A method and system for automatic detection of mineral samples

Abstract

The present invention relates to the technical field of mineral sample detection, and specifically discloses an automatic detection method and system for mineral samples. The method comprises: obtaining a data set of a collection environment of a mineral sample to be detected, obtaining a mineral category existing in the collection environment of the mineral sample to be detected by comparison, and importing a physical property characteristic value of the mineral sample to be detected obtained by comprehensive analysis and a spectral prediction characteristic value of the mineral sample to be detected into a detection model of the mineral sample to be detected, and obtaining a mineral type of the mineral sample to be detected by comparison among the mineral categories existing in the collection environment of the mineral sample to be detected. The present invention solves the problem that the traditional mineral industry mainly analyzes raw materials and products of the production process by combining manual online sampling and laboratory offline detection, and obtains mineral category information of the mineral sample to be detected more quickly, improves detection efficiency, realizes automation, reduces human resource investment, and reduces detection costs.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral sample detection, and in particular to an automatic detection method and system for mineral samples. Background Art

[0002] As my country's economic development enters a new normal, the development of the mineral mining industry is also at an important turning point. Accurate and rapid testing of mineral samples is a key link in the development and utilization of mineral resources. In the process of ore mining, rapid and accurate identification and analysis of the components and impurities contained in the raw materials and products at each stage, and timely adjustment of various parameters of production operations based on this, can effectively prevent various product problems in the production process and improve production efficiency and product quality.

[0003] Nowadays, there are still some shortcomings in the research on an automatic detection method for mineral samples. Specifically, the traditional mining industry mainly uses a combination of manual online sampling and laboratory offline detection to analyze the raw materials and products of the production process. There are problems such as low efficiency, small sample size, and large interference from human factors. Manual online sampling and laboratory offline detection require sample collection and sending to the laboratory for testing. This process usually takes a certain amount of time, resulting in delays in analysis results and affecting the real-time control and adjustment of the production process. The accuracy of laboratory offline detection is affected by factors such as sampling process, transportation, and sample preservation, and is prone to errors. In addition, traditional mineral sample detection can only achieve element-level detection and cannot meet the qualitative and quantitative analysis needs at the material level. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an automatic detection method and system for mineral samples, which can effectively solve the problems involved in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides an automatic detection method for mineral samples, comprising the following steps: obtaining a data set of a collection environment of a mineral sample to be detected, and obtaining the mineral category existing in the collection environment of the mineral sample to be detected based on the obtained data set of a collection environment of the mineral sample to be detected; obtaining a data set of physical properties of the mineral sample to be detected, and obtaining the physical property characteristic values ​​of the mineral sample to be detected based on the obtained data set of physical properties of the mineral sample to be detected; obtaining a data set of spectral predictions of the mineral sample to be detected, and obtaining the spectral prediction characteristic values ​​of the mineral sample to be detected based on the obtained data set of spectral predictions of the mineral sample to be detected; importing the physical property characteristic values ​​of the mineral sample to be detected and the spectral prediction characteristic values ​​of the mineral sample to be detected into a detection model of the mineral sample to be detected, and obtaining the mineral type of the mineral sample to be detected by comparing the mineral category existing in the collection environment of the mineral sample to be detected.

[0006] As a further method, the data set of the environment in which the mineral samples to be detected are collected specifically includes the temperature of the environment in which the mineral samples to be detected are collected, the soil moisture of the environment in which the mineral samples to be detected are collected, and the soil hardness of the environment in which the mineral samples to be detected are collected.

[0007] As a further method, based on the acquired data set of the environment in which the mineral samples to be tested are collected, the mineral categories existing in the environment in which the mineral samples to be tested are obtained by comparison. The specific analysis process is as follows: based on the acquired data set of the environment in which the mineral samples to be tested are collected, a comprehensive analysis is performed to obtain characteristic values ​​of the environment in which the mineral samples to be tested are collected, and the characteristic values ​​of the environment in which the mineral samples to be tested are used as an analysis basis for obtaining the mineral categories existing in the environment in which the mineral samples to be tested are collected; the characteristic values ​​of the environment in which the mineral samples to be tested are collected are compared with the mineral categories corresponding to the characteristic values ​​of the environment in which the mineral samples to be tested are collected and stored in the database to obtain the mineral categories corresponding to the characteristic values ​​of the environment in which the mineral samples to be tested are collected, and the mineral categories corresponding to the characteristic values ​​of the environment in which the mineral samples to be tested are collected represent the mineral categories existing in the environment in which the mineral samples to be tested correspond to the characteristic values ​​of the environment in which the mineral samples to be tested are collected.

[0008] As a further method, the environmental characteristic values ​​of the mineral samples to be tested are collected, and the specific analysis process is as follows:

[0009] ;

[0010] In the formula, Collect environmental characteristic values ​​for the mineral samples to be tested, Collect the ambient temperature for the mineral sample to be tested, Collect ambient soil moisture for the mineral samples to be tested, Collect environmental soil hardness for the mineral samples to be tested, The compensation factor for the ambient temperature of the mineral sample to be tested is set. The compensation factor of the soil moisture in the environment for the mineral sample to be tested is set. is the compensation factor for the hardness of the soil in the environment where the mineral sample to be tested is collected, and e is a natural constant.

[0011] As a further method, based on the acquired physical property data set of the mineral samples to be tested, a comprehensive analysis is performed to obtain the physical property characteristic values ​​of the mineral samples to be tested. The specific analysis process is: physical property testing is performed on the mineral samples to be tested to obtain the physical property data set of the mineral samples to be tested. The physical property data set of the mineral samples to be tested specifically includes the maximum pressure that the mineral samples to be tested can withstand without breaking, the specific heat capacity of the mineral samples to be tested, the appearance color of the mineral samples to be tested, and the refractive index of the mineral samples to be tested; based on the acquired physical property data set of the mineral samples to be tested, a comprehensive analysis is performed to obtain the physical property characteristic values ​​of the mineral samples to be tested. The physical property characteristic values ​​of the mineral samples to be tested are used as the analysis basis for obtaining the mineral type of the mineral samples to be tested by comparing with the mineral categories existing in the collection environment of the mineral samples to be tested.

[0012] As a further method, the physical property characteristic values ​​of the mineral samples to be tested, the specific analysis process is:

[0013] ;

[0014] In the formula, is the characteristic value of the physical properties of the mineral sample to be tested, The maximum pressure that the mineral sample to be tested can withstand without breaking. is the specific heat capacity of the mineral sample to be tested, is the appearance color of the mineral sample to be tested, is the refractive index of the mineral sample to be tested, The compensation factor for the maximum pressure that the mineral sample to be tested can withstand without breaking. is the compensation factor of the specific heat capacity of the mineral sample to be tested, is the compensation factor for the appearance color of the mineral sample to be tested. It is the compensation factor of the refractive index of the mineral sample to be tested.

[0015] As a further method, based on the acquired spectral prediction data set of the mineral samples to be detected, a comprehensive analysis is performed to obtain the spectral prediction characteristic values ​​of the mineral samples to be detected. The specific analysis process is: real-time online new laser induced breakdown spectroscopy detection is performed on the mineral samples to be detected to obtain the spectral prediction data set of the mineral samples to be detected. The spectral prediction data set of the mineral samples to be detected specifically includes the spectral prediction of phosphorus pentoxide content of the mineral samples to be detected, the spectral prediction of calcium oxide content of the mineral samples to be detected, and the spectral prediction of magnesium oxide content of the mineral samples to be detected; based on the acquired spectral prediction data set of the mineral samples to be detected, a comprehensive analysis is performed to obtain the spectral prediction characteristic values ​​of the mineral samples to be detected. The spectral prediction characteristic values ​​of the mineral samples to be detected are used as the analysis basis for obtaining the mineral type of the mineral samples to be detected by comparing with the mineral categories existing in the collection environment of the mineral samples to be detected.

[0016] As a further method, the characteristic value of the spectrum of the mineral sample to be detected is predicted. The specific analysis process is as follows:

[0017] ;

[0018] In the formula, Predict characteristic values ​​for the spectrum of the mineral sample to be tested, Predict the phosphorus pentoxide content for the mineral sample spectrum to be tested, Predict the calcium oxide content of the mineral sample spectrum to be tested, Predict the magnesium oxide content of the mineral sample spectrum to be tested, The compensation factor for predicting the phosphorus pentoxide content of the set mineral sample spectrum to be tested, The compensation factor for predicting the calcium oxide content of the mineral sample spectrum to be tested, is the compensation factor for predicting the magnesium oxide content of the mineral sample spectrum to be tested, and e is a natural constant.

[0019] As a further method, the characteristic values ​​of the physical properties of the mineral samples to be detected and the predicted characteristic values ​​of the spectra of the mineral samples to be detected are introduced into the detection model of the mineral samples to be detected, and the mineral types of the mineral samples to be detected are obtained by comparison in the mineral categories existing in the collection environment of the mineral samples to be detected. The specific analysis process is: the characteristic values ​​of the physical properties of the mineral samples to be detected and the predicted characteristic values ​​of the spectra of the mineral samples to be detected are introduced into the detection model of the mineral samples to be detected, and the detection characteristic values ​​of the mineral samples to be detected are obtained by comprehensive analysis. The detection characteristic values ​​of the mineral samples to be detected are used as the analysis basis for obtaining the mineral types of the mineral samples to be detected by comparison in the mineral categories existing in the collection environment of the mineral samples to be detected; the detection characteristic values ​​of the mineral samples to be detected are compared with the mineral detection characteristic values ​​corresponding to the mineral types in the mineral categories existing in the collection environment of the mineral samples to be detected, and the mineral detection characteristic values ​​equal to the detection characteristic values ​​of the mineral samples to be detected are obtained. Then the mineral types in the mineral categories existing in the collection environment of the mineral samples to be detected corresponding to the mineral detection characteristic values ​​represent the mineral types of the mineral samples to be detected corresponding to the detection characteristic values ​​of the mineral samples to be detected.

[0020] The second aspect of the present invention provides an automatic detection system for mineral samples, including a mineral category comparison module existing in a collection environment, a physical property characteristic value acquisition module, a spectrum prediction characteristic value acquisition module and a mineral type comparison module for mineral samples to be detected, wherein: the mineral category comparison module existing in the collection environment is used to obtain a data set of a collection environment of mineral samples to be detected, and compare to obtain the mineral category existing in the collection environment of the mineral samples to be detected based on the acquired data set of the collection environment of the mineral samples to be detected; the physical property characteristic value acquisition module is used to obtain a physical property data set of the mineral samples to be detected, and obtain the physical property characteristic value of the mineral samples to be detected based on the acquired physical property data set of the mineral samples to be detected; the spectrum prediction characteristic value acquisition module is used to obtain a spectrum prediction data set of the mineral samples to be detected, and obtain the spectrum prediction characteristic value of the mineral samples to be detected based on the acquired spectrum prediction data set of the mineral samples to be detected; the mineral type comparison module for mineral samples to be detected is used to import the physical property characteristic value of the mineral samples to be detected and the spectrum prediction characteristic value of the mineral samples to be detected into a detection model for mineral samples to be detected, and compare to obtain the mineral category of the mineral samples to be detected in the mineral category existing in the collection environment of the mineral samples to be detected.

[0021] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0022] (1) The present invention provides an automatic detection method and system for mineral samples, which comprehensively collects environmental data, physical property data and spectral prediction data, and combines and analyzes the characteristic values ​​from these different sources, thereby helping to improve the accuracy of the mineral type of the mineral sample to be detected, thereby reducing the possibility of misjudgment. Through comprehensive analysis of various data, the mineral category information of the mineral sample to be detected can be obtained more quickly, thereby improving the efficiency of detection and facilitating rapid response and decision-making in the production process. The collection and analysis process of environmental data, physical property data and spectral prediction data can be automated, reducing the investment in human resources and reducing the cost of detection.

[0023] (2) The present invention performs real-time online new laser induced breakdown spectroscopy detection on the mineral sample to be detected, and obtains the predicted characteristic value of the spectrum of the mineral sample to be detected through comprehensive analysis. The new laser induced breakdown spectroscopy detection technology has high sensitivity and high resolution, and can more accurately obtain the spectral characteristics of the mineral sample to be detected, thereby improving the prediction accuracy. The online detection has the characteristics of real-time monitoring, and can obtain and analyze spectral data in real time during the sampling process, and obtain the characteristic value of the mineral sample to be detected in time, which is conducive to rapid response and adjustment of production. Laser induced breakdown spectroscopy detection is a non-destructive detection technology that can obtain spectral information without destroying the sample to be detected, which is conducive to protecting the integrity of the mineral sample.

[0024] (3) The present invention imports the characteristic values ​​of the physical properties of the mineral samples to be detected and the predicted characteristic values ​​of the spectra of the mineral samples to be detected into the detection model of the mineral samples to be detected, and obtains the mineral type of the mineral samples to be detected by comparing them with the mineral categories existing in the collection environment of the mineral samples to be detected. The comparison of the physical properties and the spectral characteristic values ​​can improve the accuracy of mineral type identification. By comprehensively considering the information of different characteristic values, the mineral category to which the mineral samples to be detected belong can be determined more accurately. By comparing with known mineral categories, the detection model can be verified and adjusted, thereby improving the robustness and reliability of the model, which helps to ensure the accuracy and applicability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0026] Figure 1 It is a schematic diagram of the method steps of the present invention;

[0027] Figure 2 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] Reference Figure 1 As shown, the first aspect of the present invention provides an automatic detection method for mineral samples, including: obtaining a data set of the collection environment of the mineral samples to be detected, and based on the obtained data set of the collection environment of the mineral samples to be detected, comparing to obtain the mineral categories existing in the collection environment of the mineral samples to be detected.

[0030] Specifically, the data set of the environment in which the mineral samples to be tested are collected includes the environment temperature of the mineral samples to be tested, the soil moisture of the environment in which the mineral samples to be tested are collected, and the soil hardness of the environment in which the mineral samples to be tested are collected. The environment temperature refers to the temperature of the surrounding air recorded during the mineral sample collection process. The soil moisture describes the water content in the soil. The soil hardness describes the density and compressive resistance of the soil. The environment temperature can be monitored in real time by temperature sensors or meteorological stations and other equipment. The soil moisture can be measured by soil moisture sensors or soil moisture meters and other equipment. The soil hardness can be measured by soil hardness meters, soil permeameters or soil texture meters and other equipment to measure the hardness and density of the soil.

[0031] In a specific embodiment, an increase in ambient temperature will cause an increase in the evaporation of water in the soil, thereby reducing soil moisture. The ambient temperature will affect the temperature of the soil. The expansion and contraction of the soil at different temperatures will affect the density and hardness of the soil. High temperature will expand the gaps between soil particles, thereby reducing the hardness of the soil, while low temperature will keep the soil tight and increase the hardness of the soil. Water is the adhesive between soil particles. An appropriate amount of water can increase the plasticity and adhesion of the soil and increase the hardness of the soil. Too high or too low soil moisture will destroy the internal structure of the soil, resulting in a decrease in friction between soil particles, thereby reducing the hardness of the soil.

[0032] Furthermore, based on the acquired dataset of the environment in which the mineral samples to be tested are collected, the mineral categories existing in the environment in which the mineral samples to be tested are obtained by comparison. The specific analysis process is as follows: based on the acquired dataset of the environment in which the mineral samples to be tested are collected, a comprehensive analysis is performed to obtain characteristic values ​​of the environment in which the mineral samples to be tested are collected, and the characteristic values ​​of the environment in which the mineral samples to be tested are used as an analysis basis for obtaining the mineral categories existing in the environment in which the mineral samples to be tested are collected; the characteristic values ​​of the environment in which the mineral samples to be tested are collected are compared with the mineral categories corresponding to the characteristic values ​​of the environment in which the mineral samples to be tested are collected and stored in the database to obtain the mineral categories corresponding to the characteristic values ​​of the environment in which the mineral samples to be tested are collected, and the mineral categories corresponding to the characteristic values ​​of the environment in which the mineral samples to be tested are collected represent the mineral categories existing in the environment in which the mineral samples to be tested correspond to the characteristic values ​​of the environment in which the mineral samples to be tested are collected.

[0033] In a specific embodiment, the characteristic values ​​of the collection environment of the mineral samples to be tested obtained through comprehensive analysis can provide a more accurate and objective description of the environmental conditions in which the mineral samples are located. Comparing the mineral categories in the database can more accurately determine the mineral categories that may exist in the collection environment of the mineral samples to be tested. By using the mineral categories corresponding to the characteristic values ​​of the collection environment of the mineral samples to be tested stored in the database, the characteristic values ​​of the collection environment of the mineral samples to be tested can be quickly compared, saving the time of manual search and analysis and improving efficiency. The mineral categories corresponding to the characteristic values ​​of the collection environment of the mineral samples to be tested stored in the database usually have more comprehensive information, including the name, characteristics, origin and other data of the mineral category. More detailed information can be obtained through comparison, which is helpful for further mineral identification and research.

[0034] Specifically, the environmental characteristic values ​​of the mineral samples to be tested are collected, and the specific analysis process is as follows:

[0035] ;

[0036] In the formula, Collect environmental characteristic values ​​for the mineral samples to be tested, Collect the ambient temperature for the mineral sample to be tested, Collect ambient soil moisture for the mineral samples to be tested, Collect environmental soil hardness for the mineral samples to be tested, The compensation factor for the ambient temperature of the mineral sample to be tested is set. The compensation factor of the soil moisture in the environment for the mineral sample to be tested is set. is the compensation factor for the hardness of the soil in the environment where the mineral sample to be tested is collected, and e is a natural constant.

[0037] It needs to be explained that the above-mentioned characteristic values ​​of the collection environment of the mineral samples to be tested are calculated through the temperature of the environment in which the mineral samples to be tested are collected, the soil moisture of the environment in which the mineral samples to be tested are collected, and the soil hardness of the environment in which the mineral samples to be tested are collected. The temperature of the environment in which the mineral samples to be tested, the soil moisture of the environment in which the mineral samples to be tested are collected, and the soil hardness of the environment in which the mineral samples to be tested are collected are normalized. Considering the characteristic values ​​of the collection environment of the mineral samples can better understand the conditions and environment in which the mineral samples are located in the collection environment, which is helpful for analyzing the growth process, collection method, possible natural influences and other information of the mineral samples, and further improve the analysis and judgment of the mineral samples. Some minerals will show specific performance under specific environmental conditions such as temperature and humidity. Taking the characteristic values ​​of the collection environment into consideration in the analysis can help improve the accuracy of identifying the types of mineral samples. Combining the characteristic values ​​of the collection environment with the characteristic values ​​of the physical properties and the spectral prediction characteristic values ​​of the mineral samples to be tested for analysis can achieve a more comprehensive description and analysis of the characteristics of the mineral samples, which is helpful for a deeper understanding of the characteristics and potential influences of the mineral samples. Influencing factors, taking into account the characteristic values ​​of the collection environment of mineral samples, can increase the reliability and stability of data analysis. The environmental characteristic values ​​can be used as an auxiliary verification of the mineral sample detection results, help reduce misjudgment and improve the credibility of the data. The compensation factors of the set collection environment temperature of the mineral samples to be detected, the soil moisture of the collection environment of the mineral samples to be detected, and the soil hardness of the collection environment of the mineral samples to be detected are obtained from the database. According to historical data, a mapping set of the historically measured collection environment temperature of the mineral samples to be detected, the soil moisture of the collection environment of the mineral samples to be detected, the soil hardness of the collection environment of the mineral samples to be detected and the compensation factors of the collection environment temperature of the mineral samples to be detected, the soil moisture of the collection environment of the mineral samples to be detected, and the soil hardness of the collection environment of the mineral samples to be detected is established to obtain the compensation factors of the collection environment temperature of the mineral samples to be detected, the soil moisture of the collection environment of the mineral samples to be detected, and the soil hardness of the collection environment of the mineral samples to be detected corresponding to the current collection environment temperature of the mineral samples to be detected, the soil moisture of the collection environment of the mineral samples to be detected, and the soil hardness of the collection environment of the mineral samples to be detected.

[0038] A data set of physical properties of the mineral samples to be detected is obtained, and based on the obtained data set of physical properties of the mineral samples to be detected, characteristic values ​​of the physical properties of the mineral samples to be detected are obtained through comprehensive analysis.

[0039] Specifically, based on the acquired physical property data set of the mineral samples to be tested, a comprehensive analysis is performed to obtain the physical property characteristic values ​​of the mineral samples to be tested. The specific analysis process is: physical property testing is performed on the mineral samples to be tested to obtain the physical property data set of the mineral samples to be tested. The physical property data set of the mineral samples to be tested specifically includes the maximum pressure that the mineral samples to be tested can withstand without breaking, the specific heat capacity of the mineral samples to be tested, the appearance color of the mineral samples to be tested, and the refractive index of the mineral samples to be tested. The maximum pressure that the mineral samples to be tested can withstand without breaking represents the maximum pressure that the mineral samples to be tested can withstand under the action of force, that is, the pressure limit without breaking or rupture, reflecting the hardness and compressive resistance of the mineral samples to be tested, which is of great significance for the identification and recognition of minerals. The specific heat capacity of the mineral samples to be tested represents the heat absorbed or released by a unit mass of the mineral samples to be tested during the temperature change process, that is, the heat capacity of the mineral samples. The specific heat capacity can be used to judge the thermal stability of the mineral samples and their performance under temperature changes, which is of great significance for the thermal properties and mineralogical research of minerals. The appearance color of the mineral samples to be tested represents the color characteristics of the mineral samples to be tested in appearance. The color of a mineral is usually caused by its chemical composition and crystal structure. Therefore, color can be used as an important feature for identifying and classifying minerals. Color changes are often related to the composition and crystal structure of the mineral, which is helpful for preliminarily determining the type of mineral. The refractive index of the mineral sample to be tested indicates the degree of refraction of the incident light by the mineral sample to be tested, that is, the deflection of light when it propagates in the mineral. The refractive index is one of the optical properties of minerals. Different types of minerals have different refractive indices. They can be identified and distinguished by measuring the refractive index of the mineral, which is of great significance to mineralogy and optical research. The mineral sample to be tested remains opaque. The maximum pressure that can be withstood by raw fracture is obtained by means of equipment such as a hardness tester or a pressure gauge, the specific heat capacity of the mineral sample to be tested is measured by a heat capacity measuring instrument, such as a calorimeter or a thermal differential scanning calorimeter, the appearance color of the mineral sample to be tested is obtained by means of a spectrometer, and the refractive index of the mineral sample to be tested is obtained by means of equipment such as a refractometer or a microscope; based on the acquired data set of physical properties of the mineral sample to be tested, a comprehensive analysis is performed to obtain characteristic values ​​of the physical properties of the mineral sample to be tested, and the characteristic values ​​of the physical properties of the mineral sample to be tested are used as the basis for analysis of the mineral type of the mineral sample to be tested by comparing it with the mineral category existing in the environment in which the mineral sample to be tested is collected.

[0040] It needs to be explained that, through the comprehensive analysis of different physical property data of the mineral samples to be tested, such as the maximum pressure that can be withstood without breaking, specific heat capacity, appearance color, refractive index, etc., a relatively complete physical characteristic value of the mineral sample can be obtained, which is helpful for in-depth understanding and description of the physical properties of the mineral samples to be tested, and provides more comprehensive information basis for subsequent mineral type identification. The physical property characteristic values ​​of the mineral samples to be tested can be used as a reference standard, and compared and analyzed with the physical properties of known mineral categories to determine the possible mineral types of the mineral samples to be tested. Different minerals have certain differences in physical properties. Comprehensive consideration of multiple property parameters can improve the accuracy of mineral type identification. The characteristic values ​​obtained by comprehensive analysis of physical property data can be used as the basis for identification, which can achieve comprehensive consideration of various properties, reduce the influence of subjective factors on the identification results, and improve the objectivity and accuracy of identification.

[0041] Furthermore, the specific analysis process of the physical property characteristic values ​​of the mineral samples to be tested is as follows:

[0042] ;

[0043] In the formula, is the characteristic value of the physical properties of the mineral sample to be tested, The maximum pressure that the mineral sample to be tested can withstand without breaking. is the specific heat capacity of the mineral sample to be tested, is the appearance color of the mineral sample to be tested, is the refractive index of the mineral sample to be tested, The compensation factor for the maximum pressure that the mineral sample to be tested can withstand without breaking. is the compensation factor of the specific heat capacity of the mineral sample to be tested, is the compensation factor for the appearance color of the mineral sample to be tested. It is the compensation factor of the refractive index of the mineral sample to be tested.

[0044] It needs to be explained that the above-mentioned characteristic values ​​of the physical properties of the mineral samples to be tested are calculated through the maximum pressure that the mineral samples to be tested can withstand without breaking, the specific heat capacity of the mineral samples to be tested, the appearance color of the mineral samples to be tested, and the refractive index of the mineral samples to be tested. The maximum pressure that the mineral samples to be tested can withstand without breaking, the specific heat capacity of the mineral samples to be tested, the appearance color of the mineral samples to be tested, and the refractive index of the mineral samples to be tested are normalized. By analyzing the physical properties such as the maximum pressure that the mineral samples to be tested can withstand without breaking, the specific heat capacity, the appearance color and the refractive index, it can help to identify the physical properties of the mineral samples, which are related to the hardness, stability, color and optical properties of the minerals, and help to accurately describe the physical properties of the mineral samples to be tested. The physical properties of the mineral samples are crucial for distinguishing different types of minerals. Different types of minerals have different hardness, color, refractive index and other characteristics. Analyzing these characteristic values ​​of physical properties can better distinguish different types of minerals and improve the accuracy of detection. Quantifying and calculating the characteristic values ​​of physical properties can help to distinguish different minerals during the analysis process. The samples are quantitatively compared and measured to reduce the interference of subjective factors and improve the objectivity and accuracy of the test results. The set compensation factors of the maximum pressure that the mineral sample to be tested can withstand without breaking, the specific heat capacity of the mineral sample to be tested, the appearance color of the mineral sample to be tested, and the refractive index of the mineral sample to be tested are obtained from the database. According to the historical data, a mapping set of the maximum pressure that the mineral sample to be tested can withstand without breaking, the specific heat capacity of the mineral sample to be tested, the appearance color of the mineral sample to be tested, the refractive index of the mineral sample to be tested and the compensation factors of the maximum pressure that the mineral sample to be tested can withstand without breaking, the specific heat capacity of the mineral sample to be tested, the appearance color of the mineral sample to be tested, and the refractive index of the mineral sample to be tested corresponding to the maximum pressure that the mineral sample to be tested can withstand without breaking, the specific heat capacity of the mineral sample to be tested, the appearance color of the mineral sample to be tested, and the refractive index of the mineral sample to be tested corresponding to the maximum pressure that the mineral sample to be tested can withstand without breaking, the specific heat capacity of the mineral sample to be tested, the appearance color of the mineral sample to be tested, and the refractive index of the mineral sample to be tested corresponding to the maximum pressure that the mineral sample to be tested can withstand without breaking, the specific heat capacity of the mineral sample to be tested, the appearance color of the mineral sample to be tested, and the refractive index of the mineral sample to be tested corresponding to the maximum pressure that the mineral sample to be tested can withstand without breaking, the specific heat capacity of the mineral sample to be tested, the appearance color of the mineral sample to be tested, and the refractive index of the mineral sample to be tested corresponding to the

[0045] A spectrum prediction data set of the mineral sample to be detected is obtained, and based on the obtained spectrum prediction data set of the mineral sample to be detected, a comprehensive analysis is performed to obtain the spectrum prediction characteristic value of the mineral sample to be detected.

[0046] Specifically, based on the acquired spectral prediction data set of the mineral samples to be detected, a comprehensive analysis is performed to obtain the spectral prediction characteristic values ​​of the mineral samples to be detected. The specific analysis process is: real-time online new laser induced breakdown spectroscopy detection is performed on the mineral samples to be detected to obtain the spectral prediction data set of the mineral samples to be detected. The spectral prediction data set of the mineral samples to be detected specifically includes the spectral prediction of phosphorus pentoxide content of the mineral samples to be detected, the spectral prediction of calcium oxide content of the mineral samples to be detected, and the spectral prediction of magnesium oxide content of the mineral samples to be detected. The phosphorus pentoxide content refers to the content of phosphorus pentoxide in the mineral samples to be detected. Phosphorus pentoxide is usually one of the main components in phosphorus minerals and is of great significance for the evaluation and utilization of phosphorus-containing minerals. Through the prediction of spectral technology, the information of phosphorus pentoxide content can be obtained non-destructively. The calcium oxide content refers to the content of calcium oxide in the mineral sample to be tested. The prediction using spectral technology can quickly and accurately obtain the content information of calcium oxide in the mineral sample. The magnesium oxide content refers to the content of magnesium oxide in the mineral sample to be tested. The prediction of magnesium oxide content through spectroscopy can provide important data for the comprehensive evaluation of mineral samples. Based on the acquired spectral prediction data set of the mineral sample to be tested, the spectral prediction characteristic values ​​of the mineral sample to be tested are obtained through comprehensive analysis. The spectral prediction characteristic values ​​of the mineral sample to be tested are used as the analysis basis for obtaining the mineral type of the mineral sample to be tested by comparing with the mineral category existing in the collection environment of the mineral sample to be tested.

[0047] It needs to be explained that the above-mentioned use of spectral prediction can quickly obtain the key component content information of the mineral sample to be tested. The characteristic values ​​obtained after comprehensive analysis can comprehensively describe the spectral characteristics of the mineral sample, and can more accurately depict the chemical composition and characteristics of the mineral sample, providing more sufficient data support for the subsequent mineral type identification. The characteristic values ​​obtained by comprehensive analysis of the spectral prediction data set can be used for data mining and pattern recognition analysis, which can help discover the distinguishing features between different mineral categories, and assist in comparing and identifying different mineral categories under the collection environment. The characteristic values ​​obtained based on the spectral prediction data set are used as the analytical basis for mineral category comparison and identification, which can improve the accuracy of mineral type discrimination of the mineral sample to be tested. Spectral data contains rich chemical information, and the characteristic values ​​predicted by the spectrum can more clearly reflect the chemical composition of the mineral sample, thereby accurately classifying it.

[0048] It needs to be explained that the above-mentioned real-time online new laser induced breakdown spectroscopy detection is carried out on the mineral samples to be detected, and the predicted characteristic values ​​of the spectrum of the mineral samples to be detected are obtained through comprehensive analysis. The new laser induced breakdown spectroscopy detection technology has high sensitivity and high resolution, and can more accurately obtain the spectral characteristics of the mineral samples to be detected, thereby improving the prediction accuracy. The online detection has the characteristics of real-time monitoring, and can obtain and analyze spectral data in real time during the sampling process, and obtain the characteristic values ​​of the mineral samples to be detected in time, which is conducive to rapid response and adjustment of production. Laser induced breakdown spectroscopy detection is a non-destructive detection technology that can obtain spectral information without destroying the samples to be detected, which is conducive to protecting the integrity of mineral samples.

[0049] Furthermore, the characteristic value of the spectrum of the mineral sample to be detected is predicted, and the specific analysis process is as follows:

[0050] ;

[0051] In the formula, Predict characteristic values ​​for the spectrum of the mineral sample to be tested, Predict the phosphorus pentoxide content for the mineral sample spectrum to be tested, Predict the calcium oxide content of the mineral sample spectrum to be tested, Predict the magnesium oxide content of the mineral sample spectrum to be tested, The compensation factor for predicting the phosphorus pentoxide content of the set mineral sample spectrum to be tested, The compensation factor for predicting the calcium oxide content of the mineral sample spectrum to be tested, is the compensation factor for predicting the magnesium oxide content of the mineral sample spectrum to be tested, and e is a natural constant.

[0052] It should be explained that the above-mentioned predicted characteristic values ​​of the spectrum of the mineral sample to be tested are calculated by predicting the phosphorus pentoxide content, the calcium oxide content and the magnesium oxide content of the mineral sample to be tested. The predicted phosphorus pentoxide content, the calcium oxide content and the magnesium oxide content of the mineral sample to be tested are normalized. The chemical components such as the phosphorus pentoxide, calcium oxide and magnesium oxide content in the mineral sample can be predicted through the spectral prediction characteristic values, which is helpful to determine the type and content of organic matter contained in the mineral, and further helps to identify the type and characteristics of the mineral. The spectral prediction characteristic values ​​can realize the quantitative analysis of the content of key elements or compounds in the mineral sample to be tested, which is helpful to standardize and systematize data analysis, and improve the reliability and accuracy of data analysis. The set spectrum of the mineral sample to be tested The compensation factors for predicting the phosphorus pentoxide content, predicting the calcium oxide content, and predicting the magnesium oxide content by the spectrum of the mineral sample to be tested are obtained from the database, and a mapping set of the historically measured predictions of the phosphorus pentoxide content, the calcium oxide content, and the magnesium oxide content by the spectrum of the mineral sample to be tested and the compensation factors for predicting the phosphorus pentoxide content, the calcium oxide content, and the magnesium oxide content by the spectrum of the mineral sample to be tested and the predictions of the phosphorus pentoxide content, the calcium oxide content, and the magnesium oxide content by the spectrum of the mineral sample to be tested is established according to the historical data, and the compensation factors for predicting the phosphorus pentoxide content, the calcium oxide content, and the magnesium oxide content by the spectrum of the mineral sample to be tested corresponding to the current predictions of the phosphorus pentoxide content, the calcium oxide content, and the magnesium oxide content by the spectrum of the mineral sample to be tested are obtained.

[0053] The physical property characteristic values ​​of the mineral sample to be detected and the spectral prediction characteristic values ​​of the mineral sample to be detected are introduced into the detection model of the mineral sample to be detected, and the mineral type of the mineral sample to be detected is obtained by comparing the mineral types existing in the collection environment of the mineral sample to be detected.

[0054] Specifically, the characteristic values ​​of the physical properties of the mineral samples to be detected and the predicted characteristic values ​​of the spectra of the mineral samples to be detected are introduced into the detection model of the mineral samples to be detected, and the mineral types of the mineral samples to be detected are compared in the mineral categories existing in the collection environment of the mineral samples to be detected. The specific analysis process is: the characteristic values ​​of the physical properties of the mineral samples to be detected and the predicted characteristic values ​​of the spectra of the mineral samples to be detected are introduced into the detection model of the mineral samples to be detected, and the detection characteristic values ​​of the mineral samples to be detected are obtained through comprehensive analysis. The detection characteristic values ​​of the mineral samples to be detected are used as the analysis basis for obtaining the mineral types of the mineral samples to be detected by comparing in the mineral categories existing in the collection environment of the mineral samples to be detected; the detection characteristic values ​​of the mineral samples to be detected are compared with the mineral detection characteristic values ​​corresponding to the mineral types in the mineral categories existing in the collection environment of the mineral samples to be detected, and a mineral detection characteristic value equal to the detection characteristic value of the mineral samples to be detected is obtained. Then the mineral type in the mineral category existing in the collection environment of the mineral samples to be detected corresponding to the mineral detection characteristic value represents the mineral type of the mineral sample to be detected corresponding to the detection characteristic value of the mineral samples to be detected.

[0055] In a specific embodiment, the physical property characteristic values ​​and the spectral prediction characteristic values ​​are combined and a comprehensive analysis is performed based on the detection model, which can more comprehensively describe the characteristics of the mineral sample to be detected, and help improve the accuracy and reliability of mineral sample detection. By comparing the detection characteristic values ​​of the mineral sample to be detected with the mineral detection characteristic values ​​corresponding to each mineral type, the mineral type that matches the characteristic value of the sample to be detected can be found, thereby accurately determining the mineral type of the mineral sample to be detected, which helps to improve the accuracy and precision of mineral type identification. Using the detection model for characteristic value comparison and classification can effectively save manpower and time costs. Compared with traditional mineral detection methods, this method based on characteristic value comparison is more efficient and faster, can improve work efficiency and reduce analysis costs, and combining different types of characteristic values ​​for analysis can improve the reliability of the detection results. The physical property characteristic values ​​and the spectral prediction characteristic values ​​confirm each other, which helps to ensure the accuracy and credibility of the detection results.

[0056] In a specific embodiment, the detection model of the mineral sample to be detected has a specific analysis process as follows:

[0057] ;

[0058] In the formula, Detect characteristic values ​​of the mineral samples to be tested. is the characteristic value of the physical properties of the mineral sample to be tested, is the predicted characteristic value of the mineral sample spectrum to be detected, and e is a natural constant.

[0059] It should be explained that the above method imports the characteristic values ​​of the physical properties of the mineral samples to be detected and the predicted characteristic values ​​of the spectra of the mineral samples to be detected into the detection model of the mineral samples to be detected, and compares them with the mineral categories existing in the collection environment of the mineral samples to be detected to obtain the mineral types of the mineral samples to be detected. The comparison of physical properties and spectral characteristic values ​​can improve the accuracy of mineral type identification, and comprehensive consideration of information from different characteristic values ​​can more accurately determine the mineral category to which the mineral samples to be detected belong. By comparing with known mineral categories, the detection model can be verified and adjusted, thereby improving the robustness and reliability of the model, which helps to ensure the accuracy and applicability of the model.

[0060] Reference Figure 2 As shown, the second aspect of the present invention provides an automatic detection system for mineral samples, including a mineral category comparison module existing in the collection environment, a physical property characteristic value acquisition module, a spectral prediction characteristic value acquisition module and a mineral type comparison module for the mineral sample to be detected.

[0061] The mineral category comparison module in the collection environment is used to obtain the data set of the collection environment of the mineral samples to be tested, and compare the mineral categories existing in the collection environment of the mineral samples to be tested based on the acquired data set of the collection environment of the mineral samples to be tested.

[0062] The physical property characteristic value acquisition module is used to acquire a physical property data set of the mineral sample to be detected, and based on the acquired physical property data set of the mineral sample to be detected, comprehensively analyze and obtain the physical property characteristic value of the mineral sample to be detected.

[0063] The spectrum prediction characteristic value acquisition module is used to acquire the spectrum prediction data set of the mineral sample to be detected, and based on the acquired spectrum prediction data set of the mineral sample to be detected, comprehensively analyze and obtain the spectrum prediction characteristic value of the mineral sample to be detected.

[0064] The mineral type comparison module of the mineral sample to be detected is used to import the physical property characteristic values ​​of the mineral sample to be detected and the spectral prediction characteristic values ​​of the mineral sample to be detected into the detection model of the mineral sample to be detected, and compare the mineral types existing in the mineral sample collection environment to obtain the mineral type of the mineral sample to be detected.

[0065] It should be explained that the above-mentioned automatic detection method and system for mineral samples are provided, which comprehensively collects environmental data, physical property data and spectral prediction data, and combines the analysis of the characteristic values ​​from these different sources, which helps to improve the accuracy of the mineral type of the mineral sample to be detected, thereby reducing the possibility of misjudgment. Through the comprehensive analysis of various data, the mineral category information of the mineral sample to be detected can be obtained more quickly, the detection efficiency is improved, and it is helpful to respond and make decisions quickly in the production process. The collection and analysis process of environmental data, physical property data and spectral prediction data can be automated, reducing the input of human resources and reducing the detection cost.

[0066] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. An automatic detection method for mineral samples, characterized in that: The following steps are involved: Acquire a data set of the environment in which the mineral sample to be detected is collected, and based on the acquired data set of the environment in which the mineral sample to be detected is collected, compare and obtain the mineral category existing in the environment in which the mineral sample to be detected is collected, wherein the data set of the environment in which the mineral sample to be detected is collected specifically includes the temperature of the environment in which the mineral sample to be detected is collected, the soil humidity of the environment in which the mineral sample to be detected is collected, and the soil hardness of the environment in which the mineral sample to be detected is collected; Based on the acquired data set of the collection environment of the mineral sample to be detected, the mineral types existing in the collection environment of the mineral sample to be detected are compared. The specific analysis process is as follows: Based on the acquired data set of the collection environment of the mineral samples to be tested, a comprehensive analysis is performed to obtain characteristic values ​​of the collection environment of the mineral samples to be tested, and the characteristic values ​​of the collection environment of the mineral samples to be tested are used as the basis for comparison to obtain the analysis of the mineral categories existing in the collection environment of the mineral samples to be tested; The specific analysis process of the environmental characteristic values ​​of the mineral samples to be tested is as follows: ； In the formula, Collect environmental characteristic values ​​for the mineral samples to be tested, Collect the ambient temperature for the mineral sample to be tested, Collect ambient soil moisture for the mineral samples to be tested, Collect environmental soil hardness for the mineral samples to be tested, The compensation factor for the ambient temperature of the mineral sample to be tested is set. The compensation factor of the soil moisture in the environment for the mineral sample to be tested is set. is the compensation factor of the soil hardness of the environment in which the mineral sample to be tested is collected, and e is a natural constant; Compare the characteristic value of the environment in which the mineral sample to be detected is collected with the mineral categories corresponding to the characteristic values ​​of the environment in which the mineral sample to be detected is collected stored in the database to obtain the mineral category corresponding to the characteristic value of the environment in which the mineral sample to be detected is collected, and the mineral category corresponding to the characteristic value of the environment in which the mineral sample to be detected is collected indicates the mineral category existing in the environment in which the mineral sample to be detected is collected, corresponding to the characteristic value of the environment in which the mineral sample to be detected is collected; Acquire a data set of physical properties of the mineral sample to be detected, and obtain characteristic values ​​of the physical properties of the mineral sample to be detected by comprehensive analysis based on the acquired data set of physical properties of the mineral sample to be detected; Based on the acquired physical property data set of the mineral sample to be detected, the physical property characteristic value of the mineral sample to be detected is obtained by comprehensive analysis. The specific analysis process is as follows: Performing physical property testing on the mineral sample to be tested, and obtaining a physical property data set of the mineral sample to be tested, wherein the physical property data set of the mineral sample to be tested specifically includes the maximum pressure that the mineral sample to be tested can withstand without breaking, the specific heat capacity of the mineral sample to be tested, the appearance color of the mineral sample to be tested, and the refractive index of the mineral sample to be tested; Based on the acquired physical property data set of the mineral sample to be tested, a comprehensive analysis is performed to obtain the physical property characteristic values ​​of the mineral sample to be tested, and the physical property characteristic values ​​of the mineral sample to be tested are used as the analysis basis for obtaining the mineral type of the mineral sample to be tested by comparing with the mineral types existing in the collection environment of the mineral sample to be tested; The specific analysis process of the physical property characteristic value of the mineral sample to be detected is as follows: ； In the formula, is the characteristic value of the physical properties of the mineral sample to be tested, The maximum pressure that the mineral sample to be tested can withstand without breaking. is the specific heat capacity of the mineral sample to be tested, is the appearance color of the mineral sample to be tested, is the refractive index of the mineral sample to be tested, The compensation factor for the maximum pressure that the mineral sample to be tested can withstand without breaking. is the compensation factor of the specific heat capacity of the mineral sample to be tested, is the compensation factor for the appearance color of the mineral sample to be tested. is the compensation factor of the refractive index of the mineral sample to be tested. is the hyperbolic tangent function; Acquire a data set of spectrum prediction of the mineral sample to be detected, and obtain the spectrum prediction characteristic value of the mineral sample to be detected by comprehensive analysis based on the acquired data set of spectrum prediction of the mineral sample to be detected; Based on the acquired spectrum prediction data set of the mineral sample to be detected, the spectrum prediction characteristic value of the mineral sample to be detected is obtained by comprehensive analysis. The specific analysis process is as follows: Perform real-time online new laser-induced breakdown spectroscopy detection on the mineral samples to be detected, and obtain a spectrum prediction data set of the mineral samples to be detected, which specifically includes the spectrum prediction of phosphorus pentoxide content of the mineral samples to be detected, the spectrum prediction of calcium oxide content of the mineral samples to be detected, and the spectrum prediction of magnesium oxide content of the mineral samples to be detected; Based on the acquired spectrum prediction data set of the mineral sample to be detected, a comprehensive analysis is performed to obtain the spectrum prediction characteristic value of the mineral sample to be detected, and the spectrum prediction characteristic value of the mineral sample to be detected is used as an analysis basis for comparing the mineral categories existing in the collection environment of the mineral sample to be detected to obtain the mineral type of the mineral sample to be detected; The specific analysis process of the predicted characteristic value of the spectrum of the mineral sample to be detected is as follows: ； In the formula, Predict characteristic values ​​for the spectrum of the mineral sample to be tested, Predict the phosphorus pentoxide content for the mineral sample spectrum to be tested, Predict the calcium oxide content of the mineral sample spectrum to be tested, Predict the magnesium oxide content of the mineral sample spectrum to be tested, The compensation factor for predicting the phosphorus pentoxide content of the set mineral sample spectrum to be tested, The compensation factor for predicting the calcium oxide content of the mineral sample spectrum to be tested, is the compensation factor for predicting the magnesium oxide content of the mineral sample spectrum to be tested, and e is a natural constant; The physical property characteristic values ​​of the mineral sample to be detected and the spectral prediction characteristic values ​​of the mineral sample to be detected are introduced into the detection model of the mineral sample to be detected, and the mineral type of the mineral sample to be detected is obtained by comparing the mineral types existing in the collection environment of the mineral sample to be detected.

2. The automatic detection method for mineral samples according to claim 1, characterized in that: The physical property characteristic values ​​of the mineral sample to be detected and the spectral prediction characteristic values ​​of the mineral sample to be detected are introduced into the detection model of the mineral sample to be detected, and the mineral types of the mineral sample to be detected are obtained by comparison with the mineral types existing in the collection environment of the mineral sample to be detected. The specific analysis process is as follows: The physical property characteristic values ​​of the mineral sample to be detected and the spectral prediction characteristic values ​​of the mineral sample to be detected are introduced into the detection model of the mineral sample to be detected, and the detection characteristic values ​​of the mineral sample to be detected are obtained through comprehensive analysis. The detection characteristic values ​​of the mineral sample to be detected are used as the analysis basis for obtaining the mineral type of the mineral sample to be detected by comparing with the mineral types existing in the collection environment of the mineral sample to be detected; The detection characteristic value of the mineral sample to be detected is compared with the mineral detection characteristic values ​​corresponding to each mineral type in the mineral category existing in the collection environment of the mineral sample to be detected, and a mineral detection characteristic value equal to the detection characteristic value of the mineral sample to be detected is obtained. Then the mineral type in the mineral category existing in the collection environment of the mineral sample to be detected corresponding to the mineral detection characteristic value represents the mineral type of the mineral sample to be detected corresponding to the detection characteristic value of the mineral sample to be detected.

3. An automatic detection system for mineral samples, characterized in that: An automatic detection method for mineral samples according to any one of claims 1 to 2, comprising a mineral category comparison module in a collection environment, a physical property characteristic value acquisition module, a spectrum prediction characteristic value acquisition module and a mineral type comparison module for a mineral sample to be detected, wherein: The mineral category comparison module in the collection environment is used to obtain a data set of the collection environment of the mineral sample to be detected, and compare and obtain the mineral category existing in the collection environment of the mineral sample to be detected based on the obtained data set of the collection environment of the mineral sample to be detected; The data set of the environment in which the mineral sample to be detected is collected specifically includes the temperature of the environment in which the mineral sample to be detected is collected, the soil humidity of the environment in which the mineral sample to be detected is collected, and the soil hardness of the environment in which the mineral sample to be detected is collected; Based on the acquired data set of the collection environment of the mineral sample to be detected, the mineral types existing in the collection environment of the mineral sample to be detected are compared. The specific analysis process is as follows: Based on the acquired data set of the collection environment of the mineral samples to be tested, a comprehensive analysis is performed to obtain characteristic values ​​of the collection environment of the mineral samples to be tested, and the characteristic values ​​of the collection environment of the mineral samples to be tested are used as the basis for comparison to obtain the analysis of the mineral categories existing in the collection environment of the mineral samples to be tested; The specific analysis process of the environmental characteristic values ​​of the mineral samples to be tested is as follows: ； In the formula, Collect environmental characteristic values ​​for the mineral samples to be tested, Collect the ambient temperature for the mineral sample to be tested, Collect ambient soil moisture for the mineral samples to be tested, Collect environmental soil hardness for the mineral samples to be tested, The compensation factor for the ambient temperature of the mineral sample to be tested is set. The compensation factor of the soil moisture in the environment for the mineral sample to be tested is set. is the compensation factor of the soil hardness of the environment in which the mineral sample to be tested is collected, and e is a natural constant; Compare the characteristic value of the environment in which the mineral sample to be detected is collected with the mineral categories corresponding to the characteristic values ​​of the environment in which the mineral sample to be detected is collected stored in the database to obtain the mineral category corresponding to the characteristic value of the environment in which the mineral sample to be detected is collected, and the mineral category corresponding to the characteristic value of the environment in which the mineral sample to be detected is collected indicates the mineral category existing in the environment in which the mineral sample to be detected is collected, corresponding to the characteristic value of the environment in which the mineral sample to be detected is collected; The physical property characteristic value acquisition module is used to acquire a physical property data set of the mineral sample to be detected, and based on the acquired physical property data set of the mineral sample to be detected, comprehensively analyze and obtain the physical property characteristic value of the mineral sample to be detected; Based on the acquired physical property data set of the mineral sample to be detected, the physical property characteristic value of the mineral sample to be detected is obtained by comprehensive analysis. The specific analysis process is as follows: Performing physical property testing on the mineral sample to be tested, and obtaining a physical property data set of the mineral sample to be tested, wherein the physical property data set of the mineral sample to be tested specifically includes the maximum pressure that the mineral sample to be tested can withstand without breaking, the specific heat capacity of the mineral sample to be tested, the appearance color of the mineral sample to be tested, and the refractive index of the mineral sample to be tested; Based on the acquired physical property data set of the mineral sample to be tested, a comprehensive analysis is performed to obtain the physical property characteristic values ​​of the mineral sample to be tested, and the physical property characteristic values ​​of the mineral sample to be tested are used as the analysis basis for obtaining the mineral type of the mineral sample to be tested by comparing with the mineral types existing in the collection environment of the mineral sample to be tested; The specific analysis process of the physical property characteristic value of the mineral sample to be detected is as follows: ； In the formula, is the characteristic value of the physical properties of the mineral sample to be tested, The maximum pressure that the mineral sample to be tested can withstand without breaking. is the specific heat capacity of the mineral sample to be tested, is the appearance color of the mineral sample to be tested, is the refractive index of the mineral sample to be tested, The compensation factor for the maximum pressure that the mineral sample to be tested can withstand without breaking. is the compensation factor of the specific heat capacity of the mineral sample to be tested, is the compensation factor for the appearance color of the mineral sample to be tested. is the compensation factor of the refractive index of the mineral sample to be tested. is the hyperbolic tangent function; The spectrum prediction characteristic value acquisition module is used to acquire a spectrum prediction data set of the mineral sample to be detected, and based on the acquired spectrum prediction data set of the mineral sample to be detected, comprehensively analyze and obtain the spectrum prediction characteristic value of the mineral sample to be detected; Based on the acquired spectrum prediction data set of the mineral sample to be detected, the spectrum prediction characteristic value of the mineral sample to be detected is obtained by comprehensive analysis. The specific analysis process is as follows: Perform real-time online new laser-induced breakdown spectroscopy detection on the mineral samples to be detected, and obtain a spectrum prediction data set of the mineral samples to be detected, which specifically includes the spectrum prediction of phosphorus pentoxide content of the mineral samples to be detected, the spectrum prediction of calcium oxide content of the mineral samples to be detected, and the spectrum prediction of magnesium oxide content of the mineral samples to be detected; Based on the acquired spectrum prediction data set of the mineral sample to be detected, a comprehensive analysis is performed to obtain the spectrum prediction characteristic value of the mineral sample to be detected, and the spectrum prediction characteristic value of the mineral sample to be detected is used as an analysis basis for comparing the mineral categories existing in the collection environment of the mineral sample to be detected to obtain the mineral type of the mineral sample to be detected; The specific analysis process of the predicted characteristic value of the spectrum of the mineral sample to be detected is as follows: ； In the formula, Predict characteristic values ​​for the spectrum of the mineral sample to be tested, Predict the phosphorus pentoxide content for the mineral sample spectrum to be tested, Predict the calcium oxide content of the mineral sample spectrum to be tested, Predict the magnesium oxide content of the mineral sample spectrum to be tested, The compensation factor for predicting the phosphorus pentoxide content of the set mineral sample spectrum to be tested, The compensation factor for predicting the calcium oxide content of the mineral sample spectrum to be tested, is the compensation factor for predicting the magnesium oxide content of the mineral sample spectrum to be tested, and e is a natural constant; The mineral type comparison module for the mineral sample to be detected is used to import the physical property characteristic values ​​of the mineral sample to be detected and the spectral prediction characteristic values ​​of the mineral sample to be detected into the detection model of the mineral sample to be detected, and compare the mineral types existing in the mineral sample collection environment to obtain the mineral type of the mineral sample to be detected.

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