A method and system for detecting the total iron content of iron ore based on spectral analysis

Abstract

The present invention relates to the technical field of iron content detection, and particularly to a method and system for detecting the total iron content of iron ore based on spectral analysis. After obtaining the spectral data of the iron ore, the present invention preprocesses the spectral data by using an improved envelope method. Specifically, the sliding window method is used to determine the local maximum points of the spectral data. Specifically, the size of the window for finding the local maximum points of the spectral data by the sliding window method is determined by the number of data points of the maximum characteristic peak of the spectral curve of the spectral data, which improves the robustness of the determination of the local maximum points of the spectral data; when determining the envelope nodes, not only the local maximum values are considered, but also the intersection points of the fitting curves of the spectral curves are considered. The characteristic points of both aspects are used as the envelope nodes, so that the envelope nodes are determined more accurately and can better reflect the characteristics of the spectral curve.

Description

Technical Field

[0001] The present invention relates to the technical field of iron content detection, and in particular to a method and system for detecting the total iron content of iron ore based on spectral analysis. Background Art

[0002] Iron ore is an important mineral resource in modern society and is one of the earliest discovered and most widely used metal mineral resources by humans. In order to achieve the integration of mines and intelligent technologies and promote the development of intelligent mines, quickly and accurately obtaining the total iron content of iron ore has become the key to realizing intelligent geological exploration and accelerating the construction of intelligent mines. Traditionally, obtaining information on the total iron content of iron ore mainly involves a combination of on-site sampling and laboratory testing. There are many methods for testing iron ore in the laboratory, including chemical analysis, electrochemistry analysis, chromatographic proton method, mass spectrometry analysis, neutron activation analysis, phase analysis, photometric analysis, and atomic spectroscopy analysis. Although there are many traditional methods for determining iron ore, different methods have their specific limiting conditions, such as low sampling density of iron ore samples, sparse measuring points, large workload, long cycle, and so on. Therefore, the traditional iron ore testing method cannot meet the requirements of modern intelligent mine construction.

[0003] There are technical solutions for detecting the total iron content of iron ore based on spectral data in the prior art. For example, Chinese Patent (CN109030388A) discloses a method for detecting the total iron content of iron ore based on spectral data, including the following steps: obtaining spectral data of an iron ore sample to be detected, where the spectral data contains m spectral features; inputting the spectral data into an iron ore classification model to obtain the iron ore type of the iron ore sample to be detected; according to the obtained iron ore type, inputting the spectral data into an iron ore total iron content detection model corresponding to the iron ore type to obtain the total iron content of the iron ore corresponding to the spectral data. However, the above solution does not perform preprocessing operations on the spectral data, resulting in low accuracy of iron content detection. At the same time, there is a solution in the prior art that preprocesses the spectral data using an improved envelope method, and determines envelope nodes using the first derivative of the spectral data. However, the above method has problems of poor applicability to different spectral curves and inaccurate determination of envelope nodes, resulting in poor preprocessing effects. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for detecting the total iron content of iron ore based on spectral analysis to solve the problems existing in the prior art.

[0005] The present invention provides a method for detecting the total iron content of iron ore based on spectral analysis, including the following steps:

[0006] S1: Perform a pretreatment operation on the iron ore sample to obtain iron ore powder;

[0007] S2: Collect spectral data of the iron ore powder;

[0008] S3: Perform data pretreatment operation on the spectral data;

[0009] Among them, the data pretreatment operation on the spectral data is to perform data pretreatment operation on the spectral data by using the improved envelope method;

[0010] The data pretreatment operation on the spectral data by using the improved envelope method specifically is:

[0011] S3.1: Determine the local maximum points of the spectral data in the spectral data, and use the local maximum points as marked points;

[0012] S3.2: Perform curve fitting on the spectral curve formed by the spectral data, and use the intersection points of the fitting curve and the spectral curve as marked points;

[0013] S3.3: Remove duplicates from the marked points determined in S3.1 and S3.2 to obtain the final marked points;

[0014] S3.4: Determine the envelope nodes of the spectral data according to the final marked points;

[0015] S3.5: Determine the envelope of the spectral data according to the envelope nodes;

[0016] S3.6: Perform pretreatment operation on the spectral data according to the envelope;

[0017] S4: Perform feature band extraction operation on the spectral data pretreated in S3;

[0018] S5: Input the spectral data of the feature band into the total iron content prediction model to obtain the detection result of the total iron content of the iron ore.

[0019] Preferably, in S3.1, the sliding window method is used to determine the local maximum points of the spectral data;

[0020] Preferably, the method for determining the local maximum points of the spectral data by using the sliding window method is:

[0021] S3.1.1: Convert the spectral data into a spectral curve;

[0022] S3.1.2: Determine the number of data points of the maximum characteristic peak of the spectral curve;

[0023] S3.1.3: Determine the window size of the sliding window according to the number of data points of the maximum characteristic peak of the spectral curve;

[0024] Among them, the formula for determining the window size s of the sliding window is:

[0025] ;

[0026] In the formula, m is the number of data points of the maximum characteristic peak of the spectral curve, a is an adjustment coefficient, 0 < a < 1, and round() is a rounding function;

[0027] S3.1.4: Determine the local maximum points of the spectral data according to the window determined in S3.1.3.

[0028] Preferably, in S3.1.3, the value of a is related to the number of characteristics of the spectral curve, and the number of characteristics includes the characteristic peaks, reflection peaks, and inflection points of the spectral curve.

[0029] Preferably, the formula for the value of a is:

[0030] ;

[0031] In the formula, b is the number of characteristics of the spectral curve.

[0032] Preferably, in S3.1.1, the spectral data is converted into a spectral curve through the ASD ViewSpec Pro software.

[0033] Preferably, in S3.1.2, mark the start point and end point of the maximum characteristic peak of the spectral curve in the ASD ViewSpec Pro software, and then use the statistical function of the ASD ViewSpec Pro software to determine the number of data points of the maximum characteristic peak of the spectral curve.

[0034] Preferably, in S3.1.4, calculate the first derivative of the spectral curve of the spectral data within the window, and use the zero-crossing point of the first derivative of the spectral curve as the local maximum point of the spectral data within the window.

[0035] Preferably, S3.4 is specifically as follows: If the connection line between adjacent final marked points does not intersect with the spectral curve of the spectral data, then both of these final marked points are envelope line nodes; if the connection line between adjacent final marked points intersects with the spectral curve of the spectral data, then draw a tangent to the spectral curve of the spectral data from the lower final marked point to the higher final marked point, where the intersection point of the tangent and the spectral curve of the spectral data is the tangent point, and use the lower final marked point and the tangent point as envelope line nodes.

[0036] According to another aspect of the present invention, there is provided an iron ore total iron content detection system based on spectral analysis. The system adopts the above-mentioned iron ore total iron content detection method based on spectral analysis. The system includes:

[0037] An iron ore pretreatment module for performing pretreatment operations on iron ore samples to obtain iron ore powder;

[0038] A spectral data acquisition module for acquiring spectral data of the iron ore powder;

[0039] A data pretreatment module for performing data pretreatment operations on the spectral data;

[0040] A characteristic band extraction module for performing characteristic band extraction operations on the spectral data that has passed through the data pretreatment module;

[0041] An iron ore total iron content detection module for inputting the spectral data of the characteristic band into a total iron content prediction model to obtain an iron ore total iron content detection result.

[0042] The embodiments of the present invention have the following technical effects:

[0043] After obtaining the spectral data of iron ore, the present invention preprocesses the spectral data using an improved envelope method. Among them, the sliding window method is used to determine the local maximum points of the spectral data. Specifically, the size of the window for finding the local maximum points of the spectral data by the sliding window method is determined by the number of data points of the maximum characteristic peak of the spectral curve of the spectral data, which improves the robustness of the determination of the local maximum points of the spectral data;

[0044] When determining the envelope nodes, not only the local maximum values are considered, but also the intersection points of the fitting curves of the spectral curves are considered. The characteristic points in both aspects are used as the envelope nodes, making the determination of the envelope nodes more accurate and better reflecting the characteristics of the spectral curve. Description of the Drawings

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0046] Figure 1 It is a flowchart of an iron ore total iron content detection method based on spectral analysis provided by an embodiment of the present invention. Specific Embodiments

[0047] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0048] Embodiment 1, attached Figure 1 shows a flowchart of a method for detecting the total iron content in iron ore based on spectral analysis. As attached Figure 1 shown, a method for detecting the total iron content in iron ore based on spectral analysis includes the following steps:

[0049] S1: Perform pretreatment operations on the iron ore sample to obtain iron ore powder;

[0050] Among them, the collected iron ore sample is sealed with a black self-sealing bag and then taken back to the laboratory. After bringing the iron ore sample back to the laboratory, the iron ore sample is air-dried, then ground, and passed through a nylon sieve with a pore size of 100 mesh to obtain iron ore powder.

[0051] S2: Collect spectral data for the iron ore powder;

[0052] In this step, an ASD FieldSpec4 ground object spectrometer is used to collect spectral data in a dark room in the laboratory; the ASD FieldSpec4 ground object spectrometer is specifically used for spectral measurement and can measure the reflectance, transmittance, radiance, and irradiance spectra of ground objects. This device is known for its high signal-to-noise ratio, fast scanning ability, and wide wavelength coverage range (350 nm - 2500 nm);

[0053] The spectral range of the ground object spectrometer is 350 - 2500 nm, the spectral resolution is 3 nm in the range of 350 - 1000 nm and 6 nm in the range of 1000 - 2500 nm. During measurement, the iron ore powder is spread flat on a 10 cm * 10 cm black non-reflective cardboard, and at the same time, a reference white board is placed horizontally. The end of the probe optical fiber of the ground object spectrometer is located directly above the sample. In this step, a contact measurement method is adopted.

[0054] During the spectral data collection process, to ensure the reliability of the spectral data, the iron ore powder is collected 5 times repeatedly. When the difference in reflectance of repeated collections is within 5%, the spectral data is considered qualified, and then the average value of the qualified spectral data is taken as the spectral data of the iron ore powder.

[0055] S3: Perform data preprocessing operations on the spectral data;

[0056] Among them, the preprocessing operation on the spectral data is to perform data preprocessing on the spectral data by using the improved envelope method;

[0057] The envelope method is a commonly used spectral data preprocessing method, mainly used to highlight the absorption and reflection characteristics of spectral data, and normalize them onto a consistent spectral background for subsequent analysis and comparison. The core idea of the envelope method is to fit the "outer shell" (i.e., the envelope) of the spectral data, and then perform a ratio operation on the reflectance values of the original spectral data and the reflectance values on the envelope, so as to obtain normalized spectral data. This method can effectively eliminate background noise and non-target information, strengthen absorption characteristics, and make the morphological characteristics of spectral data more obvious; Although the envelope method can make the absorption characteristics of spectral data more obvious, there may be a situation of losing features for absorption characteristics that are not particularly obvious. Therefore, in this embodiment, the traditional envelope method is improved in the data preprocessing link;

[0058] Specifically, the preprocessing operation on the spectral data by using the improved envelope method to perform data preprocessing on the spectral data is specifically as follows:

[0059] S3.1: Determine the local maximum points of the spectral data in the spectral data, and use the local maximum points as marker points;

[0060] Among them, in this step, the sliding window method is used to determine the local maximum points of the spectral data;

[0061] Specifically, using the sliding window method to determine the local maximum points of the spectral data is as follows:

[0062] S3.1.1: Convert the spectral data into a spectral curve;

[0063] In this step, the spectral data is converted into a spectral curve through the ASD ViewSpec Pro software;

[0064] S3.1.2: Determine the number of data points of the maximum characteristic peak of the spectral curve;

[0065] In this step, mark the starting point and ending point of the maximum characteristic peak of the spectral curve in the ASD ViewSpec Pro software, and then use the statistical function of the ASD ViewSpec Pro software to determine the number of data points of the maximum characteristic peak of the spectral curve;

[0066] S3.1.3: Determine the window size of the sliding window according to the number of data points of the maximum characteristic peak of the spectral curve;

[0067] Among them, the formula for determining the window size s of the sliding window is:

[0068] ;

[0069] Wherein, m is the number of data points of the maximum characteristic peak of the spectral curve, a is an adjustment coefficient, 0 < a < 1, and round() is a rounding function;

[0070] Among them, the value of a is related to the characteristic quantity of the spectral curve, and the characteristic quantity includes the characteristic peak, reflection peak, and inflection point of the spectral curve;

[0071] As a preferred embodiment, the value formula of a is:

[0072] ;

[0073] Wherein, b is the characteristic quantity of the spectral curve;

[0074] S3.1.4: Determine the local maximum points of the spectral data according to the window determined in S3.1.3;

[0075] Among them, calculate the first derivative of the spectral curve of the spectral data within the calculation window, and use the zero-crossing point of the first derivative of the spectral curve as the local maximum point of the spectral data within the window;

[0076] In step S3.1, the size of the window for finding the local maximum points of the spectral data by the sliding window method is determined by the number of data points of the maximum characteristic peak of the spectral curve of the spectral data, which improves the robustness of the determination of the local maximum points of the spectral data;

[0077] S3.2: Perform curve fitting on the spectral curve formed by the spectral data, and use the intersection point of the fitting curve and the spectral curve as the marking point;

[0078] Among them, in this step, the spectral curve is fitted by the Lorentz fitting method;

[0079] S3.3: Remove duplicates from the marking points determined in S3.1 and S3.2 to obtain the final marking points;

[0080] S3.4: Determine the envelope nodes of the spectral data according to the final marking points;

[0081] In this step, S3.4 is specifically as follows: If the line connecting adjacent final marker points does not intersect with the spectral curve of the spectral data, then both of these final marker points are envelope nodes; if the line connecting adjacent final marker points intersects with the spectral curve of the spectral data, then draw a tangent to the spectral curve of the spectral data from the lower final marker point to the higher final marker point, where the intersection point of the tangent and the spectral curve of the spectral data is the tangent point, and the lower final marker point and the tangent point are used as envelope nodes;

[0082] In this embodiment, when determining the envelope nodes, not only local maxima are considered but also the intersection points of the fitting curves of the spectral curves are considered. The characteristic points from both aspects are used as envelope nodes, making the determination of the envelope nodes more accurate and better able to reflect the characteristics of the spectral curve.

[0083] S3.5: Determine the envelope of the spectral data according to the envelope nodes;

[0084] S3.6: Perform a preprocessing operation on the spectral data according to the envelope;

[0085] Among them, in this step, the spectral data is divided by the envelope to achieve the preprocessing operation.

[0086] S4: Perform an operation of extracting characteristic bands on the spectral data preprocessed by the S3 data;

[0087] The mineral composition of iron ore is complex and contains many impurities. During the detection process of the total iron content, the spectral information of various impurities will overlap, resulting in a large amount of noise in the detected spectral data, thus having a greater impact on the detection accuracy. Therefore, it is necessary to extract the characteristic bands related to the properties of the iron ore to be detected to improve the detection accuracy;

[0088] In this step, the correlation analysis method is used to extract the characteristic bands of the spectral data;

[0089] The correlation analysis method is a commonly used method for extracting spectral characteristic bands. By calculating the correlation between the spectral data and the target variable (characteristic band), the bands most relevant to the target variable are selected, thereby achieving the extraction of the characteristic bands;

[0090] By using the correlation analysis method to perform a correlation analysis on each band of the spectral data and the iron ore property information, calculate the correlation coefficient between each band in the spectral data and the iron ore property data respectively, and use the bands with larger absolute values of the correlation coefficients as the characteristic bands of the spectral data.

[0091] S5: Input the spectral data of the characteristic bands into the total iron content prediction model to obtain the detection result of the total iron content of the iron ore;

[0092] Among them, the total iron content detection model is a deep learning model, and the deep learning model is one of an artificial neural network model, a deep neural network model, and a recurrent neural network model;

[0093] The input of the deep learning model is the spectral data of the characteristic band, and the output is the total iron content detection value.

[0094] Embodiment 2. The present invention also provides an iron ore total iron content detection system based on spectral analysis. The system adopts a method for detecting the total iron content of iron ore based on spectral analysis in Embodiment 1. The system includes:

[0095] An iron ore pretreatment module for performing pretreatment operations on iron ore samples to obtain iron ore powder;

[0096] A spectral data acquisition module for acquiring spectral data of the iron ore powder;

[0097] A data pretreatment module for performing data pretreatment operations on the spectral data;

[0098] A characteristic band extraction module for performing characteristic band extraction operations on the spectral data that has passed through the data pretreatment module;

[0099] An iron ore total iron content detection module for inputting the spectral data of the characteristic band into the total iron content prediction model to obtain the iron ore total iron content detection result.

[0100] Embodiment 3. The present invention also provides an electronic device, including one or more processors and a memory.

[0101] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0102] The memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may run the program instructions to implement a method for detecting the total iron content of iron ore based on spectral analysis in any embodiment of the present application above and / or other desired functions. Various contents such as initial external parameters and thresholds may also be stored in the computer-readable storage medium.

[0103] In one example, the electronic device may further include: an input device and an output device, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown). The input device may include, for example, a keyboard, a mouse, and so on. The output device may output various information to the outside, including warning prompt information, braking force, and so on. The output device may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, and so on.

[0104] Of course, for simplicity, components such as buses, input / output interfaces, and so on are omitted. In addition, according to specific application scenarios, the electronic device may further include any other appropriate components.

[0105] In addition to the above methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, and when the computer program instructions are run by a processor, the processor implements the functions of a method for detecting the total iron content of iron ore based on spectral analysis provided by any embodiment of the present application.

[0106] The computer program product may be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present application. The programming languages include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as the "C" language or similar programming languages. The programming code may be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0107] In addition, an embodiment of the present application may also be a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are run by a processor, the processor implements a method for detecting the total iron content of iron ore based on spectral analysis provided by any embodiment of the present application.

[0108] The computer-readable storage medium may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting the total iron content of iron ore based on spectral analysis, characterized in that: The following steps are involved: S1: performing pretreatment operation on the iron ore sample to obtain iron ore powder; S2: collecting spectral data of the iron ore powder; S3: performing data preprocessing operation on the spectral data; Wherein, the data preprocessing operation on the spectral data is to perform data preprocessing operation on the spectral data using an improved envelope method; The preprocessing operation for the spectral data is to use an improved envelope method to perform data preprocessing operation on the spectral data, specifically: S3.1: determining a local maximum point of the spectral data in the spectral data, and using the local maximum point as a marking point; determining the local maximum point of the spectral data by using a sliding window method; The local maximum point of the spectral data is determined by the sliding window method as follows: S3.1.1: Convert the spectral data into a spectral curve; S3.1.2: Determine the number of data points of the maximum characteristic peak of the spectral curve; S3.1.3: Determine the window size of the sliding window according to the number of data points of the maximum characteristic peak of the spectral curve; The window size s of the sliding window is determined by: ； Wherein, m is the number of data points of the maximum characteristic peak of the spectral curve, a is the adjustment coefficient, 0<a<1, and round() is the rounding function; S3.1.4: Determine the local maximum point of the spectral data according to the window size determined in S3.1.3; S3.2: performing curve fitting on the spectral curve formed by the spectral data to obtain a fitting curve, and taking the intersection of the fitting curve and the spectral curve as a marking point; S3.3: Deduplication processing is performed on the marking points determined in S3.1 and S3.2 to obtain final marking points; S3.4: Determine the envelope node of the spectral data according to the final marking point; S3.5: Determine the envelope of the spectral data according to the envelope node; S3.6: performing a preprocessing operation on the spectral data according to the envelope; S4: performing a characteristic band extraction operation on the spectral data preprocessed by the S3 data; S5: Input the spectral data of the characteristic band into the total iron content prediction model to obtain the total iron content detection result of the iron ore.

2. The method for detecting the total iron content of iron ore based on spectral analysis according to claim 1, characterized in that: In S3.1.3, the value of a is related to the number of features of the spectral curve, and the number of features includes a characteristic peak, a reflection peak, and an inflection point of the spectral curve.

3. The method for detecting the total iron content of iron ore based on spectral analysis according to claim 1, characterized in that: The value formula of a is: ； Wherein, b is the characteristic number of the spectral curve.

4. The method for detecting the total iron content of iron ore based on spectral analysis according to claim 2, characterized in that: In S3.1.1, the spectral data is converted into a spectral curve using ASD ViewSpec Pro software.

5. The method for detecting the total iron content of iron ore based on spectral analysis according to claim 4, characterized in that: In S3.1.2, the starting point and the end point of the maximum characteristic peak of the spectral curve are marked in the ASD ViewSpec Pro software, and then the statistical function of the ASD ViewSpec Pro software is used to determine the number of data points of the maximum characteristic peak of the spectral curve.

6. The method for detecting the total iron content of iron ore based on spectral analysis according to claim 1, characterized in that: In S3.1.4, the first-order derivative of the spectrum curve of the spectrum data in the window is calculated, and the zero-crossing point of the first-order derivative of the spectrum curve is taken as the local maximum point of the spectrum data in the window.

7. The method for detecting the total iron content of iron ore based on spectral analysis according to claim 1, characterized in that: Specifically, S3.4 is as follows: if the line connecting adjacent final marking points has no intersection with the spectral curve of the spectral data, then the two final marking points are both envelope nodes; if the line connecting adjacent final marking points has an intersection with the spectral curve of the spectral data, then a tangent to the spectral curve of the spectral data is drawn from the lower final marking point to the higher final marking point, wherein the intersection point of the tangent with the spectral curve of the spectral data is the tangent point, and the lower final marking point and the tangent point are used as envelope nodes.

8. A system for detecting the total iron content of iron ore based on spectral analysis, characterized in that: The system adopts a method for detecting the total iron content of iron ore based on spectral analysis as described in any one of claims 1 to 7, and the system comprises: The iron ore pretreatment module is used to perform pretreatment operations on the iron ore sample to obtain iron ore powder; A spectral data acquisition module, used for acquiring spectral data of the iron ore powder; A data preprocessing module, used for performing data preprocessing operations on the spectral data; A characteristic band extraction module, used for performing a characteristic band extraction operation on the spectral data passed through the data preprocessing module; The iron ore total iron content detection module is used to input the spectral data of the characteristic band into the total iron content prediction model to obtain the iron ore total iron content detection result.

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