Screening method and system of LIBS spectral data based on plasma image

Abstract

The invention relates to the technical field of laser-induced breakdown spectroscopy, and discloses an LIBS spectral data screening method based on a plasma image, which comprises the following steps: synchronously acquiring a plasma spectrum and plasma auxiliary elements of a to-be-detected sample, respectively obtaining a plasma characteristic corresponding to the plasma spectrum and at least one auxiliary characteristic corresponding to the plasma auxiliary element; selecting an auxiliary feature strongly correlated with the plasma feature as a feature variable or the plasma feature as a feature variable, and obtaining a feature matrix formed by a plurality of feature variables at different time points; and acquiring a central characteristic variable in the characteristic matrix, and finding n characteristic variables closest to the central characteristic variable, thereby finding the n characteristic variables and the plasma spectrum corresponding to the central characteristic variable as screened spectrum data. According to the invention, effectively excited spectrum data can be screened out.

Description

Technical Field

[0001] The present invention relates to the technical field of laser induced breakdown spectroscopy, and in particular to a method and system for screening LIBS spectral data based on plasma images. Background Art

[0002] At present, laser-induced breakdown spectroscopy technology, as a new, fast and convenient material composition analysis technology, has been proven to have great application potential in more and more industrial fields. However, the technology itself still has many shortcomings that need to be overcome. Since the technology collects the plasma spectrum generated by the sample excited by the laser, the collected spectrum is analyzed to determine the material composition and content. However, the collected spectral data is easily disturbed by external factors such as the environment and laser energy fluctuations, and there is a problem of low data stability. Therefore, a suitable method is needed to screen stable spectral data, thereby optimizing the accuracy and stability of LIBS analysis.

[0003] Traditional optimization screening methods usually involve improving the analytical model or processing the data using methods such as the mean method and normalization method. However, improving the model often means an increase in model complexity, which may reduce the generalization ability of the model; in some special environments, it is difficult to collect more data to implement the mean method; and the normalization method has the limitation that the reference elements of the sample to be tested must be known.

[0004] Therefore, traditional optimization screening has many defects, and it is urgent to design a new screening method to screen LIBS spectral data. Summary of the invention

[0005] The present invention provides a method and system for screening LIBS spectrum data based on plasma images, and screens the LIBS spectrum data.

[0006] The present invention is achieved through the following technical solutions:

[0007] A screening method for LIBS spectral data based on plasma images, comprising:

[0008] Synchronously collecting the plasma spectrum and plasma auxiliary elements of the sample to be tested, and respectively obtaining a plasma feature corresponding to the plasma spectrum and at least one auxiliary feature corresponding to the plasma auxiliary element, and if there are multiple auxiliary features, determining the correlation between the plasma feature and the multiple auxiliary features;

[0009] Selecting auxiliary features that are strongly correlated with the plasma features as feature variables or taking plasma features as feature variables, and obtaining a feature matrix consisting of the feature variables contained in each plasma excited by the laser each time;

[0010] The central characteristic variable in the characteristic matrix is ​​obtained, and the n characteristic variables closest to the central characteristic variable are found, so as to find the plasma spectra corresponding to the n characteristic variables and the central characteristic variable as the screened spectral data, where n is a positive integer.

[0011] As an optimization, before collecting the plasma spectrum and plasma auxiliary factors of the sample to be tested, the changing rules between different experimental conditions and the auxiliary characteristics of the plasma auxiliary factors are first determined, or the changing rules between different experimental conditions and the plasma characteristics of the plasma spectrum are first determined, so as to determine the stable experimental conditions that can collect the stable plasma auxiliary factors or the plasma spectrum of the sample, and then the plasma spectrum and plasma auxiliary factors of the sample to be tested are synchronously collected under the stable experimental conditions.

[0012] As an optimization, the plasma characteristics include spectral line intensities of plasma spectra.

[0013] As an optimization, the auxiliary features include image features.

[0014] As optimized, the image features include plasma region size, image total intensity, image maximum intensity, plasma morphology and plasma center position.

[0015] As an optimization, the specific process of obtaining the central characteristic variable in the characteristic matrix, finding the n characteristic variables closest to the central characteristic variable, and then finding the plasma spectra corresponding to the n characteristic variables and the central characteristic variable as the screened spectral data is as follows:

[0016] Obtain the distance difference between each of the feature variables in the feature matrix and all feature variables except the feature variable to obtain a distance difference matrix corresponding to the feature variable, and find the distance difference matrix with the smallest sum of distance differences, define the distance difference matrix with the smallest sum of distance differences as the minimum distance difference matrix, and define the feature variable corresponding to the minimum distance difference matrix as the central feature variable;

[0017] The elements in the minimum distance difference matrix are sorted in descending order to find the n characteristic variables closest to the central characteristic variable, and the plasma spectra corresponding to the n characteristic variables and the central characteristic variable are selected as the screened spectral data.

[0018] The present invention discloses a screening system for LIBS spectrum data based on plasma images, comprising:

[0019] An acquisition module, used for synchronously acquiring the plasma spectrum and plasma auxiliary elements of the sample to be tested;

[0020] A feature extraction module, used to respectively obtain a plasma feature corresponding to the plasma spectrum and at least one auxiliary feature corresponding to the plasma auxiliary element, and if there are multiple auxiliary features, determine the correlation between the plasma feature and the multiple auxiliary features;

[0021] A feature matrix construction module, used to select auxiliary features having a strong correlation with the plasma features as feature variables or to use the plasma features as feature variables, and to obtain a feature matrix composed of a plurality of the feature variables at different time points;

[0022] The screening module is used to obtain the central characteristic variable in the characteristic matrix, find the n characteristic variables closest to the central characteristic variable, and thus find the plasma spectra corresponding to the n characteristic variables and the central characteristic variable as the screened spectral data, where n is a positive integer.

[0023] As an optimization, the acquisition module includes a spectrometer for acquiring plasma spectrum and an auxiliary acquisition device for acquiring auxiliary elements of the plasma.

[0024] As an optimization, the auxiliary acquisition device is an ICCD camera for acquiring plasma images.

[0025] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0026] 1. Compared with improving the analysis accuracy of LIBS technology by optimizing the hardware performance of lasers and spectrometers or limiting external environmental factors, the present invention only needs to set up a synchronous high-speed camera around the plasma excitation position to collect additional plasma image information. Its cost is relatively low and its applicability is stronger. The system diagram is as follows: Figure 2 As shown, this can lay the foundation for the commercialization of LIBS technology.

[0027] 2. In addition to spectral signals, additional signals such as images can be collected by corresponding equipment during the LIBS plasma excitation process. The above additional signals can also be used as auxiliary information features. By making full use of multiple information and combining redundant or complementary information of multiple information sources in space or time according to specific standards, a consistent explanation or description of the object under test can be obtained, so that the information system has better performance than the system composed of the various subsets it contains, that is, the spectral signal is optimized and corrected, thereby increasing the analysis accuracy of the LIBS technology.

[0028] 3. Because LIBS spectral data is collected from transient plasma data at the microsecond level, the collection process is susceptible to interference from various conditions, which may cause its spectral data to be too high or too weak. Therefore, screening effective data is one of the most important steps to improve the performance of LIBS analysis. Although the mean method and normalization method can reduce the analysis error of LIBS technology to a certain extent, if the data is not screened, abnormal data will also be processed in the above method, which may cause the original effective spectral data to lose its accurate characterization of the sample. Further, compared with the traditional data screening method, it is also necessary to use a certain feature of the spectral data as an index. However, the collected LIBS spectral data generally has thousands or even tens of thousands of data points depending on the performance of the spectrometer, which greatly increases the difficulty of selecting spectral features. In contrast, the present invention uses plasma image features as indexes to screen effective spectral data. Its image features such as plasma brightness and area size can more simply and intuitively characterize the stability and intensity of the excited plasma compared to a single spectral feature line, and also avoid the spectral interference caused by the difference in the content of different elements in the sample as much as possible. Therefore, using image features as indexes can better screen out effectively excited spectral data. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0030] Figure 1 A flowchart of a method for screening LIBS spectral data based on plasma images according to the present invention;

[0031] Figure 2 A connection diagram of a screening system for LIBS spectral data based on plasma images according to the present invention;

[0032] Figure 3 is a schematic diagram of a plasma image;

[0033] Figure 4 It is an illustration of image features;

[0034] Figure 5 This is a flowchart for screening valid data through characteristic variables. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0036] Existing studies have shown that the image features of plasma images, such as their brightness and area size, have a high correlation with the plasma temperature and electron density, that is, they are correlated with the spectral data as a whole. Therefore, the image, as an external reference signal, can be used to eliminate invalid spectral data collected, thereby improving the analysis accuracy of LIBS technology. And compared with traditional methods, because plasma images can intuitively reflect the various interference factors that affect the stability of spectral data, the image-assisted data screening method has a wider applicability.

[0037] This embodiment 1 provides a screening method for LIBS spectral data based on plasma images, such as Figure 1 As shown, including:

[0038] S1. synchronously collecting the plasma spectrum and plasma auxiliary factor of the sample to be tested, and respectively obtaining a plasma feature corresponding to the plasma spectrum and at least one auxiliary feature corresponding to the plasma auxiliary factor, and if there are multiple auxiliary features, determining the correlation between the plasma feature and the multiple auxiliary features;

[0039] In some embodiments, the plasma feature includes the spectral line intensity of the plasma spectrum. The auxiliary feature includes an image feature. Specifically, the image feature includes the size of the plasma region, the total image intensity, the maximum image intensity, the plasma morphology and the plasma center position. The extraction of the image features corresponding to the plasma image is as follows: Figure 4 shown.

[0040] Here, image features are mainly used.

[0041] For spectral data, its characteristics are mainly related to the type and content of elements contained in the measured substance, specifically, whether the characteristic spectral line peaks corresponding to the elements are excited and their intensity. Therefore, in the present invention, the plasma characteristics are the spectral line intensity. For spectral data, the plasma characteristics are mostly spectral line intensity, such as the spectral peak distortion caused by environmental interference or the self-absorption effect of the plasma itself, which will eventually be reflected in the spectral peak shape and peak intensity in the spectral data.

[0042] In the early stage of plasma formation (within a few hundred nanoseconds), the spectrum collection will cause strong background interference due to the plasma's bremsstrahlung (a characteristic of laser-excited plasma radiation), which increases the uncertainty of the characteristic spectral line intensity and thus affects the accuracy of LIBS quantitative analysis.

[0043] In summary, plasma has many mutually influencing characteristics, which will affect the peak intensity and shape of the spectrum. Without specific analysis, we generally only look at the peak intensity.

[0044] The correlation between mutually influencing features can be evaluated by the correlation coefficient, which can be achieved using the Pearson correlation coefficient. Specifically, the Pearson correlation coefficient is used to measure the correlation (linear correlation) between two variables X and Y. Its value is between -1 and 1. The closer the absolute value is to 1, the greater the correlation between the two data.

[0045] Next, the plasma auxiliary element is taken as an example of a plasma image, and its auxiliary feature is an image feature. Correspondingly, the acquisition device for acquiring the plasma auxiliary element is an ICCD camera.

[0046] like Figure 2 As shown, the plasma image is collected based on the ICCD camera, and the plasma spectrum is collected by the spectrometer. According to the working delay of the hardware device receiving the trigger signal and the appropriate collection delay after the laser focusing excites the plasma, the trigger signals between the laser, spectrometer, and camera are respectively given by the computer, thereby realizing the synchronous collection of the plasma spectrum and image signal. The plasma image is shown in Figure 3 Shown is a collection of pixels, and the computer extracts image features.

[0047] In some embodiments, before collecting the plasma spectrum and plasma auxiliary factors of the sample to be tested, the variation pattern between different experimental conditions and the auxiliary characteristics of the plasma auxiliary factors is first determined, or the variation pattern between different experimental conditions and the plasma characteristics of the plasma spectrum is first determined, so as to determine the stable experimental conditions that can collect the stable plasma auxiliary factors or the plasma spectrum of the sample, and then the plasma spectrum and plasma auxiliary factors of the sample to be tested are synchronously collected under the stable experimental conditions.

[0048] Here, the determination of stable plasma auxiliary factors or stable experimental conditions of plasma spectrum is specifically as follows:

[0049] The plasma image with side light collection should be nearly circular or mushroom-shaped. If the plasma image is unstable (i.e., the plasma image is sometimes large and sometimes small, and the degree of brightness is different), it may be due to the following reasons:

[0050] 1. The camera focus is not accurate;

[0051] 2. Improper setting of conditions. For example, setting the delay too early or too late will cause the collected plasma to be too bright or too large, or too small or too dark. The changes in the plasma in the early or late stages will be extremely drastic, or the fluctuations in the mid-term will result in a very different late state, thus causing the plasma image to be unstable.

[0052] It should be noted that stability means that there is no significant difference in the collected plasma image that is visible to the naked eye (for example, there is no obvious difference in shape, area size, brightness, etc.), which is basically consistent with the collection conditions when the spectral data is relatively stable. Therefore, if the spectrum is stable, the collected image is also stable.

[0053] LIBS is used to collect data on the sample to be tested. The pulsed laser emitted by the laser is focused on the surface of the sample to be tested through an optical path transmission system composed of optical lenses, so that the surface of the sample to be tested is quickly ablated, melted, and vaporized, forming a high-temperature, high-pressure plasma composed of a large number of atoms, ions, and free electrons. In this process, the spectrometer and the ICCD camera synchronously collect signals. Studies have shown that the various characteristics of the image signal are closely related to the intensity of the spectral lines of the plasma spectrum and the physical or chemical properties of the sample to be tested. Therefore, when it is performed on samples with similar matrices, the difference in its image characteristics is the different specific differences between its spectral data. Therefore, this image information helps to evaluate the validity of its corresponding spectral data. In this step, it is necessary to analyze the changing laws and influencing mechanisms of different experimental conditions on image characteristics, laying the foundation for the subsequent method of indexing and screening effective data for image characteristics.

[0054] LIBS spectrum and image acquisition are seriously affected by experimental conditions. The data under different conditions are different, which has a great impact on data stability. In order to test the stability of the results, the impact of experimental conditions on images or spectra needs to be analyzed, such as laser energy, defocus (the distance between the sample surface and the laser focus), and acquisition delay (acquisition starts after a certain delay after the actual laser light emission time, in order to avoid interference from strong background radiation in the early stage of plasma).

[0055] According to the variation law of the obtained image features (such as the area size and brightness of the collected plasma image), the image signal with high stability is selected as the experimental condition, and the spectrum and image data of the sample with similar matrix are collected synchronously through the system designed by the present invention. The correlation between the intensity of the element characteristic spectral line (plasma characteristic) and the image signal characteristics is analyzed, and the correlation between the physical or chemical characteristics of the sample to be tested affected by the matrix elements and trace elements and the image signal is further analyzed.

[0056] S2. Selecting auxiliary features that are strongly correlated with the plasma features as feature variables or taking plasma features as feature variables, and obtaining a feature matrix consisting of the feature variables contained in each plasma excited by the laser each time.

[0057] Here, the auxiliary feature with the greatest correlation is selected, that is, only one auxiliary feature is needed.

[0058] S3. Obtain the central characteristic variable in the characteristic matrix, find the n characteristic variables closest to the central characteristic variable, and then find the plasma spectra corresponding to the n characteristic variables and the central characteristic variable as the screened spectral data, where n is a positive integer.

[0059] The specific process is:

[0060] Obtain the distance difference between each of the feature variables in the feature matrix and all feature variables except the feature variable to obtain a distance difference matrix corresponding to the feature variable, and find the distance difference matrix with the smallest sum of distance differences, define the distance difference matrix with the smallest sum of distance differences as the minimum distance difference matrix, and define the feature variable corresponding to the minimum distance difference matrix as the central feature variable;

[0061] The elements in the minimum distance difference matrix are sorted in descending order to find the n characteristic variables closest to the central characteristic variable, and the plasma spectra corresponding to the n characteristic variables and the central characteristic variable are selected as the screened spectral data.

[0062] According to the analysis of S3, the image signal features with strong correlation with the excited plasma features are selected as feature variables, and the image feature F corresponding to one data is selected. The distance difference between it and the feature values ​​corresponding to all other data is calculated to obtain the matrix XD, and the matrix AD of the sum of the distance differences is counted. After traversing all the data, the matrix AD with the smallest sum of distance differences is selected. min The corresponding data is the central data, and the first n data closest to the central data are listed as valid data. This method uses the clustering characteristics of similar spectral data of similar plasma radiation. The specific operation process is as follows Figure 5 shown.

[0063] like Figure 5 As shown in Figure 2. Let’s take the feature variable as image feature as an example.

[0064] If plasma characteristics are taken as characteristic variables.

[0065] If there is a spectrum data (plasma spectrum) assumed to be a column vector with 1000 rows and 1 column, then 300 spectrum data are a matrix with 1000 rows and 300 columns. For the intensity of the spectrum line, 1000 rows represent the intensity at 1000 different wavelengths, most of which are background or noise, and only the intensity at some characteristic spectrum line wavelengths is meaningful. For example, the 324th row in 1000 rows is the intensity data of a characteristic spectrum line, so this data is Figure 3The subscript i of F and Fi in Fi indicates that this is the characteristic intensity of the i-th spectrum data. Then the matrix X composed of 300 data is a matrix of 1 row and 300 columns (the characteristic intensity is different from 1000 of the full spectrum data and is 1-dimensional). To find the plasma characteristics (characteristic spectral lines) from each spectrum data, it depends on the operator's experience analysis or extraction by some algorithm. This is the existing technology and will not be repeated here.

[0066] The aforementioned 300 spectral data are collected by the spectrometer. At the same time, the camera will also collect plasma images, so there are 300 images that correspond to the spectral data one by one. The image features of each image, such as area size, brightness, internal and external contrast, are all 1-dimensional. Similarly, the 300 images can form a matrix of 1 row and 300 columns.

[0067] The image features of plasma images that are easy to extract and have clear physical meanings (area size, brightness, internal and external contrast) are relatively few, and are easier to analyze than the high-dimensional spectral data of 1000 dimensions. Spectral data, especially the intensity of characteristic spectral lines, are closely related to the state of plasma, and images can also characterize the state of plasma. Therefore, the screening of image features should be convenient and fast. Each spectral data has a corresponding image collected separately, such as Figure 3 The figure shows a plasma image corresponding to a spectrum data. The laser is excited once, the plasma is generated, and the spectrum and image are collected once. Sampling 300 times means stimulating the laser 300 times. Only one spectrum and image can be collected at the same time, that is, 300 spectrum data are collected 300 times.

[0068] Using image features as feature variables to Figure 5 The process in is described.

[0069] like Figure 5 As shown, the feature matrix X composed of m image features m =[F1,F2,···,F m ] and each image feature is respectively associated with the all-1 matrix one m =[1,1,···,1] are multiplied to obtain m first elements, and then the distance differences between the m first elements and the first elements other than themselves are calculated to form m distance difference matrices. The distance difference matrix is ​​expressed as XD i =|X m -F i *one m |=[|F1-F i |,|F2-F i |,···,|F m -F i |], i∈[1,m],F iis the central feature variable, and the element in the distance difference matrix is ​​set as the second element.

[0070] Then calculate the distance difference matrix XD i The sum of all the second elements in , we get m sum matrices AD i , A.D. i =|F1-F i |+|F2-F i |+···+|F m -F i |;

[0071] Then find the smallest sum matrix AD among the m sum matrices min And the minimum distance difference matrix XD corresponding to the minimum matrix min , the minimum distance difference matrix XD min The second elements in are reordered, and then the plasma spectra corresponding to the image features corresponding to the first n smallest second elements are output.

[0072] For example, the distance difference matrix obtained for this feature is 1 row and 300 columns. For example, now take the data in the 100th column as the center and calculate the difference between the absolute value of the remaining 299 data and it. Assuming that the data value of the 100th column is 100 and the data value of the 1st column is 1, then the difference between the two is 99. Then the value of the first column of the distance difference matrix centered on the data in the 100th column is 99, that is, the second element is 99. In this distance difference matrix, the smaller the value, the closer it is to the center. For example, still for the above distance difference matrix, its center is the 100th column, the value of the first column is 99, and the difference of the 10th column is 10, which means that the 10th column is closer to the center of the 100th column than the 1st column. In other words, the smaller the second element, the closer the distance to the central feature variable, and the more effective the corresponding image feature (or plasma feature) is for spectral data.

[0073] Valid data is screened out from the collected raw data. The effective spectral data screening method proposed in the present invention avoids the disadvantage that the traditional screening method requires manual empirical selection of a certain characteristic spectrum line as a reference spectrum line, and improves the stability and accuracy of the LIBS technology measurement results as much as possible.

[0074] The present invention discloses a screening system for LIBS spectrum data based on plasma images, comprising:

[0075] An acquisition module, used for synchronously acquiring the plasma spectrum and plasma auxiliary elements of the sample to be tested;

[0076] A feature extraction module, used to respectively obtain a plasma feature corresponding to the plasma spectrum and at least one auxiliary feature corresponding to the plasma auxiliary element, and if there are multiple auxiliary features, determine the correlation between the plasma feature and the multiple auxiliary features;

[0077] A feature matrix construction module, used to select auxiliary features having a strong correlation with the plasma features as feature variables or to use the plasma features as feature variables, and to obtain a feature matrix composed of a plurality of the feature variables at different time points;

[0078] The screening module is used to obtain the central characteristic variable in the characteristic matrix, find the n characteristic variables closest to the central characteristic variable, and thus find the plasma spectra corresponding to the n characteristic variables and the central characteristic variable as the screened spectral data, where n is a positive integer.

[0079] In some embodiments, the acquisition module includes a spectrometer for acquiring a plasma spectrum and an auxiliary acquisition device for acquiring auxiliary elements of the plasma.

[0080] In some embodiments, the auxiliary acquisition device is an ICCD camera for acquiring plasma images.

[0081] Specifically, an embodiment of the present invention provides a spectral data processing method for assisting in screening effective spectral data based on plasma image signals, thereby improving the analysis accuracy of LIBS technology, and is used to solve the problem of relatively low stability of the above-mentioned collected plasma spectral signals.

[0082] The device is based on the laser, optical fiber, spectrometer, optical path transmission system and other equipment included in the traditional LIBS device, and a set of charge-coupled cameras is added as a plasma image signal acquisition device. Among them, the plasma image signal can simply and intuitively characterize the shape and size of the plasma and the temperature distribution state, and these state parameters will directly affect the strength, sensitivity and stability of the plasma spectrum signal collected by the spectrometer, thereby affecting the analysis accuracy of the LIBS technology. Therefore, the present invention proposes to use plasma image signals to analyze the quality of the synchronously collected spectral signals, thereby eliminating abnormal and invalid data, and improving the stability of the collected spectral data from the source as much as possible. Compared with traditional data processing methods such as the mean method and the normalization method, its superiority lies in avoiding the multiple acquisitions required by the mean method (multiple acquisitions will not only significantly increase the measurement time, but also the ablation pits generated by multiple acquisitions at the same sample position will interfere with the subsequent excited plasma) and the complex selection process of the internal standard spectrum line of the normalization method (this method requires a priori knowledge of whether the sample being tested has stable and high-intensity element characteristic spectrum lines).

[0083] In summary, the specific process of the present invention is: using a charge coupled ICCD camera as a plasma image signal acquisition device to collect additional plasma image reference data for assisting the screening of spectral data and eliminating abnormal data as much as possible; extracting various features contained in the plasma image data, and analyzing the correlation between the extracted image features and the characteristic spectral lines in the spectral data; using the image features as indexes, clustering and sorting are performed to obtain the corresponding cluster center, and the index value of the center data has an index error with the index value of other data, and the data distribution sorting is obtained under the cluster center when the total index error value with other data is the smallest, and a certain number of front data are selected as valid data under the data distribution sorting condition. By analyzing the correlation between the characteristic spectral line intensity of the sample to be tested and the image features (area, brightness, etc.), a screening method for finding the characteristic center and selecting the spectral data corresponding to the feature with the smallest difference from the center as valid data is proposed. This method can eliminate the interference of invalid spectral data as accurately and effectively as possible, thereby solving the problem of low measurement results of LIBS technology. Through the data fusion between the plasma image and the spectral signal of the present invention, new and complementary characteristic information can be obtained, which is helpful for the subsequent establishment of a higher-precision LIBS analysis model.

[0084] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method 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 in the scope of protection of the present invention.

Claims

1. A method for screening LIBS spectral data based on plasma images, characterized in that: include: Synchronously collecting the plasma spectrum and plasma auxiliary elements of the sample to be tested, and respectively obtaining a plasma feature corresponding to the plasma spectrum and at least one auxiliary feature corresponding to the plasma auxiliary element, and if there are multiple auxiliary features, determining the correlation between the plasma feature and the multiple auxiliary features; Selecting auxiliary features that are strongly correlated with the plasma features as feature variables or taking plasma features as feature variables, and obtaining a feature matrix consisting of the feature variables contained in each plasma excited by the laser each time; The central characteristic variable in the characteristic matrix is ​​obtained, and the n characteristic variables closest to the central characteristic variable are found, so as to find the plasma spectra corresponding to the n characteristic variables and the central characteristic variable as the screened spectral data, where n is a positive integer.

2. The method for screening LIBS spectral data based on plasma images according to claim 1, characterized in that: Before collecting the plasma spectrum and plasma auxiliary factors of the sample to be tested, the variation law between different experimental conditions and the auxiliary characteristics of the plasma auxiliary factors is first determined, or the variation law between different experimental conditions and the plasma characteristics of the plasma spectrum is first determined, so as to determine the stable experimental conditions that can collect the stable plasma auxiliary factors or the plasma spectrum of the sample, and then the plasma spectrum and plasma auxiliary factors of the sample to be tested are synchronously collected under the stable experimental conditions.

3. The method for screening LIBS spectral data based on plasma images according to claim 1, characterized in that: The plasma characteristics include the spectral line intensity of the plasma spectrum.

4. The method for screening LIBS spectral data based on plasma images according to claim 1, characterized in that: The auxiliary features include image features.

5. The method for screening LIBS spectral data based on plasma images according to claim 4, characterized in that: The image features include plasma area size, total image intensity, maximum image intensity, plasma morphology and plasma center position.

6. The method for screening LIBS spectral data based on plasma images according to claim 1, characterized in that: The specific process of obtaining the central characteristic variable in the characteristic matrix, finding the n characteristic variables closest to the central characteristic variable, and then finding the plasma spectra corresponding to the n characteristic variables and the central characteristic variable as the screened spectral data is as follows: Obtain the distance difference between each of the feature variables in the feature matrix and all feature variables except the feature variable to obtain a distance difference matrix corresponding to the feature variable, and find the distance difference matrix with the smallest sum of distance differences, define the distance difference matrix with the smallest sum of distance differences as the minimum distance difference matrix, and define the feature variable corresponding to the minimum distance difference matrix as the central feature variable; The elements in the minimum distance difference matrix are sorted in descending order to find the n characteristic variables closest to the central characteristic variable, and the plasma spectra corresponding to the n characteristic variables and the central characteristic variable are selected as the screened spectral data.

7. A screening system for LIBS spectral data based on plasma images, characterized in that: include: An acquisition module, used for synchronously acquiring the plasma spectrum and plasma auxiliary elements of the sample to be tested; A feature extraction module, used to respectively obtain a plasma feature corresponding to the plasma spectrum and at least one auxiliary feature corresponding to the plasma auxiliary element, and if there are multiple auxiliary features, determine the correlation between the plasma feature and the multiple auxiliary features; A feature matrix construction module, used to select auxiliary features having a strong correlation with the plasma features as feature variables or to use the plasma features as feature variables, and to obtain a feature matrix composed of a plurality of the feature variables at different time points; The screening module is used to obtain the central characteristic variable in the characteristic matrix, find the n characteristic variables closest to the central characteristic variable, and thus find the plasma spectra corresponding to the n characteristic variables and the central characteristic variable as the screened spectral data, where n is a positive integer.

8. The screening system of LIBS spectrum data based on plasma image according to claim 7, characterized in that: The acquisition module includes a spectrometer for acquiring plasma spectrum and an auxiliary acquisition device for acquiring auxiliary elements of the plasma.

9. The screening system of LIBS spectrum data based on plasma image according to claim 8, characterized in that: The auxiliary acquisition device is an ICCD camera for acquiring plasma images.

Images