A method, system, device and storage medium for spectral line intensity correction

Abstract

This application is applicable to the field of spectral detection technology, and provides a spectral line intensity correction method, system, device and storage medium, including: obtaining plasma information of a sample; the plasma information of the sample includes the full width at half maximum of the plasma radiation spectrum of the sample, plasma event stream data, and the number of events generated based on the plasma event stream data; reconstructing a plasma image according to the plasma event stream data; forming a plasma shape contour based on the plasma shape contour features extracted from the plasma image, and calculating the area of the region enclosed by the plasma shape contour; inputting the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum into a spectral line intensity correction model to generate a corrected spectral line intensity. This application improves the accuracy of quantitative analysis of spectral line intensity in related technologies.

Description

Technical Field

[0001] This application belongs to the technical field of spectral detection, and particularly relates to a spectral line intensity correction method, system, device, and storage medium. Background Art

[0002] In recent years, Laser-induced breakdown spectroscopy (LIBS) has received extensive attention from researchers. Due to its many advantages such as micro-damage detection, low detection limit, rapid detection, high sensitivity, and real-time analysis, it has been applied in many fields such as industry, agriculture, biology, archaeology, and aerospace.

[0003] LIBS technology is affected by the matrix effect, which has become one of the key problems restricting the wide application of LIBS quantitative analysis. The matrix effect refers to the fact that the composition and properties of the sample matrix will affect the analysis results, which will lead to changes in the signal intensity of elements, thus affecting the accuracy of quantitative and qualitative analysis. Summary of the Invention

[0004] Embodiments of this application provide a spectral line intensity correction method, system, device, and storage medium to solve the technical problem of inaccurate quantitative analysis of spectral line intensity in related technologies.

[0005] In a first aspect, embodiments of this application provide a spectral line intensity correction method, including:

[0006] Obtain the plasma information of the sample; the plasma information of the sample includes the full width at half maximum of the plasma radiation spectrum of the sample, plasma event stream data, and the number of events generated based on the plasma event stream data;

[0007] Reconstruct the plasma image according to the plasma event stream data;

[0008] Form a plasma shape contour based on the plasma shape contour features extracted from the plasma image, and calculate the area of the region enclosed by the plasma shape contour;

[0009] Input the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum into the spectral line intensity correction model to generate the corrected spectral line intensity.

[0010] It should be understood that the spectral line intensity correction method of the embodiments of this application can be applied in a processor-related circuit or in a processor or in an electronic device with a processor such as a computer, a terminal such as a client terminal, a server terminal, etc. Exemplarily, the spectral line intensity correction method can also be applied in a system for detecting the element content of a sample by laser-induced breakdown spectroscopy.

[0011] It should be understood that obtaining the plasma information of a sample may include partial plasma information during the period from when the sample is excited by a laser to generate plasma until the plasma cools down, or may also include all the plasma information during this period. Exemplarily, the plasma information may include the plasma radiation spectrum of the sample and the full width at half maximum of this spectrum, plasma event stream data, and the number of events generated based on the plasma event stream data. It may also include the plasma radiation spectrum of the sample after background removal, baseline correction, and noise reduction, as well as the full width at half maximum of this spectrum.

[0012] It can be understood that reconstructing a plasma image based on the plasma event stream data can generate a plasma image that can be produced during the period from when the sample is excited by a laser to generate plasma until the plasma cools down through reconstruction using the plasma event stream data. Exemplarily, a deep network, a spiking neural network, clustering analysis, or event accumulation can be used to reconstruct a plasma image based on the plasma event stream data, and a plasma shape profile is formed according to the plasma shape profile features extracted from the plasma image. Exemplarily, an edge detection algorithm or the like can be used to calculate the area within the region covered by the plasma shape profile.

[0013] The spectral line intensity correction model may include various correction formulas obtained by combining the spectral line intensity formula with the Saha formula. The area of the region enclosed by the plasma shape profile, the number of events, and the plasma radiation spectrum can be substituted as independent variables into the various correction formulas to obtain the corrected spectral line intensity.

[0014] In a possible implementation manner of the first aspect, the spectral line intensity correction model includes a function obtained by processing the spectral line intensity formula using the Saha formula. The independent variables of the function are the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma radiation spectrum, and the dependent variable is the corrected spectral line intensity.

[0015] In a possible implementation manner of the first aspect, the spectral line intensity correction method further includes: establishing a spectral line intensity correction model; the establishing of the spectral line intensity correction model includes:

[0016] After processing the spectral line intensity formula using the Saha formula, a first function is obtained. The independent variables of the first function include the total particle number density, plasma temperature, and electron number density, and the dependent variable is the spectral line intensity;

[0017] Performing a Taylor expansion on the first function to generate a second function;

[0018] Express the spectral line intensity as a linear function of the element concentration in the sample and substitute it into the second function to generate a third function; the dependent variable of the third function is the corrected spectral line intensity, and the independent variables include the element concentration, the total particle number density, the plasma temperature, and the electron number density;

[0019] Replace the total particle number density in the third function with the area of the region enclosed by the plasma shape profile, replace the plasma temperature in the third function with the number of events, and replace the electron number density in the third function with the full width at half maximum to generate a fourth function; the dependent variable of the fourth function is the corrected spectral line intensity, and the independent variables include the element concentration, the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma emission spectrum;

[0020] Use the element concentration, the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma emission spectrum as independent variables, and the true spectral line intensity as the dependent variable, and perform regression processing on the fourth function to determine the regression value;

[0021] Use the regression value, the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma emission spectrum as independent variables, and the ideal spectral line intensity as the dependent variable, perform regression processing on the fourth function to obtain the coefficients of the fourth function and substitute them into the fourth function to obtain the spectral line intensity correction model.

[0022] It should be understood that the regression processing for obtaining the fifth function and the regression processing for obtaining the spectral line intensity correction model both include least squares regression processing, ridge regression processing, stepwise regression processing, or elastic net regression.

[0023] In a possible implementation manner of the first aspect, the obtaining of the plasma information of the sample includes:

[0024] Obtain the event data and the number of events generated by the dynamic vision sensor; the event data includes the pixel data recorded by the dynamic vision sensor each time the brightness change of the plasma exceeds the threshold of the dynamic vision sensor when the sample generates plasma under laser ablation; the pixel data includes pixel points, timestamps, and event polarities; the number of events is the cumulative number of times the brightness change of the plasma exceeds the threshold of the dynamic vision sensor within a certain time;

[0025] Obtain the plasma event stream data generated by the dynamic vision sensor based on the accumulated event data within a certain time;

[0026] And / or, obtain the original plasma radiation spectrum of the sample, perform background removal, baseline correction, and noise reduction on the original plasma radiation spectrum to obtain a preprocessed plasma radiation spectrum; obtain the full width at half maximum of the plasma radiation spectrum of the sample based on the original plasma radiation spectrum or the preprocessed plasma radiation spectrum.

[0027] In one possible implementation of the first aspect, forming a plasma shape contour based on the plasma shape contour features extracted from the plasma image, and calculating the area of the region enclosed by the plasma shape contour includes:

[0028] Based on the plasma event stream data, reconstruct the plasma image using a deep network, spiking neural network, clustering analysis, or event accumulation, form a plasma shape contour based on the plasma shape contour features extracted from the plasma image, and calculate the area of the region enclosed by the plasma shape contour;

[0029] The plasma image is a plasma image obtained using the plasma event stream data or a Gaussian-filtered plasma image obtained by performing Gaussian filtering on the plasma image obtained using the plasma event stream data.

[0030] In one possible implementation of the first aspect, reconstructing the plasma image based on the plasma event stream data; includes:

[0031] Based on the plasma event stream data, generate a plasma event frame image;

[0032] Forming a plasma shape contour based on the plasma shape contour features extracted from the plasma image, and calculating the area of the region enclosed by the plasma shape contour: includes:

[0033] Extract the edge information of the plasma shape contour of the plasma event frame image according to the edge detection algorithm, calculate the number of pixels in the region enclosed by the plasma shape contour, and generate the area of the region enclosed by the plasma shape contour.

[0034] In a second aspect, an embodiment of the present application provides a spectral line intensity correction system, including:

[0035] An acquisition unit, configured to acquire plasma information of a sample; the plasma information of the sample includes the full width at half maximum of the plasma radiation spectrum of the sample, plasma event stream data, and the number of events generated based on the plasma event stream data;

[0036] A generation unit, configured to reconstruct a plasma image according to the plasma event stream data;

[0037] An extraction unit, configured to form a plasma shape contour according to the plasma shape contour features extracted from the plasma image;

[0038] A calculation unit, configured to calculate the area of the region enclosed by the plasma shape contour;

[0039] And a correction unit, configured to input the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum into a spectral line intensity correction model to generate a corrected spectral line intensity.

[0040] In a third aspect, an embodiment of the present application provides a laser-induced breakdown spectroscopy system, including:

[0041] An optical path system;

[0042] A laser, connected to the optical path system;

[0043] A delay controller, connected to the laser;

[0044] A spectrometer, connected to a computer, the optical path system, and the delay controller respectively;

[0045] A dynamic vision sensor;

[0046] And a computer, connected to the spectrometer and the dynamic vision sensor respectively, for:

[0047] Obtaining plasma information of a sample; the plasma information of the sample includes the full width at half maximum of the plasma radiation spectrum of the sample, plasma event stream data, and the number of events generated based on the plasma event stream data;

[0048] Reconstructing a plasma image according to the plasma event stream data;

[0049] Forming a plasma shape contour according to the plasma shape contour features extracted from the plasma image, and calculating the area of the region enclosed by the plasma shape contour;

[0050] Inputting the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum into a spectral line intensity correction model to generate a corrected spectral line intensity.

[0051] In a fourth aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where when the processor executes the computer program, the spectral line intensity correction method is implemented.

[0052] Fifth aspect, an embodiment of the present application provides a computer-readable storage medium, including: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the spectral line intensity correction method is implemented.

[0053] Sixth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device is enabled to execute the spectral line intensity correction method described in any one of the above first aspects.

[0054] It can be understood that the beneficial effects of the above second aspect to fifth aspect can be referred to the relevant descriptions in the above first aspect, and will not be elaborated here.

[0055] The beneficial effect of the embodiment of the present application compared with the prior art is:

[0056] The spectral line intensity correction method of the embodiment of the present application reconstructs a plasma image according to plasma event stream data; extracts a plasma shape contour according to the plasma image, and calculates the area of the region enclosed by the plasma shape contour; inputs the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum into a spectral line intensity correction model to generate a corrected spectral line intensity, improving the accuracy of spectral line intensity detection. Description of the Drawings

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0058] Figure 1 It is a schematic diagram of a laser-induced breakdown spectroscopy system provided by an embodiment of the present application. [[ID=2,3]]

[0059] Figure 2 It is a schematic flowchart of a spectral line intensity correction method provided by an embodiment of the present application.

[0060] Figure 3 It is a schematic flowchart of a spectral line intensity correction method provided by another embodiment of the present application.

[0061] Figure 4 It is a schematic flowchart of a method for establishing a spectral line intensity correction model provided by an embodiment of the present application.

[0062] Figure 5 It is a schematic flowchart of a spectral line intensity correction method provided by still another embodiment of the present application.

[0063] Figure 6 It is a schematic structural diagram of a spectral line intensity correction system provided by an embodiment of the present application.

[0064] Figure 7 It is a plasma image restored based on event data stream according to another embodiment of the present application; wherein, (a) is a plasma grayscale image restored from event stream data; (b) is an image obtained by performing Gaussian filtering on the image in (a).

[0065] Figure 8 It is an example diagram of the original plasma radiation spectrum provided by an embodiment of the present application.

[0066] Figure 9 It is provided by an embodiment of the present application for Figure 8 An example diagram after background removal, baseline correction and noise reduction.

[0067] Figure 10 It is the original calibration curve of Cu I 327.396 nm provided by an embodiment of the present application.

[0068] Figure 11 It is the full-spectrum area normalized calibration curve of Cu I 327.396 nm provided by an embodiment of the present application.

[0069] Figure 12 It is the calibration curve obtained by processing Cu I 327.396 nm with the spectral line intensity correction method provided by an embodiment of the present application.

[0070] Figure 13 It is the original calibration curve of Zn I 328.289 nm provided by an embodiment of the present application.

[0071] Figure 14 It is the full-spectrum area normalized calibration curve of Zn I 328.289 nm provided by an embodiment of the present application.

[0072] Figure 15 It is the calibration curve obtained by processing Zn I 328.289 nm with the spectral line intensity correction method provided by an embodiment of the present application. Detailed implementation manners

[0073] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are presented to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0074] It should be understood that, as used in the specification of this application and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups.

[0075] It should also be understood that the term "and / or" as used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items and includes such combinations.

[0076] As used in the specification of this application and the appended claims, the term "if" can be construed, depending on the context, as "when" or "once" or "in response to determining" or "in response to detecting". Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be construed, depending on the context, as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]".

[0077] In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are used only for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0078] Reference to "one embodiment" or "some embodiments" or the like described in the specification of this application means that a particular feature, structure or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0079] The technical solution of the embodiment of the present application can be applied to various laser-induced breakdown spectroscopy systems. Exemplarily, the technical solution of the embodiment of the present application can be applied to a system formed by adding a high-speed camera or a dynamic vision sensor to various traditional laser-induced breakdown spectroscopy systems, wherein the high-speed camera or the dynamic vision sensor is used to capture plasma characteristic data during the period from when the sample is ablated to generate plasma until the plasma cools down. The plasma characteristic data is used to reconstruct a plasma image, and the plasma image is used to solve for the area of the plasma representing. During the above period, the dynamic vision sensor generates less data than the high-speed camera, which is more conducive to extracting plasma characteristic data from the data to construct a reconstructed plasma image.

[0080] Figure 1 A laser-induced breakdown spectroscopy system applicable to the embodiment of the present application. Figure 1 Replacing the dynamic vision sensor in it with a high-speed camera is also applicable to the technical solution of the embodiment of the present application.

[0081] Refer to Figure 1 As shown, the laser-induced breakdown spectroscopy system includes a laser 101, a delay controller 102, a spectrometer 103, a computer 104, a dynamic vision sensor 105, an optical path system 106, and a sample and sample excitation stage 107.

[0082] The laser 101 is connected to the optical path system 106, and the laser 101, the delay controller 102, the spectrometer 103, the computer 104, and the dynamic vision sensor 105 are connected in sequence; the optical path of the optical path system points to the sample and sample excitation stage 107. The spectrometer 103 receives the plasma spectrum generated by the sample on the sample and sample excitation stage 107, and the dynamic vision sensor 105 is used to capture event data generated by the brightness change of the plasma when the sample is ablated. By selecting appropriate laser energy, light collection angle, and spectrometer delay time, higher spectral signals such as signal-to-noise ratio and signal-to-background ratio can be obtained. In addition, a set of dynamic vision sensors are added to capture plasma signals.

[0083] The principle of laser-induced breakdown spectroscopy (LIBS) is that a high-energy laser pulse is used to ablate the surface of the sample to form a plasma with high temperature and high electron density. During the cooling process of the plasma, part of the energy is radiated in the form of a spectrum. By collecting the spectral signal with a spectrometer, the wavelength and intensity of the spectral line can be obtained. The element attribution of the spectral line can be determined by the wavelength of the spectral line, and the element content can be reflected to a certain extent by the spectral line intensity.

[0084] LIBS technology is affected by matrix effects, which has become one of the key problems restricting the wide application of LIBS quantitative analysis. Matrix effect refers to the fact that the composition and properties of the sample matrix will affect the analysis results, which will lead to changes in the signal intensity of elements, thus affecting the accuracy of quantitative and qualitative analysis. Matrix effects are mainly interfered by the chemical characteristics of the sample, such as the components of the sample and the valence states of elements. In addition, it is also interfered by the physical characteristics of the sample, such as the roughness, hardness, moisture content, etc. of the sample. The existence of matrix effects severely restricts the application of LIBS in quantitative analysis.

[0085] At present, many methods have been used to correct the matrix effects of LIBS technology. The most common is to optimize the parameters of the LIBS test system, such as laser energy, laser wavelength, delay time, defocus amount, light collection angle, etc., so as to obtain a stronger signal with a higher signal-to-noise ratio, but the effect is limited. The spectral line normalization method is also relatively common, especially the full-spectrum area normalization, which can eliminate the matrix effect to a certain extent, but the effect is unstable.

[0086] The reference signal correction method realizes signal correction by using other signals generated during the LIBS process. Common signals include acoustic signals and optical signals, etc. The present invention precisely uses the optical signal in the plasma process as a reference to realize the correction of the LIBS matrix effect, and then obtains a relatively accurate corrected spectral line intensity.

[0087] To solve the technical problem of inaccurate quantitative analysis of spectral line intensity in related technologies, an embodiment of the present application provides a spectral line intensity correction method.

[0088] This method can run on the computer of the above-mentioned laser-induced breakdown spectroscopy system, or it can also run on the above-mentioned laser-induced breakdown spectroscopy system after replacing the computer with other devices. The other devices can be terminal devices such as mobile phones, tablet computers, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc. The embodiment of the present application does not impose any restrictions on the specific types of terminal devices.

[0089] For example, the terminal device may be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication function, a computing device or other processing devices connected to a wireless modem, a vehicle-mounted device, a vehicle-to-everything (V2X) terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a set top box (STB), a customer premise equipment (CPE), and / or other devices for communicating on a wireless system, as well as next-generation communication systems, such as mobile terminals in a 5G network or mobile terminals in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0090] By way of example and not limitation, when the terminal device is a wearable device, the wearable device may also be a general term for devices that are intelligently designed for daily wear using wearable technology and developed into wearable devices, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is either worn directly on the body or integrated into the user's clothing or accessories. A wearable device is not just a hardware device, but also realizes powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable intelligent devices include those with complete functions and large sizes that can achieve complete or partial functions without relying on a smartphone, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to cooperate with other devices such as smartphones, such as various smart bracelets and smart jewelry for physical sign monitoring.

[0091] Figure 2 It is a schematic flowchart of a spectral line intensity correction method provided by an embodiment of the present application. Refer to Figure 2 As shown, in an embodiment of the present application, the spectral line intensity correction method includes the steps:

[0092] S1. Obtain the plasma information of the sample; the plasma information of the sample includes the full width at half maximum of the plasma radiation spectrum of the sample, the plasma event stream data, and the number of events generated based on the plasma event stream data.

[0093] The generation process of the plasma of the sample is to ablate the surface of the sample by high-energy laser pulses to form a plasma with high temperature and high electron density. During the cooling process of the plasma, part of the energy is radiated in the form of spectrum. The spectral signal is collected by a spectrometer, and other information of the plasma such as image information can also be collected by other devices such as a high-speed camera or a dynamic vision sensor during this process.

[0094] That is, the plasma information of the sample is the data information obtained by electronic devices such as a spectrometer, a high-speed camera or a dynamic vision sensor when the sample generates plasma through ablation. This information can include the original plasma radiation spectrum, the spectrum obtained by preprocessing the original plasma radiation spectrum, such as the plasma radiation spectrum obtained after background removal, baseline correction and noise reduction, the full width at half maximum obtained from the plasma radiation spectrum, the plasma event stream data obtained by the dynamic vision sensor in response to the brightness change of the plasma, and the number of events generated based on the plasma event stream data and so on.

[0095] It can be understood that the plasma information of the sample can include the full width at half maximum of the plasma radiation spectrum of the sample, the plasma event stream data and the number of events generated based on the plasma event stream data; the plasma information of the sample can also include the original plasma radiation spectrum of the sample and the preprocessed plasma radiation spectrum obtained after background removal, baseline correction and noise reduction of the original plasma radiation spectrum.

[0096] The acquisition method can be to obtain from a hardware device storing the above data information, or to obtain the above data information obtained by processing relevant data during the period from the generation to the cooling of the plasma of the sample in real time. It can be obtained by wired means or by wireless means, and can be obtained through a local area network or through an Internet channel.

[0097] S2. Reconstruct the plasma image according to the plasma event stream data;

[0098] It can be understood that the plasma event stream data can be obtained by accumulating the event data of the plasma generated by the sample in the laser ablation state by a dynamic vision sensor within a certain period of time. When the sample generates plasma by laser ablation, each time the brightness change of the plasma exceeds the threshold of the dynamic vision sensor is recorded as an event, and the pixel data of each event, that is, the event data, is recorded. The pixel data includes pixel points, timestamps and event polarities; the plasma event stream data is generated by counting the events generated within a certain period of time, and the number of events generated within a certain period of time is accumulated to generate the number of events.

[0099] The process of reconstructing the plasma image from the plasma event stream data involves multiple steps, mainly including data acquisition, data processing and image generation.

[0100] Data processing includes: removing noise in the data, improving data quality, calibrating the camera or spectrometer to ensure data accuracy, and converting the original data into a format suitable for image processing.

[0101] On this basis, data processing also requires feature extraction. For plasma images, the boundaries of the plasma need to be identified, which usually involves using image processing algorithms such as the Canny operator and the Sobel operator to detect edges in the image. Key parameters of the plasma, such as temperature and density, are calculated based on spectral data or image data.

[0102] Image generation requires converting the plasma event stream data into images using image processing software or algorithms. These images can be two-dimensional grayscale or color images, or three-dimensional stereoscopic images.

[0103] Exemplarily, a dynamic vision sensor can be used to obtain the plasma event stream data. It can be understood that a dynamic vision sensor is different from a traditional camera or a high-speed camera. Its output is not a frame image, but has pixel coordinates (x, y), a timestamp t, and an event polarity p. When the light intensity changes beyond a threshold, the pixel will generate a response, generating an ON event (p = 1) when the brightness increases and an OFF event (p = 0) when the brightness decreases. The dynamic vision sensor outputs the above events and data in the form of event stream data.

[0104] S3. Form a plasma shape contour based on the plasma shape contour features extracted from the plasma image, and calculate the area of the region enclosed by the plasma shape contour;

[0105] S4. Input the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum into the spectral line intensity correction model to generate the corrected spectral line intensity.

[0106] In a possible implementation manner of the first aspect, the spectral line intensity correction model includes a function obtained by processing the spectral line intensity formula using the Saha formula. The independent variables of the function are the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum, and the dependent variable is the corrected spectral line intensity.

[0107] Figure 3 It is a schematic flowchart of the spectral line intensity correction method provided by another embodiment of the present application. Refer to Figure 3 As shown, in another embodiment of the application, the spectral line intensity correction method further includes: S0. Establish a spectral line intensity correction model.

[0108] Figure 4 This is a schematic flow chart of a method for establishing a spectral line intensity correction model provided by an embodiment of the present application. Refer to Figure 4 As shown, the establishment of the spectral line intensity correction model includes:

[0109] S01. After processing the spectral line intensity formula using the Saha formula, a first function is obtained. The independent variables of the first function include the total particle number density, the plasma temperature, and the electron number density, and the dependent variable is the spectral line intensity;

[0110] S02. Perform a Taylor expansion on the first function to generate a second function;

[0111] S03. Express the spectral line intensity as a linear function of the element concentration in the sample and substitute it into the second function to generate a third function; the dependent variable of the third function is the corrected spectral line intensity, and the independent variables include the element concentration, the total particle number density, the plasma temperature, and the electron number density;

[0112] S04. Replace the total particle number density in the third function with the area of the region enclosed by the plasma shape profile, replace the plasma temperature in the third function with the number of events, and replace the electron number density in the third function with the full width at half maximum to generate a fourth function; the dependent variable of the fourth function is the corrected spectral line intensity, and the independent variables include the element concentration, the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma emission spectrum;

[0113] S05. Using the element concentration, the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma emission spectrum as independent variables, and the true spectral line intensity as the dependent variable, perform a regression process on the fourth function to determine the regression value;

[0114] S06. Using the regression value, the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma emission spectrum as independent variables, and the ideal spectral line intensity as the dependent variable, perform a regression process on the fourth function to obtain the coefficients of the fourth function and substitute them into the fourth function to obtain the spectral line intensity correction model.

[0115] It can be understood that the above regression process adopts normalization processing during the solution process, which can eliminate the influence of dimensions, unify the data range, and simplify the calculation process.

[0116] Exemplarily, the establishment process of the spectral line intensity correction model is as follows:

[0117] Under ideal conditions, the LIBS spectral line intensity is given by formula (1):

[0118] (1)

[0119] Among them, F is a parameter determined by the experimental environment and system parameters, is the total number density of elemental particles in the plasma, represents the statistical weight of the high energy level i, is the transition probability, is the partition function, is the excitation energy of the high energy level, k B is the Boltzmann constant, which is approximately 1.38×10 -23 J / K, T is the plasma excitation temperature, and r is the ratio of the number of atomic or ionic particles to the total number of particles.

[0120] (1)-1

[0121] (1)-2

[0122] The ionization degree can be expressed by the ratio R of the atomic and ionic number densities. According to the Saha equation R It is expressed as:

[0123] (1)-3

[0124] (1)-4

[0125] Among them, and respectively represent the number of atoms and the number of singly ionized ions of a certain element, and respectively represent the partition functions of atoms and ions, represents the electron mass, is the ionization energy of the atom, is the correction term for the atomic ionization energy.

[0126] It is not difficult to find that is mainly affected by the electron density , the plasma temperature T and the number density of particles . Therefore, according to the Saha formula, equation (1) can be expressed as:

[0127] (2)

[0128] The spectral line intensity is mainly affected by the electron density , the plasma temperature T and the total number density of particles . k is composed ofF , m e , k B , h parameters determined by parameters such as are not affected by the electron density , the plasma temperature T and the particle number density .

[0129] Considering the deviation effects caused by the fluctuations of the electron density , the plasma temperature T and the particle number density on the LIBS spectral line intensity, the spectral line intensity can be expressed as:

[0130] (2)-1

[0131] According to the first-order Taylor formula, the above formula can be derived as follows:

[0132] (2)-2

[0133] The relationship between the ideal spectral line and the element content satisfies the following calibration relationship:

[0134] (2)-3

[0135] Therefore, Equation (2)-2 can be further expressed as Equation (3):

[0136] (3)

[0137] Use the number of events E num to characterize the plasma temperature T ; use the plasma area E s calculated based on the event data to characterize the total number of particles N s ; use the full width at half maximum FWHM of the spectral line not affected by the self-absorption effect to characterize the electron number density n e ; Equation (3) can be rewritten as:

[0138] (4)

[0139] The true spectral line intensity is also affected by other factors such as experimental instrument parameters and test environment factors. Therefore, the true spectral line intensity is different from I(N s ,n e ,T) . I(Ns ,n e ,T) The main purpose is to correct the influence of plasma fluctuations. Therefore, for the solution of Equation (4), the real spectral line intensity is used here. to replace As the dependent variable, the independent variable is C 、 dE s 、dFWHM and dE num Perform regression analysis on formula (4) and solve the regression value as , used for subsequent calculations.

[0140] In addition, another representation of formula (3) is formula (5), which is the LIBS spectrum correction model DVS-T1 established based on event data.

[0141] (5)

[0142] according to use Establish the original calibration curve to obtain the dependent variable ,Will by dEs 、 dEnum and dFWHM As the independent variable, we conduct regression analysis on (5) to obtain a1 ~ a4 , substituted into the DVS-T1 model to obtain the corrected spectral line intensity Icorr . Normalization is used in the solution process to eliminate the influence of dimension.

[0143] It should be understood that the above normalization processing can be a least squares regression processing, a ridge regression processing, a stepwise regression processing, an elastic net regression processing or the like.

[0144] Figure 5 FIG. 1 is a flow chart of a method for correcting spectral line intensity according to another embodiment of the present application. Figure 5 As shown in Figure 2, the spectral line intensity correction method includes:

[0145] S11. Acquiring event data and event counts generated by the dynamic vision sensor; the event data includes pixel data recorded by the dynamic vision sensor each time a change in plasma brightness exceeds a threshold of the dynamic vision sensor when the sample is subjected to laser ablation to generate plasma; the pixel data includes a pixel point, a timestamp, and an event polarity; the event count is the cumulative number of times the plasma brightness changes exceed the threshold of the dynamic vision sensor within a certain period of time;

[0146] S12. Obtain the plasma event stream data generated by the dynamic vision sensor based on the event data accumulated within a certain period of time;

[0147] S13. Obtain the original plasma radiation spectrum of the sample, and perform background removal, baseline correction, and noise reduction on the original plasma radiation spectrum to obtain the preprocessed plasma radiation spectrum; obtain the full width at half maximum of the plasma radiation spectrum of the sample according to the original plasma radiation spectrum or the preprocessed plasma radiation spectrum.

[0148] S21. Generate a plasma event frame image based on the plasma event stream data;

[0149] Exemplarily, refer to Figure 7 as shown. The event frame image is reconstructed by the method of event accumulation. The event data is accumulated within a certain time range to obtain the plasma event stream data. The plasma event frame image can be obtained through the plasma event stream data as Figure 7 shown. The cumulative time in the figure is 200 ms, and the cumulative time can also be selected according to the actual plasma event data. Figure 7 In (a) is the plasma grayscale image restored from the event stream data, Figure 7 in (b) is the image after Gaussian filtering of (a). The subsequent steps can use the image in Figure 7 (a) or (b) to extract the area of the region enclosed by the plasma shape contour.

[0150] S31. Based on the plasma event stream data, use a deep network, a spiking neural network, clustering analysis, or event accumulation to reconstruct a plasma image, and form a plasma shape contour according to the plasma shape contour features extracted from the plasma image, and calculate the area of the region enclosed by the plasma shape contour;

[0151] The plasma image is a plasma image obtained by using the plasma event stream data or a Gaussian-filtered plasma image obtained by performing Gaussian filtering on the plasma image obtained by using the plasma event stream data;

[0152] S321. Extract the edge information of the plasma shape contour of the plasma event frame image according to the edge detection algorithm, and calculate the number of pixels in the region enclosed by the plasma shape contour, and generate the area of the region enclosed by the plasma shape contour.

[0153] Exemplarily, following the above Figure 7 shown, the edge information of the plasma shape contour can be extracted through edge detection algorithms such as cancy, and the number of pixels in the region can be calculated, and the plasma area is characterized by the number of pixels.

[0154] S4. Input the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma radiation spectrum into the spectral line intensity correction model to generate the corrected spectral line intensity.

[0155] Exemplarily, use Figure 4 the spectral line intensity correction model and Figure 5 the method shown to conduct experimental verification on the sample.

[0156] The sample information used is shown in Table 1 below.

[0157] Table 1 Information on the main element content of the sample

[0158]

[0159] Taking Sample 1 as an example. By normalizing the spectral data, the influence of parameter fluctuations such as pulse energy and measurement efficiency is eliminated. Based on the Discrete Wavelet Transformation (DWT), baseline correction and noise reduction are performed on the normalized spectrum. The vast majority of the noise is eliminated, and the spectral lines from different channels are well corrected. The original spectrum and the spectrum after correction and noise reduction are respectively as shown in Figure 8 and Figure 9 The remaining samples are also preprocessed in the same manner as above.

[0160] After preprocessing the spectral data of the above 9 samples, it is necessary to find the peaks of the spectral lines, and peak finding directly affects the accuracy of the analysis results.

[0161] This application uses the peak finding method of the Continuous Wavelet Transform (CWT), which can effectively distinguish double peaks and has good accuracy. Referring to the Atomic Spectra Database (ASD) of the National Institute of Standards and Technology (NIST) in the United States, the central wavelength corresponding to the test spectral peak of each sample is compared with the standard wavelength in the database to determine the elemental attribution of the spectral line. For the spectral peaks obtained by peak finding processing, they are compared with the standard database to obtain the spectral element identification results. Referring to the information in the NIST database, for the above 9 samples, the representative characteristic spectral lines Cu I 327.396 nm and Zn I 328.289 nm are selected for research and analysis.

[0162] 1. Correction of the calibration curve for Cu I 327.396 nm

[0163] The brass sample was tested to verify the effectiveness of the above method and model. Leave-one-out cross-validation was used to evaluate the performance of the calibration curves obtained by different methods. The test results are shown in Figures 10 - 15 and Tables 2 and 3 below.

[0164] Table 2 Treatment effects of different methods on Cu I 327.396 nm

[0165]

[0166] As can be seen from Table 2 above, for Cu I 327.396 nm, the DVS-T1 model has better correlation and accuracy compared with the original spectrum and the normalized spectrum obtained by normalizing the original spectrum.

[0167] 2. Correction of the calibration curve for Zn I 328.289 nm

[0168] Table 3 Treatment effects of different methods on Zn I 328.289 nm

[0169]

[0170] As can be seen from Table 3 above, for Zn I 328.289 nm, the DVS-T1 model has better correlation and accuracy compared with the original spectrum and the normalized spectrum obtained by normalizing the original spectrum.

[0171] From Figures 10 - 15 and Tables 2 and 3 above, it can be seen that the spectral line intensity correction method of the embodiment of the present application has better correlation and accuracy compared with the normalized spectrum and the original spectrum obtained by normalization processing. Therefore, the embodiment of the present application solves the technical problem of inaccurate quantitative analysis of spectral line intensity in the related art through the spectral line intensity correction method. In addition, the above method has good reproducibility and good stability in quantitative analysis.

[0172] Figure 6 is a schematic structural diagram of a spectral line intensity correction system provided by an embodiment of the present application. Refer to Figure 6 As shown, the embodiment of the present application also provides a spectral line intensity correction system 200, including:

[0173] An acquisition unit 201, configured to acquire plasma information of a sample; the plasma information of the sample includes the full width at half maximum of the plasma radiation spectrum of the sample, plasma event stream data, and the number of events generated based on the plasma event stream data;

[0174] A generation unit 202, configured to reconstruct a plasma image according to the plasma event stream data;

[0175] An extraction unit 203, configured to form a plasma shape profile according to the plasma shape profile features extracted from the plasma image;

[0176] A calculation unit 204, configured to calculate the area of the region enclosed by the plasma shape profile;

[0177] And a correction unit 205, configured to input the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma radiation spectrum into a spectral line intensity correction model to generate a corrected spectral line intensity.

[0178] It can be understood that the above spectral line intensity correction system 200 can also execute the specific processes of the corresponding steps in the above method. For the sake of brevity, this is not described herein.

[0179] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0180] An embodiment of the present application further provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the processor executes the computer program, the steps in any of the foregoing method embodiments are implemented.

[0181] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the foregoing method embodiments can be implemented.

[0182] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal is enabled to execute the steps in the foregoing method embodiments.

[0183] When the integrated unit is implemented in the form of 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, to implement all or part of the processes in the above-described embodiment methods of this application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc.

[0184] In the above embodiments, the descriptions of the various embodiments have their own focuses. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0185] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0186] In the embodiments provided in this application, it should be understood that the disclosed device / network device and method can be implemented in other ways. For example, the device / network device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in an electrical, mechanical, or other form.

[0187] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the technical solution of this embodiment.

[0188] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A spectral line intensity correction method, characterized in that Including: Obtaining plasma information of a sample; The plasma information of the sample includes the full width at half maximum of the plasma radiation spectrum of the sample, plasma event stream data, and the number of events generated based on the plasma event stream data; Reconstructing a plasma image according to the plasma event stream data; Forming a plasma shape contour based on the plasma shape contour features extracted from the plasma image, and calculating the area of the region enclosed by the plasma shape contour; Inputting the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum into a spectral line intensity correction model to generate a corrected spectral line intensity; The obtaining of the plasma information of the sample includes: Obtaining event data and the number of events generated by a dynamic vision sensor; the event data includes pixel data recorded by the dynamic vision sensor each time the brightness change of the plasma exceeds the threshold of the dynamic vision sensor when the sample generates plasma under laser ablation; the pixel data includes pixel points, timestamps, and event polarities; the number of events is the cumulative number of times the brightness change of the plasma exceeds the threshold of the dynamic vision sensor within a certain time; Obtaining plasma event stream data generated by the dynamic vision sensor based on the event data accumulated within a certain time.

2. The spectral line intensity correction method according to claim 1, wherein The spectral line intensity correction model includes a function obtained by processing the spectral line intensity formula using the Saha formula. The independent variables of the function are the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum, and the dependent variable is the corrected spectral line intensity.

3. The spectral line intensity correction method according to claim 2, wherein, It also includes: Establishing a spectral line intensity correction model; The establishing of the spectral line intensity correction model includes: After processing the spectral line intensity formula using the Saha formula, obtaining a first function. The independent variables of the first function include the total particle number density, plasma temperature, and electron number density, and the dependent variable is the spectral line intensity; Performing a Taylor expansion on the first function to generate a second function; Expressing the spectral line intensity as a linear function of the element concentration in the sample and substituting it into the second function to generate a third function; the dependent variable of the third function is the corrected spectral line intensity, and the independent variables include the element concentration, the total particle number density, the plasma temperature, and the electron number density; Replacing the total particle number density in the third function with the area of the region enclosed by the plasma shape contour, replacing the plasma temperature in the third function with the number of events, and replacing the electron number density in the third function with the full width at half maximum to generate a fourth function; the dependent variable of the fourth function is the corrected spectral line intensity, and the independent variables include the element concentration, the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum; Performing a regression process on the fourth function with the element concentration, the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum as independent variables and the true spectral line intensity as the dependent variable to determine a regression value; Taking the regression value, the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma radiation spectrum as independent variables, and the ideal spectral line intensity as the dependent variable, performing regression processing on the fourth function to obtain the coefficients of the fourth function and substituting them into the fourth function to obtain the spectral line intensity correction model.

4. The spectral line intensity correction method according to claim 1, wherein, The obtaining of the plasma information of the sample includes: Obtaining the original plasma radiation spectrum of the sample, performing background removal, baseline correction, and noise reduction on the original plasma radiation spectrum to obtain the preprocessed plasma radiation spectrum; obtaining the full width at half maximum of the plasma radiation spectrum of the sample according to the original plasma radiation spectrum or the preprocessed plasma radiation spectrum.

5. The spectral line intensity correction method according to claim 4, wherein The forming of the plasma shape profile according to the plasma shape profile features extracted from the plasma image and the calculating of the area of the region enclosed by the plasma shape profile include: Reconstructing the plasma image based on the plasma event stream data using a deep network, a spiking neural network, clustering analysis, or event accumulation, forming the plasma shape profile according to the plasma shape profile features extracted from the plasma image, and calculating the area of the region enclosed by the plasma shape profile; The plasma image is the plasma image obtained using the plasma event stream data or the Gaussian-filtered plasma image obtained by performing Gaussian filtering on the plasma image obtained using the plasma event stream data.

6. The spectral line intensity correction method according to claim 4, wherein, The reconstructing of the plasma image according to the plasma event stream data includes: Generating a plasma event frame image based on the plasma event stream data; The forming of the plasma shape profile according to the plasma shape profile features extracted from the plasma image and the calculating of the area of the region enclosed by the plasma shape profile: includes: Extracting the edge information of the plasma shape profile of the plasma event frame image according to an edge detection algorithm, calculating the number of pixels in the region enclosed by the plasma shape profile, and generating the area of the region enclosed by the plasma shape profile.

7. A spectral line intensity correction system, characterized in that, Includes: An obtaining unit for obtaining the plasma information of the sample; The plasma information of the sample includes the full width at half maximum of the plasma radiation spectrum of the sample, the plasma event stream data, and the number of events generated based on the plasma event stream data; A generating unit for reconstructing the plasma image according to the plasma event stream data; An extracting unit for forming the plasma shape profile according to the plasma shape profile features extracted from the plasma image; A calculating unit for calculating the area of the region enclosed by the plasma shape profile; And a correcting unit for inputting the area of the region enclosed by the plasma shape profile, the number of events, and the full width at half maximum of the plasma radiation spectrum into the spectral line intensity correction model to generate the corrected spectral line intensity; The obtaining of the plasma information of the sample includes: Obtain the event data and the number of events generated by the dynamic vision sensor; the event data includes pixel data recorded by the dynamic vision sensor each time the brightness change of the plasma exceeds the threshold of the dynamic vision sensor when the sample generates plasma under laser ablation; the pixel data includes pixel points, timestamps, and event polarities; the number of events is the cumulative number of times the brightness change of the plasma exceeds the threshold of the dynamic vision sensor within a certain period of time; Obtain the plasma event stream data generated by the dynamic vision sensor based on the event data accumulated within a certain period of time.

8. A laser-induced breakdown spectroscopy system, characterized in that, Comprising: An optical path system; A laser, connected to the optical path system; A delay controller, connected to the laser; A spectrometer, connected to a computer, the optical path system, and the delay controller respectively; A dynamic vision sensor; And a computer, connected to the spectrometer and the dynamic vision sensor respectively, for: Obtain the plasma information of the sample; the plasma information of the sample includes the full width at half maximum of the plasma radiation spectrum of the sample, the plasma event stream data, and the number of events generated based on the plasma event stream data; Reconstruct the plasma image according to the plasma event stream data; Form a plasma shape contour based on the plasma shape contour features extracted from the plasma image, and calculate the area of the region enclosed by the plasma shape contour; Input the area of the region enclosed by the plasma shape contour, the number of events, and the full width at half maximum of the plasma radiation spectrum into the spectral line intensity correction model to generate the corrected spectral line intensity; The obtaining of the plasma information of the sample; includes: Obtain the event data and the number of events generated by the dynamic vision sensor; the event data includes pixel data recorded by the dynamic vision sensor each time the brightness change of the plasma exceeds the threshold of the dynamic vision sensor when the sample generates plasma under laser ablation; the pixel data includes pixel points, timestamps, and event polarities; the number of events is the cumulative number of times the brightness change of the plasma exceeds the threshold of the dynamic vision sensor within a certain period of time; Obtain the plasma event stream data generated by the dynamic vision sensor based on the event data accumulated within a certain period of time.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the method described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the method described in any one of claims 1 to 6 is implemented.

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