LIBS instrument based on human-computer interaction, visual teaching system and interaction method
By using a human-computer interaction-based LIBS instrument and visualization teaching system, the linkage between machine learning algorithms and physical measurement steps in the teaching of laser-induced breakdown spectroscopy instruments was realized, solving the problem of visualization teaching that is difficult to achieve in existing technologies, and improving students' understanding and operation of data analysis.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TAICHU INTELLIGENT TEST (BEIJING) TECHNOLOGY CO LTD
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-01
AI Technical Summary
In existing teaching of laser-induced breakdown spectroscopy instruments, the linkage between machine learning algorithms and physical measurement steps is difficult to visualize, making it impossible to complete the teaching process from data acquisition, spectral data preprocessing, LIBS data modeling, and data analysis and evaluation.
By using LIBS instruments and a visual teaching system based on human-computer interaction, and combining LIBS experimental data with machine learning execution processes, synchronous and visual linkage of instruments, teaching systems, and interactive methods is achieved, including steps such as data preprocessing, feature dimensionality reduction, and model validation.
It enables an intuitive understanding of the visual construction and optimization process of machine learning models, covering the complete teaching process from data collection to model evaluation, and helps students systematically learn data analysis techniques.
Smart Images
Figure CN121963565A_ABST
Abstract
Description
A LIBS instrument, visualization teaching system, and interaction method based on human-computer interaction Technical Field
[0001] This invention relates to a human-computer interaction-based LIBS instrument, a visualization teaching system, and an interaction method, belonging to the technical field of machine learning teaching and spectroscopy teaching equipment, and is applied to the teaching of laser-induced breakdown spectroscopy and chemometrics in colleges and universities. Background Technology
[0002] In existing laser-induced breakdown spectroscopy (LIBS) instrument teaching, it is mainly used for reading spectral images. With the introduction of machine learning algorithms, it has been further realized to perform quantitative analysis on dimensionality reduction and regression processing of high-dimensional spectral data.
[0003] However, due to the isolation between the execution process of machine learning algorithms and actual spectral data, it is difficult to visualize the linkage between the execution process of machine learning and the physical measurement steps in the teaching of existing laser-induced breakdown spectroscopy instruments. Consequently, it is impossible to complete the teaching process from data acquisition, spectral data preprocessing, LIBS data modeling, and data analysis and evaluation.
[0004] Therefore, how to visualize machine learning algorithms and LIBS instrument teaching in the teaching of existing laser-induced breakdown spectroscopy instruments has become an urgent problem to be solved. Summary of the Invention
[0005] The purpose of this invention is to address the technical problem of visualizing machine learning algorithms and LIBS instrument teaching in existing laser-induced breakdown spectroscopy (LIBS) instrument teaching, and to propose a human-computer interaction-based LIBS instrument, visualization teaching system, and interaction method.
[0006] The workflow of this invention is as follows: by visually matching and combining the data obtained from the LIBS experiment with the execution process of machine learning in teaching, synchronous visualization and linkage of instruments, visualization teaching system and interactive methods are achieved.
[0007] The objective of this invention is achieved through the following technical solution:
[0008] On the one hand, the present invention discloses a LIBS interaction method based on human-computer interaction, which is applied to LIBS visualization teaching, including the following steps:
[0009] Step 1: Use the LIBS execution component of the LIBS instrument to collect the same-side light and opposite-side light on the sample surface, and use the single same-side light or the combination of the same-side light and opposite-side light as the spectrum to be analyzed.
[0010] Step 1.1: Use the LIBS execution component to collect light from the same side of the sample surface to form the same-side spectrum;
[0011] Step 1.2: Use the LIBS execution component to collect the light from the opposite side of the sample surface to form the opposite side spectrum;
[0012] Step 1.3: Use the same-side spectrum or the combination of the same-side and opposite-side spectra as the spectrum to be analyzed;
[0013] Step 2: Use the data preprocessing module of the LIBS visualization teaching system to process the spectrum to be analyzed, call the stepper motor of the LIBS correction component to correct the height of the sample surface by the laser height gauging instrument, and use the LIBS teaching system to generate a visualization spectrum. At the same time, the LIBS instrument human-computer interaction module performs interactive parameter correction.
[0014] Step 2.1: Use the Raida criterion to screen for anomalous spectra in the spectrum to be analyzed, and generate an interactive spectrum for removing anomalous spectra that can be used in the LIB S teaching system.
[0015] Step 2.1.1: Mark the abnormal spectra in the spectrum to be analyzed;
[0016] Step 2.1.2: Remove the marked anomalous spectra using the Laida criterion;
[0017] Step 2.1.3: Use the LIBS teaching system to generate a spectrum with outlier spectra removed, and use the LIBS instrument human-computer interaction module to interactively correct the relevant parameters in the spectrum.
[0018] Step 2.2: Use interpolation fitting to remove the background curve of the abnormal spectrum and perform peak completion through channel intensity normalization and multiple linear regression to generate an executable interactive peak-finding spectrum for the LIBS teaching system.
[0019] Step 2.2.1: The background curve for removing abnormal spectra is removed by interpolation fitting and then peak completion is performed by channel intensity normalization and multiple linear regression.
[0020] Step 2.2.2: Use the LIBS teaching system to generate peak finding spectrum, and use the LIBS instrument human-computer interaction module to interactively correct the relevant parameters in the spectrum.
[0021] Step 3: Based on the executable interactive spectrum of the LIBS teaching system and the interactive parameter correction of the LIBS instrument human-computer interaction module, perform PCA and PLS feature analysis on the spectral features;
[0022] Step 3.1: Perform feature dimensionality reduction using unsupervised principal component analysis (PCA);
[0023] Step 3.2: Use supervised learning partial least squares (PLS) for feature dimensionality reduction;
[0024] Step 3.3: Compare the feature dimensionality reduction of PCA and PLS, generate a feature analysis projection comparison map using the LIBS teaching system, and perform interactive parameter correction using the LIBS instrument human-computer interaction module;
[0025] Step 4: Input the PCA or PLS dimensionality reduction features into the fit model to obtain the MSE, RMSE, MAE, and R of the component analysis. 2 index;
[0026] Step 5: Execute steps 1 to 4 using the LIBS visual teaching system in a human-computer interaction manner, perform interactive parameter correction of the LIBS instrument in real time, and call the LIBS correction component to adjust the stepper motor and servo motor group in real time, and display the process in a visual and synchronized manner with the teaching process.
[0027] On the other hand, this invention discloses a LIBS visualization teaching system based on human-computer interaction, used to implement the above-mentioned method. The LIBS visualization teaching system based on human-computer interaction disclosed in this invention includes a data preprocessing module, a feature dimensionality reduction module, a model validation module, and a human-computer interaction module.
[0028] The data preprocessing module is used to process the spectra collected by the spectrometer in the LIBS instrument to form spectral features in the LIBS teaching process. It consists of a data filtering module, a background removal module, a normalization module, and a feature peak finding module; and will be used as the input of the feature processing module.
[0029] Furthermore, the data filtering module uses the Laida criterion to remove abnormal spectra; this will be used as input to the background removal module.
[0030] Furthermore, the background removal module is used to remove the background curve fitted by interpolation from the spectral data, and will be used as the input of the normalization module.
[0031] Furthermore, the normalization module is used to normalize the intensity of the spectrum by channel; this will be used as the input to the feature peak finding module.
[0032] Furthermore, the feature peak finding module is used to perform peak completion on the spectrum using multiple linear regression.
[0033] The feature dimensionality reduction module is used for PCA and PLS feature parsing; it consists of a PCA projection module and a PLS projection module, and will serve as the input to the model validation module.
[0034] Furthermore, the PCA projection module is used to project the components of the feature to maximize the projection variance;
[0035] Furthermore, the PLS projection module is used to project the components of the feature to maximize the covariance.
[0036] The model validation module is used for cross-validation of performance metrics during model training, utilizing the adaptive model to evaluate the MSE, RMSE, MAE, and R-squared values of component analysis. 2 The indicators will be validated and used as input for the human-computer interaction module.
[0037] The human-computer interaction module is used to visualize the teaching process and interact synchronously with the LIBS instrument, and output the interaction data in real time.
[0038] Furthermore, this invention discloses a LIBS instrument based on human-computer interaction, used to implement the aforementioned system and method. The LIBS instrument based on human-computer interaction disclosed in this invention comprises a LIBS execution component, a LIBS correction component, and a LIBS visualization component.
[0039] The LIBS execution component consists of a same-side light-receiving component, an opposite-side light-receiving component, a spectrometer, and a laser altitude stabilizer.
[0040] Furthermore, the same-side light receiving component consists of a laser emitting a parallel laser beam, which is reflected by a mirror to a dichroic mirror, focused onto the sample surface by a first single plano-convex lens to form plasma, which is then transmitted back to the dichroic mirror and focused onto an optical fiber by a second plano-convex lens to form same-side light.
[0041] Furthermore, the opposite-side light receiving component uses a plano-convex lens array to focus plasma onto the sample surface and then onto the fiber optic array to generate opposite-side light.
[0042] Furthermore, the spectrometer is used to collect light from the same side and light from the opposite side;
[0043] Furthermore, the laser height stabilizer is used to adjust the height of the sample surface;
[0044] The LIBS correction component consists of a stepper motor and a servo motor assembly;
[0045] Furthermore, a stepper motor is used to correct the height of the laser altimeter relative to the sample surface in real time;
[0046] Furthermore, the servo motor group is used to correct the offset angle of the plano-convex lens array in real time to collect plasma;
[0047] The LIBS visualization component is used to display the corrected data in a visual form in sync with the teaching process;
[0048] Compared with existing technologies, it has the following beneficial effects:
[0049] 1. In terms of deep interactive experience, this invention enables students to more intuitively understand the construction and optimization process of machine learning models through 3D visualization and real-time operation.
[0050] 2. In terms of the teaching process, this invention covers the complete machine learning process from data collection to model evaluation, helping students to systematically learn data analysis techniques. Attached Figure Description
[0051] Figure 1 is a flowchart of the present invention;
[0052] Figure 2 is a schematic diagram of the structure of the present invention; Detailed Implementation
[0053] To better illustrate the purpose and advantages of this invention, the invention will be further described below with reference to the accompanying drawings and examples. It should be noted that the implementation of this invention is not limited to the following embodiments, and any modifications or alterations made to this invention will fall within the scope of protection of this invention.
[0054] Example
[0055] As shown in Figure 1, this embodiment presents a LIBS interaction method based on human-computer interaction, applied to LIBS visualization teaching. The specific implementation steps are as follows:
[0056] Step 1: Use the LIBS execution component of the LIBS instrument to collect the same-side light and opposite-side light on the sample surface, and use the single same-side light or the combination of the same-side light and opposite-side light as the spectrum to be analyzed.
[0057] Step 1.1: Use the LIBS execution component to collect light from the same side of the sample surface to form the same-side spectrum;
[0058] Step 1.2: Use the LIBS execution component to collect the light from the opposite side of the sample surface to form the opposite side spectrum;
[0059] Step 1.3: Use the same-side spectrum or the combination of the same-side and opposite-side spectra as the spectrum to be analyzed;
[0060] Step 2: Use the data preprocessing module of the LIBS visualization teaching system to process the spectrum to be analyzed, call the stepper motor of the LIBS correction component to correct the height of the sample surface by the laser height gauging instrument, and use the LIBS teaching system to generate a visualization spectrum. At the same time, the LIBS instrument human-computer interaction module performs interactive parameter correction.
[0061] Step 2.1: Use the Raida criterion to screen for anomalous spectra in the spectrum to be analyzed, and generate an interactive spectrum for removing anomalous spectra that can be used in the LIB S teaching system.
[0062] Step 2.1.1: Mark the abnormal spectra in the spectrum to be analyzed;
[0063] Step 2.1.2: Remove the marked anomalous spectra using the Laida criterion;
[0064] Step 2.1.3: Use the LIBS teaching system to generate a spectrum with outlier spectra removed, and use the LIBS instrument human-computer interaction module to interactively correct the relevant parameters in the spectrum.
[0065] Step 2.2: Use interpolation fitting to remove the background curve of the abnormal spectrum and perform peak completion through channel intensity normalization and multiple linear regression to generate an executable interactive peak-finding spectrum for the LIBS teaching system.
[0066] Step 2.2.1: The background curve for removing abnormal spectra is removed by interpolation fitting and then peak completion is performed by channel intensity normalization and multiple linear regression.
[0067] Step 2.2.2: Use the LIBS teaching system to generate peak finding spectrum, and use the LIBS instrument human-computer interaction module to interactively correct the relevant parameters in the spectrum.
[0068] Step 3: Based on the executable interactive spectrum of the LIBS teaching system and the interactive parameter correction of the LIBS instrument human-computer interaction module, perform PCA and PLS feature analysis on the spectral features;
[0069] Step 3.1: Perform feature dimensionality reduction using unsupervised principal component analysis (PCA);
[0070] Step 3.2: Use supervised learning partial least squares (PLS) for feature dimensionality reduction;
[0071] Step 3.3: Compare the feature dimensionality reduction of PCA and PLS, generate a feature analysis projection comparison map using the LIBS teaching system, and perform interactive parameter correction using the LIBS instrument human-computer interaction module;
[0072] Step 4: Input the PCA or PLS dimensionality reduction features into the fit model to obtain the MSE, RMSE, MAE, and R of the component analysis. 2 index;
[0073] Step 5: Execute steps 1 to 4 using the LIBS visual teaching system in a human-computer interaction manner, perform interactive parameter correction of the LIBS instrument in real time, and call the LIBS correction component to adjust the stepper motor and servo motor group in real time, and display the process in a visual and synchronized manner with the teaching process.
[0074] In this embodiment, the device includes a laser, focusing optics, a sample stage, an optical fiber coupling system, a spectrometer, and a computer terminal. Students can use this device to complete basic LIBS experimental training, including optical path adjustment, laser focusing, sample breakdown, and spectral signal acquisition. The teaching process is as follows: The instructor first demonstrates the influence of parameters such as laser output energy, pulse frequency, and focusing position on the intensity and peak shape of the spectral signal, helping students understand the impact of experimental conditions on the measured signal. By adjusting parameters such as focal length and integration time, students can observe changes in the spectral signal in real time and understand the relationship between experimental repeatability and signal-to-noise ratio. The signal data acquired by the spectrometer is transmitted to the computer via USB or local area network for subsequent software demonstrations. This section primarily aims to enable students to master the basic operation of LIBS experiments and the spectral acquisition process, forming an intuitive understanding of experimental errors and data sources.
[0075] On the other hand, this embodiment provides a LIBS visualization teaching system based on human-computer interaction to implement the above method. The LIBS visualization teaching system based on human-computer interaction disclosed in this invention includes a data preprocessing module, a feature dimensionality reduction module, a model validation module, and a human-computer interaction module.
[0076] The data preprocessing module is used to process the spectra collected by the spectrometer in the LIBS instrument to form spectral features in the LIBS teaching process. It consists of a data filtering module, a background removal module, a normalization module, and a feature peak finding module; and will be used as the input of the feature processing module.
[0077] Furthermore, the data filtering module uses the Laida criterion to remove abnormal spectra; this will be used as input to the background removal module.
[0078] Furthermore, the background removal module is used to remove the background curve fitted by interpolation from the spectral data, and will be used as the input of the normalization module.
[0079] Furthermore, the normalization module is used to normalize the intensity of the spectrum by channel; this will be used as the input to the feature peak finding module.
[0080] Furthermore, the feature peak finding module is used to perform peak completion on the spectrum using multiple linear regression.
[0081] The feature dimensionality reduction module is used for PCA and PLS feature parsing; it consists of a PCA projection module and a PLS projection module, and will serve as the input to the model validation module.
[0082] Furthermore, the PCA projection module is used to project the components of the feature to maximize the projection variance;
[0083] Furthermore, the PLS projection module is used to project the components of the feature to maximize the covariance.
[0084] The model validation module is used for cross-validation of performance metrics during model training, utilizing the adaptive model to evaluate the MSE, RMSE, MAE, and R-squared values of component analysis. 2 The indicators will be validated and used as input for the human-computer interaction module.
[0085] The human-computer interaction module is used to visualize the teaching process and interact synchronously with the LIBS instrument, and output the interaction data in real time.
[0086] In this embodiment, the system mainly includes the following functional modules: Data preprocessing module: Students select preprocessing methods (such as baseline removal, normalization, smoothing filtering, etc.) through the interface and observe the changes in spectral curves before and after processing in real time. The system provides a slider and graphic linkage function to demonstrate the impact of parameter adjustment on the results. Feature reduction module: The software has built-in PCA and PLS algorithm examples and provides a three-dimensional interactive graphical interface. Students can observe the distribution of samples in different principal component spaces through rotation, scaling, and other operations, and distinguish different sample categories through color labeling. This module also displays multicollinearity and correlation visualization to help students understand the core differences between the two algorithms. Model validation module: The software provides examples of training and test set partitioning through a simulation data generator. Students can select different hyperparameters and immediately view model performance indicators (MSE, RMSE, R-value). 2 Changes in (etc.). Students can intuitively understand the difference between overfitting and underfitting.
[0087] Furthermore, as shown in Figure 2, this embodiment provides a LIBS instrument based on human-computer interaction, used to implement the aforementioned system and method. The LIBS instrument based on human-computer interaction disclosed in this invention comprises a LIBS execution component, a LIBS correction component, and a LIBS visualization component.
[0088] The LIBS execution component consists of a same-side light-receiving component, an opposite-side light-receiving component, a spectrometer, and a laser altitude stabilizer.
[0089] Furthermore, the same-side light receiving component consists of a laser emitting a parallel laser beam, which is reflected by a mirror to a dichroic mirror, focused onto the sample surface by a first single plano-convex lens to form plasma, which is then transmitted back to the dichroic mirror and focused onto an optical fiber by a second plano-convex lens to form same-side light.
[0090] Furthermore, the opposite-side light receiving component uses a plano-convex lens array to focus plasma onto the sample surface and then onto the fiber optic array to generate opposite-side light.
[0091] Furthermore, the spectrometer is used to collect light from the same side and light from the opposite side;
[0092] Furthermore, the laser height stabilizer is used to adjust the height of the sample surface;
[0093] The LIBS correction component consists of a stepper motor and a servo motor assembly;
[0094] Furthermore, a stepper motor is used to correct the height of the laser altimeter relative to the sample surface in real time;
[0095] Furthermore, the servo motor group is used to correct the offset angle of the plano-convex lens array in real time to collect plasma;
[0096] The LIBS visualization component is used to display the corrected data in a visual form in sync with the teaching process;
[0097] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A LIBS interactive method based on human-computer interaction, applied to LIBS visualization teaching, characterized by: The process includes the following steps: Step 1: Using the LIBS execution component of the LIBS instrument, collect both same-side and opposite-side light from the sample surface, and use a single same-side light or a combination of same-side and opposite-side light as the spectrum to be analyzed; Step 2: Use the data preprocessing module of the LIBS visualization teaching system to process the spectrum to be analyzed, and call the stepper motor of the LIBS correction component to correct the height of the laser altimeter on the sample surface and generate a visualization spectrum using the LIBS teaching system. Simultaneously, the LIBS instrument's human-computer interaction module performs interactive parameter correction; Step 2.1: Use the Laida criterion to screen for anomalous spectra in the spectrum to be analyzed, generating data for LIB... Step 2.2: Use interpolation fitting to remove the background curve of the abnormal spectrum and perform peak completion through channel intensity normalization and multiple linear regression to generate an interactive peak-finding spectrum for the LIBS teaching system; Step 3: Perform PCA and PLS feature analysis on the spectral features based on the interactive spectrum of the LIBS teaching system and the interactive parameter correction of the LIBS instrument human-computer interaction module; Step 3.1: Use unsupervised learning principal component analysis (PCA) for feature dimensionality reduction; Step 3.2: Use supervised learning partial least squares (PLS) for feature dimensionality reduction; Step 3.3: Compare the PCA and PLS feature dimensionality reduction, generate a feature analysis projection comparison map using the LIBS teaching system, and perform interactive parameter correction using the LIBS instrument human-computer interaction module; Step 4: Input the PCA or PLS dimensionality reduction features into the adaptability model to obtain the MSE, RMSE, MAE, and R of the component analysis. 2 Indicators; Step 5: Execute steps 1 to 4 using the LIBS visual teaching system in a human-computer interaction manner, perform interactive parameter correction of the LIBS instrument in real time, and call the LIBS correction component to adjust the stepper motor and servo motor group in real time, and display the results in a visual synchronization with the teaching process.
2. The LIBS interaction method based on human-computer interaction as described in claim 1, characterized in that: The implementation method of step 1 is as follows: Step 1.1: Use the LIBS execution component to collect the same-side light on the sample surface to form the same-side spectrum; Step 1.2: Use the LIBS execution component to collect the opposite-side light on the sample surface to form the opposite-side spectrum; Step 1.3: Use the same-side spectrum or the spectrum combining the same-side and opposite-side as the spectrum to be analyzed.
3. The LIBS interaction method based on human-computer interaction as described in claim 1, characterized in that: Step 2.1 is implemented as follows: Step 2.1.1: Mark the abnormal spectra in the spectrum to be analyzed; Step 2.1.2: Remove the marked abnormal spectra using the Laida criterion; Step 2.1.3: Use the LIBS teaching system to generate a spectrum of removed abnormal spectra, and use the LIBS instrument human-computer interaction module to interactively correct the relevant parameters in the spectrum.
4. The LIBS interaction method based on human-computer interaction as described in claim 1, characterized in that: Step 2.2 is implemented as follows: Step 2.2.1: The background curve of the abnormal spectrum is removed by interpolation fitting and peak completion is performed by channel intensity normalization and multiple linear regression; Step 2.2.2: The peak-finding spectrum is generated using the LIBS teaching system, and the relevant parameters in the spectrum are interactively corrected using the LIBS instrument human-computer interaction module.
5. A LIBS-based visual teaching system based on human-computer interaction, as described in claim 1, characterized in that: The system includes a data preprocessing module, a feature reduction module, a model validation module, and a human-computer interaction module. The data preprocessing module processes the spectra acquired by the spectrometer in the LIBS instrument to form spectral features, and consists of a data filtering module, a background removal module, a normalization module, and a feature peak finding module; this data serves as the input to the feature processing module. The feature reduction module performs PCA and PLS feature analysis; it consists of a PCA projection module and a PLS projection module, and serves as the input to the model validation module. Furthermore, the PCA projection module projects the feature components to maximize the projection variance; the PLS projection module projects the feature components to maximize the covariance. The model validation module performs cross-validation of the model training performance metrics, using the fit model to evaluate the MSE, RMSE, MAE, and R-squared values of the component analysis. 2 The indicators will be validated and used as input for the human-computer interaction module. The human-computer interaction module is used to visualize the teaching process and interact synchronously with the LIBS instrument, and output the interaction data in real time.
6. The LIBS visualization teaching system based on human-computer interaction as described in claim 5, characterized in that: Furthermore, the data filtering module uses the Laida criterion to remove abnormal spectra; The input will be used as the background removal module; further, the background removal module is used to remove the background curve fitted by interpolation in the spectral data, and the input will be used as the normalization module; further, the normalization module is used to normalize the intensity of each channel of the spectrum; the input will be used as the feature peak finding module; further, the feature peak finding module is used to perform peak completion of multiple linear regression on the spectrum.
7. A LIBS instrument based on human-computer interaction that implements the method as described in claim 1, characterized in that: The system comprises a LIBS execution component, a LIBS correction component, and a LIBS visualization component. The LIBS execution component consists of a same-side light-collecting component, an opposite-side light-collecting component, a spectrometer, and a laser altimeter. Specifically, the same-side light-collecting component involves a laser emitting a parallel laser beam, which is reflected by a mirror to a dichroic mirror, focused onto the sample surface by a first single plano-convex lens to form plasma, which is then returned to the dichroic mirror and focused onto an optical fiber by a second plano-convex lens, forming same-side light. The opposite-side light-collecting component uses a plano-convex lens array to focus plasma from the sample surface onto an optical fiber array, forming opposite-side light. The spectrometer is used to collect both same-side and opposite-side light. The LIBS correction component consists of a stepper motor and a servo motor assembly. The stepper motor is used to correct the laser altimeter's height relative to the sample surface in real time. The servo motor assembly is used to correct the offset angle of the plasma collected by the plano-convex lens array in real time. The LIBS visualization component is used to display the correction data in a visual form synchronously with the teaching process.
8. A LIBS instrument based on human-computer interaction as described in claim 7, characterized in that: Furthermore, the laser height stabilizer is used to adjust the height of the sample surface.