Spectrum screening method and device for deep sea rare earth element LIBS quantitative detection and storage medium

Abstract

The invention provides a spectrum screening method and device for deep sea rare earth element LIBS quantitative detection and a storage medium, rare earth sample LIBS spectrums under different conditions are collected, and the spectrums are mapped to a CIE L * a * b * color space in combination with spectral intensity and a CIE standard chromaticity observer spectrum tristimulus value function to realize characterization of plasma colors. Quantitative analysis is carried out on LIBS spectrums under different conditions, the condition with the optimal quantitative effect is taken as a standard, a central area of a spectrum mapping point at the moment is delimited, the spectrum mapping points under other conditions are constrained by utilizing the standard area, spectrum screening based on plasma luminescence characteristics is realized, and the linear fitting degree of a spectrum calibration curve under other conditions is improved. According to the method, a complicated data processing means is not needed, the mapping from the spectrum to the color space can be realized only by combining a mathematical formula for operation, the operation time is short, the required data volume is small, the applicability is high, and a result has an actual physical meaning and has interpretability.

Description

Technical Field

[0001] The present invention belongs to the technical field of rare earth spectral element analysis and identification, and in particular relates to a spectral screening method, equipment and storage medium for LIBS quantitative detection of deep-sea rare earth elements. Background Art

[0002] Rare earth elements (REEs) are a general term for 15 lanthanide elements and two transition metals (scandium and yttrium), which have been widely used in advanced technology and manufacturing. Among them, deep-sea rare earth resources have great potential. "Deep-sea rare earth-rich deposits" are produced in deep-sea basins. The total amount of rare earths is generally greater than 700μg / g. The highest known amount is 8000μg / g, which is more than 2000 times the rare earth reserves on land. In recent years, it has become the focus of attention of various countries. It is estimated that by 2035, the demand for rare earth resources will increase to 70,900 tons. As the second country in the world to conduct deep-sea rare earth surveys and research, my country urgently needs to develop new technologies and methods for deep-sea rare earth resource detection and detection to provide more convenience and guarantee for future resource exploitation.

[0003] Laser-induced breakdown spectroscopy (LIBS) is an emerging elemental analysis technology based on plasma radiation in recent years. It uses high-energy lasers to converge through lenses and act on samples to ablate the sample surface to form plasma, obtain plasma spectral data, and then achieve qualitative and quantitative analysis of elements in the sample. LIBS has the advantages of no need for sample pretreatment, on-site real-time, and pollution-free. It has been widely used in nuclear fusion, cultural relics archaeology, environmental monitoring, metallurgy and other fields. Therefore, LIBS technology is fully capable of conducting deep-sea rare earth exploration.

[0004] LIBS quantitative analysis is a method to infer the concentration of an element in a sample by detecting the spectral intensity of the characteristic spectral line of the target element. The core principle is that the intensity of the characteristic spectral line of the target element is proportional to the concentration of the element. In this method, it is first necessary to measure the characteristic spectral intensity of a series of standard samples of known concentrations, draw a curve of the relationship between spectral intensity and concentration, that is, the calibration curve, and perform linear fitting to obtain a mathematical model of spectral intensity and element concentration. Subsequently, for unknown samples, the concentration of the element can be inferred by measuring its spectral intensity and substituting it into the calibration curve. However, LIBS quantitative analysis requires high accuracy of the calibration curve, and this process may be affected by multiple factors, such as matrix effects (interference of sample composition and its physical state on measurement), fluctuations in plasma temperature and electron density, etc. These factors may lead to measurement errors, thereby affecting the accuracy of quantitative results. Therefore, LIBS quantitative analysis often needs to be combined with signal optimization, data screening and other methods to minimize errors and improve the reliability and accuracy of analysis.

[0005] To address the problem of decreased accuracy of quantitative analysis due to fluctuations in the plasma state, existing methods use machine learning or deep learning techniques to optimize the fit of the calibration curve through complex algorithms, making the relationship model between spectral intensity and element concentration more accurate. However, this method requires a large spectral data set and a long running time, consumes a lot of computing resources, and the training process is complex, requiring high equipment performance and data processing capabilities. In addition, these algorithms fail to fully reflect the physical properties of LIBS spectra, such as the color of plasma, from the essence, and are not universal and interpretable. Summary of the invention

[0006] In view of the above problems, the present invention discloses a CIE L*a*b* data screening method for deep-sea rare earth element LIBS quantification. The plasma emission spectrum, that is, the coordinate point of the CIE L*a*b* color space corresponding to the LIBS spectrum, is calculated according to the formula to achieve the characterization of the plasma color characteristics. Since the detection conditions have a significant impact on the quantitative analysis, the LIBS spectrum under the optimal quantitative conditions is selected, and its area in the color space is defined as the standard area. The spectra under different conditions are screened to improve the fitting degree of the rare earth element calibration curve after the condition fluctuations.

[0007] The first aspect of the present invention provides a spectral screening method for LIBS quantitative detection of deep-sea rare earth elements, comprising the following process:

[0008] Step 1: Based on the constructed LIBS detection system, LIBS spectrum data of multiple groups of rare earth samples with target element concentration gradients under different detection delays of the spectrometer are obtained, and each delay condition includes a total of K LIBS spectrum data of multiple groups of rare earth samples;

[0009] Step 2: Establish an element calibration curve for the acquired LIBS spectral data, and find a total of K LIBS spectral data of multiple groups of rare earth samples under the detection delay condition with the best quantitative effect;

[0010] Step 3, based on the K pieces of LIBS spectrum data under the detection delay condition with the best quantitative effect, calculate the CIE L*a*b* value corresponding to each piece of spectrum data, so as to obtain the coordinate set of the spectrum mapping point;

[0011] Step 4, based on the spectral mapping point coordinate set obtained in step 3, a standard area is defined according to the center of the point set;

[0012] Step 5: According to the standard area, the LIBS spectral data under other delay conditions are converted to the CIEL*a*b* color space according to step 3 to obtain the coordinates of the spectral data mapping points under other delay conditions. The spectral data falling outside the standard area are screened out.

[0013] Preferably, the constructed LIBS detection system includes a pulsed laser, a sample platform, a spectral signal receiving system, a spectral detection system, a timing control system and a computer control system;

[0014] A reflector is installed at the pulse laser light outlet to guide the pulse laser into the subsequent light path;

[0015] The sample platform is a three-dimensional moving platform, on which the sample pressed into a sheet is placed, and the laser is focused on the sample surface through the microscope objective lens after passing through the dichroic mirror;

[0016] The spectral signal receiving system includes a lens and an optical fiber probe. The spectral signal receiving system adopts backward collection. The light emitted by the plasma is reflected by the dichroic mirror. The lens converges the LIBS spectral signal into the optical fiber probe and connects the spectral detection system through an optical fiber. The spectral detection system is controlled by a timing control system and a computer control system.

[0017] The spectrum detection system is realized by a spectrometer;

[0018] The timing control system is implemented by a PIN tube; a small portion of the laser reaches the PIN tube through a reflector, and the PIN tube responds and triggers the spectrometer to collect data;

[0019] The computer control system is connected to the spectrum detection system and is used to set spectrometer parameters and display the LIBS spectrum of the collected samples.

[0020] Preferably, the method of acquiring LIBS spectral data of multiple groups of rare earth samples under different detection delays of the spectrometer is to continuously collect LIBS spectra of p laser pulses at each point of each group of samples under different detection delays of the spectrometer, collect m points in total, and obtain multiple LIBS spectra for each group of samples in total, thereby realizing the acquisition of LIBS spectra of samples under different delays.

[0021] Preferably, the calibration curve in step 2 is a regression curve of characteristic spectral line intensity and element content established by using a series of standard samples. First, according to the acquired spectral data, the characteristic spectral line of the rare earth element is selected, and the element content is used as the horizontal coordinate and the intensity of the characteristic spectral line of the element at different contents is used as the vertical coordinate. A linear fit is performed to establish a calibration curve of a total of K LIBS spectral data of multiple groups of samples under each detection delay and calculate the determination coefficient R 2 The maximum value is 1 and the minimum is 0. The closer the value is to 1, the better the model is. K LIBS spectral data under the detection delay condition closest to 1 are selected.

[0022] Preferably, the specific process of step 3 is:

[0023] Convert the spectral data to the CIE L*a*b* color space using the following calculation formula:

[0024]

[0025] In the formula, X, Y, and Z are the three stimulus values ​​of the CIE1931 standard colorimetric system, X n , Y n , Z n is the tristimulus value of the CIE standard illuminant irradiated on a completely diffuse reflector and then reflected into the observer's eyes, I(λ) is the relative spectral power distribution of the light source radiation, k is the normalization coefficient, is the CIE1931 standard colorimetric observer spectrum tristimulus value, Δλ is the wavelength interval; "L*" represents lightness, indicating colors from black to white; "a*" represents green-red color saturation, "+a*" represents red, and "-a*" represents green; "b*" represents blue-yellow color saturation, "+b*" represents yellow, and "-b*" represents blue;

[0026] According to the calculation formula, the 380-780nm visible light band of the LIBS spectrum was selected, and MATLAB was used for interpolation to obtain the spectral line intensity values ​​at a certain wavelength interval. Combined with the spectral tristimulus values, the XYZ tristimulus values ​​were solved to obtain the L*a*b* values ​​in the CIE L*a*b* color space, and the mapping of LIBS spectral data to the CIE L*a*b* color space was realized. The high-dimensional spectral data was converted into three-dimensional color features, and the coordinate set of the spectral data mapping points was obtained.

[0027] Preferably, the specific process of step 4 is:

[0028] Calculate the center point position of the spectral mapping points of samples with different element concentrations, that is, solve the average values ​​of the coordinate points according to the three dimensions respectively, and calculate the Euclidean distance between each point and the center point; sort the distances from small to large, and retain the spectral mapping points corresponding to the first 1 / e distances. The area covered by this part of the point set is the standard area.

[0029] The second aspect of the present invention provides a spectral screening device for LIBS quantitative detection of deep-sea rare earth elements, the device comprising at least one processor and at least one memory, the processor and the memory being coupled; a computer execution program is stored in the memory; when the processor executes the computer execution program stored in the memory, the processor executes the spectral screening method for LIBS quantitative detection of deep-sea rare earth elements as described in the first aspect.

[0030] The third aspect of the present invention provides a computer-readable storage medium, which stores a computer program or instruction. When the program or instruction is executed by a processor, the processor executes the spectral screening method for LIBS quantitative detection of deep-sea rare earth elements as described in the first aspect.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The present invention proposes a method for improving the accuracy of LIBS quantitative analysis of deep-sea rare earth elements. The plasma color is characterized by introducing the CIE L*a*b* color space, and the linear fit of the calibration curve can be improved by screening using the standard area. The method does not require complex calculations, requires a small amount of data, and can screen the spectrum from the perspective of the physical characteristics of plasma luminescence, thereby improving the quantitative analysis capability of LIBS.

[0033] The present invention collects LIBS spectra of rare earth samples under different conditions, combines the spectral intensity with the CIE standard colorimetric observer spectrum tristimulus value function, maps the spectrum to the CIE L*a*b* color space, and realizes the characterization of plasma color. The LIBS spectra under different conditions are quantitatively analyzed, and the LIBS spectrum obtained under the condition of the best quantitative effect is used as the standard to delineate the central area of ​​the spectrum mapping point at this time, and the standard area is used to constrain other spectrum mapping points to realize spectrum screening based on plasma luminescence characteristics, and improve the linear fit of the spectrum calibration curve under other conditions.

[0034] The method of the present invention does not require complicated data processing means, and can realize the mapping of the spectrum to the color space only by combining mathematical formulas for calculation. It has a short running time, requires a small amount of data, has strong applicability, and the results have actual physical meaning and are interpretable. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart for realizing the method of the present invention;

[0036] Figure 2 This is a structural principle diagram of the deep-sea rare earth LIBS spectrum detection device required by the present invention;

[0037] Figure 3 This is a schematic diagram of the LIBS spectrum conversion process according to an embodiment of the present invention;

[0038] Figure 4 Schematic diagram of the distribution of the LIBS spectrum of an embodiment of the present invention in the CIE L*a*b* color space;

[0039] Figure 5 Schematic diagram of the screening process of an embodiment of the present invention;

[0040] Figure 6 It is a comparison chart of the quantitative results before and after screening of the embodiment of the present invention.

[0041] Figure 7 A simplified diagram of the spectral screening device of the present invention.

[0042] Figure 2 Middle: 1- pulse laser, 2- reflector, 3- dichroic mirror, 4- microscope objective, 5- sample platform, 6- lens, 7- optical fiber, 8- spectrometer, 9- PIN tube, 10- computer control system. DETAILED DESCRIPTION

[0043] Principle of the invention: The present invention aims to provide a CIE L*a*b* data screening method for quantitative analysis of deep-sea rare earth element laser induced breakdown spectroscopy. Since the detection conditions have a significant impact on quantitative analysis, a series of LIBS spectra under different conditions are collected. The spectrum under the optimal quantitative conditions is selected, and the corresponding color space coordinates are solved according to the calculation formula to achieve the mapping of the LIBS spectrum to the CIE L*a*b* color space. The area of ​​the color space is defined as the standard area, and the plasma state reflected in this area is considered to be suitable for quantification. By judging whether the spectral mapping points under other conditions are in this area, the spectrum is screened, thereby improving the linear fit of the spectral calibration curve after the condition fluctuations.

[0044] In order to achieve the above object, the present invention adopts the following technical solutions:

[0045] 1. Based on the constructed LIBS experimental system, LIBS spectral data of multiple groups of samples with target element concentration gradients are obtained under different detection delay conditions of the spectrometer.

[0046] The deep-sea rare earth laser induced breakdown spectroscopy detection system disclosed in the present invention comprises a pulse laser 1, a reflector 2, a dichroic mirror 3, a microscope objective 4, a sample platform 5, a lens 6, an optical fiber 7, a spectrometer 8, a PIN tube 9 and a computer control system 10. Figure 1 shown.

[0047] The pulse laser is a Q-switched Nd:YAG laser with an excitation wavelength of 1064 nm and a pulse repetition frequency of 1 Hz. A reflector is installed at the laser light outlet to introduce the pulse laser into the subsequent optical path.

[0048] The sample platform is a three-dimensional moving platform on which the pressed sample is placed. After the laser passes through the dichroic mirror, it is focused on the sample surface through the microscope objective.

[0049] The spectral signal receiving system includes a lens and a fiber optic probe. The spectral signal receiving system adopts backward collection. The light emitted by the plasma is reflected by the dichroic mirror. The lens converges the LIBS spectral signal into the fiber optic probe and connects to the spectrometer through an optical fiber. The spectrometer is controlled by the timing control system and the computer control system.

[0050] The spectrum detection system is realized by a spectrometer.

[0051] The timing control system is realized by the PIN tube. A small part of the laser reaches the PIN tube through the reflector, and the PIN tube responds and triggers the spectrometer to collect data.

[0052] The computer control system is connected to the spectrum detection system to set the parameters of the spectrometer, such as the delay and integration time, and to display the LIBS spectrum of the collected samples.

[0053] Multiple spectrometer detection delays are set. Under each spectrometer detection delay, K pieces of LIBS spectral data are collected for multiple groups of samples to achieve the acquisition of sample LIBS spectral data under different detection delays.

[0054] 2. Based on the acquired LIBS spectral data, an element calibration curve is established to find a total of K LIBS spectral data of multiple groups of samples under the detection delay conditions with the best quantitative effect.

[0055] LIBS quantitative analysis requires the use of a series of standard samples to establish a regression curve between the intensity of characteristic spectral lines and the content of elements, namely the calibration curve. This curve reflects the quantitative relationship between the intensity of the emission spectral lines of elements and their content under specific experimental conditions. In order to establish the calibration curve, firstly, according to the acquired spectrum, the characteristic spectral lines of the target element are selected, and the element content is used as the horizontal coordinate and the intensity of the characteristic spectral lines of the elements at different contents is used as the vertical coordinate. A linear fit is performed to establish the calibration curve. For a total of K LIBS spectral data of multiple groups of samples under each detection delay, the calibration curve is established and R is calculated. 2Value (coefficient of determination, used to quantify the degree of fit of the model to the data), the specific calculation formula is as follows:

[0056]

[0057] Among them, y i represents the actual observed value, represents the mean of the true observations, Represents the predicted value.

[0058] R 2 The maximum value is 1 and the minimum is 0. The closer the value is to 1, the better the model is, and the closer it is to 0, the worse the model is. Find R 2 A total of K LIBS spectral data of multiple groups of samples under the detection delay whose value is closest to 1 are considered to have the best quantitative effect.

[0059] 3. Based on the LIBS spectral data with the best quantitative effect, calculate the corresponding CIE L*a*b* values ​​to obtain the spectral mapping point coordinate set with the best quantitative effect.

[0060] The LIBS spectrum based on plasma radiation has certain color characteristics, so the CIE L*a*b* color space is used to characterize the spectral color. The calculation formula for converting the spectrum to the CIE L*a*b* color space is as follows:

[0061]

[0062] In the formula, X, Y, and Z are the three stimulus values ​​of the CIE1931 standard colorimetric system, Xn, Yn, and Zn are the three stimulus values ​​of the CIE standard illuminant irradiated on a completely diffuse reflector and then reflected into the observer's eyes, I(λ) is the relative spectral power distribution of the light source radiation, and k is the normalization coefficient. It is the CIE1931 standard colorimetric observer spectrum tristimulus value, and Δλ is the wavelength interval. "L*" represents lightness, indicating the color from black to yellow; "a*" represents green-red color saturation, "+a*" represents red, and "-a*" represents green; "b*" represents blue-yellow color saturation, "+b*" represents yellow, and "-b*" represents blue.

[0063] like Figure 3As shown in the figure, the wavelength range of the CIE standard colorimetric observer spectrum tristimulus function is 380nm-780nm. Therefore, according to the calculation formula, the 380-780nm visible light band of the LIBS spectrum needs to be selected, and MATLAB is used for interpolation to obtain the spectral line intensity values ​​at a certain wavelength interval. Combined with the spectral tristimulus values, the XYZ tristimulus values ​​are solved, and the L*a*b* value in the CIE L*a*b* color space is further obtained to realize the mapping of the LIBS spectrum to the CIE L*a*b* color space, so that the high-dimensional spectral data is converted into three-dimensional color features.

[0064] Select the detection delay with the best linear fit of the calibration curve, and map the spectrum under this condition to the CIE L*a*b* color space according to the above conversion method to obtain the coordinate set of the spectrum mapping point, such as Figure 4 shown.

[0065] 4. Based on the quantitatively optimal spectral mapping point coordinate set, the standard area is delineated according to the center of the point set.

[0066] Since the points on the calibration curve are calculated using the average value of the characteristic spectral line intensity at each concentration, the center point position of the spectrum mapping points of samples with different element concentrations is calculated, that is, the average values ​​of the coordinate points are solved in three dimensions respectively, and the Euclidean distance between each point and the center point is calculated. The distances are sorted from small to large, and the spectrum mapping points corresponding to the first 1 / e distances are retained. The area covered by this part of the point set is the standard area.

[0067] 5. According to the standard area, the spectral mapping points under other time delays can be screened.

[0068] The LIBS spectra under other time-delay conditions (non-quantitative optimal time-delay conditions) are converted to the CIE L*a*b* color space according to step 3. The converted mapping points are screened according to the defined standard area, such as Figure 5 If the spectral mapping point falls within the standard area, the spectrum corresponding to this point will be retained; conversely, if it falls outside the standard area, it will be eliminated. Through this screening mechanism, the spectral data that can most accurately reflect the rare earth element content can be retained more accurately.

[0069] 6. Establish element calibration curves for the screened spectra to verify the quantitative improvement effects before and after.

[0070] A rare earth element calibration curve was established for the screened spectral data, and the linear fit of the calibration curve before and after screening was compared. Figure 6 As shown in the figure, after screening, the R 2The value (coefficient of determination, used to quantify the degree of fit of the model to the data) has been significantly improved, verifying that the method of screening through the CIE L*a*b* color space can effectively optimize the LIBS quantitative analysis of rare earth elements, thereby improving the accuracy and reliability of the analysis.

[0071] The method of the present invention is further described below in conjunction with specific embodiments.

[0072] Figure 1 This is a flowchart for the implementation of the method of the present invention. Build a LIBS experimental system, obtain the LIBS spectra of samples under different conditions and perform simple quantitative analysis to find the spectrum with the best quantitative analysis. According to the calculation formula, select the 380-780nm visible light band of the spectrum, use MATLAB to interpolate, obtain the spectral line intensity values ​​with an interval of 1nmm, combine the spectral tristimulus values, solve the XYZ tristimulus values, further obtain the L*a*b* value in the CIE L*a*b* color space, and map the quantitatively optimal spectrum to the CIE L*a*b* color space. Calculate the center point position of the spectral mapping points of different types of samples at this time, retain a part of the spectral mapping points closest to the center point of each type of sample, and use the area composed of this part of points as the standard to constrain the spectral feature points of different types of samples under other conditions. If the spectral mapping point falls within the standard area, then this point will be retained; conversely, if it falls outside the standard area, it will be eliminated. Establish a calibration curve for the screened spectrum, which can improve its R 2 value (coefficient of determination, used to quantify the degree of fit of the model to the data) to improve the accuracy of quantification.

[0073] like Figure 2 As shown, the deep-sea rare earth laser induced breakdown spectroscopy system disclosed in the present invention includes a 1064nm pulse laser 1, a sample platform 5, a spectral signal receiving system 6-7, a spectrometer 8, a timing control system 9, and a computer control system 10. The pulse laser 1 adopts a Q-switched Nd:YAG laser, whose excitation wavelength is 1064nmmm and the pulse repetition frequency is 1Hz. A reflector 2 is installed at the laser light outlet to introduce the laser into the subsequent optical path, wherein part of the laser passes through the reflector 2, and the PIN tube 9 converts the optical signal into an electrical signal to synchronously trigger the spectrometer. The laser can pass through the dichroic mirror 3 and focus on the position of the sample surface through the microscope objective 4 to excite the sample to generate plasma. The light emitted by the plasma is reflected by the dichroic mirror 3 and converged into the optical fiber probe 7 by the lens 6. The optical fiber introduces the detected signal into the spectrometer 8, and the spectrum is observed and collected in the computer control system 10.

[0074] The specific sample spectrum acquisition process is as follows: First, use a tablet press to compress the rare earth sample at a pressure of 10MPa to obtain 5 different types of rare earth sample tablets. Place the tablet under the microscope objective and fix it on a three-dimensional displacement platform. Move the Z axis to place the rare earth sample tablet at the focus of the microscope objective. Set the delay of the spectrometer to 500ns, turn on the Q-switched Nd:YAG laser, turn on the pulse repetition frequency to 1Hz, and the laser energy to 50mJ to generate plasma on the surface of the rare earth sample tablet. The light emitted by the plasma is reflected by the dichroic mirror, and the generated spectral signal is collected by the lens. Continuously collect 10 LIBS spectra of the laser pulse for each point of the sample, collect 10 points of the sample by moving the XY axis of the displacement stage, and obtain 100 LIBS spectra for each sample. Change the delay of the spectrometer to 1000ns and 1500ns, repeat the above acquisition steps, and obtain LIBS spectra of 5 rare earth samples under different delays.

[0075] like Figure 3 As shown, taking the LIBS spectrum of the GBW07590 rare earth sample obtained under a delay of 1500ns as an example, the above figure is the visible light band of the selected LIBS spectrum. Although the wavelength range of the spectrometer used in the present invention is 235-800nm, only the 380-780nm band can characterize the color, so this band is selected. The figure below shows the response of the LIBS spectrum to the CIE standard chromaticity observer spectral tristimulus function. Since the resolution of the system spectrometer used is 0.7nm, the spectral tristimulus value of Δλλ=1nm is selected. MATLAB is used to obtain the spectral line intensity with an interval of 1nm by interpolation, and the summation calculation is performed to match the spectral tristimulus values, and then the L*a*b* value can be obtained according to the formula.

[0076] By establishing calibration curves for the spectra at 500ns, 1000ns, and 1500ns delays, it was found that the calibration curve R2 at 1500ns delay was close to 1, indicating that the quantitative effect was optimal at this time. Figure 3 The process obtains the L*a*b* value of each sample spectrum under the condition, maps the LIBS spectrum to the CIE L*a*b* color space, and realizes the characterization of the luminescent colors of different sample plasmas, such as Figure 4 shown.

[0077] Calculate the center point position of the spectrum mapping points of different types of samples under the delay, retain a part of the spectrum mapping points closest to the center point of each type of sample, and use the area formed by this part of points as the standard, such as Figure 5As shown in the red area in the middle. After mapping the 500ns and 1000ns spectra to the CIE L*a*b* color space, the spectral mapping points are screened according to the standard area with the best quantitative effect. If the point is within the area, the corresponding spectrum is retained, otherwise it is deleted, completing the screening of LIBS spectra when the conditions fluctuate.

[0078] Establish element calibration curve for the screened spectrum and calculate the determination coefficient R 2 value, Figure 6 For the LIBS quantitative analysis of rare earth elements such as Dy, the determination coefficient R of the calibration curve before and after screening is 2 The performance of the 389.9nm characteristic line was significantly improved. 2 It increased from 0.339 to 0.905, proving that the screening method based on the CIE L*a*b* color space can improve the accuracy of LIBS quantitative analysis and is a fast and effective spectral screening method.

[0079] like Figure 7 As shown, the present invention also provides a spectral screening device for deep-sea rare earth element LIBS quantitative detection, the device includes at least one processor and at least one memory, and also includes a communication interface and an internal bus; a computer execution program is stored in the memory; a computer execution program is stored in the memory; when the processor executes the computer execution program stored in the memory, the processor can execute the spectral screening method for deep-sea rare earth element LIBS quantitative detection as described above. The internal bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus. The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and may also be a U disk, a mobile hard disk, a read-only memory, a disk or an optical disk.

[0080] The device may be provided as a terminal, a server or other forms of device. In an exemplary embodiment, the electronic device may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0081] The present invention also provides a computer-readable storage medium, in which a computer execution program is stored. When the computer execution program is executed by a processor, the processor can execute the spectral screening method for LIBS quantitative detection of deep-sea rare earth elements as described above.

[0082] Specifically, a system, device or equipment equipped with a readable storage medium may be provided, on which a software program code for implementing the functions of any of the above-mentioned embodiments is stored, and a computer or processor of the system, device or equipment reads and executes the instructions stored in the readable storage medium. In this case, the program code read from the readable medium itself can implement the functions of any of the above-mentioned embodiments, so the machine-readable code and the readable storage medium storing the machine-readable code constitute a part of the present invention.

[0083] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0084] Although the above describes the specific implementation methods of the present invention, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A spectral screening method for LIBS quantitative detection of deep-sea rare earth elements, characterized in that: The process includes: Step 1: Based on the constructed LIBS detection system, LIBS spectrum data of multiple groups of rare earth samples with target element concentration gradients under different detection delays of the spectrometer are obtained, and each delay condition includes a total of K LIBS spectrum data of multiple groups of rare earth samples; Step 2: Establish an element calibration curve for the acquired LIBS spectral data, and find a total of K LIBS spectral data of multiple groups of rare earth samples under the detection delay condition with the best quantitative effect; Step 3, based on the K pieces of LIBS spectrum data under the detection delay condition with the best quantitative effect, calculate the CIE L*a*b* value corresponding to each piece of spectrum data, so as to obtain the coordinate set of the spectrum mapping point; Step 4, based on the spectral mapping point coordinate set obtained in step 3, a standard area is defined according to the center of the point set; Step 5: According to the standard area, the LIBS spectral data under other delay conditions are converted to the CIE L*a*b* color space according to step 3 to obtain the coordinates of the spectral data mapping points under other delay conditions. The spectral data falling outside the standard area are screened out.

2. A spectral screening method for LIBS quantitative detection of deep-sea rare earth elements according to claim 1, characterized in that: The constructed LIBS detection system includes a pulsed laser, a sample platform, a spectral signal receiving system, a spectral detection system, a timing control system and a computer control system; A reflector is installed at the pulse laser light outlet to guide the pulse laser into the subsequent light path; The sample platform is a three-dimensional moving platform, on which the sample pressed into a sheet is placed, and the laser is focused on the sample surface through the microscope objective lens after passing through the dichroic mirror; The spectral signal receiving system includes a lens and an optical fiber probe. The spectral signal receiving system adopts backward collection. The light emitted by the plasma is reflected by the dichroic mirror. The lens converges the LIBS spectral signal into the optical fiber probe and connects the spectral detection system through an optical fiber. The spectral detection system is controlled by a timing control system and a computer control system. The spectrum detection system is realized by a spectrometer; The timing control system is implemented by a PIN tube; a small portion of the laser reaches the PIN tube through a reflector, and the PIN tube responds and triggers the spectrometer to collect data; The computer control system is connected to the spectrum detection system and is used to set spectrometer parameters and display the LIBS spectrum of the collected samples.

3. The spectral screening method for LIBS quantitative detection of deep-sea rare earth elements according to claim 1, characterized in that: The method of acquiring LIBS spectral data of multiple groups of rare earth samples under different detection delays of the spectrometer is to continuously collect LIBS spectra of p laser pulses at each point of each group of samples under different detection delays of the spectrometer, collect m points in total, and obtain multiple LIBS spectra for each group of samples in total, thereby realizing the acquisition of LIBS spectra of samples under different delays.

4. The spectral screening method for LIBS quantitative detection of deep-sea rare earth elements according to claim 1, characterized in that: The calibration curve in step 2 is a regression curve of characteristic spectral line intensity and element content established by using a series of standard samples. First, according to the acquired spectral data, the characteristic spectral line of the rare earth element is selected, and the element content is used as the horizontal axis and the intensity of the characteristic spectral line of the element at different contents is used as the vertical axis. A linear fit is performed to establish a calibration curve of a total of K LIBS spectral data of multiple groups of samples under each detection delay and calculate the determination coefficient R 2 The maximum value is 1 and the minimum is 0. The closer the value is to 1, the better the model is. K LIBS spectral data under the detection delay condition closest to 1 are selected.

5. The spectral screening method for LIBS quantitative detection of deep-sea rare earth elements according to claim 1, characterized in that: The specific process of step 3 is as follows: Convert the spectral data to the CIE L*a*b* color space using the following calculation formula: In the formula, X, Y, and Z are the three stimulus values ​​of the CIE1931 standard colorimetric system, X n , Yn, Z n is the tristimulus value of the CIE standard illuminant irradiated on a completely diffuse reflector and then reflected into the observer's eyes, I(λ) is the relative spectral power distribution of the light source radiation, k is the normalization coefficient, is the CIE1931 standard colorimetric observer spectrum tristimulus value, Δλ is the wavelength interval; "L*" represents lightness, indicating color from black to white; "a*" represents green-red color saturation, "+a*" represents red, "-a*" represents green; "b*" represents blue-yellow color saturation, "+b*" represents yellow, "-b*" represents blue; According to the calculation formula, the 380-780nm visible light band of the LIBS spectrum was selected, and MATLAB was used for interpolation to obtain the spectral line intensity values ​​at a certain wavelength interval. Combined with the spectral tristimulus values, the XYZ tristimulus values ​​were solved to obtain the L*a*b* values ​​in the CIE L*a*b* color space, and the mapping of LIBS spectral data to the CIE L*a*b* color space was realized. The high-dimensional spectral data was converted into three-dimensional color features, and the coordinate set of the spectral data mapping points was obtained.

6. A spectral screening method for LIBS quantitative detection of deep-sea rare earth elements according to claim 1, characterized in that: The specific process of step 4 is as follows: Calculate the center point position of the spectral mapping points of samples with different element concentrations, that is, solve the average values ​​of the coordinate points according to the three dimensions respectively, and calculate the Euclidean distance between each point and the center point; sort the distances from small to large, and retain the spectral mapping points corresponding to the first 1 / e distances. The area covered by this part of the point set is the standard area.

7. A spectral screening device for LIBS quantitative detection of deep-sea rare earth elements, characterized by: The device includes at least one processor and at least one memory, the processor and the memory are coupled; a computer execution program is stored in the memory; when the processor executes the computer execution program stored in the memory, the processor executes the spectral screening method for LIBS quantitative detection of deep-sea rare earth elements as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or instruction, and when the program or instruction is executed by the processor, the processor executes the spectral screening method for LIBS quantitative detection of deep-sea rare earth elements as described in any one of claims 1 to 6.

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