Foreign matter hardness detection method, system and equipment and storage medium

Abstract

The invention provides a foreign matter hardness detection method, system and equipment and a storage medium, and relates to the technical field of physical testing. The foreign matter hardness detection method comprises the following steps: acquiring spectral data and shock wave data generated by laser ablation of a sample; determining the material type of the sample according to the spectral data; based on the material type, determining a correction weight matrix between the spectral data and the shock wave data; correcting the corresponding spectral data by using the shock wave data and the correction weight matrix to obtain the corrected spectral data; and determining the detection hardness of the sample by using the corrected spectral data and the hardness quantitative relationship corresponding to the material type. According to the detection method disclosed by the invention, the interference of the surface state of the sample and the environment on a test result is effectively reduced by fusing the spectral data and the shock wave data, so that the hardness of the foreign matter is stably and accurately measured in a complex environment.

Description

Technical Field

[0001] The present disclosure relates to the field of physical testing technology, and in particular to a method, system, device and storage medium for detecting the hardness of a foreign body. Background Art

[0002] Laser-induced breakdown spectroscopy (LIBS) is an atomic optical emission spectrometry technique widely used for analyzing the composition of materials. Laser pulses form a plasma on the sample surface, and the plasma spectrum collected can accurately identify the elemental composition of the material. LIBS spectra are quantitatively correlated with the hardness of the test sample, allowing non-contact hardness measurements. LIBS testing technology allows for remote, in-situ, and real-time sample analysis, making it ideal for use in the harsh environments of nuclear power plants.

[0003] During nuclear power plant overhauls, hardness testing of foreign matter within containers is crucial for ensuring safe system operation. However, the complex environments of nuclear power plants, such as high temperature, high pressure, and radiation, as well as untreated foreign matter surfaces, can severely interfere with test signals. For example, in actual testing, the spectral signal from LIBS measurements is susceptible to interference from these harsh conditions and the surface condition of foreign matter, impacting analytical stability and accuracy.

[0004] Therefore, it is necessary to provide a method, system, device and storage medium for detecting the hardness of foreign matter to improve the above problems. Summary of the Invention

[0005] The purpose of the present disclosure is to provide a method, system, device and storage medium for detecting the hardness of foreign matter, so as to improve the technical problem that the current LIBS test spectrum cannot accurately obtain the hardness of the sample because it is easily affected by the sample surface state and environmental interference.

[0006] To achieve the above-mentioned and other related objectives, in a first aspect, the present disclosure provides a method for detecting the hardness of a foreign body, the method comprising the following steps:

[0007] Acquire spectral data and shock wave data generated by laser ablation samples;

[0008] determining the material type of the sample according to the spectral data;

[0009] determining a correction weight matrix between the spectral data and the shock wave data based on the material type;

[0010] Correcting the corresponding spectral data using the shock wave data and the correction weight matrix to obtain corrected spectral data;

[0011] The detected hardness of the sample is determined using the corrected spectral data and a quantitative hardness relationship corresponding to the material type, wherein the quantitative hardness relationship is a quantitative relationship between the hardness of the sample and the intensity ratio of the characteristic spectral lines in the spectral data.

[0012] In an example of the present disclosure, determining the material type of the sample according to the spectral data includes:

[0013] Acquiring characteristic spectral line information from the spectral data, the characteristic spectral line information including the wavelength and intensity of the characteristic spectral line;

[0014] The material type of the sample is determined based on the characteristic spectral line information.

[0015] In an example of the present disclosure, determining the material type of the sample based on the characteristic spectral line information includes:

[0016] Determining the types of elements contained in the sample and the content ratio of each element based on the wavelength and intensity of the characteristic spectral line;

[0017] The material type of the sample is determined based on the types of elements contained in the sample and the content ratio of each element.

[0018] In an example of the present disclosure, determining a correction weight matrix between the spectral data and the shock wave data based on the material type includes:

[0019] Based on the material type, the theoretical hardness corresponding to the material of the material type is determined; based on the quantitative relationship between the theoretical hardness and multiple groups of the spectral data and the shock wave data, a correction weight matrix between the spectral data and the shock wave data is determined.

[0020] In an example of the present disclosure, the process of obtaining the quantitative relationship of hardness includes: performing linear regression fitting on the theoretical hardness and the characteristic spectral line intensity ratio of the corrected spectral data to obtain the quantitative relationship of hardness.

[0021] In an example of the present disclosure, the process of obtaining the quantitative relationship of hardness includes: obtaining the actual hardness of the sample, and performing linear regression fitting on the actual hardness and the characteristic spectral line intensity ratio of the corrected spectral data to obtain the quantitative relationship of hardness.

[0022] In an example of the present disclosure, the detection method further includes: obtaining the actual hardness of the sample; and adjusting the correction weight matrix based on the loss between the detected hardness and the actual hardness.

[0023] In a second aspect, the present disclosure provides a Doppler point detection system for a pressurized water nuclear reactor, the Doppler point detection system comprising:

[0024] A data acquisition module, used to acquire spectrum data and shock wave data generated by laser ablation samples;

[0025] A material determination module, configured to determine the material type of the sample based on the spectral data;

[0026] a matrix determination module, which determines a correction weight matrix between the spectral data and the shock wave data based on the material type;

[0027] a spectrum correction module, which corrects the corresponding spectrum data using the shock wave data and the correction weight matrix to obtain the corrected spectrum data;

[0028] The hardness acquisition module determines the detected hardness of the sample using the corrected spectral data and the hardness quantitative relationship corresponding to the material type, where the hardness quantitative relationship is the quantitative relationship between the hardness of the sample and the intensity ratio of the characteristic spectral lines in the spectral data.

[0029] In a third aspect, the present disclosure provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above-mentioned examples when executing the computer program.

[0030] In a fourth aspect, the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described in any of the above examples are implemented.

[0031] The foreign body hardness detection method provided by the present disclosure utilizes synchronously acquired shock wave signals to correct spectral data to reduce interference from the sample surface and environment on the spectral data, thereby improving the accuracy and robustness of hardness detection and ensuring that LIBS testing can stably measure the composition and hardness of foreign bodies in high-temperature, high-pressure, or strong radiation environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The features and advantages of the present disclosure will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present disclosure in any way. In the accompanying drawings:

[0033] Figure 1 Shown is a flow chart of a method for detecting hardness of a foreign body in one embodiment of the present disclosure;

[0034] Figure 2 Shown is a flow chart of step S2 in one embodiment of the present disclosure;

[0035] Figure 3 Shown is a flow chart of step S3 in one embodiment of the present disclosure;

[0036] Figure 4 The display shows a comparison of the spectral data before correction, the spectral data corrected by pure machine learning, and the spectral data corrected using the correction weight matrix;

[0037] Figure 5 Shown is a schematic diagram of a quantitative relationship curve between the test hardness and the spectral data in one embodiment of the present disclosure;

[0038] Figure 6 Shown is a structural block diagram of a detection system in one embodiment of the present disclosure;

[0039] Figure 7 Shown is a structural block diagram of a computer device in one embodiment of the present disclosure.

[0040] Component number description:

[0041] 10. Detection system; 11. Data acquisition module; 12. Material determination module; 13. Matrix determination module; 14. Spectrum correction module; 15. Hardness acquisition module. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0043] See also Figures 1 to 7 It should be noted that the diagrams provided in this embodiment are merely schematic illustrations of the basic concept of the present disclosure. Therefore, the diagrams only show components related to the present disclosure and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0044] See Figures 1 to 5 In its first aspect, the present disclosure provides a method for detecting foreign body hardness. This method combines a correction weight matrix with shock wave data to correct spectral data, and then determines the sample's measured hardness based on the corrected spectral data. This method effectively reduces the influence of sample surface conditions and environmental factors on test results, thereby achieving stable and accurate foreign body hardness measurement in complex environments.

[0045] like Figure 1 As shown, the foreign body hardness detection method includes the following steps:

[0046] Step S1: Acquire spectrum data and shock wave data generated by laser ablation of a sample.

[0047] In step S1, multiple sets of spectral data and shock wave data generated by burning a sample with pulsed laser light output by a pulsed laser are acquired. Specifically, a pulsed laser output by an Nd:YAG pulsed laser is used to burn different locations of the sample multiple times, generating a stable plasma emission spectrum and shock wave with each burn. The Nd:YAG pulsed laser has an output wavelength of 1064 nm and an energy of 20 to 100 mJ. A spectrometer is used to collect the plasma emission spectrum generated by each burn, and a piezoelectric sensor is used to collect shock wave data.

[0048] In step S1 , the spectrum data includes the wavelength and intensity of light excited by the sample under laser burning, and the shock wave data includes the amplitude and propagation speed of the sound pressure generated by the sample under laser burning.

[0049] Next, step S2 is executed to determine the material type of the sample based on the spectral data.

[0050] like Figure 2 As shown, in some embodiments, step S2 includes the following steps:

[0051] S21. Acquire characteristic spectral line information from the spectral data, where the characteristic spectral line information includes the wavelength and intensity of the characteristic spectral line.

[0052] In step S21, the characteristic spectral lines in the spectral data are spectral lines with specific wavelengths emitted by elements through electronic transitions after being excited in the laser-induced plasma. The wavelengths of these spectral lines correspond to the atomic or ionic structures of the elements and can be used to identify the elements in the sample.

[0053] S22. Determine the material type of the sample based on the characteristic spectral line information.

[0054] In step S22, each characteristic line in the spectral data is compared with the NIST atomic spectrum database to determine the element types contained in the sample. Then, based on the ratio of the intensities of the characteristic lines, the content ratio of the elements corresponding to the characteristic lines is determined. Finally, based on the element types and content ratios contained in the sample, the material type of the sample is determined. For example, the intensity of the characteristic lines corresponding to Fe and Cr in the spectral data can be identified to determine that the sample is made of the corresponding grade of stainless steel.

[0055] Next, step S3 is executed to determine a correction weight matrix between the spectral data and the shock wave data based on the material type.

[0056] like Figure 3As shown, in some embodiments, step S3 includes the following steps:

[0057] S31. Based on the material type, determine the theoretical hardness corresponding to the material of the material type.

[0058] S32. Based on the quantitative relationship between the theoretical hardness and the multiple sets of spectral data and shock wave data, determine a correction weight matrix between the spectral data and the shock wave data.

[0059] In some embodiments, in step S32, the theoretical hardness of the sample is used as a reference, and the correlation weights between the shock wave data and the characteristic spectral line intensities in the corresponding spectral data are analyzed using multiple regression to determine a correction weight matrix between the spectral data and the shock wave data.

[0060] In other embodiments, in step S32, a correction weight matrix is pre-set between the spectral data and the shock wave data based on the sample material type. Based on this correction weight matrix, a quantitative relationship is established between the predicted hardness and the intensity ratio of characteristic spectral lines in the spectral data and the shock wave data. For example, for a 304 stainless steel sample, a quantitative relationship is established between the predicted hardness and the intensity ratio of the characteristic spectral lines FeII / FeI, as well as the acoustic pressure intensity and propagation velocity of the shock wave. Subsequently, using multiple sets of spectral data and shock wave data, a training method is used to minimize the loss between the predicted hardness and the theoretical hardness, thereby obtaining a trained correction weight matrix.

[0061] Next, step S4 is executed to correct the corresponding spectral data using the shock wave data and the correction weight matrix to obtain the corrected spectral data.

[0062] Since the sound pressure intensity in the shock wave data is correlated with the sample surface state, and the propagation velocity is correlated with the environmental state, in step S4, the corresponding spectral data is corrected by combining the shock wave data and the correction weight matrix, which can effectively reduce the interference error caused by the sample surface state and the test environment on the spectral data. Figure 4 As shown, in one embodiment of the present disclosure, compared with the spectral data before correction, the spectral data corrected by using the shock wave data and the correction weight matrix has a significantly reduced spectral jitter.

[0063] Next, step S5 is executed to determine the test hardness of the sample using the corrected spectral data and the quantitative relationship between the hardness of the corresponding material type.

[0064] In step S5, a hardness quantitative relationship is retrieved based on the sample material type. This hardness quantitative relationship is the quantitative relationship between the sample's hardness and the intensity ratio of the characteristic spectral lines in the spectral data. The sample's measured hardness is then determined based on the hardness quantitative relationship and the intensity ratio of the characteristic spectral lines in the corrected spectral data.

[0065] In some embodiments, the hardness quantitative relationship used in step S5 can be determined based on the theoretical hardness and the corrected spectral data. Specifically, a linear regression fit is performed on the theoretical hardness and the characteristic line intensity ratio of multiple sets of corrected spectral data, using the characteristic line intensity ratio of the corrected spectral data as the independent variable and the theoretical hardness as the dependent variable, to obtain a calibration curve between the characteristic line intensity ratio and the Vickers hardness, and a hardness quantitative relationship corresponding to the calibration curve.

[0066] In other embodiments, the actual hardness of the sample can be first obtained through physical testing, and the hardness quantitative relationship used in step S5 can be determined based on the actual hardness and the corrected spectral data. Specifically, using the characteristic line intensity of the corrected spectral data as the independent variable and the actual hardness as the dependent variable, a linear regression fit is performed on the actual hardness and the characteristic line intensity ratio of multiple sets of corrected spectral data to obtain a calibration curve of the characteristic line intensity ratio and Vickers hardness, as well as a hardness quantitative relationship corresponding to the calibration curve.

[0067] like Figure 5 As shown in the figure, in an example, the intensity ratio of multiple sets of characteristic spectral lines FeII and FeI after correction is linearly regressed and fitted with the actual hardness at the corresponding position, the obtained calibration curve of the characteristic spectral line intensity ratio and Vickers hardness and the quantitative relationship of the hardness corresponding to the calibration curve are obtained.

[0068] In addition, in some embodiments, the foreign body hardness detection method further includes the step of adjusting a correction weight matrix using the actual hardness. This step specifically includes obtaining the actual hardness at the sample test location corresponding to the multiple sets of spectral data; and adjusting the correction weight matrix based on a loss function between the detected hardness determined by the multiple sets of corrected spectral data and the corresponding actual hardness until the loss function is minimized, thereby obtaining an optimized correction weight matrix, thereby further improving the consistency between subsequent test results and the actual hardness.

[0069] In summary, this foreign body hardness detection method determines the sample material type based on spectral data, determines a corresponding correction weight matrix based on the material type, combines the correction weight matrix with the simultaneously acquired shock wave signal to correct the spectral data, and finally substitutes the corrected spectral data into the quantitative hardness relationship corresponding to the sample material type to obtain the detected hardness of the sample. By fusing spectral data with shock wave data, this foreign body hardness detection method effectively reduces the interference of the sample surface condition and environment on the test results, thereby achieving stable and accurate foreign body hardness measurement in complex environments.

[0070] In a second aspect, in some embodiments, the present invention provides a foreign body hardness detection system 10 , which corresponds one-to-one to the foreign body hardness detection method in the above-mentioned embodiment.

[0071] like Figure 6 As shown, the detection system 10 includes a data acquisition module 11, a material determination module 12, a matrix determination module 13, a spectrum correction module 14 and a hardness acquisition module 15. The functional modules are described in detail as follows:

[0072] A data acquisition module 11 is used to acquire spectrum data and shock wave data generated by laser ablation of the sample;

[0073] A material determination module 12 is used to determine the material type of the sample based on the spectral data;

[0074] a matrix determination module 13, which determines a correction weight matrix between the spectral data and the shock wave data based on the material type;

[0075] The spectrum correction module 14 corrects the corresponding spectrum data using the shock wave data and the correction weight matrix to obtain the corrected spectrum data;

[0076] The hardness acquisition module 15 determines the detected hardness of the sample using the corrected spectral data and the hardness quantitative relationship corresponding to the material type, where the hardness quantitative relationship is the quantitative relationship between the hardness of the sample and the intensity ratio of the characteristic spectral lines in the spectral data.

[0077] In one embodiment, the material determination module 12 is specifically configured to:

[0078] Acquiring characteristic spectral line information from the spectral data, the characteristic spectral line information including the wavelength and intensity of the characteristic spectral line;

[0079] The material type of the sample is determined based on the characteristic spectral line information.

[0080] In one embodiment, the material determination module 12 is specifically configured to:

[0081] Determining the types of elements contained in the sample and the content ratio of each element based on the wavelength and intensity of the characteristic spectral line;

[0082] The material type of the sample is determined based on the types of elements contained in the sample and the content ratio of each element.

[0083] In one embodiment, the matrix determination module 13 is specifically configured to:

[0084] Based on the material type, determining a theoretical hardness corresponding to the material type;

[0085] Based on the quantitative relationship between the theoretical hardness and the multiple sets of the spectral data and the shock wave data, a correction weight matrix between the spectral data and the shock wave data is determined.

[0086] In one embodiment, the hardness acquisition module 15 is specifically configured to:

[0087] A linear regression fitting is performed on the theoretical hardness and the characteristic spectral line intensity ratio of the corrected spectral data to obtain a quantitative relationship of hardness.

[0088] In one embodiment, the hardness acquisition module 15 is specifically configured to:

[0089] The actual hardness of the sample is obtained, and a linear regression fitting is performed on the actual hardness and the characteristic spectral line intensity ratio of the corrected spectral data to obtain a quantitative relationship of hardness.

[0090] In one embodiment, the hardness acquisition module 15 is specifically configured to:

[0091] Obtaining the actual hardness of the sample;

[0092] The correction weight matrix is adjusted based on the loss between the detected hardness and the actual hardness.

[0093] The specific definitions of the foreign body hardness detection system 10 can be found in the definitions of the foreign body hardness detection method described above and will not be further elaborated here. Each module of the foreign body hardness detection system 10 can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor within a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0094] In one embodiment, a computer device is provided, wherein the internal structure of the computer device can be as follows: Figure 7 As shown. The computer device includes a processor, memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When executed by the processor, the computer program implements the functions or steps of a Doppler point detection method.

[0095] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed:

[0096] Acquire spectral data and shock wave data generated by laser ablation samples;

[0097] determining the material type of the sample according to the spectral data;

[0098] determining a correction weight matrix between the spectral data and the shock wave data based on the material type;

[0099] Correcting the corresponding spectral data using the shock wave data and the correction weight matrix to obtain corrected spectral data;

[0100] The detected hardness of the sample is determined using the corrected spectral data and a quantitative hardness relationship corresponding to the material type, wherein the quantitative hardness relationship is a quantitative relationship between the hardness of the sample and the intensity ratio of the characteristic spectral lines in the spectral data.

[0101] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0102] Acquire spectral data and shock wave data generated by laser ablation samples;

[0103] determining the material type of the sample according to the spectral data;

[0104] determining a correction weight matrix between the spectral data and the shock wave data based on the material type;

[0105] Correcting the corresponding spectral data using the shock wave data and the correction weight matrix to obtain corrected spectral data;

[0106] The detected hardness of the sample is determined using the corrected spectral data and a quantitative hardness relationship corresponding to the material type, wherein the quantitative hardness relationship is a quantitative relationship between the hardness of the sample and the intensity ratio of the characteristic spectral lines in the spectral data.

[0107] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can be found in the relevant descriptions of the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0108] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink), DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

Claims

1. A method for detecting the hardness of a foreign body, characterized in that: include: Acquire spectral data and shock wave data generated by laser ablation samples; determining the material type of the sample according to the spectral data; determining a correction weight matrix between the spectral data and the shock wave data based on the material type; Correcting the corresponding spectral data using the shock wave data and the correction weight matrix to obtain corrected spectral data; The detected hardness of the sample is determined using the corrected spectral data and a quantitative hardness relationship corresponding to the material type, wherein the quantitative hardness relationship is a quantitative relationship between the hardness of the sample and the intensity ratio of the characteristic spectral lines in the spectral data.

2. The detection method according to claim 1, characterized in that Determining the material type of the sample according to the spectral data includes: Acquiring characteristic spectral line information from the spectral data, the characteristic spectral line information including the wavelength and intensity of the characteristic spectral line; The material type of the sample is determined based on the characteristic spectral line information.

3. The detection method according to claim 2, characterized in that The determining the material type of the sample based on the characteristic spectral line information includes: Determining the types of elements contained in the sample and the content ratio of each element based on the wavelength and intensity of the characteristic spectral line; The material type of the sample is determined based on the types of elements contained in the sample and the content ratio of each element.

4. The detection method according to claim 1, wherein The determining of a correction weight matrix between the spectral data and the shock wave data based on the material type includes: Based on the material type, determining a theoretical hardness corresponding to the material type; Based on the quantitative relationship between the theoretical hardness and the multiple sets of the spectral data and the shock wave data, a correction weight matrix between the spectral data and the shock wave data is determined.

5. The detection method according to claim 4, characterized in that The process of obtaining the quantitative relationship of hardness includes: A linear regression fitting is performed on the theoretical hardness and the characteristic spectral line intensity ratio of the corrected spectral data to obtain a quantitative relationship of hardness.

6. The detection method according to claim 4, characterized in that The process of obtaining the quantitative relationship of hardness includes: The actual hardness of the sample is obtained, and a linear regression fitting is performed on the actual hardness and the characteristic spectral line intensity ratio of the corrected spectral data to obtain a quantitative relationship of hardness.

7. The detection method according to claim 4, characterized in that Also includes: Obtaining the actual hardness of the sample; The correction weight matrix is adjusted based on the loss between the detected hardness and the actual hardness.

8. A foreign body hardness detection system, characterized in that: include: A data acquisition module, used to acquire spectrum data and shock wave data generated by laser ablation samples; A material determination module, configured to determine the material type of the sample based on the spectral data; a matrix determination module, which determines a correction weight matrix between the spectral data and the shock wave data based on the material type; a spectrum correction module, which corrects the corresponding spectrum data using the shock wave data and the correction weight matrix to obtain the corrected spectrum data; The hardness acquisition module determines the detected hardness of the sample using the corrected spectral data and the hardness quantitative relationship corresponding to the material type, where the hardness quantitative relationship is the quantitative relationship between the hardness of the sample and the intensity ratio of the characteristic spectral lines in the spectral data.

9. A computer device, characterized in that: include: processor and memory; The memory is used to store computer programs; The processor is connected to the memory, and is configured to execute a computer program stored in the memory, so that the computer device executes the detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the detection method according to any one of claims 1 to 7 are implemented.

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