Method, system and equipment for improving signal stability of LIBS (Laser-induced Breakdown Spectroscopy) technology and medium

Abstract

The invention discloses a method, a system, equipment and a medium for improving signal stability of an LIBS (laser-induced breakdown spectroscopy) technology, and belongs to the technical field of laser-induced breakdown spectroscopy, and the method comprises the following steps: building an LIBS correction system which is used for acquiring an asynchronous event data stream; characteristic parameters of plasma are extracted from the asynchronous event data flow, and a signal correction model is established based on the characteristic parameters; and correcting the LIBS spectral signal based on the signal correction model, and determining the corrected spectral line intensity. According to the method, the plasma is captured by introducing the event camera, and the signal correction model is established in combination with the spectral signal characteristics, so that the signal stability of the LIBS technology is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of laser-induced breakdown spectroscopy (LIBS) technology, and in particular to a method, system, device, and medium for improving the signal stability of LIBS technology. Background Technology

[0002] Laser-induced breakdown spectroscopy (LIBS), as a rapid, non-contact, and near-non-destructive elemental analysis method, has broad application prospects in materials science, environmental monitoring, archaeology, geological exploration, and medical diagnostics. However, LIBS technology faces serious signal instability problems in practical applications, which severely restricts its promotion and application in precision analysis and quantitative detection.

[0003] The signal instability of traditional LIBS technology mainly stems from the following aspects: First, the microstructure, roughness, and uniformity of the sample surface can lead to significant differences in the laser breakdown process; second, minute fluctuations in laser pulse energy can cause inconsistencies in plasma formation and radiation characteristics; third, environmental factors such as changes in temperature, humidity, and atmospheric pressure can also affect plasma formation and spectral characteristics. These factors collectively pose significant challenges to the repeatability and accuracy of LIBS technology. Existing signal stability improvement methods, such as averaging multiple measurements and using internal standard correction, can reduce signal fluctuations to some extent, but cannot fundamentally solve the signal instability problem. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: how to effectively improve the signal stability of LIBS technology by introducing an event camera to capture plasma and combining it with spectral signal characteristics to establish a signal correction model.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for improving the signal stability of LIBS technology, comprising the following steps,

[0007] A LIBS correction system is constructed to acquire asynchronous event data streams; plasma characteristic parameters are extracted from the asynchronous event data streams, and a signal correction model is established based on the characteristic parameters; the LIBS spectral signal is corrected based on the signal correction model to determine the corrected spectral line intensity.

[0008] As a preferred embodiment of the method for improving the signal stability of LIBS technology described in this invention, the method of acquiring asynchronous event data stream includes: using laser pulses to ablate and excite the sample to generate plasma; receiving the radiation spectrum of the plasma and simultaneously photographing the plasma.

[0009] As a preferred embodiment of the method for improving the signal stability of LIBS technology described in this invention, the method involves extracting plasma characteristic parameters from the asynchronous event data stream, including: reconstructing the plasma morphology through the asynchronous event data stream, extracting plasma area information Es to represent the total particle number density, extracting the event count Enum to represent the plasma temperature, and extracting the full width at half maximum (FWHM) to represent the electron density. The beneficial effect of this preferred embodiment is that by extracting characteristic parameters such as plasma area information Es, event count Enum, and FWHM from the asynchronous event data stream to represent the total particle number density, plasma temperature, and electron density, accurate quantification of the internal state of the plasma is achieved, laying a physical foundation for establishing an accurate signal correction model.

[0010] As a preferred embodiment of the method for improving the signal stability of LIBS technology according to the present invention, the method includes: establishing a signal correction model based on the characteristic parameters, comprising: expressing the LIBS spectral line intensity as a first function of total particle number density, plasma temperature, and electron density using Taylor expansion based on the acquired characteristic parameters; adjusting the first function using the Saha equation to obtain a second function; performing Taylor expansion on the second function to obtain a third function; performing variable substitution on the third function in conjunction with the characteristic parameters to establish a signal correction model; and solving the coefficients of the signal correction model through regression analysis to obtain the spectral line intensity I(N). s ,n e The beneficial effects of this preferred technical solution are that by combining Taylor expansion and Saha equations to derive the signal correction model, the complex plasma physics process is transformed into a mathematical expression, establishing a functional relationship between spectral line intensity and physical parameters, providing a theoretically complete signal correction method, and improving the accuracy and reliability of LIBS signals.

[0011] As a preferred embodiment of the method for improving the stability of LIBS (Limited Indication Broadband) signals according to the present invention, the method for correcting the LIBS spectral signal based on the signal correction model includes: performing a formal transformation on the third function based on event data to establish a LIBS spectral signal stability improvement model; performing regression analysis on the LIBS spectral signal stability improvement model to solve for its coefficients; and substituting the coefficients of the LIBS spectral signal stability improvement model into the LIBS spectral signal stability improvement model to obtain the corrected spectral line intensity. The beneficial effect of this preferred embodiment is that by performing a formal transformation on the Taylor expansion function based on event data, a LIBS spectral signal stability improvement model directly related to measured parameters is established, and by solving for the model coefficients through regression analysis, efficient correction of the LIBS signal is achieved, reducing measurement variability.

[0012] As a preferred embodiment of the method for improving the signal stability of LIBS technology described in this invention, the step of solving the coefficients of the signal correction model through regression analysis includes: using the actual spectral line intensity to replace the spectral line intensity I(N) s ,n e Using elemental content C, plasma area change dEs, full width at half maximum (FWHM) change dFWHM, and event number change dEnum as independent variables, regression analysis is performed on the signal correction model to obtain its coefficients. The beneficial effect of this preferred technical solution is that by using elemental content C and changes in plasma characteristic parameters as independent variables for regression analysis, an accurate method for solving the signal correction model coefficients is established, realizing signal correction based on a physical model and effectively eliminating the interference of plasma fluctuations on spectral line intensity.

[0013] As a preferred embodiment of the method for improving the signal stability of LIBS technology according to the present invention, the following steps are included: performing regression analysis on the LIBS spectral signal stability improvement model to solve for the coefficients of the LIBS spectral signal stability improvement model, including: establishing an original calibration curve using actual spectral line intensities to obtain the dependent variable, i.e., the ideal spectral line intensity, and setting the spectral line intensity I(N) as the coefficient of the LIBS spectral signal stability improvement model. s ,n e The coefficients of the LIBS spectral signal stability improvement model were obtained by using the changes in plasma area information dEs, event number dEnum, and full width at half maximum (FWHM) as independent variables.

[0014] Another objective of this invention is to provide a system for improving the signal stability of LIBS technology.

[0015] To address the aforementioned technical problems, this invention provides the following technical solution: a system for improving the signal stability of LIBS technology, comprising: a data acquisition module for building a LIBS correction system, wherein the LIBS correction system is used to acquire asynchronous event data streams; a model building module for extracting plasma characteristic parameters from the asynchronous event data streams and establishing a signal correction model based on the characteristic parameters; and a signal correction module for correcting the LIBS spectral signal based on the signal correction model and determining the corrected spectral line intensity.

[0016] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for improving the signal stability of LIBS technology.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements the steps of the method for improving the signal stability of LIBS technology.

[0018] The beneficial effects of this invention are as follows: By introducing an event camera to capture plasma and combining it with spectral signal characteristics, a signal correction model is established, thereby effectively improving the signal stability of LIBS technology. Specifically, by constructing a LIBS correction system including an event camera and acquiring asynchronous event data streams, real-time, high-precision capture of plasma morphology is achieved, providing rich and accurate raw data for subsequent signal correction, thus improving the accuracy and reliability of the correction from the source. Secondly, by extracting feature parameters from the asynchronous event data stream, physical models characterizing total particle number density, plasma temperature, and electron density are established. This parameter extraction method based on plasma physical characteristics achieves precise quantification of key plasma parameters, overcoming the limitations of traditional methods that cannot obtain information about the internal state of plasma, and providing a solid theoretical foundation for signal correction. Finally, through mathematical model derivation based on Taylor expansion and the Saha equation, a correction model that correlates plasma physical parameters with spectral signals is established, and the model coefficients are solved through regression analysis, achieving precise correction of the LIBS spectral signal. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0020] Figure 1 This is a schematic diagram of the experimental platform in Example 1.

[0021] Figure 2 This is a schematic diagram of the computer device in Example 4.

[0022] Figure 3 This is a schematic diagram of the original spectrum in Example 5.

[0023] Figure 4 This is a schematic diagram of the spectrum after correction and noise reduction in Example 5.

[0024] Figure 5 This is a schematic diagram of the original spectral calibration curve in Example 5.

[0025] Figure 6 This is a schematic diagram of the normalized spectral calibration curve in Example 5.

[0026] Figure 7 This is a schematic diagram of the calibration curve of the model proposed in this invention in Example 5.

[0027] Figure 8 This is a schematic diagram illustrating the effect of different methods on the RSD of spectral line intensity in Example 5. Detailed Implementation

[0028] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0029] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0030] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0031] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for improving the signal stability of LIBS technology, comprising:

[0032] S100: Build the LIBS correction system, which is used to obtain asynchronous event data streams.

[0033] S200: Extract plasma characteristic parameters from asynchronous event data streams and establish a signal correction model based on the characteristic parameters.

[0034] S300: Corrects the LIBS spectral signal based on the signal correction model and determines the intensity of the corrected spectral lines.

[0035] It should be noted that laser-induced breakdown spectroscopy (LIBS), as a rapid and non-destructive elemental analysis technique, has broad application prospects in the field of materials composition analysis. However, LIBS technology faces signal instability issues in practical applications. Factors such as plasma temperature fluctuations, electron density variations, and unstable ablation quality lead to significant fluctuations in spectral line intensity, affecting the accuracy of quantitative analysis. Existing signal stability improvement methods, such as averaging multiple measurements and using internal standard correction, can reduce signal fluctuations to some extent, but cannot fundamentally solve the signal instability problem. At the same time, traditional spectral acquisition methods cannot capture the entire process of plasma formation and evolution, and lack the ability to monitor and correct the internal physical parameters of the plasma in real time.

[0036] Therefore, to address the aforementioned signal instability issues in LIBS technology, a LIBS correction system incorporating an event camera is constructed through steps S100-S300 to acquire asynchronous event data streams and reconstruct the plasma morphology. Characteristic parameters such as plasma area information Es, event number Enum, and full width at half maximum (FWHM) are extracted to characterize the total particle number density, plasma temperature, and electron density, respectively. A signal correction model is established based on Taylor expansion and the Saha equation, correlating plasma physical parameters with the spectral signal. The model coefficients are solved through regression analysis to achieve accurate correction of the LIBS spectral signal. Simultaneously, precise quantification and monitoring of key plasma physical parameters are achieved, establishing a signal correction mechanism based on plasma physical characteristics, fundamentally improving the stability of LIBS technology in practical applications.

[0037] Example 2, refer to Figure 1 This is the second embodiment of the present invention. Based on the above embodiments, a method for improving the signal stability of LIBS technology is provided.

[0038] In this embodiment of the invention, obtaining the asynchronous event data stream in step S100 includes:

[0039] A laser pulse is generated by using a laser to ablate and excite the sample, thus generating plasma.

[0040] The plasma's radiation spectrum is received by a spectrometer, and the plasma is simultaneously photographed using an event camera.

[0041] It should be noted that when acquiring asynchronous event data streams, selecting appropriate laser energy, light collection angle, and spectrometer delay time can yield spectral signals with high signal-to-noise ratio and signal-to-background ratio.

[0042] In an alternative implementation, the asynchronous event data stream in step S100 can also be acquired by using a high-speed ICCD camera combined with time-resolved imaging technology. For example, the plasma evolution process can be captured in frames using nanosecond-level exposure time, and the plasma dynamic features can be extracted through image sequence analysis to replace the asynchronous event stream output of the event camera.

[0043] In another optional implementation, the asynchronous event data stream can also be acquired in step S100 using a multispectral imaging system in conjunction with a compressed sensing algorithm. For example, plasma radiation signals can be acquired synchronously using multi-band filters, and the asynchronous event data stream can be reconstructed using sparse characterization theory to achieve high timeliness characterization of plasma parameters.

[0044] It should be noted that traditional LIBS technology commonly suffers from signal instability due to factors such as laser energy fluctuations, sample surface inhomogeneity, and matrix effects. These issues typically only mitigate errors by averaging multiple measurements, failing to fundamentally address the signal fluctuation problem. This invention solves the technical challenge of accurately capturing dynamic plasma changes by constructing a LIBS correction system incorporating an event camera. Unlike traditional frame rate cameras, the event camera allows for capturing the rapid evolution of plasma at microsecond or even nanosecond temporal resolutions, acquiring asynchronous event data streams. This high temporal resolution data acquisition method records the entire process of plasma change from formation to dissipation, rather than merely acquiring static information at a single moment. Furthermore, in selecting laser energy, light collection angle, and spectrometer delay time, this invention not only improves the signal-to-noise ratio and signal-to-background ratio but also achieves simultaneous acquisition of plasma morphology and spectral information, providing a more comprehensive data foundation for subsequent signal correction. This dual-information acquisition method transcends the limitations of traditional LIBS technology's singular focus on spectral information, enabling the understanding and correction of signal fluctuations from a physical perspective.

[0045] In this embodiment of the invention, step S200, which involves extracting plasma characteristic parameters from the asynchronous event data stream, includes:

[0046] The plasma morphology is reconstructed using asynchronous event data streams. The plasma area information Es is extracted to represent the total particle number density, the number of events Enum is extracted to represent the plasma temperature, and the full width at half maximum (FWHM) is extracted to represent the electron density.

[0047] In an optional implementation, in addition to extracting plasma characteristic parameters from the asynchronous event data stream, step S200 can also be achieved using a high-speed ICCD camera combined with image processing technology. For example, time-resolved imaging can be used to capture the plasma evolution process, and edge detection algorithms can be used to extract the plasma morphology contour and calculate the plasma area information. Event counts can be counted by light intensity integration or pulse counting. The broadening of specific spectral lines can be measured using a spectrometer, and the electron density can be directly calculated using Stark broadening theory. This approach is suitable for scenarios requiring high spatiotemporal resolution, such as transient plasma dynamics research or complex matrix sample analysis.

[0048] In another optional implementation, in addition to extracting plasma characteristic parameters from the asynchronous event data stream, step S200 can also be achieved through a combination of electrical diagnostics and spectral analysis. This involves using a Rogowski coil or current probe to monitor the laser-induced plasma discharge current waveform and indirectly characterizing the total particle number density through current integration; combining the Langmuir probe to measure plasma potential and electron temperature to replace the event number; and directly measuring electron density using Thomson scattering or interferometry to replace the full width at half maximum (FWHM) characterization method.

[0049] In another optional implementation, the method for extracting and characterizing plasma characteristic parameters in step S200 can also expand the characteristic dimensions through various technical means to improve the comprehensiveness of plasma state description; for example, when characterizing the total particle number density, in addition to plasma area information, the plasma refractive index change can be measured by laser interferometry, or the particle number density distribution can be inverted by Rayleigh scattering intensity, or even the mass of ablated material can be directly obtained by combining mass spectrometry analysis technology; when characterizing plasma temperature, in addition to the number of events, the temperature field can be calculated by multi-wavelength radiation ratio using two-color thermometry, or the characteristic spectral lines can be fitted based on the Boltzmann oblique line method, or the temperature parameters can be derived by combining the blackbody radiation model with Planck's formula; for the characterization of electron density, in addition to the full width at half maximum (FWHM), the broadening amount of specific spectral lines can be analyzed by Stark broadening.

[0050] In this embodiment of the invention, step S200, which involves establishing a signal correction model based on feature parameters, includes:

[0051] Based on the acquired characteristic parameters, the LIBS spectral line intensity is expressed as a first function of the total particle number density, plasma temperature, and electron density using Taylor expansion.

[0052] The second function is obtained by adjusting the first function using the Saha equation.

[0053] The third function is obtained by performing a Taylor expansion on the second function.

[0054] By combining the characteristic parameters, the third function is substituted with variables to establish a signal correction model.

[0055] The coefficients of the signal correction model are solved by regression analysis to obtain the spectral line intensity I(N). s ,n e ,T).

[0056] Specifically, under ideal conditions, the formula for expressing the LIBS spectral line intensity as a first function of the total particle number density, plasma temperature, and electron density using Taylor expansion is as follows:

[0057]

[0058] Where F is a parameter determined by the experimental environment and system parameters; N s g represents the total density of elemental particles in the plasma. i For the statistical weight of higher energy level i; A ij U represents the transition probability; s (T) is the partition function; E i λ is the excitation energy of the high energy level; kB is the Boltzmann constant, which is approximately 1.38 × 10⁻²³ J / K; T is the plasma temperature; and r is the ratio of the number of atomic or ionic particles to the total number of particles.

[0059] Furthermore, by adjusting the first function using the Saha equation, the second function is obtained, as shown in the following formula:

[0060] I ij =kN s f(n e ,T)=I(N s ,n e ,T);

[0061] Among them, I ij The intensity of a spectral line is primarily determined by the electron density n. e Plasma temperature T and total particle number density N s The influence of; k is determined by F, m e k B The parameters, such as h, are not affected by the electron density n. e Plasma temperature T and total particle number density N s The effect; f(n e I(N,T) is a function of electron density and plasma temperature; s ,n e ,T) represents the spectral line intensity.

[0062] Furthermore, under standard conditions, the total particle number density N of elemental particles in the plasma during stoichiometric ablation satisfies the requirements. sThe ideal spectral line intensity I is directly proportional to the elemental content C in the sample. ideal Proportional to the element content C, the second function is expanded using a second-order Taylor series to obtain the third function, the specific formula of which is as follows:

[0063]

[0064] Among them, I(N) s ,n e T) represents the spectral line intensity; N s N represents the total number density of elemental particles in the plasma. s0 The total particle number density under standard conditions (reference conditions); dN s N is the small change in the total particle number density relative to the standard state. s -N s0 ;n e Electron density in plasma, typically expressed as electrons per centimeter. 3 Electron density is one of the key parameters determining the properties of plasma; n e0 The electron density under standard conditions; dn e n is the small change in electron density relative to the standard state. e -n e0 T represents the plasma temperature, typically measured in electron volts (eV) or Kelvin (K). In plasma, temperature reflects the average kinetic energy of particles. T0 represents the plasma temperature under standard conditions. dT represents the minute change in plasma temperature relative to the standard state, i.e., T - T0. ideal For ideal spectral line intensity, it is typically achieved under standard conditions (N... s0 ,n e0 Strength value at T0); Spectral intensity I versus total particle number density N s The first-order partial derivative; For spectral intensity I versus electron density n e The first-order partial derivative; The first partial derivative of spectral intensity I with respect to plasma temperature T; Spectral intensity I versus total particle number density N s The second-order partial derivative; For spectral intensity I versus electron density n e The second-order partial derivative; Let I be the second partial derivative of the spectral intensity I with respect to the plasma temperature T; Spectral intensity I versus total particle number density N s and electron density n e The cross-second partial derivatives of N represent N s and n eThe effect of coupling on I; For spectral intensity I versus electron density n e The cross-second partial derivative with plasma temperature T represents n e The effect of coupling between T and I; The spectral intensity I is relative to the plasma temperature T and the total particle number density N. s The cross-second partial derivatives, representing T and N s The effect of coupling on I.

[0065] In this embodiment of the invention, a signal correction model is established by performing variable substitution on the third function in conjunction with characteristic parameters, including the following steps:

[0066] Transforming the right side of the third function, we obtain the following formula for the right side of the transformed third function:

[0067]

[0068] Where k4, k5, k6, k7, and k8 are constants, i.e., the results obtained according to Taylor's formula.

[0069] Substitute the formula on the right side of the transformed third function into the third function, and replace the variable with the number of events E. num Plasma area E s The signal correction model is obtained by combining the full width at half maximum (FWHM) representation with the half height (HWHM).

[0070] Specifically, the formula for the signal correction model is as follows:

[0071]

[0072] Among them, I(N) s ,n e ,T) represents the spectral line intensity; k0-k9 are constants, i.e., the coefficients of the signal correction model.

[0073] It should be noted that the actual spectral line intensity is affected not only by changes in parameters such as plasma temperature, electron density, and ablation mass caused by matrix effects, but also by other factors such as experimental instrument parameters and testing environment. Therefore, the actual spectral line intensity I real With spectral line intensity I(N) s ,n e ,T) are different.

[0074] Therefore, in this embodiment of the invention, the step S200 of solving the coefficients of the signal correction model through regression analysis includes:

[0075] Using actual spectral line intensity I real Replace spectral line intensity I(N) s ,ne Using element content C, plasma area information change dEs, full width at half maximum (FWHM) change dFWHM, and event number change dEnum as independent variables, regression analysis was performed on the signal correction model to obtain the coefficients k0-k9 of the signal correction model.

[0076] In one alternative implementation, the coefficients of the signal correction model in step S200 can be solved using various alternative schemes. For example, a nonlinear modeling framework based on neural networks can be used, with feature parameters as input layers. A multilayer perceptron or convolutional neural network can be used to fit the complex mapping relationship between spectral line intensity and physical parameters, and an attention mechanism can be introduced to dynamically allocate the weights of each feature. In addition, a Bayesian optimization algorithm can be combined to iteratively search for the optimal combination of coefficients in the parameter space using a Gaussian process surrogate model and a collection function, taking into account both computational efficiency and global convergence. For parameter drift problems in dynamic environments, online learning techniques can be used to correct model parameters in real time through a sliding window update mechanism or incremental learning algorithm, such as using recursive least squares or Kalman filtering to track time-varying systems. For multi-objective optimization requirements (such as simultaneously minimizing prediction error and model complexity), a multi-objective particle swarm optimization algorithm can be introduced to adaptively select the optimal trade-off solution in the Pareto front.

[0077] In another optional implementation, the coefficients of the signal correction model in step S200 can also be solved by transfer learning combined with domain knowledge. For example, the neural network parameters can be initialized using a pre-trained physical model and then fine-tuned with a small amount of experimental data to solve the model generalization problem in real-world scenarios. Alternatively, a symbolic regression algorithm can be used, taking feature parameters and spectral intensity as input, to automatically search for mathematical expression structures that conform to physical constraints and generate an interpretable correction model. For the complex relationship between nonlinear features and spectral line intensity, a kernel method can be introduced to capture potential correlations through high-dimensional feature mapping. In addition, a dynamic weight allocation model based on an attention mechanism can be designed to adjust the influence weight of each feature parameter in the correction model according to the real-time contribution of the feature parameters. Under the requirement of uncertainty quantification, a probabilistic graphical model can be used to jointly model the conditional probability distribution of feature parameters and spectral signals, and the posterior distribution of model coefficients can be estimated through sampling methods. The coefficients of the LIBS spectral signal stability improvement model in step S300 are solved in the same way.

[0078] It should be noted that in LIBS technology, plasma physical properties directly affect spectral line intensity, but these parameters are difficult to measure directly, leading to a lack of physical basis in traditional correction methods. This invention establishes a bridge between physical parameters and measurable features by extracting plasma characteristic parameters from asynchronous event data streams. This includes effectively characterizing the total particle number density through plasma area information, solving the problem of difficulty in quantifying ablation fluctuations; characterizing plasma temperature through event count, overcoming the complexity and limited accuracy of temperature measurement in traditional methods; and characterizing electron density through full width at half maximum (FWHM), avoiding the technical complexity of directly measuring electron density. In terms of model building, this invention first establishes a functional relationship between spectral line intensity and basic physical parameters based on the principles of spectroscopic physics. Then, it considers the influence of ionization equilibrium through the Saha equation, and simplifies the complex physical model into an engineering-usable mathematical expression through Taylor expansion. Finally, it transforms theoretical parameters into directly measurable characteristic parameters through variable substitution. This step-by-step derivation process from theory to practice not only has a rigorous physical basis but also has practical application feasibility. It solves the dilemma of traditional correction methods being either too theoretical to implement or too empirical to have a physical basis, accurately reflecting the influence of plasma parameter fluctuations on spectral line intensity and providing a scientific basis for signal correction.

[0079] In this embodiment of the invention, step S300, which corrects the LIBS spectral signal based on a signal correction model, includes:

[0080] Based on event data, the third function is transformed to establish a LIBS spectral signal stability improvement model.

[0081] Regression analysis was performed on the LIBS spectral signal stability enhancement model to solve for the coefficients of the LIBS spectral signal stability enhancement model.

[0082] Substituting the coefficients of the LIBS spectral signal stability enhancement model into the LIBS spectral signal stability enhancement model yields the corrected spectral line intensity.

[0083] Specifically, the formula for the LIBS spectral signal stability enhancement model is as follows:

[0084]

[0085] Among them, I ideal For ideal spectral line intensity; I(N) s ,n e T) represents the spectral line intensity; a1-a9 are constants, i.e., the coefficients of the LIBS spectral signal stability enhancement model; E num E represents the number of events. s is the plasma area; FWHM is the full width at half maximum (FWHM).

[0086] In this embodiment of the invention, step S300 involves performing regression analysis on the LIBS spectral signal stability enhancement model to solve for the coefficients of the LIBS spectral signal stability enhancement model, including:

[0087] According to I ideal =aC+b, using the actual spectral line intensity I real Establish the original calibration curve and obtain the dependent variable, i.e., the ideal spectral line intensity I. ideal and the spectral line intensity I(N) s ,n e Using the changes in plasma area information (dEs), the number of events (dEnum), and the full width at half maximum (FWHM) (dFWHM) as independent variables, regression analysis was performed on the LIBS spectral signal stability improvement model to obtain the coefficients a1-a9 of the LIBS spectral signal stability improvement model.

[0088] Furthermore, the corrected spectral line intensity is obtained by substituting the coefficients a1-a9 of the LIBS spectral signal stability enhancement model into the LIBS spectral signal stability enhancement model and calculating based on the known independent variables to obtain the corrected spectral line intensity I. corr .

[0089] It should be noted that the present invention employs normalization in the above-mentioned solution process to eliminate the influence of dimensions.

[0090] It should be noted that the matrix effect and signal instability caused by fluctuations in experimental conditions are the main obstacles limiting the accuracy of quantitative analysis in traditional LIBS technology. Existing correction methods often rely on internal standard elements or simple mathematical processing, which are difficult to cope with complex sample analysis scenarios. Based on the aforementioned physical model, this invention establishes a LIBS spectral signal stability improvement model through formal transformation. It not only considers the influence of matrix effects on changes in plasma temperature, electron density, and ablation mass, but also considers experimental instrument parameters and test environment factors, achieving comprehensive correction of the original signal. By solving the model coefficients through regression analysis, a quantitative relationship between characteristic parameters and signal correction is established, giving the correction process a clear mathematical basis. In practical applications, this invention does not require the addition of internal standard elements or detailed knowledge of the sample composition. It can achieve effective signal correction solely through synchronously acquired plasma event data, greatly simplifying the operation process and improving analysis efficiency. At the same time, the corrected spectral line intensity significantly improves stability and repeatability, enhancing the accuracy of LIBS technology in quantitative analysis and laying a technical foundation for its application expansion in materials analysis, environmental monitoring, and industrial online detection.

[0091] In summary, this invention effectively improves the signal stability of LIBS technology by introducing an event camera to capture plasma and combining it with spectral signal characteristics to establish a signal correction model. Specifically, by constructing a LIBS correction system incorporating an event camera and acquiring asynchronous event data streams, real-time, high-precision capture of plasma morphology is achieved, providing rich and accurate raw data for subsequent signal correction and improving the accuracy and reliability of the correction from the source. Secondly, by extracting feature parameters from the asynchronous event data stream, physical models characterizing total particle number density, plasma temperature, and electron density are established. This parameter extraction method based on plasma physical characteristics achieves precise quantification of key plasma parameters, overcoming the limitations of traditional methods that cannot obtain information about the internal state of plasma, and providing a solid theoretical foundation for signal correction. Finally, through mathematical model derivation based on Taylor expansion and the Saha equation, a correction model relating plasma physical parameters to spectral signals is established, and the model coefficients are solved through regression analysis, achieving precise correction of the LIBS spectral signal.

[0092] Example 3 is the third embodiment of the present invention. This embodiment provides a system for improving the signal stability of LIBS technology, including: a data acquisition module for building a LIBS correction system, which is used to acquire asynchronous event data streams; a model building module for extracting plasma characteristic parameters from the asynchronous event data streams and establishing a signal correction model based on the characteristic parameters; and a signal correction module for correcting the LIBS spectral signal based on the signal correction model and determining the corrected spectral line intensity.

[0093] Example 4 is the fourth embodiment of the present invention, which differs from the previous three embodiments in that:

[0094] like Figure 2 As shown, if the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0096] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0097] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0098] Example 5, refer to Figures 3-8 This is the fifth embodiment of the present invention, which provides a method for improving the signal stability of LIBS technology. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0099] This example utilizes a laser to generate high-power, high-stability laser pulses to ablate and excite a small sample to produce plasma. The plasma radiation spectrum is received by a spectrometer, and an event camera is used to capture images of the plasma. By extracting characteristic information from the plasma and establishing a correction model, the signal of the LIBS technique is corrected, thereby improving its signal stability. Table 1 shows the content information of the main elements in the sample.

[0100] Table 1. Information on the content of major elements in the samples.

[0101] Sample number model Fe 1 Q195 99.07％ 2 Q215 98.50％ 3 Q235 98.29％ 4 Q255 98.34％ 5 Q275 98.82％ 6 Q345 98.53％ 7 Q390 98.08％ 8 Q420 97.58％ 9 Q460 97.46％

[0102] For the plasma radiation spectra generated by the samples in Table 1 above, the influence of fluctuations in parameters such as pulse energy and measurement efficiency was eliminated by normalizing the spectral data. Based on Discrete Wavelet Transform (DWT), baseline correction and noise reduction were performed on the normalized spectra. Most of the noise was eliminated, and spectral lines from different channels were well corrected. The original spectrum and the spectrum after correction and noise reduction are shown in the figures below. Figure 3 and Figure 4 As shown.

[0103] After preprocessing the spectral data, peak finding is required, as it directly affects the accuracy of the analysis results. This embodiment employs the Continuous Wavelet Transform (CWT) peak finding method, which effectively identifies duplicate peaks with good accuracy. Referring to the Atomic Spectra Database (ASD) of the National Institute of Standards and Technology (NIST), the center wavelengths corresponding to the tested spectral peaks are compared with the standard wavelengths in the database to determine the elemental affiliation of the spectral lines. The peaks obtained from peak finding are then compared with the standard database to obtain the spectral element identification results. Based on the information in the NIST database, the representative characteristic spectral line FeI 355.907 nm is selected for further analysis, including... Figure 5 , Figure 6 , Figure 7 as well as Figure 8The figures show the RSD of the calibration curve and spectral intensity of Fe 355.907nm under different processing methods, including the original spectral calibration curve, the normalized spectral calibration curve, the calibration curve of the model proposed in this invention, and the influence of different methods on the RSD of the spectral intensity. It can be seen that the R2 of the original calibration curve of Fe 355.907nm is 0.5584, the R2 of the normalized calibration curve is 0.5890, and the R2 of the calibration curve after processing by the method proposed in this invention is 0.9971. The RSD of the signal after processing is significantly reduced, and the RSD fluctuates within 1%, which is much smaller than that of the original data and the normalized data.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for improving the signal stability of LIBS technology, characterized in that: include, A LIBS correction system is established, which is used to acquire asynchronous event data streams; The plasma characteristic parameters are extracted from the asynchronous event data stream, and a signal correction model is established based on the characteristic parameters; The LIBS spectral signal is corrected based on the aforementioned signal correction model to determine the corrected spectral line intensity.

2. The method for improving the signal stability of LIBS technology as described in claim 1, characterized in that: The process of obtaining the asynchronous event data stream includes: The sample is ablated and excited by a laser pulse to generate plasma. It receives the radiation spectrum of the plasma and simultaneously photographs the plasma.

3. The method for improving the signal stability of LIBS technology as described in claim 2, characterized in that: Extracting plasma characteristic parameters from the asynchronous event data stream includes: The plasma morphology is reconstructed using asynchronous event data streams. The plasma area information Es is extracted to represent the total particle number density, the number of events Enum is extracted to represent the plasma temperature, and the full width at half maximum (FWHM) is extracted to represent the electron density.

4. The method for improving the signal stability of LIBS technology as described in claim 3, characterized in that: A signal correction model is established based on the aforementioned feature parameters, including: Based on the acquired characteristic parameters, the LIBS spectral line intensity is expressed as a first function of the total particle number density, plasma temperature, and electron density using Taylor expansion. The second function is obtained by adjusting the first function using the Saha equation; Performing a Taylor expansion on the second function yields the third function; By combining the characteristic parameters, the third function is substituted with variables to establish a signal correction model; The coefficients of the signal correction model are solved by regression analysis to obtain the spectral line intensity I(N). s ,n e ,T).

5. The method for improving the signal stability of LIBS technology as described in claim 4, characterized in that: The LIBS spectral signal is corrected based on the aforementioned signal correction model, including: Based on the event data, the third function is transformed to establish a LIBS spectral signal stability improvement model. Regression analysis was performed on the LIBS spectral signal stability enhancement model to solve for the coefficients of the LIBS spectral signal stability enhancement model; Substituting the coefficients of the LIBS spectral signal stability enhancement model into the LIBS spectral signal stability enhancement model yields the corrected spectral line intensity.

6. The method for improving the signal stability of LIBS technology as described in claim 5, characterized in that: The process of solving the coefficients of the signal correction model through regression analysis includes: Using actual spectral line intensity to replace spectral line intensity I(N) s ,n e Using element content C, plasma area information change dEs, full width at half maximum (FWHM) change dFWHM, and event number change dEnum as independent variables, regression analysis was performed on the signal correction model to obtain the coefficients of the signal correction model.

7. The method for improving the signal stability of LIBS technology as described in claim 6, characterized in that: Regression analysis was performed on the LIBS spectral signal stability enhancement model to solve for the coefficients of the LIBS spectral signal stability enhancement model, including: The original calibration curve is established using the actual spectral line intensities to obtain the dependent variable, i.e., the ideal spectral line intensity, and the spectral line intensity I(N) is then expressed as... s ,n e The coefficients of the LIBS spectral signal stability improvement model were obtained by using the changes in plasma area information dEs, event number dEnum, and full width at half maximum (FWHM) as independent variables.

8. A system for improving the signal stability of LIBS technology, comprising the method for improving the signal stability of LIBS technology as described in any one of claims 1 to 7, characterized in that: include, The data acquisition module is used to build the LIBS correction system, which is used to acquire asynchronous event data streams. The model building module is used to extract plasma characteristic parameters from asynchronous event data streams and build a signal correction model based on the characteristic parameters. The signal correction module is used to correct the LIBS spectral signal based on the signal correction model and determine the intensity of the corrected spectral lines.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for improving the signal stability of LIBS technology as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for improving the signal stability of LIBS technology as described in any one of claims 1 to 7.

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