Precious metal analyzer coating detection method and system based on X fluorescence spectrum

Abstract

The invention provides a precious metal analyzer coating detection method and system based on an X fluorescence spectrum. The method comprises the following steps: on the basis of curvature data of each curved surface detection point in three-dimensional shape space data, adjusting a cooperative pose of an X-ray source and a dual-laser positioning device so as to control an X-ray beam to vertically enter each curved surface detection point; when the X-ray beam is vertically incident to the curved surface detection point, the precious metal sample is excited to generate an X fluorescence spectrum signal; according to the curvature data, correcting geometric attenuation factors in the X fluorescence spectrum intensity attenuation model by using a preset curved surface distance correction algorithm; and extracting the characteristic peak intensity value of the noble metal element, inputting the characteristic peak intensity value of the noble metal element into the fluorescence spectrum intensity attenuation model after geometric attenuation factor correction, and analyzing and outputting the thickness value of the noble metal coating. According to the technical scheme provided by the invention, high-precision and lossless online detection on the thickness of the precious metal coating on the complex curved surface is realized through a dynamic cooperative positioning and curved surface correction algorithm.

Description

Technical Field

[0001] The present application relates to the technical field of coating detection, and in particular to a coating detection method and system of a precious metal analyzer based on X-ray fluorescence spectroscopy. Background Art

[0002] In high-end precision manufacturing, the demand for accurate, non-destructive, in-line testing of precious metal coating thickness is increasingly urgent. Traditional planar testing methods are particularly difficult to apply when the coating substrate or sample itself exhibits complex three-dimensional curved topography. This requires testing technology that can adapt to complex surface geometries, ensuring that the measuring probe maintains an optimal geometric relationship with each test point and accurately correcting for signal attenuation caused by the curved surface geometry. This ensures highly accurate and stable measurement of coating thickness across the entire curved surface.

[0003] Existing solutions use X-rays to excite the sample, producing characteristic X-ray fluorescence. A detector receives the fluorescence signal intensity and calculates the thickness based on a theoretical model that correlates the known fluorescence intensity with the coating thickness. To accommodate curved surfaces, this approach may involve rotating the sample stage or moving it along multiple axes, moving different points on the sample sequentially to a predetermined fixed measurement position, or using a wide beam of X-rays to cover a specific area.

[0004] However, a drawback of existing solutions is that it is difficult to ensure that the X-ray beam is strictly perpendicular to all detection points on the entire surface. When the curvature of the surface changes, the fixed-angle X-ray beam will produce an incidence angle deviation relative to the local surface normal. This not only reduces the excitation and detection efficiency, but more importantly, the calculation of the geometric attenuation factor is based on the assumption of ideal vertical incidence and fixed distance, which is seriously inconsistent with the actual surface geometry. This mismatch in geometric relationships will introduce measurement errors, causing the measurement accuracy of coating thickness to deteriorate severely on complex surfaces, especially in areas with drastic curvature changes, and cannot meet the needs of high-precision detection. Summary of the Invention

[0005] The present application provides a coating detection method and system for a precious metal analyzer based on X-ray fluorescence spectroscopy, which is used to solve the problem in the prior art that geometric attenuation distortion is caused by the deviation of the X-ray incident angle on complex curved surfaces, thereby failing to meet high-precision detection requirements.

[0006] In a first aspect, the present application provides a precious metal analyzer coating detection method based on X-ray fluorescence spectroscopy, comprising: A dual laser positioning device is used to locate the area to be tested on the surface of the precious metal sample to obtain the spatial coordinates; Based on the spatial coordinates, driving the zoom camera to scan the surface morphology of the area to be detected to generate three-dimensional morphology spatial data; Based on the curvature data of each curved surface detection point in the three-dimensional topography space data, adjusting the coordinated posture of the X-ray source and the dual laser positioning device to control the X-ray beam to be vertically incident on each curved surface detection point; When the X-ray beam is vertically incident on the curved surface detection point, the precious metal sample is excited to generate an X-ray fluorescence spectrum signal, and the X-ray fluorescence spectrum signal is collected by an X-ray fluorescence detector; According to the curvature data of the surface detection point, the preset surface distance correction algorithm is used to correct the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model; The characteristic peak intensity value of the precious metal element is extracted from the X-ray fluorescence spectrum signal, and the characteristic peak intensity value of the precious metal element is input into the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor to analyze and output the precious metal coating thickness value.

[0007] Optionally, extracting a characteristic peak intensity value of a noble metal element from the X-ray fluorescence spectrum signal, inputting the characteristic peak intensity value of the noble metal element into a fluorescence spectrum intensity attenuation model corrected by a geometric attenuation factor, and analyzing and outputting a noble metal coating thickness value includes: Based on the preset characteristic energy of the noble metal element, locating the corresponding characteristic peak position in the X-ray fluorescence spectrum signal, and using the intensity value of the characteristic peak position as the characteristic peak intensity value of the noble metal element; Combining the characteristic peak intensity value of the noble metal element with the geometric attenuation factor corresponding to the curvature data of the curved surface detection point to form input parameters for a fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor; Iteratively solving the model-predicted intensity value using the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor and the input parameters; During the iterative solution process, the thickness value of the precious metal coating is updated based on the difference between the characteristic peak intensity value of the precious metal element and the intensity value predicted by the model until a preset convergence condition is met to obtain the thickness value of the precious metal coating.

[0008] Optionally, combining the characteristic peak intensity value of the noble metal element with the geometric attenuation factor corresponding to the curvature data of the curved surface detection point to form input parameters for a fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor includes: Multiplying the characteristic peak intensity value of the noble metal element by the geometric attenuation factor value to generate a compensated characteristic peak intensity value; Combining the compensated characteristic peak intensity value and the geometric attenuation factor value into a parameter vector; The parameter vector is mapped to a standard input format of the fluorescence spectrum intensity attenuation model after correction of the geometric attenuation factor to obtain input parameters.

[0009] Optionally, the multiplying the characteristic peak intensity value of the noble metal element by the geometric attenuation factor value to generate a compensated characteristic peak intensity value includes: Converting the characteristic peak intensity value of the noble metal element into a characteristic peak intensity value in the form of photon counting; Resolving a path length correction factor and an angle of incidence correction factor from the geometric attenuation factor value; multiplying the path length correction factor by the incident angle correction factor to generate a composite compensation weight value; Multiplying the characteristic peak intensity value of the photon counting form by the composite compensation weight value to generate a compensated photon counting intensity value; The compensated photon counting intensity value is converted into a compensated characteristic peak intensity value.

[0010] Optionally, the method of correcting the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model using a preset surface distance correction algorithm based on the curvature data of the surface detection point includes: Extracting the principal curvature radius value and the normal vector direction angle value according to the curvature data of the surface detection point; Calculating a path length correction factor based on the principal radius of curvature value and the nominal distance of the X-ray source; determining an incident angle correction coefficient based on the normal vector direction angle value and the incident direction of the X-ray beam; multiplying the path length correction factor by the angle of incidence correction factor to generate an uncorrected geometric attenuation factor value; The uncorrected geometric attenuation factor value is adjusted for distance weight using a preset surface distance correction algorithm, and a corrected geometric attenuation factor value is output.

[0011] Optionally, when the X-ray beam is vertically incident on the curved surface detection point, exciting the precious metal sample to generate an X-ray fluorescence spectrum signal, and collecting the X-ray fluorescence spectrum signal using an X-ray fluorescence detector, includes: When the X-ray beam is vertically incident on a curved surface detection point, the pulse emission timing and pulse width of the X-ray source are adjusted based on the curvature data of the curved surface detection point to generate pulse parameters that adapt to the curved surface topography; triggering the X-ray source to emit the X-ray beam according to the pulse parameters, exciting the noble metal sample to generate an X-ray fluorescence spectrum signal at the curved surface detection point; The X-ray fluorescence spectrum signal is collected using an X-ray fluorescence detector at a preset sampling frequency.

[0012] Optionally, adjusting the pulse emission timing and pulse width of the X-ray source based on the curvature data of the curved surface detection point to generate pulse parameters adapted to the curved surface topography includes: Calculating a normal vector offset angle according to a normal vector change rate in the curvature data of the surface detection point; determining a compensation amount for the pulse emission timing according to a proportional relationship between the normal vector offset angle and a preset X-ray source position offset threshold; combining the compensation amount of the pulse emission timing and the pulse width into a pulse parameter instruction set; The pulse parameter instruction set is parsed by an X-ray source controller to generate pulse parameters that are adapted to the curved surface morphology.

[0013] In a second aspect, the present application provides a precious metal analyzer coating detection system based on X-ray fluorescence spectroscopy, comprising: A positioning module is used to locate the area to be tested on the surface of the precious metal sample using a dual laser positioning device to obtain spatial coordinates; A driving module, configured to drive a zoom camera to scan the surface topography of the area to be detected based on the spatial coordinates, and generate three-dimensional topography spatial data; a detection module, configured to adjust the coordinated posture of the X-ray source and the dual laser positioning device based on curvature data of each curved surface detection point in the three-dimensional topography space data, so as to control the X-ray beam to be vertically incident on each curved surface detection point; an acquisition module, configured to excite the precious metal sample to generate an X-ray fluorescence spectrum signal when the X-ray beam is vertically incident on the curved surface detection point, and to acquire the X-ray fluorescence spectrum signal using an X-ray fluorescence detector; The positioning module is used to correct the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model based on the curvature data of the surface detection point using a preset surface distance correction algorithm; The correction module is used to extract the characteristic peak intensity value of the precious metal element from the X-ray fluorescence spectrum signal, input the characteristic peak intensity value of the precious metal element into the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor, and analyze and output the precious metal coating thickness value.

[0014] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a precious metal analyzer coating detection method based on X-ray fluorescence spectroscopy as described in any one of the first aspects.

[0015] In a fourth aspect, the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a precious metal analyzer coating detection method based on X-ray fluorescence spectroscopy as described in any one of the first aspects.

[0016] The present application provides a coating detection method for a precious metal analyzer based on X-ray fluorescence spectroscopy, the method comprising: using a dual-laser positioning device to locate a to-be-detected area on the surface of a precious metal sample to obtain spatial coordinates; based on the spatial coordinates, driving a zoom camera to scan the surface morphology of the to-be-detected area to generate three-dimensional morphology spatial data; based on curvature data of each curved surface detection point in the three-dimensional morphology spatial data, adjusting the coordinated posture of an X-ray source and the dual-laser positioning device to control the X-ray beam to be vertically incident on each curved surface detection point; when the X-ray beam is vertically incident on the curved surface detection point, exciting the precious metal sample to generate an X-ray fluorescence spectral signal, and collecting the X-ray fluorescence spectral signal using an X-ray fluorescence detector; based on the curvature data of the curved surface detection point, using a preset curved surface distance correction algorithm to correct the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model; extracting characteristic peak intensity values ​​of precious metal elements from the X-ray fluorescence spectrum signals, inputting the characteristic peak intensity values ​​of the precious metal elements into the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor, and analyzing and outputting the precious metal coating thickness value.

[0017] This application realizes high-precision, adaptive non-destructive detection of the thickness of precious metal coatings on complex surfaces by dynamically coordinating dual laser positioning and three-dimensional topography scanning to control the detection points of the X-ray beam vertically incident on the curved surface, and corrects the geometric attenuation factor in real time in combination with curvature data.

[0018] Furthermore, after extracting the characteristic peak intensity values ​​of the precious metal elements, the characteristic peak intensity values ​​of the precious metal elements are multiplied by the geometric attenuation factor value calculated based on the curvature data of the current surface detection point to generate the compensated characteristic peak intensity value. Next, the compensated characteristic peak intensity value and the geometric attenuation factor value are combined into a parameter vector and mapped to a standard input format. Finally, the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor and the input parameters are iteratively solved until the convergence conditions are met, and the final precious metal coating thickness value is output. By mathematically compensating the original characteristic peak intensity value with the curvature-related geometric attenuation factor and constructing standardized input parameters, the distortion effect of surface geometric deformation on the original fluorescence signal intensity is reduced. Combined with an iterative optimization algorithm to solve the corrected physical model, the accuracy of the analysis of precious metal coating thickness on complex surfaces and the reliability of model convergence are effectively improved.

[0019] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 A flowchart of a precious metal analyzer coating detection method based on X-ray fluorescence spectroscopy provided in an embodiment of the present application; Figure 2 A schematic structural diagram of a precious metal analyzer coating detection system based on X-ray fluorescence spectroscopy provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0023] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 11, 12, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0024] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0025] In order to solve the problem in the prior art that geometric attenuation distortion is caused by the deviation of X-ray incident angle on complex surfaces, which in turn leads to the inability to meet high-precision detection requirements, an embodiment of the present application provides a precious metal analyzer coating detection method based on X-ray fluorescence spectroscopy. The method adopts the following concept: Aiming at the two core problems of X-ray incident angle deviation and geometric attenuation distortion in the detection of precious metal coatings on complex surfaces, a four-step progressive strategy of "positioning-modeling-dynamic calibration-model correction" is adopted: first, dual laser positioning is combined with three-dimensional morphology scanning to construct a digital twin of the surface, and then the X-ray source posture is adjusted in real time according to the curvature data to achieve full-domain vertical incidence, and the geometric factor of the fluorescence attenuation model is dynamically corrected based on the curvature characteristics. Finally, the pure fluorescence signal excited by vertical incidence is input into the correction model to achieve precise coupling of the physical mechanism of the surface coating thickness and the data model.

[0026] Figure 1 A flow chart of a precious metal analyzer coating detection method based on X-ray fluorescence spectroscopy provided in an embodiment of the present application is as follows: Figure 1 As shown, the method includes: S11. Use a dual laser positioning device to locate the area to be tested on the surface of the precious metal sample to obtain the spatial coordinates.

[0027] Among them, the dual laser positioning device refers to a device that calculates the target spatial position through the reflection time difference of two crossed laser beams, and is used to establish a three-dimensional coordinate system for the surface of the precious metal sample. The precious metal sample refers to the object to be detected whose surface contains precious metal coatings such as gold, silver, and platinum, and its curved surface geometric characteristics affect the detection accuracy. The area to be detected refers to the local curved surface area pre-demarcated on the surface of the precious metal sample where the coating thickness measurement is required. The spatial coordinates refer to the detection point position data described in a three-dimensional rectangular coordinate system obtained by the dual laser positioning device. In an embodiment of the present application, a crossed laser beam is first projected onto the area to be detected on the surface of the precious metal sample by the dual laser positioning device, and the spatial coordinates of the area to be detected are calculated based on the time difference and phase difference of the laser reflection points to form three-dimensional positioning grid data.

[0028] S12. Based on the spatial coordinates, drive the zoom camera to scan the surface morphology of the area to be detected to generate three-dimensional morphology spatial data.

[0029] A zoom camera refers to an optical imaging device with dynamically adjustable focal length, used to capture images of curved surfaces at varying depths of field for topography reconstruction. Surface topography refers to the three-dimensional geometric topology of the precious metal sample surface in the area to be inspected. Three-dimensional topography spatial data refers to a structured point cloud dataset containing the coordinates, normal vectors, and curvature values ​​of the surface inspection points.

[0030] In an embodiment of the present application, the zoom camera is driven to scan the area to be inspected along a preset path based on the spatial coordinates, and the surface morphology is reconstructed through multi-focal length image stitching and stereo vision algorithm, and finally three-dimensional morphology spatial data including curvature gradient distribution is generated.

[0031] S13. Based on the curvature data of each curved surface detection point in the three-dimensional morphology space data, adjust the coordinated posture of the X-ray source and the dual laser positioning device to control the X-ray beam to be vertically incident on each curved surface detection point.

[0032] Curvature data refers to mathematical parameters that describe the local curvature of a surface, including the principal radius of curvature and the normal vector angle. An X-ray source is a device that generates a high-energy X-ray beam, with an adjustable position to match the surface normal. Collaborative position refers to the coordinated control relationship between the spatial position and orientation of the dual laser positioning device and the X-ray source.

[0033] In an embodiment of the present application, the curvature data of each surface detection point is then extracted from the three-dimensional morphology space data, and the posture adjustment vector is calculated in real time in combination with the robotic arm motion model of the X-ray source; finally, the dual laser positioning device and the X-ray source are controlled to move in coordination to ensure that the axis of the X-ray beam coincides with the normal vector of the surface detection point to achieve vertical incidence.

[0034] S14. When the X-ray beam is vertically incident on the curved surface detection point, the precious metal sample is excited to generate an X-ray fluorescence spectrum signal, and the X-ray fluorescence spectrum signal is collected using an X-ray fluorescence detector.

[0035] Normal incidence refers to the geometric state in which the angle between the propagation direction of the X-ray beam and the normal vector of the surface detection point is zero. The X-ray fluorescence spectrum signal refers to the characteristic energy photon flux released by precious metal elements after being excited by X-rays. The X-ray fluorescence detector is the detection device that converts X-ray fluorescence photons into electrical signals and generates an energy spectrum curve.

[0036] In an embodiment of the present application, when the X-ray beam is vertically incident on the curved surface detection point, the X-ray source is first triggered to emit a focused X-ray beam to excite the atoms of the precious metal sample; secondly, the X-ray fluorescence detector is used to receive the radially diffused X-ray fluorescence spectrum signal, and it is converted into a digital spectrum sequence through a multi-channel pulse height analyzer.

[0037] S15. According to the curvature data of the surface detection point, a preset surface distance correction algorithm is used to correct the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model.

[0038] The surface distance correction algorithm refers to a mathematical model that dynamically calculates X-ray path length and incident angle based on curvature data. The geometric attenuation factor describes the intensity attenuation of X-ray fluorescence due to path length and angle when propagating on a curved surface. For example, the surface distance correction algorithm can be the Curvature-Weighted Distance Attenuation Correction (CWDAC) algorithm.

[0039] In an embodiment of the present application, based on the curvature data of the surface detection point, the path length correction coefficient and the incident angle correction coefficient are first calculated using a surface distance correction algorithm; secondly, the two are multiplied together to generate a geometric attenuation factor; finally, this factor is substituted into the geometric term of the X-ray fluorescence spectrum intensity attenuation model to complete the dynamic correction.

[0040] S16. Extract the characteristic peak intensity value of the precious metal element from the X-ray fluorescence spectrum signal, input the characteristic peak intensity value of the precious metal element into the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor, and analyze and output the precious metal coating thickness value.

[0041] The characteristic peak intensity value refers to the photon count statistics of the characteristic energy channel of the precious metal element in the X-ray fluorescence spectrum. The precious metal coating thickness value refers to the physical dimension of the coating in the vertical direction analyzed by the fluorescence intensity attenuation model.

[0042] In an embodiment of the present application, the characteristic energy peak of the precious metal element is first identified from the X-ray fluorescence spectrum signal, and the characteristic peak intensity value in the form of photon counts is extracted; secondly, the intensity value and the corrected geometric attenuation factor are input into the fluorescence spectrum intensity attenuation model; finally, the thickness equation is solved by the Newton iteration method, and the thickness value of the precious metal coating is output.

[0043] The following is a specific example: First, the surface of gold alloy jewelry is scanned by a dual laser positioning device to generate a spatial coordinate grid of the area to be inspected; secondly, the zoom camera is driven to collect multi-angle images along the coordinate grid, and the images are fused to generate three-dimensional morphology data including curvature distribution; then, the position of the X-ray source robotic arm is adjusted according to the curvature data, so that the X-ray beam is vertically directed to the inspection point on the ring setting surface; when the X-rays excite the sample, the X-ray fluorescence spectrum signal is collected using a silicon drift detector; at the same time, the geometric attenuation factor is calculated based on the main curvature radius of the current inspection point, and the fluorescence intensity attenuation model is corrected; finally, the characteristic peak intensity value of the gold element is extracted and input into the correction model, and the thickness of the gold plating layer of the ring setting is iteratively solved.

[0044] By executing S11 to S16, the embodiment of the present application ensures the full vertical incidence of the X-ray beam through dynamic posture control, and combines the curvature-adaptive geometric attenuation correction model to effectively eliminate the signal distortion caused by complex surfaces and realize high-precision non-destructive testing of coating thickness.

[0045] In one possible embodiment, S16, extracting the characteristic peak intensity value of the noble metal element from the X-ray fluorescence spectrum signal, inputting the characteristic peak intensity value of the noble metal element into the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor, and analyzing and outputting the noble metal coating thickness value, includes: Step 161: Based on the preset characteristic energy of the noble metal element, locate the corresponding characteristic peak position in the X-ray fluorescence spectrum signal, and use the intensity value of the characteristic peak position as the characteristic peak intensity value of the noble metal element.

[0046] The preset characteristic energy refers to the energy value of the characteristic X-ray photons released by the precious metal element upon stimulation. It is pre-set based on the element's atomic number and is used to identify the target peak position in the spectral signal. The characteristic peak position refers to the energy channel position on the X-ray fluorescence spectrum curve corresponding to the preset characteristic energy value, and its intensity reflects the element content.

[0047] In the embodiment of the present application, first, a preset characteristic energy value of the noble metal element is determined based on the atomic energy level structure of the noble metal element; second, a peak position corresponding to the characteristic energy value is located on the energy axis of the X-ray fluorescence spectrum signal; and finally, the photon counting intensity value at the peak position is extracted as the characteristic peak intensity value of the noble metal element.

[0048] Step 162 : Combining the characteristic peak intensity value of the noble metal element with the geometric attenuation factor corresponding to the curvature data of the surface detection point to form input parameters for the fluorescence spectrum intensity attenuation model after correction of the geometric attenuation factor.

[0049] The input parameters refer to the data vector constructed by the compensated characteristic peak intensity value and the geometric attenuation factor, which is used as the initial input of the thickness analysis model after normalization.

[0050] In an embodiment of the present application, the characteristic peak intensity value is first multiplied by the geometric attenuation factor value corresponding to the curvature data of the surface detection point to generate a compensated characteristic peak intensity value; secondly, the compensation value and the geometric attenuation factor are combined into a multidimensional parameter vector; finally, the vector is converted into the standard input parameter of the fluorescence spectrum intensity attenuation model after the geometric attenuation factor is corrected through a standardized mapping layer.

[0051] Step 163: Using the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor and the input parameters, iteratively solve the model-predicted intensity value.

[0052] Iterative solution refers to a mathematical optimization process that gradually approaches the true solution by repeatedly calculating the difference between the model prediction value and the measured value and updating the thickness assumption value. The model-predicted intensity value refers to the theoretical X-ray fluorescence intensity calculated by the fluorescence spectrum intensity attenuation model based on the current coating thickness assumption value. The equation of the fluorescence spectrum intensity attenuation model is: in, is the predicted intensity value, is the geometric attenuation factor, is the saturated fluorescence intensity of an infinitely thick sample, is the mass absorption coefficient of the noble metal element, is the coating density, is the thickness of the precious metal coating. The inverse function is: The association between the model equation and inverse function and the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor provides a mathematical basis for thickness analysis by introducing the equation and inverse function of the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor; the iterative process uses the partial derivatives derived from the inverse function to dynamically update the thickness assumption value, and finally achieves high-precision inversion from compensation intensity to coating thickness.

[0053] In an embodiment of the present application, the input parameters are first loaded into the fluorescence spectrum intensity attenuation model after correction of the geometric attenuation factor; secondly, the model equation is solved by the Newton-Raphson iterative algorithm, and each iteration outputs the model predicted intensity value corresponding to the current thickness assumption value.

[0054] Step 164 , during the iterative solution process, based on the difference between the characteristic peak intensity value of the precious metal element and the model predicted intensity value, the precious metal coating thickness value is updated until the preset convergence condition is met, thereby obtaining the precious metal coating thickness value.

[0055] The iterative solution process refers to the calculation process of cyclically executing the predicted intensity calculation, measured value comparison, and thickness value update. The difference refers to the absolute value of the residual between the measured value of the characteristic peak intensity and the model predicted value, which drives the direction of the thickness value update. The preset convergence condition refers to the judgment standard for terminating the iterative calculation, usually when the difference value is less than the set threshold or the number of iterations reaches the upper limit. The preset convergence condition must simultaneously meet the following dual judgment criteria: the absolute difference between the measured value of the characteristic peak intensity of the current iteration step and the model predicted value is less than the preset residual threshold, and when the number of iterations reaches the preset maximum number of iterations, it prevents infinite loops in the case of non-convergence.

[0056] In an embodiment of the present application, the absolute difference between the characteristic peak intensity value and the model predicted intensity value is first calculated; secondly, the assumed value of the precious metal coating thickness is adjusted according to the direction of the difference value; finally, the iterative process is repeated until the difference value is less than the preset convergence threshold, and the precious metal coating thickness value that finally meets the accuracy requirements is output.

[0057] The following is a specific example: First, based on the preset characteristic energy of the gold element, the specific peak position in the X-ray fluorescence spectrum is located, and the photon counting intensity is extracted as the characteristic peak intensity value. Second, this intensity value is multiplied by the geometric attenuation factor of the current surface detection point to generate a compensation value, which is combined into a standardized input parameter. The input parameter is then loaded into the modified fluorescence intensity attenuation model, and the predicted intensity corresponding to different thickness assumptions is solved using the Newton iteration method. Finally, the predicted intensity is continuously compared with the measured characteristic peak intensity. When the difference is less than the set threshold, the precise thickness of the gold plating layer of the ring is output.

[0058] By executing steps 161 to 164, the embodiment of the present application dynamically compensates for surface geometric distortion and establishes a standardized model input, combined with an iterative optimization algorithm, to effectively improve the accuracy and calculation stability of complex surface coating thickness analysis.

[0059] In one possible embodiment, step 162, combining the characteristic peak intensity value of the noble metal element with the geometric attenuation factor corresponding to the curvature data of the surface detection point to form input parameters for the fluorescence spectrum intensity attenuation model after the geometric attenuation factor correction, includes: Step a1: multiply the characteristic peak intensity value of the noble metal element by the geometric attenuation factor value to generate a compensated characteristic peak intensity value.

[0060] The multiplication operation refers to the arithmetic multiplication of the characteristic peak intensity value and the geometric attenuation factor value to compensate for the light intensity attenuation caused by surface geometric deformation. The compensated characteristic peak intensity value is the corrected value obtained by multiplying the original characteristic peak intensity value by the geometric attenuation factor, reflecting the true fluorescence intensity after eliminating the influence of surface distortion.

[0061] In an embodiment of the present application, the original characteristic peak intensity value of the precious metal element and the geometric attenuation factor value corresponding to the surface detection point are first obtained; secondly, the original characteristic peak intensity value and the geometric attenuation factor value are numerically multiplied by an arithmetic multiplication unit; finally, the product result is output as the compensated characteristic peak intensity value.

[0062] Step a2: Combining the compensated characteristic peak intensity value and the geometric attenuation factor value into a parameter vector.

[0063] The parameter vector refers to a two-dimensional array data structure consisting of the compensated characteristic peak intensity value and the geometric attenuation factor, which carries the dual variable input required by the model.

[0064] In the embodiment of the present application, the compensated characteristic peak intensity value is first converted into floating-point data; secondly, the digital form of the current geometric attenuation factor value is read; finally, the two are sequentially combined into a two-dimensional parameter vector through a vector constructor.

[0065] Step a3: Map the parameter vector to the standard input format of the fluorescence spectrum intensity attenuation model after the correction of the geometric attenuation factor to obtain input parameters.

[0066] The standard input format refers to the standardized data interface requirements defined by the fluorescence spectral intensity attenuation model, including numerical range, dimension order, and data type constraints.

[0067] In an embodiment of the present application, the predefined standard input format specification of the fluorescence spectral intensity attenuation model after geometric attenuation factor correction is first loaded; secondly, the numerical range of the parameter vector is mapped to the numerical interval required by the standard input format through the linear transformation layer; finally, the formatted input parameters that meet the requirements of the model interface are output.

[0068] The following is a specific example: First, the characteristic peak intensity value of the gold element and the geometric attenuation factor value calculated based on the principal curvature radius are input into the multiplier to generate the compensated characteristic peak intensity value; secondly, the compensation value and the geometric attenuation factor are combined into a parameter vector through the data encapsulation module; finally, based on the standardization requirements defined by the fluorescence intensity attenuation model, the parameter vector is normalized to generate formatted parameters that can be directly input into the thickness analysis model.

[0069] By executing steps a1 to a3, the embodiment of the present application establishes a standardized model input interface through intensity compensation and vectorized data reconstruction, ensures data compatibility of different surface detection points, and improves the generalization ability of the thickness analysis model.

[0070] In a possible embodiment, step a1, multiplying the characteristic peak intensity value of the noble metal element by the geometric attenuation factor value to generate a compensated characteristic peak intensity value, includes: Step a11: convert the characteristic peak intensity value of the noble metal element into a characteristic peak intensity value in the form of photon counting.

[0071] Among them, the photon counting form refers to the discrete digital signal form output by the X-ray fluorescence detector, which directly reflects the radiation intensity by counting the number of photon collisions per unit time.

[0072] Step a12: derive the path length correction coefficient and the incident angle correction coefficient from the geometric attenuation factor value.

[0073] The path length correction factor is a scalar coefficient calculated based on the principal radius of curvature of the surface. It is used to compensate for the intensity attenuation of X-rays due to path length differences when propagating on the surface. The incident angle correction factor is a cosine correction term calculated based on the angle between the surface normal vector and the X-ray beam. It is used to correct for the loss of effective excitation area caused by non-perpendicular incidence.

[0074] Step a13: multiply the path length correction coefficient by the incident angle correction coefficient to generate a composite compensation weight value.

[0075] The composite compensation weight value refers to the integrated correction factor obtained by multiplying the path length correction coefficient by the incident angle correction coefficient, which comprehensively represents the degree of fluorescence attenuation caused by surface geometric distortion.

[0076] Step a14: multiply the characteristic peak intensity value in the form of photon counting by the composite compensation weight value to generate a compensated photon counting intensity value.

[0077] Step a15: converting the compensated photon counting intensity value into a compensated characteristic peak intensity value.

[0078] Among them, the compensated photon counting intensity value is converted by inverse restoration of the detector response function. The specific process is to call the photon count and voltage conversion coefficients pre-stored in the X-ray fluorescence detector, multiply the compensated photon counting intensity value by the conversion coefficient, output an analog voltage signal, and scale it to the standard characteristic peak intensity value dimension according to the reference voltage ratio based on the energy spectrometer gain setting to generate the final compensated characteristic peak intensity value.

[0079] The following is a specific example: first, the characteristic peak intensity value of the gold element is converted into a digital signal in the form of photon counts; second, the path length correction factor and the incident angle correction factor are parsed from the geometric attenuation factor; then the two are multiplied together to generate a composite compensation weight value; then the weight value is multiplied by the photon counting intensity value to obtain the compensated photon count; finally, the compensated photon count is converted into an analog characteristic peak intensity value to complete the fine correction of the surface geometric distortion.

[0080] By executing steps a11 to a15, the embodiment of the present application accurately eliminates the path length and incident angle coupling errors in surface detection by analyzing the geometric attenuation factor step by step and implementing photon counting level compensation, thereby improving the physical authenticity of the thickness inversion model.

[0081] In a possible embodiment, S15, correcting the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model using a preset surface distance correction algorithm based on the curvature data of the surface detection point, includes: Step 151: Extract the principal curvature radius value and the normal vector direction angle value according to the curvature data of the surface detection point.

[0082] The principal radius of curvature is the radius of curvature in the direction of maximum curvature at the surface inspection point. It is calculated using differential geometry algorithms based on 3D topography data and reflects the local steepness of the surface. The normal vector angle is the angle between the normal line at the surface inspection point and the reference axis of the global coordinate system. It is used to determine the deviation of the X-ray incident direction from the normal.

[0083] Step 152: Calculate a path length correction factor based on the principal curvature radius value and the nominal distance of the X-ray source.

[0084] Among them, the nominal distance of the X-ray source refers to the vertical design distance from the focus of the X-ray source to the detection point on the surface of the precious metal sample in an ideal plane state. The source and determination method of the nominal distance of the X-ray source are based on the mechanical design value as the basic parameter of the equipment, and the vertical distance between the installation position of the X-ray source and the reference plane of the sample stage is pre-set. It is then obtained by inversion optimization of the actual measured value of the X-ray fluorescence intensity of the plane standard sample and the theoretical model to eliminate mechanical assembly errors. The nominal distance of the X-ray source is used as the benchmark reference value for geometric attenuation calculation, and is compared with the actual path length in curved surface detection to generate a correction coefficient.

[0085] The nominal distance refers to the straight-line distance from the focus of the X-ray source to the normal projection position of the surface detection point, which serves as the benchmark reference value for geometric attenuation calculation.

[0086] Step 153: Determine an incident angle correction coefficient based on the normal vector direction angle value and the incident direction of the X-ray beam.

[0087] Among them, the incident direction of the X-ray beam refers to the propagation vector of the high-energy photon beam emitted by the X-ray source in three-dimensional space. The source and determination method of the incident direction of the X-ray beam are based on the reference emission direction preset in the coordinate system of the mechanical base of the X-ray source, and are determined by solving the current beam direction vector through the real-time Euler angle feedback from the rotary encoder of the posture adjustment mechanism.

[0088] Step 154: Multiply the path length correction factor by the incident angle correction factor to generate an uncorrected geometric attenuation factor value.

[0089] Among them, the uncorrected geometric attenuation factor value refers to the primary product of the path length correction coefficient and the incident angle correction coefficient, and the intermediate variable of the surface distance weight compensation has not yet been considered.

[0090] Step 155: Perform distance weight adjustment on the uncorrected geometric attenuation factor value using a preset surface distance correction algorithm, and output a corrected geometric attenuation factor value.

[0091] Among them, distance weight adjustment refers to applying distance-related compensation to the uncorrected geometric attenuation factor through the weight function in the surface distance correction algorithm to eliminate the model error in the curvature mutation area.

[0092] The following is a specific example: First, the principal curvature radius and normal vector angle values ​​are extracted based on the curvature data of the ring surface detection point; then, the path length correction factor is calculated based on the principal curvature radius and the nominal distance of the X-ray source, and the incident angle correction factor is determined based on the angle between the normal vector angle and the incident light beam; then, the two correction factors are multiplied together to generate an uncorrected geometric attenuation factor value; finally, the value is distance-weighted using the surface distance correction algorithm, and the final corrected geometric attenuation factor value is output for fluorescence intensity compensation.

[0093] By executing steps 151 to 155, the embodiment of the present application decouples the influence of path length and incident angle in steps and introduces curvature-adaptive distance weight compensation to achieve refined modeling of the geometric attenuation factor.

[0094] In a possible embodiment, S14, when the X-ray beam is vertically incident on the curved surface detection point, exciting the precious metal sample to generate an X-ray fluorescence spectrum signal, and collecting the X-ray fluorescence spectrum signal using an X-ray fluorescence detector, includes: Step 141: When the X-ray beam is vertically incident on the curved surface detection point, the pulse emission timing and pulse width of the X-ray source are adjusted based on the curvature data of the curved surface detection point to generate pulse parameters that adapt to the curved surface morphology.

[0095] Among them, pulse emission timing refers to the time series parameters that control the emission of pulses by the X-ray source, including the starting time and cycle interval, which are used to match the dynamic position changes of the surface detection point. Pulse width refers to the duration of a single X-ray pulse, which is adaptively adjusted according to the surface curvature to optimize the excitation photon flux. Adapting the surface morphology refers to the process of dynamically matching the X-ray pulse parameters to the local geometric characteristics, including timing synchronization and pulse width scaling. Pulse parameters refer to the set of instructions including pulse emission timing, pulse width and energy intensity, which drive the X-ray source to perform surface adaptive excitation.

[0096] Step 142: trigger the X-ray source to emit an X-ray beam according to the pulse parameters, and excite the noble metal sample to generate an X-ray fluorescence spectrum signal at the curved surface detection point.

[0097] Step 143: Use an X-ray fluorescence detector to collect X-ray fluorescence spectrum signals at a preset sampling frequency.

[0098] The preset sampling frequency refers to the signal acquisition rate set by the X-ray fluorescence detector to ensure that enough data points are obtained per unit time to reconstruct the energy spectrum.

[0099] The following is a specific example: First, when the X-ray beam is vertically incident on the detection point of the ring surface, the delay amount of the pulse emission timing and the scaling ratio of the pulse width are adjusted based on the normal vector change rate of the current curvature data to generate pulse parameters adapted to the local morphology; then, the X-ray source is triggered to emit a pulse beam according to the parameters, exciting the precious metal coating to generate an X-ray fluorescence spectrum signal; finally, the spectrum signal is collected and digitized by the X-ray fluorescence detector at a set sampling frequency.

[0100] By executing steps 141 to 143, the embodiment of the present application optimizes the X-ray excitation efficiency and signal-to-noise ratio through pulse parameter control adaptive to the surface morphology, thereby ensuring the stability of the fluorescence signal quality in complex surface detection.

[0101] In a possible embodiment, in step 141, based on the curvature data of the curved surface detection point, adjusting the pulse emission timing and pulse width of the X-ray source to generate pulse parameters adapted to the curved surface topography includes: Step b1: Calculate the normal vector offset angle according to the normal vector change rate in the curvature data of the surface detection point.

[0102] The normal vector rate of change refers to the change in the surface normal direction per unit displacement. It is calculated using vector differentials based on 3D topography data and reflects the degree of surface geometric abruptness. The normal vector offset angle refers to the spatial angle between the normal at the current detection point and the ideal perpendicular incident normal. It is calculated using the inverse cosine function of the vector dot product.

[0103] Step b2: determining a compensation amount for the pulse emission timing according to a proportional relationship between the normal vector offset angle and a preset X-ray source position offset threshold.

[0104] The position offset threshold refers to the maximum allowable position deviation angle of the X-ray source mechanical system, serving as the critical criterion for triggering pulse timing compensation. The proportional relationship, defined as the arithmetic ratio of the normal vector offset angle to the position offset threshold, is used to linearly map the pulse timing compensation. The compensation amount refers to the time adjustment required to advance or delay the pulse emission to ensure synchronization of X-ray excitation with dynamic position changes.

[0105] Step b3: combining the compensation amount of the pulse emission timing and the pulse width into a pulse parameter instruction set.

[0106] Among them, the pulse parameter instruction set refers to structured control instructions including timing compensation amount, pulse width and energy intensity, which are encoded as machine-parseable data packets.

[0107] Step b4: parse the pulse parameter instruction set through the X-ray source controller to generate pulse parameters that adapt to the surface morphology.

[0108] Among them, the X-ray source controller refers to an embedded hardware system that receives pulse parameter instruction sets and converts them into high-voltage pulse drive signals to achieve precise execution of excitation parameters.

[0109] The following is a specific example: first, the normal vector offset angle is calculated based on the normal vector change rate of the ring ring surface detection point; second, the pulse emission timing compensation amount is determined according to the proportional relationship between this angle and the position offset threshold; then the compensation amount and the pulse width are combined into a pulse parameter instruction set; finally, the X-ray source controller parses the instruction set to generate pulse parameters adapted to the local surface, achieving precise matching of the excitation process and the dynamic posture.

[0110] By executing steps b1 to b4, the embodiment of the present application realizes closed-loop dynamic compensation of pulse timing through real-time quantization of normal vector offset and threshold ratio mapping, thereby ensuring precise synchronization of X-ray excitation and posture changes in high-speed detection of curved surfaces.

[0111] Figure 2 A schematic diagram of the structure of a precious metal analyzer coating detection system based on X-ray fluorescence spectroscopy provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the system includes: The positioning module 21 is used to locate the area to be detected on the surface of the precious metal sample using a dual laser positioning device to obtain spatial coordinates.

[0112] The driving module 22 is used to drive the zoom camera to scan the surface morphology of the area to be detected based on the spatial coordinates to generate three-dimensional morphology spatial data.

[0113] The detection module 23 is used to adjust the coordinated posture of the X-ray source and the dual laser positioning device based on the curvature data of each curved surface detection point in the three-dimensional morphology space data, so as to control the X-ray beam to be vertically incident on each curved surface detection point.

[0114] The acquisition module 24 is used to excite the precious metal sample to generate an X-ray fluorescence spectrum signal when the X-ray beam is vertically incident on the curved surface detection point, and to acquire the X-ray fluorescence spectrum signal using an X-ray fluorescence detector.

[0115] The positioning module 25 is used to correct the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model using a preset surface distance correction algorithm according to the curvature data of the surface detection point.

[0116] The correction module 26 is used to extract the characteristic peak intensity value of the precious metal element from the X-ray fluorescence spectrum signal, input the characteristic peak intensity value of the precious metal element into the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor, and analyze and output the precious metal coating thickness value.

[0117] Figure 2 The X-ray fluorescence spectrum-based precious metal analyzer coating detection system can perform Figure 1 The implementation principles and technical effects of the X-ray fluorescence spectroscopy-based precious metal analyzer coating detection method described in the illustrated embodiment are not further elaborated. The specific manner in which the various modules and units perform operations in the X-ray fluorescence spectroscopy-based precious metal analyzer coating detection system described in the above embodiment have been described in detail in the related embodiments of the method and will not be further elaborated here.

[0118] In one possible design, Figure 2 The embodiment shown is a precious metal analyzer coating detection system based on X-ray fluorescence spectroscopy that can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32 .

[0119] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0120] The processing component 32 is used to execute the following process: using a dual laser positioning device to locate the area to be inspected on the surface of the precious metal sample to obtain spatial coordinates; based on the spatial coordinates, driving the zoom camera to scan the curved surface morphology of the area to be inspected to generate three-dimensional morphological spatial data; based on the curvature data of each curved surface detection point in the three-dimensional morphological spatial data, adjusting the coordinated posture of the X-ray source and the dual laser positioning device to control the X-ray beam to be vertically incident on each curved surface detection point; when the X-ray beam is vertically incident on the curved surface detection point, exciting the precious metal sample to generate an X-ray fluorescence spectrum signal, and using an X-ray fluorescence detector to collect the X-ray fluorescence spectrum signal; based on the curvature data of the curved surface detection point, using a preset curved surface distance correction algorithm to correct the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model; extracting the characteristic peak intensity value of the precious metal element from the X-ray fluorescence spectrum signal, inputting the characteristic peak intensity value of the precious metal element into the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor, and analyzing and outputting the precious metal coating thickness value.

[0121] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0122] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as random access memory (RAM), static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0123] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0124] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0125] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0126] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0127] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a precious metal analyzer coating detection method based on X-ray fluorescence spectroscopy.

[0128] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0129] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0130] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A precious metal analyzer coating detection method based on X-ray fluorescence spectroscopy, characterized in that: include: A dual laser positioning device is used to locate the area to be tested on the surface of the precious metal sample to obtain the spatial coordinates; Based on the spatial coordinates, driving the zoom camera to scan the surface morphology of the area to be detected to generate three-dimensional morphology spatial data; Based on the curvature data of each curved surface detection point in the three-dimensional topography space data, adjusting the coordinated posture of the X-ray source and the dual laser positioning device to control the X-ray beam to be vertically incident on each curved surface detection point; When the X-ray beam is vertically incident on the curved surface detection point, the precious metal sample is excited to generate an X-ray fluorescence spectrum signal, and the X-ray fluorescence spectrum signal is collected by an X-ray fluorescence detector; According to the curvature data of the surface detection point, the preset surface distance correction algorithm is used to correct the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model; The characteristic peak intensity value of the precious metal element is extracted from the X-ray fluorescence spectrum signal, and the characteristic peak intensity value of the precious metal element is input into the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor to analyze and output the precious metal coating thickness value.

2. The method according to claim 1, characterized in that The method of extracting the characteristic peak intensity value of the noble metal element from the X-ray fluorescence spectrum signal, inputting the characteristic peak intensity value of the noble metal element into the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor, and analyzing and outputting the noble metal coating thickness value includes: Based on the preset characteristic energy of the noble metal element, locating the corresponding characteristic peak position in the X-ray fluorescence spectrum signal, and using the intensity value of the characteristic peak position as the characteristic peak intensity value of the noble metal element; Combining the characteristic peak intensity value of the noble metal element with the geometric attenuation factor corresponding to the curvature data of the curved surface detection point to form input parameters for a fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor; Iteratively solving the model-predicted intensity value using the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor and the input parameters; During the iterative solution process, the thickness value of the precious metal coating is updated based on the difference between the characteristic peak intensity value of the precious metal element and the intensity value predicted by the model until a preset convergence condition is met to obtain the thickness value of the precious metal coating.

3. The method according to claim 2, characterized in that The step of combining the characteristic peak intensity value of the noble metal element with the geometric attenuation factor corresponding to the curvature data of the curved surface detection point to form input parameters for a fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor includes: Multiplying the characteristic peak intensity value of the noble metal element by the geometric attenuation factor value to generate a compensated characteristic peak intensity value; Combining the compensated characteristic peak intensity value and the geometric attenuation factor value into a parameter vector; The parameter vector is mapped to a standard input format of the fluorescence spectrum intensity attenuation model after correction of the geometric attenuation factor to obtain input parameters.

4. The method according to claim 3, characterized in that The multiplying the characteristic peak intensity value of the noble metal element by the geometric attenuation factor value to generate a compensated characteristic peak intensity value includes: Converting the characteristic peak intensity value of the noble metal element into a characteristic peak intensity value in the form of photon counting; Resolving a path length correction factor and an angle of incidence correction factor from the geometric attenuation factor value; multiplying the path length correction factor by the incident angle correction factor to generate a composite compensation weight value; Multiplying the characteristic peak intensity value of the photon counting form by the composite compensation weight value to generate a compensated photon counting intensity value; The compensated photon counting intensity value is converted into a compensated characteristic peak intensity value.

5. The method according to claim 1, wherein The method of correcting the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model using a preset surface distance correction algorithm based on the curvature data of the surface detection point includes: Extracting the principal curvature radius value and the normal vector direction angle value according to the curvature data of the surface detection point; Calculating a path length correction factor based on the principal radius of curvature value and the nominal distance of the X-ray source; determining an incident angle correction coefficient based on the normal vector direction angle value and the incident direction of the X-ray beam; multiplying the path length correction factor by the angle of incidence correction factor to generate an uncorrected geometric attenuation factor value; The uncorrected geometric attenuation factor value is adjusted for distance weight using a preset surface distance correction algorithm, and a corrected geometric attenuation factor value is output.

6. The method according to claim 1, characterized in that When the X-ray beam is vertically incident on the curved surface detection point, the precious metal sample is excited to generate an X-ray fluorescence spectrum signal, and the X-ray fluorescence spectrum signal is collected by using an X-ray fluorescence detector, including: When the X-ray beam is vertically incident on a curved surface detection point, the pulse emission timing and pulse width of the X-ray source are adjusted based on the curvature data of the curved surface detection point to generate pulse parameters that adapt to the curved surface topography; triggering the X-ray source to emit the X-ray beam according to the pulse parameters, exciting the noble metal sample to generate an X-ray fluorescence spectrum signal at the curved surface detection point; The X-ray fluorescence spectrum signal is collected using an X-ray fluorescence detector at a preset sampling frequency.

7. The method according to claim 6, characterized in that The method of adjusting the pulse emission timing and pulse width of the X-ray source based on the curvature data of the curved surface detection point to generate pulse parameters adapted to the curved surface morphology includes: Calculating a normal vector offset angle according to a normal vector change rate in the curvature data of the surface detection point; determining a compensation amount for the pulse emission timing according to a proportional relationship between the normal vector offset angle and a preset X-ray source position offset threshold; combining the compensation amount of the pulse emission timing and the pulse width into a pulse parameter instruction set; The pulse parameter instruction set is parsed by an X-ray source controller to generate pulse parameters that are adapted to the curved surface morphology.

8. A precious metal analyzer coating detection system based on X-ray fluorescence spectroscopy, characterized in that: include: A positioning module is used to locate the area to be tested on the surface of the precious metal sample using a dual laser positioning device to obtain spatial coordinates; A driving module, configured to drive a zoom camera to scan the surface topography of the area to be detected based on the spatial coordinates, and generate three-dimensional topography spatial data; a detection module, configured to adjust the coordinated posture of the X-ray source and the dual laser positioning device based on curvature data of each curved surface detection point in the three-dimensional topography space data, so as to control the X-ray beam to be vertically incident on each curved surface detection point; an acquisition module, configured to excite the precious metal sample to generate an X-ray fluorescence spectrum signal when the X-ray beam is vertically incident on the curved surface detection point, and to acquire the X-ray fluorescence spectrum signal using an X-ray fluorescence detector; The positioning module is used to correct the geometric attenuation factor in the X-ray fluorescence spectrum intensity attenuation model based on the curvature data of the surface detection point using a preset surface distance correction algorithm; The correction module is used to extract the characteristic peak intensity value of the precious metal element from the X-ray fluorescence spectrum signal, input the characteristic peak intensity value of the precious metal element into the fluorescence spectrum intensity attenuation model corrected by the geometric attenuation factor, and analyze and output the precious metal coating thickness value.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a precious metal analyzer coating detection method based on X-ray fluorescence spectroscopy as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the method for detecting coatings using a precious metal analyzer based on X-ray fluorescence spectroscopy as claimed in any one of claims 1 to 7 is implemented.

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