Method and system for judging low-grade mineral analysis accuracy through simulation test

Abstract

The invention belongs to the technical field of mineral analysis, and discloses a method for judging the analysis accuracy of low-grade minerals through a simulation test, according to the method, low-grade lithium-containing china clay ores are used as raw materials, and an automatic quantitative mineral analysis system is used for testing a prepared resin polished section so as to obtain detailed data of minerals and elements in each particle. And then carrying out a simulation test by applying a written program, obtaining an automatic quantitative mineral analysis error through a simulation test result, and further judging whether the test result is statistically meaningful or not. The used automatic quantitative mineral analysis system has the characteristics of high test accuracy, accurate image splicing, support of a surface scanning mode, 1 [mu] m of EDS scanning precision and the like, and resin polished section preparation needs to follow strict process requirements. According to the method, the defect of the prior art in the aspect of accurately evaluating the automatic quantitative mineral analysis error is overcome, and important technical support is provided for the field of mineral analysis.

Description

Technical Field

[0001] The present invention belongs to the technical field of mineral analysis, and particularly relates to a method and system for judging the accuracy of low-grade mineral analysis through simulation tests. Background Art

[0002] In the field of modern mineral analysis, with the continuous progress of technology, automatic quantitative mineral analysis systems have gradually become important tools in the industry. Among them, devices such as mineral liberation analyzers (MLA) and automatic mineral analyzers (MapsMin and AMICS) are widely used in the research and analysis of various ores due to their high efficiency and automation characteristics.

[0003] However, in the actual application process, these automatic quantitative mineral analysis systems face many challenges and problems. On the one hand, despite the continuous development of technology, due to the complex composition and diverse structures of ores themselves, as well as the possible interference of various factors during the analysis process, it is difficult to absolutely guarantee the accuracy of the analysis results. For example, ores from different origins and different ore veins have significant differences in mineral composition, crystal structure, etc., which poses high requirements for the adaptability of the analysis system. Even for ores in the same mining area, their internal structures and component distributions are not completely uniform, and there may be local variations and anomalies, which may all lead to deviations in the analysis results.

[0004] On the other hand, there is currently a lack of a unified and effective method to comprehensively evaluate the errors generated by automatic quantitative mineral analysis systems. Some existing evaluation methods often can only detect specific aspects or under limited conditions, and cannot accurately reflect the true performance of the system under various complex actual working conditions. For example, some traditional methods may only focus on the hardware performance indicators of the instrument itself, while ignoring the comprehensive effects of sample preparation processes, analysis software algorithms, and environmental factors on the final results.

[0005] In the analysis of low-grade ores, the situation is more complex. The content of useful minerals in low-grade ores is relatively small, and often accompanied by trace elements, such as low-grade lithium-bearing kaolin ores co-associated with rubidium, cesium, tantalum, and niobium. The presence of these trace elements not only increases the difficulty of analysis, but also requires higher accuracy of the analysis results. Because in the process of ore dressing and resource development, even a slight change in the content of trace elements may have a significant impact on the comprehensive utilization value of the ore. However, when the existing automatic quantitative mineral analysis systems process such low-grade complex ores, the sources of their errors are more difficult to trace and evaluate, further highlighting the necessity of developing an accurate and reliable error analysis method.

[0006] In summary, in order to improve the accuracy and reliability of the automatic quantitative mineral analysis system in the analysis of various ores, especially low-grade complex ores, there is an urgent need for a method that can comprehensively and systematically obtain analysis errors, so as to provide a solid basis for accurately judging the statistical significance of analysis results, and further promote the in-depth development and application of mineral analysis technology in the mining field.

[0007] Through the above analysis, the problems and defects existing in the prior art are as follows:

[0008] When the existing automatic quantitative mineral analysis system processes such low-grade complex ores, its error sources are more difficult to trace and evaluate, further highlighting the necessity of developing an accurate and reliable error analysis method. Summary of the Invention

[0009] In view of the problems existing in the prior art, the present invention provides a method for judging the accuracy of low-grade mineral analysis through simulation tests.

[0010] The present invention is implemented as follows. A method for judging the accuracy of low-grade mineral analysis through simulation tests includes:

[0011] Step 1: Using low-grade ore as raw material, testing the resin polished section after grinding, polishing and carbon spraying with a mineral liberation analyzer and an automatic mineral analyzer automatic quantitative mineral analysis system; obtaining detailed data of minerals and elements in each tested particle.

[0012] Step 2: Using a programmed procedure to process the test data of the automatic quantitative mineral analysis system for simulation tests, obtaining the automatic quantitative mineral analysis error through the simulation test results, so as to judge whether the automatic quantitative mineral analysis results are statistically significant.

[0013] Furthermore, the resin polished section tested by the automatic quantitative mineral analysis system should undergo standard grinding, polishing and carbon spraying processes. Both the grinding and polishing processes need to be carried out 3 times using grinding discs and polishing cloths of different specifications, and the carbon spraying on the surface of the resin polished section should be uniform and of appropriate thickness.

[0014] Furthermore, the automatic mineral analysis system should be able to obtain all data of different content minerals and elements in each ore particle, and can export these data to an Excel spreadsheet.

[0015] Furthermore, a simulation test program should be written using Matlab or Python programming software according to the data obtained by the automatic quantitative mineral analysis system;

[0016] When writing in Matlab, its rich mathematical calculation library and data processing toolbox need to be utilized. First, a data matrix is constructed based on the data characteristics, where the rows correspond to different mineral particles and the columns correspond to various minerals and element information. Then, efficient data processing and resampling operations are achieved through defining simulation test parameters, initializing variables for storing error results, performing simulation test loops, and calculating the final error.

[0017] When writing in Python, relying on its concise and flexible syntax structure, libraries such as NumPy are used for array operations to process data, and the Pandas library is used for data reading, cleaning, and sorting to ensure the accuracy and integrity of the data input into the simulation test program. At the same time, a reasonable random sampling function should be designed according to the principle of resampling. This function should be able to draw samples from the original dataset with or without replacement according to the set rules. During the sampling process, the sampling ratio and the number of samplings should be strictly controlled to fully simulate different sampling situations. Then, representative automatic quantitative mineral analysis error results are obtained through statistical analysis of multiple groups of sampled data.

[0018] Furthermore, detailed comments need to be added during the program writing process for subsequent maintenance and optimization work. The program should also have a data visualization function, which can intuitively display the original data distribution, sampled data distribution, and the final error analysis results in the form of charts, so as to assist analysts in more clearly understanding and evaluating the automatic quantitative mineral analysis error situation.

[0019] Furthermore, the written program can conduct simulation tests and understand whether the test results of the automatic quantitative mineral analysis system are statistically significant based on the obtained error analysis results.

[0020] Another object of the present invention is to provide a system for judging the accuracy of low-grade mineral analysis through simulation tests, including:

[0021] An analysis module, which uses a mineral liberation analyzer and an automatic mineral analyzer automatic quantitative mineral analysis system to test the resin polished section after grinding, polishing, and carbon spraying with low-grade ore as the raw material, and obtains detailed data of minerals and elements in each tested particle.

[0022] A judgment module, which uses the written program to process the test data of the automatic quantitative mineral analysis system for simulation tests, obtains the automatic quantitative mineral analysis error through the simulation test results, and thus judges whether the automatic quantitative mineral analysis results are statistically significant.

[0023] 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 is caused to execute the steps of the method for judging the accuracy of low-grade mineral analysis through simulation testing.

[0024] 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 a processor, the processor is caused to execute the steps of the method for judging the accuracy of low-grade mineral analysis through simulation testing.

[0025] Another object of the present invention is to provide an information data processing terminal for implementing the system for judging the accuracy of low-grade mineral analysis through simulation testing.

[0026] Combined with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0027] First, an automatic quantitative mineral analysis error evaluation method for low-grade complex ores is proposed, filling the gap in the field of analysis error evaluation for such ores.

[0028] By comprehensively considering various factors such as raw material characteristics, analysis system requirements, sample preparation processes, and program writing, a complete error evaluation system is constructed, improving the accuracy and reliability of the evaluation results.

[0029] Through the writing and application of simulation test programs, new ideas and methods are provided for the performance evaluation of automatic quantitative mineral analysis systems, contributing to the further development and improvement of mineral analysis technologies.

[0030] Second, as creative auxiliary evidence for 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 mining production link, it can greatly improve the accuracy of low-grade mineral analysis, making the ore dressing process more precise and efficient. Enterprises can optimize the ore dressing process based on accurate analysis results, effectively improving the recovery rate of useful minerals, reducing the tailings discharge, reducing resource waste and environmental pressure, and thus reducing production costs and increasing product revenues. For example, in the development of low-grade lithium ores, accurate analysis helps enterprises accurately separate lithium and associated elements, significantly improving product purity and market competitiveness, and bringing direct economic benefits.

[0033] In the technology market, this invention fills a key gap and becomes the core driving force for the innovation of mineral analysis technology. Equipment manufacturers improve their analysis systems around it to enhance equipment performance and accuracy; software developers develop supporting software based on it to expand functions and application scenarios. This will give rise to new technological products and service systems, promoting industry technology upgrading and economic growth.

[0034] From the perspective of industry development, its application scope is extensive and it plays a key role in many fields such as geological exploration and environmental science. For example, it can improve the success rate of ore prospecting in geological exploration and accurately evaluate the pollution status in environmental monitoring, attracting a diverse customer group, broadening the market boundary, enhancing the market share and influence of the technology, creating continuous economic and social benefits for enterprises and the industry, consolidating the industry's innovation position and leading the development direction.

[0035] (2) The technical solution of this invention fills the technical gap in the domestic and international industries:

[0036] This invention has successfully filled the technical gap in the domestic and international industries in the field of mineral analysis. In the past, for the analysis of low-grade minerals, traditional methods were limited to high-grade or simple mineral systems. When facing complex low-grade ores such as low-grade lithium-bearing kaolin ores co-associated with rubidium, cesium, tantalum, and niobium, the existing analysis systems were seriously insufficient in error tracking and evaluation.

[0037] This invention constructs a new process. In the sample preparation link, the resin polished slice process is strictly standardized to ensure that the sample reflects the characteristics of the ore; the functional requirements such as high-precision detection of the analysis system are clarified. The core innovative simulation test program evaluates errors from a statistical perspective and provides a new basis for judging the validity of the results. Previously, the industry lacked a comprehensive evaluation method for such systems. This invention breaks through this dilemma, points the way for the analysis of low-grade minerals, strongly promotes the development and utilization of global mining resources, enhances China's international status and influence in this field, and becomes a key turning point in the industry.

[0038] (3) The technical solution of this invention solves the technical problems that people have always been eager to solve but have never succeeded in:

[0039] For a long time, when facing the analysis of low-grade minerals, how to ensure the accuracy and reliability of the analysis results has always troubled mining practitioners and scientific researchers. Due to the complex composition, numerous trace elements and uneven distribution of low-grade ores, coupled with the influence of various factors such as sample preparation differences, limitations of analysis instruments, and environmental factors in the analysis process, the sources of errors are intricate and difficult to effectively track and evaluate. Although the industry has been constantly exploring, it has never found a comprehensive and effective method to solve this dilemma.

[0040] Starting from the root cause, the present invention proposes a systematic solution to these key problems. In the sample preparation process, by strictly standardizing the grinding, polishing, and carbon spraying processes of resin polished slices, the consistency and representativeness of test samples are ensured, effectively reducing errors caused by sample differences. In the selection and application of the analysis system, the key indicators of high-precision and multi-functional mineral liberation analyzers and automatic mineral analyzers are defined, improving the accuracy and comprehensiveness of data collection.

[0041] Most importantly, the innovative simulation test program uses advanced mathematical algorithms and statistical methods to deeply mine the potential error information in the analysis data and strictly verify the analysis results from a statistical perspective. The synergistic effect of this set of technical solutions effectively solves the problem of the accuracy of low-grade mineral analysis that has long troubled the industry, greatly promoting the application process of mineral analysis technology in actual production and scientific research and meeting the urgent need for accurate low-grade mineral analysis.

[0042] (4) The technical solution of the present invention overcomes the technical prejudice:

[0043] In the past concepts, people often overly relied on the improvement of the hardware performance of the automatic quantitative mineral analysis system to improve the analysis results, believing that as long as the indicators such as the resolution and sensitivity of the instrument are high enough, accurate mineral analysis data can be obtained, while ignoring the comprehensive influence of the sample preparation process, analysis software algorithms, and complex environmental factors on the final results. This technical prejudice has led to the fact that in practical applications, even if the analysis instrument equipment is continuously updated, the problem of analysis errors for low-grade complex ores has still not been effectively solved.

[0044] The present invention breaks this traditional cognitive limitation and constructs an overall framework covering raw material characteristics, comprehensive requirements of the analysis system, fine sample preparation processes, and innovative program writing. It emphasizes the importance of the standard grinding, polishing, and carbon spraying processes of resin polished slices in the sample preparation process, recognizing its key role in the authenticity of test results and no longer simply focusing on instrument hardware. During the analysis process, the complex composition and structural differences of the ore are comprehensively considered. Through programming for simulation testing and using the simulation testing method to comprehensively evaluate errors, the influence of software algorithms and data processing on the result accuracy is fully considered, rather than being limited to the improvement of hardware performance.

[0045] The present invention, with a brand-new perspective and method system, corrects the previous one-sided technical concepts, successfully overcomes the long-existing technical prejudice, opens up a new path for the development of mineral analysis technology, guides the industry to develop in a more scientific and comprehensive direction, and makes the judgment of the accuracy of low-grade mineral analysis more reliable and effective. Description of the Drawings

[0046] Figure 1It is a flowchart of a method for judging the accuracy of low-grade mineral analysis through simulation tests provided by an embodiment of the present invention.

[0047] Figure 2 It is a block diagram of the system structure for judging the accuracy of low-grade mineral analysis through simulation tests provided by an embodiment of the present invention.

[0048] Figure 3 It is a schematic diagram of the test process of an automatic quantitative mineral analysis system provided by an embodiment of the present invention.

[0049] Figure 4 It is a diagram of conducting simulation tests on a simple data set provided by an embodiment of the present invention.

[0050] Figure 5 It is a diagram of the distribution and error analysis results of mica at a dissociation level of 80% - 100% provided by an embodiment of the present invention. Detailed implementation manners

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with 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.

[0052] As Figure 1 shown, a method for judging the accuracy of low-grade mineral analysis through simulation tests provided by an embodiment of the present invention includes the following steps:

[0053] S101, using a low-grade ore as a raw material, and using a mineral liberation analyzer and an automatic mineral analyzer of an automatic quantitative mineral analysis system to test the resin polished section after grinding, polishing and carbon spraying; obtaining detailed data of minerals and elements in each tested particle;

[0054] S102, using a compiled program to process the test data of the automatic quantitative mineral analysis system for simulation tests, and obtaining the automatic quantitative mineral analysis error through the simulation test results, so as to judge whether the automatic quantitative mineral analysis results are statistically significant.

[0055] Taking the low-grade ore as the research object, sample preparation is carried out first. Specifically, it includes grinding, polishing and carbon spraying of the ore sample to form a resin polished section with uniform surface characteristics. Subsequently, the sample is tested using a mineral liberation analyzer and an automatic mineral analyzer. The mineral liberation analyzer can accurately record the mineral composition and its element distribution of each particle in the polished section through high-resolution image scanning and mineral identification technology, forming accurate basic data.

[0056] Import the above test data into the automatic quantitative mineral analysis system, complete data sorting and statistics through the built-in algorithm, and generate the quantitative results of each mineral. To improve the analysis accuracy, the system combines the spatial position, morphological characteristics and element combination of particles to preliminarily correct the possible error sources. The processing at this stage can form an intuitive mineral distribution map and the content distribution data of each mineral in the overall sample.

[0057] Conduct a simulation test on the results of the automatic quantitative mineral analysis system through a self-written program. The core of the simulation test is to use the resampling algorithm to repeatedly calculate the analysis result deviation under different conditions to evaluate the error range. The system compares the analysis data with the simulation results, calculates the error value of each mineral, and outputs the results in the form of a confidence interval. This method can effectively identify the errors caused by sparse samples or insufficient test sensitivity in low-grade ores.

[0058] According to the error evaluation results, combined with statistical methods (such as t-test or variance analysis), judge whether the analysis data has statistical significance. If the error value is within the acceptable range, it indicates that the results of the automatic quantitative mineral analysis system are credible; if the error exceeds the range, it indicates that there may be systematic deviations or operation problems in the analysis system. Through this process, the reliability and repeatability of the results of low-grade mineral analysis can be ensured in scientific research and industrial applications.

[0059] The resin polished sections tested by the automatic quantitative mineral analysis system provided in the embodiments of the present invention should undergo standard grinding, polishing and carbon spraying processes. The grinding and polishing processes both need to be carried out 3 times using grinding discs and polishing cloths of different specifications, and the carbon spraying on the surface of the resin polished section should be uniform and of appropriate thickness.

[0060] The automatic mineral analysis system provided in the embodiments of the present invention should be able to obtain all the data of different content minerals and elements in each ore particle, and can export these data to an Excel spreadsheet.

[0061] According to the data obtained by the automatic quantitative mineral analysis system provided in the embodiments of the present invention, a simulation test program should be written using Matlab or Python programming software;

[0062] When writing using Matlab, it is necessary to utilize its rich mathematical calculation library and data processing toolbox. First, construct a data matrix according to the data characteristics, where the rows correspond to different mineral particles and the columns correspond to various mineral and element information. Then, through defining simulation test parameters, initializing error result storage variables, simulation test loops, and calculating the final error operations, efficient data processing and resampling operations are realized;

[0063] When writing in Python, by virtue of its concise and flexible syntax structure, use libraries such as NumPy for array operations to process data, and use the Pandas library for data reading, cleaning, and sorting to ensure the accuracy and integrity of the data input into the simulation test program. At the same time, a reasonable random sampling function should be designed based on the principle of resampling. This function should be able to draw samples from the original dataset with or without replacement according to the set rules. During the sampling process, strictly control the sampling ratio and the number of samplings to fully simulate different sampling situations, and then obtain representative automatic quantitative mineral analysis error results through the statistical analysis of multiple groups of sampled data.

[0064] In the embodiment of the present invention, detailed comments need to be added during the program writing process for subsequent maintenance and optimization work. The program should also have a data visualization function, which can visually display the original data distribution, sampled data distribution, and the final error analysis results in the form of charts, so as to assist analysts in more clearly understanding and evaluating the automatic quantitative mineral analysis error situation.

[0065] The program written in the embodiment of the present invention can conduct simulation tests and understand whether the test results of the automatic quantitative mineral analysis system are statistically significant based on the obtained error analysis results.

[0066] As Figure 2 shown, a system for judging the accuracy of low-grade mineral analysis through simulation tests provided by the embodiment of the present invention includes:

[0067] An analysis module, which uses a low-grade ore as a raw material and uses an automatic quantitative mineral analysis system of a mineral dissociation analyzer and an automatic mineral analyzer to test the resin polished section after grinding, polishing, and carbon spraying; obtain detailed data of minerals and elements in each tested particle;

[0068] A judgment module, which uses the written program to process the test data of the automatic quantitative mineral analysis system for simulation tests, obtains the automatic quantitative mineral analysis error through the simulation test results, and thus judges whether the automatic quantitative mineral analysis results are statistically significant.

[0069] A system for judging the accuracy of low-grade mineral analysis through simulation tests provided by the embodiment of the present invention aims to improve the accuracy and reliability of mineral analysis in low-grade ores. The system mainly consists of two core modules: an analysis module and a judgment module. The analysis module is responsible for comprehensively analyzing the minerals and elements in low-grade ore samples to obtain detailed data; the judgment module uses these data to evaluate the error of the automatic quantitative mineral analysis system through simulation tests, and then judges the statistical significance of the analysis results. The entire system ensures the accuracy and scientific nature of low-grade mineral analysis by organically combining experimental analysis and data processing.

[0070] The analysis module is the fundamental part of the system and is responsible for conducting detailed mineral quantitative analysis on low-grade ore samples. First, the low-grade ore, as the raw material, undergoes processes such as grinding, polishing, and carbon spraying to prepare resin polished sections. These processing steps ensure that the sample surface is smooth, suitable for observation and analysis under the microscope. Subsequently, the mineral liberation analyzer and the automatic mineral analyzer of the automatic quantitative mineral analysis system test these resin polished sections. The mineral liberation analyzer dissociates minerals from the rock matrix through chemical or physical methods, while the automatic mineral analyzer conducts high-precision quantitative analysis on the dissociated mineral particles to obtain detailed data on the minerals and elements in each particle.

[0071] After the analysis module completes the testing of the samples, the system generates a large amount of detailed data. This data includes information such as the type, quantity, elemental composition, and content of each mineral particle. To ensure the accuracy and usability of the data, the system conducts preliminary processing on the original data, such as removing noise and correcting errors. The processed data will be stored in a structured form for subsequent analysis and calculation. This process not only improves the quality of the data but also provides a reliable data basis for the judgment module, ensuring the effectiveness and accuracy of subsequent simulation tests.

[0072] The judgment module is a key part of the system used to evaluate the accuracy of the analysis results. This module conducts in-depth processing and analysis on the test data generated by the automatic quantitative mineral analysis system through a dedicated program written. Specifically, the judgment module uses this data to conduct simulation tests, simulating various errors and deviations that may occur during the actual analysis process. By comparing the results of the simulation tests with the actual test data, the judgment module can quantify the error range of the automatic quantitative mineral analysis system, and then evaluate the reliability and accuracy of the analysis results.

[0073] The simulation test is the core step in the judgment module, aiming to evaluate the performance and errors of the analysis system through simulation methods. First, the system constructs simulation test scenarios based on historical data and known mineral characteristics, simulating the mineral analysis process under different conditions. Then, virtual test data is generated using these simulation scenarios and compared with the actual test data for analysis. Through multiple simulation tests, the system can identify the error patterns and deviation degrees of the automatic quantitative mineral analysis system under different circumstances. This simulation method can not only detect potential problems in advance but also provide data support for system optimization.

[0074] After completing the simulation test, the judgment module will conduct a statistical evaluation of the analysis error to determine the significance of the results of automatic quantitative mineral analysis. Specifically, the system will calculate statistical indicators such as the mean and variance of the error, and use methods such as hypothesis testing to determine whether the error is within an acceptable range. If the analysis results are statistically significant, it indicates that the results of the automatic quantitative mineral analysis system are reliable; otherwise, the analysis process or equipment needs to be further optimized. Ultimately, this system can not only provide accurate mineral analysis results, but also provide a scientific basis for fields such as ore exploration, resource assessment, and metallurgical processing, with broad application prospects and practical value.

[0075] In summary, through the collaborative work of the analysis module and the judgment module, the present invention realizes a comprehensive evaluation and optimization of the accuracy of low-grade mineral analysis. This system not only improves the accuracy and reliability of the analysis results, but also ensures its scientificity and practicality through simulation tests and statistical evaluations, providing advanced technical support for the field of mineral analysis.

[0076] The technical solution adopted by the present invention is as follows:

[0077] (1) Raw material selection

[0078] Select low-grade ores as raw materials. The low-grade ores are ores with less useful minerals or containing trace elements, such as low-grade lithium-bearing kaolin ores co-associated with rubidium, cesium, tantalum, and niobium. The characteristics of these ores can fully test the ability of the automatic quantitative mineral analysis system in complex composition analysis, ensuring that the developed error evaluation method has broad applicability.

[0079] (2) Requirements for the analysis system

[0080] Use automatic quantitative mineral analysis systems such as Mineral Liberation Analyzer (MLA) and Automatic Mineral Analyzer (MapsMin and AMICS). This system should have the following characteristics:

[0081] High test accuracy, capable of accurately detecting the content and distribution of minerals and elements in ore particles, and reducing measurement deviations caused by the system itself.

[0082] The attached software accurately stitches the images obtained from the test, ensuring a comprehensive and accurate analysis of the entire resin polished section, and avoiding inaccurate data caused by image stitching errors.

[0083] Support the surface scanning method to obtain comprehensive information of ore particles. At the same time, the scanning accuracy of EDS can reach 1μm, realizing fine analysis at the microscopic level.

[0084] (3) Resin polished section processing

[0085] As Figure 3As shown, the resin polished sections tested by the automatic quantitative mineral analysis system need to undergo standard grinding, polishing, and carbon spraying processes.

[0086] Both the grinding and polishing processes need to use grinding disks and polishing cloths of different specifications three times. Through a gradually refined processing method, it is ensured that the surface flatness of the resin polished section reaches the best state, making the test results better reflect the true situation of the ore.

[0087] The carbon spraying on the surface of the resin polished section should be uniform and of appropriate thickness, which helps to improve the stability and accuracy of the signal during the test and lays a foundation for accurately obtaining mineral and element data subsequently.

[0088] (4) Data acquisition and export

[0089] The automatic mineral analysis system should have strong data acquisition capabilities, be able to obtain all the data of different content minerals and elements in each ore particle, and can conveniently export these data to an Excel table for subsequent data processing and analysis.

[0090] (5) Program writing and simulation testing

[0091] As Figure 4 shown, according to the data obtained by the automatic quantitative mineral analysis system, simulation test programs are written using programming software such as Matlab or Python.

[0092] When writing using Matlab, it is necessary to utilize its rich mathematical calculation library and data processing toolbox. First, a data matrix is constructed based on the data characteristics, where the rows correspond to different mineral particles and the columns correspond to various mineral and element information. Then, through operations such as defining simulation test parameters, initializing error result storage variables, simulation test loops, and calculating the final error, efficient data processing and resampling operations are achieved.

[0093] When writing using Python, relying on its concise and flexible syntax structure, operations such as using the NumPy library for array operations to process data and using the Pandas library for data reading, cleaning, and sorting are adopted to ensure the accuracy and integrity of the data input into the simulation test program. At the same time, a reasonable random sampling function is designed based on the principle of resampling. This function can draw samples from the original dataset with or without replacement according to the set rules, and strictly control the sampling ratio and sampling times during the sampling process to fully simulate different sampling situations.

[0094] The written program can conduct simulation tests, and through the statistical analysis of multiple groups of sampled data, representative automatic quantitative mineral analysis error results can be obtained.

[0095] During the programming process, detailed comments should be added for subsequent maintenance and optimization work. Moreover, the program also has a data visualization function, which can visually display the original data distribution, sampled data distribution, and the final error analysis results in the form of charts. For example, a bar chart can be drawn to show the comparison of error ranges of different mineral components, and a line chart can be drawn to reflect the trend of error changing with the number of samplings, etc., so as to assist analysts in more clearly understanding and evaluating the automatic quantitative mineral analysis error situation.

[0096] Example 1:

[0097] Select low-grade lithium-bearing kaolin ore co-associated with rubidium, cesium, tantalum, and niobium as the raw material. First, sample the ore and prepare it into small pieces suitable for testing. Then, perform grinding and polishing processes. Use grinding disks of different specifications to grind the ore pieces in sequence, and clean them thoroughly after each grinding to ensure a flat surface. Next, carry out the polishing process. Also use polishing cloths of different specifications to perform polishing 3 times to make the surface of the resin polished slice reach the required smoothness. After that, carry out carbon spraying treatment, control the parameters of the carbon spraying equipment to ensure uniform carbon spraying and appropriate thickness.

[0098] Put the processed resin polished slice into a Mineral Liberation Analyzer (MLA) for testing. Set the test parameters according to the instrument operation specifications, start the surface scanning mode for testing, obtain detailed data of minerals and elements in each particle, and export the data to an Excel spreadsheet.

[0099] According to the exported data, use Matlab or Python to write a simulation test program. In the program, set the parameters of the simulation test, such as the number of resamplings, sample size, etc. Run the program for simulation testing. The written program performs multiple resamplings and analyses on the original data to obtain the simulation test results, and then calculates the automatic quantitative mineral analysis error. The pseudo-code of the simulation test program written in Matlab or Python is as follows.

[0100] Matlab program pseudo-code

[0101] % Utilize Matlab's rich mathematical calculation library and data processing toolbox

[0102] % Assume that the original data has been stored in a matrix named data_matrix, where each row represents a particle, and the columns contain information such as particle ID, particle proportion, and the content of each mineral (such as mica)

[0103] % 1. Construct the data matrix

[0104] data_matrix = load_data_from_excel('your_data_file.xlsx'); % Load data from an Excel file and construct the matrix, adjust the function parameters according to the actual situation

[0105] % 2. Define simulation test parameters

[0106] num_resamples = 100; % Number of resamplings, can be adjusted according to requirements

[0107] sample_size = size(data_matrix, 1); % Sample size, here it is the amount of original data, can be modified

[0108] % 3. Initialize the error result storage variable

[0109] error_results = zeros(num_resamples, size(data_matrix, 2)); % Used to store the error results after each resampling

[0110] % 4. Simulation test loop

[0111] for i = 1:num_resamples

[0112] % 4.1 Resampling operation

[0113] resampled_data = resample_data(data_matrix, sample_size); % Custom resampling function to implement sampling with or without replacement

[0114] % 4.2 Data processing and analysis (here it is assumed to simply calculate the average mineral content, and should be written according to the specific analysis method in reality)

[0115] mean_values = mean(resampled_data, 1); % Calculate the average value of each row of the resampled data

[0116] % 4.3 Calculate the error (assuming comparison with the average value of the original data, and the error calculation method can be modified according to the actual situation)

[0117] error_results(i, :) = mean_values - mean(data_matrix, 1);

[0118] end

[0119] % 5. Calculate the final error (here taking the average value plus or minus 1.96 standard deviations as an example, can be adjusted according to the requirements of statistical analysis)

[0120] final_error_mean = mean(error_results, 1);

[0121] final_error_std=std(error_results,1);

[0122] error_range=[final_error_mean-1.96*final_error_std; final_error_mean+1.96*final_error_std];

[0123] %6. Data visualization (need to write detailed code according to the actual drawing library function)

[0124] %7. Add comments (add comments to key parts of the program to facilitate maintenance and optimization)

[0125] Python program pseudocode

[0126] import numpy as np

[0127] import pandas as pd

[0128] #Assume that the original data is stored in an Excel file named your_data_file.xlsx and the file format meets the requirements

[0129] #1. Data reading and organization

[0130] data=pd.read_excel('your_data_file.xlsx')

[0131] #The data can be further cleaned and preprocessed according to the actual situation, such as processing missing values, etc.

[0132] #2. Define simulation test parameters

[0133] num_resamples=1000

[0134] sample_size = len(data)

[0135] #3. Initialize the error result storage list

[0136] error_results = []

[0137] #4. Simulation test loop

[0138] for i in range(num_resamples):

[0139] #4.1 Resampling operation (using custom functions to implement sampling with or without replacement)

[0140] resampled_data = resample_data(data, sample_size)

[0141] #4.2 Data processing and analysis (here it is assumed to simply calculate the average mineral content, and the actual code should be written according to the specific analysis method)

[0142] mean_values = np.mean(resampled_data, axis = 0)

[0143] #4.3 Calculate the error (assuming comparison with the average value of the original data, and the error calculation method can be modified according to the actual situation)

[0144] error = mean_values - np.mean(data, axis = 0)

[0145] error_results.append(error)

[0146] #5. Convert the error results to a suitable format (such as an array) for subsequent calculations

[0147] error_results_array = np.array(error_results)

[0148] #6. Calculate the final error (here, taking the average plus or minus 1.96 standard deviations as an example, which can be adjusted according to the statistical analysis requirements)

[0149] final_error_mean = np.mean(error_results_array, axis = 0)

[0150] final_error_std = np.std(error_results_array, axis = 0)

[0151] error_range = np.vstack((final_error_mean - 1.96 * final_error_std, final_error_mean + 1.96 * final_error_std))

[0152] #7. Data visualization (detailed code needs to be written according to the actual plotting library functions)

[0153] #8. Add comments (add comments at key parts of the program for easy maintenance and optimization)

[0154] Figure 5The results of error analysis using simulation tests for the dissociation distribution of mica in 5107 particles tested are shown in the figure. The error bars given in the figure are the mean of the simulated resampling plus or minus 1.96 standard deviations.

[0155] By analyzing the errors, it is judged whether the results of this automatic quantitative mineral analysis are statistically significant. For example, if the errors are within an acceptable range, the analysis results are considered to have a certain degree of reliability; if the errors are too large, it is necessary to further check whether there are problems in the test process or analysis system.

[0156] The present invention has extensive and important applications in multiple fields and related products.

[0157] In the fields of mining and ore dressing, it can be directly applied to various ore analysis processes. For low-grade metal ores such as copper, lead, and zinc ores, it can accurately analyze the mineral composition and content therein, helping enterprises formulate reasonable ore dressing processes, improve metal recovery rates, reduce the grade of tailings, reduce resource waste and mining costs, and optimize the entire mining production chain. In the analysis of rare earth ores, in the face of complex symbiotic situations of rare earth elements, the present invention can accurately judge the analysis accuracy, ensuring the efficient development and utilization of rare earth resources.

[0158] In geological exploration work, it can be used for rapid detection and analysis of field samples. After geological personnel collect rock samples, with the help of the technology of the present invention, they can preliminarily judge the ore grade and potential value on site, improve exploration efficiency, accurately lock in areas with further exploration value, reduce unnecessary exploration investment, and accelerate the discovery process of new mineral resources.

[0159] In the field of environmental science, it is of great significance for the analysis of mineral components in soil and water sediments. By monitoring the content and changes of heavy metal minerals therein, the degree of environmental pollution and ecological risks are evaluated, providing key data support for environmental restoration and pollution control, and assisting in formulating scientific and effective environmental protection strategies.

[0160] The embodiments of the present invention have obtained remarkable technical effects and are supported by sufficient evidence.

[0161] In terms of experimental data, taking low-grade lithium-bearing porcelain clay ores co-associated with rubidium, cesium, tantalum, and niobium as an example, through multiple repeated tests, a large number of resin polished sections are analyzed using the method of the present invention. In terms of data accuracy, compared with traditional analysis methods, the deviation of the mineral element content data obtained by the present invention is significantly smaller, and the stability of multiple test results is higher, with the standard deviation at a relatively low level, strongly proving its excellent effectiveness in improving analysis accuracy.

[0162] From the feedback of actual applications, field application tests were conducted in cooperation with multiple mining enterprises. On the ore dressing production line, after adjusting the process according to the analysis results of the present invention, the recovery rate of valuable minerals increased by an average of 3%, and the residual amount of valuable minerals in the tailings decreased significantly, effectively improving the economic benefits of the enterprises and directly reflecting the positive impact and technical advantages of the present invention on production practice.

[0163] In terms of verifying technical indicators, by testing low-grade ore samples from different origins and different ore veins, the simulation test program of the present invention successfully identified various potential error sources and accurately defined the error range of the analysis system. For example, in the analysis of a certain low-grade copper ore, the analysis deviation caused by the local structural differences of the samples was discovered and corrected, greatly improving the credibility of the final analysis results and meeting the requirements of high-precision and high-reliability for low-grade mineral analysis in actual production and scientific research, providing a solid evidence basis for the technical effects.

[0164] 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 an appropriate instruction execution system, such as a microprocessor or dedicated designed hardware. Those of ordinary skill in the art can understand that the above-mentioned 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 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 programmable logic devices such as field programmable gate arrays, or can 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.

[0165] The above is only the specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention by those skilled in the art within the technical scope disclosed by the present invention shall be covered by the protection scope of the present invention.

Claims

1. A method for determining the accuracy of low-grade mineral analysis through simulation testing, characterized in that: The following steps are involved: Step 1, using low-grade ore as raw material, using a mineral dissociation analyzer and an automatic mineral analyzer automatic quantitative mineral analysis system to test the resin optical film after grinding, polishing and carbon spraying; obtaining detailed data of minerals and elements in each particle tested; Step 2, use the written program to process the test data of the automatic quantitative mineral analysis system to perform a simulation test, and obtain the automatic quantitative mineral analysis error through the simulation test results, so as to determine whether the automatic quantitative mineral analysis results are statistically significant.

2. The method for determining the accuracy of low-grade mineral analysis by simulation testing as claimed in claim 1, characterized in that: The resin optical slices tested by the automatic quantitative mineral analysis system should undergo standard grinding, polishing and carbon spraying processes. The grinding and polishing processes need to be performed three times using grinding discs and polishing cloths of different specifications. The carbon spraying on the surface of the resin optical slice should be uniform and of appropriate thickness.

3. The method for determining the accuracy of low-grade mineral analysis by simulation testing as claimed in claim 1, characterized in that: The automatic mineral analysis system should be able to obtain all data on the different contents of minerals and elements in each ore particle, and can export these data into an Excel spreadsheet.

4. The method for determining the accuracy of low-grade mineral analysis by simulation testing as claimed in claim 1, characterized in that: The simulation test program should be written using Matlab or Python programming software based on the data obtained from the automatic quantitative mineral analysis system; When using Matlab to write, you need to use its rich mathematical calculation library and data processing toolbox. First, build a data matrix based on the data characteristics, where the rows correspond to different mineral particles and the columns correspond to various mineral and element information. Then, define simulation test parameters, initialize error result storage variables, simulate test cycles, and calculate the final error operation to achieve efficient data processing and resampling operations. When using Python to write, with the help of its concise and flexible syntax structure, the NumPy library is used for array operations to process data, and the Pandas library is used to read, clean and organize data to ensure the accuracy and completeness of the data input into the simulation test program. At the same time, a reasonable random sampling function should be designed based on the principle of resampling. The function should be able to extract samples with or without replacement from the original data set according to the set rules. The sampling ratio and number of samplings should be strictly controlled during the sampling process to fully simulate different sampling situations, and then the representative automatic quantitative mineral analysis error results are obtained through statistical analysis of multiple groups of sampling data.

5. The method for determining the accuracy of low-grade mineral analysis by simulation testing as claimed in claim 4, characterized in that: Detailed comments need to be added during the program writing process for subsequent maintenance and optimization. The program should also have data visualization capabilities, which can intuitively display the original data distribution, sampled data distribution and final error analysis results in the form of charts, thereby assisting analysts to more clearly understand and evaluate the errors of automatic quantitative mineral analysis.

6. The method for determining the accuracy of low-grade mineral analysis by simulation testing as claimed in claim 4, characterized in that: The programmed program can be used to conduct simulation tests and understand whether the test results of the automatic quantitative mineral analysis system are statistically significant based on the error analysis results obtained.

7. A system for determining the accuracy of low-grade mineral analysis by simulation testing, which implements the method for determining the accuracy of low-grade mineral analysis by simulation testing as claimed in any one of claims 1 to 6, characterized in that: The system for judging the accuracy of low-grade mineral analysis through simulation testing includes: The analysis module is used to test the resin optical film after grinding, polishing and carbon injection using low-grade ore as raw material using the mineral dissociation analyzer and the automatic quantitative mineral analysis system of the automatic mineral analyzer; and obtain the detailed data of minerals and elements in each particle tested; The judgment module is used to process the test data of the automatic quantitative mineral analysis system using the written program to perform simulation tests, and obtain the automatic quantitative mineral analysis error through the simulation test results, so as to judge whether the automatic quantitative mineral analysis results are statistically significant.

8. A computer device, characterized in that: The computer device includes 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 method for determining the accuracy of low-grade mineral analysis through simulation testing as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the method for determining the accuracy of low-grade mineral analysis through simulation testing as described in any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the system for determining the accuracy of low-grade mineral analysis through simulation testing as described in claim 7.

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