FL-LIBS non-gated acquisition-based calibration-free method, device, equipment and medium

Abstract

The application relates to the technical field of atomic spectrum detection, in particular to an FL-LIBS calibration-free method and device based on non-gated acquisition, equipment and a medium. The method comprises the following steps: obtaining a corrected spectrum and determining a target reference element; obtaining a plurality of candidate vectors and determining candidate simulation spectra; iteratively updating each candidate vector; determining a target simulation spectrum and an optimal vector; determining a target electron temperature and a target particle number density; determining the particle number densities of other elements in the sample to be measured according to the target electron temperature; and performing normalization calculation on the target particle number density and the particle number densities of the other elements to obtain the concentration composition of the sample to be measured. The application generates a plurality of unknown parameter groups, i.e. candidate vectors, and finds the optimal vector that best matches by means of an optimization algorithm, thereby providing a method for obtaining the electron temperature required for time-integrated spectrum in the case of being free from a high-speed gated detector, and realizing fast and accurate calibration-free quantitative analysis of FL-LIBS.

Description

Technical Field

[0001] This application relates to the field of atomic spectrum detection technology, and in particular, to a calibration-free method, apparatus, equipment, and medium for FL-LIBS based on non-gated acquisition. Background Technology

[0002] Laser-induced breakdown spectroscopy (LIBS) has become a promising atomic spectroscopy detection technique due to its advantages such as speed, in-situ accuracy, non-contact operation, and online diagnostics. Traditional LIBS systems typically use solid-state lasers with low pulse output frequencies as the plasma excitation source. These lasers cannot maintain stable pulse energy over long periods of operation. Unstable pulse energy leads to fluctuations in the LIBS spectrum, resulting in decreased analytical accuracy. Fiber lasers, on the other hand, can maintain more stable pulse energy over extended periods, and some models cost less than one-tenth of solid-state lasers. This makes fiber laser-induced breakdown spectroscopy based on fiber lasers (FL-LIBS) a new direction for improving LIBS technology.

[0003] Despite the numerous advantages of FL-LIBS, its excessively high repetition frequency (≥20 kHz) makes synchronization between acquisition and pulse difficult. While gated detectors equipped with high-speed shutters can achieve timing synchronization with the pulses, their high cost and stringent operating temperature requirements limit their application to laboratory settings. Furthermore, the low single-pulse energy of fiber lasers results in poor signal-to-noise ratios for the spectra of individual plasmas (spectroscopy after the accumulation of a few plasma ions). Therefore, ungated detectors are typically used to acquire FL-LIBS spectra with exposure times much longer than the plasma generation period. However, the FL-LIBS spectra obtained under this acquisition strategy encompass the entire evolution of the plasma, making quantitative analysis using calibration-free LIBS methods unsatisfactory. Consequently, most studies on FL-LIBS still employ standard calibration methods. In conclusion, there is currently no calibration-free model for FL-LIBS based on ungated acquisition. Summary of the Invention

[0004] This application provides a calibration-free method, apparatus, equipment, and medium for FL-LIBS based on non-gated acquisition, to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of the embodiments of this application, a calibration-free FL-LIBS method based on non-gated acquisition is provided, the method comprising:

[0007] A corrected spectrum is acquired, and a target reference element is determined based on the corrected spectrum, wherein the corrected spectrum is obtained from the FL-LIBS spectrum acquired by the non-gated acquisition system for the sample to be tested;

[0008] A number of candidate vectors containing information on electron temperature and particle number density of a reference element are obtained, and a candidate simulated spectrum corresponding to each candidate vector is determined. The candidate vector corresponds to the target reference element, and the value of the candidate vector is randomly generated within a preset range.

[0009] Each candidate vector is iteratively updated according to a preset optimization algorithm. In each iteration, the fitness of each candidate vector is evaluated in a parallel evaluation manner. The parallel evaluation method is to compare the similarity of each candidate simulated spectrum with the corrected spectrum.

[0010] Based on the fitness of each candidate vector in the final iteration round, the target simulated spectrum with the highest similarity to the modified spectrum is determined, and the candidate simulated vector corresponding to the target simulated spectrum is determined as the optimal vector;

[0011] The target electron temperature and the target particle number density of the target reference element are determined based on the optimal vector.

[0012] The particle number density of elements other than the target reference element in the sample to be tested is determined based on the target electron temperature.

[0013] The concentration composition of the sample to be tested is obtained by normalizing the target particle number density and the particle number densities of each of the other elements.

[0014] The method further includes the following steps before obtaining the corrected spectrum:

[0015] Acquire the FL-LIBS spectrum of the sample to be tested by the non-gated acquisition system;

[0016] The FL-LIBS spectrum is preprocessed by background removal, noise reduction, and peak finding to obtain the preprocessed measurement spectrum;

[0017] The corrected spectrum is obtained through the following steps:

[0018] Among the various spectral lines of the measured spectrum, identify the target spectral line with the highest intensity;

[0019] The corrected spectrum is determined based on the spectral parameters of the target spectral line and a preset correction coefficient;

[0020] Each candidate vector consists of an initial temperature, a power of 1, and an electron temperature.

[0021] The preset range is obtained through the following steps:

[0022] The second spectral line intensity parameter is determined based on the first calculation formula, the target parameter, and the preset electron temperature;

[0023] The third spectral line intensity parameter is determined according to the preset second calculation formula, the target parameter, and the preset electron temperature;

[0024] The theoretical self-absorption coefficient curve related to the particle number density and the preset electron temperature is determined based on the ratio of the second spectral line intensity parameter and the third spectral line intensity parameter.

[0025] The range of values ​​for the particle number density is determined based on the theoretical self-absorption coefficient curve, and this range is used as the preset range.

[0026] In one embodiment of this application, based on the foregoing scheme, determining the target reference element according to the corrected spectrum includes:

[0027] Among the elements of the corrected spectrum, elements that meet preset conditions are selected as the target reference elements;

[0028] The preset condition is having the largest number of observed spectral lines that exceed a preset signal-to-noise ratio threshold.

[0029] In one embodiment of this application, based on the foregoing scheme, the candidate simulated spectrum corresponding to a single candidate vector is obtained through the following steps:

[0030] The first spectral line intensity parameter is determined according to the preset first calculation formula, the target parameter, and the candidate vector;

[0031] The candidate simulated spectrum is obtained by integrating the first spectral line intensity parameter over time.

[0032] According to one aspect of the embodiments of this application, an FL-LIBS calibration-free device based on non-gated acquisition is provided. The device is used to implement the FL-LIBS calibration-free method based on non-gated acquisition as described above. The device includes:

[0033] The first acquisition unit is used to acquire the corrected spectrum and determine the target reference element based on the corrected spectrum, wherein the corrected spectrum is obtained based on the FL-LIBS spectrum acquired by the non-gated acquisition system for the sample to be tested.

[0034] The second acquisition unit is used to acquire a number of candidate vectors containing information on electron temperature and particle number density of a reference element, and to determine the candidate simulated spectrum corresponding to each candidate vector. The candidate vector corresponds to the target reference element, and the value of the candidate vector is randomly generated within a preset range.

[0035] An iterative unit is used to iteratively update each of the candidate vectors according to a preset optimization algorithm. In each iteration, the fitness of each candidate vector is evaluated in a parallel evaluation manner. The parallel evaluation manner is to compare the similarity of each candidate simulated spectrum with the corrected spectrum.

[0036] The first determining unit is used to determine the target simulated spectrum with the highest similarity to the modified spectrum based on the fitness of each candidate vector in the final iteration round, and to determine the candidate simulated vector corresponding to the target simulated spectrum as the optimal vector;

[0037] The second determining unit is used to determine the target electron temperature and the target particle number density of the target reference element based on the optimal vector.

[0038] The third determining unit is used to determine the particle number density of elements other than the target reference element in the sample to be tested based on the target electron temperature.

[0039] The concentration calculation unit is used to normalize the number density of the target particles and the number density of each of the other elements to obtain the concentration composition of the sample to be tested.

[0040] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided that stores a computer program thereon, the computer program including executable instructions that, when executed by a processor, implement the method described in the above embodiments.

[0041] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a memory for storing executable instructions of the processors, which, when executed by the one or more processors, cause the one or more processors to perform the method as described in the above embodiments.

[0042] The beneficial effects of this application are as follows: The corrected spectrum obtained from the FL-LIBS spectrum of the sample under test using a non-gated acquisition system allows for a direct visualization of the target reference element, which has a large number of spectral lines and a high signal-to-noise ratio. Therefore, subsequent concentration calibration based on the target reference element yields more accurate results. Furthermore, by acquiring several candidate vectors (i.e., by introducing randomly generated unknowns), a preset optimization algorithm iteratively updates each candidate vector. In each iteration, the fitness of each candidate vector is evaluated in parallel. This parallel evaluation involves comparing the similarity of each candidate simulated spectrum with the corrected spectrum to determine the target simulated spectrum with the highest similarity to the corrected spectrum, thus obtaining the optimal vector.

[0043] The target electron temperature can be determined by obtaining the optimal vector, which in turn yields the electron temperature of the laser-induced plasma for the sample under test, eliminating the need for expensive gated detectors as required by existing technologies. Furthermore, by using the determined target electron temperature and the target particle number density of the target reference element, the particle number density of other elements in the sample under test can be obtained, thereby determining the concentration of each element and calibrating the sample under test.

[0044] This application generates several sets of unknown parameters, i.e., candidate vectors, and uses an optimization algorithm to find the best matching optimal vector, thereby obtaining the electron temperature that evolves over time. It provides a method for obtaining the time-resolved electron temperature required for time-integrated spectroscopy without the need for a high-speed gated detector, and can also quickly and accurately achieve calibration-free quantitative analysis of FL-LIBS.

[0045] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0046] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0047] Figure 1 This is a flowchart illustrating an FL-LIBS calibration-free method based on non-gated acquisition according to an embodiment of this application;

[0048] Figure 2 This is a flowchart illustrating the specific logic of the FL-LIBS calibration-free method based on non-gated acquisition proposed in this application.

[0049] Figure 3 This is a diagram illustrating the specific steps of the optimization algorithm proposed in the embodiments of this application;

[0050] Figure 4 This is a block diagram of an FL-LIBS calibration-free device based on non-gated acquisition, according to an embodiment of this application.

[0051] Figure 5 This is a structural diagram of an electronic device according to this application. Detailed Implementation

[0052] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0053] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0054] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller node devices.

[0055] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0056] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0057] The background technology of the embodiments of this application is described in detail below:

[0058] Laser-induced breakdown spectroscopy (LIBS) is an atomic spectroscopy technique that combines the advantages of in-situ, rapid, and non-contact analysis, enabling qualitative or quantitative analysis of most samples. Fiber lasers have gradually replaced traditional solid-state or gas lasers over the past decade, finding widespread application in industrial processing and intelligent manufacturing. Combining the advantages of both, fiber laser-induced breakdown spectroscopy based on fiber lasers (FL-LIBS) offers several strengths: first, it can operate stably for extended periods; second, the ultra-high output frequency of fiber lasers significantly improves acquisition efficiency compared to solid-state pulsed lasers; and third, its lower maintenance costs facilitate the commercialization of LIBS detection equipment. Therefore, promoting research into FL-LIBS calibration methods can not only improve the maturity of this technology but also expand the application scenarios of LIBS, providing new solutions for rapid and online detection.

[0059] FL-LIBS spectra contain spectral information for each analyte, with peak intensities reflecting the particle number density of each element. Deducing the concentration composition of the sample from these peak intensities requires research into calibration methods and models.

[0060] Existing technical solutions for quantitative analysis of FL-LIBS include:

[0061] ① Standard calibration is currently the most common quantitative analysis method for FL-LIBS. Various improvement methods have been derived around hardware system improvements, spectral preprocessing, and machine learning, and it is suitable for non-gated acquisition strategies.

[0062] ② Very few researchers use the free calibration method to quantitatively analyze FL-LIBS. However, the detection of the spectrum depends on the gating acquisition strategy, which requires expensive gating detectors with high-speed shutters, and cannot be separated from triggering and synchronization equipment such as photoelectric modulators.

[0063] ③ Few researchers use the free calibration method to quantitatively analyze FL-LIBS based on ungated acquisition. This is because its quantitative analysis accuracy is very poor, and the data obtained through forced calculation are only recorded as experimental results.

[0064] The shortcomings of the existing solution:

[0065] ① The standard sample calibration method cannot be applied in scenarios where there are no standard samples.

[0066] ② Calibration-free methods, which are free from sample limitations, hold research potential; however, currently only free calibration methods based on the Boltzmann plane are available. The use of free calibration methods involves an important assumption: the particles in the plasma follow a Boltzmann distribution, i.e., obey the Local Thermal Equilibrium (LTE) theory. In reality, only near-transient (<1 μs) plasmas can be approximated as conforming to the LTE theory. Therefore, to acquire plasma spectra within extremely short observation windows, ICCD detectors equipped with high-speed shutters are typically used. These devices are generally expensive, and their use in FL-LIBS not only negates the low-cost advantage but also makes the system more complex, requiring the detector to synchronize pulse output frequencies above tens of kHz.

[0067] ③ Non-gated detectors, such as CCDs and CMOS sensors, lack high-speed shutters, and their exposure time determines the minimum acquisition time, which is generally longer than the evolution time of fiber laser-induced plasma. When using these detectors to acquire fiber laser-induced plasma spectra, the acquired plasma temperature inevitably changes over time. Therefore, FL-LIBS acquired using non-gated detectors deviates from LTE theory, making it difficult for existing free calibration methods to accurately calculate the plasma temperature and particle number density.

[0068] In traditional LIBS using solid-state lasers, a novel calibration-free method for non-gated acquisition strategies has been proposed: the time-integration free calibration method. While this calibration model improves the accuracy of quantitative analysis in LIBS, it is only suitable for traditional LIBS and does not completely eliminate the need for gating devices.

[0069] ④ Fiber lasers have low single-pulse energy (<1 mJ), requiring the accumulation of multiple plasmas to obtain a high signal-to-noise ratio spectrum. For gated acquisition strategies, plasma can be accumulated by opening and closing the shutter multiple times within a single exposure time. For ungated acquisition strategies, the exposure time can be set much longer than the plasma generation period. This acquisition method using ungated detectors is called continuous acquisition, and it is impossible to set a delay to avoid the continuous spectral background caused by bremsstrahlung during the initial stage of each plasma expansion. Therefore, ungated FL-LIBS contains unavoidable bremsstrahlung background.

[0070] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0071] According to one aspect of this application, a calibration-free FL-LIBS method based on non-gated acquisition is provided. Figure 1 The flowchart below illustrates an FL-LIBS calibration-free method based on non-gated acquisition according to an embodiment of this application. This FL-LIBS calibration-free method based on non-gated acquisition includes at least steps S1 to S5, which are described in detail below:

[0072] In step S1, a corrected spectrum is acquired, and a target reference element is determined based on the corrected spectrum, wherein the corrected spectrum is obtained from the FL-LIBS spectrum acquired by the non-gated acquisition system for the sample to be tested.

[0073] Specifically, the non-gated acquisition system consists of an ultraviolet fused silica lens group, a CT-type spectrometer, a CMOS detector (non-gated detector), and a data terminal. The sample to be tested is TC4 titanium alloy as an example. The working principle of the non-gated acquisition system is as follows: the ultraviolet fused silica lens group couples the light emitted by the laser-induced plasma into the optical fiber probe of the spectrometer. The coupling fiber transmits the light signal to the CMOS detector for exposure, and then the data is transmitted to the terminal device (computer, embedded system, etc.).

[0074] The corrected spectrum is obtained through the following steps:

[0075] Before obtaining the corrected spectrum, the method further includes:

[0076] Acquire the FL-LIBS spectrum of the sample to be tested by the non-gated acquisition system;

[0077] The FL-LIBS spectrum is preprocessed by background removal, noise reduction, and peak finding to obtain the preprocessed measurement spectrum.

[0078] Among the various spectral lines of the measured spectrum, identify the target spectral line with the highest intensity;

[0079] The corrected spectrum is determined based on the spectral parameters of the target spectral line and a preset correction coefficient.

[0080] Specifically, the corrected spectrum is the spectrum obtained by correcting the measured spectrum acquired and preprocessed by the non-gated acquisition system. The spectral lines of each element and the signal-to-noise ratio can be seen intuitively from the corrected spectrum.

[0081] The measured spectrum is obtained by preprocessing the FL-LIBS spectrum through denoising, background removal, peak finding, intensity calibration, and intensity normalization. Finally, the corrected spectrum is obtained by combining the target spectral line in the measured spectrum with the preset correction coefficient.

[0082] In step S2, several candidate vectors containing information on electron temperature and reference element particle number density are obtained, and candidate simulated spectra corresponding to each candidate vector are determined. The candidate vectors correspond to the target reference element, and the values ​​of the candidate vectors are randomly generated within a preset range.

[0083] Specifically, the core of the method proposed in this application is that candidate vectors (i.e., electron temperature and particle number density) are randomly generated, the similarity between candidate simulated spectra and corrected spectra under different combinations of electron temperature and particle number density is judged, and the candidate vector corresponding to the candidate simulated spectra that achieves the highest similarity is found as the final result (i.e. the optimal vector described in this application).

[0084] Furthermore, since the non-gated acquisition system described in this application is implemented using a non-gated detector, and considering that the exposure time of the non-gated detector is greater than the plasma evolution time, the electron temperature changes with time during the exposure period. Therefore, let the initial temperature be... T 0, exponentiation is a Let the electron temperature curve be expressed by equation (1), and let the parameters be... F 1 represents the particle number density of the target reference element. Wherein, T 0 、a and temperature t Form a single candidate vector.

[0085]

[0086] The corrected spectrum is determined based on the spectral parameters of the target spectral line and a preset correction coefficient. Specifically, the correction coefficient is determined using equation (2). F 2. I obs ( λ m The value is the reading of the strongest spectral line in the measured spectrum (which can be directly seen from the measured spectrum). L ( λ m , T 10000K ) represents the blackbody radiation intensity at 10000 K (Kelvin, a unit of temperature) at the wavelength of this spectral line. t It is time, determined by the evolutionary lifetime of the plasma. Considering that the single-pulse energy of a fiber laser is less than 1 mJ, t The value is 10 -6 .

[0087]

[0088] In one embodiment of this application, determining the target reference element based on the corrected spectrum includes:

[0089] Among the elements of the corrected spectrum, elements that meet preset conditions are selected as the target reference elements;

[0090] The preset condition is having the largest number of observed spectral lines that exceed a preset signal-to-noise ratio threshold.

[0091] The more spectral lines, the more representative the overall spectrum. If the matrix of the sample is known, then the matrix elements are selected. The preset quantity threshold and the preset signal-to-noise ratio threshold can be set according to actual needs.

[0092] In step S3, each candidate vector is iteratively updated according to a preset optimization algorithm. In each iteration, the fitness of each candidate vector is evaluated in a parallel evaluation manner. The parallel evaluation method is to compare the similarity of each candidate simulated spectrum with the corrected spectrum.

[0093] In one embodiment of this application, the candidate simulated spectrum corresponding to a single candidate vector is obtained through the following steps:

[0094] The first spectral line intensity parameter is determined according to the preset first calculation formula, the target parameter, and the candidate vector;

[0095] The candidate simulated spectrum is obtained by integrating the first spectral line intensity parameter over time.

[0096] Specifically, the preset first calculation formula is equation (3) in the embodiments of this application. Through equations (1), (3), (4) and (8) in the embodiments of this application, where equation (4) expresses the theoretical intensity of the spectral line neglecting the self-absorption effect. ι This is reflected in equation (3), which is used to obtain the candidate simulated spectra. The candidate vectors used above include electron temperature and particle number density, while the target parameters include the following parameters:

[0097] h is Planck's constant, k B Boltzmann's constant, π is pi, c is the speed of light, and m e This refers to the electron mass; all of the above are known values. λ It is the center wavelength of the spectral line. A ki It is the spontaneous emission coefficient, E k It is the energy that jumps to a higher energy level. g k It is the degeneracy of the upper energy level, subscript k and i Representative spectral lines λ The transition between upper and lower energy levels, E ion, s It is an element s One ionization energy, U s ( T ) is an element s At electron temperature T The partition function and superscript below l andll These represent atomic lines and ion lines. The above parameters are obtained by querying a preset database, such as the NIST database.

[0098] In one embodiment of this application, the preset range is obtained through the following steps:

[0099] The preset range is obtained through the following steps:

[0100] The second spectral line intensity parameter is determined based on the first calculation formula, the target parameter, and the preset electron temperature;

[0101] The third spectral line intensity parameter is determined according to the preset second calculation formula, the target parameter, and the preset electron temperature;

[0102] The theoretical self-absorption coefficient curve related to the particle number density and the preset electron temperature is determined based on the ratio of the second spectral line intensity parameter and the third spectral line intensity parameter.

[0103] The range of values ​​for the particle number density is determined based on the theoretical self-absorption coefficient curve, and this range is used as the preset range.

[0104] The second spectral line intensity parameter here is the theoretical intensity of the spectral line. ι * It is also calculated using equation (3), but unlike the first spectral line intensity parameter, the temperature used is different. T Different. In the first spectral line intensity parameter, temperature... T It refers to the electron temperature in the candidate vector, while the temperature in the second spectral line intensity parameter is... T This refers to the preset electron temperature, which can be set to 10000 K (Kelvin). In other words, the theoretical self-absorption coefficient can be calculated using the preset electron temperature and the target parameters, without needing to use the electron temperature in the candidate vector. That is, the resulting theoretical self-absorption coefficient curve represents the relationship between the theoretical self-absorption coefficient and the particle number density (not specifically referring to the target reference element, but simply indicating that the target reference element is applicable to this function's variation curve).

[0105] The preset second calculation formula is formula (4) below. The target parameter is the parameter used in formula (4). In fact, the target parameter used in formula (3) is the same as the target parameter used in formula (4). Simulate particle number density in the entire real number domain. F There are difficulties, so the theoretical self-absorption coefficient is used to evaluate... F The approximate range of 1: Based on spontaneous emission theory, Boltzmann distribution, Saha ionization equation, and blackbody radiation reference self-absorption correction method, the theoretical intensity of any spectral line. ι* Expressed by equation (3), the parameters in equation (3) are the first target parameters. The theoretical intensity of the spectral lines is ignored due to the self-absorption effect. ι It is expressed by equation (4). The parameter in equation (4) is the second target parameter. Degree of ionization χ Expressed by equation (5); factor R Expressed by equation (6). Electron temperature T In this step, we assume a value of 10000 K; N e This is the electron density, which can be calculated using the Saha–Boltzmann method when the temperature is known. From this, the self-absorption coefficient curve can be obtained. SAC As expressed by equation (7), it is related to F A function of 1, and F 1 is the only dependent variable in the self-absorption coefficient curve. F The reasonable range for 1 lies in the case that 0.1 < SAC The range <0.9 (i.e., the preset range).

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] In step S4, the target simulated spectrum with the highest similarity to the corrected spectrum is determined based on the fitness of each candidate vector in the final iteration round, and the candidate simulated vector corresponding to the target simulated spectrum is determined as the optimal vector.

[0114] In one embodiment of this application, determining the target simulated spectrum with the highest similarity to the corrected spectrum includes:

[0115] Based on a preset similarity comparison formula, the target simulated spectrum that is closest in similarity to the corrected spectrum is determined from among the candidate simulated spectra.

[0116] Specifically, by substituting the temperature from equation (1) into equation (3), equation (8) is used to calculate the candidate simulated spectra corresponding to each candidate vector of the target reference element. Equation (9) is then used to calculate the corrected spectrum (i.e., the corrected spectrum described in this application), where... Iobs This represents the actual measured spectrum (i.e., the measured spectrum described in this application). Clearly, the simulated spectrum... I sim The candidate vector composed of the three unknown variables in [ T 0, a , F [1] refers to the data to be simulated. The similarity between candidate simulated spectra and corrected spectra is used to evaluate these randomly generated data. The tasks of generation, evaluation, iteration, and optimization are handled by an optimization algorithm (i.e., a pre-defined optimization algorithm), such as a genetic algorithm or particle swarm optimization. Figure 2 and Figure 3 The flowchart is shown.

[0117] Figure 2 This is a flowchart illustrating the specific logic of the FL-LIBS calibration-free method based on non-gated acquisition proposed in this application. Figure 3 This is a diagram illustrating the specific steps of the optimization algorithm. Figure 2 In the step of "determining the optimal vector through an optimization algorithm", the logical steps can be specifically described as follows: Figure 5 As shown, the specific steps of the optimization algorithm can be found by referring to... Figure 3 As shown in the steps, through Figure 3 The optimal vector can be finally determined through each step.

[0118] To improve computational speed, each iteration uses PyTorch to calculate the fitness of all candidate vectors in parallel (corresponding to a predefined similarity evaluation formula). Finally, the target electron temperature and target reference element are obtained from the globally optimal vector. F 1.

[0119] The preset similarity evaluation formula can be specifically as follows:

[0120]

[0121] Here, "fitness" characterizes the similarity between the candidate simulated spectrum and the corrected spectrum; the smaller the "fitness" value, the higher the similarity between the two. u This represents the number of spectral lines.

[0122] In step S5, the target electron temperature and the target particle number density of the target reference element are determined based on the optimal vector.

[0123] Specifically, once the optimal vector is obtained, the target electron temperature and the target particle number density of the target reference element in the optimal vector are naturally obtained.

[0124] In step S6, the particle number density of elements other than the target reference element in the sample to be tested is determined based on the target electron temperature.

[0125] Specifically, using the obtained target electron temperature, the values ​​of other elements are calculated sequentially. F 1: Since substituting the target electron temperature into equation (8) leaves only F One unknown. Therefore, the solution is to find the candidate simulated spectra of other elements that are closest to the corrected spectra. F The process described in step 1 involves finding the minimum point of a univariate function. In other words, in the generated function curve, the horizontal axis is... F 1. If the vertical axis represents fitness, then find a value in the curve of this function's change. F The point 1 minimizes the aforementioned fitness value, thus determining the particle number density of the current element. F The value of 1 represents the particle number density of the current element.

[0126] Because different analytes have different spectral lines, the spectral line intensities calculated using formulas (8) and (9) are also different. Therefore, the fitness value is different for different elements. In step S7, the target particle number density and the particle number densities of each of the other elements are normalized to obtain the concentration composition of the sample to be tested.

[0127] Finally, using all the obtained elements F 1. Calculate the concentration composition of the sample to be tested: all elements F The result of the normalization calculation is the concentration of each element in the sample to be tested.

[0128] The key innovation of this method lies in the generation of unknowns through simulation (i.e., first assuming candidate vectors, then using subsequent simulated spectra of the target to deduce the optimal vector, thereby obtaining the target electron temperature; finally, using the target electron temperature and particle number density to calculate the particle number density and concentration of other elements, without needing to obtain the electron temperature through an ICCD detector as in existing methods). It should be noted that the expression for any candidate simulated spectrum in this application is derived through… I sim To indicate, I cor The expression for the corrected spectrum in this application is provided.

[0129] In summary, the embodiments of this application realize calibration-free quantitative analysis of FL-LIBS based on non-gated acquisition. By acquiring several candidate vectors, that is, by introducing randomly generated unknowns, each candidate vector is iteratively updated through a preset optimization algorithm. In each iteration, the fitness of each candidate vector is evaluated in a parallel evaluation manner. The parallel evaluation method is to compare the similarity of each candidate simulated spectrum with the corrected spectrum to determine the target simulated spectrum with the highest similarity to the corrected spectrum, thereby obtaining the optimal vector.

[0130] The target electron temperature can be determined by obtaining the optimal vector, which in turn yields the electron temperature of the laser-induced plasma applied to the sample, eliminating the need for expensive gated detectors as required by existing technologies. Furthermore, the determined target electron temperature allows for the determination of the particle number density of each element in the sample, and consequently, the concentration of each element, thus enabling the calibration of the sample.

[0131] This application generates several sets of unknown parameters, i.e., candidate vectors, and uses an optimization algorithm to find the best-matching candidate vector, thereby obtaining the electron temperature evolving over time. It provides a method for obtaining the time-resolved electron temperature required for time-integrated spectroscopy without relying on high-speed gated detectors, and simultaneously enables rapid and accurate calibration-free quantitative analysis of FL-LIBS. This application realizes the application of this high-efficiency, low-maintenance, and long-term-operation LIBS system, achieving cost reduction and efficiency improvement, and providing a new and improved solution for online monitoring of industrial processes.

[0132] According to one aspect of the embodiments of this application, a calibration-free FL-LIBS device 300 based on non-gated acquisition is proposed. Figure 4 This is a schematic diagram of the FL-LIBS calibration-free device 300 based on non-gated acquisition proposed in the embodiments of this application. The system 300 includes: a first acquisition unit 301, a second acquisition unit 302, an iteration unit 303, a first determination unit 304, a second determination unit 305, a third determination unit 306, and a concentration calculation unit 307.

[0133] The first acquisition unit 301 is used to acquire a corrected spectrum and determine a target reference element based on the corrected spectrum. The corrected spectrum is obtained based on the FL-LIBS spectrum acquired by the non-gated acquisition system for the sample to be tested.

[0134] The second acquisition unit 302 is used to acquire a plurality of candidate vectors containing information on electron temperature and reference element particle number density, and to determine the candidate simulated spectrum corresponding to each candidate vector. The candidate vector corresponds to the target reference element, and the value of the candidate vector is randomly generated within a preset range.

[0135] The iteration unit 303 is used to iteratively update each of the candidate vectors according to a preset optimization algorithm. In each iteration round, the fitness of each candidate vector is evaluated in a parallel evaluation manner. The parallel evaluation manner is to compare the similarity of each candidate simulated spectrum with the modified spectrum.

[0136] The first determining unit 304 is used to determine the target simulated spectrum with the highest similarity to the modified spectrum based on the fitness of each candidate vector in the final iteration round, and to determine the candidate simulated vector corresponding to the target simulated spectrum as the optimal vector;

[0137] The second determining unit 305 is used to determine the target electron temperature and the target particle number density of the target reference element based on the optimal vector.

[0138] The third determining unit 306 is used to determine the particle number density of elements other than the target reference element in the sample to be tested based on the target electron temperature.

[0139] The concentration calculation unit 307 is used to perform normalization calculations on the target particle number density and the particle number densities of each of the other elements to obtain the concentration composition of the sample to be tested.

[0140] In another aspect, this application also provides a computer-readable storage medium storing a program product capable of implementing the methods provided above in this specification. In some possible implementations, various aspects of this application may also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the "Embodiment Methods" section of this specification according to various exemplary embodiments of this application.

[0141] According to the embodiments of this application, the program product used to implement the above-described method may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of this application is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0142] The program product may employ 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 be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, 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.

[0143] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0144] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0145] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0146] The following reference Figure 5 To describe an electronic device 400 according to this embodiment of the present application. Figure 5 The electronic device 400 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0147] like Figure 5As shown, the electronic device 400 is manifested in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, and a bus 430 connecting different system components (including storage unit 420 and processing unit 410).

[0148] The storage unit stores program code that can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the "Embodiment Methods" section above according to various exemplary embodiments of this application.

[0149] Storage unit 420 may include readable media in the form of volatile storage units, such as random access memory (RAM) 421 and / or cache memory 422, and may further include read-only memory (ROM) 423.

[0150] Storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of these examples or some combination thereof may include an implementation of a network environment.

[0151] Bus 430 can represent one or more of several types of bus structures, including a memory cell bus or memory cell control node, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0152] Electronic device 400 can also communicate with one or more external devices 1200 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. As shown, network adapter 460 communicates with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0153] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this application.

[0154] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0155] It should be understood that this application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A calibration-free FL-LIBS method based on non-gated acquisition, characterized in that, The method includes: A corrected spectrum is acquired, and a target reference element is determined based on the corrected spectrum, wherein the corrected spectrum is obtained from the FL-LIBS spectrum acquired by the non-gated acquisition system for the sample to be tested; A number of candidate vectors containing information on electron temperature and particle number density of a reference element are obtained, and a candidate simulated spectrum corresponding to each candidate vector is determined. The candidate vector corresponds to the target reference element, and the value of the candidate vector is randomly generated within a preset range. Each candidate vector is iteratively updated according to a preset optimization algorithm. In each iteration, the fitness of each candidate vector is evaluated in a parallel evaluation manner. The parallel evaluation method is to compare the similarity of each candidate simulated spectrum with the corrected spectrum. Based on the fitness of each candidate vector in the final iteration round, the target simulated spectrum with the highest similarity to the modified spectrum is determined, and the candidate simulated vector corresponding to the target simulated spectrum is determined as the optimal vector; The target electron temperature and the target particle number density of the target reference element are determined based on the optimal vector. The particle number density of elements other than the target reference element in the sample to be tested is determined based on the target electron temperature. The concentration composition of the sample to be tested is obtained by normalizing the target particle number density and the particle number densities of each of the other elements. Before obtaining the corrected spectrum, the method further includes: Acquire the FL-LIBS spectrum of the sample to be tested by the non-gated acquisition system; The FL-LIBS spectrum is preprocessed by background removal, noise reduction, and peak finding to obtain the preprocessed measurement spectrum; The corrected spectrum is obtained through the following steps: Among the various spectral lines of the measured spectrum, identify the target spectral line with the highest intensity; The corrected spectrum is determined based on the spectral parameters of the target spectral line and a preset correction coefficient; The preset correction coefficient is determined by the following formula: ； In the formula, The preset correction coefficient is... t For time, L(λ m , T 10000K ) It is the blackbody radiation intensity at 10000 Kelvin at the wavelength of the spectral line. This is the reading of the strongest spectral line in the measured spectrum; The single candidate vector is composed of T 0、 a as well as F 1. Composition T 0 represents the initial temperature. a For power, F 1 represents the simulated particle number density.

2. The FL-LIBS calibration-free method based on non-gated acquisition according to claim 1, characterized in that, The step of determining the target reference element based on the corrected spectrum includes: Among the elements of the corrected spectrum, elements that meet preset conditions are selected as the target reference elements; The preset condition is having the largest number of observed spectral lines that exceed a preset signal-to-noise ratio threshold.

3. The FL-LIBS calibration-free method based on non-gated acquisition according to claim 2, characterized in that, The candidate simulated spectrum corresponding to a single candidate vector is obtained through the following steps: The first spectral line intensity parameter is determined according to the preset first calculation formula, the target parameter, and the candidate vector; The candidate simulated spectrum is obtained by integrating the first spectral line intensity parameter over time. The preset first calculation formula is used to characterize the theoretical intensity of any spectral line.

4. The FL-LIBS calibration-free method based on non-gated acquisition according to claim 3, characterized in that, The preset range is obtained through the following steps: The second spectral line intensity parameter is determined based on the first calculation formula, the target parameter, and the preset electron temperature; The third spectral line intensity parameter is determined according to the preset second calculation formula, the target parameter, and the preset electron temperature; The theoretical self-absorption coefficient curve related to the particle number density and the preset electron temperature is determined based on the ratio of the second spectral line intensity parameter and the third spectral line intensity parameter. The range of values ​​for the particle number density is determined based on the theoretical self-absorption coefficient curve, and the range of values ​​is used as the preset range. The preset second calculation formula is used to characterize the theoretical intensity of spectral lines when self-absorption effects are ignored.

5. A calibration-free FL-LIBS acquisition device based on non-gated data acquisition, characterized in that, The FL-LIBS calibration-free device based on non-gated acquisition is used to implement the FL-LIBS calibration-free method based on non-gated acquisition as described in any one of claims 1-4, and the device includes: The first acquisition unit is used to acquire the corrected spectrum and determine the target reference element based on the corrected spectrum, wherein the corrected spectrum is obtained based on the FL-LIBS spectrum acquired by the non-gated acquisition system for the sample to be tested. The second acquisition unit is used to acquire a number of candidate vectors containing information on electron temperature and particle number density of a reference element, and to determine the candidate simulated spectrum corresponding to each candidate vector. The candidate vector corresponds to the target reference element, and the value of the candidate vector is randomly generated within a preset range. An iterative unit is used to iteratively update each of the candidate vectors according to a preset optimization algorithm. In each iteration, the fitness of each candidate vector is evaluated in a parallel evaluation manner. The parallel evaluation manner is to compare the similarity of each candidate simulated spectrum with the corrected spectrum. The first determining unit is used to determine the target simulated spectrum with the highest similarity to the modified spectrum based on the fitness of each candidate vector in the final iteration round, and to determine the candidate simulated vector corresponding to the target simulated spectrum as the optimal vector; The second determining unit is used to determine the target electron temperature and the target particle number density of the target reference element based on the optimal vector. The third determining unit is used to determine the particle number density of elements other than the target reference element in the sample to be tested based on the target electron temperature. The concentration calculation unit is used to normalize the number density of the target particles and the number density of each of the other elements to obtain the concentration composition of the sample to be tested.

6. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the FL-LIBS calibration-free method based on non-gated acquisition as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the FL-LIBS calibration-free method based on non-gated acquisition as described in any one of claims 1 to 4.

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