Adaptive analysis method and system for multi-material component detection, device, and medium

Abstract

The present invention relates to the technical field of material component detection. Disclosed are an adaptive analysis method and system for multi-material component detection, a device, and a medium. The method comprises: acquiring spectral information of a material to be detected; determining the type of said material on the basis of the spectral information of said material and a material characteristic spectrum library; and matching a corresponding quantitative analysis model from a quantitative analysis model library on the basis of the type of said material to perform component detection on said material.

Description

An adaptive analysis method, system, device and medium for multi-material component detection

[0001] Related cross-references

[0002] This application is based on and claims priority to Chinese Patent Application No. 202411709122.8, filed on November 27, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to the field of material composition detection technology, and in particular to an adaptive analysis method, system, device and medium for multi-material composition detection. Background Technology

[0004] In industries such as metallurgy, material composition is a core parameter for process control and an evaluation indicator for product quality. Composition detection plays a crucial role in various technical and economic indicators, including the degree of smelting, product quality, and metal recovery rate. Online real-time detection is one of the key bottlenecks in the intelligent upgrading of smelting processes. Currently, material composition detection in smelting processes mostly relies on manual sampling combined with offline laboratory testing, which suffers from insufficient real-time performance, reliability, guidance, and safety. This hinders real-time monitoring, precise control, and process improvement of material composition, and fails to support the intelligent upgrading of corresponding process nodes. Composition analyzers based on Laser Induced Breakdown Spectroscopy (LIBS) can detect the composition of process materials. They are characterized by being sampling-free, sample-free, non-contact, and radiation-free, facilitating closed-loop control based on real-time composition guidance for process regulation. This enables precise process control, optimizes process connection efficiency, and supports intelligent construction.

[0005] In industrial applications, LIBS technology often requires the testing of multiple materials at the same point of application. Examples include the steel industry's conveyor belt alternately transporting different types of solid materials like flux and blended ore, or the alternating flow of molten iron and slag from the taphole of a large blast furnace. In these situations, a fixed, single quantitative analysis model is unsuitable for the field, while manually switching models requires prior confirmation of the online sample type, increasing labor intensity and making practical application in industrial settings difficult. Summary of the Invention

[0006] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide an adaptive analysis method, system, device and medium for multi-material component detection, which solves the technical problems that the fixed single quantitative analysis model cannot be applied on site and that manually switching models will increase the intensity of manual labor in the prior art.

[0007] To achieve the above and other related objectives, the present invention provides an adaptive analysis method for the detection of multiple material components, comprising:

[0008] Obtain the spectral information of the material to be tested;

[0009] Based on the spectral information of the material to be tested and the material characteristic spectral library, the type of the material to be tested is determined. The material characteristic spectral library includes the spectral information of different types of material samples in a preferred band. The preferred band refers to the spectral band in which the spectral correlation coefficients between different types of material samples are all lower than a first threshold and include all characteristic elements of different types of material samples.

[0010] Based on the type of the material to be tested, a corresponding material analysis model is matched in the material analysis model library to perform component detection of the material to be tested, wherein the material analysis model library includes material analysis models corresponding to different types of material samples.

[0011] Based on the same inventive concept, the present invention also provides an adaptive analysis system for multi-material component detection, comprising:

[0012] The distance measuring module is used to measure the distance between the material to be tested and the spectrometer;

[0013] A motion control module is used to adjust the focal length of the spectrometer based on the results of the ranging module;

[0014] The laser module uses laser pulses to generate a plasma spectrum on the surface of the material to be tested;

[0015] A spectral acquisition module is used to acquire the spectral information of the material to be tested;

[0016] The model selection module is used to determine the type of the test material based on its spectral information and a material characteristic spectral library. The material characteristic spectral library includes spectral information of different types of material samples within a preferred wavelength band. The preferred wavelength band refers to a spectral band where the spectral correlation coefficients between different types of material samples are all below a first threshold and include all characteristic elements of the different types of material samples. Based on the type of the test material, the module matches a corresponding material analysis model from a material analysis model library, which includes material analysis models corresponding to different types of material samples.

[0017] The quantitative analysis module is used to detect the composition of the test material according to the corresponding quantitative analysis model.

[0018] Based on the same inventive concept, the present invention also provides an electronic device, which includes:

[0019] One or more processors;

[0020] A storage device for storing one or more programs, which, when executed by the one or more processors, enable the electronic device to implement the adaptive analysis method for multi-material component detection as described above.

[0021] Based on the same inventive concept, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer processor, causes the computer to perform the adaptive analysis method for multi-material component detection as described above.

[0022] As described above, the adaptive analysis method, system, equipment, and medium for multi-material component detection provided by this invention can be applied to online scenarios involving alternating multiple materials in industrial settings. By acquiring the spectral information of the material to be tested, and based on this spectral information and a material characteristic spectral library, the type of the material to be tested is determined. Then, a corresponding material analysis model is matched in a material analysis model library according to the type of the material to be tested to perform component detection. The adaptive analysis method for multi-material component detection of this invention can perform adaptive LIBS quantitative analysis based on different material types, obtaining online detection results for multiple material components without manually switching models. Compared to existing methods of manual sampling + offline laboratory testing, it features strong real-time performance and high accuracy. Furthermore, since no manual confirmation of the type of material to be tested is required, the intensity of manual labor is reduced. Attached Figure Description

[0023] Figure 1 is a flowchart illustrating an adaptive analysis method for detecting multiple material components according to an embodiment of the present invention;

[0024] Figure 2 is a schematic diagram of an application scenario of an adaptive analysis method for multi-material component detection provided by an embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of the system flow of an adaptive analysis method for multi-material component detection provided in an embodiment of the present invention;

[0026] Figure 4 shows the first measured spectrum of an adaptive analysis method for multi-material component detection provided in an embodiment of the present invention;

[0027] Figure 5 shows a second measured spectrum of an adaptive analysis method for multi-material component detection provided in an embodiment of the present invention;

[0028] Figure 6 is a schematic diagram of the structure of an adaptive analysis system for multi-material component detection provided in an embodiment of the present invention;

[0029] Figure 7 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0030] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0031] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0032] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0033] This invention provides an adaptive analysis method for multi-material component detection, applicable to online scenarios involving alternating multiple materials in industrial settings. It enables adaptive LIBS quantitative analysis based on different material types, obtaining online detection results for multiple material components without manual model switching. Compared to existing methods involving manual sampling and offline laboratory testing, it offers advantages such as high real-time performance and accuracy. Furthermore, since manual confirmation of the material type is unnecessary, it reduces labor intensity. Referring to Figure 1, the adaptive analysis method for multi-material component detection includes the following steps:

[0034] S100: Acquire the spectral information of the material to be tested;

[0035] S200: Determine the type of the material to be tested based on the spectral information of the material to be tested and the material characteristic spectral library, wherein the material characteristic spectral library includes the spectral information of different types of material samples in a preferred band, and the preferred band refers to the spectral band in which the spectral correlation coefficients between different types of material samples are all lower than a first threshold and include all characteristic elements of different types of material samples.

[0036] S300: Match the corresponding material analysis model in the material analysis model library according to the type of the material to be tested, so as to perform component detection of the material to be tested, wherein the material analysis model library includes material analysis models corresponding to different types of material samples.

[0037] First, before executing step S100, in other words, before performing online detection of the components of the material to be tested, it is necessary to establish a material characteristic spectral library and a material analysis model library. The establishment of the material characteristic spectral library includes the following steps:

[0038] S010: Obtain spectral information of different types of material samples.

[0039] S020: Obtain the wavelengths of all characteristic elements corresponding to different types of material samples from the standard spectral database.

[0040] S030: Segmentally compare the spectral correlation of the spectral information of different types of material samples to obtain multiple sets of spectral correlation coefficients.

[0041] S040: Select the spectral bands in which the spectral correlation coefficients between different types of material samples are all lower than the first threshold and include all characteristic elements of the different types of material samples as preferred bands.

[0042] S050: Obtain spectral information of different types of material samples in the preferred wavelength band to form a material characteristic spectral library.

[0043] It should be noted that, in step S030, the step of segmenting and comparing the spectral correlation of the spectral information of different types of material samples to obtain multiple sets of spectral correlation coefficients further includes the following steps:

[0044] S031: Divide the full spectrum wavelength of different types of material samples into multiple equally spaced band intervals;

[0045] S032: Perform pairwise correlation analysis on the spectral information of different types of material samples in each band interval to obtain multiple sets of spectral correlation coefficients corresponding to each band interval.

[0046] Specifically, firstly, a laser uses laser pulses to generate plasma on the surface of a material sample, and a spectrometer collects the spectra of different types of material samples. It should be noted that LIBS technology uses high-power-density laser pulses to irradiate the surface of the material sample, causing the surface to rapidly evaporate, vaporize, and ionize, forming plasma. During the cooling process, this plasma emits a spectrum of specific wavelengths, which contains characteristic information about the elements in the material sample. Next, based on a standard spectral database (the National Institute of Standards and Technology (NIST) database) and combined with known elements of the material in the field, the wavelengths of all characteristic elements corresponding to different types of material samples are obtained. Then, the spectral information of all material samples collected in step S010 is segmented and their spectral correlations are compared. For example, if there are N types of material samples, and the full-spectrum wavelength range of these N samples is M0 to M1, and the segmentation interval is Len, then the full-spectrum wavelength is divided into segments. Several waveband intervals, among which... The symbol represents the rounding up sign. Next, pairwise correlation analysis is performed on the spectra of different material samples within each spectral band interval to obtain multiple sets of spectral correlation coefficients for each band interval. In this embodiment, the Peason correlation coefficient formula is used to calculate the spectral correlation coefficients between different types of materials:

[0047] Where x and y are normalized intensity arrays of the same characteristic peaks in the spectra of the two sets of material samples, and r is the spectral correlation coefficient between the two sets of material samples.

[0048] Finally, the spectral bands in which the spectral correlation coefficients between different types of material samples are all lower than the first threshold and include all characteristic elements of different types of material samples are selected as preferred bands. The spectral information of different types of material samples in the preferred bands is obtained and saved to form a material characteristic spectral library.

[0049] The establishment of the material analysis model library includes: preprocessing the spectra of different types of material samples, extracting features, integrating data, and training models to form the material analysis model library.

[0050] Specifically, after the spectral information of all material samples is collected, it undergoes preprocessing, feature extraction, data integration, and model training. The corresponding quantitative analysis models for all material samples are then saved. If the internal standard method is used, the formula for the quantitative analysis model is:

[0051] Wherein, the horizontal axis represents the ratio I of the spectral line intensities of the measured element and the internal standard element. C / I R The vertical axis represents the reference concentration C obtained by other testing methods. CK is the slope of the linear fitting curve, and B is the intercept of the linear fitting curve.

[0052] If the external standard method is used, the formula for the quantitative analysis model is: C C =KI C +B

[0053] Where the horizontal axis represents the intensity I of the spectral line of the element being measured. C The vertical axis represents the reference concentration C obtained by other testing methods. C K is the slope of the linear fitting curve, and B is the intercept of the linear fitting curve.

[0054] Next, after the material characteristic spectral library and the material analysis model library are established, step S100 is executed to obtain the spectral information of the material to be tested.

[0055] The acquisition of the spectral information of the material to be tested further includes the following steps:

[0056] S110: Obtain the distance information between the material to be tested and the spectrometer;

[0057] S120: Adjust the focal length of the spectrometer according to the distance information, and use the spectrometer to collect the spectrum of the material to be tested, so as to obtain the spectral information of the material to be tested.

[0058] Specifically, as shown in Figure 2, a high-energy pulsed laser emitted by a laser is focused onto the surface of the sample to be tested via a laser focusing lens, ablating the sample and generating plasma. Then, the optical signal emitted by the plasma is coupled into an optical fiber through a collection optical path and transmitted to a spectral dispersive and detection device to obtain the spectral signal of the sample containing elemental composition information, thus completing the LIBS spectral data acquisition of the sample. It should be noted that when detecting the material under test online, the distance information between the material under test and the spectrometer is obtained through a ranging module. The motion control module adjusts the focal length of the spectrometer based on this distance information before acquiring the spectrum of the material under test.

[0059] Next, step S200 is executed to determine the type of the material to be tested based on its spectral information and the material characteristic spectral library. The material characteristic spectral library includes spectral information of different types of material samples within a preferred wavelength band. The preferred wavelength band refers to a spectral band where the spectral correlation coefficients between different types of material samples are all below a first threshold and include all characteristic elements of the different types of material samples.

[0060] It should be noted that determining the type of the test material based on its spectral information and a material characteristic spectral library further includes the following steps:

[0061] S210: Extract the spectral information of the material to be tested within the preferred wavelength band;

[0062] S220: The spectral information in the preferred band is compared with the spectral information of different types of materials in the material characteristic spectral library in turn to determine the spectral correlation, and the type of the material to be tested is determined according to the comparison result.

[0063] It is worth noting that the step of sequentially comparing the spectral correlation between the spectral information within the preferred band and the spectral information of different types of materials in the material characteristic spectral library, and determining the type of the material to be tested based on the comparison results, further includes the following steps:

[0064] S221: Sequentially determine whether the spectral correlation coefficient between the spectral information of the material to be tested in the preferred band and the spectral information of different types of materials in the feature spectral library is higher than the second threshold.

[0065] S222: If yes, then determine that the type of the material to be tested is the corresponding material type in the matching feature spectral library; otherwise, return to step S221.

[0066] Specifically, in this embodiment, the Peason correlation coefficient formula is used to calculate the spectral correlation between the spectrum of the material to be tested in the preferred wavelength band and the spectrum of each material in the material characteristic spectral library in the preferred wavelength band. When the Peason correlation coefficient is higher than a second threshold, it is determined to be a known material of this type.

[0067] Finally, step S300 is executed, matching the corresponding quantitative analysis model in the quantitative analysis model library according to the type of the analyte to perform component detection of the analyte. The quantitative analysis model library includes quantitative analysis models corresponding to different types of the analyte samples. The spectra of the analyte are preprocessed, feature extracted, and data integrated, and quantitative analysis of the elements contained in the analyte is achieved through spectral data processing methods based on the corresponding quantitative analysis model.

[0068] In summary, as shown in Figure 3, the adaptive analysis method for multi-material component detection provided by this invention acquires the spectral information of the material to be tested, determines the type of the material to be tested based on the spectral information and a material characteristic spectral library, and matches the corresponding quantitative analysis model in a quantitative analysis model library according to the type of the material to be tested to perform component detection. This adaptive analysis method for multi-material component detection can be applied to online scenarios of alternating multiple materials in industrial settings. For example, it can be applied to scenarios where multiple materials are alternately transported in conveyor belts or drainage systems, and perform adaptive LIBS quantitative analysis, obtaining online detection results of multiple material components without manual operation. Compared to existing methods of manual sampling + offline laboratory testing, the adaptive analysis method for multi-material component detection provided by this invention has the characteristics of strong real-time performance and high accuracy, and can truly reflect the current state of the materials. Please refer to Figures 4 and 5, which show the first and second measured spectra of various materials, respectively. As can be seen from Figures 4 and 5, different types of materials have different elemental compositions, and the corresponding spectral characteristics are clearly distinguishable. If the same material model is used for quantitative analysis, the deviation will be significant. Therefore, the adaptive LIBS quantitative analysis method of this invention is needed to realize the online detection of multiple material components.

[0069] Based on the same inventive concept, as shown in Figure 6, another embodiment of the present invention also provides an adaptive analysis system 11 for multi-material component detection, which includes:

[0070] Distance measuring module 111 is used to measure the distance between the material to be measured and the spectrometer;

[0071] Motion control module 112 is used to adjust the focal length of the spectrometer based on the result of the ranging module 111;

[0072] Laser module 113 uses laser pulses to generate a plasma spectrum on the surface of the material to be tested;

[0073] The spectral acquisition module 114 is used to acquire the spectral information of the material to be tested;

[0074] The model selection module 115 is used to determine the type of the test material based on the spectral information of the test material and a material characteristic spectral library, wherein the material characteristic spectral library includes spectral information of different types of material samples in preferred wavelength bands, wherein the preferred wavelength bands refer to spectral bands in which the spectral correlation coefficients between different types of material samples are all lower than a first threshold and include all characteristic elements of different types of material samples; and to match the corresponding quantitative analysis model in a quantitative analysis model library based on the type of the test material, wherein the quantitative analysis model library includes quantitative analysis models corresponding to different types of material samples.

[0075] The quantitative analysis module 116 is used to detect the composition of the material to be tested according to the corresponding quantitative analysis model.

[0076] It should be noted that the adaptive analysis system 11 for multi-material component detection includes the adaptive analysis method for multi-material component detection described in any of the above embodiments. Since the adaptive analysis system 11 for multi-material component detection provided in this embodiment belongs to the same inventive concept as the adaptive analysis method for multi-material component detection provided in any of the above embodiments, it has at least the same beneficial effects, and will not be described in detail here.

[0077] Based on the same inventive concept, as shown in Figure 7, another embodiment of the present invention also provides an electronic device 1, which may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as an adaptive analysis program for multi-material composition detection.

[0078] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 12 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 12 can include both internal and external storage units of the electronic device 1. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 1, such as code for adaptive analysis of multi-material composition detection, but also to temporarily store data that has been output or will be output.

[0079] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the electronic device 1, connecting various components of the electronic device 1 via various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., adaptive analysis programs for multi-material composition detection) and calls data stored in the memory 12 to perform various functions and process data in the electronic device 1.

[0080] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the adaptive analysis method for multi-material component detection described above.

[0081] For example, the computer program may be divided into one or more modules, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into a ranging module 111, a motion control module 112, a laser module 113, a spectral acquisition module 114, a model selection module 115, and a quantitative analysis module 116.

[0082] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium, which can be non-volatile or volatile. The software functional module, stored in the storage medium, includes several instructions to cause a computer device (which may be a personal computer, a computer device, or a network device, etc.) or processor to execute some functions of the adaptive analysis method for multi-material component detection described in the various embodiments of this application.

[0083] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method of adaptive analysis of multi-material composition detection, characterized in that, include: Obtain the spectral information of the material to be tested; Based on the spectral information of the material to be tested and the material characteristic spectral library, the type of the material to be tested is determined. The material characteristic spectral library includes the spectral information of different types of material samples in a preferred band. The preferred band refers to the spectral band in which the spectral correlation coefficients between different types of material samples are all lower than a first threshold and include all characteristic elements of different types of material samples. Based on the type of the material to be tested, a corresponding material analysis model is matched in the material analysis model library to perform component detection of the material to be tested, wherein the material analysis model library includes material analysis models corresponding to different types of material samples.

2. The adaptive analysis method for multi-material composition detection of claim 1, wherein, The acquisition of the spectral information of the material to be tested includes: Obtain the distance information between the material to be tested and the spectrometer; The focal length of the spectrometer is adjusted according to the distance information, and the spectrometer is used to collect the spectrum of the material to be tested in order to obtain the spectral information of the material to be tested.

3. The adaptive analysis method for multi-material composition detection of claim 1, wherein, The establishment of the material characteristic spectral library includes: Obtain spectral information of different types of material samples; The wavelengths of all characteristic elements corresponding to different types of material samples were obtained from the standard spectral database. The spectral correlation of the spectral information of different types of material samples is compared in segments to obtain multiple sets of spectral correlation coefficients; The preferred bands are those whose spectral correlation coefficients between different types of material samples are all lower than the first threshold and include all characteristic elements of the different types of material samples. Spectral information of different types of material samples in preferred wavelength bands is obtained to form a material characteristic spectral library.

4. The adaptive analysis method for multi-material composition detection of claim 3, wherein, The spectral correlation of the spectral information of different types of material samples is compared segment by segment to obtain multiple sets of spectral correlation coefficients, including: The full spectrum wavelengths of the different types of material samples are divided into multiple equally spaced band intervals; Pairwise correlation analysis was performed on the spectral information of different types of material samples in each band interval to obtain multiple sets of spectral correlation coefficients corresponding to each band interval.

5. The adaptive analysis method for multi-material component detection according to any one of claims 1 to 4, characterized in that, The establishment of the material analysis model library includes: The spectra of different types of material samples were preprocessed, features were extracted, data were integrated, and models were trained to form a material quantitative analysis model library.

6. The adaptive analysis method for multi-material composition detection according to any one of claims 1 to 5, characterized in that, The step of determining the type of the test material based on its spectral information and a material characteristic spectral library includes: Extract the spectral information of the material to be tested within the preferred wavelength band; The spectral information within the preferred band is compared sequentially with the spectral information of different types of materials in the material characteristic spectral library to determine the type of the material to be tested based on the comparison results.

7. The adaptive analysis method for multi-material composition detection of claim 6, wherein, The step of sequentially comparing the spectral correlation between the spectral information within the preferred wavelength band and the spectral information of different types of materials in the material characteristic spectral library, and determining the type of the material to be tested based on the comparison results, includes: The spectral correlation coefficient between the spectral information of the material to be tested in the preferred band and the spectral information of different types of materials in the feature spectral library is determined sequentially to be higher than a second threshold. If so, the type of the material to be tested is determined to be the corresponding material type in the matching feature spectral library.

8. An adaptive analysis system for multi-material composition detection, characterized by, include: The distance measuring module is used to measure the distance between the material to be tested and the spectrometer; A motion control module is used to adjust the focal length of the spectrometer based on the results of the ranging module; The laser module uses laser pulses to generate a plasma spectrum on the surface of the material to be tested; A spectral acquisition module is used to acquire the spectral information of the material to be tested; The model selection module is used to determine the type of the test material based on its spectral information and a material characteristic spectral library. The material characteristic spectral library includes spectral information of different types of material samples within a preferred wavelength band. The preferred wavelength band refers to a spectral band where the spectral correlation coefficients between different types of material samples are all below a first threshold and include all characteristic elements of the different types of material samples. The module also matches a corresponding material analysis model in a material analysis model library based on the type of the test material. This material analysis model library includes material analysis models corresponding to different types of material samples. The quantitative analysis module is used to detect the composition of the test material according to the corresponding quantitative analysis model.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the adaptive analysis method for multi-material component detection as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the adaptive analysis method for multi-material composition detection as described in any one of claims 1 to 7.

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