A method and system for determining iron ore composition based on spectral technology

Abstract

The present application relates to the technical field of iron ore composition determination, and specifically to a method and system for determining the composition of iron ore based on spectral technology, the method comprising: collecting an energy intensity diagram of the photon energy-count rate of an iron ore sample; obtaining the element characteristic peaks of each element except iron in the energy intensity diagram of the iron ore composition; calculating the active characteristic value of each element characteristic peak; and then obtaining the X-ray absorption intensity factor of each element characteristic peak; calculating the matrix interference characteristic value of each element characteristic peak; based on the difference between the matrix interference characteristic value and the photon energy of the peak of the element characteristic peak, obtaining the corrected count rate of the peak of each element characteristic peak, and determining and correcting the composition of the iron ore. The present application reduces the error caused by the matrix effect and improves the accuracy of X-ray fluorescence spectroscopy analysis.

Description

Technical Field

[0001] The present application relates to the technical field of iron ore composition determination, and in particular to a method and system for determining iron ore composition based on spectroscopy technology. Background Art

[0002] The application of spectroscopy in the determination of iron ore composition is an important branch of modern analytical chemistry. It uses the absorption, emission or scattering characteristics of light to determine the composition and content of a substance. With the development of technology, the determination of iron ore composition has become faster and more accurate. Spectroscopy is of great significance in the determination of iron ore composition. It provides a scientific basis for the exploration and mining of iron ore, and helps geologists and mining engineers evaluate the quality and value of ore deposits. Secondly, accurate composition analysis is crucial for the processing and utilization of iron ore, which directly affects the efficiency of the smelting process and the quality of the final product.

[0003] When detecting other elements or compounds present in the sample, the elements or compounds will interfere with the spectral signal of the target element. This interference may change the number of excited atoms or ions of the target element through physical or chemical processes, thereby affecting the intensity of its spectral signal and forming a matrix effect. The matrix effect can occur in many ways, such as spectral line overlap, particle size effect, physical adsorption or chemical adsorption, etc. The influence of the matrix effect is manifested as a deviation in the analysis results, which may lead to an overestimation or underestimation of the element content. Iron ore usually contains a variety of minerals, including iron oxides, silicates, carbonates, etc. In addition to iron, it may also contain silicon, aluminum, calcium, magnesium, sulfur, phosphorus and other elements. Due to the complex composition of iron ore, the chemical composition and physical properties of different ores may vary greatly. This complexity leads to relatively severe matrix interference during analysis, thereby affecting the accuracy of the analysis results. When dealing with matrix effects in the prior art, the mutual influence between different matrices and the differences in the degree of influence are ignored, resulting in errors in the detection of element content in the sample. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and system for determining iron ore composition based on spectral technology. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for determining the composition of iron ore based on spectroscopy technology, the method comprising the following steps:

[0006] Collecting energy intensity graphs of photon energy versus count rate for iron ore samples;

[0007] Combining the relationship between the energy intensity diagram and the energy table of characteristic X-ray spectral lines of each element, a characteristic peak recognition algorithm is used to obtain the element characteristic peaks of each element except iron in the energy intensity diagram of the iron ore composition;

[0008] Based on the number of minimum values ​​and the difference between the minimum values ​​in the local area where the characteristic peak of the element is located, the active characteristic value of the characteristic peak of each element is obtained;

[0009] Based on the discrete degree of the count rate in the adjacent local area of ​​the element characteristic peak and the active characteristic value of the element characteristic peak, the X-ray absorption intensity factor of each element characteristic peak is obtained;

[0010] Construct multiple neighborhoods of the peak points of the characteristic peaks of each element;

[0011] Based on the change trend of the data in the neighborhood of the element characteristic peak, the change trend between the endpoint data in the neighborhood, the difference between the photon energy at both ends in the neighborhood of the element characteristic peak and the X-ray absorption intensity factor, the matrix interference characteristic value of each element characteristic peak is obtained;

[0012] Based on the difference between the matrix interference characteristic value and the photon energy of the peak value of the element characteristic peak, the corrected count rate of the peak value of each element characteristic peak is obtained to correct the iron ore composition.

[0013] Further, the method for obtaining the characteristic peak of the element is:

[0014] The characteristic peak recognition algorithm is used to identify the characteristic peaks in the energy intensity diagram, and the energy intensity diagram is compared with the energy table of characteristic X-ray spectrum lines of each element to determine the iron ore composition of each characteristic peak;

[0015] The characteristic peaks corresponding to the elements other than iron in the iron ore composition in the energy intensity diagram are taken as the characteristic peaks of each element.

[0016] Furthermore, the method for obtaining the active feature value is:

[0017] For each element characteristic peak, division is performed at the peak points of two characteristic peaks adjacent to the left and right of the element characteristic peak to obtain the local energy intensity area of ​​each element characteristic peak;

[0018] Using a minimum value detection method to obtain all minimum value points in the local energy intensity region of the characteristic peak of each element, and calculating the difference between the photon energies of any two minimum value points as the first difference;

[0019] For the local energy intensity region of each element's characteristic peak, the average of all the first differences is calculated, and the product of the average and the number of all minimum points in the local energy intensity region is calculated as the active characteristic value of each element's characteristic peak.

[0020] Furthermore, the method for obtaining the X-ray absorption intensity factor is:

[0021] Calculate the range of all count rates in the local energy intensity region of the characteristic peak of each element as the first range;

[0022] The product of the first extreme difference and the active characteristic value is calculated as the X-ray absorption intensity factor of the characteristic peak of each element.

[0023] Furthermore, the construction of multiple neighborhoods of the peak points of the characteristic peaks of each element includes:

[0024] The peak point of each characteristic peak of each element in the energy intensity diagram is obtained, and a neighborhood with a preset side length is constructed with the peak point as the center. For the neighborhood of each peak point, the neighborhood is increased from the left and right sides of the neighborhood at the same time according to the preset step size each time to obtain the neighborhood after each increase of the peak point.

[0025] Furthermore, the method for obtaining the matrix interference characteristic value is:

[0026] Based on the change trend of the data in the neighborhood of the element characteristic peak and the change trend between the endpoint data in the neighborhood, the endpoint slope and the fitting slope of each element characteristic peak are obtained;

[0027] Based on the difference between the endpoint slope and the fitting slope and the difference between the photon energies at both ends in the neighborhood of the element characteristic peak, the matrix masking characteristic value of each element characteristic peak is obtained;

[0028] The matrix interference characteristic value of the characteristic peak of each element is obtained based on the X-ray absorption intensity factor and the matrix masking characteristic value.

[0029] Furthermore, the method for obtaining the endpoint slope and the fitting slope is:

[0030] For the neighborhood after each growth of the peak point, the slope of the line between the leftmost data point and the rightmost data point of the neighborhood is calculated as the endpoint slope; a straight line fitting algorithm is used to fit all data points in the neighborhood to obtain a fitting straight line and its slope, and the slope of the fitting straight line is used as the fitting slope.

[0031] Furthermore, the method for obtaining the matrix masking characteristic value is:

[0032] The absolute value of the difference between the endpoint slope and the fitting slope is calculated as the first absolute value of the difference. When the first absolute value of the difference is greater than a preset difference threshold, the neighborhood of the peak point stops growing. The absolute value of the difference between the photon energy of the leftmost data point and the photon energy of the rightmost data point in the neighborhood after the peak point stops growing is calculated as the second absolute value of the difference.

[0033] The product of the first absolute value of the difference and the second absolute value of the difference is calculated as the matrix masking characteristic value of the characteristic peak of each element.

[0034] Further, the corrected count rate is: , ; In the formula, is the corrected count rate of the characteristic peak value of the jth element; represents the count rate of the peak value of the characteristic peak of the jth element, are the matrix interference characteristic values ​​of the pth element characteristic peak, D is the number of element characteristic peaks, is the absolute value of the difference in photon energy between the peak value of the characteristic peak of the jth element and the peak value of the characteristic peak of the pth element, L is the adjustment function, is the sigmoid function, which is used to normalize the value. is the preset adjustment factor, and Z is the preset energy threshold.

[0035] In a second aspect, an embodiment of the present application also provides an iron ore composition determination system based on spectral technology, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0036] This application has at least the following beneficial effects:

[0037] The present application proposes a method and system for determining the composition of iron ore based on spectral technology, which uses an XRF spectrometer to collect energy intensity diagrams, and uses a characteristic peak recognition algorithm and a minimum detection algorithm to reflect the degree of substrate fluctuation and substrate active characteristics in order to solve the problem of elemental analysis errors caused by matrix effects in iron ore samples, and solve the problem of interference of matrix effects on X-ray fluorescence signals. In order to solve the problem that characteristic peak signals may be affected by matrix masking, matrix masking characteristic values ​​are used to calculate the intensity and range of the matrix masking effect, and the influence of matrix masking on characteristic peak signals is eliminated. By establishing a correction model based on energy differences and matrix interference characteristic values, the influence of matrix interference between different elements is analyzed, so as to achieve the beneficial effects of reducing errors caused by matrix effects and improving the accuracy of X-ray fluorescence spectral analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 A flowchart of a method for determining the composition of iron ore based on spectroscopy technology provided in one embodiment of the present application;

[0040] Figure 2 A flowchart for obtaining a matrix interference characteristic value provided by an embodiment of the present application;

[0041] Figure 3 A photon energy-count rate energy intensity diagram provided for one embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the iron ore composition determination method and system based on spectral technology proposed in the present application, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0044] The following is a detailed description of a method and system for determining the composition of iron ore based on spectroscopy technology provided by the present application with reference to the accompanying drawings.

[0045] See also Figure 1 , which shows a flow chart of the steps of a method for determining the composition of iron ore based on spectroscopy technology provided by an embodiment of the present application, the method comprising the following steps:

[0046] Step S1, collecting an energy intensity diagram of photon energy-count rate of an iron ore sample.

[0047] The iron ore sample is crushed and ground to a particle size of 200 mesh or less, and then the ground iron ore sample is treated with a mixture of hydrochloric acid or nitric acid and heated to 80°C for dissolution to ensure that the iron and other elements are completely dissolved. The dissolved sample is cooled to room temperature and the treated sample is placed in an XRF spectrometer to excite the elements in the sample to emit characteristic X-rays. The XRF spectrometer can qualitatively and quantitatively analyze the elemental composition in the sample by measuring the energy and intensity of these X-rays, thereby outputting an energy intensity diagram of the photon energy-count rate of the iron ore sample. The photon energy-count rate energy intensity diagram is shown in the figure. Figure 3 As shown, the horizontal axis is the photon energy intensity (KeV), and the vertical axis is the count rate per second (cps). Figure 3 The peak points in the graph represent the elements in the iron ore composition, such as Ar, Ca, Fe, etc.

[0048] Step S2, combining the relationship between the energy intensity diagram and the energy table of the characteristic X-ray spectral lines of each element, using the characteristic peak recognition algorithm to obtain the element characteristic peaks of each element except iron in the energy intensity diagram of the iron ore composition; based on the number of minimum values ​​in the local area where the element characteristic peak is located and the difference between the minimum values, obtaining the active characteristic value of each element characteristic peak; based on the discrete degree of the counting rate in the adjacent local area of ​​the element characteristic peak and the active characteristic value of the element characteristic peak, obtaining the X-ray absorption intensity factor of each element characteristic peak.

[0049] When performing X-ray fluorescence spectrometry, because atoms of different elements exhibit different characteristics when absorbing and scattering X-rays, the matrix effect mainly refers to the influence of the main elements (matrix) in the sample on the X-ray fluorescence signal of the analyzed element, which includes enhancing or weakening the signal of the analyzed element. Specifically, when X-rays interact with the sample, the matrix elements may absorb part of the X-rays, reduce the number of X-rays reaching the analyzed element, or change the path of the X-rays through scattering, thereby affecting the fluorescence signal intensity of the analyzed element.

[0050] Matrix effect mainly refers to the influence of other components (matrix) in the sample other than the element to be measured on the analysis results. These influences are usually directly related to the measurement of the element to be measured, but the matrix effect is not limited to affecting only the element to be measured, it will also produce mutual influence between matrix components.

[0051] For the energy intensity diagram obtained, the characteristic peaks of the spectrum in the energy intensity diagram are identified by the characteristic peak recognition algorithm. The horizontal coordinates of the output characteristic peaks represent the photon energy of different elements. The energy intensity diagram is compared with the energy table of characteristic X-ray spectrum lines of each known element to determine the qualitative and quantitative analysis results of the iron ore composition of each characteristic peak.

[0052] The characteristic peaks corresponding to the elements other than iron in the iron ore composition in the energy intensity diagram are taken as the characteristic peaks of each element; for each characteristic peak of each element, the peak values ​​of the two characteristic peaks adjacent to the left and right of the element characteristic peak are divided to obtain the local energy intensity area of ​​each characteristic peak of each element.

[0053] Furthermore, the base fluctuation between the two characteristic peaks on the left and right sides of the characteristic peak of each element in the local energy intensity area of ​​the characteristic peak of each element is analyzed. The base fluctuation is usually caused by the inhomogeneity of the matrix composition, the interaction between different components, and the secondary radiation generated by the interaction between X-rays and the sample.

[0054] According to the above analysis, the spectrum in the local energy intensity region of the characteristic peak of each element is processed using the minimum detection method to obtain all the minimum points of the spectrum in each local energy intensity region, and the difference between the photon energies of any two minimum points is calculated as the first difference. The range of all count rates of the spectrum in the local energy intensity region of the characteristic peak of each element is calculated as the first range.

[0055] Furthermore, in order to reflect the activity of the elements in the substrate in the local energy intensity region of the characteristic peak of each element, the active characteristic value b of the characteristic peak of each element is calculated, and the calculation relationship is: ; Where b is the active characteristic value of each element's characteristic peak; m is the number of minimum points in the local energy intensity region of each element's characteristic peak, is the average of all the first differences in the local energy intensity region of the characteristic peak of each element.

[0056] It should be noted that the number of minimum points in the local energy intensity region of the element characteristic peak represents the period of the average energy change of the interaction between the elements in the substrate. The active eigenvalue evaluates the activity of the elements in the substrate and their potential impact on the X-ray fluorescence signal. The larger the active eigenvalue, the stronger the activity and interaction of the elements in the substrate, which increases the complexity and uncertainty of the X-ray fluorescence signal.

[0057] Furthermore, in order to evaluate the intensity of the matrix effect caused by the matrix represented by the characteristic peak of each element, the X-ray absorption intensity factor A of the matrix element near the characteristic peak of the element is calculated based on the fluctuation degree of the substrate near the characteristic peak of the element and the active characteristic value b of the substrate near the characteristic peak of the element. The calculation formula is: ; In the formula, A is the X-ray absorption intensity factor of the characteristic peak of each element, a is the first extreme difference, and b is the active characteristic value of the characteristic peak of each element.

[0058] Among them, the first range reflects the degree of fluctuation of the substrate near the characteristic peak of each element. It is used to quantify the degree of substrate fluctuation. It is an indicator to measure the substrate inhomogeneity or the secondary radiation intensity generated by the interaction between X-rays and samples. It reflects the comprehensive effect of matrix elements in the sample on X-ray absorption and scattering. This effect reflects the influence on the intensity and accuracy of the X-ray fluorescence signal. The larger the value of the first range, the more violent the substrate fluctuation, which leads to increased interference in the X-ray fluorescence signal. Therefore, the larger the value of the first range, the larger the value of the X-ray absorption intensity factor will be, reflecting a stronger matrix effect; the larger the active characteristic value, the larger the value of the X-ray absorption intensity factor will be, reflecting a stronger matrix effect, that is, the greater the interference to the X-ray fluorescence signal; conversely, the smaller the interference to the X-ray fluorescence signal.

[0059] Step S3, constructing the neighborhood of each element characteristic peak after each growth of the peak point; based on the change trend of the data in the neighborhood of the element characteristic peak, the change trend between the endpoint data of the neighborhood, the difference between the photon energy at both ends of the neighborhood of the element characteristic peak and the X-ray absorption intensity factor, obtaining the matrix interference characteristic value of each element characteristic peak.

[0060] In addition to the fluctuation of the substrate near the characteristic peak, the transition region between the substrate and the characteristic peak can also provide information about the sample matrix effect. If the transition between the substrate and the characteristic peak is very smooth, it means that the characteristic peak is not masked by significant background noise or matrix absorption. If there is an obvious jump or sharp transition between the substrate and the characteristic peak, it means that the characteristic peak may be affected by matrix absorption or enhancement.

[0061] The peak point of each characteristic peak of the spectrum in the energy intensity diagram is obtained, a neighborhood with a side length of 2 is constructed with the peak point as the center, and the neighborhood of each peak point is increased from the left and right sides of the neighborhood at the same time with a step size of N each time to obtain the neighborhood of the peak point after each increase; in this embodiment, the value of N is 1, and the implementer can select other values ​​according to actual conditions.

[0062] For the neighborhood after each growth of the peak point, the slope of the line between the leftmost data point and the rightmost data point of the neighborhood is calculated as the endpoint slope; further, for the neighborhood after each growth of the peak point, a straight line fitting algorithm is used to fit all data points in the neighborhood to obtain a fitting straight line and the slope of the fitting straight line, and the slope of the fitting straight line is used as the fitting slope. The straight line fitting algorithm includes the least squares method, the gradient descent method, etc. This embodiment uses the least squares method for straight line fitting, and the implementer can select other straight line fitting algorithms according to actual conditions.

[0063] Furthermore, the absolute value of the difference between the endpoint slope and the fitting slope is calculated as the first absolute value of the difference, and a difference threshold is set. In this embodiment, the value of the difference threshold is 0.3, and the implementer can select other values ​​according to actual conditions; when the first absolute value of the difference is greater than the difference threshold, the neighborhood of the peak point stops growing. At this time, it is considered that there is an obvious jump or sharp transition between the base and the characteristic peak. The absolute value of the difference between the photon energy of the leftmost data point and the photon energy of the rightmost data point in the neighborhood after the peak point stops growing is calculated as the second absolute value of the difference.

[0064] Furthermore, in order to reflect the degree of matrix masking of characteristic peak signals, the matrix masking characteristic value of the characteristic peak of each element is calculated, and the calculation formula is: ; Where c is the matrix masking characteristic value of the characteristic peak of each element, is the absolute value of the first difference of the characteristic peak values ​​of each element, It is the absolute value of the second difference of the characteristic peak value of each element.

[0065] It should be noted that the absolute value of the first difference reflects the steepness of the transition between the substrate and the characteristic peak, and the absolute value of the second difference reflects the range of energy variation. The two are multiplied to quantify the intensity and range of the matrix masking effect, so as to more accurately evaluate the influence of the matrix on the characteristic peak signal. When the absolute value of the first difference is smaller, the steepness of the transition between the substrate and the characteristic peak is smaller. When the absolute value of the second difference is smaller, the range of energy variation is smaller. At this time, the matrix masking characteristic value is smaller, and the degree of matrix masking of the characteristic peak signal is smaller; conversely, the matrix masking characteristic value is larger, and the degree of matrix masking of the characteristic peak signal is greater.

[0066] Furthermore, in order to reflect the comprehensive influence of the matrix effect on the characteristic peak signal, the matrix interference characteristic value B of the element represented by the element characteristic peak is calculated based on the X-ray absorption intensity factor A of the substrate element near the element characteristic peak and the matrix masking characteristic value c of the element characteristic peak. The specific calculation formula is: ; Wherein, B is the matrix interference characteristic value of each element characteristic peak; A is the X-ray absorption intensity factor of each element characteristic peak, and c is the matrix masking characteristic value of each element characteristic peak. The flowchart for obtaining matrix interference characteristic value is as follows: Figure 2 shown.

[0067] In the formula, the value of the X-ray absorption intensity factor reflects the comprehensive effect of the matrix elements in the sample on X-ray absorption and scattering. The larger the value of the X-ray absorption intensity factor, the stronger the matrix effect. Therefore, the matrix interference characteristic value B will also increase accordingly, reflecting a stronger matrix interference. On the contrary, the matrix interference characteristic value B decreases, and the matrix interference is weaker.

[0068] The matrix masking characteristic value reflects the degree of matrix masking on the characteristic peak signal, that is, the influence of matrix absorption or enhancement. The larger the matrix masking characteristic value, the stronger the matrix masking effect on the characteristic peak signal, thereby increasing the uncertainty and complexity of the X-ray fluorescence signal, and the larger the matrix interference characteristic value will also be.

[0069] The matrix interference characteristic value describes the comprehensive influence of the matrix effect on the characteristic peak signal, including matrix fluctuation, interaction between elements and matrix masking effect. The larger the matrix interference characteristic value, the stronger the interference of the matrix effect on the characteristic peak signal, and the more likely it is to cause the weakening or distortion of the characteristic peak signal, thereby affecting the accuracy and reliability of X-ray fluorescence spectrometry analysis.

[0070] At this point, the matrix interference characteristic values ​​of the element characteristic peaks of the elements other than iron are obtained.

[0071] Step S4, based on the difference between the matrix interference characteristic value and the photon energy of the peak value of the element characteristic peak, the corrected count rate of the peak value of each element characteristic peak is obtained to correct the iron ore composition.

[0072] In X-ray fluorescence spectrometry (XRF) analysis, the correction of matrix effects can ensure the accuracy of the analysis results. Here, a specific correction model is constructed to eliminate or reduce the influence of matrix effects. When the atomic number of the matrix element is similar to that of the analyzed element, the characteristic peak intensity of the secondary element may be enhanced, while when the atomic number of the matrix element is significantly different from that of the analyzed element, the characteristic peak intensity of the secondary element may be suppressed.

[0073] For the characteristic peak of the jth element, its matrix interference characteristic value is , calculate the energy difference between the peak value of the characteristic peak of the jth element and the peak value of the characteristic peak of the pth element , set the energy threshold Z, in this embodiment, the value of Z is 10, and establish a correction model for the characteristic peak of the jth element. The correction model relationship is: , ;in, represents the raw count rate of the characteristic peak-to-peak value of the jth element, is the corrected count rate of the characteristic peak-to-peak value of the jth element, are the matrix interference characteristic values ​​of the pth element characteristic peak, D is the number of element characteristic peaks, p is the summation index, is the absolute value of the difference in photon energy between the characteristic peak of the jth element and the characteristic peak of the pth element, L is the adjustment function, when the energy difference is greater than or equal to the energy threshold, L is 1, indicating that the matrix effect near the characteristic peak of the pth element is far away from the characteristic peak of the ith element, suppressing the intensity of the characteristic peak of the ith element, thereby amplifying the intensity of the characteristic peak of the ith element, when the energy difference is less than the threshold, L is , similarly, it means that the matrix effect near the characteristic peak of the pth element amplifies the intensity of the characteristic peak of the ith element, thereby suppressing the intensity of the characteristic peak of the ith element. is the sigmoid function, which is used to normalize the value. is a preset adjustment factor, which in this embodiment is set to 3 and is used to adjust the influence of matrix effects of different matrices on the characteristic peak of the jth element.

[0074] The established correction model takes into account the mutual influence of the substrate effects near all characteristic peaks, where L adjusts the influence weight of each element characteristic peak according to the size of the energy difference. Subsequently, the established correction model is used for all element characteristic peaks. By correcting the matrix effect, the model can provide more accurate element content measurements and reduce the errors caused by the matrix effect.

[0075] Based on the same inventive concept as the above method, an embodiment of the present application also provides an iron ore composition determination system based on spectral technology, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned iron ore composition determination methods based on spectral technology are implemented.

[0076] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0077] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0078] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for determining iron ore composition based on spectral technology, characterized in that: The method comprises the following steps: Collecting energy intensity graphs of photon energy versus count rate for iron ore samples; Combining the relationship between the energy intensity diagram and the energy table of characteristic X-ray spectral lines of each element, a characteristic peak recognition algorithm is used to obtain the element characteristic peaks of each element except iron in the energy intensity diagram of the iron ore composition; Based on the number of minimum values ​​and the difference between the minimum values ​​in the local area where the characteristic peak of the element is located, the active characteristic value of the characteristic peak of each element is obtained; Based on the discrete degree of the count rate in the adjacent local area of ​​the element characteristic peak and the active characteristic value of the element characteristic peak, the X-ray absorption intensity factor of each element characteristic peak is obtained; Construct multiple neighborhoods of the peak points of the characteristic peaks of each element; Based on the change trend of the data in the neighborhood of the element characteristic peak, the change trend between the endpoint data in the neighborhood, the difference between the photon energy at both ends in the neighborhood of the element characteristic peak and the X-ray absorption intensity factor, the matrix interference characteristic value of each element characteristic peak is obtained; Based on the difference between the matrix interference characteristic value and the photon energy of the peak value of the element characteristic peak, the corrected count rate of the peak value of each element characteristic peak is obtained to correct the iron ore composition; The corrected count rate is: , ; In the formula, is the corrected count rate of the characteristic peak value of the jth element; represents the count rate of the peak value of the characteristic peak of the jth element, is the matrix interference characteristic value of the pth element characteristic peak, D is the number of element characteristic peaks, is the absolute value of the difference in photon energy between the peak value of the characteristic peak of the jth element and the peak value of the characteristic peak of the pth element, L is the adjustment function, is the sigmoid function, which is used to normalize the value. is the preset adjustment factor, and Z is the preset energy threshold.

2. The method for determining the composition of iron ore based on spectroscopy technology according to claim 1, characterized in that: The method for obtaining the characteristic peak of the element is: The characteristic peak recognition algorithm is used to identify the characteristic peaks in the energy intensity diagram, and the energy intensity diagram is compared with the energy table of characteristic X-ray spectrum lines of each element to determine the iron ore composition of each characteristic peak; The characteristic peaks corresponding to the elements other than iron in the iron ore composition in the energy intensity diagram are taken as the characteristic peaks of each element.

3. The method for determining the composition of iron ore based on spectroscopy technology according to claim 1, characterized in that: The method for obtaining the active feature value is: For each element characteristic peak, division is performed at the peak points of two characteristic peaks adjacent to the left and right of the element characteristic peak to obtain the local energy intensity area of ​​each element characteristic peak; Using a minimum value detection method to obtain all minimum value points in the local energy intensity region of the characteristic peak of each element, and calculating the difference between the photon energies of any two minimum value points as the first difference; For the local energy intensity region of each element's characteristic peak, the average of all the first differences is calculated, and the product of the average and the number of all minimum points in the local energy intensity region is calculated as the active characteristic value of each element's characteristic peak.

4. The method for determining the composition of iron ore based on spectroscopy technology according to claim 3, characterized in that: The method for obtaining the X-ray absorption intensity factor is: Calculate the range of all count rates in the local energy intensity region of the characteristic peak of each element as the first range; The product of the first extreme difference and the active characteristic value is calculated as the X-ray absorption intensity factor of the characteristic peak of each element.

5. The method for determining the composition of iron ore based on spectroscopy technology according to claim 1, characterized in that: The step of constructing multiple neighborhoods of the peak points of the characteristic peaks of each element includes: The peak point of each characteristic peak of each element in the energy intensity diagram is obtained, and a neighborhood with a preset side length is constructed with the peak point as the center. For the neighborhood of each peak point, the neighborhood is increased from the left and right sides of the neighborhood at the same time according to the preset step size each time to obtain the neighborhood after each increase of the peak point.

6. The method for determining the composition of iron ore based on spectroscopy technology according to claim 1, characterized in that: The method for obtaining the matrix interference characteristic value is: Based on the change trend of the data in the neighborhood of the element characteristic peak and the change trend between the endpoint data in the neighborhood, the endpoint slope and the fitting slope of each element characteristic peak are obtained; Based on the difference between the endpoint slope and the fitting slope and the difference between the photon energies at both ends in the neighborhood of the element characteristic peak, the matrix masking characteristic value of each element characteristic peak is obtained; The matrix interference characteristic value of the characteristic peak of each element is obtained based on the X-ray absorption intensity factor and the matrix masking characteristic value.

7. The method for determining the composition of iron ore based on spectroscopy technology according to claim 6, characterized in that: The method for obtaining the endpoint slope and the fitting slope is: For the neighborhood after each growth of the peak point, the slope of the line between the leftmost data point and the rightmost data point of the neighborhood is calculated as the endpoint slope; a straight line fitting algorithm is used to fit all data points in the neighborhood to obtain a fitting straight line and its slope, and the slope of the fitting straight line is used as the fitting slope.

8. The method for determining the composition of iron ore based on spectroscopy technology according to claim 6, characterized in that: The method for obtaining the matrix masking characteristic value is: The absolute value of the difference between the endpoint slope and the fitting slope is calculated as the first absolute value of the difference. When the first absolute value of the difference is greater than a preset difference threshold, the neighborhood of the peak point stops growing. The absolute value of the difference between the photon energy of the leftmost data point and the photon energy of the rightmost data point in the neighborhood after the peak point stops growing is calculated as the second absolute value of the difference. The product of the first absolute value of the difference and the second absolute value of the difference is calculated as the matrix masking characteristic value of the characteristic peak of each element.

9. A system for determining iron ore composition based on spectroscopy technology, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for determining the composition of iron ore based on spectroscopy technology as described in any one of claims 1 to 8 are implemented.

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