Material chemical component detection method and system based on spectrum technology

Abstract

The invention relates to the technical field of component detection, in particular to a material chemical component detection method and system based on the spectrum technology, and the method comprises the steps: obtaining a preset number of initial spectrum images of a to-be-detected material through a detector, screening out a matched standard scattering peak corresponding to each target scattering peak from scattering peaks of all standard components obtained in advance; determining a drift possible factor corresponding to each target scattering peak; determining a spectrum overlapping cause characterization coefficient; determining an equipment environment influence factor and a target compensation coefficient corresponding to each initial spectral image; and correcting the initial spectral image according to the target compensation coefficient, and detecting the chemical components of the material based on the corrected initial spectral image. According to the method, the correction of the initial spectral image is realized by analyzing the spectral overlapping condition of the initial spectral image, and the spectral overlapping degree in the spectral image is corrected to a certain extent, so that the detection accuracy of the chemical components of the material is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of component detection, in particular to a material chemical component detection method and system based on spectral technology. BACKGROUND

[0002] With the rapid development of modern industry, the types and application scenarios of materials are increasingly complex, and the performance of different materials in structural performance, functional characteristics and safety stability highly depends on the accurate control of their chemical components. For example, in the fields of metallurgy, chemical industry, medicine, new energy and environmental monitoring, the chemical composition of materials often directly determines the product reliability, and therefore, the rapid, accurate and non-destructive detection of material chemical components has become a key technical requirement in scientific research and industrial development.

[0003] At present, when performing component detection, the method commonly used is to obtain a spectrum image through a detector and perform component detection based on the obtained spectrum image. The basic principle is to identify the chemical composition of the material through the characteristic absorption peak or scattering peak corresponding to the molecular vibration mode. However, the molecular chain length of high polymer materials is long and the structure is complex, and they usually contain multiple functional groups, and the coupling effect between their vibration energy levels is significant, which may result in a large number of overlapping peaks in the spectrum image collected by the detector, making it difficult to accurately identify specific functional groups, and thus leading to poor accuracy of material chemical component detection. SUMMARY

[0004] In order to solve the technical problem of poor accuracy of material chemical component detection, the present application provides a material chemical component detection method and system based on spectral technology.

[0005] In a first aspect, the present application provides a material chemical component detection method based on spectral technology, which comprises: obtaining a preset number of initial spectrum images of the material to be detected through a detector, and selecting a matching standard scattering peak corresponding to each target scattering peak from the scattering peaks of all standard components, wherein the target scattering peak is a scattering peak in the initial spectrum image; determining a drift possible factor corresponding to each target scattering peak according to the wave number difference between each target scattering peak and its corresponding matching standard scattering peak, and the wavelength distribution of each target scattering peak; determining a spectrum overlap cause representation coefficient according to the difference between the drift possible factors corresponding to the target scattering peaks in all initial spectrum images, and the interval between the adjacent scattering peaks in the initial spectrum image; determining a device environment influence factor corresponding to each initial spectrum image according to the interval between the adjacent scattering peaks in each initial spectrum image, and the intensity and wave number thereof; According to the spectral overlap cause characterization coefficient, the device environment influence factor corresponding to each initial spectral image, and the intensity of the target scattering peak and the matching standard scattering peak corresponding to the target scattering peak, a target compensation coefficient corresponding to each initial spectral image is determined; According to the target compensation coefficient, the initial spectral image is corrected, and material chemical composition detection is performed based on the corrected initial spectral image.

[0006] In combination with the first aspect, in a possible implementation, the determining of the drift possibility factor corresponding to each target scattering peak according to the wave number difference between each target scattering peak and the matching standard scattering peak corresponding to the target scattering peak, and the wavelength distribution of each target scattering peak comprises: The difference between the wave number corresponding to the maximum intensity point of each target scattering peak and the wave number corresponding to the maximum intensity point of the matching standard scattering peak corresponding to the target scattering peak is determined as the target wave number difference corresponding to each target scattering peak; The intersection of the half peak of each target scattering peak and the horizontal line is determined as the half peak reference point corresponding to each target scattering peak, and two half peak reference points corresponding to each target scattering peak are obtained; The difference between the wavelength corresponding to the maximum intensity point of each target scattering peak and the wavelength corresponding to each half peak reference point corresponding to the target scattering peak is determined as the half peak wavelength interval between each target scattering peak and each half peak reference point corresponding to the target scattering peak, and two half peak wavelength intervals corresponding to each target scattering peak are obtained; The drift possibility factor corresponding to each target scattering peak is determined according to the difference between the two half peak wavelength intervals corresponding to each target scattering peak and the target wave number difference corresponding to each target scattering peak.

[0007] In combination with the first aspect, in a possible implementation, the determining of the spectral overlap cause characterization coefficient according to the difference between the drift possibility factors corresponding to the target scattering peaks in all initial spectral images and the interval between the scattering peaks adjacent in position in the initial spectral image comprises: If there is one scattering peak adjacent in position to the target scattering peak in the initial spectral image to which the target scattering peak belongs, the wavelength interval between the target scattering peak and the scattering peak adjacent in position to the target scattering peak is determined as the wavelength representative interval corresponding to the target scattering peak; If there are two scattering peaks adjacent in position to the target scattering peak in the initial spectral image to which the target scattering peak belongs, the mean value of the wavelength intervals between the target scattering peak and the two scattering peaks adjacent in position to the target scattering peak is determined as the wavelength representative interval corresponding to the target scattering peak; The target scattering peaks representing the same composition are screened out from all the initial spectral images to form a scattering peak representative sequence; According to differences between the drift possible factors corresponding to adjacent target scattering peaks in the scattering peak representative sequence and differences between the wavelength representative intervals corresponding to adjacent target scattering peaks in the scattering peak representative sequence, the spectral overlap cause representation coefficient is determined.

[0008] With reference to the first aspect above, in a possible implementation manner, the device environment influence factor corresponding to each initial spectral image is determined according to the interval between adjacent scattering peaks in each initial spectral image, and intensity conditions and wave number conditions corresponding to the interval. The spectral line resolution interference degree corresponding to each initial spectral image is determined according to a mean value of wavelength intervals between all adjacent scattering peaks in each initial spectral image, a theoretical resolution of a detector when the initial spectral image is acquired, a difference value between a maximum wave number and a minimum wave number corresponding to each initial spectral image, and a total number of pixels of the detector along a spectral direction when the initial spectral image is acquired. The background interference coefficient corresponding to each initial spectral image is determined according to the matching standard scattering peak corresponding to each scattering peak in each initial spectral image and intensity conditions corresponding to each initial spectral image. The product between the spectral line resolution interference degree and the background interference coefficient corresponding to each initial spectral image is determined as the device environment influence factor corresponding to each initial spectral image.

[0009] With reference to the first aspect above, in a possible implementation manner, the background interference coefficient corresponding to each initial spectral image is determined according to the matching standard scattering peak corresponding to each scattering peak in each initial spectral image and intensity conditions corresponding to each initial spectral image. The union of the matching standard scattering peaks corresponding to all scattering peaks in each initial spectral image is determined as the real component peak set corresponding to each initial spectral image. The wavelength range corresponding to the real component peak set corresponding to each initial spectral image is determined as the real component wavelength range corresponding to each initial spectral image. All wavelengths in each initial spectral image that do not belong to the real component wavelength range corresponding to the initial spectral image are constructed into the background interference wavelength range corresponding to each initial spectral image. The background interference coefficient corresponding to each initial spectral image is determined according to a mean value of intensities corresponding to all wavelengths in the background interference wavelength range corresponding to each initial spectral image and a maximum intensity of a target scattering peak in each initial spectral image.

[0010] With reference to the first aspect above, in a possible implementation manner, the background interference coefficient corresponding to each initial spectral image is determined according to a mean value of intensities corresponding to all wavelengths in the background interference wavelength range corresponding to each initial spectral image and a maximum intensity of a target scattering peak in each initial spectral image. The mean value of the intensity corresponding to all wavelengths in the background interference wavelength range corresponding to each initial spectral image is determined as the background interference representative intensity corresponding to each initial spectral image; The background interference coefficient corresponding to each initial spectral image is determined according to the ratio between the background interference representative intensity corresponding to each initial spectral image and the maximum intensity of the target scattering peak therein.

[0011] In combination with the first aspect, in a possible implementation manner, the target compensation coefficient corresponding to each initial spectral image is determined according to the spectral overlap cause representation coefficient, the device environment influence factor corresponding to each initial spectral image, and the intensity of the target scattering peak and the matching standard scattering peak corresponding thereto, and the target compensation coefficient corresponding to each initial spectral image is determined according to the spectral overlap cause representation coefficient, the device environment influence factor corresponding to each initial spectral image, and the intensity of the target scattering peak and the matching standard scattering peak corresponding thereto. The real spectral overlap intensity factor corresponding to each initial spectral image is determined according to the spectral overlap cause representation coefficient and the device environment influence factor corresponding to each initial spectral image. The target compensation coefficient corresponding to each initial spectral image is determined according to the real spectral overlap intensity factor corresponding to each initial spectral image, and the ratio between the maximum intensity of the target scattering peak in each initial spectral image and the maximum intensity of the matching standard scattering peak corresponding thereto.

[0012] In combination with the first aspect, in a possible implementation manner, the real spectral overlap intensity factor corresponding to each initial spectral image is determined according to the spectral overlap cause representation coefficient and the device environment influence factor corresponding to each initial spectral image, and the real spectral overlap intensity factor corresponding to each initial spectral image is determined according to the spectral overlap cause representation coefficient and the device environment influence factor corresponding to each initial spectral image. The difference between the constant 1 and the device environment influence factor corresponding to each initial spectral image is determined as the interference suppression coefficient corresponding to each initial spectral image. The product between the spectral overlap cause representation coefficient and the interference suppression coefficient corresponding to each initial spectral image is determined as the real spectral overlap intensity factor corresponding to each initial spectral image.

[0013] In combination with the first aspect, in a possible implementation manner, the initial spectral image is corrected according to the target compensation coefficient, and the initial spectral image is corrected according to the target compensation coefficient corresponding to each initial spectral image. The maximum intensity of each target scattering peak in each initial spectral image is corrected according to the target compensation coefficient corresponding to each initial spectral image, to obtain an intensity correction value corresponding to each target scattering peak in each initial spectral image. The wave number corresponding to the maximum intensity point of each target scattering peak in each initial spectral image is corrected according to the target compensation coefficient corresponding to each initial spectral image, to obtain a wave number correction value corresponding to each target scattering peak in each initial spectral image. The maximum intensity of each target scattering peak in each initial spectral image is updated to the corresponding intensity correction value, and the wave number corresponding to the maximum intensity point of each target scattering peak in each initial spectral image is updated to the corresponding wave number correction value, to obtain a corrected initial spectral image.

[0014] In a second aspect, the present application provides a material chemical composition detection system based on spectral technology, which comprises: The acquisition and screening module is configured to acquire a preset number of initial spectral images of the material to be detected through the detector, and screen out the matching standard scattering peak corresponding to each target scattering peak from the scattering peaks of all standard components. The drift possibility factor determination module is configured to determine the drift possibility factor corresponding to each target scattering peak according to the wave number difference between each target scattering peak and the matching standard scattering peak corresponding thereto, and the wavelength distribution of each target scattering peak. The spectral overlap cause characterization coefficient determination module is configured to determine the spectral overlap cause characterization coefficient according to the difference between the drift possibility factors corresponding to the target scattering peaks in all initial spectral images, and the interval between the adjacent scattering peaks in the initial spectral image. The device environment influence factor determination module is configured to determine the device environment influence factor corresponding to each initial spectral image according to the interval between the adjacent scattering peaks in each initial spectral image, and the intensity and wave number corresponding thereto. The target compensation coefficient determination module is configured to determine the target compensation coefficient corresponding to each initial spectral image according to the spectral overlap cause characterization coefficient, the device environment influence factor corresponding to each initial spectral image, and the intensity of the target scattering peak and the matching standard scattering peak corresponding thereto. The correction and composition detection module is configured to correct the initial spectral image according to the target compensation coefficient, and detect the chemical composition of the material based on the corrected initial spectral image.

[0015] In a third aspect, a server is provided, comprising a memory and a processor. The memory is configured to store executable program code, and the processor is configured to call and run the executable program code from the memory, so that the device executes the method in the first aspect or any possible implementation manner of the first aspect.

[0016] In a fourth aspect, a computer program product is provided, which comprises computer program code. When the computer program code runs on a computer, it makes the computer execute the method in the first aspect or any possible implementation manner of the first aspect.

[0017] In a fifth aspect, a computer-readable storage medium is provided, which stores computer program codes, when the computer program codes are run on a computer, the computer is caused to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0018] The present application has the following beneficial effects: The material chemical component detection method based on the spectrum technology has the following beneficial effects: BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0020] Figure 1 A flow chart of a material chemical component detection method based on the spectrum technology according to the present application; Figure 2 A composition structure schematic diagram of a material chemical component detection system based on the spectrum technology according to the present application; Figure 3 A structure schematic diagram of a computer device according to the present application. DETAILED DESCRIPTION

[0021] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined purposes, the specific implementation manners, structures, features and effects of the technical solutions according to the present application are described in detail as follows by combining the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0023] The material chemical composition detection method and system based on spectral technology are often applied to the quality control of medical materials and food packaging materials. For example, in the production process of drug sustained-release carriers, medical polymer colloids and other materials, it is often necessary to accurately detect the main chain structure and trace impurity components therein to ensure the safety and stability of the materials, or in food packaging materials, it is often necessary to quickly identify and quantitatively analyze the plasticizers, stabilizers and potential harmful organic matters therein.

[0024] Reference Figure 1 , shows the flow of some embodiments of the material chemical composition detection method based on spectral technology of the application. The material chemical composition detection method based on spectral technology comprises the following steps: Step S1, acquiring a preset number of initial spectral images of the material to be detected by the detector, and screening out the matching standard scattering peak corresponding to each target scattering peak from the scattering peaks of all standard components obtained in advance.

[0025] Wherein, the material to be detected can be a material to be detected for chemical composition detection, which can be a polymer or an organic material. The preset number can be a preset number, which can be 6. The initial spectral image can be a spectral image of the material to be detected collected by the detector, such as a Raman spectral image. The standard component can be a component of a pure known component type. The scattering peak of the standard component can be the scattering peak reflected in the spectral image of the standard component. The target scattering peak can be a scattering peak in the initial spectral image. The matching standard scattering peak corresponding to the target scattering peak can be a scattering peak similar to the target scattering peak. The scattering peak is also called characteristic peak.

[0026] As an example, the present step can comprise the following steps: First, by the detector, collect a Raman spectral image of the material to be detected every preset time interval within a preset time period, and record the Raman spectral image collected at this time as the initial spectral image, and obtain a preset number of initial spectral images.

[0027] Specifically, in the process of acquiring the initial spectral image, first, the high polymer or organic material to be detected is prepared into a form suitable for spectral detection, such as a thin film, a powder or a solution, and the sample is subjected to necessary pretreatment (such as drying, grinding, tabletting or dissolution) to reduce spectral errors caused by surface roughness and uneven thickness; then, a suitable spectral detection method (such as Raman spectroscopy) is selected, and then a light source of a specific wavelength is used to excite or irradiate the sample to cause scattering of the light; then, the transmitted light, reflected light or scattered light is spectrally dispersed by a spectral acquisition module, and then a detector (such as a photodiode array, a CCD or an InGaAs detector) receives the light signal and converts it into an electrical signal; finally, the original spectral curve is preliminarily acquired, such as the scattering spectral data in the process of Raman spectroscopy detection, and the spectral image obtained at this time is the initial spectral image.

[0028] Second step, acquire the scattering peaks of the standard components.

[0029] It should be noted that in spectral analysis, the Raman characteristic peaks of different functional groups and chemical bonds often have public databases and standard literature, and before detection, a standard sample of pure and known components can be measured as a reference spectrum, that is, the peak positions of different functional groups are obtained, and the scattering peaks in the reference spectrum are recorded as the scattering peaks of the standard components.

[0030] Third step, screen out the matching standard scattering peak corresponding to each target scattering peak from the scattering peaks of all standard components.

[0031] The target distance index between the target scattering peak and the matching standard scattering peak corresponding thereto can be less than a preset distance threshold, and the target distance index between the target scattering peak and the matching standard scattering peak corresponding thereto can be equal to the minimum value of the target distance index between the target scattering peak and the scattering peaks of all standard components. The preset distance threshold can be a threshold set in advance, which can be 0.3. The target distance index between the target scattering peak and the scattering peak of the standard component can be obtained by normalizing the Euclidean distance between the coordinates of the maximum intensity point of the target scattering peak and the coordinates of the maximum intensity point of the scattering peak of the standard component to obtain the target distance index therebetween. The maximum intensity point of the scattering peak can be a position point corresponding to the maximum intensity in the scattering peak in the spectral image. The coordinates corresponding to the maximum intensity point can be two-dimensional coordinates with wavelength as abscissa and intensity as ordinate.

[0032] It should be noted that in the actual monitoring process, after the Raman scattering spectral data in one spectral detection process is acquired, for a peak position in the spectral data, the peak positions in the known reference spectrum are matched to find the most similar group of peak values (peak points), and the scattering peak of the standard component matching the scattering peak in the spectral data is obtained.

[0033] Step S2, according to the wave number difference between each target scattering peak and its corresponding matching standard scattering peak, and the wavelength distribution of each target scattering peak, determine the drift possible factor corresponding to each target scattering peak.

[0034] As an example, the present step can include the following steps: Firstly, the difference between the wave number corresponding to the maximum intensity point of each target scattering peak and the wave number corresponding to the maximum intensity point of the matching standard scattering peak corresponding to each target scattering peak is determined as the target wave number difference corresponding to each target scattering peak.

[0035] Wherein, the wave number corresponding to the maximum intensity point can be the characteristic wave number value at the position in the spectrum, which represents the intrinsic energy of the specific molecular vibration, lattice vibration or other meta-excitation causing the scattering.

[0036] For example, the formula for determining the target wave number difference corresponding to the target scattering peak can be: ; Wherein, is the target wave number difference corresponding to the i-th target scattering peak. i is the serial number of the target scattering peak. is the absolute value function. is the wave number corresponding to the maximum intensity point of the i-th target scattering peak. is the wave number corresponding to the maximum intensity point of the matching standard scattering peak corresponding to the i-th target scattering peak.

[0037] Secondly, the intersection of the half peak of each target scattering peak and the horizontal line is determined as the half peak reference point corresponding to each target scattering peak, and two half peak reference points corresponding to each target scattering peak are obtained.

[0038] Wherein, the half peak is also called the half-height peak.

[0039] Thirdly, the difference between the wavelength corresponding to the maximum intensity point of each target scattering peak and the wavelength corresponding to each half peak reference point corresponding to it is determined as the half peak wavelength interval between each target scattering peak and each half peak reference point corresponding to it, and two half peak wavelength intervals corresponding to each target scattering peak are obtained.

[0040] For example, the formula for determining the half peak wavelength interval between the target scattering peak and its corresponding two half peak reference points can be: ; ; Wherein, is the half peak wavelength interval between the i-th target scattering peak and the first half peak reference point corresponding to it. is the half-peak wavelength interval between the ith target scattering peak and the first half-peak reference point corresponding thereto. i is the serial number of the target scattering peak. is an absolute value function. is the wavelength corresponding to the first half-peak reference point corresponding to the ith target scattering peak. is the wavelength corresponding to the maximum intensity point of the ith target scattering peak. is the wavelength corresponding to the second half-peak reference point corresponding to the ith target scattering peak.

[0041] Fourthly, the drift possibility factor corresponding to each target scattering peak is determined according to the difference between the two half-peak wavelength intervals corresponding to each target scattering peak and the target wavenumber difference corresponding to each target scattering peak.

[0042] For example, the formula corresponding to the determination of the drift possibility factor corresponding to the target scattering peak can be: ; wherein, is the drift possibility factor corresponding to the ith target scattering peak. i is the serial number of the target scattering peak. is a normalization function. is the target wavenumber difference corresponding to the ith target scattering peak. is an absolute value function. is the half-peak wavelength interval between the ith target scattering peak and the first half-peak reference point corresponding thereto. is the half-peak wavelength interval between the ith target scattering peak and the second half-peak reference point corresponding thereto.

[0043] It should be noted that when is smaller, it often indicates that the center position of the scattering peak of the spectrum (the wavenumber corresponding to the maximum intensity point) is basically fixed, often indicating that the signal is not significantly affected by stress, temperature or molecular interaction, and often indicating that the detection condition is relatively ideal. When is smaller, it often indicates that the single characteristic peak of the spectrum is closer to the symmetric distribution of the Gaussian type or the Lorentz type, i.e. the left and right half-peak widths are similar, and it often indicates that it is more likely to be in an undisturbed or ideal situation. In actual situations, if only the peak position shift is considered, noise or instrument error may be misjudged as drift; if only the symmetry change is considered, it is often difficult to determine whether it is a real drift or not, which may only be peak broadening; the product of the two can reflect the actual shift degree of the scattering peak to some extent, and can effectively avoid the misjudgment caused by noise or broadening of a single characteristic. Therefore, can represent the drift of the ith target scattering peak, and the larger the value is, the greater the probability of drift of the ith target scattering peak is.

[0044] Step S3, determining the spectral overlap cause representation coefficient according to the difference between the drift possible factors corresponding to the target scattering peak in all initial spectral images, and the interval between the scattering peaks adjacent to the target scattering peak in the initial spectral images.

[0045] It should be noted that this step mainly analyzes the composition of the spectral signal and the cause of the peak overlap, which can to some extent determine the characteristic response law of different chemical groups in a complex analysis system, thereby providing a basis for subsequent interference recognition and signal decoupling, and to some extent, the problem of inaccurate component recognition caused by signal misjudgment in the subsequent detection process can be avoided.

[0046] As an example, this step can include the following steps: First, if there is one scattering peak adjacent to the target scattering peak in the initial spectral image to which the target scattering peak belongs, the wavelength interval between the target scattering peak and the scattering peak adjacent to it is determined as the wavelength representative interval corresponding to the target scattering peak.

[0047] For example, the wavelength interval between two scattering peaks can be obtained by taking the absolute value of the difference between the wavelengths corresponding to the maximum intensity points of the two scattering peaks as the wavelength interval between the two scattering peaks.

[0048] It should be noted that when the target scattering peak is located at the endpoint of the initial spectral image, there is usually only one scattering peak adjacent to the target scattering peak in the initial spectral image.

[0049] Second, if there are two scattering peaks adjacent to the target scattering peak in the initial spectral image to which the target scattering peak belongs, the mean value of the wavelength intervals between the target scattering peak and the two scattering peaks adjacent to it is determined as the wavelength representative interval corresponding to the target scattering peak.

[0050] It should be noted that when the target scattering peak is located in the middle of the initial spectral image, there are usually two scattering peaks adjacent to the target scattering peak in the initial spectral image.

[0051] Third, selecting target scattering peaks representing the same component from all initial spectral images to form a scattering peak representative sequence.

[0052] The scattering peak representative sequence can be a time sequence.

[0053] It should be noted that the same material component is represented by different initial spectral images, so the scattering peaks in different initial spectral images can be one-to-one corresponding.

[0054] For example, any one target scattering peak can be determined as a marker scattering peak, the scattering peak with the minimum distance between the maximum intensity point of the marker scattering peak and the maximum intensity point of each initial spectral image is determined as a representative scattering peak corresponding to the marker scattering peak, and all representative scattering peaks corresponding to the marker scattering peak are used to form a scattering peak representative sequence.

[0055] In the fourth step, a spectral overlap cause representation coefficient is determined according to the difference between the drift possibility factors corresponding to adjacent target scattering peaks in all scattering peak representative sequences and the difference between the wavelength representative intervals corresponding to the adjacent target scattering peaks in all scattering peak representative sequences.

[0056] For example, the formula corresponding to the spectral overlap cause representation coefficient can be as follows: ; Wherein, h is the spectral overlap cause representation coefficient. N is the number of scattering peak representative sequences. b is the serial number of the scattering peak representative sequence. m is the number of target scattering peaks in the scattering peak representative sequence. j is the serial number of the target scattering peak in the scattering peak representative sequence. is an absolute value function. is the drift possibility factor corresponding to the jth target scattering peak in the bth scattering peak representative sequence. is the drift possibility factor corresponding to the j+1th target scattering peak in the bth scattering peak representative sequence. is the wavelength representative interval corresponding to the jth target scattering peak in the bth scattering peak representative sequence. is the wavelength representative interval corresponding to the j+1th target scattering peak in the bth scattering peak representative sequence.

[0057] It should be noted that, can represent the change of the drift probability of the same peak in two adjacent detections, and the greater the value, the more likely the single peak drift probability changes significantly in continuous measurement, indicating that the peak is easily affected by external interference or there is peak overlap. can represent the change of the interval between the scattering peak and the adjacent peak, and when the adjacent peaks are close or there is energy coupling, even if the central peak drifts very small, it will often cause significant changes in the peak interval. Therefore, h can be used as a quantitative representation of the whole spectrum overlap cause, and the greater the value, the more likely the scattering peak drifts and the more likely the adjacent peaks interact, that is, it often significantly contributes to the overall spectral overlap.

[0058] In step S4, the device environment influence factor corresponding to each initial spectral image is determined according to the interval between the adjacent scattering peaks in each initial spectral image and the intensity and wave number conditions corresponding thereto.

[0059] It should be noted that in the actual detection process, the spectral overlap and signal interference often not only come from the material itself, but also are affected by the detection equipment parameters. Analyzing the influence of the parameters on the spectral resolution and background fluorescence helps to distinguish whether the signal complexity comes from the material characteristics or the instrument limitation. This step mainly analyzes the influence of the detection equipment parameters on the spectral detection results in the spectral detection process of the chemical composition of the material, and then quantifies the overlapping influence caused by the instrument limitation or background interference in the spectral overlap cause characterization coefficient.

[0060] As an example, this step can include the following steps: In the first step, the spectral resolution interference degree corresponding to each initial spectral image is determined according to the mean value of the wavelength interval between all adjacent scattering peaks in each initial spectral image, the theoretical resolution of the detector when the initial spectral image is acquired, the difference between the maximum wave number and the minimum wave number corresponding to each initial spectral image, and the total number of pixels of the detector along the spectral direction when the initial spectral image is acquired.

[0061] The wave number corresponding to the maximum intensity point can be a characteristic wave number value at the position in the spectrum. The theoretical resolution of the detector can be represented by the grating resolution index provided by the manufacturer. The grating resolution can represent the minimum resolvable interval, which refers to the minimum interval of two adjacent peaks that can be clearly separated by the grating instrument in theory. The maximum wave number corresponding to the initial spectral image can be the maximum value among the wave numbers corresponding to different positions in the initial spectral image. The minimum wave number corresponding to the initial spectral image can be the minimum value among the wave numbers corresponding to different positions in the initial spectral image.

[0062] For example, the formula for determining the spectral resolution interference degree corresponding to the initial spectral image can be: ; wherein, is the spectral resolution interference degree corresponding to the xth initial spectral image. x is the serial number of the initial spectral image. is a normalization function. is the theoretical resolution of the detector when the xth initial spectral image is acquired. is the mean value of the wavelength interval between all adjacent scattering peaks in the xth initial spectral image. is the difference between the maximum wave number and the minimum wave number corresponding to the xth initial spectral image. is the total number of pixels of the detector along the spectral direction when the xth initial spectral image is acquired.

[0063] It should be noted that, reflects the matching degree between the theoretical capability of the spectral instrument and the actual scattering peak interval. The greater the value, the more likely the instrument cannot completely separate the peaks, and the more likely there is potential interference. The wave number width covered by each pixel can be represented, and the smaller the value is, the more likely the sampling is more fine; the larger the value is, the more likely the peak shape discretization can be more serious, and false overlaps can be generated, because the discretization often leads to peak shape blurring or false overlaps. Therefore, when The larger the value is, the more likely the instrument parameters and sampling conditions can limit the spectral line resolution, which can lead to false peak overlaps or peak shape distortion.

[0064] Secondly, according to the matching standard scattering peaks corresponding to all scattering peaks in each initial spectral image and the intensity corresponding to each initial spectral image, the background interference coefficient corresponding to each initial spectral image is determined.

[0065] It should be noted that if the background fluorescence is strong, the weak peak can still be covered, and then the drift probability or the inter-peak coupling index is affected.

[0066] For example, determining the background interference coefficient corresponding to each initial spectral image can include the following sub-steps: Firstly, the union of the matching standard scattering peaks corresponding to all scattering peaks in each initial spectral image is determined as the real component peak set corresponding to each initial spectral image.

[0067] Secondly, the wavelength range corresponding to the real component peak set corresponding to each initial spectral image is determined as the real component wavelength range corresponding to each initial spectral image.

[0068] The wavelength range corresponding to the real component peak set can include all wavelengths within the real component peak set.

[0069] Thirdly, all wavelengths in each initial spectral image that do not belong to the real component wavelength range corresponding to the initial spectral image form the background interference wavelength range corresponding to each initial spectral image.

[0070] Fourthly, according to the mean value of the intensity corresponding to all wavelengths in the background interference wavelength range corresponding to each initial spectral image and the maximum intensity of the target scattering peak in each initial spectral image, the background interference coefficient corresponding to each initial spectral image is determined.

[0071] For example, determining the background interference coefficient corresponding to each initial spectral image can include the following steps: Firstly, the mean value of the intensity corresponding to all wavelengths in the background interference wavelength range corresponding to each initial spectral image is determined as the background interference representative intensity corresponding to each initial spectral image.

[0072] It should be noted that the background interference wavelength range is often composed of background fluorescence and noise.

[0073] Then, according to the ratio between the background interference representative intensity corresponding to each initial spectral image and the maximum intensity of the target scattering peak in the initial spectral image, the background interference coefficient corresponding to each initial spectral image is determined.

[0074] For example, the formula corresponding to the determination of the background interference coefficient corresponding to the initial spectral image can be: ; wherein, is the background interference coefficient corresponding to the xth initial spectral image. x is the serial number of the initial spectral image. is the normalization function. is the number of target scattering peaks in the xth initial spectral image. t is the serial number of the target scattering peak in the xth initial spectral image. is the background interference representative intensity corresponding to the xth initial spectral image. is the maximum intensity of the tth target scattering peak in the xth initial spectral image.

[0075] It should be noted that when is larger, it often means that the interference intensity of background fluorescence, dark noise and other non-scattering signals is larger, it often means that the background section intensity is higher, it often means that the background fluorescence is stronger, it often means that the weak peak is more likely to be covered, and it often means that the background interference is larger.

[0076] Thirdly, the product between the spectral line resolution interference degree corresponding to each initial spectral image and the background interference coefficient is determined as the device environment influence factor corresponding to each initial spectral image.

[0077] It should be noted that when the device environment influence factor corresponding to the initial spectral image is larger, it often means that in the actual spectral detection process, the evaluation of spectral overlap phenomenon is more likely to be affected due to the limitation of the device and the monitoring condition, that is, the false overlap or drift phenomenon is more likely to occur.

[0078] Step S5, according to the spectral overlap cause representation coefficient and the device environment influence factor corresponding to each initial spectral image, and the intensity of the target scattering peak and the matching standard scattering peak corresponding thereto, the target compensation coefficient corresponding to each initial spectral image is determined.

[0079] As an example, this step can include the following steps: Firstly, according to the spectral overlap cause representation coefficient and the device environment influence factor corresponding to each initial spectral image, the true spectral overlap intensity factor corresponding to each initial spectral image is determined, which can include the following sub-steps: First sub-step, the difference between the constant 1 and the device environment influence factor corresponding to each initial spectral image is determined as the interference suppression coefficient corresponding to each initial spectral image.

[0080] It should be noted that the smaller the interference suppression coefficient corresponding to the initial spectral image is, the more likely it is that there is greater interference of device parameters and environment in the detection process, which masks the overlapping performance of the material itself, at which time the spectral overlap cause representation coefficient needs to be reduced, thereby resulting in the real spectral overlap intensity factor of the real spectral overlap intensity caused by the material composition in the spectral detection process.

[0081] Second sub-step, the product between the above spectral overlap cause representation coefficient and the interference suppression coefficient corresponding to each initial spectral image is determined as the real spectral overlap intensity factor corresponding to each initial spectral image.

[0082] Among them, the real spectral overlap intensity factor can represent the real spectral overlap intensity caused by the material composition in the spectral detection process.

[0083] It should be noted that the larger the spectral overlap cause representation coefficient is, the more obvious the overlapping phenomenon in the spectral detection process is, but the overlapping phenomenon often contains natural overlapping caused by the real chemical composition of the material and false overlapping caused by spectral device parameters and background noise. In order to better reflect the real material information, it is often necessary to use the interference suppression coefficient to strip the false phenomenon. When the device and background interference are very slight, the interference suppression coefficient is more likely to be close to 1, at which time the original representation coefficient often needs little adjustment; when the device and background interference are serious, the interference suppression coefficient is more likely to be close to 0, at which time it is often indicated that most of the overlapping in the detection result is false, at which time the spectral overlap cause representation coefficient should be greatly reduced. The real spectral overlap intensity factor represents the real overlapping degree related to the real chemical composition of the material to some extent, and the larger the value is, the higher the overlapping degree of the real material composition is.

[0084] Second step, according to the real spectral overlap intensity factor corresponding to each initial spectral image, and the ratio between the maximum intensity of the target scattering peak in each initial spectral image and the maximum intensity of the matching standard scattering peak corresponding thereto, the target compensation coefficient corresponding to each initial spectral image is determined.

[0085] It should be noted that in the monitoring process of the chemical composition of the material, in addition to considering the spectral overlap, the substrate effect often needs to be considered to realize the detection of the material composition. The substrate material often absorbs the excitation light or scatters the Raman signal, so that the detected spectral peak intensity is lower than the ideal case, thereby reflecting the inhibition of the substrate to the signal.

[0086] For example, the formula for determining the target compensation coefficient corresponding to the initial spectral image can be: ; Among them, is the target compensation coefficient corresponding to the xth initial spectral image. x is the serial number of the initial spectral image. is a normalization function. is the true spectral overlap intensity factor corresponding to the xth initial spectral image. is the number of target scattering peaks in the xth initial spectral image. t is the serial number of the target scattering peak in the xth initial spectral image. is the maximum intensity of the tth target scattering peak in the xth initial spectral image. is the maximum intensity of the matching standard scattering peak corresponding to the tth target scattering peak in the xth initial spectral image.

[0087] It should be noted that, can represent the intensity generated by the same material under ideal conditions (no substrate or no interfering substrate), which is the ideal maximum value; in actual samples, due to substrate absorption, scattering or thickness effect, part of the excitation light or scattered light is often weakened, and the peak intensity detected is usually lower than its corresponding When is closer to 1, it often indicates that the substrate has less effect on the signal, and the value closer to 0 often indicates that the attenuation is more serious. to a certain extent, it reflects the systematic interference of the substrate material on the spectral signal in the spectral detection process, and the greater the value, the more serious the substrate interference. reflects the scattering peak overlap caused by the material composition itself, excluding instrument noise and background fluorescence interference, and the greater the value, the more serious the overlap caused by the material composition. Therefore, when is greater, it often means that the overlap peak intensity and substrate interference may be greater in the actual monitoring process, often requiring stronger spectral signal correction; used for weighting correction or peak separation of the actual measured spectral signal, to reduce false overlap or substrate-induced misjudgment, thereby improving the accuracy of chemical component identification.

[0088] Step S6, according to the target compensation coefficient, the initial spectral image is corrected, and the material chemical component detection is carried out based on the corrected initial spectral image.

[0089] As an example, this step can include the following steps: First, according to the target compensation coefficient corresponding to each initial spectral image, the maximum intensity of each target scattering peak in each initial spectral image is corrected to obtain the intensity correction value corresponding to each target scattering peak in each initial spectral image.

[0090] For example, the formula for determining the intensity correction value corresponding to the target scattering peak can be: ; wherein, is the intensity correction value corresponding to the tth target scattering peak in the xth initial spectral image. x is the serial number of the initial spectral image. t is the serial number of the target scattering peak in the xth initial spectral image. is the target compensation coefficient corresponding to the xth initial spectral image. is the maximum intensity of the tth target scattering peak in the xth initial spectral image.

[0091] It should be noted that, to a certain extent, can represent the true peak intensity close to the material itself.

[0092] Secondly, according to the target compensation coefficient corresponding to each initial spectral image, the wave number corresponding to the maximum intensity point of each target scattering peak in each initial spectral image is corrected to obtain the wave number correction value corresponding to each target scattering peak in each initial spectral image.

[0093] For example, the formula for determining the wave number correction value corresponding to the target scattering peak can be: ; Wherein, is the wave number correction value corresponding to the tth target scattering peak in the xth initial spectral image. x is the serial number of the initial spectral image. t is the serial number of the target scattering peak in the xth initial spectral image. is the target compensation coefficient corresponding to the xth initial spectral image. is the wave number corresponding to the maximum intensity point of the tth target scattering peak in the xth initial spectral image.

[0094] It should be noted that, to a certain extent, can represent the true wave number close to the material itself.

[0095] Thirdly, the maximum intensity of each target scattering peak in each initial spectral image is updated to the intensity correction value corresponding thereto, and the wave number corresponding to the maximum intensity point of each target scattering peak in each initial spectral image is updated to the wave number correction value corresponding thereto, to obtain the corrected initial spectral image.

[0096] It should be noted that after the intensity of the scattering peak is corrected, the wave number points within the peak width (half peak width) of the scattering peak are also corrected simultaneously according to the correction ratio of the scattering peak value point, so as to maintain the relative distribution of the peak shape curve, and at the same time, the intensity is enlarged as a whole to avoid peak shape distortion; finally, the scattering spectrum detection result of the material chemical composition after compensation can be obtained.

[0097] Fourthly, the material chemical composition is detected based on the corrected initial spectral image.

[0098] For example, the corrected initial spectral image can be used as the final spectral image for component detection, and the component detection can be performed by using the existing spectral technology.

[0099] It should be noted that the improvement point of the present application is mainly to correct the spectral image involved in the material chemical component detection, and to weaken the spectral overlap in the spectral image to a certain extent. Therefore, based on the corrected initial spectral image, the material chemical component detection is performed, and the accuracy of the material chemical component detection is improved to a certain extent.

[0100] Reference Figure 2 Based on the same inventive concept as the above method embodiment, the present application provides a material chemical component detection system based on spectral technology, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the above computer program is executed by the processor, the steps of a material chemical component detection method based on spectral technology are realized, which can specifically include: The acquisition and screening module 201 is configured to acquire a preset number of initial spectral images of the material to be detected by the detector, and screen out the matching standard scattering peak corresponding to each target scattering peak from the scattering peaks of all standard components. The drift possible factor determination module 202 is configured to determine the drift possible factor corresponding to each target scattering peak according to the wave number difference between each target scattering peak and its corresponding matching standard scattering peak, and the wavelength distribution of each target scattering peak. The spectral overlap cause representation coefficient determination module 203 is configured to determine the spectral overlap cause representation coefficient according to the difference between the drift possible factors corresponding to the target scattering peaks in all initial spectral images, and the interval between the adjacent scattering peaks in the initial spectral image. The device environment influence factor determination module 204 is configured to determine the device environment influence factor corresponding to each initial spectral image according to the interval between the adjacent scattering peaks in each initial spectral image, and the intensity and wave number thereof. The target compensation coefficient determination module 205 is configured to determine the target compensation coefficient corresponding to each initial spectral image according to the spectral overlap cause representation coefficient, the device environment influence factor corresponding to each initial spectral image, and the intensity of the target scattering peak and its corresponding matching standard scattering peak. The correction and component detection module 206 is configured to correct the initial spectral image according to the target compensation coefficient, and perform material chemical component detection based on the corrected initial spectral image.

[0101] Figure 3 is a structural schematic diagram of a computer device provided by an embodiment of the present application. For example, Figure 3As shown, the computer device 300 comprises a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302, wherein the processor 302 executes the computer program 303, so that the computer device can execute any one of the aforementioned material chemical composition detection methods based on the spectrum technology.

[0102] Based on the same inventive concept as the above method embodiments, the present application provides a server comprising a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes any one of the aforementioned material chemical composition detection methods based on the spectrum technology.

[0103] Based on the same inventive concept as the above method embodiments, the present application provides a computer program product comprising computer program code, which, when running on a computer, causes the computer to execute any one of the aforementioned material chemical composition detection methods based on the spectrum technology.

[0104] Based on the same inventive concept as the above method embodiments, the present application provides a computer readable storage medium storing computer program code, which, when running on a computer, causes the computer to execute any one of the aforementioned material chemical composition detection methods based on the spectrum technology.

[0105] In summary, by analyzing the preset number of initial spectrum images of the material to be detected, the present application quantifies a plurality of indicators related to the spectrum overlap condition, such as the drift possible factor, the spectrum overlap cause representation coefficient, and the device environment influence factor, thereby quantifying the target compensation coefficient corresponding to each initial spectrum image, and based on the target compensation coefficient, the initial spectrum image is corrected, which to some extent weakens the spectrum overlap condition in the spectrum image. Therefore, based on the corrected initial spectrum image, the material chemical composition is detected, which to some extent improves the accuracy of the material chemical composition detection.

[0106] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for detecting chemical composition of a material based on spectroscopy, characterized in that, The method comprises the following steps: obtaining a preset number of initial spectral images of the material to be detected by the detector, and selecting a matching standard scattering peak corresponding to each target scattering peak from the scattering peaks of all standard components, wherein the target scattering peak is a scattering peak in the initial spectral image; determining a drift possible factor corresponding to each target scattering peak according to the wave number difference between each target scattering peak and its corresponding matching standard scattering peak, and the wavelength distribution of each target scattering peak; determining a spectral overlap cause representation coefficient according to the difference between the drift possible factors corresponding to the target scattering peaks in all initial spectral images, and the interval between the scattering peaks adjacent in position in the initial spectral image; determining a device environment influence factor corresponding to each initial spectral image according to the interval between the adjacent scattering peaks in each initial spectral image, and the intensity and wave number thereof; determining a target compensation coefficient corresponding to each initial spectral image according to the spectral overlap cause representation coefficient, the device environment influence factor corresponding to each initial spectral image, and the intensity of the target scattering peak and its corresponding matching standard scattering peak; correcting the initial spectral image according to the target compensation coefficient, and performing material chemical component detection based on the corrected initial spectral image.

2. The method for detecting chemical composition of material based on spectrum technology according to claim 1, characterized in that, The method of determining the drift possible factor corresponding to each target scattering peak according to the wave number difference between each target scattering peak and its corresponding matching standard scattering peak, and the wavelength distribution of each target scattering peak comprises: determining a target wave number difference corresponding to each target scattering peak as the difference between the wave number corresponding to the maximum intensity point of each target scattering peak and the wave number corresponding to the maximum intensity point of its corresponding matching standard scattering peak; determining two half-peak reference points corresponding to each target scattering peak as the intersection points between the half-peak of each target scattering peak and the horizontal line; determining a half-peak wavelength interval between each target scattering peak and each half-peak reference point corresponding thereto as the difference between the wavelength corresponding to the maximum intensity point of each target scattering peak and the wavelength corresponding to each half-peak reference point corresponding thereto, to obtain two half-peak wavelength intervals corresponding to each target scattering peak; determining the drift possible factor corresponding to each target scattering peak according to the difference between the two half-peak wavelength intervals corresponding to each target scattering peak and the target wave number difference corresponding to each target scattering peak.

3. The method for detecting chemical composition of material based on spectrum technology according to claim 1, characterized in that, The method of determining the spectral overlap cause representation coefficient according to the difference between the drift possible factors corresponding to the target scattering peaks in all initial spectral images and the interval between the scattering peaks adjacent in position in the initial spectral image comprises: if there is one scattering peak adjacent in position to the target scattering peak in the initial spectral image to which the target scattering peak belongs, determining a wavelength representative interval corresponding to the target scattering peak as the wavelength interval between the target scattering peak and the scattering peak adjacent in position thereto; if there are two scattering peaks adjacent in position to the target scattering peak in the initial spectral image to which the target scattering peak belongs, determining a wavelength representative interval corresponding to the target scattering peak as the mean value of the wavelength intervals between the target scattering peak and the two scattering peaks adjacent in position thereto, respectively. Screening target scattering peaks with same components from all initial spectral images to form a scattering peak representative sequence; Determining a spectral overlap cause representation coefficient according to the difference between drift possible factors corresponding to adjacent target scattering peaks in all scattering peak representative sequences, and the difference between wavelength representatives corresponding to adjacent target scattering peaks in all scattering peak representative sequences.

4. The method for detecting chemical composition of material based on spectrum technology according to claim 1, characterized in that, The determining of the device environment influence factor corresponding to each initial spectral image according to the interval between adjacent scattering peaks in each initial spectral image, and the intensity condition and wave number condition thereof, comprises: Determining a spectral line resolution interference degree corresponding to each initial spectral image according to the mean value of the wavelength intervals between all adjacent scattering peaks in each initial spectral image, the theoretical resolution of the detector when the initial spectral image is acquired, the difference between the maximum wave number and the minimum wave number corresponding to each initial spectral image, and the total number of pixels of the detector along the spectral direction when the initial spectral image is acquired; Determining a background interference coefficient corresponding to each initial spectral image according to the matching standard scattering peaks corresponding to all scattering peaks in each initial spectral image, and the intensity condition corresponding to each initial spectral image; Determining the product between the spectral line resolution interference degree and the background interference coefficient corresponding to each initial spectral image as the device environment influence factor corresponding to each initial spectral image.

5. The method according to claim 4, wherein the method is characterized by, The determining of the background interference coefficient corresponding to each initial spectral image according to the matching standard scattering peaks corresponding to all scattering peaks in each initial spectral image, and the intensity condition corresponding to each initial spectral image, comprises: Determining the union of the matching standard scattering peaks corresponding to all scattering peaks in each initial spectral image as the real component peak set corresponding to each initial spectral image; Determining the wavelength range corresponding to the real component peak set corresponding to each initial spectral image as the real component wavelength range corresponding to each initial spectral image; Constructing all wavelengths in each initial spectral image that do not belong to the real component wavelength range corresponding to the initial spectral image as the background interference wavelength range corresponding to each initial spectral image; Determining the background interference coefficient corresponding to each initial spectral image according to the mean value of the intensities corresponding to all wavelengths in the background interference wavelength range corresponding to each initial spectral image, and the maximum intensity of the target scattering peak in each initial spectral image.

6. The method for detecting chemical composition of material based on spectrum technology according to claim 5, characterized in that, The determining of the background interference coefficient corresponding to each initial spectral image according to the mean value of the intensities corresponding to all wavelengths in the background interference wavelength range corresponding to each initial spectral image, and the maximum intensity of the target scattering peak in each initial spectral image, comprises: Determining the mean value of the intensities corresponding to all wavelengths in the background interference wavelength range corresponding to each initial spectral image as the background interference representative intensity corresponding to each initial spectral image; Determining the background interference coefficient corresponding to each initial spectral image according to the ratio between the background interference representative intensity corresponding to each initial spectral image and the maximum intensity of the target scattering peak therein.

7. The method of claim 1, wherein the method is a method of detecting chemical composition of a material based on spectroscopy. The target compensation coefficient corresponding to each initial spectral image is determined according to the spectral overlap cause characterization coefficient, the device environment influence factor corresponding to each initial spectral image, and the intensity of the target scattering peak and the intensity of the matching standard scattering peak corresponding to the target scattering peak. The real spectral overlap intensity factor corresponding to each initial spectral image is determined according to the spectral overlap cause characterization coefficient and the device environment influence factor corresponding to each initial spectral image. The target compensation coefficient corresponding to each initial spectral image is determined according to the real spectral overlap intensity factor corresponding to each initial spectral image, and the ratio between the maximum intensity of the target scattering peak in each initial spectral image and the maximum intensity of the matching standard scattering peak corresponding to the target scattering peak.

8. The method according to claim 7, wherein the method is characterized by, The real spectral overlap intensity factor corresponding to each initial spectral image is determined according to the spectral overlap cause characterization coefficient and the device environment influence factor corresponding to each initial spectral image, including: The difference between the constant 1 and the device environment influence factor corresponding to each initial spectral image is determined as the interference suppression coefficient corresponding to each initial spectral image. The product between the spectral overlap cause characterization coefficient and the interference suppression coefficient corresponding to each initial spectral image is determined as the real spectral overlap intensity factor corresponding to each initial spectral image.

9. The method of claim 1, wherein the method is a method of detecting chemical composition of a material based on spectroscopy. The initial spectral image is corrected according to the target compensation coefficient, including: The maximum intensity of each target scattering peak in each initial spectral image is corrected according to the target compensation coefficient corresponding to each initial spectral image, to obtain the intensity correction value corresponding to each target scattering peak in each initial spectral image. The wave number corresponding to the maximum intensity point of each target scattering peak in each initial spectral image is corrected according to the target compensation coefficient corresponding to each initial spectral image, to obtain the wave number correction value corresponding to each target scattering peak in each initial spectral image. The maximum intensity of each target scattering peak in each initial spectral image is updated to the intensity correction value corresponding to the target scattering peak, and the wave number corresponding to the maximum intensity point of each target scattering peak in each initial spectral image is updated to the wave number correction value corresponding to the target scattering peak, to obtain the corrected initial spectral image.

10. A material chemical composition detection system based on spectroscopic technology, characterized in that, The processor is configured to process instructions stored in the memory to implement the method for detecting the chemical composition of a material based on a spectral technology according to any one of claims 1-9.

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