A rapid identification and quantification method for fluorite minerals based on full-spectrum fitting

Abstract

This invention discloses a rapid identification and quantification method for fluorite minerals based on full-spectrum fitting. The method includes: acquiring a sample to be tested; detecting the sample using an XRD detection module to determine the mineral type and proportion of a first mineral; detecting the sample using a single-wavelength X-ray fluorescence detection module to determine the element type and content of a first element; calculating the proportion of a second mineral for each first mineral based on the mineral type of the first mineral, and determining the cumulative mineral proportion; when the cumulative mineral proportion is within a preset mineral proportion threshold range, normalizing the proportion of the second mineral for each first mineral to determine the first identification data; otherwise, adjusting the mineral type and proportion of the first mineral to determine the second identification data. This invention achieves accurate identification and efficient quantification of fluorite minerals by integrating XRD phase identification with single-wavelength X-ray fluorescence detection technology capable of measuring ultralight elements.

Description

Technical Field

[0001] This application relates to the field of mineral detection technology, and more specifically, to a rapid identification and quantification method for fluorite minerals based on full-spectrum fitting. Background Technology

[0002] Fluorite (mainly composed of calcium fluoride, CaF2) is an important raw material for industries such as fluorochemicals, metallurgy, and building materials. Its identification and quantification typically rely on single methods such as chemical analysis, X-ray diffraction (XRD), or X-ray fluorescence analysis (single-wavelength X-ray fluorescence). Traditional detection methods, such as chemical titration (GB / T5195.1), are cumbersome and time-consuming. XRD can provide mineral phase composition and quantitative information, but its ability to directly analyze elemental content is limited, and it has a large error for low-content samples. Traditional single-wavelength X-ray fluorescence, while capable of rapidly detecting elemental content, struggles to distinguish mineral phases.

[0003] Therefore, there is an urgent need for an efficient method that combines phase identification and elemental quantification. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide a rapid identification and quantification method for fluorite minerals based on full-spectrum fitting. Single-wavelength X-ray fluorescence detection can obtain the elemental content, such as fluorine and carbon, which cannot be calculated using standard-free methods based on full-spectrum fitted matrix parameters. By integrating XRD phase identification with single-wavelength X-ray fluorescence detection technology capable of measuring ultralight elements, the identification speed of fluorite minerals can be effectively improved, the detection accuracy of fluorite mineral quantitative analysis can be enhanced, and accurate identification and efficient quantification of fluorite minerals can be achieved.

[0005] The first aspect of this invention provides a rapid identification and quantification method for fluorite minerals based on full-spectrum fitting, comprising:

[0006] Obtain the sample to be tested;

[0007] The XRD detection module is used to detect the sample to determine the type and proportion of the first mineral present in the sample.

[0008] The sample to be tested is detected by a single-wavelength X-ray fluorescence detection module to determine the types and contents of the first element in the sample. Combined with the mineral type of the first mineral, the proportion of the second mineral for each first mineral is calculated.

[0009] The proportions of the second minerals in all first minerals are summed to determine the total mineral proportions.

[0010] When the accumulated mineral percentage is within a preset mineral percentage threshold range, the second mineral percentage of each first mineral is normalized to determine the first identification data;

[0011] Conversely, the mineral type of the first mineral and the proportion of the second mineral are adjusted to determine the second identification data.

[0012] In this solution, the step of using an XRD detection module to detect the sample to be tested and determine the mineral type and proportion of the first mineral present in the sample includes:

[0013] The sample to be tested is detected by the XRD detection module to determine the diffraction pattern;

[0014] Extract the characteristic peak images corresponding to each mineral from the diffraction pattern;

[0015] Calculate the image similarity between each characteristic peak image and the standard diffraction image of the corresponding mineral, and determine the mineral with an image similarity greater than a first preset similarity threshold as the first mineral;

[0016] The percentage of the first mineral in each first mineral is calculated using a preset mineral content calculation method.

[0017] In this scheme, the sample to be tested is detected using a single-wavelength X-ray fluorescence detection module to determine the types and contents of the first element present in the sample. Combined with the mineral type of the first mineral, the proportion of the second mineral for each first mineral is calculated, including:

[0018] The fluorescence spectrum of the sample to be tested was obtained using a single-wavelength X-ray fluorescence detection module.

[0019] Segment the elemental spectral images of preset element types from the fluorescence spectrum;

[0020] The elemental spectral images of the preset element types are analyzed to determine the elemental type and content of the first element present in the sample to be tested.

[0021] The first mineral and the first element contained within the first mineral are correlated to determine the associated minerals of the first element;

[0022] Count the number of associated minerals for each first element, and sort the first elements in descending order according to the number of associated minerals;

[0023] The associated minerals of each first element are analyzed in descending order, and the mass percentage of the first element in the associated minerals is calculated.

[0024] The proportion of the second mineral in the associated mineral is determined by multiplying the element content of the first element by the mass percentage of the first element in the associated mineral.

[0025] This plan also includes:

[0026] When there are other first elements whose content is not determined in the associated mineral, the content of the other first elements is calculated based on the proportion of the second mineral in the associated mineral and the mass percentage of the other first elements.

[0027] Calculate the difference between the element content of the other first element and the element content of the first element, and update the element content of the other first element.

[0028] This plan also includes:

[0029] When the content of the first element in the first mineral is determined, the first mineral is removed from the associated minerals of the first element, and the associated quantity of the associated minerals of the first element is updated.

[0030] This solution also includes verifying the first identification data:

[0031] Calculate the difference between the proportion of the second mineral and the proportion of the corresponding first mineral for each first mineral;

[0032] If the difference in the mineral proportion of each first mineral is less than the corresponding first preset difference threshold, then the first identification data is output.

[0033] Conversely, the mineral types of the first mineral and the proportion of the second mineral are adjusted.

[0034] In this scheme, adjusting the mineral type of the first mineral and the proportion of the second mineral to determine the second identification data includes:

[0035] Minerals whose image similarity to the feature peak image falls between a first preset similarity threshold and a second preset similarity threshold are identified as questionable minerals.

[0036] When there are multiple questionable minerals, the questionable minerals are randomly combined based on a preset combination quantity threshold to determine one or more groups of questionable minerals.

[0037] The image similarity of all questionable minerals within the questionable mineral group is weighted and calculated to determine the first similarity of the questionable mineral group.

[0038] The questionable minerals and questionable mineral groups are comprehensively sorted according to the image similarity and the first similarity score from largest to smallest.

[0039] The questionable minerals or groups of questionable minerals are analyzed in descending order in conjunction with the first mineral to determine the proportion of the third mineral in each second mineral, generating unverified identification data containing the mineral types of the second minerals and the proportion of the third minerals; the second minerals include both the questionable minerals and the first mineral.

[0040] The identification data to be verified is verified;

[0041] When the identification data to be verified meets the verification conditions, the identification data to be verified is determined as the second identification data.

[0042] This plan also includes:

[0043] The number of data points in the second identification data is counted. When the number of data points in the second identification data is less than a preset threshold, the next suspected mineral or suspected mineral group is selected for analysis according to the sorting order.

[0044] When the amount of data equals a preset threshold, the second identification data is output.

[0045] In this solution, the verification of the identification data to be verified includes:

[0046] Calculate the average difference between the mineral percentage differences of all second minerals in the identification data to be verified and the corresponding first mineral percentages;

[0047] When the average difference is less than the second preset difference threshold, the identification data to be verified meets the verification conditions.

[0048] This invention discloses a rapid identification and quantification method for fluorite minerals based on full-spectrum fitting. The method includes: acquiring a sample to be tested; detecting the sample using an XRD detection module to determine the mineral type and proportion of a first mineral; detecting the sample using a single-wavelength X-ray fluorescence detection module to determine the element type and content of a first element; calculating the proportion of a second mineral for each first mineral based on the mineral type of the first mineral, and determining the cumulative mineral proportion; when the cumulative mineral proportion is within a preset mineral proportion threshold range, normalizing the proportion of the second mineral for each first mineral to determine the first identification data; otherwise, adjusting the mineral type and proportion of the first mineral to determine the second identification data. This invention achieves accurate identification and efficient quantification of fluorite minerals by integrating XRD phase identification with single-wavelength X-ray fluorescence detection technology capable of measuring ultralight elements. Attached Figure Description

[0049] Figure 1 The flowchart of a rapid identification and quantification method for fluorite minerals based on full-spectrum fitting provided by the present invention is shown.

[0050] Figure 2 A flowchart illustrating the method for calculating the mineral type and percentage of the first mineral present in the sample to be tested, provided by the present invention, is shown.

[0051] Figure 3The flowchart illustrates the element type and content of the first element present in the sample to be tested, as well as the method for calculating the proportion of the second mineral in the first mineral, provided by the present invention. Detailed Implementation

[0052] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0054] Figure 1 The flowchart shows a rapid identification and quantification method for fluorite minerals based on full-spectrum fitting provided by the present invention.

[0055] like Figure 1 As shown, this invention discloses a rapid identification and quantification method for fluorite minerals based on full-spectrum fitting, comprising:

[0056] S102, Obtain the sample to be tested;

[0057] S104, The XRD detection module is used to detect the sample to be tested to determine the type and proportion of the first mineral present in the sample.

[0058] S106, the sample to be tested is detected by a single-wavelength X-ray fluorescence detection module to determine the types and contents of the first element in the sample, and the proportion of the second mineral for each first mineral is calculated in combination with the mineral type of the first mineral.

[0059] S108, sum up the proportions of the second minerals for all first minerals to determine the total mineral proportions;

[0060] S110, when the cumulative mineral percentage is within the preset mineral percentage threshold range, the second mineral percentage of each first mineral is normalized to determine the first identification data;

[0061] S112, conversely, the mineral type of the first mineral and the proportion of the second mineral are adjusted to determine the second identification data.

[0062] According to an embodiment of the present invention, the sample to be tested is first pretreated by grinding it to 200 mesh (particle size ≤ 75 μm) to ensure uniform distribution of mineral particles and reduce particle size effects in XRD and single-wavelength X-ray fluorescence detection. Then, the ground powder is pressed using a tablet press to obtain a sample that meets the detection requirements.

[0063] The sample is analyzed using both XRD and single-wavelength X-ray fluorescence (XRF) modules to determine if it is fluorite and to perform quantitative analysis, identifying the mineral percentages of each mineral within the fluorite. XRD can quantitatively analyze the content of each phase in the sample, and the percentage of the primary mineral (e.g., fluorite, calcite, and dolomite) can be calculated using methods such as Rietveld refinement. However, this may only provide relative proportions, not absolute contents, especially when amorphous substances are present in the sample, which XRD may not detect. Single-wavelength XRF can identify the types and contents of the primary element present in the sample (including elements that can be calculated using standard-free quantification with full-spectrum fitting matrix parameters and elements that cannot be obtained), such as Ca, F, Si, and Fe. The primary mineral and the primary element contained within it are correlated to identify associated minerals. Priority is given to analyzing the primary element with a unique associated mineral, calculating its mass percentage in the associated mineral, and combining this with the primary element's content to calculate the percentage of the secondary mineral in that associated mineral. The proportion of the second mineral in each first mineral is determined through multiple iterative analyses. Since the elemental content detected by single-wavelength X-ray fluorescence may exist in other impurities undetectable by XRD, the cumulative mineral proportion obtained by summing the proportions of the second minerals of all first minerals may not equal 100%. A preset mineral proportion threshold range (e.g., 95-105%) is set to determine this. When the cumulative mineral proportion falls within this threshold range, it indicates that only a small amount of the first element exists in other impurities, having a minimal impact on the identification results of the sample. The final mineral proportion of each first mineral is determined by normalizing the proportion of its second mineral. Alternatively, when the cumulative mineral proportion is between 95-100%, the proportion of the second mineral in each first mineral can be directly output, with the remaining mineral proportion represented by impurities.

[0064] The preset mineral percentage threshold range can be set by those skilled in the art according to actual needs.

[0065] When the cumulative mineral percentage falls outside the preset mineral percentage threshold range, the diffraction pattern obtained from XRD detection is analyzed again to determine the possible presence of questionable minerals in the sample. This is then combined with the already identified first mineral and first element for analysis to generate second identification data containing the mineral percentage of the questionable minerals, for reference by relevant personnel. In this second identification data, the questionable minerals are marked accordingly, allowing relevant personnel to verify them through other methods, thereby determining more accurate quantitative detection data for fluorite.

[0066] Figure 2 A flowchart is shown showing the method for calculating the mineral type and proportion of the first mineral present in the sample to be tested, as provided by the present invention.

[0067] like Figure 2 As shown, according to an embodiment of the present invention, the XRD detection module is used to detect the sample to be tested to determine the mineral type and proportion of the first mineral present in the sample, including:

[0068] S202, the XRD detection module is used to detect the sample to be tested and determine the diffraction pattern;

[0069] S204, extracting characteristic peak images of each mineral from the diffraction pattern;

[0070] S206, calculate the image similarity between each characteristic peak image and the standard diffraction image of the corresponding mineral, and determine the mineral with the image similarity of the characteristic peak image greater than the first preset similarity threshold as the first mineral;

[0071] S208, calculate the proportion of the first mineral for each first mineral using a preset mineral content calculation method.

[0072] It should be noted that the XRD module parameters are set, and the XRD detection module is started according to these parameters to detect the sample and obtain the diffraction pattern. For example, the XRD module parameters include the radiation source, Cu target Kα rays. Voltage 40kV, current 40mA; scanning range 2θ=5°-70°, step size 0.02°, scanning speed 0.5° / s; detector, one-dimensional array detector, real-time acquisition of diffraction intensity.

[0073] Each mineral is individually detected using an X-ray diffractometer. The corresponding characteristic peak images are extracted from the acquired diffraction patterns, and these peak images are used to determine the standard diffraction images for each mineral. The system analyzes and compares the characteristic peak images of each mineral with their corresponding standard diffraction images using model analysis or curve similarity calculations to determine the image similarity of the characteristic peak images for each mineral. By statistically analyzing the first mineral, the mineral species present in the sample are determined. The preset mineral content calculation method can be Rietveld refinement or the Reference Intensity Ratio (RIR) method. The proportion of the first mineral is calculated using the preset mineral content calculation method.

[0074] The first preset similarity threshold is set by those skilled in the art according to actual needs.

[0075] Figure 3 The flowchart illustrates the element type and content of the first element present in the sample to be tested, as well as the method for calculating the proportion of the second mineral in the first mineral, provided by the present invention.

[0076] like Figure 3 As shown in the embodiment of the present invention, the sample to be tested is detected by a single-wavelength X-ray fluorescence detection module to determine the type and content of the first element present in the sample, and the proportion of the second mineral for each first mineral is calculated in combination with the mineral type of the first mineral, including:

[0077] S302, acquires the fluorescence spectrum of the sample to be tested through a single-wavelength X-ray fluorescence detection module;

[0078] S304, segment elemental spectral images of preset element types from fluorescence spectra;

[0079] S306, Analyze the elemental spectral images of the preset element types to determine the element type and content of the first element present in the sample to be tested;

[0080] S308, Correlate the first mineral and the first element contained in the first mineral to determine the associated mineral of the first element;

[0081] S310, count the number of associated minerals for each first element, and sort the first elements in descending order according to the number of associated minerals;

[0082] S312, Analyze the associated minerals of each first element in descending order and calculate the mass percentage of the first element in the associated minerals;

[0083] S314, calculate the product of the element content of the first element and the mass percentage of the first element in the associated mineral to determine the proportion of the second mineral in the associated mineral.

[0084] It should be noted that the single-wavelength X-ray fluorescence detection module parameters are set, and the module is started according to these parameters to detect the sample and obtain the fluorescence spectrum. For example, the single-wavelength X-ray fluorescence module parameters include: excitation source, single-wavelength Rh target Kα line (energy 20.2 keV), excitation voltage 50 kV; detectable elements: Ca (Kα = 3.69 keV), F (Kα = 0.68 keV), Si (Kα = 1.74 keV), Fe (Kα = 6.40 keV), etc.; spectroscopic system: silicon drift detector (SDD), energy resolution ≤ 130 eV (@Mn-Kα).

[0085] The preset element types are the element types that may be present in the sample to be tested, determined by the system based on the detection requirements of the sample. For example, when detecting whether the sample is fluorite, the preset element types are the element types that may be contained in fluorite and associated minerals, such as calcium (Ca), fluorine (F), sulfur (Si), and iron (Fe). By calculating the characteristic peak area of ​​each element's spectral image, the system determines whether the sample contains the element of the corresponding element type, and determines the element content based on the size of the characteristic peak area.

[0086] The first element may exist in one or more first minerals. For example, calcium (Ca) is found in fluorite (CaF2), calcite (CaCO3), and dolomite (CaMg(CO3)2), with a correlation number of 3 for calcium. Fluorine (F) is only found in fluorite (CaF2), with a correlation number of 1 for fluorine. Furthermore, since the same first element exists in multiple associated minerals, the actual elemental content in each associated mineral cannot be determined, making it impossible to calculate the proportion of the second mineral in the associated minerals based on the mass percentage of the first element. Therefore, the first elements are sorted in descending order according to the correlation number of associated minerals, with priority given to analyzing the first element with a correlation number of 1.

[0087] The mass percentage of the first element in the associated mineral is the ratio of the relative molecular mass of the first element to the relative molecular mass of the associated mineral. For example, the mass percentage of fluorine (F) in fluorite (CaF2) is:

[0088]

[0089] Wherein, 19.00 is the relative molecular mass of fluorine (F) and 78.07 is the relative molecular mass of fluorite (CaF2).

[0090] According to an embodiment of the present invention, it further includes:

[0091] When there are other first elements whose content is not determined in the associated minerals, the content of the other first elements is calculated based on the proportion of the second minerals in the associated minerals and the mass percentage of the other first elements.

[0092] Calculate the difference between the element content of other first elements and the element content of the first element, and update the element content of other first elements.

[0093] It should be noted that, taking fluorite (CaF2) as an example, in addition to fluorine (F), fluorite (CaF2) also contains calcium (Ca). By calculating the ratio of the proportion of the second mineral in fluorite (CaF2) to the mass percentage of calcium (Ca), the content of the first element calcium (Ca) in fluorite (CaF2) can be determined.

[0094] The mass percentage of fluorine (F) in fluorite (CaF2) is:

[0095]

[0096] Wherein, 40.08 is the relative molecular mass of calcium (Ca).

[0097] By calculating the difference between the calcium (Ca) content and the first element content, the calcium content in minerals other than fluorite can be determined, and the calcium content can be updated.

[0098] According to an embodiment of the present invention, it further includes:

[0099] When the content of the first element in the first mineral is determined, the first mineral is removed from the associated minerals of the first element, and the associated quantity of the associated minerals of the first element is updated.

[0100] It should be noted that, taking calcium as an example, calcium (Ca) exists in fluorite (CaF2), calcite (CaCO3), and dolomite (CaMg(CO3)2). The proportion of the second mineral in fluorite (CaF2) and dolomite (CaMg(CO3)2) can be calculated using the elemental contents of fluorine (F) and magnesium (Mg), respectively, and the primary elemental content of calcium (Ca) in each can be determined, updating the elemental content of calcium (Ca). After removing fluorite (CaF2) and dolomite (CaMg(CO3)2) from the associated minerals of calcium (Ca), only calcite (CaCO3) remains among the associated minerals of calcium (Ca). The proportion of the second mineral in calcite (CaCO3) can be determined by calculating the mass percentage of calcium (Ca) in calcite (CaCO3) and the updated elemental content of calcium (Ca).

[0101] Remove the first mineral with a known proportion of the second mineral from the association list of the corresponding first element, update the association number of the associated minerals of each first element, adjust the sorting order of the first elements, and determine the proportion of the second mineral in each first mineral in the sample to be tested through multiple iterations.

[0102] According to an embodiment of the present invention, the method further includes verifying the first identification data:

[0103] Calculate the difference between the proportion of the second mineral and the proportion of the corresponding first mineral for each first mineral;

[0104] If the difference in the mineral proportion of each first mineral is less than the corresponding first preset difference threshold, then the first identification data is output.

[0105] Conversely, the mineral types of the first mineral and the proportion of the second mineral are adjusted.

[0106] It should be noted that the difference between the proportion of the second mineral and the corresponding proportion of the first mineral is the absolute value of their difference. The system sets different first preset difference thresholds for each first mineral based on its proportion; the larger the proportion of the first mineral, the larger the corresponding first preset difference threshold. If the difference in the proportion of each first mineral is less than the corresponding first preset difference threshold, it proves that the mineral type of the first mineral in the sample is correctly identified. First identification data is generated based on the mineral type and the proportion of the second mineral, and output through display terminals such as monitors and mobile phones for relevant personnel to view the identification data of the sample. Conversely, there are identification errors, and XRD detection and single-wavelength X-ray fluorescence detection have detection errors. For example, XRD detection cannot identify first minerals with small proportions; due to the influence of other elements, the element content obtained by single-wavelength X-ray fluorescence detection has a certain error compared to the actual element content, etc.

[0107] According to an embodiment of the present invention, the mineral type of the first mineral and the proportion of the second mineral are adjusted to determine the second identification data, including:

[0108] Minerals whose image similarity to the feature peak image falls between a first preset similarity threshold and a second preset similarity threshold are identified as questionable minerals.

[0109] When there are multiple questionable minerals, the questionable minerals are randomly combined based on a preset combination quantity threshold to determine one or more groups of questionable minerals.

[0110] The image similarity of all questionable minerals within the questionable mineral group is weighted and calculated to determine the first similarity of the questionable mineral group.

[0111] The questionable minerals and questionable mineral groups are comprehensively sorted according to the image similarity and the first similarity score from largest to smallest.

[0112] The questionable minerals or groups of questionable minerals are analyzed in descending order in conjunction with the first mineral to determine the proportion of the third mineral in each second mineral, generating unverified identification data containing the mineral types of the second minerals and the proportion of the third minerals; the second minerals include both the questionable minerals and the first mineral.

[0113] Verify the identification data to be verified;

[0114] When the identification data to be verified meets the verification conditions, the identification data to be verified is determined as the second identification data;

[0115] The number of data points in the second identification data is counted. When the number of data points in the second identification data is less than the preset threshold, the next suspected mineral or suspected mineral group is selected for analysis according to the sorting order.

[0116] When the amount of data equals the preset threshold, the second identification data is output.

[0117] It should be noted that the minerals in the image similarity of the feature peak image that are between the first preset similarity threshold and the second preset similarity threshold are minerals that may exist in the sample to be tested, and are represented by doubtful minerals.

[0118] During the generation of questionable mineral groups, the number of questionable minerals within a questionable mineral group is less than or equal to a preset combination quantity threshold. For example, when the preset combination quantity threshold is 3, the number of questionable minerals within a questionable mineral group can be 2 or 3. In calculating the first similarity of a questionable mineral group, it is determined based on the sum of the element content of the first element of each questionable mineral within the group. The larger the sum of element content, the higher the influence threshold of the corresponding questionable mineral. The image similarity and influence threshold of each questionable mineral within the group are multiplied, and the calculation results are accumulated to determine the first similarity of the questionable mineral group. Questionable minerals and questionable mineral groups are comprehensively sorted, and the questionable mineral with the highest image similarity or the questionable mineral group with the highest first similarity is selected. Combining the known element content of the first mineral and the first element, the proportion of the third mineral in each second mineral (including the questionable mineral and the first mineral) is calculated, generating verification data. The verification data is then verified based on the system's preset verification method. When verification is successful, the verification data is determined as the second identification data.

[0119] The second preset similarity threshold and the preset combination number threshold are both set by those skilled in the art according to actual needs.

[0120] According to an embodiment of the present invention, it further includes:

[0121] The number of data points in the second identification data is counted. When the number of data points in the second identification data is less than the preset threshold, the next suspected mineral or suspected mineral group is selected for analysis according to the sorting order.

[0122] When the amount of data equals the preset threshold, the second identification data is output.

[0123] It should be noted that since the second identification data is predicted based on XRD and single-wavelength X-ray fluorescence detection data, there may be some error compared with the actual mineral types and contents present in the sample. Multiple sets of second identification data are generated by selecting multiple questionable minerals or groups of questionable minerals to facilitate understanding of the identification status of the sample. The preset quantity threshold is set by those skilled in the art according to actual needs.

[0124] In addition, after all the doubtful minerals and doubtful mineral groups have been analyzed, if the number of second identification data is still less than the preset number threshold, then the verification score of each identification data to be verified is calculated, and the identification data to be verified is selected to supplement the second identification data in descending order of score.

[0125] According to an embodiment of the present invention, verifying the identification data to be verified includes:

[0126] Calculate the average difference between the proportion of the third mineral and the corresponding proportion of the first mineral in all second minerals in the data to be verified;

[0127] When the average difference is less than the second preset difference threshold, the identification data to be verified meets the verification conditions.

[0128] It should be noted that the difference between the proportion of the third mineral in the second mineral and the proportion of the corresponding first mineral is the absolute value of the difference between the two. By calculating the average difference of the proportion differences of all second minerals in the data to be verified, and comparing it with a second preset difference threshold, if the average difference is less than the second preset difference threshold, the data to be verified is determined to meet the verification conditions; otherwise, the data to be verified is determined not to meet the verification conditions.

[0129] All information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals (including but not limited to signals transmitted between user terminals and other devices) involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the "sample to be tested" and "standard diffraction images of minerals" involved in this disclosure were obtained with full authorization.

[0130] This invention discloses a rapid identification and quantification method for fluorite minerals based on full-spectrum fitting. The method includes: acquiring a sample to be tested; detecting the sample using an XRD detection module to determine the mineral type and proportion of a first mineral; detecting the sample using a single-wavelength X-ray fluorescence detection module to determine the element type and content of a first element; calculating the proportion of a second mineral for each first mineral based on the mineral type of the first mineral, and determining the cumulative mineral proportion; when the cumulative mineral proportion is within a preset mineral proportion threshold range, normalizing the proportion of the second mineral for each first mineral to determine the first identification data; otherwise, adjusting the mineral type and proportion of the first mineral to determine the second identification data. This invention achieves accurate identification and efficient quantification of fluorite minerals by integrating XRD phase identification with single-wavelength X-ray fluorescence detection technology capable of measuring ultralight elements.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0132] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0133] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0134] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A rapid identification and quantification method for fluorite minerals based on full-spectrum fitting, characterized in that, include: Obtain the sample to be tested; The XRD detection module is used to detect the sample to determine the type and proportion of the first mineral present in the sample. The sample to be tested is detected by a single-wavelength X-ray fluorescence detection module to determine the types and contents of the first element in the sample. Combined with the mineral type of the first mineral, the proportion of the second mineral for each first mineral is calculated. The proportions of the second minerals in all first minerals are summed to determine the total mineral proportions. When the accumulated mineral percentage is within a preset mineral percentage threshold range, the second mineral percentage of each first mineral is normalized to determine the first identification data; The first identification data is verified, and the mineral percentage difference between the second mineral percentage of each first mineral and the corresponding first mineral percentage is calculated. If the difference in the mineral proportion of each first mineral is less than the corresponding first preset difference threshold, then the first identification data is output. Conversely, the mineral type of the first mineral and the proportion of the second mineral are adjusted to determine the second identification data; The adjustment of the mineral type of the first mineral and the proportion of the second mineral to determine the second identification data includes: Minerals whose image similarity to the feature peak image falls between a first preset similarity threshold and a second preset similarity threshold are identified as questionable minerals. When there are multiple questionable minerals, the questionable minerals are randomly combined based on a preset combination quantity threshold to determine one or more groups of questionable minerals. The image similarity of all questionable minerals within the questionable mineral group is weighted and calculated to determine the first similarity of the questionable mineral group. The questionable minerals and questionable mineral groups are comprehensively sorted according to the image similarity and the first similarity score from largest to smallest. The questionable minerals or groups of questionable minerals are analyzed in descending order in conjunction with the first mineral to determine the proportion of the third mineral in each second mineral, generating unverified identification data containing the mineral types of the second minerals and the proportion of the third minerals; the second minerals include both the questionable minerals and the first mineral. The identification data to be verified is verified; When the identification data to be verified meets the verification conditions, the identification data to be verified is determined as the second identification data.

2. The rapid identification and quantification method for fluorite minerals based on full-spectrum fitting according to claim 1, characterized in that, The step of detecting the sample to be tested using an XRD detection module to determine the mineral type and proportion of the first mineral present in the sample includes: The sample to be tested is detected by the XRD detection module to determine the diffraction pattern; Extract the characteristic peak images corresponding to each mineral from the diffraction pattern; Calculate the image similarity between each characteristic peak image and the standard diffraction image of the corresponding mineral, and determine the mineral with an image similarity greater than a first preset similarity threshold as the first mineral; The percentage of the first mineral in each first mineral is calculated using a preset mineral content calculation method.

3. The rapid identification and quantification method for fluorite minerals based on full-spectrum fitting according to claim 1, characterized in that, The step involves detecting the sample using a single-wavelength X-ray fluorescence detection module to determine the types and content of the first element present in the sample. Combined with the mineral type of the first mineral, the proportion of the second mineral for each first mineral is calculated, including: The fluorescence spectrum of the sample to be tested was obtained using a single-wavelength X-ray fluorescence detection module. Segment the elemental spectral images of preset element types from the fluorescence spectrum; The elemental spectral images of the preset element types are analyzed to determine the elemental type and content of the first element present in the sample to be tested. The first mineral and the first element contained within the first mineral are correlated to determine the associated minerals of the first element; Count the number of associated minerals for each first element, and sort the first elements in descending order according to the number of associated minerals; The associated minerals of each first element are analyzed in descending order, and the mass percentage of the first element in the associated minerals is calculated. The proportion of the second mineral in the associated mineral is determined by multiplying the element content of the first element by the mass percentage of the first element in the associated mineral.

4. The rapid identification and quantification method for fluorite minerals based on full-spectrum fitting according to claim 3, characterized in that, Also includes: When there are other first elements whose content is not determined in the associated mineral, the content of the other first elements is calculated based on the proportion of the second mineral in the associated mineral and the mass percentage of the other first elements. Calculate the difference between the element content of the other first element and the element content of the first element, and update the element content of the other first element.

5. The rapid identification and quantification method for fluorite minerals based on full-spectrum fitting according to claim 4, characterized in that, Also includes: When the content of the first element in the first mineral is determined, the first mineral is removed from the associated minerals of the first element, and the associated quantity of the associated minerals of the first element is updated.

6. The rapid identification and quantification method for fluorite minerals based on full-spectrum fitting according to claim 1, characterized in that, Also includes: The number of data points in the second identification data is counted. When the number of data points in the second identification data is less than a preset threshold, the next suspected mineral or suspected mineral group is selected for analysis according to the sorting order. When the amount of data equals a preset threshold, the second identification data is output.

7. The rapid identification and quantification method for fluorite minerals based on full-spectrum fitting according to claim 1, characterized in that, The verification of the identification data to be verified includes: Calculate the average difference between the mineral percentage differences of all second minerals in the identification data to be verified and the corresponding first mineral percentages; When the average difference is less than the second preset difference threshold, the identification data to be verified meets the verification conditions.

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