Rapid detection method and system of ore rare earth elements by laser-induced breakdown spectroscopy

Abstract

The application discloses a mineral rare earth element laser-induced breakdown spectroscopy rapid detection method and system, relates to the technical field of spectral analysis and determination, and comprises the following steps: controlling a pulsed laser to break a mineral sample to be measured to generate plasma, acquiring emission image data of the plasma in a preset time window, extracting form feature of iron ions and target rare earth ions in the emission image data, and obtaining corresponding iron ion initial velocity data, iron ion radius data, target rare earth ion initial velocity data and target rare earth ion radius data; the method has the beneficial effects that the method of relying on pure mathematical deconvolution or forcibly stripping overlapping spectra by using a complex software model is avoided, and the risk of error accumulation and model overfitting caused by nonlinear signal fluctuation when facing a complex mineral matrix is eliminated.

Description

Technical Field

[0001] This invention relates to the field of spectroscopic analysis and measurement technology, and in particular to a rapid detection method and system for laser-induced breakdown spectroscopy of rare earth elements in ores. Background Technology

[0002] In the detection and analysis of rare earth ores, rapid and quantitative in-situ detection of rare earth elements such as lanthanum, cerium, neodymium, and praseodymium is a core aspect of rare earth ore composition analysis and quality evaluation. Traditional chemical analysis methods, such as inductively coupled plasma mass spectrometry (ICP-MS), require the complete digestion of complex ore matrices into a liquid state. This cumbersome pretreatment process typically takes several hours, making it difficult to meet the needs of rapid detection of large batches of ore samples. Therefore, laser-induced breakdown spectroscopy (LIBS) technology, with its advantages of requiring no complex sample pretreatment, extremely short single analysis time, and support for in-situ excitation, has gradually become the preferred solution in the field of rapid detection of rare earth ores. This technology generates high-temperature plasma by ablating the ore surface with a pulsed laser, and uses a spectrometer to collect the atomic and ion characteristic spectra emitted during the plasma cooling process, thereby achieving quantitative elemental analysis.

[0003] Existing LIBS technology has certain shortcomings when applied to the detection of complex associated rare earth ores: due to the high abundance of associated elements such as iron (Fe) in the raw ore, extremely dense emission lines are generated under plasma excitation. These spectral lines severely cross and overlap with the characteristic spectral lines of the target rare earth elements in optical space. Currently, the conventional methods used by those skilled in the art to overcome this spectral interference often involve using backend pure software algorithms (such as multivariate deconvolution, peak fitting, or complex machine learning models) to mathematically forcibly separate the mixed spectra. However, this purely algorithmic "mathematical deconvolution" not only fails to eliminate the overlap of physical luminescence centers at the source, but also easily leads to error accumulation and overfitting when faced with nonlinear signal fluctuations caused by the complex matrix of the ore. This results in the weak rare earth signal being annihilated by the strong radiation background of iron or algorithm noise, ultimately causing the detection limit of rare earth element quantitative detection to be too high and the quantitative error to be difficult to control, making it difficult to meet the needs of high-precision quantitative detection of rare earth ores. Summary of the Invention

[0004] In view of the above-mentioned prior art, this application is hereby made. The embodiments of this application provide a rapid detection method and system for laser-induced breakdown spectroscopy of rare earth elements in ores, which avoids the practice of relying on pure mathematical deconvolution or forcibly stripping overlapping spectra using complex software models, and eliminates the risk of error accumulation and model overfitting caused by nonlinear signal fluctuations when dealing with complex ores matrices.

[0005] According to one aspect of this application, a rapid laser-induced breakdown spectroscopy method for detecting rare earth elements in ores is provided, comprising:

[0006] The pulsed laser is controlled to penetrate the ore sample to generate plasma. The emission image data of the plasma within a preset time window is obtained. The morphological characteristics of iron ions and target rare earth ions in the emission image data are extracted to obtain the corresponding iron ion initial velocity data, iron ion radius data, target rare earth ion initial velocity data, and target rare earth ion radius data.

[0007] Based on the charge-to-mass ratio difference data between iron ions and target rare earth ions, combined with the separation momentum difference determined by the initial velocity data of iron ions and the initial velocity data of target rare earth ions, and the spatial separation boundary parameters determined by the radius data of iron ions and the radius data of target rare earth ions, the target separation magnetic field strength data is calculated and generated.

[0008] Based on the target separation magnetic field strength data, the spatial deflection trajectory of the target rare earth ion is determined, target spatial coordinate data is generated, and a transient magnetic field with a strength corresponding to the target separation magnetic field strength data is triggered, causing the iron ion and the target rare earth ion to separate their tracks.

[0009] Directional spectral acquisition is performed at the location indicated by the target spatial coordinate data to obtain the target rare earth characteristic spectral data. The matrix characteristic parameters in the target rare earth characteristic spectral data are extracted. After matching with a preset benchmark dataset, the target rare earth characteristic spectral data is mapped to concentration data for output.

[0010] According to another aspect of this application, a rapid detection system for rare earth elements in ores using laser-induced breakdown spectroscopy is provided, comprising:

[0011] The feature extraction module is used to control the pulsed laser to break down the ore sample to be tested to generate plasma, acquire the emission image data of the plasma within a preset time window, extract the morphological features of iron ions and target rare earth ions in the emission image data, and obtain the corresponding iron ion initial velocity data, iron ion radius data, target rare earth ion initial velocity data, and target rare earth ion radius data.

[0012] The separation magnetic field calculation module is used to calculate and generate target separation magnetic field strength data based on the charge-to-mass ratio difference data between iron ions and target rare earth ions, combined with the separation momentum difference determined by the initial velocity data of iron ions and the initial velocity data of target rare earth ions, and the spatial separation boundary parameters determined by the radius data of iron ions and the radius data of target rare earth ions.

[0013] The track-separation trigger module is used to determine the spatial deflection trajectory of the target rare earth ion based on the target separation magnetic field strength data, generate target spatial coordinate data, and trigger a transient magnetic field of intensity corresponding to the target separation magnetic field strength data, so that the iron ion and the target rare earth ion separate their tracks.

[0014] The acquisition and mapping module is used to perform directional spectral acquisition at the location indicated by the target spatial coordinate data, acquire target rare earth characteristic spectral data, extract matrix characteristic parameters from the target rare earth characteristic spectral data, and map the target rare earth characteristic spectral data into concentration data output after matching a preset benchmark dataset.

[0015] According to another aspect of this application, an electronic device is provided, including a memory and a processor, the memory being used to store computer-executable instructions, and the processor being used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method described above.

[0016] According to another aspect of this application, a computer storage medium is provided that stores computer-executable instructions thereon, which, when executed by a processor, implement the steps of the method described above.

[0017] Compared with the prior art, the rapid detection method and system for laser-induced breakdown spectroscopy of rare earth elements in ores according to the embodiments of this application utilizes the difference in cyclotron deflection of ions in a magnetic field to separate the interfering iron ions and the target rare earth ions in physical space. The spatial isolation between the interfering elements and the target elements is completed in the acquisition stage, avoiding the practice of relying on pure mathematical deconvolution or complex software models to forcibly remove overlapping spectra. It also eliminates the risk of error accumulation and model overfitting caused by nonlinear signal fluctuations when facing complex ores matrices.

[0018] This approach avoids the strong radiation background of iron and the accompanying baseline rise interference, preventing weak rare earth signals from being annihilated by iron spectral noise or back-end algorithm noise. Furthermore, by extracting matrix characteristic parameters from rare earth characteristic spectral data and matching them with a benchmark dataset for concentration mapping, the mixed matrix effect is further corrected, reducing the quantitative detection limit of trace rare earth elements and effectively controlling the final output concentration quantification error.

[0019] By placing the complex interference removal process at the plasma physics evolution stage, pure rare earth spectra are directly obtained through front-end image feature extraction and dynamic magnetic field intervention. This "physical filtering" method eliminates the computational cost of a large number of iterative peak finding, deconvolution calculations, and complex black-box models in the back end. At the same time, the high signal-to-noise ratio spectral data acquired in a directional manner directly reduces the computation time for benchmark dataset matching and concentration mapping. The overall process simplifies the data processing links, shortens the closed-loop analysis cycle of a single ore sample, improves the detection efficiency of rare earth elements in ores, and meets the demand for high detection efficiency in batch detection of rare earth ores. Attached Figure Description

[0020] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0021] Figure 1 This is a schematic diagram of the overall process of the rapid detection method for rare earth elements in ores by laser-induced breakdown spectroscopy according to the present invention.

[0022] Figure 2 This is a schematic diagram of the logical framework of the rapid detection method for rare earth elements in ores using laser-induced breakdown spectroscopy according to the present invention.

[0023] Figure 3 This is a schematic diagram of the extended structure of the rapid detection method for rare earth elements in ores using laser-induced breakdown spectroscopy according to the present invention. Detailed Implementation

[0024] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0025] Example 1:

[0026] To address the problem of severe spatial overlap between iron spectral lines and target rare earth elements in laser-induced breakdown spectroscopy detection of rare earth ores, and the tendency of traditional software deconvolution algorithms to accumulate errors, this application controls laser breakdown of the ore to generate plasma and acquires early emission image data. It extracts the initial velocity and radius data of iron ions and target rare earth ions, and calculates the target separation magnetic field strength data based on the charge-to-mass ratio difference between the two. Subsequently, a transient magnetic field of corresponding strength is triggered to physically separate the two in the air. Finally, directional spectral acquisition is performed at the generated target spatial coordinates, and the extracted matrix feature parameters are matched with a benchmark dataset to map the target rare earth characteristic spectrum into concentration data for output.

[0027] This invention uses a pre-processing dynamic magnetic field to directly isolate the interference source from the target element during the physical acquisition stage. This avoids the risks of overfitting and nonlinear fluctuations caused by forcibly stripping overlapping spectra using complex mathematical models. This approach not only obtains pure rare earth spectra with high signal-to-noise ratio and effectively reduces the quantitative detection limit and detection error of trace elements, but also simplifies the back-end data processing and calculation chain, improving the overall analysis and detection efficiency of rare earth ore samples.

[0028] Reference Figures 1-3As an embodiment of the present invention, a rapid detection method for rare earth elements in ores by laser-induced breakdown spectroscopy is provided, comprising: S1-S4.

[0029] For ease of understanding, the following explanation uses a rare earth ore sample mainly composed of bastnaesite and iron minerals as an example. Assume the sample number is ORE-0017, the main mineral composition is bastnaesite and iron minerals, the estimated iron content is approximately 25 wt%, the target rare earth element is neodymium (Nd), and the estimated Nd content is approximately 1.2 wt%. The detection device uses an Nd:YAG pulsed laser as the breakdown light source, an enhanced charge-coupled device (ICCD) camera for image data acquisition, a pulsed coil combined with a programmable current source to generate a transient magnetic field, and a fiber optic probe combined with a spectrometer to acquire the characteristic spectra of the target rare earth element. Subsequent steps will be described using this ore sample and this device configuration.

[0030] Figure 1 and Figure 2 The illustration shows a rapid detection method for rare earth elements in ores using laser-induced breakdown spectroscopy according to an embodiment of this application, specifically including:

[0031] like Figure 1 As shown, in step S1, the pulsed laser is controlled to break down the ore sample to be tested to generate plasma, and the emission image data of the plasma within a preset time window is obtained. The morphological characteristics of iron ions and target rare earth ions in the emission image data are extracted to obtain the corresponding iron ion initial velocity data, iron ion radius data, target rare earth ion initial velocity data, and target rare earth ion radius data.

[0032] Specifically, pulsed laser breakdown refers to irradiating the surface of the ore sample with a high-energy short-pulse laser focused by a focusing lens, causing the surface material to vaporize and ionize instantly, forming a high-temperature plasma composed of atoms, ions and free electrons. The pulsed laser can be a 1064nm wavelength pulsed laser output from an Nd:YAG solid-state laser, with a pulse width on the order of nanoseconds and a single pulse energy on the order of tens to hundreds of millijoules. The laser repetition frequency can be set according to the surface renewal requirements of the ore sample.

[0033] The emission image data refers to the two-dimensional spatial luminescence intensity distribution sequence of plasma acquired by an ICCD camera within a preset time window at multiple different delay times. Each frame of the emission image reflects the spatial morphology of the plasma at the corresponding delay time. The gate delay of the ICCD camera is used to control the acquisition start time, and the gate width is used to control the exposure duration of a single frame. The gate delay is synchronized from the laser pulse trigger time.

[0034] It should be noted that the preset time window refers to the time interval for acquiring emission image data. The basis for determining the preset time window is as follows: in the very early stage of plasma generation, continuous radiation such as bremsstrahlung is dominant, and the characteristic spectral lines are obscured by the broadband continuous background, making it difficult to clearly identify the ion morphology from the image; while in the late stage of plasma evolution, the signal intensity decays significantly, the morphological features tend to be diffuse and are not easy to extract stably. Therefore, the starting time of the preset time window is usually selected after the end of the continuous radiation-dominated stage and when the characteristic spectral lines begin to appear clearly, and the ending time is usually selected when the plasma morphology still maintains identifiable boundaries and the signal-to-noise ratio still meets the analysis requirements. The preset time window can be taken from about 500 ns to 3 μs after the laser pulse is triggered. The specific value can be pre-calibrated according to the type of ore matrix and the laser energy before the experiment.

[0035] Specifically, the morphological features are extracted as follows: spectral channels corresponding to the characteristic spectral lines of iron ions and the characteristic spectral lines of target rare earth ions are selected from the emission image data. The characteristic spectral lines can be selected with reference to a known atomic spectral database. The two-dimensional spatial luminescence intensity distribution of each ion in the wavelength channel is obtained at each delay time. Boundary recognition is performed on the spatial luminescence intensity distribution of each frame according to a preset luminescence intensity threshold. After fitting the recognized boundary into an approximate ellipsoidal contour, the equivalent radius of the ellipsoid is used as the radius data of the ion at that delay time. The spatial position of the centroid of the ion distribution at different delay times is removed to obtain the initial velocity data of the ions by corresponding time intervals.

[0036] Using the aforementioned mineral sample example, a pulsed laser was focused onto the surface of mineral sample ORE-0017 to trigger breakdown. An ICCD camera acquired emission images in 100-ns increments starting 500 ns after laser pulse triggering, ending at 3 μs, for a total of 26 image frames. In the images at each delay time, channels near Fe II 259.94 nm and Nd II 401.22 nm were selected to extract the two-dimensional spatial emission distribution of iron and neodymium ions at that wavelength. After intensity threshold boundary identification and ellipsoid fitting, the radius data of iron ions was approximately 1.2 mm and the radius data of neodymium ions was approximately 0.8 mm. After removing the emission centroids of iron and neodymium ions at each delay time according to the corresponding time interval, the corresponding initial velocity data of iron and neodymium ions were obtained.

[0037] The above scheme transforms the two-dimensional luminescence intensity distribution in the early stages of plasma evolution into morphological feature parameters that can be quantitatively extracted, providing input for the subsequent magnetic field calculation based on the charge-to-mass ratio difference. After extracting the plasma morphological feature parameters, the method enters the calculation stage of separating the magnetic field strength parameters. The core of this stage is to reverse-calculate the magnetic field strength parameters required to cause the two types of ions to spatially separate based on the differences in mass, charge, and motion state between iron ions and target rare earth ions, thereby providing a basis for subsequent transient magnetic field triggering.

[0038] return Figure 1 In step S2, based on the charge-to-mass ratio difference data between iron ions and target rare earth ions, combined with the separation momentum difference determined by the initial velocity data of iron ions and the initial velocity data of target rare earth ions, and the spatial separation boundary parameters determined by the radius data of iron ions and the radius data of target rare earth ions, the target separation magnetic field strength data is calculated and generated, including:

[0039] Construct a spatial feature array of plasma orbital phases that uses the momentum difference as the momentum evolution dimension and the spatial separation boundary parameters as the geometric displacement dimension;

[0040] Specifically, phase space refers to the abstract space spanned by spatial position coordinates and momentum coordinates, describing the motion state of particles. The plasma phase space feature array refers to a discrete two-dimensional data array formed by rasterizing the motion states of iron ions and target rare earth ions using the momentum difference as the momentum evolution dimension and the spatial separation boundary parameter as the geometric displacement dimension. Among them, the momentum difference is obtained by subtracting the product of the initial velocity data of iron ions and the initial velocity data of target rare earth ions with their respective ion masses, reflecting the momentum broadening benchmark of the two types of ions before the action of the magnetic field; the spatial separation boundary parameter is jointly determined by the radius data of iron ions and the radius data of target rare earth ions, reflecting the boundary of the geometrically achievable distribution range of the two types of ions.

[0041] Extract the equivalent electromagnetic coupling characteristic parameters from the charge-to-mass ratio difference data of iron ions and target rare earth ions;

[0042] The equivalent electromagnetic coupling characteristic parameter characterizes the strength of the coupling effect of a magnetic field on a unit mass of ions, and is derived from the gyroradius formula. The key coupling term that determines the size of the gyro radius is, among which, The gyroradius (m) represents the ion's mass physical constant, v represents the ion's velocity perpendicular to the magnetic field direction (m / s), i.e., the extracted initial velocity data of iron ions or the initial velocity data of target rare earth ions, q represents the charge carried by the ion (C), and B represents the magnetic induction intensity (T). Due to the different charge numbers, the equivalent electromagnetic coupling characteristic parameters of the two types of ions show differences, and this difference causes the separability of the gyroradius under the action of a unified magnetic field.

[0043] Using the spatial de-overlap limit state as the objective optimization constraint, dynamic boundary optimization is performed based on the equivalent electromagnetic coupling characteristic parameters in the plasma orbital phase spatial characteristic array. The solution outputs target separation magnetic field strength data that satisfies the objective optimization constraint, including:

[0044] In the plasma phase-space feature array, the dynamic distribution probability tensor of iron ions and target rare earth ions is generated based on the equivalent electromagnetic coupling feature parameter mapping. The specific formula is as follows:

[0045] ;

[0046] Where i represents the ion type. Let be the dynamic distribution probability tensor element of ion i at coordinates (r, p) in phase space, representing the probability density ( ), where r is the spatial coordinate variable (m) of the geometric displacement dimension. The momentum coordinate variable of the momentum evolution dimension ( ), The initial central momentum parameter of ion i ( (), obtained by multiplying the initial velocity data by the ion mass, Let be the initial spatial center coordinate parameter (m) of ion i. Here is the radius data (m) of ion i. To separate momentum differences, the momentum broadening reference for phase space is ( ), The equivalent electromagnetic coupling characteristic parameter is defined as the ionic charge. Magnetic field strength separating from the target The product ( ), Pi is a constant.

[0047] It should be noted that the modeling of the dynamic distribution probability tensor is based on the following: In the early stage of plasma evolution, the initial state distribution of the same ion in phase space can be approximated as a two-dimensional joint distribution with the initial central momentum as the momentum center, the initial spatial center coordinates as the position center, the ion radius data as the spatial expansion scale, and the separation momentum difference as the momentum expansion scale; After the introduction of the magnetic field, the equivalent electromagnetic coupling characteristic parameter forms a coupling constraint on the momentum evolution through the Lorentz force, so that the distribution in phase space unfolds in the form of the probability density containing the equivalent electromagnetic coupling characteristic parameter.

[0048] Extract the spatial interference overlap data of the dynamic distribution probability tensors of iron ions and target rare earth ions in the geometric displacement dimension; where the spatial interference overlap data refers to the integral value of the point-by-point minimum value of the dynamic distribution probability tensors of iron ions and target rare earth ions along the geometric displacement dimension, which is used to quantify the degree of overlap of the two types of ions in spatial distribution.

[0049] The convergence state of the spatial interference overlap data to the basis noise tolerance parameter is set as the spatial de-overlap limit state.

[0050] Specifically, the floor noise tolerance parameter refers to the minimum signal ratio that the spectral acquisition system can distinguish between the target signal and the background noise under the current detection device conditions. It is determined as follows: a pure matrix mineral sample without the target element is subjected to multiple blank measurements under the same experimental conditions as the sample to be tested. The standard deviation of the obtained signal fluctuations is taken as the floor noise level, and a preset multiple (e.g., 3 times) of the standard deviation is taken as the floor noise tolerance parameter. When the spatial interference overlap data decreases to close to the floor noise tolerance parameter, the signal contribution of iron ions distributed at the spatial position of the target rare earth ions has fallen within the noise background. At this time, the two types of ions reach the de-overlap limit state in spatial distribution.

[0051] The virtual magnetic field strength parameters are mapped and traversed in the spatial feature array of plasma orbit phases. The virtual magnetic field strength parameters that match the spatial de-overlap limit state are selected and confirmed as the target separation magnetic field strength data that meet the target optimization constraint conditions.

[0052] Specifically, the traversal mapping can scan the virtual magnetic field strength parameter successively within a preset value range according to a preset step size. The preset value range can be set with reference to the intensity range achievable by a conventional pulse magnetic field device. In one embodiment, the preset value range can be 0.1T to 5T, and the preset step size can be 0.05T. Each time a virtual magnetic field strength parameter is substituted, the corresponding spatial interference overlap data is calculated once according to the dynamic distribution probability tensor formula. The virtual magnetic field strength parameter that first satisfies the spatial de-overlap limit state is confirmed as the target separation magnetic field strength data. Other alternative optimization methods include numerical optimization methods such as binary search and gradient descent, which are not limited in this embodiment.

[0053] It should be noted that the gyrocoel radius of an ion in a magnetic field is determined by its momentum and charge-to-mass ratio. Ions with different charge-to-mass ratios exhibit different gyrocoel radii under the same magnetic field, thus creating the possibility of spatial separation. This step is constructed based on the aforementioned physical laws. An alternative approach is to directly use the gyrocoel radius formula to calculate the magnetic field strength, i.e., to perform single-point calculations based on the average velocity and average radius of the two types of ions. However, this method ignores the fact that a large number of ions in the plasma exhibit distribution broadening in terms of velocity and spatial position. The calculated magnetic field strength usually only separates ions near the distribution center, while ions on both sides of the distribution still have spatial overlap. This application further constructs the solution process as a boundary optimization problem in the sense of phase space distribution, so that the calculated magnetic field strength can cover the distribution range of most iron ions and target rare earth ions in the plasma.

[0054] It should be noted that this embodiment uses associated iron (Fe) as a typical interfering element because iron is usually abundant in rare earth ores, and its emission lines under plasma excitation are extremely dense, with the most significant optical overlap with the characteristic spectral lines of the target rare earth element. Therefore, iron ions are selected as representative interfering ions; however, the method provided in this application is not limited to iron. As can be seen from the aforementioned physical laws, under the same magnetic field, the gyroscopic radius of an ion is determined by its momentum and charge-to-mass ratio. Interfering ions that differ from the target rare earth ions in charge-to-mass ratio will produce distinguishable spatial deflection trajectories due to their different gyroscopic radii. Therefore, this method is also applicable to the spatial separation and isolation between other interfering elements and the target rare earth element. These other interfering elements can be, for example, common associated elements in rare earth ores such as calcium (Ca), silicon (Si), aluminum (Al), manganese (Mn), titanium (Ti), and barium (Ba), as well as other elements with different charge-to-mass ratios from the target rare earth ions.

[0055] In practical implementation, it is only necessary to replace the parameters extracted and calculated for iron ions in steps S1 to S4 with the corresponding parameters extracted and calculated for the interfering ion. That is, replace the initial velocity data and radius data of iron ions with the initial velocity data and radius data of the interfering ion, and replace the charge-to-mass ratio difference data between iron ions and target rare earth ions with the charge-to-mass ratio difference data between the interfering ion and target rare earth ions. Then, following the same phase space boundary optimization, transient magnetic field triggering, and directional spectral acquisition process, the physical separation of the interfering ion and target rare earth ion can be achieved. The implementation principle and processing are the same as those for iron ions, and will not be repeated here. The only requirement for the type of interfering ion in this method is that the charge-to-mass ratio difference between the interfering ion and the target rare earth ion is sufficient to form a distinguishable cyclotron radius difference under the applied target separation magnetic field strength, so that the spatial interference overlap data of the two types of ions in the geometric displacement dimension can converge to within the basis noise tolerance parameter.

[0056] Furthermore, when multiple interfering elements are present in the ore sample to be tested, the corresponding target separation magnetic field strength data can be calculated for one or more interfering ions that significantly interfere with the target rare earth characteristic spectral lines, and the orbital separation can be performed sequentially; or when constructing the plasma orbital phase spatial characteristic array, the dynamic distribution probability tensor of multiple interfering ions can be included in the optimization constraint condition of the spatial de-overlap limit state, so that the calculated target separation magnetic field strength data can enable the target rare earth ions to simultaneously avoid the deflection and aggregation regions of multiple interfering ions. This application does not limit the number and type of interfering ions.

[0057] Using the aforementioned mineral sample example, the initial velocity data of iron ions, the initial velocity data of neodymium ions, and the radius data of iron ions and neodymium ions obtained in step S1 are substituted into the constructed plasma phase separation spatial feature array. The equivalent electromagnetic coupling feature parameters are obtained by multiplying the charge of iron ions and the charge of neodymium ions by the virtual magnetic field strength parameter. The virtual magnetic field strength parameter is substituted into the array in the range of 0.1T to 5T with a step size of 0.05T. The base noise tolerance parameter is pre-calibrated to 3 times the standard deviation of the corresponding blank baseline. When the virtual magnetic field strength parameter is about 0.8T, the spatial interference overlap data of iron ions and neodymium ions first drops below the base noise tolerance parameter. At this point, 0.8T is confirmed as the target separation magnetic field strength data.

[0058] The above scheme extends the calculation of magnetic field strength from single-point cyclotron radius estimation to boundary optimization based on the full distribution probability tensor. This allows the obtained target separation magnetic field strength data to cover the distribution range of most iron ions and target rare earth ions in the plasma, avoiding the residual overlap of ions on both sides of the distribution caused by calculating the magnetic field strength based on only a few ion states. After obtaining the target separation magnetic field strength data, the method enters the transient magnetic field triggering execution stage. Considering that the plasma itself has the characteristic of kinetic energy decaying over time, this embodiment further modulates the magnetic field strength according to the time sequence, so that the magnetic field can provide a deflection force that matches the current ion kinetic energy state at different moments of plasma evolution.

[0059] return Figure 1 In step S3, the spatial deflection trajectory of the target rare earth ions is determined based on the target separation magnetic field strength data, and target spatial coordinate data is generated.

[0060] Specifically, the calculation of the spatial deflection trajectory uses the plasma generation location as the origin of the coordinate system, the gyration radius determined by the target separation magnetic field strength data as the deflection radius, and the laser ablation direction as a reference to determine the deflection direction. This allows the spatial position coordinates of the target rare earth ions after being deflected by the magnetic field to be obtained as the target spatial coordinate data.

[0061] A transient magnetic field of intensity corresponding to the target separation magnetic field strength data is triggered, causing iron ions to separate from the target rare earth ions, including:

[0062] Acquire time-domain kinetic energy decay characteristic data of iron ions and target rare earth ions within a preset time window;

[0063] Specifically, the time-domain kinetic energy decay characteristic data can be obtained from the same set of emission image data obtained in step S1: within a preset time window, the displacement of the centroid of the ion distribution at each delay moment is calculated by difference with the corresponding time interval to obtain the instantaneous velocity at each moment; combined with the mass of the ion, the instantaneous kinetic energy at each moment is obtained according to the definition of kinetic energy; after arranging in time series, the time-domain kinetic energy decay characteristic data is obtained.

[0064] The target separation magnetic field strength data is mapped to a time-domain sequence based on the time-domain kinetic energy decay characteristic data to generate transient magnetic field time-varying drive array data, including:

[0065] Extract the temporal decay gradient parameter from the temporal kinetic energy decay feature data;

[0066] Specifically, the temporal decay gradient parameter refers to the relative rate of plasma kinetic energy dissipation over time within a preset time window. This temporal decay gradient parameter is determined by fitting the time-domain kinetic energy decay characteristic data in an exponential decay manner along the time dimension, resulting in... The decay expression of the form, where This is the time-series decay gradient parameter; in one implementation, the natural logarithm of the time-domain kinetic energy decay characteristic data can be taken first, and then least squares linear fitting can be performed. The absolute value of the slope of the fitted line is the time-series decay gradient parameter.

[0067] The preset time window is divided into discrete time-series node sequences according to the preset sampling frequency;

[0068] It should be noted that the preset sampling frequency is determined based on the following: According to the sampling theorem, the sampling frequency must be higher than twice the highest effective frequency component in the time-domain kinetic energy decay characteristic data in order to avoid spectral aliasing of the reconstructed continuous magnetic field strength signal; for example, for a preset time window on the order of hundreds of nanoseconds to several microseconds, the preset sampling frequency can be on the order of 10MHz to 100MHz.

[0069] Specifically, the process of dividing a preset time window into a discrete time-series node sequence is as follows: according to the formula Calculate the total number N of discrete time nodes, where, The time span (s) of the preset time window. The preset sampling frequency (Hz) is used; then the preset time window starts at the beginning of the sampling time. Using the time base, according to the sampling time step Generate discrete time series (Index number k=1, 2, ..., N), the set of this discrete time series constitutes the discrete time series node sequence. Through this segmentation process, the continuous plasma evolution time is transformed into a discrete computation grid, providing a computational framework for the subsequent discrete node weight allocation of the time-varying magnetic field driven array.

[0070] Based on the temporal decay gradient parameter, node weights are assigned to the target separated magnetic field strength data to generate temporal weighted concatenated data. The specific formula is as follows:

[0071] ;

[0072] in, This represents the time-series weighted concatenated data (unit: T, Tesla) for the k-th discrete time-series node. Separate the magnetic field strength (T, Tesla) for the target. The time-series decay gradient parameter characterizes the relative rate (1 / s) of plasma kinetic energy dissipation over time. This is the index number of the discrete time-series node sequence. It is the reciprocal of the preset sampling frequency, i.e., the system's sampling time interval (s);

[0073] It should be noted that the above time-series weighted cascaded data formula modulates the target separation magnetic field strength data node by exponential function, so that the magnetic field strength increases exponentially along the discrete time-series node sequence. The physical basis is that the plasma kinetic energy decays exponentially with time, and the corresponding ion momentum decreases synchronously. In order to maintain the ion cyclotron radius at each moment consistent with the target separation condition, the magnetic field strength needs to increase synchronously with the decrease of ion kinetic energy. The magnetic field strength and ion kinetic energy form a mutually inverse compensating relationship in the form of an exponential function.

[0074] The time-series weighted concatenated data is injected into the discrete time-series node sequence for array reconstruction, outputting transient magnetic field time-varying driving array data. The specific formula is as follows:

[0075] ;

[0076] in, The transient magnetic field time-varying drive array data (T, Tesla) is in the continuous time domain t, where t is the continuous time variable (s) within a preset time window. The total number of discrete-time nodes. Let S represent the standard Singer interpolation function, used for continuous reconstruction of discrete signals. The expression for the standard Singer interpolation function is: Its function is to combine the magnetic field strength values ​​at discrete time nodes into a differentiable magnetic field strength function in the continuous time dimension based on the Nyquist-Shannon reconstruction theorem.

[0077] The transient magnetic field time-varying drive array data is sent down as a timing trigger signal, and the transient magnetic field dynamically modulated in the time domain is output to separate iron ions from the target rare earth ions.

[0078] Specifically, the transient magnetic field can be generated by a pulse coil or a Helmholtz coil combined with a programmable current source device. The programmable current source uses the transient magnetic field time-varying drive array data as a timing trigger signal to control the time-varying amplitude of the current pulse, thereby forming a transient magnetic field in the space near the plasma generation location that matches the decay state of ion kinetic energy.

[0079] Under the action of this transient magnetic field, the physical mechanism by which iron ions and target rare earth ions separate their orbits is as follows: When iron ions and target rare earth ions, which are in a state of high-speed outward expansion, pass through the vertical transient magnetic field, they are radially deflected by the Lorentz force. Since the mass of iron ions is much smaller than that of target rare earth ions, their charge-to-mass ratio is larger, and they exhibit a smaller gyro radius under the same dynamic magnetic field, resulting in a drastic curvature of their deflection trajectory. On the other hand, the target rare earth ions have a large mass and high mechanical inertia, exhibiting a larger gyro radius, resulting in a gentler curvature of their deflection trajectory. The time-domain dynamically modulated transient magnetic field compensates for the momentum loss caused by the ion deceleration, ensuring that the difference in the gyro radius between these two ions with different charge-to-mass ratios remains consistent throughout the entire window period during which their kinetic energy decays over time. As a result, they are continuously and sequentially separated in physical space, eventually falling into non-overlapping macroscopic spatial distribution regions, thus completing the separation of their orbits in physical form.

[0080] The kinetic energy of ions within the plasma exhibits an overall decay characteristic within a preset time window: in the early stages of evolution, the ion kinetic energy is higher, corresponding to a larger gyroscope radius; in the late stages of evolution, the ion kinetic energy is lower, corresponding to a smaller gyroscope radius. If a single constant magnetic field is applied throughout the entire evolution time window, the high-kinetic-energy ions in the early stages of evolution may not separate sufficiently due to insufficient magnetic field deflection, while the low-kinetic-energy ions in the late stages of evolution may be over-deflected due to a relatively strong magnetic field, resulting in a mismatch between the final luminescent centroid position and the target spatial coordinate data. This application constructs the intensity of the transient magnetic field as a time-varying form that matches the decay of plasma kinetic energy, ensuring that the magnetic field intensity matches the ion kinetic energy at each moment of plasma evolution. Alternative implementation methods include applying segmented magnetic fields such as trapezoidal pulsed magnetic fields and step magnetic fields, but segmented magnetic fields exhibit abrupt changes at switching points, resulting in discontinuities in their impact on ion trajectories. In contrast, the continuous time-varying method based on Singer interpolation used in this application is differentiable overall in the time dimension, thus solving the problem of trajectory discontinuity.

[0081] Using the aforementioned mineral sample example, the time-domain kinetic energy decay characteristic data is obtained from the emission image data obtained in step S1 after differential calculation. The natural logarithm of this data is then used for least-squares linear fitting to obtain the time-series decay gradient parameter, which is approximately... The preset time window of 500ns to 3μs is divided into 125 discrete time nodes according to the preset sampling frequency of 50MHz. After substituting the target separation magnetic field strength data of 0.8T into the time weight concatenation data formula, the magnetic field strength corresponding to node k=0 is 0.8T, and the magnetic field strength corresponding to node k=124 rises to about 1.61T. The obtained time weight concatenation data is output to the programmable current source after Singer interpolation. The pulse coil forms a time-varying transient magnetic field in the plasma evolution space. Iron ions are deflected to one side according to their smaller gyrocoel radius, and neodymium ions are deflected to the other side according to their larger gyrocoel radius, and the two types of ions are spatially separated.

[0082] The above scheme ensures that the intensity of the transient magnetic field matches the decay state of ion kinetic energy in the time dimension of plasma evolution, thus avoiding trajectory scattering and acquisition position offset caused by the mismatch of early and late ion forces under constant magnetic field application.

[0083] After the iron ions and the target rare earth ions complete spatial separation, the method enters the directional spectral acquisition and concentration mapping stage. The core of this stage is to lock the spatial position of the spectral acquisition to the deflection and aggregation region of the target rare earth ions, avoid the deflection and aggregation region of iron ions, eliminate the spatial aliasing interference of iron spectral lines on the target spectral lines from the source, and then output the concentration data after correcting the remaining matrix effects by combining matrix characteristic parameters.

[0084] return Figure 1 In step S4, directional spectral acquisition is performed at the location indicated by the target spatial coordinate data to obtain the target rare earth characteristic spectral data, the matrix characteristic parameters in the target rare earth characteristic spectral data are extracted, and after matching with a preset benchmark dataset, the target rare earth characteristic spectral data is mapped to concentration data for output.

[0085] Specifically, directional spectral acquisition refers to limiting the spatial acquisition field of the spectrometer to the vicinity of the target spatial coordinate data, so that the acquired spectral data mainly comes from plasma emission at that location. Matrix characteristic parameters refer to several spectral parameters that reflect the physical state of the plasma and the influence of the background, including at least the plasma continuous radiation background intensity, the electron density estimated by Stark broadening, and the plasma temperature estimated by the Boltzmann diagram method. These parameters are used to correct for fluctuations in the intensity of the target rare earth spectral lines caused by differences in the physical state of the plasma itself.

[0086] It should be noted that the preset benchmark dataset refers to the set of correspondences consisting of target rare earth characteristic spectral data, corresponding matrix characteristic parameters, and corresponding true concentrations, obtained by standard mineral samples with known rare earth element concentrations under the same experimental conditions (including laser parameters, ICCD gating parameters, spectrometer acquisition distance and acquisition angle, etc.) as the mineral sample to be tested. The benchmark dataset is constructed by selecting several standard mineral samples with rare earth element concentration gradients covering the expected detection range (for example, at least 10 concentration gradient points between 0.05wt% and 5wt% can be selected). After processing each standard mineral sample according to steps S1 to S3 of this embodiment, target rare earth characteristic spectral data are collected and corresponding matrix characteristic parameters are extracted. Then, the true value of rare earth element concentration of the standard mineral sample is independently calibrated using an independent chemical analysis method (such as inductively coupled plasma mass spectrometry, ICP-MS). The resulting triplet of "target rare earth characteristic spectral data - matrix characteristic parameters - concentration true value" is collected to form a preset benchmark dataset. The matching method can be partial least squares regression, support vector regression, nearest neighbor matching based on matrix characteristic parameter index, etc.

[0087] Taking partial least squares regression as an example, the specific implementation process of mapping target rare earth feature spectral data to concentration data output is as follows: The target rare earth feature spectral data matrix of each standard mineral sample in the preset benchmark dataset is fused and dimension-reduced with the matrix feature parameter matrix to form the principal component independent variable matrix X, and the corresponding known true concentration values ​​are used to form the dependent variable matrix Y; the maximum covariance latent variable of X and Y is extracted using partial least squares, a linear regression model is established, and the mapped regression coefficient matrix W is obtained; during actual detection, the pure target rare earth feature spectral data currently obtained from the target spatial location is fused with the extracted matrix feature parameters to form the input feature row vector. Through the linear mapping formula The predicted concentration of the target rare earth element in the tested mineral sample can then be calculated. This completes the mapping output from the pure spectral feature space to the elemental mass percentage concentration.

[0088] Traditional LIBS methods acquire spectra of the entire plasma. In the acquired spectra, the dense spectral lines of iron overlap with the characteristic spectral lines of the target rare earth elements in wavelength. This requires back-end processing using mathematical methods such as deconvolution, multiple linear regression, or machine learning to separate the mixed spectra. However, this back-end processing method struggles to eliminate the overlap of physical luminescence centers at the source and is highly sensitive to nonlinear signal fluctuations caused by complex ore matrices. An alternative approach is to use narrowband filters to acquire only the characteristic spectral lines of the target rare earth elements. However, this method only selects wavelengths and cannot distinguish the emission contributions of iron and the target rare earth elements at wavelength overlap locations. This step uses targeted spatial coordinate data for directional spectral acquisition. The imaging optical axis of the fiber optic probe or spectrometer is aligned with the deflection and aggregation location of the target rare earth ions, directly isolating the signal contribution of the iron ion luminescence region in spatial dimension. The obtained characteristic spectrum is dominated by the emission signal of the target rare earth elements. After correction for residual matrix effects using matrix characteristic parameters, the concentration data is output.

[0089] Using the aforementioned mineral sample example, after aligning the incident end of the fiber optic probe with the Nd:20 ion deflection and aggregation position indicated by the target spatial coordinate data generated in step S3, spectral acquisition is performed within a preset time window to obtain characteristic spectral data near Nd:20 1.22 nm; the continuous radiation background intensity is extracted from the characteristic spectral data, and... The electron density estimated by broadening the spectral line (i.e., the first characteristic spectral line of the Balmer series of hydrogen atoms, with a wavelength of approximately 656.28 nm) and the plasma temperature obtained from the relative intensity of Nd multi-spectral lines were used as the corresponding matrix characteristic parameters. The characteristic spectral data and matrix characteristic parameters were substituted into a preset benchmark dataset constructed from standard rare earth mineral samples of the same type for matching. Partial least squares regression was used for matching, and the final output was the concentration of neodymium in the mineral sample as 1.18 wt%.

[0090] By using the above scheme, the interference element removal process is moved from the back-end mathematical deconvolution to the front-end spatial isolation acquisition. The obtained characteristic spectrum is mainly the emission signal of the target rare earth element. After correcting the remaining matrix effect by combining matrix characteristic parameters, the concentration data is mapped and output, which shortens the data processing link and improves the quantitative analysis of trace rare earth element concentration.

[0091] Based on the basic method consisting of S1-S4, since the plume boundary of the target rare earth ion continues to expand during the outward expansion evolution of the plasma, when the transient magnetic field is a single magnetic field with a relatively uniform spatial distribution, the radial constraint force of the magnetic field on the target rare earth ion in the plume boundary region is insufficient. The target rare earth ion may spatially diverge during the deflection process, causing the position indicated by the target spatial coordinate data to deviate from the actual centroid position of the ion distribution, thereby reducing the alignment accuracy of subsequent directional spectral acquisition.

[0092] To address this deviation issue, this embodiment further introduces a local magnetic field gradient compensation step to update the target spatial coordinate data, such as... Figure 3 As shown, before generating the target spatial coordinate data, the following steps are also included:

[0093] Extract the spatial divergence feature data of the target rare earth ion from the emission image data; specifically, the spatial divergence feature data refers to the expansion state data of the outer contour position of the luminescent region of the target rare earth ion relative to the initial luminescent center at different delay times, reflecting the degree of spatial divergence of the target rare earth ion plume.

[0094] Spatial divergence characteristic data, iron ion radius data, and target rare earth ion radius data are calculated to generate local magnetic field gradient compensation data, including:

[0095] Extract the plumose boundary expansion rate data of the target rare earth ions based on the spatial divergence characteristic data;

[0096] Specifically, the method for extracting the feather boundary expansion rate data is as follows: the difference between the equivalent boundary radius of the target rare earth ion luminescent region at each delay time and the radius at the initial delay time is divided by the corresponding time interval to obtain the instantaneous velocity of feather boundary expansion at each time; the representative value of the expansion instantaneous velocity at each time is taken according to the time series (in one embodiment, the arithmetic mean can be taken) as the feather boundary expansion rate data.

[0097] By combining the iron ion radius data and the target rare earth ion radius data, the local magnetic pressure distribution data of the target rare earth ion is calculated. The specific formula is as follows:

[0098] ;

[0099] in, Radial distance from the light-emitting center Local magnetic pressure distribution data (Pa) at the location. Let be the radial spatial variable (m) from the geometric center of Yu Hui. The square (m²) represents the radius of the target rare earth ion. The square (m²) represents the radius of the iron ion. The physical constant of vacuum permeability ( ), Separate the target magnetic field strength (T, Tesla);

[0100] It should be noted that the physical meaning of the above local magnetic pressure distribution data is: the equivalent pressure generated by the magnetic field on the circulation of charged particles, that is, the equivalent mechanical confinement corresponding to the magnetic field energy density; its physical basis is: in plasma magnetic confinement theory, the magnetic field energy density corresponding to the magnetic induction intensity B is... This energy density is dimensionally equivalent to pressure (Pa), and can be used as the equivalent strength of the radial confinement of the plasma by the magnetic field. The formula further expresses the magnetic pressure in radial space through the geometric relationship between the squares of the iron ion radius and the squares of the target rare earth ion radius. The relevant distribution function allows the magnetic pressure to exhibit differentiated constraints on iron ions and target rare earth ions at different radial positions. Among these, the physical constant of vacuum permeability... It is a fundamental physical constant, defined by the relationship between magnetic induction and magnetic field strength in vacuum, and its value is... T·m / A does not need to be determined experimentally in this method;

[0101] The spatial rate of change of magnetic induction intensity required to calculate the offsetting feint boundary expansion rate data is used as local magnetic field gradient compensation data. The specific formula is as follows:

[0102] ;

[0103] in, This is local magnetic field gradient compensation data, representing the spatial derivative of magnetic induction intensity (T / m). The mass physical constant (kg) of a single target rare earth ion. This represents the boundary expansion rate data of the feather (m / s). Separate the magnetic field strength (T, Tesla) for the target. For target rare earth ion radius data (m);

[0104] In this step, the construction of the aforementioned spatial change rate data of magnetic induction intensity is based on the mechanical equilibrium relationship: the target rare earth ion acquires an outward expansion inertia in the radial direction that is proportional to the square of the expansion rate. To constrain this expansion motion in the radial direction, a non-uniform magnetic field in space is required to provide a reverse gradient force to balance this expansion inertia. The spatial change rate of magnetic induction intensity along the radial direction corresponds to the required local magnetic field gradient compensation data. In terms of formula form, this gradient data increases with the increase of the mass of a single target rare earth ion and the square of the expansion rate, and decreases with the increase of the target separation magnetic field strength and the radius of the target rare earth ion, which conforms to the mechanical law that the compensation gradient required by the magnetic field weakens relatively when the basic strength increases.

[0105] Local magnetic field gradient compensation data is fused with target separation magnetic field strength data to generate composite magnetic field modulation data.

[0106] Specifically, the composite magnetic field modulation data is composed of two parameters: the target separated magnetic field strength data as the magnetic induction intensity at the center position and the local magnetic field gradient compensation data as the spatial rate of change of magnetic induction intensity along the radial direction. The composite magnetic field modulation data is used to drive a composite magnetic field generating device that simultaneously possesses a central field strength and a radial gradient.

[0107] Based on the composite magnetic field modulation data, the corresponding gradient magnetic field is triggered to obtain the centroid data of the distribution of target rare earth ions in a state of morphological contraction.

[0108] Specifically, the gradient magnetic field refers to a magnetic field with a rate of change in magnetic induction intensity in its spatial distribution, which can be generated by a combination of multiple coils with different current directions; the morphological contraction state refers to the state in which the expansion of the plume boundary of the target rare earth ions is canceled out by the reverse constraint force provided by the magnetic field gradient under the action of the gradient magnetic field, and the ion distribution tends to converge radially; the distribution centroid data refers to the geometric center coordinates obtained by re-acquiring the emission image of the target rare earth ions by the ICCD camera in the morphological contraction state, and then calculating them by weighting the ion emission intensity.

[0109] The target spatial coordinate data is updated based on the centroid distribution data, using the following formula:

[0110] ;

[0111] in, The updated target spatial coordinate data is a three-dimensional vector (m). The initial generated target spatial coordinate data is a three-dimensional vector (m). This is the centroid offset vector data of the target rare earth ion distribution extracted during morphological contraction.

[0112] In contrast, alternative processing methods include empirically biasing the original target spatial coordinates or using a spectral probe with a larger acquisition aperture to cover the divergence region. However, the former is difficult to adapt to the divergence differences of different mineral samples under different conditions, while the latter will introduce more background signals from the adjacent space. The gradient magnetic field compensation method based on the physical model in this application has interpretability for the correction of the target spatial coordinates and can reuse the same set of magnetic field generation devices with the transient magnetic field triggering link.

[0113] Using the aforementioned mineral sample example, the outer contour radius of neodymium ions at each delay time is extracted from the emission image sequence obtained in step S1. The plume boundary expansion rate data is calculated to be approximately 350 m / s in the manner described above. Substituting this data, along with the iron ion radius data, neodymium ion radius data, and the target separation magnetic field strength of 0.8 T, into the local magnetic pressure distribution data formula and the magnetic induction intensity spatial change rate data formula, respectively, the local magnetic field gradient compensation data is approximately 12 T / m. This gradient compensation data is then fused with the target separation magnetic field strength of 0.8 T to obtain composite magnetic field modulation data with a central strength of 0.8 T and a radial gradient of 12 T / m. After the gradient magnetic field generating device is driven to act on the plasma according to this composite magnetic field modulation data, the emission image is acquired again by the ICCD camera. The centroid of the neodymium ion distribution in the morphological contraction state is found to have an offset vector of approximately 0.15 mm relative to the initial target spatial coordinates. This offset vector is then substituted into the target spatial coordinate data update formula to obtain the updated target spatial coordinate data, which is used for directional spectral acquisition in step S4.

[0114] Example 2:

[0115] This is one embodiment of the present invention, which differs from the previous embodiment in that:

[0116] A rapid detection system for rare earth elements in ores using laser-induced breakdown spectroscopy includes:

[0117] The feature extraction module is used to control the pulsed laser to break down the ore sample to generate plasma, acquire the emission image data of the plasma within a preset time window, extract the morphological features of iron ions and target rare earth ions in the emission image data, and obtain the corresponding iron ion initial velocity data, iron ion radius data, target rare earth ion initial velocity data, and target rare earth ion radius data.

[0118] The separation magnetic field calculation module is used to calculate and generate target separation magnetic field strength data based on the charge-to-mass ratio difference data between iron ions and target rare earth ions, combined with the separation momentum difference determined by the initial velocity data of iron ions and the initial velocity data of target rare earth ions, and the spatial separation boundary parameters determined by the radius data of iron ions and the radius data of target rare earth ions.

[0119] The track separation triggering module is used to determine the spatial deflection trajectory of the target rare earth ion based on the target separation magnetic field strength data, generate target spatial coordinate data, and trigger a transient magnetic field of the intensity corresponding to the target separation magnetic field strength data, so that the iron ion and the target rare earth ion separate their tracks.

[0120] The acquisition and mapping module is used to perform directional spectral acquisition at the location indicated by the target spatial coordinate data, acquire the target rare earth characteristic spectral data, extract the matrix characteristic parameters in the target rare earth characteristic spectral data, and after matching with a preset benchmark dataset, map the target rare earth characteristic spectral data into concentration data output.

[0121] Example 3:

[0122] In one embodiment of the present invention, which differs from the previous embodiment, the electronic device includes one or more processors and a memory.

[0123] A processor can be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and can control other components in an electronic device to perform desired functions.

[0124] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0125] In one example, the electronic device may also include input devices and output devices, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). In addition, depending on the specific application, the electronic device may include any other suitable components.

[0126] Example 4:

[0127] Embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps described in the "Embodiment 1" section of this specification according to the various embodiments of this application.

[0128] Computer-readable storage media may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0129] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not restrict the application from being implemented using the specific details described above.

[0130] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0131] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0132] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0133] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A rapid detection method for rare earth elements in ores using laser-induced breakdown spectroscopy, characterized in that, include: The pulsed laser is controlled to penetrate the ore sample to generate plasma. The emission image data of the plasma within a preset time window is obtained. The morphological characteristics of iron ions and target rare earth ions in the emission image data are extracted to obtain the corresponding iron ion initial velocity data, iron ion radius data, target rare earth ion initial velocity data, and target rare earth ion radius data. Based on the charge-to-mass ratio difference data between iron ions and target rare earth ions, combined with the separation momentum difference determined by the initial velocity data of iron ions and the initial velocity data of target rare earth ions, and the spatial separation boundary parameters determined by the radius data of iron ions and the radius data of target rare earth ions, the target separation magnetic field strength data is calculated and generated. Based on the target separation magnetic field strength data, the spatial deflection trajectory of the target rare earth ion is determined, target spatial coordinate data is generated, and a transient magnetic field with a strength corresponding to the target separation magnetic field strength data is triggered, causing the iron ion and the target rare earth ion to separate their tracks. Directional spectral acquisition is performed at the location indicated by the target spatial coordinate data to obtain the target rare earth characteristic spectral data. The matrix characteristic parameters in the target rare earth characteristic spectral data are extracted. After matching with a preset benchmark dataset, the target rare earth characteristic spectral data is mapped to concentration data for output.

2. The rapid detection method for rare earth elements in ores using laser-induced breakdown spectroscopy according to claim 1, characterized in that, Before generating the target spatial coordinate data, the following steps are also included: Extract the spatial divergence feature data of the target rare earth ions from the emission image data; The spatial divergence characteristic data, the iron ion radius data, and the target rare earth ion radius data are calculated to generate local magnetic field gradient compensation data. The local magnetic field gradient compensation data and the target separated magnetic field intensity data are fused together to generate composite magnetic field modulation data. Based on the composite magnetic field modulation data, the corresponding gradient magnetic field is triggered to obtain the centroid data of the distribution of the target rare earth ions in the morphological contraction state. The target spatial coordinate data is updated based on the distribution centroid data.

3. The rapid detection method for rare earth elements in ores using laser-induced breakdown spectroscopy according to claim 2, characterized in that, The calculation generates local magnetic field gradient compensation data, including: Extract the plumose boundary expansion rate data of the target rare earth ion based on the spatial divergence characteristic data; By combining the iron ion radius data and the target rare earth ion radius data, the local magnetic pressure distribution data of the target rare earth ion is calculated; Calculate the spatial rate of change of magnetic induction intensity required to offset the expansion rate data of the feathery boundary, and use it as the local magnetic field gradient compensation data.

4. The rapid detection method for rare earth elements in ores using laser-induced breakdown spectroscopy according to claim 1, characterized in that, The calculation generates target separation magnetic field strength data, including: Construct a plasma phase-spacing spatial feature array that uses the separation momentum difference as the momentum evolution dimension and the spatial separation boundary parameter as the geometric displacement dimension; Extract the equivalent electromagnetic coupling characteristic parameters from the charge-to-mass ratio difference data between the iron ions and the target rare earth ions; Using the spatial de-overlap limit state as the target optimization constraint, dynamic boundary optimization is performed based on the equivalent electromagnetic coupling characteristic parameters in the plasma phase-separation spatial feature array, and the target separation magnetic field strength data that satisfies the target optimization constraint is calculated and output.

5. The rapid detection method for rare earth elements in ores using laser-induced breakdown spectroscopy according to claim 4, characterized in that, The solution output includes target separation magnetic field strength data that satisfies the target optimization constraints, including: In the plasma phase space feature array, a dynamic distribution probability tensor of iron ions and target rare earth ions is generated based on the equivalent electromagnetic coupling feature parameter mapping. Extract the spatial interference overlap data of the dynamic distribution probability tensor of the iron ions and the target rare earth ions in the geometric displacement dimension; The convergence state of the spatial interference overlap data approximating the basis noise tolerance parameter is set as the spatial de-overlap limit state. The virtual magnetic field strength parameters are traversed in the spatial feature array of the plasma orbital phase, and the virtual magnetic field strength parameters that match the spatial de-overlap limit state are selected and confirmed as the target separation magnetic field strength data that satisfy the target optimization constraint conditions.

6. The rapid detection method for rare earth elements in ores using laser-induced breakdown spectroscopy according to claim 1, characterized in that, The triggering of the transient magnetic field includes: Obtain the time-domain kinetic energy decay characteristic data of the iron ions and the target rare earth ions within the preset time window; The target separation magnetic field strength data is mapped to the time-domain sequence according to the time-domain kinetic energy decay characteristic data to generate transient magnetic field time-varying drive array data. The transient magnetic field time-varying drive array data is sent down as a timing trigger signal, and a time-domain dynamically modulated transient magnetic field is output to separate the iron ions from the target rare earth ions.

7. The rapid detection method for rare earth elements in ores using laser-induced breakdown spectroscopy according to claim 6, characterized in that, The generation of transient magnetic field time-varying drive array data includes: Extract the temporal decay gradient parameter from the temporal kinetic energy decay feature data; The preset time window is divided into discrete time-series node sequences according to a preset sampling frequency; Based on the time-series decay gradient parameter, node weight allocation is performed on the target separated magnetic field strength data to generate time-series weighted concatenated data. The time-series weighted concatenated data is injected into the discrete time-series node sequence for array reconstruction operation, and the transient magnetic field time-varying drive array data is output.

8. A rapid detection system for rare earth elements in ores using laser-induced breakdown spectroscopy, characterized in that, include: The feature extraction module is used to control the pulsed laser to break down the ore sample to be tested to generate plasma, acquire the emission image data of the plasma within a preset time window, extract the morphological features of iron ions and target rare earth ions in the emission image data, and obtain the corresponding iron ion initial velocity data, iron ion radius data, target rare earth ion initial velocity data, and target rare earth ion radius data. The separation magnetic field calculation module is used to calculate and generate target separation magnetic field strength data based on the charge-to-mass ratio difference data between iron ions and target rare earth ions, combined with the separation momentum difference determined by the initial velocity data of iron ions and the initial velocity data of target rare earth ions, and the spatial separation boundary parameters determined by the radius data of iron ions and the radius data of target rare earth ions. The track-separation trigger module is used to determine the spatial deflection trajectory of the target rare earth ion based on the target separation magnetic field strength data, generate target spatial coordinate data, and trigger a transient magnetic field of intensity corresponding to the target separation magnetic field strength data, so that the iron ion and the target rare earth ion separate their tracks. The acquisition and mapping module is used to perform directional spectral acquisition at the location indicated by the target spatial coordinate data, acquire target rare earth characteristic spectral data, extract matrix characteristic parameters from the target rare earth characteristic spectral data, and map the target rare earth characteristic spectral data into concentration data output after matching a preset benchmark dataset.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method as described in any one of claims 1 to 7.

10. A computer storage medium storing computer-executable instructions thereon, characterized in that: When the computer-executable instructions are executed by a processor, they implement the steps of the method as described in any one of claims 1 to 7.

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