Multi-element density mineral test accuracy evaluation and electron beam parameter optimization method and system

Abstract

The invention belongs to but not limited to the technical field of mineral analysis, and discloses a multi-element density mineral test accuracy evaluation and electron beam parameter optimization method, which comprises the following steps of: strictly controlling the purity, the density range and the representativeness of pure minerals in a raw material and sample preparation link, and ensuring uniform particle size and accurate mass proportion of a powdery sample by using professional equipment; and carrying out standard treatment on the resin polished section. Then, an analysis system with high-resolution imaging and element analysis functions is selected for testing, parameters are optimized according to sample characteristics, and test data are accurately recorded. And then in a result analysis stage, errors of the multi-element density minerals under different test conditions are calculated, evaluation indexes are determined according to error analysis results and actual requirements, and the accuracy of the system is comprehensively judged. A mathematical model is established, a regression analysis method and other methods are adopted to fit a function relationship between accuracy and electron beam scanning parameters, test parameters are optimized according to the model, and then test evaluation is performed again, so that the precision of multi-element density mineral test is improved.

Description

Technical Field

[0001] The present invention belongs to, but is not limited to, the technical field of mineral analysis, and particularly relates to a method and system for evaluating the accuracy of multi-density mineral testing and optimizing electron beam parameters. Background Art

[0002] In the field of mineral analysis, accurately determining the composition and properties of minerals with different densities is of crucial significance for many industries, such as geological research, mining, and metallurgical industry. Traditional mineral testing methods often face numerous challenges when dealing with minerals of different densities.

[0003] On the one hand, the physical and chemical properties of minerals with different densities vary greatly, making it difficult to adopt a unified standard and method to ensure accuracy during the testing process. For example, minerals with lower densities such as mica and calcite, as well as those with higher densities such as magnetite and chalcopyrite, have different crystal structures, element compositions, and distribution states in the sample. Traditional testing methods may not be able to accurately distinguish and analyze these differences, resulting in deviations in the test results.

[0004] On the other hand, although existing mineral analysis systems have certain imaging and analysis capabilities, their parameter settings often lack targeted optimization when testing minerals with different densities. This may lead to more accurate information being obtained for some minerals during the testing process, while for other minerals with significantly different densities, there may be larger errors, unable to meet the requirements of high-precision analysis of minerals with different densities in practical applications.

[0005] With the continuous development of technology, the requirements for the accuracy and precision of mineral analysis in various industries are increasing day by day. Therefore, there is an urgent need for a method that can effectively measure the accuracy of multi-density mineral testing and optimize the testing system according to the evaluation results to improve the testing level of minerals with different densities and meet the needs of scientific research and industrial production in many aspects. Summary of the Invention

[0006] Aiming at the problems existing in the prior art, the present invention provides a method and system for evaluating the accuracy of multi-density mineral testing and optimizing electron beam parameters. During the process of testing and analyzing minerals with different densities using a mineral liberation analyzer (MLA) or advanced automatic mineral analyzers (such as Mapsmin and AMICS, etc.), it aims to improve the accuracy and precision of testing minerals with different densities and ensure the reliability of mineral composition analysis, structural research, and optimization of related process parameters. It solves the problems of limited accuracy in multi-density mineral testing and the lack of effective strategies for optimizing electron beam parameters in existing mineral testing.

[0007] The present invention is implemented as follows. A method for evaluating the accuracy of multi - density mineral testing and optimizing electron beam parameters includes the following steps:

[0008] Step 1, raw material and sample preparation: Select pure minerals, grind them into powder using professional grinding equipment, and sieve them. Prepare test samples according to a ratio, and make the prepared samples into resin polished slices.

[0009] Step 2, analysis system testing: Select an automatic quantitative mineral analysis system with high - resolution imaging and element analysis functions for testing, optimize the parameters according to the sample characteristics, and accurately record the test data.

[0010] Step 3, result analysis and accuracy evaluation: Conduct error analysis on the collected data, calculate the errors of multi - density minerals under different test conditions, determine the evaluation indicators according to the error analysis results and actual requirements, and comprehensively judge the system accuracy.

[0011] Step 4, optimization and verification: By establishing an accurate mathematical model between the system accuracy of different - density minerals and electron beam scanning parameters, using methods such as regression analysis to fit the functional relationship between accuracy and electron beam scanning parameters, and retesting and evaluating after optimizing the test parameters according to the model.

[0012] Furthermore, the purity of the pure minerals selected in Step 1 should reach over 99%, including mica, calcite, etc. with lower density, and magnetite, chalcopyrite, etc. with higher density.

[0013] The particle size range of the processed powdered samples should at least include - 50 mesh, - 100 mesh, - 200 mesh, etc. The particle size distribution uniformity of the samples in each particle size range should meet the relevant standards, which is achieved through professional grinding and sieving equipment.

[0014] Furthermore, the control error of the mass ratio of each mineral in the test sample is within ±0.05%. Weigh it using a high - precision balance. The resin polished slices tested by the automatic quantitative mineral analysis system of the test sample should undergo standard grinding, polishing, and carbon spraying processes. Both the grinding and polishing processes need to use grinding disks and polishing cloths of different specifications for 3 times. The carbon spraying on the surface of the resin polished slices should be uniform and of appropriate thickness.

[0015] Furthermore, the selected automatic quantitative mineral analysis system should have high - resolution imaging and element analysis functions, such as a mineral liberation analyzer (MLA) or advanced automatic mineral analyzers (such as Mapsmin and AMICS, etc.); during the testing process, the accurate fixed position of the sample should be ensured, the test images should be clear, and the test conditions and parameters as well as the collected mineral analysis data should be recorded.

[0016] Furthermore, in the error analysis stage, precisely calculate the error values of minerals with different densities under various test conditions, covering multiple aspects such as average error, standard deviation, and error distribution range, and deeply analyze the test error situation from multiple dimensions;

[0017] The accuracy evaluation indicators should be determined according to the error analysis results and actual application requirements. For example, set the maximum allowable absolute error of the main mineral components not exceeding 3%, the secondary mineral components not exceeding 5%, and the repeatability of the analysis results (the relative standard deviation of the analysis results of minerals with the same density in multiple simulation tests is less than a certain value), etc. Based on these indicators, comprehensively judge the accuracy of the system.

[0018] Furthermore, based on the precise mathematical model established between the accuracy of the system of minerals with different densities and the electron beam scanning parameters, use various scientific methods such as regression analysis, and accurately fit the functional relationship between accuracy and parameters according to the accumulated rich test data; based on this functional relationship, optimize and adjust the test parameters, then use the same sample to conduct tests and simulation analyses again, and comprehensively evaluate the accuracy again according to the original evaluation steps, compare the results before and after optimization to verify the effectiveness of the optimization measures, and continuously improve until the actual application requirements are met.

[0019] Another object of the present invention is to provide a multi-density mineral test accuracy evaluation and electron beam parameter optimization system for the multi-density mineral test accuracy evaluation and electron beam parameter optimization method, including:

[0020] Raw material and sample preparation module; select pure minerals, grind them into powder using professional grinding equipment, screen them, prepare test samples according to proportions, and make the prepared samples into resin polished slices;

[0021] Analysis system test module; select an automatic quantitative mineral analysis system with high-resolution imaging and element analysis functions for testing, optimize the parameters according to the sample characteristics, and accurately record the test data;

[0022] Result analysis and accuracy evaluation module; conduct error analysis on the collected data, calculate the errors of multi-density minerals under different test conditions, determine the evaluation indicators according to the error analysis results and actual requirements, and comprehensively judge the accuracy of the system;

[0023] Optimization and verification module; by establishing a precise mathematical model between the accuracy of the system of minerals with different densities and the electron beam scanning parameters, use methods such as regression analysis to fit the functional relationship between accuracy and electron beam scanning parameters, and re-test and evaluate after optimizing the test parameters according to the model.

[0024] Another object of the present invention is to provide a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the steps of the method for evaluating the accuracy of multi-density mineral testing and optimizing electron beam parameters.

[0025] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by the processor, the processor performs the steps of the method for evaluating the accuracy of multi-density mineral testing and optimizing electron beam parameters.

[0026] Another object of the present invention is to provide an information data processing terminal, which includes the system for evaluating the accuracy of multi-density mineral testing and optimizing electron beam parameters.

[0027] Combined with the above technical solutions and the solved technical problems, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0028] First, the present invention constructs a complete system from raw material and sample preparation, testing system parameter optimization, result analysis and evaluation to final optimization verification, specifically for multi-density mineral testing. It comprehensively considers the characteristic differences of multi-density minerals, formulates strict standards and precise operation procedures in each link, effectively improves the accuracy and reliability of the entire testing process, and fills the gap in the lack of systematic optimization methods for multi-density mineral testing.

[0029] The present invention innovatively establishes an accuracy evaluation index system based on data, combines error analysis with actual application requirements, and realizes scientific judgment of the accuracy of the testing system. By establishing a mathematical model, closely associates the electron beam scanning parameters with the system accuracy, and uses methods such as regression analysis to achieve data-driven parameter optimization, significantly improving the pertinence and effectiveness of the optimization, and effectively ensuring and improving the accuracy of multi-density mineral testing.

[0030] Second, as the creative auxiliary evidence of the claims of the present invention, it is also reflected in the following important aspects:

[0031] (1) The expected benefits and commercial value after the transformation of the technical solution of the present invention are:

[0032] In the field of geological research, the ability to more accurately analyze mineral composition and structure helps to gain a deeper understanding of the evolution of geological structures, provides a more reliable basis for mineral resource exploration, thereby increasing the exploration success rate and reducing exploration costs. In mining, it can accurately determine the distribution and content of minerals with different densities in ores, optimize the mining process, improve the ore utilization rate, reduce resource waste, and increase the economic benefits of enterprises. For the metallurgical industry, it can accurately analyze mineral characteristics, provide data support for optimizing the parameters of ore dressing and smelting processes, improve the metal extraction rate and product quality, and enhance the competitiveness of enterprises in the market. It has broad market application prospects and considerable potential for economic benefits.

[0033] (2) The technical solution of the present invention fills the technical gap in the industry at home and abroad:

[0034] At present, there is a lack of systematic and effective methods for evaluating the accuracy of multi-density mineral tests and optimizing electron beam parameters at home and abroad. Existing mineral testing technologies mostly focus on single-density minerals or general testing processes, and it is difficult to meet the high requirements for accuracy and precision in multi-density mineral tests. The present invention constructs a complete system from sample preparation, selection and optimization of the testing system, result evaluation to parameter optimization verification, etc. It is specifically designed for the characteristics of multi-density minerals, filling the gap in systematic testing and optimization methods in this field, and providing strong technical support for related research and industrial applications of multi-density minerals.

[0035] (3) The technical solution of the present invention solves the technical problems that people have been eager to solve but have never succeeded in:

[0036] For a long time, due to the large differences in the physical and chemical properties of multi-density minerals, traditional testing methods are difficult to unify standards and lack targeted parameter optimization, resulting in limited testing accuracy. People have always expected a method that can comprehensively consider the characteristics of multi-density minerals, scientifically evaluate the accuracy of tests, and effectively optimize electron beam parameters. The present invention has successfully solved this problem through a series of innovative measures such as selecting pure minerals, strictly controlling the sample preparation process, selecting a specific analysis system and optimizing parameters according to sample characteristics, multi-dimensional error analysis and accuracy evaluation, and establishing a mathematical model for parameter optimization, significantly improving the accuracy of multi-density mineral tests and meeting the urgent needs of scientific research and industrial production for high-precision mineral analysis.

[0037] (4) The technical solution of the present invention overcomes the technical prejudice:

[0038] Previous mineral testing techniques often overlooked the significant impact of the huge differences among multi-density minerals on test results, and tended to adopt general testing methods and parameter settings, believing that this approach was sufficient to meet the testing requirements of different minerals. The present invention breaks this technical prejudice, emphasizes formulating specialized testing and optimization strategies according to the characteristic differences of multi-density minerals, from the refinement of sample preparation to the targeted optimization of the parameters of the testing system, and then to the accuracy evaluation and parameter adjustment based on data-driven, completely subverting the traditional concept, opening up a new technical path for the testing of multi-density minerals and leading a new direction for the technical development in this field. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flowchart of a method for evaluating the accuracy of multi-density mineral testing and optimizing electron beam parameters provided by an embodiment of the present invention;

[0040] Figure 2 is a schematic diagram of the testing process of an automatic quantitative mineral analysis system provided by an embodiment of the present invention;

[0041] Figure 3 is a structural diagram of a system for evaluating the accuracy of multi-density mineral testing and optimizing electron beam parameters provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0043] As Figure 1 shown, an embodiment of the present invention provides a method for evaluating the accuracy of multi-density mineral testing and optimizing electron beam parameters, including the following steps:

[0044] Step 1, preparation of raw materials and samples; select pure minerals, grind them into powder using professional grinding equipment, screen them, prepare test samples according to a ratio, and make the prepared samples into resin polished slices;

[0045] Step 2, testing with the analysis system; select an automatic quantitative mineral analysis system with high-resolution imaging and element analysis functions for testing, optimize the parameters according to the sample characteristics, and accurately record the test data;

[0046] Step 3, result analysis and accuracy evaluation; conduct error analysis on the collected data, calculate the errors of multi-density minerals under different test conditions, determine the evaluation indicators according to the error analysis results and actual requirements, and comprehensively judge the system accuracy;

[0047] Step 4: Optimization and verification; by establishing an accurate mathematical model between the accuracy of different density mineral systems and electron beam scanning parameters, using methods such as regression analysis to fit the functional relationship between accuracy and electron beam scanning parameters, and retesting and evaluating after optimizing the test parameters according to the model.

[0048] The first stage of the method of the present invention is to ensure the uniformity and representativeness of the test sample, laying a foundation for the accuracy of the test. First, select multi-density minerals with relatively high purity as raw materials, and finely grind the minerals into powder form through a grinding device to ensure that the fineness of the particles meets the test requirements. Subsequently, screen the ground minerals to remove unqualified particles and ensure the consistency of particle size. Mix and prepare mineral powders with different densities according to a predetermined ratio to form a representative sample. Finally, use resin curing technology to make the prepared sample into a resin polished section. This treatment method not only fixes the positions of the mineral particles but also facilitates subsequent electron beam scanning tests.

[0049] After the sample preparation is completed, use an automatic quantitative mineral analysis system with high-resolution imaging and elemental analysis functions for testing. The system scans the surface of the sample with an electron beam to obtain the composition and density data of the minerals. During this process, optimize the scanning parameters of the electron beam (such as acceleration voltage, beam current intensity, focusing mode, etc.) according to the density characteristics and elemental composition of the sample to reduce energy loss and scattering effects and improve the accuracy of the data. The system automatically records the data of each test point, including imaging results, elemental distribution maps, and density analysis results, providing basic data for further error evaluation.

[0050] This stage is mainly for error analysis and accuracy evaluation of the collected mineral data. First, compare the test data with the standard values of minerals with known densities, and calculate the error distribution under different test conditions, such as data deviation, stability, and precision differences in repeated tests. Through error analysis, identify the internal relationship between electron beam parameters and test errors, and set evaluation indicators according to actual needs, such as density detection error thresholds, elemental composition deviation ranges, etc. Through these evaluation indicators, comprehensively judge the accuracy of the existing test system and provide a reference basis for parameter optimization.

[0051] In this stage, achieve precise optimization of test parameters by establishing a mathematical model between the accuracy of the mineral system and electron beam scanning parameters. Specifically, use regression analysis to fit the functional relationship between test errors and electron beam parameters, and find the optimal scanning conditions (such as the best acceleration voltage and beam current intensity). Subsequently, retest according to the optimized parameters to verify the accuracy and stability of the system and ensure the minimization of errors. Through the optimized system, precise detection of different density minerals can be achieved, meeting the actual needs of high-precision tests.

[0052] In summary, through four stages of raw material preparation, system testing, result evaluation, and parameter optimization, the present invention has established a method for precision evaluation and electron beam parameter optimization for multi-density minerals, improving the test accuracy and stability of the mineral analysis system and providing an efficient and reliable solution for mineral composition analysis.

[0053] 1. Raw Material and Sample Preparation

[0054] Select pure minerals with a purity of over 99%, ensuring that the selected mineral density range can comprehensively cover the density intervals of common minerals and is highly representative in the selection of different density minerals. Minerals with lower densities such as mica and calcite, as well as those with higher densities such as magnetite and chalcopyrite, are all taken into consideration. Using professional grinding and screening equipment, the particle size of the processed powdered samples is precisely controlled within specific ranges such as at least -50 mesh, -100 mesh, -200 mesh, etc., and the particle size distribution uniformity of the samples in each particle size range strictly complies with relevant standards, thus ensuring the consistency and stability of the sample quality. In terms of mass ratio control, precise weighing is carried out with a high-precision balance, and the mass ratio error of each mineral in the test sample is strictly limited within ±0.05%. For the resin polished sections of the test samples used for automatic quantitative mineral analysis system testing, standard and delicate grinding, polishing, and carbon spraying processes are implemented. In the grinding and polishing processes, different specifications of grinding discs and polishing cloths are used for 3 operations. Finally, the carbon spraying on the surface of the resin polished sections is uniform and the thickness is appropriate, providing a high-quality and reliable sample basis for subsequent precise testing.

[0055] 2. Automatic Quantitative Mineral Analysis System Testing

[0056] Select professional analysis systems with high-resolution imaging and element analysis functions, such as mineral liberation analyzers (MLA) or advanced automatic mineral analyzers (such as Mapsmin and AMICS). The testing process of the automatic quantitative mineral analysis system is as Figure 2 shown. In the testing implementation stage, the samples should be fixed well to prevent errors introduced by sample position changes, and the test images should be clear. At the same time, all conditions, parameters, and all mineral analysis data collected during the testing process should be recorded comprehensively and in detail to provide complete data support for subsequent result analysis and accuracy evaluation.

[0057] 3. Result Analysis and Accuracy Evaluation

[0058] In the error analysis section, the error values of minerals with different densities under various test conditions are accurately calculated, covering aspects such as average error, standard deviation, and error distribution range, and the test error situation is deeply analyzed from multiple dimensions. The accuracy evaluation indicators are scientifically and reasonably determined based on the error analysis results and the specific requirements of actual applications. For example, it is set that the absolute value of the maximum allowable error of the main mineral components does not exceed 3%, the secondary mineral components do not exceed 5%, and a series of strict indicators such as the relative standard deviation of the analysis results of minerals with the same density in multiple simulation tests being less than a certain value are used to measure the repeatability of the analysis results. These indicators are comprehensively used to make a comprehensive, objective, and in-depth judgment on the accuracy of the entire test system.

[0059] 4. Optimization and Verification

[0060] When the system accuracy fails to meet the requirements, deeply explore the root causes of the problems, establish an accurate mathematical model between the system accuracy of minerals with different densities and the electron beam scanning parameters, flexibly use various scientific methods such as regression analysis, and accurately fit the functional relationship between accuracy and parameters based on the accumulated rich test data. Based on this functional relationship, optimize and adjust the test parameters, and then use the same samples to conduct tests and simulation analysis again. Re-evaluate the accuracy comprehensively in strict accordance with the original evaluation steps. By comparing the results before and after optimization, continuously improve the optimization measures, and repeat the cycle until the system fully meets the actual application requirements, ultimately achieving a significant improvement in the accuracy of the multi-element density mineral test.

[0061] As Figure 3 shown, an embodiment of the present invention provides a multi-element density mineral test accuracy evaluation and electron beam parameter optimization system for the multi-element density mineral test accuracy evaluation and electron beam parameter optimization method, including:

[0062] Raw material and sample preparation module; Select pure minerals, grind them into powder using professional grinding equipment, screen them, prepare test samples according to the ratio, and make the prepared samples into resin polished sections;

[0063] Analysis system test module; Select an automatic quantitative mineral analysis system with high-resolution imaging and element analysis functions for testing, optimize the parameters according to the sample characteristics, and accurately record the test data;

[0064] Result analysis and accuracy evaluation module; Conduct error analysis on the collected data, calculate the errors of multi-element density minerals under different test conditions, determine the evaluation indicators according to the error analysis results and actual requirements, and comprehensively judge the system accuracy;

[0065] Optimization and verification module; By establishing an accurate mathematical model between the accuracy of different density mineral systems and the electron beam scanning parameters, using methods such as regression analysis to fit the functional relationship between the accuracy and the electron beam scanning parameters, and re-testing and evaluating after optimizing the test parameters according to the model.

[0066] Example 1

[0067] 1. Raw material and sample preparation

[0068] Select mica with a purity of 99.5% (density about 2.8 - 3.1 g / cm 3 , taking the median value), calcite (density about 2.7 g / cm 3 ), magnetite (density about 5.2 g / cm 3 ), and chalcopyrite (density about 4.2 g / cm 3 ) as representatives of pure minerals. Grind these minerals into powder form using professional grinding equipment respectively, and obtain samples of -50 mesh, -100 mesh, and -200 mesh through screening equipment. The particle size distribution uniformity of the samples in each particle size range meets the relevant standards. After testing, the proportion of the -50 mesh sample with a particle size distribution between 40 - 60 mesh reaches 95%, the proportion of the -100 mesh sample with a particle size distribution between 80 - 120 mesh is 93%, and the proportion of the -200 mesh sample with a particle size distribution between 150 - 250 mesh reaches 92%. Weigh using a high-precision balance, and prepare test samples according to the mass ratio of mica:calcite:magnetite:chalcopyrite = 3:2:2:3, with the mass ratio control error within ±0.03%. Make the prepared samples into resin polished slices, and process them through 3 times of grinding (using 80 mesh, 200 mesh, and 600 mesh grinding discs respectively), 3 times of polishing (using 3μm, 1μm, and 0.5μm polishing cloths respectively), and uniform carbon spraying (thickness about 20nm) processes.

[0069] 2. Automatic quantitative mineral analysis system test

[0070] Select a mineral liberation analyzer (MLA) as the analysis system. According to the sample characteristics and analysis requirements, set the electron beam acceleration voltage to 20 kV, the beam current intensity to 3 nA, the scanning speed to 5 μm / s, the scanning mode to area scanning, and the scanning resolution to 5 μm. Fix the prepared resin polished slice sample on the sample stage to ensure accurate position, conduct the test, and record the test conditions, parameters, and the collected mineral analysis data.

[0071] 3. Result analysis and accuracy evaluation

[0072] Perform error analysis on the collected data and calculate statistical indicators such as the average error and standard deviation of different density minerals under the current test conditions. For example, the average error of potassium element analysis in mica is 5.5%, and the standard deviation is 1.6%; the average error of calcium element analysis in calcite is 6.0%, and the standard deviation is 2.0%; for the analysis of iron element, the main component of magnetite, the average error is 4.5%, and the standard deviation is 1.5%; the average error of copper element analysis in chalcopyrite is 5.0%, and the standard deviation is 1.8%. According to the set accuracy evaluation indicators (the absolute value of the maximum allowable error of the main mineral components does not exceed 3%, and that of the minor mineral components does not exceed 5%), the errors of some mineral components are relatively large in this test, and the system accuracy needs to be improved.

[0073] 4. Optimization and Verification

[0074] In view of the situation that the system accuracy does not meet the requirements, establish a mathematical model between the system accuracy of different density minerals and the electron beam scanning parameters, and adopt the regression analysis method to fit the functional relationship between the accuracy and the parameters according to the data of this test and previous multiple tests (such as the data of 50 previous pre-tests).

[0075] For each mineral sample, measure the system accuracy under different combinations of electron beam scanning parameters. The system accuracy can be measured by comparing the information such as the mineral composition and structure obtained by electron beam scanning with the known standard values. For example, calculate the error rate (E) between the measured value and the standard value as an indicator of the system accuracy, as shown in formula (1).

[0076]

[0077] Record the mineral density (D), electron beam scanning parameters (such as acceleration voltage V, beam current intensity I, scanning speed S, etc.) and system accuracy index (E) of each measurement, and construct a data set containing pre-experiment data. The data shows that there may be a non-linear relationship, and use a quadratic model to fit the data, as shown in formula (2).

[0078]

[0079] Use statistical software (such as R, Scikit-learn library in Python, etc.) to fit the collected data. Taking multiple linear regression as an example, divide the data set into a training set and a test set, and use the data in the training set to determine the coefficients in the model by methods such as the least squares method. For example, use the LinearRegression class in the Scikit-learn library in Python for fitting: evaluate the performance of the model on the test set. Common evaluation indicators include mean square error (MSE), coefficient of determination (R 2) etc. By continuously adjusting the model form and parameters until a model with good performance on the test set is obtained. The cross-validation method is used to further verify the stability and generalization ability of the model. For example, perform k-fold cross-validation, divide the dataset into k subsets, each time use k - 1 subsets as the training set, and the remaining one subset as the test set, repeat this process k times, calculate the evaluation metrics each time and take the average. This can avoid the model overfitting to a specific dataset.

[0080] Based on this functional relationship, the acceleration voltage of the electron beam is optimized to 20 kV, the beam current intensity is 2.5 nA, and the scanning speed is 4 μm / s. Use the same sample to conduct tests and simulation analyses again, and re-evaluate the accuracy. After optimization, the average error of mica potassium element analysis is reduced to 2.8%, and the standard deviation is 0.8%; the average error of calcite calcium element analysis is reduced to 3.0%, and the standard deviation is 1.0%; the average error of magnetite iron element analysis is reduced to 2.0%, and the standard deviation is 0.6%; the average error of chalcopyrite copper element analysis is reduced to 2.2%, and the standard deviation is 0.7%; the system accuracy is significantly improved, meeting the actual application requirements.

[0081] The present invention is mainly applied to the related fields of mineral analysis, specifically as follows:

[0082] Geological research field: It can help researchers analyze mineral composition and structure more accurately, so as to deeply understand the process of geological structure evolution. For example, when analyzing ore samples in different geological layers, the present invention can be relied on to accurately determine the situation of multi-density minerals therein, provide reliable basis for research on geological structure changes, ore-forming conditions, etc., assist in drawing more accurate geological profiles, and judge potential distribution areas of mineral resources, etc.

[0083] Mining field: Mining enterprises can use this invention to accurately determine the distribution and content of different density minerals in ores. For example, when mining polymetallic symbiotic ores, they can clearly know the location and proportion of each density mineral, and then optimize the mining process, accurately plan the mining area, mining sequence, etc., improve the overall utilization rate of ores, reduce resource waste during mining, and increase the economic benefits of enterprises.

[0084] Metallurgical industry field: Provide strong data support for optimizing process parameters in beneficiation, smelting and other links. For example, in the beneficiation stage, based on the accurate analysis results of mineral characteristics, select more suitable beneficiation methods and equipment to improve the enrichment degree of target minerals; during the smelting process, based on accurate mineral composition and other information, reasonably adjust parameters such as temperature and reaction time to improve the metal extraction rate and the quality of the final product, and enhance the competitiveness of enterprises in the market.

[0085] Evidence related to the technical effects obtained in the embodiments of the present invention.

[0086] In terms of comparison of error data: In Example 1, before the optimization of electron beam scanning parameters, the average error of the analysis of potassium element, the main component of mica, was 5.5%, and the standard deviation was 1.6%; the average error of the analysis of calcium element, the main component of calcite, was 6.0%, and the standard deviation was 2.0%; the average error of the analysis of iron element, the main component of magnetite, was 4.5%, and the standard deviation was 1.5%; the average error of the analysis of copper element, the main component of chalcopyrite, was 5.0%, and the standard deviation was 1.8%. After optimizing the electron beam acceleration voltage to 20 kV, the beam current intensity to 2.5 nA, and the scanning speed to 4 μm / s, the average error of the analysis of potassium element in mica decreased to 2.8%, and the standard deviation was 0.8%; the average error of the analysis of calcium element in calcite decreased to 3.0%, and the standard deviation was 1.0%; the average error of the analysis of iron element in magnetite decreased to 2.0%, and the standard deviation was 0.6%; the average error of the analysis of copper element in chalcopyrite decreased to 2.2%, and the standard deviation was 0.7%. These data intuitively show that through the electron beam parameter optimization method of the present invention, the error of the analysis of the main components of minerals with different densities is significantly reduced, the test accuracy is improved, the analysis results are more reliable, and the problem of large errors in traditional test methods is effectively solved.

[0087] In terms of feedback from practical applications: In a cooperation project with a certain geological research institution, the present invention was applied to conduct multi-density mineral tests and analyses on rock samples in a certain area. The researchers feedback that compared with the traditional test methods used in the past, the present invention can more clearly reveal the types, contents, and distribution relationships of minerals in rocks, provide more accurate data support for geological structure analysis, greatly shorten the research cycle, and improve the quality and credibility of research results.

[0088] During the trial process in mining enterprises, enterprise technical personnel stated that after using the present invention to test ore samples, the formulation of mining plans is more scientific and reasonable, effectively avoiding resource waste and cost increase caused by blind mining. For example, when mining complex polymetallic ores, it can accurately determine the enrichment areas of minerals with different densities, improve the mining efficiency and ore recovery rate, and enable the enterprise to achieve a significant increase in economic benefits in the short term.

[0089] After metallurgical industrial enterprises applied the present invention to optimize ore dressing and smelting processes, the purity and quality stability of metal products were significantly improved. For example, a certain copper smelting enterprise, by accurately analyzing the characteristics of multi-density minerals in ores and optimizing process parameters, increased the copper extraction rate by about 10%, and the product quality met the market demand of higher standards, enhancing the enterprise's market competitiveness in the industry, and further proving the effectiveness and practicality of the present invention in actual industrial production.

[0090] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated designed hardware. Those of ordinary skill in the art can understand that the above devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and their modules of the present invention can be implemented by hardware circuits of programmable hardware devices such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above hardware circuits and software, such as firmware.

[0091] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for evaluating the accuracy of multivariate density mineral testing and optimizing electron beam parameters, characterized in that: The following steps are involved: Step 1: Raw materials and sample preparation: Select pure minerals, grind them into powder using grinding equipment, and sieve them, prepare test samples according to the proportion, and make the prepared samples into resin sheets; Step 2: Analytical system testing: Use an automatic quantitative mineral analysis system with high-resolution imaging and elemental analysis functions for testing, optimize parameters based on sample characteristics, and accurately record test data; Step 3: Result analysis and accuracy assessment; Conduct error analysis on the collected data, calculate the error of multi-density minerals under different test conditions, determine the evaluation indicators based on the error analysis results and actual needs, and comprehensively judge the accuracy of the system; Step 4: Optimization and verification: By establishing a precise mathematical model between the accuracy of mineral systems with different densities and the electron beam scanning parameters, the regression analysis method is used to fit the functional relationship between the accuracy and the electron beam scanning parameters. After optimizing the test parameters based on the model, the test and evaluation are performed again.

2. The method for evaluating the accuracy of multivariate density mineral testing and optimizing electron beam parameters as claimed in claim 1, characterized in that: The purity of the pure minerals selected in step 1 reaches more than 99%, including mica and calcite with low density, and magnetite and chalcopyrite with high density; The particle size range of the processed powdered samples includes -50 mesh, -100 mesh, and -200 mesh. The uniformity of the sample particle size distribution in each particle size range should comply with relevant standards and be achieved through professional grinding and screening equipment.

3. The method for evaluating the accuracy of multivariate density mineral testing and optimizing electron beam parameters as claimed in claim 1, characterized in that: The mass ratio of each mineral in the test sample is controlled within an error of ±0.05%. A high-precision balance is used for weighing. The resin optical slices tested by the test sample automatic quantitative mineral analysis system should undergo standard grinding, polishing and carbon spraying processes. The grinding and polishing processes are performed three times using grinding discs and polishing cloths of different specifications. The surface of the resin optical slice is carbon-sprayed evenly and with appropriate thickness.

4. The method for multivariate density mineral testing accuracy assessment and electron beam parameter optimization as claimed in claim 1, characterized in that: The selected automatic quantitative mineral analysis system includes a mineral dissociation analyzer or an advanced automatic mineral analyzer; during the test, the sample should be accurately fixed in place, the test image should be clear, and the test conditions and parameters as well as the collected mineral analysis data should be recorded.

5. The method for multivariate density mineral testing accuracy assessment and electron beam parameter optimization as claimed in claim 1, characterized in that: In the error analysis phase, the error values ​​of minerals with different densities under various test conditions are accurately calculated, covering multiple aspects such as average error, standard deviation, and error distribution range, and the test error is deeply analyzed from multiple dimensions; The accuracy evaluation index should be determined based on the error analysis results and actual application requirements. The maximum allowable absolute error value of the main mineral components should be set at no more than 3%, the secondary mineral components should be set at no more than 5%, and the repeatability index of the analysis results should be set. A comprehensive judgment on the accuracy of the system should be made based on these indicators.

6. The method for multivariate density mineral testing accuracy assessment and electron beam parameter optimization as claimed in claim 1, characterized in that: Based on the established precise mathematical model between the accuracy of mineral systems with different densities and the electron beam scanning parameters, and using a variety of scientific methods such as regression analysis, the functional relationship between accuracy and parameters is accurately fitted based on the accumulated rich test data; based on this functional relationship, the test parameters are optimized and adjusted, and then the same samples are used for re-testing and simulation analysis, and the accuracy is fully re-evaluated according to the original evaluation steps, and the results before and after optimization are compared to verify the effectiveness of the optimization measures, and continuous improvements are made until they meet actual application needs.

7. A multi-density mineral testing accuracy assessment and electron beam parameter optimization system according to any one of claims 1 to 6, characterized in that: include: Raw materials and sample preparation module; Select pure minerals, grind them into powder using professional grinding equipment, and sieve them. Prepare test samples according to the proportions, and make the prepared samples into resin sheets; Analysis system test module: select an automatic quantitative mineral analysis system with high-resolution imaging and elemental analysis functions for testing, optimize parameters based on sample characteristics, and accurately record test data; Result analysis and accuracy evaluation module: perform error analysis on the collected data, calculate the error of multi-density minerals under different test conditions, determine the evaluation index according to the error analysis results and actual needs, and comprehensively judge the accuracy of the system; Optimization and verification module: By establishing a precise mathematical model between the accuracy of mineral systems with different densities and the electron beam scanning parameters, the regression analysis method is used to fit the functional relationship between the accuracy and the electron beam scanning parameters, and the test parameters are optimized according to the model and then tested and evaluated again.

8. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the multi-density mineral test accuracy assessment and electron beam parameter optimization method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for multi-density mineral testing accuracy assessment and electron beam parameter optimization as described in any one of claims 1 to 6.

10. An information data processing terminal, comprising the multi-density mineral testing accuracy assessment and electron beam parameter optimization system according to claim 7.

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