Rare metal isotope data calibration method, system, device and medium

Abstract

The application provides a rare metal isotope data calibration method, system, device and medium. According to pre-processed element composition information, the matrix similarity of each candidate standard substance and a rare metal sample to be measured is determined, and then an isotope calibration set of the rare metal sample to be measured is selected. The actual isotope measurement values of the rare metal sample to be measured and each candidate standard substance are determined. The known isotope measurement values of each candidate standard substance are used as a reference, and the corresponding actual isotope measurement values of each candidate standard substance are used as observation values. A matrix-fractionation response model is constructed. The actual isotope measurement value of the rare metal sample to be measured is brought into the inverse function of the matrix-fractionation response model, and then the isotope calibration value of the rare metal sample to be measured is output. According to the scheme of the application, the calibration model of the isotope can be constructed based on the element difference between the rare metal sample to be measured and the candidate standard substance.

Description

Technical Field

[0001] This application relates to the field of isotope data calibration technology, and in particular to a method, system, device and medium for calibrating rare metal isotope data. Background Technology

[0002] Isotope data calibration refers to the process of correcting the original isotope data obtained by the instrument from measuring the sample using standard substances with known isotope measurement values. Its core function is to eliminate systematic errors introduced during the instrument measurement process, mainly the mass fractionation effect, so as to obtain accurate data that can truly reflect the isotope composition of the sample.

[0003] Rare metal isotope data calibration is an isotope data calibration technique developed specifically for the unique characteristics of rare metal samples. It not only needs to solve the common problem of mass fractionation, but also needs to address the additional challenges brought about by the complex and variable matrix of rare metal samples. Traditional isotope data calibration methods often rely on the experience of analysts to select standard materials, and it is difficult to find a standard material that is completely consistent with the matrix composition of the rare metal sample to be tested. For many non-traditional or novel rare metal isotope systems, there may not even be a suitable standard material. If the wrong standard material is selected, the matrix will differ from that of the rare metal sample to be tested, and the calibration results will inevitably be biased. Therefore, how to construct an isotope calibration model based on the elemental differences between the rare metal sample to be tested and the candidate standard material has become a difficult problem for the industry. Summary of the Invention

[0004] Based on this, this application provides a rare metal isotope data calibration method, system, device and medium for constructing an isotope calibration model based on the elemental differences between the rare metal sample to be tested and the candidate standard material.

[0005] In a first aspect, this application provides a method for calibrating rare metal isotope data, comprising the following steps:

[0006] Obtain the elemental composition information of the rare metal sample to be tested and multiple candidate standard substances, wherein the elemental composition information includes the concentration data of the main matrix elements and trace elements;

[0007] Based on the preprocessed elemental composition information, the matrix similarity between each candidate standard material and the rare metal sample to be tested is determined, and then the isotope calibration set of the rare metal sample to be tested is selected from all the candidate standard materials based on the matrix similarity.

[0008] Determine the actual isotope measurement values ​​of the rare metal sample to be tested and each candidate standard material in the isotope calibration set;

[0009] Based on the known isotope measurements of each candidate standard material in the isotope calibration set, and using the actual isotope measurements corresponding to each candidate standard material in the isotope calibration set as the observation values, a matrix-fractionation response model is constructed.

[0010] The actual isotope measurement value of the rare metal sample to be tested is substituted into the inverse function of the matrix-fractionation response model, thereby outputting the isotope calibration value of the rare metal sample to be tested.

[0011] In some embodiments, determining the matrix similarity between each candidate standard substance and the rare metal sample to be tested based on the preprocessed elemental composition information specifically includes:

[0012] The element composition information is standardized and preprocessed to obtain preprocessed element composition information;

[0013] Based on the preprocessed elemental composition information, the relative distances between the rare metal sample to be tested and each candidate standard material in all elemental dimensions are determined.

[0014] The numerical values ​​of the relative distances of each candidate standard material across all elemental dimensions are converted into the corresponding matrix similarity.

[0015] In some embodiments, the isotope calibration set of the rare metal sample to be tested, selected from all candidate standard materials based on the similarity of each matrix, specifically includes:

[0016] All candidate reference materials were sorted in descending order based on the similarity of each matrix to obtain the candidate reference material sequence;

[0017] According to a preset number, a corresponding number of candidate standard substances are selected from the candidate standard substance sequence as the isotope calibration set of the rare metal sample to be tested.

[0018] In some embodiments, determining the actual isotopic measurement values ​​of the rare metal sample to be tested and each candidate standard material in the isotopic calibration set specifically includes:

[0019] Mass spectrometry analysis sample solutions were prepared for the rare metal sample to be tested and each candidate standard material in the isotope calibration set.

[0020] Under the same instrument conditions, mass spectrometry measurements were performed on the rare metal sample to be tested and each candidate standard material in the isotope calibration set based on the prepared mass spectrometry analysis sample solution, so as to obtain the actual isotope measurement values ​​of the rare metal sample to be tested and each candidate standard material in the isotope calibration set.

[0021] In some embodiments, the matrix-fractionation response model is constructed using the known isotopic measurements of each candidate standard material in the isotopic calibration set as a reference, and the actual isotopic measurements corresponding to each candidate standard material in the isotopic calibration set as observations. This specifically includes:

[0022] Obtain the known isotope measurement values ​​and corresponding actual isotope measurement values ​​for each candidate standard material in the isotope calibration set;

[0023] Based on the known isotope measurement values ​​of each candidate standard material in the isotope calibration set, and using the actual isotope measurement values ​​corresponding to each candidate standard material in the isotope calibration set as the observation values, a fractionation response function is established through nonlinear fitting.

[0024] Complete the construction of the matrix-fractionation response model.

[0025] In some embodiments, substituting the actual isotopic measurement value of the rare metal sample to be tested into the inverse function of the matrix-fractionation response model to output the isotopic calibration value of the rare metal sample to be tested specifically includes:

[0026] Verify whether the goodness of fit of the matrix-fractionation response model meets the preset standard;

[0027] Input the actual isotope measurement values ​​of the rare metal sample to be tested into the validated inverse function of the model;

[0028] Perform inverse function calculations and output the isotope calibration values ​​of the rare metal sample to be tested.

[0029] In some embodiments, the method specifically includes determining the screening conditions for each candidate reference material based on the basic properties of the rare metal sample to be tested.

[0030] Secondly, this application provides a rare metal isotope data calibration system, comprising:

[0031] The acquisition module is used to acquire the elemental composition information of the rare metal sample to be tested and multiple candidate standard substances, the elemental composition information including the concentration data of the main matrix elements and trace elements;

[0032] The processing module is used to determine the matrix similarity between each candidate standard material and the rare metal sample to be tested based on the preprocessed elemental composition information, and then select the isotope calibration set of the rare metal sample to be tested from all the candidate standard materials based on the matrix similarity.

[0033] The processing module is also used to determine the actual isotope measurement values ​​of the rare metal sample to be tested and each candidate standard material in the isotope calibration set.

[0034] The processing module is also used to construct a matrix-fractionation response model based on the known isotope measurement values ​​of each candidate standard material in the isotope calibration set and the actual isotope measurement values ​​corresponding to each candidate standard material in the isotope calibration set.

[0035] The execution module is used to input the actual isotope measurement value of the rare metal sample to be tested into the inverse function of the matrix-fractionation response model, and then output the isotope calibration value of the rare metal sample to be tested.

[0036] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described rare metal isotope data calibration method.

[0037] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described rare metal isotope data calibration method.

[0038] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0039] The rare metal isotope data calibration method, system, equipment, and medium provided in this application firstly determine the matrix similarity between each candidate standard material and the rare metal sample to be tested based on the preprocessed elemental composition information. This matrix similarity reflects the degree of matching between the candidate standard material and the rare metal sample to be tested in terms of chemical elemental composition. This step enables an objective quantitative assessment of the matrix consistency between the rare metal sample to be tested and the candidate standard materials, thereby transforming the selection of standard materials from subjective judgment based on experience to data-driven scientific decision-making. Secondly, based on each matrix similarity, an isotope calibration set for the rare metal sample to be tested is selected from all candidate standard materials. This step can replace traditional single standard material calibration with group calibration, significantly reducing systematic errors caused by matrix mismatch. Then, the rare metal sample to be tested and the matrix similarity are determined. The actual isotopic measurements of each candidate standard material in the isotopic calibration set are used as a reference, and the actual isotopic measurements of each candidate standard material in the isotopic calibration set are used as observations to construct a matrix-fractionation response model. This step can establish a response model reflecting fractionation behavior and matrix characteristics based on actual measurement data, thereby describing the complex law of instrument fractionation with matrix changes, and providing a highly targeted calibration benchmark for the rare metal sample to be tested. Finally, the actual isotopic measurement of the rare metal sample to be tested is substituted into the inverse function of the matrix-fractionation response model to output the isotopic calibration value of the rare metal sample to be tested. In summary, the scheme of this application constructs an isotopic calibration model based on the elemental differences between the rare metal sample to be tested and the candidate standard materials. Attached Figure Description

[0040] Figure 1 This is an exemplary flowchart of a rare metal isotope data calibration method according to some embodiments of this application;

[0041] Figure 2 This is a schematic diagram illustrating an application scenario of a rare metal isotope data processing system according to some embodiments of this application;

[0042] Figure 3 This is a schematic flowchart illustrating the construction of a matrix-fractionation response model according to some embodiments of this application;

[0043] Figure 4 This is a schematic diagram of the structure of a rare metal isotope data calibration system according to some embodiments of this application;

[0044] Figure 5 This is a schematic diagram of the structure of a computer device for implementing a rare metal isotope data calibration method according to some embodiments of this application. Detailed Implementation

[0045] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0046] refer to Figure 1 The figure is an exemplary flowchart of a rare metal isotope data calibration method according to some embodiments of this application. The rare metal isotope data calibration method mainly includes the following steps:

[0047] In step 101, the elemental composition information of the rare metal sample to be tested and multiple candidate standard substances is obtained. The elemental composition information includes the concentration data of the main matrix elements and trace elements.

[0048] In specific implementation, the elemental composition information of the rare metal sample to be tested and multiple candidate standard substances can be obtained in the following way: the elemental composition information of each candidate standard substance can be read in batches from a known standard substance database, and the concentration data of the main matrix elements and trace elements of the rare metal sample to be tested output by analytical instruments such as X-ray fluorescence spectroscopy or inductively coupled plasma mass spectrometry can be imported at the same time; then all the obtained elemental composition information is integrated into a unified data matrix, where each row represents a sample, that is, the rare metal sample to be tested or a candidate standard substance, and each column represents the specific concentration value of an element. Other methods can also be used in other embodiments, and this application does not limit them.

[0049] It should be noted that the standard material database is pre-structured to ensure that the elemental composition information directly read includes complete composition data of major matrix elements expressed as percentage concentrations and trace elements expressed as parts per million (ppm). The concentration data of major matrix elements and trace elements of the rare metal sample to be tested are also expressed as percentage concentrations and ppm concentrations, respectively.

[0050] In addition, it should be noted that after obtaining the elemental composition information of the rare metal sample to be tested and multiple candidate standard substances, the elemental composition information also needs to be cleaned and verified. That is, check all element concentration data in the elemental composition information, identify and record missing values ​​or outliers that are obviously outside the reasonable geological range; for missing values, if the number is small, the median of the peer data can be used to fill them, otherwise the element dimension is removed; for outliers, they are marked and removed or corrected after confirmation by the expert system or operator. Other methods can be used in other embodiments, and this application does not limit them.

[0051] Furthermore, it should be noted that the screening conditions for each candidate reference material can be determined based on the basic properties of the rare metal sample to be tested. That is, firstly, the screening conditions are determined based on the basic properties of the rare metal sample to be tested, including sample matrix type, geological origin, or metallurgical process characteristics; then, reference materials with similar matrix composition to the rare metal sample to be tested are selected from the international reference material database; finally, it is ensured that the selected reference materials have known certified isotope reference values ​​and cover the expected isotope ratio range of the rare metal sample to be tested, and the final selected reference materials are used as candidate reference materials. More specifically, for example, for copper-nickel sulfide ore samples, reference materials of the same deposit type or similar mineralization environment are preferred. In other embodiments, similarity judgment can also be made based on trace element distribution patterns or rare earth element distribution characteristics, thereby selecting the top few reference materials with the highest similarity as candidate reference materials for the rare metal sample to be tested. This application does not limit this.

[0052] In some embodiments, reference Figure 2 As shown in the figure, this figure is a schematic diagram of the application scenario of the rare metal isotope data processing system shown in some embodiments of this application. The figure includes three main components: acquisition device, server and data storage device. The acquisition device is responsible for collecting the elemental composition information of the rare metal sample to be tested and multiple candidate standard materials, and sending all the collected elemental composition information to the server through the communication network. The rare metal isotope data processing system runs in the server, and the server stores the processing results in the data storage device and visualizes them.

[0053] In step 102, the matrix similarity between each candidate standard material and the rare metal sample to be tested is determined based on the preprocessed elemental composition information. Then, based on the matrix similarity, the isotope calibration set of the rare metal sample to be tested is selected from all the candidate standard materials.

[0054] In some embodiments, determining the matrix similarity between each candidate standard substance and the rare metal sample to be tested based on the preprocessed elemental composition information can be achieved by the following steps:

[0055] The element composition information is standardized and preprocessed to obtain preprocessed element composition information;

[0056] Based on the preprocessed elemental composition information, the relative distances between the rare metal sample to be tested and each candidate standard material in all elemental dimensions are determined.

[0057] The numerical values ​​of the relative distances of each candidate standard material across all elemental dimensions are converted into the corresponding matrix similarity.

[0058] In specific implementation, the elemental composition information is standardized and preprocessed to obtain the preprocessed elemental composition information. This can be achieved by scaling the concentration data of the main matrix elements and trace elements in the elemental composition information using the Z-score standardization algorithm. More specifically, the concentration values ​​of the main matrix elements and trace elements corresponding to each element column are calculated. First, the average value of the data in each element column is subtracted, and then the average value is divided by the standard deviation of the corresponding column data, so that the mean of each element data after processing is 0 and the standard deviation is 1. This eliminates the influence of large differences in the concentration magnitude of different elements on the subsequent similarity calculation, and finally outputs the preprocessed elemental composition information. Other methods can also be used in other embodiments, and this application does not limit them.

[0059] In specific implementation, the relative distance between the rare metal sample to be tested and each candidate standard substance in all elemental dimensions can be determined in the following way: it can be achieved by calculating the Euclidean distance between the corresponding element concentration coordinates of the rare metal sample to be tested and each candidate standard substance in the multidimensional space formed by the preprocessed elemental composition information. More specifically, for example, the standardized concentration values ​​of the rare metal sample to be tested and a candidate standard substance in all elemental dimensions can be read sequentially from the data matrix, and then the sum of squares of the differences between the concentration values ​​of the rare metal sample to be tested and the corresponding candidate standard substance can be calculated. Then, the square root operation is performed on the sum of squares, and the result is the relative distance between the corresponding candidate standard substance and the sample to be tested. Other methods can also be used in other embodiments, and this application does not limit them.

[0060] In specific implementation, the conversion of the relative distance values ​​of each candidate standard material across all elemental dimensions into the corresponding matrix similarity can be achieved in the following way: the distance values ​​representing differences can be mapped to the values ​​representing similarities through mathematical transformation. More specifically, the reciprocal of each relative distance value calculated in the aforementioned steps can be taken as the matrix similarity between the candidate standard material and the rare metal sample to be tested, that is, 1 divided by the corresponding relative distance value, so that a smaller distance is converted into a larger matrix similarity, and a larger distance is converted into a smaller matrix similarity, thereby completing the conversion from relative distance to matrix similarity. In order to prevent numerical overflow caused by the relative distance being too small, when the relative distance is less than a very small threshold, such as 1e-10, the matrix similarity is directly set to a very large value, such as 1e10. Other methods can also be used in other embodiments, which are not limited in this application.

[0061] It should be noted that the matrix similarity in this application reflects the degree of matching between the candidate standard material and the rare metal sample to be tested in terms of chemical elemental composition. The higher the matrix similarity value, the higher the degree of matching between the candidate standard material and the rare metal sample to be tested in terms of chemical elemental composition. The role of matrix similarity is to quantify the matrix consistency between different standard materials and the rare metal sample to be tested, and to provide a basis for screening the most suitable candidate standard material.

[0062] In addition, it should be noted that the above steps can achieve an objective quantitative assessment of the matrix consistency between the rare metal sample to be tested and the candidate standard material, thereby transforming the screening of standard materials from subjective judgment based on experience to data-driven scientific decision-making.

[0063] In some embodiments, selecting the isotope calibration set of the rare metal sample to be tested from all candidate standard materials based on the similarity of each matrix can be achieved by the following steps:

[0064] All candidate reference materials were sorted in descending order based on the similarity of each matrix to obtain the candidate reference material sequence;

[0065] According to a preset number, a corresponding number of candidate standard substances are selected from the candidate standard substance sequence as the isotope calibration set of the rare metal sample to be tested.

[0066] In specific implementation, the preset number can be achieved in the following way: the preset number can be determined according to the total number of candidate standard substances and the distribution of matrix similarity. For example, all candidate standard substances with matrix similarity greater than a preset threshold (such as 0.7) can be selected, or it can be directly set to a fixed reasonable value, usually not less than 3 and not more than one-third of the total number of candidate standard substances. Other methods can also be used in other embodiments, and this application does not limit them.

[0067] It should be noted that the above steps can replace traditional single standard material calibration with group calibration, which can significantly reduce systematic errors caused by matrix mismatch.

[0068] In step 103, the actual isotope measurements of the rare metal sample to be tested and each candidate standard material in the isotope calibration set are determined.

[0069] In some embodiments, determining the actual isotopic measurements of the rare metal sample to be tested and each candidate standard material in the isotopic calibration set can be achieved by the following steps:

[0070] Mass spectrometry analysis sample solutions were prepared for the rare metal sample to be tested and each candidate standard material in the isotope calibration set.

[0071] Under the same instrument conditions, mass spectrometry measurements were performed on the rare metal sample to be tested and each candidate standard material in the isotope calibration set based on the prepared mass spectrometry analysis sample solution, so as to obtain the actual isotope measurement values ​​of the rare metal sample to be tested and each candidate standard material in the isotope calibration set.

[0072] In specific implementation, determining the actual isotope measurement values ​​of the rare metal sample to be tested and each candidate standard material in the isotope calibration set can be achieved in the following way: by converting the solid sample into an analyzable solution and combining it with mass spectrometry analysis technology. More specifically, firstly, the rare metal sample to be tested and each candidate standard material in the isotope calibration set are dissolved in high-purity acid and diluted to the same concentration range to prepare a solution suitable for mass spectrometry analysis; then, on a multi-receiver inductively coupled plasma mass spectrometer, all sample solutions are measured sequentially using the same instrument parameter settings (including plasma power, nebulizer flow rate, receiver configuration, etc.); finally, the target isotope signal intensity ratio of each sample is recorded in real time through the instrument's data acquisition system, and these actual measurement values ​​are associated with and stored with the corresponding sample identifier to form a dataset of actual isotope measurement values ​​for subsequent modeling. Other methods can also be used in other embodiments, and this application does not limit them.

[0073] In step 104, a matrix-fractionation response model is constructed based on the known isotope measurements of each candidate standard material in the isotope calibration set and the actual isotope measurements corresponding to each candidate standard material in the isotope calibration set.

[0074] In some embodiments, reference Figure 3 As shown in the figure, this is a schematic flowchart of the matrix-fractionation response model in some embodiments of this application. In this embodiment, the known isotope measurements of each candidate standard material in the isotope calibration set are used as the reference, and the actual isotope measurements corresponding to each candidate standard material in the isotope calibration set are used as the observation values. The matrix-fractionation response model can be constructed by the following steps:

[0075] First, in step 1031, the known isotope measurement values ​​and corresponding actual isotope measurement values ​​of each candidate standard material in the isotope calibration set are obtained.

[0076] Then, in step 1032, based on the known isotope measurement values ​​of each candidate standard material in the isotope calibration set, and using the actual isotope measurement values ​​corresponding to each candidate standard material in the isotope calibration set as the observation values, a fractionation response function is established through nonlinear fitting.

[0077] Finally, in step 1033, the matrix-fractionation response model is constructed.

[0078] In specific implementation, the known isotope measurement values ​​and corresponding actual isotope measurement values ​​of each candidate standard material in the isotope calibration set can be obtained in the following way: the known actual isotope measurement values ​​of each candidate standard material in the isotope calibration set can be read from the known standard material database, and the actual isotope measurement values ​​of each candidate standard material in the isotope calibration set determined in step 103 can be obtained. Other methods can also be used in other embodiments, and this application does not limit them.

[0079] It should be noted that the known isotope measurements in this application refer to the certified and recognized reference values ​​of the candidate standard materials, which are derived from known standard material certificates or authoritative databases, and serve as the benchmark truth for calibration.

[0080] In specific implementation, the fractionation response function is established through nonlinear fitting, using the known isotope measurement values ​​of each candidate standard material in the isotope calibration set as the benchmark and the corresponding actual isotope measurement values ​​of each candidate standard material in the isotope calibration set as the observation values. This can be achieved in the following way: First, the known actual isotope measurement values ​​of each candidate standard material in the isotope calibration set can be used as the independent variable benchmark dataset, and the corresponding actual isotope measurement values ​​can be used as the dependent variable observation dataset. Then, a nonlinear function form suitable for describing isotope fractionation behavior is selected from a preset model function library. In this application, a power-law function is selected as the nonlinear function form, and the nonlinear function form can also be an exponential function or a quadratic polynomial function. Next, a nonlinear least squares fitting algorithm is called to perform curve fitting using each value in the independent variable benchmark dataset as the X value and each corresponding value in the dependent variable observation dataset as the Y value. The function parameters that minimize the sum of squared residuals are found through iterative optimization. Finally, the specific mathematical expression of the fractionation response function is determined based on the optimal parameters. In a preferred embodiment, the nonlinear least squares fitting can employ the Levenberg-Marquardt algorithm to improve convergence speed and stability. More specifically, for example, the curve_fit function of the scipy.optimize module in Python can be used, setting the fitting model to a power-law function y=a*x^b, with the independent variable benchmark dataset as x and the dependent variable observation dataset as y. The leastsq algorithm is called for iterative fitting, and after 50 iterations, the optimal parameters a=1.00012 and b=1.00005 are obtained, finally establishing the fractionation response function y=1.00012*x^1.00005. Other methods can also be used in other embodiments, and this application does not limit them.

[0081] It should be noted that the pre-defined model function library in this application includes functions such as power law functions, exponential functions, and quadratic polynomial functions. The specific steps for selecting a nonlinear function form suitable for describing isotope fractionation behavior from the pre-defined model function library are as follows: different function forms are used to fit the benchmark dataset and the observation dataset in turn. Then, the coefficient of determination and the sum of squared residuals after fitting each function form are calculated. Finally, the function form with the highest coefficient of determination and the smallest sum of squared residuals is selected as the final fractionation response function.

[0082] In specific implementation, the matrix-fractionation response model can be constructed in the following way: First, the specific parameter values ​​and function expressions in the fitted fractionation response function can be structurally encapsulated to form a complete model description file; then, the model description file is stored in a specified model database or file system to complete the construction of the matrix-fractionation response model. In a preferred embodiment, the structural encapsulation can be organized using XML or JSON format, encapsulating the model type, parameter values, creation timestamp, and model validation indicators into a standardized data structure. More specifically, for example, after fitting the fractionation response function, the power-law function type, parameter a=1.00012, parameter b=1.00005, goodness-of-fit R²=0.9987, and model creation time can be encapsulated into a JSON format model description file. Other methods can be used in other embodiments, and this application does not limit this approach.

[0083] It should be noted that the above steps can establish a response model reflecting the fractionation behavior and matrix properties based on actual measurement data, thereby describing the complex law of instrument fractionation with changes in the matrix, and providing a highly targeted calibration benchmark for the rare metal samples to be tested.

[0084] In step 105, the actual isotope measurement value of the rare metal sample to be tested is substituted into the inverse function of the matrix-fractionation response model, and then the isotope calibration value of the rare metal sample to be tested is output.

[0085] In some embodiments, the process of substituting the actual isotopic measurement value of the rare metal sample to be tested into the inverse function of the matrix-fractionation response model to output the isotopic calibration value of the rare metal sample to be tested can be achieved by the following steps:

[0086] Verify whether the goodness of fit of the matrix-fractionation response model meets the preset standard;

[0087] Input the actual isotope measurement values ​​of the rare metal sample to be tested into the validated inverse function of the model;

[0088] Perform inverse function calculations and output the isotope calibration values ​​of the rare metal sample to be tested.

[0089] In specific implementation, the goodness of fit of the matrix-fractionation response model can be verified in the following way: the goodness of fit can be evaluated by calculating the coefficient of determination of the matrix-fractionation response model, and the calculated goodness of fit is compared with a preset threshold of 0.98. When the goodness of fit is greater than or equal to the threshold, the matrix-fractionation response model is determined to meet the preset standard and the verification result is recorded; otherwise, a warning message that the model is unusable is issued. In a preferred embodiment, the coefficient of determination can be calculated using standard statistical methods in the prior art, that is, by calculating the sum of squares and the sum of squares of residuals between the observed values ​​and the model predicted values. In other embodiments, the adjusted coefficient of determination or root mean square error can also be used as the evaluation index of goodness of fit, and this application does not limit it.

[0090] In practice, the actual isotope measurement value of the rare metal sample to be tested is input into the validated inverse function of the model. The calculation and output of the isotope calibration value of the rare metal sample can be achieved in the following way: The appropriate inverse function solution method can be selected according to the function type of the matrix-fractionation response model. For example, for a power-law function model, the inverse function expression can be directly solved through algebraic transformation, and the calibration result can be calculated by substituting the actual measurement value of the sample. For complex function models, a numerical iteration method can be used for approximation. After obtaining the isotope calibration value, this isotope calibration value can be compared with the measured value of the rare metal sample. The identification information of the rare metal sample is output to the designated result display interface or data storage area. In a preferred embodiment, the inverse function can be solved using a direct algebraic method. For example, for the power-law function y=a*x^b, its inverse function is x=exp((lny-lna) / b), and the isotope calibration value of the rare metal sample to be tested can be directly calculated through the expression of this inverse function. In other embodiments, for complex models that cannot be solved directly by inverse function, numerical methods such as the bisection method or Newton's iteration method can be used for approximate calculation. This application does not limit this.

[0091] It should be noted that the isotope calibration value in this application refers to the isotope measurement result obtained after the rare metal sample to be measured has been corrected by the matrix-fractionation response model. The purpose of the isotope calibration value is to eliminate the influence of mass fractionation effect and matrix effect generated during the instrument measurement process, and to provide isotope composition data that is closer to the true value.

[0092] In another aspect, in some embodiments, this application provides a rare metal isotope data calibration system, with reference to... Figure 4 The figure is a schematic diagram of a rare metal isotope data calibration system according to some embodiments of this application. The rare metal isotope data calibration system includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described below:

[0093] The acquisition module 401 in this application is mainly used to acquire the elemental composition information of the rare metal sample to be tested and multiple candidate standard substances. The elemental composition information includes the concentration data of the main matrix elements and trace elements.

[0094] Processing module 402, in this application, is mainly used to determine the matrix similarity between each candidate standard material and the rare metal sample to be tested based on the preprocessed elemental composition information, and then select the isotope calibration set of the rare metal sample to be tested from all candidate standard materials based on each matrix similarity.

[0095] The processing module 402 described in this application is also used to determine the actual isotope measurement values ​​of the rare metal sample to be tested and each candidate standard material in the isotope calibration set.

[0096] The processing module 402 described in this application is also used to construct a matrix-fractionation response model based on the known isotope measurement values ​​of each candidate standard material in the isotope calibration set and the actual isotope measurement values ​​corresponding to each candidate standard material in the isotope calibration set as observation values.

[0097] The execution module 403 in this application is mainly used to input the actual isotope measurement value of the rare metal sample to be tested into the inverse function of the matrix-fractionation response model, and then output the isotope calibration value of the rare metal sample to be tested.

[0098] Each module in the aforementioned rare metal isotope data calibration system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0099] In another embodiment, this application provides a computer device, which may be a server, and its internal structure diagram may be as follows. Figure 5 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores rare metal isotope calibration data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a rare metal isotope calibration method.

[0100] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0101] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described rare metal isotope data calibration method embodiment.

[0102] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described rare metal isotope data calibration method embodiment.

[0103] In one embodiment, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps described in the rare metal isotope data calibration method embodiment.

[0104] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0105] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for calibrating rare metal isotope data, characterized in that, Includes the following steps: Obtain the elemental composition information of the rare metal sample to be tested and multiple candidate standard substances, wherein the elemental composition information includes the concentration data of the main matrix elements and trace elements; Based on the preprocessed elemental composition information, the matrix similarity between each candidate standard material and the rare metal sample to be tested is determined, and then the isotope calibration set of the rare metal sample to be tested is selected from all the candidate standard materials based on the matrix similarity. Determine the actual isotope measurement values ​​of the rare metal sample to be tested and each candidate standard material in the isotope calibration set; Based on the known isotope measurements of each candidate standard material in the isotope calibration set, and using the actual isotope measurements corresponding to each candidate standard material in the isotope calibration set as the observation values, a matrix-fractionation response model is constructed. The actual isotope measurement value of the rare metal sample to be tested is substituted into the inverse function of the matrix-fractionation response model, and then the isotope calibration value of the rare metal sample to be tested is output. Wherein, the actual isotope measurement value is the ratio of the isotope signal intensity obtained by the instrument from the rare metal sample to be tested and each candidate standard material in the isotope calibration set, and the known isotope measurement value refers to the certified and recognized reference value of the candidate standard material.

2. The method as described in claim 1, characterized in that, Determining the matrix similarity between each candidate standard and the rare metal sample to be tested based on the preprocessed elemental composition information specifically includes: The element composition information is standardized and preprocessed to obtain preprocessed element composition information; Based on the preprocessed elemental composition information, the relative distances between the rare metal sample to be tested and each candidate standard material in all elemental dimensions are determined. The numerical values ​​of the relative distances of each candidate standard material across all elemental dimensions are converted into the corresponding matrix similarity.

3. The method as described in claim 1, characterized in that, The isotope calibration set for the rare metal sample to be tested, selected from all candidate standard materials based on matrix similarity, specifically includes: All candidate reference materials were sorted in descending order based on the similarity of each matrix to obtain the candidate reference material sequence; According to a preset number, a corresponding number of candidate standard substances are selected from the candidate standard substance sequence as the isotope calibration set of the rare metal sample to be tested.

4. The method as described in claim 1, characterized in that, Determining the actual isotopic measurements of the rare metal sample to be tested and each candidate standard material in the isotopic calibration set specifically includes: Mass spectrometry analysis sample solutions were prepared for the rare metal sample to be tested and each candidate standard material in the isotope calibration set. Under the same instrument conditions, mass spectrometry measurements were performed on the rare metal sample to be tested and each candidate standard material in the isotope calibration set based on the prepared mass spectrometry analysis sample solution, so as to obtain the actual isotope measurement values ​​of the rare metal sample to be tested and each candidate standard material in the isotope calibration set.

5. The method as described in claim 1, characterized in that, Based on the known isotopic measurements of each candidate standard material in the isotopic calibration set, and using the actual isotopic measurements corresponding to each candidate standard material in the isotopic calibration set as observations, the matrix-fractionation response model is constructed as follows: Obtain the known isotope measurement values ​​and corresponding actual isotope measurement values ​​for each candidate standard material in the isotope calibration set; Based on the known isotope measurement values ​​of each candidate standard material in the isotope calibration set, and using the actual isotope measurement values ​​corresponding to each candidate standard material in the isotope calibration set as the observation values, a fractionation response function is established through nonlinear fitting. Complete the construction of the matrix-fractionation response model.

6. The method as described in claim 1, characterized in that, The actual isotopic measurement value of the rare metal sample to be tested is substituted into the inverse function of the matrix-fractionation response model to output the isotopic calibration value of the rare metal sample to be tested. Specifically, this includes: Verify whether the goodness of fit of the matrix-fractionation response model meets the preset standard; Input the actual isotope measurement values ​​of the rare metal sample to be tested into the validated inverse function of the model; Perform inverse function calculations and output the isotope calibration values ​​of the rare metal sample to be tested.

7. The method as described in claim 1, characterized in that, Specifically, this includes determining the screening conditions for each candidate reference material based on the basic properties of the rare metal sample to be tested.

8. A rare metal isotope data calibration system, wherein the rare metal isotope data calibration is performed using the method described in any one of claims 1 to 7, characterized in that, The system includes: The acquisition module is used to acquire the elemental composition information of the rare metal sample to be tested and multiple candidate standard substances, the elemental composition information including the concentration data of the main matrix elements and trace elements; The processing module is used to determine the matrix similarity between each candidate standard material and the rare metal sample to be tested based on the preprocessed elemental composition information, and then select the isotope calibration set of the rare metal sample to be tested from all the candidate standard materials based on the matrix similarity. The processing module is also used to determine the actual isotope measurement values ​​of the rare metal sample to be tested and each candidate standard material in the isotope calibration set. The processing module is also used to construct a matrix-fractionation response model based on the known isotope measurement values ​​of each candidate standard material in the isotope calibration set and the actual isotope measurement values ​​corresponding to each candidate standard material in the isotope calibration set. The execution module is used to input the actual isotope measurement value of the rare metal sample to be tested into the inverse function of the matrix-fractionation response model, and then output the isotope calibration value of the rare metal sample to be tested.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the rare metal isotope data calibration method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the rare metal isotope data calibration method as described in any one of claims 1 to 7.

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