A method and system for detecting the content of rare earth elements

Abstract

The application discloses a method and system for detecting rare earth element content, and belongs to the technical field of spectrum analysis and material detection. The roughness parameter and the particle equivalent diameter parameter of a sample surface are acquired, a coupling compensation model of sample structure characteristics and plasma physical parameters is constructed, the signal generated by laser-induced breakdown spectroscopy is corrected, and the influence of sample particle structure difference on the rare earth element spectral line intensity is reduced. Through structure-temperature double-constraint normalization processing on the compensated characteristic spectral line, a quantitative regression model of the element content is established, and high-precision, stable and rapid detection of the rare earth element content is realized. The method is simple in model structure, small in parameter quantity, strong in engineering applicability, and suitable for on-site rapid detection in the fields of rare earth materials, metallurgical materials and functional ceramics.

Description

Technical Field

[0001] This invention relates to the field of spectral analysis and materials testing technology, and in particular to a method and system for detecting the content of rare earth elements. Background Technology

[0002] With the rapid development of rare earth materials, metallurgical materials, new energy materials, and functional ceramics, the demand for rapid, accurate, and online quantitative detection of the content of multiple elements in materials is increasing. In catalytic materials, polishing materials, energy storage materials, and alloy systems, the ratio of rare earth elements directly affects the physical properties and process stability of the materials. Cerium, as the most abundant typical rare earth element, is widely used in metallurgy, polishing, catalysis, functional ceramics, and new energy materials, making the efficiency and accuracy of its content detection increasingly urgent.

[0003] Currently, the mainstream methods for detecting cerium content include chemical analysis, inductively coupled plasma mass spectrometry (ICP-MS), and atomic absorption spectrometry. These methods all suffer from common problems such as complex sample preparation processes, long detection cycles, and insufficient on-site detection capabilities, making them unsuitable for the rapid, in-situ detection needs of industrial settings. While laser-induced breakdown spectroscopy (LAS-MS) offers advantages such as rapid, in-situ, and simultaneous multi-element detection, making it an important technological direction for cerium detection, its practical engineering applications are significantly affected by matrix effects. Existing compensation methods are ineffective, resulting in low detection accuracy and poor result stability. Furthermore, the interpretability of compensation strategies is weak, and the engineering implementation of related improvement methods is difficult. Overall, these methods struggle to meet the actual needs of relevant fields for accurate and efficient cerium content detection. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for detecting rare earth element content. Under the premise of ensuring the simplicity of the model structure and engineering feasibility, a detection method is introduced that can characterize the microstructure state of the sample surface and establish a clear coupling relationship between the key physical parameters and the plasma emission characteristics, thereby achieving effective suppression of matrix effects and stable and reliable quantitative detection of rare earth element content.

[0005] To achieve the above objectives, the present invention provides a method for detecting the content of rare earth elements, comprising the following steps: S0. Using multiple sets of standard samples with known rare earth element content, and under the same testing conditions as the sample to be tested, obtain calibration datasets to complete the calibration and parameter storage of the two-parameter matrix effect coupling compensation model, the structure-temperature dual-constraint normalization model, and the quantitative regression model. S1. Obtain the surface roughness parameters and particle equivalent diameter parameters of the sample to be tested; S2. Excite the surface of the sample under test with a pulsed laser, collect the raw spectral data of plasma emission and perform anomaly verification. If the verification is successful, proceed to S3; otherwise, return to this step and repeat. S3. Based on the original spectral data that passed the verification in S2, select the characteristic spectral lines of the rare earth element to be measured to invert the plasma temperature parameters and perform a rationality verification. If the verification is successful, proceed to S4; otherwise, return to this step and repeat the process. S4. Based on the surface roughness parameters and particle equivalent diameter parameters of S1 and the plasma temperature parameters that have passed the verification of S3, the two-parameter matrix effect coupling compensation model calibrated by S0 is called to correct the original spectral data that has passed the verification of S2 and obtain the corrected spectral data. S5. Extract the characteristic spectral line intensities of the rare earth elements to be measured from the calibration spectral data of S4, combine them with the surface roughness parameters of S1, the equivalent particle diameter parameters, and the plasma temperature parameters that have passed the verification of S3, and call the structure-temperature dual-constraint normalization model after S0 calibration to process and obtain the normalized characteristic intensities. S6. Input the normalized characteristic intensity of S5 into the quantitative regression model after S0 calibration, calculate, output and store the content of the rare earth element to be measured.

[0006] Preferably, S1 specifically includes: The sample to be tested is fixed so that the surface of the test area is perpendicular to the laser emission direction. A planar scan of the test area is performed, and a two-dimensional coordinate grid is established according to a preset spatial resolution. Surface height values ​​are acquired at each sampling point, forming a height distribution matrix. ; For height distribution matrix After smoothing, the height distribution matrix after denoising is obtained. Calculate the average height of the sampling points The formula is: ; in, The height is the arithmetic mean of the height values ​​of all sampling points on the sample surface. , This represents the number of sampling points in the row and column directions of the height distribution matrix. For the height distribution matrix The matrix after neighborhood smoothing. For the row index of the height distribution matrix, For column indices of the height distribution matrix, This represents the lateral sampling step size during the topography scanning process. This represents the longitudinal sampling step size during the topography scanning process; Based on the average height of the sampling points Calculate the surface roughness parameters of the sample. The formula is: ; in, This is a surface roughness parameter used to characterize the degree of height undulation on the sample surface; The height distribution matrix after smoothing Region segmentation and boundary identification are performed to identify continuous height abrupt changes in the surface as individual effective particle regions, and the projected area of ​​each individual effective particle region is calculated. The formula is: ; in, For the first The projected area of ​​each particle region on the sample surface. For the first The number of sampling points contained in each particle region. , During the morphology scanning process direction and Sampling step size in the direction; Projected area of ​​a single effective particle region The area of ​​the particle is equivalent to that of a circle with equal area, and the equivalent diameter of a single particle is calculated accordingly. The formula is: ; in, For the first The equivalent diameter of each particle; The single-particle equivalent diameter of all identified effective particle regions The arithmetic mean was taken as the equivalent particle diameter parameter of the sample. .

[0007] Preferably, S2 specifically includes: The pulsed laser excitation unit emits single-pulse or multi-pulse lasers towards the sample to be tested area. The laser ablates a local area on the sample surface to form a plasma plume. The spectral acquisition unit acquires the plasma emission light signal through the optical fiber acquisition channel within a preset delay time window synchronized with the laser emission timing. At the same detection point or adjacent micro-region within the same test area of ​​the sample, multiple pulsed laser excitations are continuously triggered, and the plasma emission spectrum signal of each excitation is collected accordingly. During the acquisition process, intensity saturation detection and background anomaly verification are performed on a single spectral signal simultaneously. If spectral saturation or background anomaly occurs, one or more acquisition parameters among the acquisition gain, delay time window, and laser energy are automatically adjusted, and excitation and acquisition are re-executed. All spectral signals that pass the verification are superimposed point by point along the wavelength dimension and then averaged to obtain stable raw spectral data. .

[0008] Preferably, S3 specifically includes: Based on the raw spectral data S(λ) obtained in step S2, at least two characteristic spectral lines of the rare earth element to be measured are selected within a preset wavelength range. Background fitting and subtraction are performed on the local band of each spectral line to obtain the net spectral signal. The net spectral signal is integrated within the corresponding spectral line band to calculate the effective integrated intensity value of each spectral line. The effective integrated intensity value, along with the excited state energy, transition probability, statistical weight, and wavelength parameter of the corresponding spectral line, are substituted into the plasma temperature inversion formula to calculate the plasma temperature parameters. The calculation formula is: ; in, This is a plasma temperature parameter used to characterize the thermal state of the plasma formed after laser ablation. , These are the atomic energy levels corresponding to the two selected characteristic spectral lines. , These are the effective intensity values ​​of the two characteristic spectral lines. , These are the center wavelengths of the two characteristic spectral lines, , These represent the transition probabilities of the two characteristic spectral lines. , These are the statistical weights of the energy levels corresponding to the two characteristic spectral lines. Boltzmann's constant; The calculated plasma temperature parameters Perform a rationality check, if the temperature parameter If the result exceeds the preset reasonable range, the system will automatically replace the alternative characteristic spectral line pair and recalculate until the inversion result meets the reasonable requirements.

[0009] Preferably, S4 specifically includes: Surface roughness parameters obtained in step S1 Particle equivalent diameter parameters The plasma temperature parameters obtained in step S3 The two-parameter matrix effect coupling compensation function calibrated in step S0 is invoked to process the raw spectral data obtained in step S2. Perform calibration to obtain calibrated spectral data. ; The expression for the two-parameter matrix effect coupling compensation function is as follows: ; in, , , The coupling weight coefficients determined in step S0 are... This is the preset reference temperature; During the calibration process, the overall intensity change and baseline stability of the spectrum before and after calibration are checked. If there are significant intensity fluctuations or baseline shifts in the calibration results, the coupling weight parameters are automatically adjusted and the calibration calculation is re-executed until stable and compliant calibrated spectral data are obtained.

[0010] Preferably, S5 specifically includes: Based on the calibrated spectral data from step S4, the characteristic spectral bands of the rare earth element to be tested are located, and local background subtraction and integration processing are performed to obtain the characteristic spectral intensity values ​​of the rare earth element to be tested; at the same time, the reference spectral intensity values ​​are extracted within the preset matrix element spectral bands. Combined with the surface roughness parameters obtained in step S1 Particle equivalent diameter parameters The plasma temperature parameters obtained in step S3 After performing structure-temperature dual-constraint normalization, the normalized characteristic intensity values ​​of the rare earth elements to be measured are obtained. The normalization calculation formula is as follows: ; In the formula, The normalized characteristic intensity value of the rare earth element to be measured is... The intensity values ​​of the characteristic spectral lines of the rare earth element to be measured are: The reference spectral line intensity values ​​are for the matrix elements. The temperature constraint coefficient determined in step S0. The structural constraint function determined in step S0.

[0011] Preferably, S6 specifically includes: The structure-temperature dual-constraint normalized characteristic intensity of the rare earth element to be tested in S5 is input into the quantitative regression model calibrated in S0 to calculate the content detection result of rare earth element in the sample; the content detection result is displayed in real time, and the sample identifier and detection timestamp are associated to complete the data storage.

[0012] A system for detecting rare earth element content is also provided, used to perform a method for detecting rare earth element content as described above. The system includes a sample stage for carrying the sample to be tested, a surface morphology acquisition unit on the detection side of the sample stage, a laser pulse excitation unit next to the surface morphology acquisition unit, a focusing lens mounted on the output light path of the laser pulse excitation unit, the focusing lens corresponding to the detection area of ​​the sample to be tested on the sample stage, and a spectral acquisition unit for the sample stage. The surface morphology acquisition unit, laser pulse excitation unit, and spectral acquisition unit are all bidirectionally connected to a computer. The surface morphology acquisition unit transmits the acquired sample surface structure parameters to the computer. The computer controls the laser pulse excitation unit to emit pulsed laser light towards the sample to be tested through the focusing lens. The spectral acquisition unit transmits the spectral data converted from the acquired plasma plume light signal to the computer. The computer performs data processing, model calculations, and quantitative calculations of the rare earth element content.

[0013] Preferably, the laser pulse excitation unit includes a laser body, an energy adjustment module, and a beam shaping component, which can independently control the laser output energy, pulse width, and emission frequency; The surface morphology acquisition unit includes a low-power scanning light source and an image acquisition module, which can emit a non-ablative measurement beam and construct a sample surface height distribution matrix to calculate surface roughness parameters and particle equivalent diameter parameters. The spectral acquisition unit includes an optical fiber acquisition channel, an acquisition unit, a beam splitter, and a spectral detector. It can acquire emission spectral data within a specified wavelength range within a preset time window and convert it into electrical signals or digital spectral data. The computer includes a data processing and control unit and a display and storage unit. The data processing and control unit is equipped with a processor, a storage module and a communication interface, which can coordinate the working sequence of each unit, perform spectral data processing and model building calculations. The display and storage unit is equipped with a display screen and a data storage module, which can visually display the test results and archive the test data by associating timestamps and sample identification information.

[0014] Therefore, the present invention, employing the above-described method and system for detecting rare earth element content, has the following beneficial effects: (1) By establishing a two-parameter matrix effect coupling model, the influence of particle structure differences on spectral intensity can be significantly reduced; (2) The structure-temperature dual constraint method is used to obtain the spectral intensity characteristic values ​​of rare earth elements and metal elements, which can effectively reduce the systematic influence of sample structure differences on quantitative results; (3) No complex chemical pretreatment is required, the detection process is efficient and fast, and it can fully meet the needs of on-site detection of rare earth elements. (4) The model has a simple structure with few parameters, strong physical interpretability, and is easy to integrate into industrial systems and perform real-time calculations. It is also easy to implement and promote.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the laser-induced breakdown spectroscopy system for detecting the cerium content of bulk rare earth materials in an embodiment of the present invention. Figure 2 This is a schematic flowchart of a method for detecting rare earth element content according to the present invention; Figure Labels 1. Laser pulse excitation unit; 2. Surface morphology acquisition unit; 3. Sample stage; 4. Plasma plume; 5. Spectral acquisition unit; 6. Computer; 7. Acquisition device; 8. Focusing lens. Detailed Implementation

[0017] Example like Figure 2 As shown, this invention discloses a method for detecting rare earth element content, the steps of which include: S0. Using multiple sets of standard samples with known rare earth element content, and under the same testing conditions as the sample to be tested, obtain calibration datasets to complete the calibration and parameter storage of the two-parameter matrix effect coupling compensation model, the structure-temperature dual-constraint normalization model, and the quantitative regression model. S1. Obtain the surface roughness parameters and particle equivalent diameter parameters of the sample to be tested.

[0018] S1 specifically includes: In this implementation, several blocky rare earth material samples were selected as the testing objects. The sample surfaces were initially smoothed by mechanical polishing to ensure that there was no obvious oil or attached impurities. The samples to be tested were fixed on the sample support stage, with the surface of the test area perpendicular to the laser emission direction. A planar scan was performed on the test area of ​​the sample, with the scanning light source in... and The optimal sampling step size for the direction is 1 mm. A two-dimensional coordinate grid is established according to a preset spatial resolution, and the surface height value is obtained at each sampling point to form a height distribution matrix. ; For height distribution matrix Smoothing is performed to remove isolated noise points and abnormal height values. The resulting height distribution matrix is ​​then denoised. Calculate the average height of the sampling points The formula is: ; in, The height is the arithmetic mean of the height values ​​of all sampling points on the sample surface. , This represents the number of sampling points in the row and column directions of the height distribution matrix. For the height distribution matrix The matrix after neighborhood smoothing, For the row index of the height distribution matrix, For column indices of the height distribution matrix, This represents the lateral sampling step size during the topography scanning process. This represents the longitudinal sampling step size during the topography scanning process; Based on the average height of the sampling points (It is recommended that the roughness parameter calculation area be within a range of 1mm×1mm to 5mm×5mm.) Calculate the surface roughness parameters of the sample. The formula is: ; in, This is a surface roughness parameter used to characterize the degree of height undulation on the sample surface; The height distribution matrix after smoothing Region segmentation and boundary identification are performed to identify continuous height abrupt changes in the surface as individual effective particle regions, and the projected area of ​​each individual effective particle region is calculated. The formula is: ; in, For the first The projected area of ​​each particle region on the sample surface. For the first The number of sampling points contained in each particle region. , During the morphology scanning process direction and Sampling step size in the direction; Projected area of ​​a single effective particle region The area of ​​the particle is equivalent to that of a circle with equal area, and the equivalent diameter of a single particle is calculated accordingly. The formula is: ; in, For the first The equivalent diameter of each particle; The single-particle equivalent diameter of all identified effective particle regions The arithmetic mean was taken as the equivalent particle diameter parameter of the sample. .

[0019] S2. Excite the surface of the sample under test using a pulsed laser, collect the raw spectral data of plasma emission, and perform anomaly verification. If the verification is successful, proceed to S3; otherwise, return to this step and repeat. S2 specifically includes: The pulsed laser excitation unit controls the emission of a single-pulse or multi-pulse laser with an energy of 10-100 mJ to the sample to be tested area. The laser ablates a local area on the sample surface to form a plasma plume 4. The spectral acquisition unit 5 acquires the plasma emission light signal through the optical fiber acquisition channel within a preset delay time window synchronized with the laser emission timing. To improve spectral stability and signal-to-noise ratio, the system continuously triggers 10-20 pulse laser excitations at the same detection point or adjacent small regions, and collects the plasma emission spectrum signal of each excitation accordingly. During the acquisition process, intensity saturation detection and background anomaly verification are performed on a single spectral signal simultaneously. If spectral saturation or background anomaly occurs, one or more acquisition parameters among the acquisition gain, delay time window, and laser energy are automatically adjusted, and excitation and acquisition are re-executed. All spectral signals that pass the verification are superimposed point by point along the wavelength dimension and then averaged to obtain stable raw spectral data. .

[0020] S3. Based on the original spectral data that passed the verification in S2, select the characteristic spectral lines of cerium to invert the plasma temperature parameters and perform a rationality verification. If the verification is successful, proceed to S4; otherwise, return to this step and repeat. S3 specifically includes: Based on the original spectral data S(λ) obtained in step S2, at least two characteristic spectral lines of cerium are selected within the preset wavelength range of 400nm-500nm; background fitting and subtraction are performed on the local band of each spectral line to obtain the net spectral signal; the net spectral signal is integrated within the corresponding spectral line band to calculate the effective integrated intensity value of each spectral line. and Substituting the effective integrated intensity value along with the excited state energy, transition probability, statistical weight, and wavelength parameter of the corresponding spectral line into the plasma temperature inversion formula, the plasma temperature parameters are calculated. The calculation formula is: ; in, This is a plasma temperature parameter used to characterize the thermal state of the plasma formed after laser ablation. , These are the atomic energy levels corresponding to the two selected characteristic spectral lines. , These are the effective intensity values ​​of the two characteristic spectral lines. , These are the center wavelengths of the two characteristic spectral lines, , These represent the transition probabilities of the two characteristic spectral lines. , These are the statistical weights of the energy levels corresponding to the two characteristic spectral lines. Boltzmann's constant; The calculated plasma temperature parameters Perform a rationality check, if the temperature parameter If the result exceeds the preset reasonable range, the system will automatically replace the alternative characteristic spectral line pair and recalculate until the inversion result meets the reasonable requirements.

[0021] S4. Based on the surface roughness parameters and equivalent particle diameter parameters of S1, and the plasma temperature parameters verified in S3, the two-parameter matrix effect coupling compensation model calibrated in S0 is called to correct the original spectral data verified in S2, obtaining the corrected spectral data. S4 specifically includes: Surface roughness parameters obtained in step S1 Particle equivalent diameter parameters The plasma temperature parameters obtained in step S3 The two-parameter matrix effect coupling compensation function calibrated in step S0 is invoked to process the raw spectral data obtained in step S2. Perform calibration to obtain calibrated spectral data. ; The expression for the two-parameter matrix effect coupling compensation function is as follows: ; in, , , The coupling weight coefficients determined in step S0 are... This is the preset reference temperature; During the calibration process, the overall intensity change and baseline stability of the spectrum before and after calibration are checked. If there are significant intensity fluctuations or baseline shifts in the calibration results, the coupling weight parameters are automatically adjusted and the calibration calculation is re-executed until stable and compliant calibrated spectral data are obtained.

[0022] S5. Extract the characteristic spectral line intensities of cerium from the corrected spectral data of S4. Combine this with the surface roughness parameters, equivalent particle diameter parameters from S1, and the plasma temperature parameters verified in S3. Then, use the structure-temperature dual-constraint normalization model calibrated in S0 to process the data and obtain the normalized characteristic intensities. S5 specifically includes: Based on the corrected spectral data from step S4, the characteristic spectral line bands of cerium are located, and local background subtraction and integration are performed to obtain the characteristic spectral line intensity values ​​of cerium; at the same time, reference spectral line intensity values ​​are extracted within the preset matrix element spectral line bands. Combined with the surface roughness parameters obtained in step S1 Particle equivalent diameter parameters The plasma temperature parameters obtained in step S3 The structure-temperature dual-constraint normalization process is performed to obtain the normalized characteristic intensity value of cerium. In this embodiment, the rare earth element to be tested is cerium. Therefore, the normalization calculation formula is as follows: (The normalized characteristic intensity value of the rare earth element to be tested) is (Normalized characteristic intensity value of cerium); (Intensity values ​​of characteristic spectral lines of the rare earth element to be measured) are (Characteristic spectral line intensity values ​​of cerium), the specific formula is as follows: ; in, The effective intensity value of the matrix reference spectral line is obtained by determining the center wavelength and transition parameters of the corresponding spectral line through an atomic spectral database, and then performing background subtraction and integration on the spectral band during the spectral acquisition process. The temperature constraint coefficient determined in step S0. The structural constraint function determined in step S0 is given by the coefficients. and A joint decision.

[0023] Note: The atomic spectral line wavelengths, energy level energies, transition probabilities, and statistical weighting parameters involved in the formulas are derived from the National Institute of Standards and Technology (NIST) Atomic Spectroscopy Database. The Boltzmann constant adopts the international standard value recommended by CODATA. The normalized weight parameters and first-order linear regression coefficients were obtained through the offline calibration phase using national standard samples.

[0024] S6. Input the normalized characteristic intensity of S5 into the quantitative regression model calibrated by S0 to calculate, output, and store the cerium content. S6 specifically includes: The cerium element structure-temperature dual-constraint normalized characteristic intensity of S5 is input into the quantitative regression model calibrated by S0 to calculate the content detection result of cerium element in the sample to be tested; the content detection result is displayed in real time, and the sample identifier and detection timestamp are associated to complete the data storage.

[0025] like Figure 1As shown, a system for detecting rare earth element content is also disclosed, which is used to perform the above-mentioned method for detecting rare earth element content: it includes a sample stage 3 for carrying the sample to be tested, a surface morphology acquisition unit 2 is provided on the detection side of the sample stage 3, a laser pulse excitation unit 1 is provided on the side of the surface morphology acquisition unit 2, a focusing lens 8 is installed on the output light path of the laser pulse excitation unit 1, the focusing lens 8 corresponds to the detection area of ​​the sample to be tested on the sample stage 3, the detection of the sample stage 3 is also equipped with a spectral acquisition unit 5, and the surface morphology acquisition unit 2, the laser pulse excitation unit 1, and the spectral acquisition unit 5 are all bidirectionally connected to a computer 6; The surface morphology acquisition unit 2 includes a low-power scanning light source and an image acquisition module, which can emit a non-ablative measurement beam and construct a sample surface height distribution matrix, calculate surface roughness parameters and particle equivalent diameter parameters; the surface morphology acquisition unit 2 transmits the acquired sample surface structure parameters to the computer 6; Computer 6 includes a data processing and control unit and a display and storage unit. The data processing and control unit is equipped with a processor, a storage module and a communication interface, which can coordinate the working sequence of each unit, perform spectral data processing and model building calculations. The display and storage unit is equipped with a display screen and a data storage module, which can visually display the test results and archive the test data by associating timestamps and sample identification information.

[0026] The computer 6 controls the laser pulse excitation unit 1 to emit pulsed laser to the sample under test through the focusing lens 8. The laser pulse excitation unit 1 includes a laser body, an energy adjustment module and a beam shaping component, and can independently control the laser output energy, pulse width and emission frequency. The spectral acquisition unit 5 includes an optical fiber acquisition channel, an acquisition unit 7, a beam splitter and a spectral detector, which can acquire emission spectral data of a specified wavelength range within a preset time window and convert it into electrical signals or digital spectral data; The spectral acquisition unit 5 transmits the spectral data converted from the optical signal of the acquired plasma plume 4 to the computer 6, and the computer 6 completes data processing, model calculation and quantitative calculation of rare earth element content.

[0027] Therefore, this invention employs the aforementioned method and system for detecting rare earth element content. This system features a simple structure, a small number of parameters, and strong engineering applicability, making it suitable for rapid on-site detection in rare earth materials, metallurgical materials, and functional ceramics. This method obtains the surface roughness parameters of the sample. Equivalent diameter parameter of particles A coupled compensation model between sample structural characteristics and plasmonic physical parameters was constructed to constrain and correct the signal generated by laser-induced breakdown spectroscopy, thereby reducing the influence of sample particle structure differences on the intensity of rare earth element spectral lines. A quantitative regression model for elemental content was established by performing structure-temperature dual-constraint normalization on the compensated characteristic spectral lines, achieving high-precision, stable, and rapid detection of rare earth element content.

[0028] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for detecting rare earth element content, characterized in that the steps include... include: S0. Using multiple sets of standard samples with known rare earth element content, and under the same testing conditions as the sample to be tested, obtain calibration datasets to complete the calibration and parameter storage of the two-parameter matrix effect coupling compensation model, the structure-temperature dual-constraint normalization model, and the quantitative regression model. S1. Obtain the surface roughness parameters and particle equivalent diameter parameters of the sample to be tested; S2. Excite the surface of the sample under test with a pulsed laser, collect the raw spectral data of plasma emission and perform anomaly verification. If the verification is successful, proceed to S3; otherwise, return to this step and repeat. S3. Based on the original spectral data that passed the verification in S2, select the characteristic spectral lines of the rare earth element to be measured to invert the plasma temperature parameters and perform a rationality verification. If the verification is successful, proceed to S4; otherwise, return to this step and repeat the process. S4. Based on the surface roughness parameters and particle equivalent diameter parameters of S1 and the plasma temperature parameters that have passed the verification of S3, the two-parameter matrix effect coupling compensation model calibrated by S0 is called to correct the original spectral data that has passed the verification of S2 and obtain the corrected spectral data. S5. Extract the characteristic spectral line intensities of the rare earth elements to be measured from the calibration spectral data of S4, combine them with the surface roughness parameters of S1, the equivalent particle diameter parameters, and the plasma temperature parameters that have passed the verification of S3, and call the structure-temperature dual-constraint normalization model after S0 calibration to process and obtain the normalized characteristic intensities. S6. Input the normalized characteristic intensity of S5 into the quantitative regression model after S0 calibration, calculate, output and store the content of the rare earth element to be measured.

2. The method for detecting rare earth element content according to claim 1, characterized in that, S1 specifically includes: The sample to be tested is fixed so that the surface of the test area is perpendicular to the laser emission direction. A planar scan of the test area is performed, and a two-dimensional coordinate grid is established according to a preset spatial resolution. Surface height values ​​are acquired at each sampling point, forming a height distribution matrix. ; For height distribution matrix After smoothing, the height distribution matrix after denoising is obtained. Calculate the average height of the sampling points The formula is: ； in, The height is the arithmetic mean of the height values ​​of all sampling points on the sample surface. , This represents the number of sampling points in the row and column directions of the height distribution matrix. For the height distribution matrix The matrix after neighborhood smoothing, For the row index of the height distribution matrix, For column indices of the height distribution matrix, This represents the lateral sampling step size during the topography scanning process. This represents the longitudinal sampling step size during the topography scanning process; Based on the average height of the sampling points Calculate the surface roughness parameters of the sample. The formula is: ； in, This is a surface roughness parameter used to characterize the degree of height undulation on the sample surface; The height distribution matrix after smoothing Region segmentation and boundary identification are performed to identify continuous height abrupt changes in the surface as individual effective particle regions, and the projected area of ​​each individual effective particle region is calculated. The formula is: ； in, For the first The projected area of ​​each particle region on the sample surface. For the first The number of sampling points contained in each particle region. , During the morphology scanning process direction and Sampling step size in the direction; Projected area of ​​a single effective particle region The equivalent area of ​​a circle with equal area is used to calculate the equivalent diameter of a single particle. The formula is: ； in, For the first The equivalent diameter of each particle; The single-particle equivalent diameter of all identified effective particle regions The arithmetic mean was taken as the equivalent particle diameter parameter of the sample. .

3. The method for detecting rare earth element content according to claim 2, characterized in that, S2 specifically includes: The pulsed laser excitation unit emits single-pulse or multi-pulse lasers towards the sample to be tested area. The laser ablates a local area on the sample surface to form a plasma plume. The spectral acquisition unit acquires the plasma emission light signal through the optical fiber acquisition channel within a preset delay time window synchronized with the laser emission timing. At the same detection point or adjacent micro-region within the same test area of ​​the sample, multiple pulsed laser excitations are continuously triggered, and the plasma emission spectrum signal of each excitation is collected accordingly. During the acquisition process, intensity saturation detection and background anomaly verification are performed on a single spectral signal simultaneously. If spectral saturation or background anomaly occurs, one or more acquisition parameters among the acquisition gain, delay time window, and laser energy are automatically adjusted, and excitation and acquisition are re-executed. All spectral signals that pass the verification are superimposed point by point along the wavelength dimension and then averaged to obtain stable raw spectral data. .

4. The method for detecting rare earth element content according to claim 3, characterized in that, S3 specifically includes: Raw spectral data obtained based on S2 At least two characteristic spectral lines of the rare earth element to be measured are selected within a preset wavelength range. Background fitting and subtraction are performed on the local band of each spectral line to obtain the net spectral signal. The net spectral signal is integrated within the corresponding spectral line band to calculate the effective integrated intensity value of each spectral line. The effective integrated intensity value, along with the excited state energy, transition probability, statistical weight, and wavelength parameter of the corresponding spectral line, are substituted into the plasma temperature inversion formula to calculate the plasma temperature parameters. The calculation formula is: ； in, This is a plasma temperature parameter used to characterize the thermal state of the plasma formed after laser ablation. , These represent the atomic energy levels corresponding to the two selected characteristic spectral lines. , These are the effective intensity values ​​of the two characteristic spectral lines. , These are the center wavelengths of the two characteristic spectral lines, , These represent the transition probabilities of the two characteristic spectral lines. , These are the statistical weights of the energy levels corresponding to the two characteristic spectral lines. Boltzmann's constant; The calculated plasma temperature parameters Perform a rationality check, if the temperature parameter If the result exceeds the preset reasonable range, the system will automatically replace the alternative characteristic spectral line pair and recalculate until the inversion result meets the reasonable requirements.

5. The method for detecting rare earth element content according to claim 4, characterized in that, S4 specifically includes: Surface roughness parameters obtained in step S1 Particle equivalent diameter parameters The plasma temperature parameters obtained in step S3 The two-parameter matrix effect coupling compensation function, calibrated in step S0, is invoked to process the raw spectral data obtained in S2. Perform calibration to obtain calibrated spectral data. ; The expression for the two-parameter matrix effect coupling compensation function is as follows: ； in, , , The coupling weight coefficients determined in step S0 are... This is the preset reference temperature; During the calibration process, the overall intensity change and baseline stability of the spectrum before and after calibration are checked. If there are significant intensity fluctuations or baseline shifts in the calibration results, the coupling weight parameters are automatically adjusted and the calibration calculation is re-executed until stable and compliant calibrated spectral data are obtained.

6. The method for detecting rare earth element content according to claim 5, characterized in that, S5 specifically includes: Based on the calibrated spectral data from step S4, the characteristic spectral bands of the rare earth element to be tested are located, and local background subtraction and integration processing are performed to obtain the characteristic spectral intensity values ​​of the rare earth element to be tested; at the same time, the reference spectral intensity values ​​are extracted within the preset matrix element spectral bands. Combined with the surface roughness parameters obtained in step S1 Particle equivalent diameter parameters The plasma temperature parameters obtained in step S3 After performing structure-temperature dual-constraint normalization, the normalized characteristic intensity values ​​of the rare earth elements to be measured are obtained. The normalization calculation formula is as follows: ； in, The normalized characteristic intensity value of the rare earth element to be measured is... These are the characteristic spectral line intensity values ​​of the rare earth element to be measured. The reference spectral line intensity values ​​are for the matrix elements. The temperature constraint coefficient determined in step S0. The structural constraint function determined in step S0.

7. The method for detecting rare earth element content according to claim 1, characterized in that, S6 specifically includes: The structure-temperature dual-constraint normalized characteristic intensity of the rare earth element to be tested in S5 is input into the quantitative regression model calibrated in S0 to calculate the content detection result of rare earth element in the sample; the content detection result is displayed in real time, and the sample identifier and detection timestamp are associated to complete the data storage.

8. A system for detecting rare earth element content, used to perform a method for detecting rare earth element content as described in any one of claims 1-7, characterized in that: The system includes a sample stage for carrying the sample to be tested. A surface morphology acquisition unit is installed on the detection side of the sample stage, and a laser pulse excitation unit is installed next to the surface morphology acquisition unit. A focusing lens is installed on the output light path of the laser pulse excitation unit, and the focusing lens corresponds to the detection area of ​​the sample to be tested on the sample stage. The sample stage is also equipped with a spectral acquisition unit. The surface morphology acquisition unit, the laser pulse excitation unit, and the spectral acquisition unit are all bidirectionally connected to a computer. The surface morphology acquisition unit transmits the acquired sample surface structure parameters to the computer. The computer controls the laser pulse excitation unit to emit pulsed laser light towards the sample to be tested through the focusing lens. The spectral acquisition unit transmits the spectral data converted from the acquired plasma plume light signal to the computer. The computer completes data processing, model calculation, and quantitative calculation of rare earth element content.

9. The system for detecting rare earth element content according to claim 8, characterized in that: The laser pulse excitation unit includes a laser body, an energy adjustment module, and a beam shaping component, which can independently control the laser output energy, pulse width, and emission frequency. The surface morphology acquisition unit includes a low-power scanning light source and an image acquisition module, which can emit a non-ablative measurement beam and construct a sample surface height distribution matrix to calculate surface roughness parameters and particle equivalent diameter parameters. The spectral acquisition unit includes an optical fiber acquisition channel, an acquisition unit, a beam splitter, and a spectral detector. It can acquire emission spectral data within a specified wavelength range within a preset time window and convert it into electrical signals or digital spectral data. The computer includes a data processing and control unit and a display and storage unit. The data processing and control unit is equipped with a processor, a storage module and a communication interface, which can coordinate the working sequence of each unit, perform spectral data processing and model building calculations. The display and storage unit is equipped with a display screen and a data storage module, which can visually display the test results and archive the test data by associating timestamps and sample identification information.

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