair of adjacent mineral phases (i, j), the basic orientation function is calculated: R(α) = (r²·cos²α + sin²α / r)^(-3 / 2), where r = 1 + εi is the orientation parameter, and α is the angle between the crystal plane normal and the preferred orientation direction. Then, the mass fraction weight is introduced: wij = (wi·wj)^(1 / 2). Considering the lattice evolution effect: Sij = exp(-|εi-εj| / ε0), where ε0 is the standard strain parameter (taken as 0.01). The final formula for calculating the orientation interference intensity is: Iij = wij·R(α)·Sij. For each anisotropic mineral phase, the orientation interference intensity between it and all isotropic mineral phases is calculated, forming a complete interference intensity matrix.

[0062] Step 1033: Construct an asymmetric interference correlation matrix between mineral phases based on the orientation interference intensity, and mark mineral phases with orientation interference intensity greater than a preset intensity threshold as orientation interference nodes to generate a mineral phase interference network.

[0063] The asymmetric interference correlation matrix is ​​an n×n square matrix (where n is the number of mineral phases) describing the interactions between mineral phases. Oriented interference nodes refer to mineral phases whose oriented interference intensity exceeds a threshold. The mineral phase interference network is a topological structure representing the interaction relationships between mineral phases. The preset intensity threshold is a critical value for determining significant interference, typically set to 0.1.

[0064] Specifically, an asymmetric interference correlation matrix M is constructed based on the calculated orientation interference intensity. The matrix element Mij represents the interference intensity of mineral phase i to mineral phase j, where Mij ≠ Mji. The matrix construction steps are as follows: First, initialize an n×n zero matrix, where n is the total number of mineral phases. For each pair of mineral phases (i, j), fill the matrix element Mij with its orientation interference intensity Iij. Set a preset intensity threshold It = 0.1 and scan all elements in matrix M. When Mij > It, mark the mineral phase pair (i, j) as an orientation interference node, and record its node number and edge weight (orientation interference intensity value). Based on all marked orientation interference nodes and edge weights, construct a mineral phase interference network G. The network construction uses an adjacency list storage structure: G = {V, E}, where V is the set of nodes (mineral phases), and E is the set of edges (interference relationships). Each edge e ∈ E contains start point, end point, and weight information: e = (vi, vj, wij), where vi and vj are mineral phase nodes, and wij is the corresponding orientation interference intensity. The resulting mineral phase interference network will be used for subsequent interfacial radiative attenuation and secondary fluorescence calculations.

[0065] In one possible implementation, based on the elemental composition and density characteristics of each mineral phase in the mineral phase interference network, the interfacial radiation attenuation rate and secondary fluorescence excitation probability between each mineral phase are calculated to generate an interphase coupling interference matrix. Specifically, this includes steps 1034-1037, as follows:

[0066] Step 1034: Extract the elemental composition data of each mineral phase from the mineral phase interference network, and calculate the mass absorption coefficient of each mineral phase relative to the incident X-rays and characteristic fluorescent X-rays based on the elemental composition data; calculate the X-ray penetration depth and fluorescence escape depth at the interface of adjacent mineral phases based on the mass absorption coefficient and the density characteristics of each mineral phase.

[0067] Density characteristics include true density and bulk density, measured in g / cm³. X-ray penetration depth is the depth at which X-ray intensity decays to 1 / e of the incident intensity, measured in μm. Fluorescence escape depth is the maximum depth to which characteristic fluorescent X-rays can escape from the sample, measured in μm.

[0068] Specifically, the chemical composition information of each mineral phase node is obtained from the mineral phase interference network. For each mineral phase, its elemental composition is calculated using stoichiometry. Taking kaolinite (Al2Si2O5(OH)4) as an example: first, the chemical formula is converted to an atomic ratio of Al:Si:O:H = 2:2:9:4, and then the mass fraction is calculated based on the atomic weight of each element: wAl = 20.90%, wSi = 21.76%, wO = 55.78%, wH = 1.56%. For each mineral phase, the mass absorption coefficient μm for incident X-rays (E0) and characteristic fluorescent X-rays (Ef) is calculated. The calculation formula is: μm = Σ(wi·μi), where wi is the mass fraction of element i, and μi is the mass absorption coefficient of element i at the corresponding energy (queried from the NIST database). Based on the calculated mass absorption coefficient μm and the mineral phase density ρ, the X-ray penetration depth d and the fluorescence escape depth d' are calculated. The calculation formulas are: d = 1 / (μm(E0)·ρ), d' = 1 / (μm(Ef)·ρ). These two characteristic depths are calculated for each pair of adjacent mineral phases in the mineral phase interference network.

[0069] Step 1035: Determine the interfacial radiation attenuation rate between each mineral phase based on the ratio of X-ray penetration depth to fluorescence emission depth.

[0070] The ratio of X-ray penetration depth to fluorescence escape depth is a dimensionless parameter characterizing interfacial radiative transport properties. Interfacial radiative attenuation rate describes the proportion of X-ray energy lost at the interface between adjacent mineral phases, and its value ranges from 0 to 1.

[0071] Specifically, for each edge e(vi, vj) in the mineral phase interference network, the corresponding X-ray penetration depths di, dj and fluorescence escape depths di', dj' are obtained. The depth ratios on both sides of the interface are calculated: ri = di / di' and rj = dj / dj'. Based on these ratios, the interface radiation attenuation rate η is calculated. A modified exponential attenuation model is used for the calculation: η = 1 - exp(-|ri - rj| / r0), where r0 is the standard depth ratio (taken as 1.0). When ri ≈ rj, η is close to 0, indicating that the interface radiation attenuation is very small; when |ri - rj| is large, η is close to 1, indicating that the interface radiation attenuation is significant. For each pair of adjacent mineral phases, the forward and reverse interface radiation attenuation rates ηij and ηji are calculated respectively, because interface radiation transmission is directional. The calculated interface radiation attenuation rates are stored in an n×n attenuation rate matrix, which will be used for subsequent interphase coupling interference calculations.

[0072] Step 1036: Calculate the fluorescence excitation energy transfer efficiency of elements with atomic numbers greater than or equal to the preset number in each mineral phase to elements with atomic numbers less than the preset number in adjacent mineral phases, and determine the secondary fluorescence excitation probability between each mineral phase.

[0073] Atomic number is the position of an element in the periodic table. The default atomic number is the threshold value that distinguishes heavy and light elements, usually taken as 20 (the atomic number of Ca). Fluorescence excitation energy transfer efficiency describes the efficiency of high-energy X-rays exciting low-energy elements to produce characteristic fluorescence, with a value ranging from 0 to 1. Secondary fluorescence excitation probability represents the probability that an element in one mineral phase will be excited by characteristic X-rays emitted from elements in an adjacent phase, with a value ranging from 0 to 1.

[0074] Specifically, firstly, elements with atomic numbers Z ≥ 20 (heavy elements) and Z < 20 (light elements) in each mineral phase are identified. For each edge e(vi, vj) in the mineral phase interference network, the fluorescence excitation energy transfer efficiency Eij of the heavy element in vi to the light element in vj is calculated. The calculation uses the Sherman equation: Eij = ωj·(1-1 / Jj)·(μm,j / μm,i)·Qij, where ωj is the fluorescence yield of the light element, Jj is the absorption edge transition ratio, μm,j and μm,i are the mass absorption coefficients of the light and heavy elements, respectively, and Qij is the excitation coefficient. The excitation coefficient Qij is calculated as: Qij = ln(Ei / Ej) / (Ei / Ej - 1), where Ei is the characteristic X-ray energy of the heavy element and Ej is the absorption edge energy of the light element. The secondary fluorescence excitation probability P is calculated based on the energy transfer efficiency. Calculation formula: Pij = Eij·ci·cj·exp(-μeff·x), where ci and cj are the mass fractions of the elements, μeff is the effective mass absorption coefficient, and x is the average action distance (usually taken as 10μm).

[0075] Step 1037: Using the interfacial radiation attenuation rate as the attenuation weight and the secondary fluorescence excitation probability as the enhancement weight, construct an interphase coupling interference matrix that reflects the interphase fluorescence signal transmission relationship of minerals.

[0076] The interfacial radiation attenuation rate, used as an attenuation weight, represents the energy loss effect of X-rays at the interface. The secondary fluorescence excitation probability, used as an enhancement weight, represents the generation effect of secondary fluorescence. The interphase coupling interference matrix is ​​an n×n square matrix describing the interphase fluorescence signal transmission relationship of minerals.

[0077] Specifically, first, an n×n zero matrix is ​​initialized, where n is the total number of mineral phases. For each edge e(vi, vj) in the mineral phase interference network, the corresponding interfacial radiation attenuation rate ηij and secondary fluorescence excitation probability Pij are obtained. The coupling coefficient is calculated as: Mij = (1-ηij)·Pij, where (1-ηij) represents the proportion of X-rays passing through the interface, and Pij represents the probability of secondary fluorescence generation. Since both interfacial radiation transmission and secondary fluorescence excitation are directional, Mij ≠ Mji, forming an asymmetric matrix. The matrix M is normalized as: M' = M / max(M), mapping the values ​​of all elements to the interval [0, 1]. The final interphase coupling interference matrix M' reflects the transmission relationship of fluorescence signals between mineral phases, where the larger the value of the matrix element M'ij, the stronger the interference effect of mineral phase i on mineral phase j. This matrix will be used for subsequent matrix effect correction calculations.

[0078] Step 104: Based on the temperature, humidity and porosity parameters in the environmental fingerprint data of the mining area, construct an environmental stress field model, calculate the lattice distortion coefficient of each mineral phase under different environmental stresses, and use the lattice distortion coefficient to perform weighted correction on the interphase coupling interference matrix to obtain the environmental interference model.

[0079] The temperature and humidity parameters include time-series data for ambient temperature (°C) and relative humidity (%). The porosity parameter represents the proportion of pores in the sample volume. The environmental stress field model describes the distribution of mechanical stress caused by changes in temperature and humidity. The lattice distortion coefficient represents the degree of lattice deformation under environmental stress, expressed as strain (%). The interphase coupling interference matrix is ​​an n×n square matrix describing the transmission relationship of fluorescence signals between mineral phases. The environmental interference model is a mineral phase interaction model that considers the influence of environmental factors.

[0080] Specifically, an environmental stress field model is first constructed based on temperature and humidity parameters (T, RH) and porosity φ. Temperature stress σT is calculated as follows: σT = αT·ΔT·E / (1-ν), where αT is the coefficient of thermal expansion, ΔT is the temperature change, E is Young's modulus, and ν is Poisson's ratio. Humidity stress σH is calculated as follows: σH = βH·ΔRH·E / (1-ν), where βH is the coefficient of humidity expansion, and ΔRH is the relative humidity change. Pore stress σP is calculated as follows: σP = P·(1-φ) / φ, where P is the pore pressure (determined by capillary action). Total environmental stress σE = σT + σH + σP. For each mineral phase i, the lattice distortion coefficient εi is calculated based on its crystal structure characteristics. The calculation employs a modified Hooke's law: εi = σE / Ei·[1 + γi·ln(1+φ)], where Ei is the elastic modulus of the mineral phase, and γi is the porosity sensitivity coefficient (typically taken as 0.5-1.5). The interphase coupling interference matrix M is weighted and corrected using the lattice distortion coefficient. The correction formula is: M'ij = Mij·exp[-(εi - εj)² / ε0²], where ε0 is the standard strain parameter (taken as 0.01). When the lattice distortion difference between adjacent mineral phases is large, the exp term is smaller, reducing the coupling strength; when the distortion difference is small, the exp term is close to 1, maintaining the original coupling strength. The final environmental interference model M' reflects the influence of environmental factors on the interphase interaction.

[0081] Step 105: Input the symbiotic mineral phase combination in the mineral phase interference network into the environmental disturbance model to determine the nonlinear matrix effect deviation value of the symbiotic minerals relative to the target metal element.

[0082] A symbiotic mineral phase assemblage refers to two or more mineral phases coexisting in the same sample. An environmental interference model is a mathematical model of mineral phase interactions that takes into account the influence of environmental factors. A target metal element is a specific metal element whose content needs to be determined. The nonlinear matrix effect deviation value represents the degree of nonlinear influence of the symbiotic minerals on the fluorescence intensity of the target element, expressed as a relative deviation (%).

[0083] Specifically, firstly, all coexisting mineral phase combinations are extracted from the mineral phase interference network, with each combination containing two or more adjacent mineral phase nodes. For each coexisting combination C(i, j, ..., k), the corresponding coupling coefficient is obtained from the environmental interference model M''. The matrix effect deviation is calculated using an iterative method: First, the initial matrix effect coefficient B0 is calculated. The calculation formula is: B0 = Σ(M''ij·wi·wj), where M''ij is the coupling coefficient in the environmental interference model, and wi and wj are the mass fractions of the mineral phases. Second, considering the absorption and enhancement effects of elemental characteristic X-rays, the correction coefficient K is calculated. The absorption effect Ka = exp[-μm(Ec)·ρ·d], where μm(Ec) is the mass absorption coefficient at the target element's characteristic X-ray energy Ec, ρ is the sample density, and d is the characteristic layer thickness. The enhancement effect Ke = Σ(ωt·Jt·Qt), where ωt is the fluorescence yield of the target element, Jt is the transition ratio, and Qt is the excitation coefficient. Third, the nonlinear correction term δ is calculated. The calculation formula is: δ = B0·(1 + α·B0 + β·B0²), where α and β are nonlinear coefficients obtained through standard sample calibration. The fourth step is to calculate the total matrix effect deviation value ΔB. The calculation formula is: ΔB = Ka·Ke·δ. Finally, for each symbiotic mineral phase combination, the nonlinear matrix effect deviation value for the target metal element is output. The larger the matrix effect deviation value, the stronger the interference of the symbiotic combination on the determination of the target element. A specific calculation example: Assume the target element is Fe, and the symbiotic combination is quartz (SiO2) and hematite (Fe2O3). First, obtain M''12 = 0.85, mass fraction w1 = 0.6, and w2 = 0.4 from the environmental interference model. Calculate B0 = 0.85·0.6·0.4 = 0.204. The Kα line energy of Fe is 6.4keV, and Ka is calculated as 0.92. Considering the enhancing effect of Si, Ke is calculated as 1.15. With nonlinear coefficients α = 0.3 and β = 0.1, the calculated δ = 0.204·(1 + 0.3·0.204 + 0.1·0.204²) = 0.218. The final matrix effect deviation ΔB = 0.92·1.15·0.218 = 0.231, or 23.1%.

[0084] In one possible implementation, a coded identifier is generated based on the cable well location information, channel type, and cable segment attributes, specifically including steps 1051-1054, as follows:

[0085] Step 1051: Based on the topology of the mineral phase interference network, input the symbiotic mineral phase combination in the mineral phase interference network into the environmental disturbance model, determine the nonlinear matrix effect deviation value of the symbiotic minerals relative to the target metal element, and generate the in-situ symbiotic topological cluster to be analyzed.

[0086] The environmental disturbance model is a mathematical model of mineral phase interactions that takes into account the influence of environmental factors. The nonlinear matrix effect deviation value represents the degree of nonlinear influence of the coexisting minerals on the fluorescence intensity of the target element, expressed as a relative deviation (%). The in-situ coexisting topological cluster is a set of mineral phase sub-networks with significant interactions.

[0087] Specifically, the Bron-Kerbosch algorithm is first used to search for maximal cliques in the mineral phase interference network to identify all fully connected co-occurring mineral phase assemblages. An edge weight threshold θ = 0.1 is set, retaining only edges with weights greater than the threshold to ensure that the identified co-occurring assemblages have significant interactions. For each identified co-occurring assembly C = {v1, v2, ..., vk}, the average coupling strength within the assembly is calculated: S = [Σi,j∈C(M''ij)] / (k·(k-1)), where M''ij is the coupling coefficient in the environmental interference model. When S>0.3, the co-occurring assembly is marked as a candidate topological cluster. For each candidate topological cluster, its matrix effect deviation value ΔB for the target metal element is calculated. The calculation uses an improved matrix effect equation: ΔB = Σi,j∈C(M''ij·wi·wj·fij), where wi and wj are the mass fractions of the mineral phases, and fij is the elemental composition correlation function. The formula for calculating the elemental composition correlation function is: fij = exp(-|Zi - Zj| / Z0)·(1 + κ·cij), where Zi and Zj are the atomic numbers of the heaviest elements in the mineral phase, Z0 is the reference atomic number (taken as 20), κ is the composition sensitivity coefficient (taken as 0.5), and cij is the mass fraction ratio of common elements. Finally, candidate topological clusters are classified according to the calculated matrix effect deviation value. A matrix effect significance threshold ΔB0 = 0.15 is set; when ΔB > ΔB0, the candidate topological cluster is determined as an in-situ co-occurring topological cluster. For all determined in-situ co-occurring topological clusters, their node set, edge set, internal coupling strength, and matrix effect deviation value are recorded to form a complete topological cluster description. These in-situ co-occurring topological clusters will be used for subsequent matrix effect compensation calculations.

[0088] Step 1052: Analyze the environmental-lattice coupling transport tensor of the carrier mineral phase node and each associated disturbed phase node under the current environmental stress from the environmental disturbance model.

[0089] The carrier mineral phase node refers to the main mineral phase containing the target metallic element. Associated perturbation phase nodes refer to other mineral phases that coexist with the carrier mineral phase and interfere with it. Environmental stress is the mechanical stress caused by environmental factors such as temperature, humidity, and porosity. The environment-lattice coupling transport tensor is a second-order tensor describing the energy and mass transfer characteristics between mineral phases under environmental stress, including directional transport coefficients.

[0090] Specifically, the process begins by identifying carrier mineral phases and all associated perturbed phase nodes from the environmental disturbance model. For each carrier phase-perturbed phase pair, a three-dimensional environment-lattice coupled transport tensor is constructed. Three main factors need to be considered during tensor construction: first, the coupling coefficient in the environmental disturbance model, which reflects the strength of the fundamental interactions between mineral phases; second, directional correlation, calculated based on the relationship between crystallographic directions and transport directions; and third, lattice distortion coupling effects, calculated using the lattice distortion coefficient. Each element of the transport tensor needs to further consider the anisotropic effects of environmental stress, including the differences in stress components in different directions and their modulation effect on the transport process. Eigenvalue decomposition is performed on the constructed transport tensor to obtain the main transport directions and corresponding transport intensities. A significance criterion is set based on the maximum eigenvalue to identify carrier phase-perturbed phase pairs with strong coupled transport characteristics. All significant environment-lattice coupled transport tensors are recorded in a tensor field data structure to form a complete description of transport characteristics. These transport tensors contain information on the influence of environmental stress on the interactions between mineral phases and will be used for subsequent transport effect compensation calculations. The analytical results of the transport tensor directly reflect the coupling transport characteristics between the carrier mineral phase and each associated perturbation phase under the current environmental conditions, providing a theoretical basis for accurately assessing and compensating for matrix effects.

[0091] Step 1053: Combining the mass fraction of each associated perturbation phase node, the fluorescence flux modulation of the in-situ co-occurring topological cluster is calculated using the environment-lattice coupling transport tensor, and the matrix radiation index of the characteristic fluorescence of each associated perturbation phase node for the target metal element is quantified.

[0092] Associated perturbation phase nodes refer to other mineral phases that coexist with the carrier mineral phase and cause interference. The environment-lattice coupling transport tensor describes the energy and mass transfer characteristics between mineral phases under environmental stress. In-situ co-occurring topological clusters are sets of mineral phase sub-networks with significant interactions. Fluorescence flux modulation calculation is a quantitative calculation process for analyzing the perturbation of fluorescence X-ray intensity. The matrix radiation index is a dimensionless parameter that quantifies the degree of influence of associated phases on the fluorescence signal of the target element.

[0093] Specifically, the mass fraction *wi* of the associated phase and the environment-lattice coupling transport tensor *T* are first obtained. The fluorescence flux modulation factor *F* is calculated through three-dimensional spatial integration. The integration process employs a partitioned integration method, dividing the sample space into N×N×N micro-volumes (N is typically 50). For each micro-volume, the transport paths of incident X-rays and fluorescent X-rays are calculated, and the energy transfer efficiency is calculated using the transport tensor. The contributions of all micro-volumes are summed to obtain the total flux modulation factor. The matrix radiation index *R* is calculated based on the flux modulation factor. The calculation process considers the elemental composition, density, and spatial distribution characteristics of the associated phase. Finally, the matrix radiation index of each associated perturbation phase node for the characteristic fluorescence of the target element is obtained.

[0094] Step 1054: Based on the detection sensitivity of the X-ray fluorescence spectrometer, map the matrix radiation index to an equivalent grade drift vector in the concentration dimension, and set the modulus of the equivalent grade drift vector.

[0095] Typical values ​​range from 0.1 to 10 cps / ppm. The matrix irradiance index is a dimensionless parameter quantifying the influence of associated fluorescence signals on the target element, typically ranging from -1 to 1. The equivalent grade drift vector is a three-dimensional vector describing the apparent concentration deviation caused by matrix effects; its components represent concentration deviations along different crystal planes. The concentration dimension refers to the dimensional space of elemental content, usually expressed as mass fraction (%) or parts per million (ppm). It reflects the total concentration deviation.

[0096] Specifically, the process of mapping the matrix radiation index to an equivalent grade drift vector includes the following steps: First, obtain the basic detection parameters of the X-ray fluorescence spectrometer, including detection sensitivity S, energy resolution ΔE / E, and counting efficiency η. For each matrix radiation index Ri, perform an intensity-to-concentration conversion based on the detection sensitivity. The conversion process considers three directional components: first, the concentration deviation parallel to the crystal plane normal direction, calculated using the normal component of the matrix radiation index; second, the concentration deviation parallel to the two orthogonal directions of the crystal plane, calculated using the tangential component of the matrix radiation index. The concentration deviation calculation in each direction needs to consider the detector response characteristics correction. The corrected three directional components constitute the equivalent grade drift vector D. Finally, calculate the modulus |D| of the equivalent grade drift vector, which is the square root of the sum of the squares of the three directional components. This modulus directly represents the concentration measurement deviation caused by the matrix effect, and its value is the same as the unit of the target element's content. The modulus calculation result will be used for subsequent matrix effect compensation and uncertainty assessment.

[0097] Step 106: Use the nonlinear matrix effect deviation value to compensate and correct the initial concentration value until the concentration difference between two adjacent corrections is less than the preset threshold, and output the target grade data of the target metal element.

[0098] The nonlinear matrix effect deviation value describes the degree of nonlinear influence of the coexisting minerals on the fluorescence intensity of the target element, expressed as a relative deviation (%). The initial concentration value is the preliminary elemental content data obtained after basic correction, expressed as a mass fraction (%). Compensation correction is the process of eliminating the influence of the matrix effect through iterative calculation. The preset threshold is the concentration difference standard for determining the convergence of the compensation correction, typically set at 0.1% of the initial concentration. The target grade data is the final elemental content result after matrix effect compensation.

[0099] Specifically, initial values ​​are set: C0 is the initial concentration value, and ΔB is the nonlinear matrix effect deviation value. The first iteration calculates: C1 = C0 / (1 + ΔB). The concentration difference is calculated: δC1 = |C1 - C0|. If δC1 is greater than a preset threshold, a second iteration is performed: the matrix effect deviation value ΔB' is recalculated based on C1, taking into account the nonlinear characteristics of the matrix effect. The updated deviation value is used to calculate C2 = C1 / (1 + ΔB'). The new concentration difference is calculated: δC2 = |C2 - C1|. This iterative process is repeated until, in the nth iteration, the concentration difference δCn is less than the preset threshold. The concentration value Cn obtained in the last iteration is the target grade data. During the compensation and correction process, the matrix effect deviation value needs to be updated in each iteration to ensure that the feedback effect of concentration changes on the matrix effect is considered. The entire iterative process typically converges within 3-5 iterations. The output target grade data includes the final concentration value, the number of iterations, and the convergence accuracy. This result has eliminated the influence of nonlinear matrix effects caused by symbiotic mineral phases and can accurately reflect the true content of the target metal element.

[0100] In the above embodiments, the environmental effects of the sample were characterized through analysis of mining area environmental parameters and stress field modeling. To further improve the quantitative accuracy of the influence of environmental stress on mineral phase lattice distortion and reduce the interference of multi-factor coupling on lattice strain calculation, this application also provides an X-ray fluorescence spectrometry detection method for bauxite. This method constructs a unified environmental stress field model by analyzing the spatial distribution characteristics of temperature, humidity, and porosity, stress transmission mechanisms, and lattice response laws, and performs lattice strain calculation, enabling the system to more accurately characterize the structural deformation characteristics of mineral phases under complex environmental conditions. The following is a combination of... Figure 2 Another X-ray fluorescence spectrometry detection method for bauxite in this application embodiment is described below:

[0101] Please see Figure 2 This is a schematic flowchart of an X-ray fluorescence spectrometry detection method for bauxite in an embodiment of this application.

[0102] Step 201: Obtain the temperature, humidity and porosity parameters from the environmental fingerprint data of the mining area, and determine the thermal expansion stress and hydration expansion stress values ​​inside the sample to be tested based on the temperature and humidity parameters.

[0103] Mining area environmental fingerprint data is a set of digital parameters describing the environmental characteristics of a mining area. Temperature parameters represent the temperature value and variation characteristics of the sample's environment, expressed in degrees Celsius. Humidity parameters describe the relative humidity value and distribution characteristics of the environment, expressed as a percentage. Porosity parameters represent the proportion of pore volume to total volume in the sample. Thermal expansion stress values ​​are the internal stresses caused by temperature changes, expressed in MPa. Hydration expansion stress values ​​are the internal stresses caused by moisture absorption, expressed in MPa.

[0104] Specifically, temperature parameters T(x, y, z, t), humidity parameters H(x, y, z, t), and porosity parameters P(x, y, z) are first extracted from the mining area environmental fingerprint database. Based on the temperature parameters, thermal expansion stress values ​​are calculated, taking into account the thermal expansion coefficient α, elastic modulus E, and Poisson's ratio ν of the sample material. In three-dimensional space, the thermal stress components in the x, y, and z directions are calculated separately. For isotropic materials, the volume change caused by the temperature gradient ΔT must be considered in the thermal stress calculation. Based on the humidity parameters, hydration expansion stress values ​​are calculated, taking into account the water absorption expansion coefficient β and moisture diffusion coefficient D of the material. A nonlinear diffusion model is used for hydration stress calculation, considering the volume change caused by the humidity gradient ΔH. Finally, the thermal expansion stress tensor and hydration expansion stress tensor are output for subsequent stress field model construction.

[0105] Step 202: Determine the pore distribution characteristics of the sample to be tested based on the porosity parameters, and calculate the stress concentration factor at the pore boundary based on the pore distribution characteristics; construct an environmental stress field model describing the stress distribution state inside the sample based on the thermal expansion stress value, hydration expansion stress value and stress concentration factor.

[0106] Pore ​​distribution characteristics describe the spatial distribution of pores in a sample. The stress concentration factor is a dimensionless parameter describing the stress amplification effect at pore boundaries. The environmental stress field model is a mathematical model describing the stress distribution state inside the sample.

[0107] Specifically, a three-dimensional pore distribution model is constructed based on porosity parameters. Statistical analysis methods are used to determine the pore size distribution, shape characteristics, and spatial connectivity. For each identified pore, the stress concentration factor K at its boundary is calculated. The stress concentration factor calculation considers the pore geometry, size, and orientation. For ellipsoidal pores, the stress concentration factor is calculated based on the major and minor axis ratios and direction cosines. The thermal expansion stress tensor σT and the hydration expansion stress tensor σH are coupled with the stress concentration factor K to construct an environmental stress field model. The stress field model is described in tensor form, including anisotropic stress distribution and stress concentration effects. The model output includes principal stress distribution, stress intensity, and stress gradient, which are used to assess the impact of environmental stress on the sample structure.

[0108] Step 203: Use the thermal expansion coefficient and hydration sensitivity coefficient of each mineral phase in the mineral phase interference network as input parameters, and substitute them into the environmental stress field model to calculate the equivalent environmental stress on each mineral phase.

[0109] The coefficient of thermal expansion represents the rate of volume change of a mineral when the temperature changes, and its unit is K^-1. The hydration sensitivity coefficient describes the degree of response of a mineral to moisture, and its unit is % / %RH. The environmental stress field model is a mathematical model describing the stress distribution inside a sample. The equivalent environmental stress is the total stress value combining thermal stress and hydration stress, and its unit is MPa.

[0110] Specifically, material parameters for each mineral phase node are extracted from the mineral phase interference network. For each mineral phase, its thermal expansion coefficient αi and hydration sensitivity coefficient βi are read. These material parameters are substituted into the environmental stress field model. The calculation process consists of three steps: First, the thermal stress component is calculated by multiplying the thermal expansion coefficient by the temperature field gradient; second, the hydration stress component is calculated by multiplying the hydration sensitivity coefficient by the humidity field gradient; finally, considering the stress concentration effect, the equivalent environmental stress σe borne by each mineral phase is calculated. The calculation of equivalent environmental stress needs to consider the spatial location of the mineral phase, the surrounding porosity distribution, and the stress transmission path. The equivalent environmental stress tensor for each mineral phase node is output for subsequent lattice distortion analysis.

[0111] Step 204: Calculate the lattice distortion coefficient of each mineral phase under the current environmental stress based on the elastic modulus and equivalent environmental stress of each mineral phase.

[0112] The elastic modulus is a physical quantity that describes a material's resistance to elastic deformation, measured in gigabytes of pressure (GPa). Equivalent environmental stress is the combined stress acting on a mineral phase. The lattice distortion coefficient is a dimensionless parameter that describes the degree of deformation in a crystal structure.

[0113] Specifically, for each mineral phase, its elastic modulus tensor E is first obtained. The elastic modulus tensor contains elastic constants along the principal axes and shear directions. Based on the calculated equivalent environmental stress σe and the elastic modulus tensor E, the lattice distortion coefficient ε is calculated. The calculation process employs the generalized Hooke's law, considering the linear relationship between stress and strain. For anisotropic crystals, the strain components in different crystal orientations need to be calculated separately. The calculation of the lattice distortion coefficient also needs to consider the symmetry of the crystal structure and the non-uniformity of stress distribution. For each mineral phase, its lattice distortion coefficient tensor under the current environmental stress is output, which describes the direction and extent of lattice deformation. The lattice distortion coefficient will be used in subsequent environmental-lattice coupling analysis.

[0114] In one possible implementation, the lattice distortion coefficient of each mineral phase under the current environmental stress is calculated based on the elastic modulus and equivalent environmental stress of each mineral phase, specifically including steps 2041-2042, as follows:

[0115] Step 2041: For each mineral phase, calculate the elastic strain of the mineral phase under environmental stress based on the elastic modulus and equivalent environmental stress of the mineral phase, and use the elastic strain as the lattice strain.

[0116] Elastic modulus is a parameter describing a material's resistance to deformation, including Young's modulus, Poisson's ratio, and shear modulus, with units of GPa, dimensionless, and GPa, respectively. Equivalent environmental stress is the total stress value combining thermal and hydration stresses, with units of MPa. Elastic strain describes the degree of deformation of a material within its elastic deformation range and is a dimensionless tensor. Lattice strain represents the relative change in crystal unit cell parameters relative to an ideal state and is also a dimensionless tensor.

[0117] Specifically, firstly, the elastic constant matrix C of the mineral phase is obtained, which describes the elastic properties of the material in various directions. For the cubic crystal system, the elastic constant matrix contains three independent components C11, C12, and C44; for the hexagonal crystal system, it contains five independent components C11, C12, C13, C33, and C44; and for the orthorhombic crystal system, it contains nine independent components. Then, the equivalent environmental stress tensor σe is decomposed into principal stress components and shear stress components. Based on the generalized Hooke's law, a set of stress-strain equations is established. For anisotropic crystals, the stress-strain relationship in different crystal orientations needs to be considered. The elastic strain tensor ε is calculated by solving the set of equations. The elastic strain tensor contains the normal strain component εii and the shear strain component εij. The principal axis transformation is performed on the calculated elastic strain tensor to obtain the principal strain values ​​and principal strain directions. The elastic strain tensor is directly used as the lattice strain tensor to characterize the deformation state of the lattice. The lattice strain tensor describes the relative changes in cell parameters, including the stretching of cell edges and the changes in cell angles. These deformation parameters will be used for subsequent lattice distortion analysis and environment-lattice coupling calculations.

[0118] Step 2042: Correct the interplanar spacing of the mineral phase based on the lattice strain, and determine the deviation rate between the corrected interplanar spacing and the standard interplanar spacing as the lattice distortion coefficient.

[0119] Lattice strain is a tensor describing the degree of deformation in a crystal structure, containing both normal and shear strain components. Interplanar spacing refers to the perpendicular distance between adjacent crystal faces in a crystal. Standard interplanar spacing is the interplanar spacing under ideal stress-free conditions. Lattice distortion coefficient is a dimensionless parameter representing the deviation of the actual interplanar spacing from the standard interplanar spacing. Mineral phases are solid substances with specific chemical compositions and crystal structures. Deviation rate is the relative difference between the actual value and the standard value.

[0120] Specifically, the crystal plane indices are first determined based on the crystal structure of the mineral phase. For each crystal plane, its standard interplanar spacing d0 is obtained. The interplanar spacing correction calculation uses the strain tensor transformation method. The lattice strain tensor ε is transformed from the laboratory coordinate system to the crystal coordinate system. For each crystal plane, the strain component εn in the normal direction is calculated. The strain component calculation must consider the crystal symmetry and crystal orientation relationship. The corrected interplanar spacing calculation formula is d = d0(1 + εn), where d0 is the standard interplanar spacing and εn is the normal strain component. The lattice distortion coefficient of each crystal plane is calculated using the formula ε = (d - d0) / d0. Statistical analysis is performed on the lattice distortion coefficients of all crystal planes to obtain the average distortion coefficient and distortion distribution characteristics. The output lattice distortion coefficient reflects the degree of crystal structure deformation caused by environmental stress and will be used for subsequent environmental-lattice coupling analysis.

[0121] The following describes an X-ray fluorescence spectrometry detection system for bauxite from the perspective of hardware processing in an embodiment of this invention. Please refer to [link to relevant documentation]. Figure 3 This is a schematic diagram of the structure of an X-ray fluorescence spectrometry detection system for bauxite in an embodiment of this application.

[0122] It should be noted that, Figure 3 The structure of the X-ray fluorescence spectrometry detection system for bauxite shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0123] like Figure 3 As shown, an X-ray fluorescence spectrometry detection system for bauxite includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 302 or a program loaded from storage section 308 into random access memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0124] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0125] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0126] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0128] Specifically, the X-ray fluorescence spectroscopy detection system for bauxite in this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the X-ray fluorescence spectroscopy detection method for bauxite provided in the above embodiment.

[0129] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the bauxite X-ray fluorescence spectrometry detection system described in the above embodiments; or it may exist independently and not incorporated into the bauxite X-ray fluorescence spectrometry detection system. The storage medium carries one or more computer programs, which, when executed by a processor of the bauxite X-ray fluorescence spectrometry detection system, enable the bauxite X-ray fluorescence spectrometry detection system to implement the bauxite X-ray fluorescence spectrometry detection method based on IoT data encryption transmission provided in the above embodiments.

Claims

1. A method for X-ray fluorescence spectrometry detection of bauxite, characterized in that, The method includes: Acquire the test samples and mining area environmental fingerprint data of the target production area; The fluorescence spectral response data of the sample to be tested was obtained by X-ray fluorescence spectrometry, and the background noise was removed from the fluorescence spectral response data based on the environmental fingerprint data of the mining area to determine the initial concentration value of the target metal element. A mineral phase interference network of the sample to be tested is constructed, and based on the elemental composition and density characteristics of each mineral phase in the mineral phase interference network, the interfacial radiation attenuation rate and secondary fluorescence excitation probability between each mineral phase are calculated to generate an interphase coupling interference matrix. Based on the temperature, humidity and porosity parameters in the environmental fingerprint data of the mining area, an environmental stress field model is constructed, the lattice distortion coefficients of each mineral phase under different environmental stresses are calculated, and the interphase coupling interference matrix is ​​weighted and corrected using the lattice distortion coefficients to obtain an environmental interference model. The coexisting mineral phase combination in the mineral phase interference network is input into the environmental disturbance model to determine the nonlinear matrix effect deviation value of the coexisting mineral relative to the target metal element; The initial concentration value is compensated and corrected using the nonlinear matrix effect deviation value until the concentration difference between two adjacent corrections is less than a preset threshold, and the target grade data of the target metal element is output. The step of calculating the interfacial radiation attenuation rate and secondary fluorescence excitation probability between each mineral phase based on the elemental composition and density characteristics of each mineral phase in the mineral phase interference network, and generating an interphase coupling interference matrix, includes: Elemental composition data of each mineral phase is extracted from the mineral phase interference network, and the mass absorption coefficient of each mineral phase relative to incident X-rays and characteristic fluorescent X-rays is calculated based on the elemental composition data. Based on the mass absorption coefficient and the density characteristics of each mineral phase, the X-ray penetration depth and fluorescence emission depth at the interface of adjacent mineral phases are calculated. The interfacial radiation attenuation rate between each mineral phase is determined based on the ratio of the X-ray penetration depth to the fluorescence escape depth. Calculate the fluorescence excitation energy transfer efficiency of elements with atomic numbers greater than or equal to a preset number in each mineral phase to elements with atomic numbers less than a preset number in adjacent mineral phases, and determine the secondary fluorescence excitation probability between each mineral phase. Using the interface radiation attenuation rate as the attenuation weight and the secondary fluorescence excitation probability as the enhancement weight, an interphase coupling interference matrix reflecting the interphase fluorescence signal transmission relationship of minerals is constructed. The step of inputting the symbiotic mineral phase combination in the mineral phase interference network into the environmental disturbance model to determine the nonlinear matrix effect deviation value of the symbiotic minerals relative to the target metal element includes: Based on the topology of the mineral phase interference network, the carrier mineral phase node carrying the target metal element and the associated perturbation phase node connected to the carrier mineral phase node are determined, and the in-situ symbiotic topological cluster to be analyzed is generated. The environmental-lattice coupling transport tensor of the carrier mineral phase node and each of the associated disturbed phase nodes under the current environmental stress is analyzed from the environmental disturbance model. Based on the mass fraction of each associated perturbation phase node, the fluorescence flux modulation of the in-situ co-occurring topological cluster is calculated using the environment-lattice coupling transport tensor, quantifying the matrix radiation index of the characteristic fluorescence of each associated perturbation phase node for the target metal element. Based on the detection sensitivity of the X-ray fluorescence spectrometer, the matrix radiation index is mapped to an equivalent grade drift vector in the concentration dimension, and the modulus of the equivalent grade drift vector is determined as the nonlinear matrix effect deviation value.

2. The method according to claim 1, characterized in that, The step of removing background noise from the fluorescence spectral response data based on the environmental fingerprint data of the mining area to determine the initial concentration value of the target metal element includes: Atmospheric scattering index and dust concentration parameters of the mining area are extracted from the environmental fingerprint data of the mining area to construct an environmental background noise benchmark library. The fluorescence spectral response data is spectrally matched with the environmental background noise reference spectral library to identify characteristic noise peaks and calculate the noise contribution coefficients corresponding to the characteristic noise peaks. Based on the noise contribution coefficient, the spectral intensity of the corresponding peak in the fluorescence spectral response data is subtracted from the background to obtain the net fluorescence spectral data after noise removal. The characteristic fluorescence peak intensity of the target metal element is extracted from the net fluorescence spectral data, and the moisture absorption attenuation coefficient is calculated by combining the sample moisture content parameter in the mining area environmental fingerprint data. The intensity of the characteristic fluorescence peak is corrected by the moisture absorption attenuation coefficient. The corrected intensity of the characteristic fluorescence peak is then substituted into the standard sample concentration-intensity calibration curve to determine the initial concentration value of the target metal element.

3. The method according to claim 1, characterized in that, The construction of the mineral phase interference network of the sample to be tested includes: X-ray diffraction analysis was performed on the sample to obtain the mineral phase types and mass fractions of each mineral phase. Identify anisotropic and isotropic mineral phases among the mineral phase types, and determine the lattice structure evolution coefficient of the anisotropic mineral phase based on the weathering parameters in the environmental fingerprint data of the mining area; Based on the lattice structure evolution coefficient and the mass fraction of each mineral phase, the orientation interference intensity between the anisotropic mineral phase and the isotropic mineral phase is calculated; Based on the orientation interference intensity, an asymmetric interference correlation matrix between mineral phases is constructed, and mineral phases with orientation interference intensity greater than a preset intensity threshold are marked as directional interference nodes to generate the mineral phase interference network.

4. The method according to claim 1, characterized in that, The environmental stress field model is constructed based on the temperature, humidity, and porosity parameters in the environmental fingerprint data of the mining area. The lattice distortion coefficients of each mineral phase under different environmental stresses are calculated, including: The temperature, humidity and porosity parameters in the environmental fingerprint data of the mining area are obtained, and the thermal expansion stress and hydration expansion stress values ​​inside the sample to be tested are determined based on the temperature and humidity parameters, respectively. The porosity parameters are used to determine the pore distribution characteristics of the sample under test, and the stress concentration factor at the pore boundary is calculated based on the pore distribution characteristics. Based on the thermal expansion stress value, the hydration expansion stress value, and the stress concentration factor, an environmental stress field model describing the stress distribution state inside the sample is constructed. The thermal expansion coefficient and hydration sensitivity coefficient of each mineral phase in the mineral phase interference network are used as input parameters and substituted into the environmental stress field model to calculate the equivalent environmental stress on each mineral phase. Based on the elastic modulus and equivalent environmental stress of each mineral phase, the lattice distortion coefficient of each mineral phase under the current environmental stress is calculated.

5. The method according to claim 4, characterized in that, The step of calculating the lattice distortion coefficient of each mineral phase under the current environmental stress based on the elastic modulus and equivalent environmental stress of each mineral phase includes: For each mineral phase, the elastic strain of the mineral phase under environmental stress is calculated based on the elastic modulus and equivalent environmental stress of the mineral phase, and the elastic strain is used as the lattice strain. The interplanar spacing of the mineral phase is corrected based on the lattice strain, and the deviation rate between the corrected interplanar spacing and the standard interplanar spacing is determined as the lattice distortion coefficient.

6. An X-ray fluorescence spectrometry detection system for bauxite, characterized in that, The X-ray fluorescence spectrometry detection system for bauxite includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the X-ray fluorescence spectrometry detection system for bauxite to perform the method as described in any one of claims 1-5.

7. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the bauxite X-ray fluorescence spectrometry detection system, the bauxite X-ray fluorescence spectrometry detection system performs the method as described in any one of claims 1-5.

8. A computer program product, characterized in that, When the computer program product is run on the bauxite X-ray fluorescence spectrometry detection system, the bauxite X-ray fluorescence spectrometry detection system performs the method as described in any one of claims 1-5.

Images