An oil element detection device and method for image-assisted micro-area XRF spectral analysis

Abstract

This invention discloses an image-assisted micro-area XRF spectral analysis device and method for oil element detection. The detection device includes a microscopic image acquisition unit, a micro-area XRF detection unit, and an element quantitative analysis unit. The microscopic image acquisition unit includes a camera, a microscope objective, a ring light source, an XY motorized displacement stage, and a displacement stage driver. The camera, microscope objective, and ring light source are connected in sequence. The XY motorized displacement stage is located below the ring light source and is connected to the displacement stage driver. The XY motorized displacement stage is used to place the oil to be tested. The micro-area XRF detection unit includes an X-ray source, a capillary lens, an SDD detector, and a multichannel analyzer. The X-ray source and the SDD detector are respectively located on both sides of the microscope objective. X-rays emitted by the X-ray source are directed towards the oil to be tested after passing through the capillary lens. X-rays reflected by the oil to be tested are transmitted to the SDD detector after passing through the capillary lens. The SDD detector is connected to the multichannel analyzer.

Description

Technical Field

[0001] This invention belongs to the field of spectral analysis, and in particular to a method and apparatus for detecting oil elements by image-assisted micro-area XRF spectral analysis. Background Technology

[0002] XRF spectroscopy is an important method for elemental analysis in oils, offering advantages such as being unaffected by particle size and requiring no sample preparation. XRF spectroscopy can be divided into two categories: wavelength dispersive X-ray fluorescence (WD-XRF) and energy dispersive X-ray fluorescence (ED-XRF). The latter, with its low cost and miniaturization capabilities, is currently widely used. Later developments in micro-area (microbeam or microfocal spot) X-ray technology have further improved the energy density of ED-XRF, enabling the application of low-power X-ray sources. Micro-area XRF has extremely high spatial resolution; therefore, sample homogeneity significantly impacts the measurement results. Oil abrasive particles are affected by wear conditions and fluid motion, exhibiting large differences in particle size / shape and uneven distribution, leading to significant variations in micro-area XRF spectral analysis results and poor repeatability. Furthermore, the lubricating oil used in real-world applications differs greatly from the standard oil sample containing a homogeneous mixture of soluble organic salts used in the laboratory, limiting the effectiveness of calibration using standard oil samples. Therefore, the aforementioned methods and devices for XRF elemental analysis in oils suffer from poor repeatability and low calibration accuracy. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an image-assisted micro-area XRF spectral analysis device and method for oil element detection. This method simultaneously acquires microscopic images of oil abrasive particles and employs a fundamental parameter method to analyze the influence of parameters such as particle size, shape, and distribution on the fluorescence spectrum, constructing a fluorescence intensity correction model based on the irradiation area of ​​the abrasive particles. This solves the problems of poor repeatability and low correction accuracy in existing traditional XRF oil element detection methods. Furthermore, the provided XRF oil element detection device can be widely used for on-site oil element detection in various equipment systems, including aero-engines and gas turbines.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] An image-assisted micro-area XRF spectral analysis device for oil element detection includes a microscopic image acquisition unit, a micro-area XRF detection unit, and an element quantitative analysis unit. The microscopic image acquisition unit includes a camera, a microscope objective, a ring light source, an XY motorized displacement stage, and a displacement stage driver. The camera, microscope objective, and ring light source are connected sequentially. The XY motorized displacement stage is positioned below the ring light source and connected to the displacement stage driver. The XY motorized displacement stage is used to place the oil to be tested. The micro-area XRF detection unit includes an X-ray source, a capillary lens, an SDD detector, and a multichannel analyzer. The X-ray source and SDD detector are respectively positioned on opposite sides of the microscope objective. X-rays emitted from the X-ray source pass through the capillary lens and are directed towards the oil to be tested. X-rays reflected by the oil to be tested pass through the capillary lens and are transmitted to the SDD detector. The SDD detector is connected to the multichannel analyzer.

[0006] The elemental quantitative analysis unit includes a computer, which is connected to a displacement stage driver, a multichannel analyzer, and a camera. The computer is used to control the displacement stage driver and simultaneously acquire image data from the camera and spectral data from the multichannel analyzer to achieve quantitative analysis of each element in the oil to be tested.

[0007] This invention also provides a method for oil element detection using image-assisted micro-area XRF spectral analysis, comprising two processes: quantitative calibration and quantitative prediction; as detailed below:

[0008] (1) Quantitative calibration;

[0009] 101) XRF spectral data were acquired using standard oil samples;

[0010] 102) Using known element content, select characteristic wavelengths based on partial least squares (PLS), genetic algorithm (GA), or continuous projection algorithm (SPA);

[0011] 103) Quantitative calibration is performed using the selected characteristic wavelengths and based on PLS regression analysis;

[0012] (2) Quantitative prediction;

[0013] 201) Using the oil sample to be tested, acquire its XRF spectral data and microscopic image data;

[0014] 202) Combining the XRF spectral data of standard oil samples and the microscopic image data of the oil sample to be tested, the fluorescence intensity of the XRF spectral data of the oil sample to be tested is corrected based on the basic parameter method.

[0015] 203) Using the corrected XRF spectral data of the oil sample to be tested, its characteristic wavelengths are extracted, and quantitative prediction is performed based on PLS regression analysis;

[0016] 204) Using the quantitative prediction results and the microscopic image data of the oil sample to be tested, principal component analysis (PCA) is used to extract the principal components of the microscopic image, which are then fused with the XRF spectral data to perform quantitative prediction again, thereby achieving iterative optimization.

[0017] Furthermore, based on the fundamental parameter method, fluorescence intensity correction was performed on the XRF spectral data of the oil sample using the microscopic image data of the sample. According to the fundamental parameter method, the fundamental parameters of the XRF spectral data include the mass absorption coefficient, fluorescence yield, absorption limit transition factor, and spectral line fraction. To obtain the relationship between XRF fluorescence intensity and the content of the analyte element, the Sherman equation was used, and the relationship between fluorescence intensity and element content is expressed as:

[0018]

[0019] in

[0020]

[0021] In the formula, i is the element to be measured, j is a matrix element, i.e., the element affected by the matrix effect, n is the total number of elements, s represents the substance to be measured, λ0 is the minimum wavelength of the incident X-ray, and λ edgei λ is the critical excitation wavelength of the element to be measured, and λ is the incident X-ray wavelength. i λ is the characteristic wavelength of the element to be measured. j It is the characteristic wavelength of the matrix element, I i (λ i ) is the fluorescence intensity of the analyte, I0(λ) is the incident X-ray intensity, and G i It is a correction factor related to the analytical instrument, C i It is the content of the element to be measured, C j It is the content of matrix elements, k i It is the sensitivity of the element to be measured, k j It is the sensitivity of the matrix elements, δ ij (λ) is the sensitivity to matrix effects, L ij (λ) is the coefficient of the matrix effect, r j It is the absorption step coefficient of the matrix element, ω j It is the fluorescence yield of the matrix element, f j It is the spectral line fraction of the matrix elements, Φ' is the incident angle, Φ" is the exit angle, and μ i (λ) is the mass absorption coefficient of element i to wavelength λ, μ j (λ j ) is a matrix pair with wavelength λ j The mass absorption coefficient, μ' s (λ) and μ" s (λ i) are the effective mass absorption coefficients of the analyte at the incident and exit points, respectively, μ' s (λ j ) and μ" s (λ j These are the effective mass absorption coefficients of the matrix elements at the incident and exit points, respectively.

[0022] The above formula is the basis for correcting the relationship between fluorescence intensity and the content of the analyte using the basic parameter method. In this formula, the basic parameters constitute the correction factor, while the fluorescence intensity is the spectral data obtained by detection, and the content of the analyte is the amount to be detected. By correcting the fluorescence intensity according to the basic parameter method, the relationship between fluorescence intensity and the content of the analyte can be obtained more accurately.

[0023] Using standard samples as a reference, a relative fluorescence intensity calculation model is established: for a specific analyte, the theoretical fluorescence intensity I of a standard sample with a given content is calculated using the basic parameter method. si The measured fluorescence intensity I' of the standard sample was obtained by XRF measurement. si The ratio of the two is α i =I si / I' si Using m standard samples, [α1, α2, ..., α] are obtained. m Then, the curve of the instrument correction factor changing with fluorescence intensity is obtained by fitting a custom function. α Using interpolation J α And measured fluorescence intensity I' i The theoretical fluorescence intensity I of a certain element was calculated. si Then, based on the theoretical formula of the basic parameter method, the content C is calculated. i This section describes the process of correcting fluorescence intensity using the basic parameter method, a common application in the industry. The image-assisted method of this invention, as described below, differs primarily from traditional methods in that: traditional methods rely on experience and experiments to calculate the theoretical fluorescence intensity of a standard sample with a set concentration, leading to deviations from actual measurements, particularly in S... xi and S ti Since it is impossible to obtain the value from the collected oil microscopic images, it is estimated based on experience and existing experimental data. xi S represents the effective irradiation area of ​​X-rays on a specific element i within the micro-region. ti The total area of ​​all abrasive grains of a specific element i in the entire sampling area; while the method S of the present invention xi and S ti By acquiring microscopic images of the oil, more accurate information can be obtained, thus leading to more accurate results.

[0024] I i =J α Ii ′=f(C i (3)

[0025] a) For the oil sample to be tested, the elements exist in the form of insoluble abrasive particles. The expected total fluorescence intensity of the analyte in the entire sampling area is:

[0026]

[0027] Among them, S xi S represents the effective irradiation area of ​​X-rays on a specific element i within the micro-region. ti I represents the total area of ​​all abrasive grains of a specific element i within the entire sampling region. xi To measure the fluorescence intensity of the elemental micro-regions in the obtained oil sample;

[0028] b) For oil standard samples, the elements are uniformly distributed in the form of free sulfonates, therefore the expected fluorescence intensity across the entire sampling area is:

[0029]

[0030] Where S0 is the total area of ​​the entire sampling region, S x I' is the area of ​​the X-ray micro-region. xi The fluorescence intensity of the elemental micro-regions in the obtained standard sample was measured;

[0031] c) Substitute equations (5) and (4) into equation (3) to correct the fluorescence intensity of the oil sample to be tested and the standard sample, and calculate the content of the oil sample to be tested.

[0032]

[0033] I xi 、I' xi S0, S x All are quantities that are obtained through measurement or can be known through design, S xi and S ti Quantitative calibration of the spectral data of the sample to be tested is achieved by acquiring the collected oil microscopic images.

[0034] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of the oil element detection method.

[0035] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the oil element detection method.

[0036] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0037] This invention simultaneously acquires sample images within a micro-region using X-rays and employs the fundamental parameter method. By analyzing the influence of parameters such as the particle size, shape, and distribution of oil abrasive particles on the fluorescence spectrum, a fluorescence intensity correction model based on the abrasive particle irradiation area is constructed. This addresses the problem of inaccurate sample homogeneity estimation in the classical fundamental parameter method, improves the effectiveness of correcting for matrix effects in oil abrasive particles using the fundamental parameter method, and enhances the repeatability of oil element detection. Simultaneously, by utilizing the fluorescence intensity correction model based on the abrasive particle irradiation area, the relationship between sample distribution and fluorescence intensity is established based on the acquired sample images. This solves the problem of insufficient correction accuracy caused by inconsistencies in homogeneity between standard oil samples and actual lubricating oil, improving the accuracy of oil element detection. Therefore, this invention addresses the problems of poor repeatability and low correction accuracy in existing traditional XRF oil element detection methods.

[0038] The provided XRF oil element detection device is small in size and low in power consumption, and can be powered by rechargeable batteries. It can be widely used for on-site oil element detection in various equipment systems, including aero-engines and gas turbines. Because the instrument uses a confocal structure, its spectral analysis of oil wear particles has spatial resolution, allowing for more accurate identification of the elemental composition of abnormal wear particles. This enables more precise determination of the location of abnormal wear, facilitating the rapid identification of faulty or malfunctioning components. This has significant practical implications for the maintenance of complex and difficult-to-disassemble equipment such as aero-engines. Attached Figure Description

[0039] Figure 1a This is a schematic diagram of the structure of the oil element detection device of the present invention.

[0040] Figure 1b yes Figure 1a A schematic diagram showing the specific setup of each component in the oil liquid element detection device.

[0041] Figure 2 This is a schematic diagram of the oil element detection and calibration process.

[0042] Figure reference numerals: 1-Camera, 2-Microscope objective, 3-Ring light source, 4-XY motorized stage, 5-Stage driver, 6-Oil to be tested, 7-X-ray source, 8-Capillary lens, 9-SDD detector, 10-Multichannel analyzer, 11-Computer. Detailed Implementation

[0043] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0044] like Figure 1a and Figure 1b As shown, this embodiment provides an image-assisted micro-area XRF spectral analysis device for oil element detection, including a microscopic image acquisition unit, a micro-area XRF detection unit, and an element quantitative analysis unit. The microscopic image acquisition unit includes a camera 1, a microscope objective 2, a ring light source 3, an XY motorized displacement stage 4, and a displacement stage driver 5. The camera 1, microscope objective 2, and ring light source 3 are connected in sequence. The XY motorized displacement stage 4 is located below the ring light source 3 and is connected to the displacement stage driver 5. The XY motorized displacement stage 4 is used to place the oil to be tested. The micro-area XRF detection unit includes an X-ray source 7, a capillary lens 8, an SDD detector 9, and a multichannel analyzer 10. The X-ray source 7 and the SDD detector 9 are respectively located on both sides of the microscope objective 2. The X-rays emitted by the X-ray source 7 pass through the capillary lens 8 and are directed towards the oil to be tested. The X-rays reflected by the oil to be tested pass through the capillary lens 8 and are transmitted to the SDD detector 9. The SDD detector 9 is connected to the multichannel analyzer 10.

[0045] The elemental quantitative analysis unit includes a computer 11, which is connected to a displacement stage driver 5, a multichannel analyzer 10, and a camera 1. The computer 11 is used to control the displacement stage driver 5 and simultaneously acquire image data from the camera 1 and spectral data from the multichannel analyzer 10 to realize the quantitative analysis of each element in the oil to be tested.

[0046] Based on the above-mentioned oil element detection device, the present invention also provides a method for oil element detection by image-assisted micro-area XRF spectral analysis, as follows:

[0047] According to the fundamental parameter method, important fundamental parameters of XRF spectroscopy include mass absorption coefficient, fluorescence yield, absorption limit transition factor, and spectral line fraction. To obtain the relationship between XRF fluorescence intensity and the content of the analyte, the Sherman equation can be used to express the relationship between fluorescence intensity and elemental content as follows:

[0048]

[0049] in

[0050]

[0051] In the formula, i is the element to be measured, j is a matrix element, i.e., the element affected by the matrix effect, n is the total number of elements, s represents the substance to be measured, λ0 is the minimum wavelength of the incident X-ray, and λ edgei λ is the critical excitation wavelength of the element to be measured, and λ is the incident X-ray wavelength. i λ is the characteristic wavelength of the element to be measured. j It is the characteristic wavelength of the matrix element, I i (λ i ) is the fluorescence intensity of the analyte, I0(λ) is the incident X-ray intensity, and Gi It is a correction factor related to the analytical instrument, C i It is the content of the element to be measured, C j It is the content of matrix elements, k i It is the sensitivity of the element to be measured, k j It is the sensitivity of the matrix elements, δ ij (λ) is the sensitivity to matrix effects, L ij (λ) is the coefficient of the matrix effect, r j It is the absorption step coefficient of the matrix element, ω j It is the fluorescence yield of the matrix element, f j It is the spectral line fraction of the matrix elements, Φ' is the incident angle, Φ" is the exit angle, and μ i (λ) is the mass absorption coefficient of element i to wavelength λ, μ j (λ j ) is a matrix pair with wavelength λ j The mass absorption coefficient, μ' s (λ) and μ" s (λ i ) are the effective mass absorption coefficients of the analyte at the incident and exit points, respectively, μ' s (λ j ) and μ" s (λ j These are the effective mass absorption coefficients of the matrix elements at the incident and exit points, respectively.

[0052] Using standard samples as a reference, a relative fluorescence intensity calculation model is established: for a specific analyte, the theoretical fluorescence intensity I of a standard sample with a specific concentration is calculated using the fundamental parameter method. si The measured fluorescence intensity I' of the standard sample was obtained by XRF measurement. si The ratio of the two is α i =I si / I' si Using m standard samples, [α1, α2, ..., α] can be obtained. m Then, the curve of the instrument correction factor changing with fluorescence intensity is obtained by fitting a custom function. α Using interpolation J α And measured fluorescence intensity I' i The theoretical fluorescence intensity I of a certain element can be calculated. si Then, based on the theoretical formula of the basic parameter method, the content C can be calculated. i .

[0053] I i =J α I i ′=f(C i (3)

[0054] a) For the oil sample to be tested, the elements exist in the form of insoluble abrasive particles. The expected total fluorescence intensity of the analyte in the entire sampling area is:

[0055]

[0056] Among them, S xi S represents the effective irradiation area of ​​X-rays on a specific element i within the micro-region. ti I represents the total area of ​​all abrasive grains of a specific element i within the entire sampling region. xi The fluorescence intensity of the elemental micro-regions in the obtained oil sample was measured.

[0057] b) For oil standard samples, the elements are uniformly distributed in the form of free sulfonates, therefore the expected fluorescence intensity across the entire sampling area is:

[0058]

[0059] Where S0 is the total area of ​​the entire sampling region, S x I' is the area of ​​the X-ray micro-region. xi The fluorescence intensity of the elemental micro-regions in the standard sample was measured.

[0060] c) Substitute equations (5) and (4) into equation (3) to correct the fluorescence intensity of the oil sample to be tested and the standard sample, and calculate the content of the oil sample to be tested.

[0061]

[0062] I xi 、I' xi S0, S x All are quantities that are obtained through measurement or can be known through design, S xi and S ti Based on the acquired microscopic images of the oil, quantitative calibration of the spectral data of the sample to be tested is achieved. The quantitative calibration process, as shown in Figure 1, includes two steps: quantitative calibration and quantitative prediction. Figure 2 :

[0063] (1) Quantitative calibration

[0064] 101) XRF spectral data were acquired using standard oil samples;

[0065] 102) Using known element content, select characteristic wavelengths based on partial least squares (PLS), genetic algorithm (GA), continuous projection algorithm (SPA), etc.

[0066] 103) Quantitative calibration is performed using the selected characteristic wavelengths and based on PLS regression analysis;

[0067] (2) Quantitative prediction

[0068] 201) Using the oil sample to be tested, acquire its XRF spectral data and microscopic image data;

[0069] 202) Combining the XRF spectral data of the standard oil sample and the microscopic image data of the oil sample to be tested, the fluorescence intensity of the XRF spectral data of the oil sample to be tested is corrected.

[0070] 203) Using the corrected XRF spectral data of the sample to be tested, extract its characteristic wavelengths and perform quantitative prediction based on PLS regression analysis;

[0071] 204) Using the quantitative prediction results, the principal components of the image are extracted by combining the microscopic image data of the sample to be tested with the principal component analysis (PCA) method. The components are then fused with the characteristic wavelengths of the XRF spectrum and quantitative prediction is performed again to achieve iterative optimization.

[0072] Compared to traditional methods, the image-assisted micro-area XRF spectral analysis element detection method described in this invention uses a basic parameter method to correct the detection error caused by oil abrasive particles. It obtains the effective irradiation area of ​​X-rays on a specific element i in the micro-area through image data, and the total area of ​​all abrasive particles of the specific element i in the entire sampling area. It then corrects the micro-area fluorescence intensity of the element, thereby improving the accuracy of oil element detection.

[0073] Preferably, embodiments of this application also provide a specific implementation of an electronic device capable of performing all steps in the oil element detection method for image-assisted micro-area XRF spectral analysis described in the above embodiments. The electronic device specifically includes the following:

[0074] Processor, memory, communications interface, and bus;

[0075] The processor, memory, and communication interface communicate with each other via a bus; the communication interface is used to realize information transmission between server-side devices, metering devices, and user-side devices.

[0076] The processor is used to call the computer program in the memory. When the processor executes the computer program, it implements all the steps in the oil element detection method of image-assisted micro-area XRF spectral analysis in the above embodiments.

[0077] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the oil element detection method of image-assisted micro-area XRF spectral analysis in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the oil element detection method of image-assisted micro-area XRF spectral analysis in the above embodiments.

[0078] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0079] While this application provides method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the method can be executed sequentially as shown in the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0080] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0082] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0083] This invention is not limited to the embodiments described above. The above description of specific embodiments is intended to illustrate and explain the technical solutions of this invention. The specific embodiments described above are merely illustrative and not restrictive. Without departing from the spirit and scope of the claims, those skilled in the art can make many specific modifications based on the teachings of this invention, and these modifications all fall within the scope of protection of this invention.

Claims

1. A method for elemental detection in oil liquid using image-assisted micro-area XRF spectral analysis, based on an oil liquid elemental detection device, comprising a microscopic image acquisition unit, a micro-area XRF detection unit, and an elemental quantitative analysis unit. The microscopic image acquisition unit includes a camera, a microscope objective, a ring light source, an XY motorized displacement stage, and a displacement stage driver. The camera, microscope objective, and ring light source are connected in sequence. The XY motorized displacement stage is positioned below the ring light source and connected to the displacement stage driver. The XY motorized displacement stage is used to place the oil liquid to be tested. The micro-area XRF detection unit includes an X-ray source, a capillary lens, an SDD detector, and a multichannel analyzer. The X-ray source and the SDD detector are respectively positioned on opposite sides of the microscope objective. X-rays emitted by the X-ray source pass through the capillary lens and are directed towards the oil liquid to be tested. X-rays reflected by the oil liquid are transmitted to the SDD detector after passing through the capillary lens. The SDD detector is connected to the multichannel analyzer; The elemental quantitative analysis unit includes a computer connected to a displacement stage driver, a multichannel analyzer, and a camera. The computer controls the displacement stage driver and simultaneously acquires image data from the camera and spectral data from the multichannel analyzer to achieve quantitative analysis of each element in the oil being tested. Its key feature is that... It includes two processes: quantitative calibration and quantitative prediction; the details are as follows: (1) Quantitative calibration; 101) XRF spectral data were acquired using standard oil samples; 102) Using known element content, select characteristic wavelengths based on partial least squares (PLS), genetic algorithm (GA), or continuous projection algorithm (SPA); 103) Quantitative calibration is performed using the selected characteristic wavelengths and based on PLS regression analysis; specifically: Using standard samples as a reference, a relative fluorescence intensity calculation model is established: for a specific analyte, the theoretical fluorescence intensity of a standard sample with a given concentration is calculated using the fundamental parameter method. I si The measured fluorescence intensity of the standard sample was obtained by XRF measurement. I' si The ratio of the two is the instrument correction factor. α i = I si / I' si Using m types of standard samples to obtain [ α 1 , α 2 ,..., α m Establish instrument calibration factors. α i With fluorescence intensity I' si Change relationship J α ,use J α and measured fluorescence intensity I' si The theoretical fluorescence intensity of a certain element was calculated. I si Then, based on the theoretical formula of the basic parameter method, the content is calculated. C i ; (3) a) For the oil sample to be tested, the elements exist in the form of insoluble abrasive particles. The expected total fluorescence intensity of the analyte in the entire sampling area is: (4) in, S xi X-rays targeting specific elements within a micro-region i The effective irradiation area, S ti Specific elements for the entire sampling area i Total area of ​​all abrasive grains I xi To measure the fluorescence intensity of the elemental micro-regions in the obtained oil sample; b) For the oil standard sample, the elements are uniformly distributed in the form of free sulfonates, therefore the expected fluorescence intensity across the entire sampling area is: (5) in, S 0 The total area of ​​the entire sampling area. S x The area of ​​the X-ray micro-region, I' xi The fluorescence intensity of the elemental micro-regions in the obtained standard sample was measured; c) Substitute equations (5) and (4) into equation (3) to correct the fluorescence intensity of the oil sample to be tested and the standard sample, and calculate the content of the oil sample to be tested. (6) I xi , I' xi , S 0 , S x All of these are quantities obtained through measurement or that can be known through design. S xi and S ti Quantitative calibration of the spectral data of the sample to be tested is achieved by acquiring the collected oil microscopic images. (2) Quantitative prediction; 201) Using the oil sample to be tested, acquire its XRF spectral data and microscopic image data; 202) Combining the XRF spectral data of standard oil samples and the microscopic image data of the oil sample to be tested, the fluorescence intensity of the XRF spectral data of the oil sample to be tested is corrected based on the basic parameter method. 203) Using the corrected XRF spectral data of the oil sample to be tested, its characteristic wavelengths are extracted, and quantitative prediction is performed based on PLS regression analysis; 204) Using the quantitative prediction results and the microscopic image data of the oil sample to be tested, principal component analysis (PCA) is used to extract the principal components of the microscopic image, which are then fused with the XRF spectral data to perform quantitative prediction again, thereby achieving iterative optimization.

2. The method for detecting oil elements by image-assisted micro-area XRF spectroscopy analysis according to claim 1, characterized in that, Based on the fundamental parameter method, the XRF spectral data of the oil sample to be tested are corrected for fluorescence intensity using the microscopic image data of the oil sample to be tested. According to the fundamental parameter method, the fundamental parameters of XRF spectral data include mass absorption coefficient, fluorescence yield, absorption limit transition factor, and spectral line fraction. In order to obtain the relationship between XRF fluorescence intensity and the content of the element to be tested, the relationship between fluorescence intensity and element content is obtained according to the Sherman equation.

3. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the oil element detection method according to any one of claims 1 to 2.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the oil element detection method according to any one of claims 1 to 2.

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