Precision point measurement method and system for precious metal analyzers based on micro-area X-ray fluorescence spectroscopy

Abstract

The present invention provides a precise point measurement method and system for a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy. The method applies an X-ray beam to a preset point to be measured, uses microbeam focusing technology to adjust the focus of the X-ray beam, and generates a micro-area spot aligned with the point measurement position of the preset point to be measured in real time. The X-ray fluorescence signal generated by the micro-area spot is collected. A preset adaptive digital filter is used to dynamically suppress noise on the X-ray fluorescence signal to generate a de-noised X-ray fluorescence signal. The de-noised X-ray fluorescence signal is input into a preset spectral peak automatic identification algorithm to obtain characteristic peak data. The characteristic peak data is then matched with a preset database of precious metal element types to generate an element point measurement result. The present invention solves the complex problems of displacement drift, signal distortion, and element misidentification in micro-area point measurement, significantly improving the accuracy and efficiency of precious metal analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of analyzer point measurement, and in particular to a precise point measurement method and system for a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy. Background Art

[0002] The increasing importance of precious metals testing in fields such as jewelry authentication, materials science, and industrial recycling has placed higher demands on analyzers for accuracy and efficiency, particularly in point-of-sale testing scenarios involving high-speed moving samples, such as rapid laboratory screening. These applications require equipment capable of achieving stable, high-precision element identification at predetermined test points, even with slight sample displacement or continuous movement, to avoid data distortion and misjudgment caused by positional deviations.

[0003] The current mainstream solution is to use a fixed spot size combined with static signal processing for point measurement and analysis. This method forms an X-ray excitation area of ​​a certain size by setting standard focusing parameters, and combines a pre-calibrated signal acquisition window to excite and read data at specific locations on the sample surface. The system usually relies on a highly repeatable mechanical positioning platform to ensure the consistency of the relative position between the sample and the detector during each measurement, thereby improving the stability of the measurement results. Existing solutions have some inherent defects, including the difficulty of fixed spots to adapt to the spatial differences between different test points, causing the actual excitation area to deviate from the target position, affecting detection accuracy; the static signal processing mechanism cannot effectively deal with noise interference caused by environmental disturbances or sample movement during the measurement process, resulting in inaccurate feature signal extraction, which ultimately affects the reliability of element identification. Summary of the Invention

[0004] The present invention provides a precise point measurement method and system for a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy, which is used to solve the problems in the prior art where a fixed light spot is difficult to adapt to the spatial differences of different test points, causing the actual excitation area to deviate from the target position, affecting detection accuracy; the static signal processing mechanism is unable to effectively cope with noise interference caused by environmental disturbances or sample movement during the measurement process, resulting in inaccurate feature signal extraction, which ultimately affects the reliability of element identification.

[0005] In a first aspect, the present invention provides a method for precise point measurement of precious metals using a micro-area X-ray fluorescence spectroscopy analyzer, comprising:

[0006] Applying an X-ray beam to a preset point to be measured, adjusting the focus position of the X-ray beam using micro-beam focusing technology, and generating a micro-area light spot that is aligned with the point measurement position of the preset point to be measured in real time;

[0007] collecting X-ray fluorescence signals generated by the excitation of the micro-area light spot;

[0008] Performing dynamic noise suppression processing on the X-ray fluorescence signal using a preset adaptive digital filter to generate a noise-reduced X-ray fluorescence signal;

[0009] Inputting the de-noised X-ray fluorescence signal into a preset spectral peak automatic recognition algorithm to obtain characteristic peak data;

[0010] The characteristic peak data is matched with a preset database of precious metal element types to generate element point measurement results.

[0011] Optionally, applying an X-ray beam to a preset point to be measured, adjusting the focus position of the X-ray beam using a micro-beam focusing technology, and generating a micro-area spot aligned with the point measurement position of the preset point to be measured in real time, includes:

[0012] Obtain the current actual position of the preset test point to obtain position acquisition data;

[0013] Calculating a vector difference between the position acquisition data and the coordinates of the preset measured point as a position deviation;

[0014] Converting the position deviation into a beam deflection angle parameter to generate a focus adjustment instruction;

[0015] Applying the focus adjustment instruction to change the focus position of the X-ray beam, and obtaining the current actual position of the changed focus position to obtain new position acquisition data;

[0016] Calculating the vector difference between the new position acquisition data and the coordinates of the preset measured point as the new position deviation;

[0017] When the new position deviation is smaller than a preset tolerance threshold, a micro-area light spot is generated that is aligned with the point measurement position of the preset point to be measured in real time.

[0018] Optionally, collecting the X-ray fluorescence signal generated by the micro-area spot excitation includes:

[0019] Using a preset multi-channel acquisition device to receive the physical signal excited by the micro-area light spot to generate an original signal sequence;

[0020] Extracting key parameters of characteristic components in the original signal sequence to generate a characteristic parameter set;

[0021] The characteristic parameter set is mapped to a preset energy spectrum channel range to generate a structured signal as an X-ray fluorescence signal.

[0022] Optionally, performing dynamic noise suppression processing on the X-ray fluorescence signal using a preset adaptive digital filter to generate a noise-reduced X-ray fluorescence signal includes:

[0023] Acquiring the displacement velocity of the preset test point and the time domain characteristic parameters of the X-ray fluorescence signal, correlating the displacement velocity and the time domain characteristic parameters to generate dynamic noise model parameters;

[0024] The frequency response characteristics of a preset adaptive digital filter are adjusted using the dynamic noise model parameters to generate an adaptive filtering rule;

[0025] Applying the adaptive filtering rule to filter the X-ray fluorescence signal to generate an intermediate processed signal;

[0026] Analyzing the characteristic distribution state of the intermediate processed signal to generate characteristic distribution parameters;

[0027] The effective characteristic components of the intermediate processed signal are reconstructed based on the characteristic distribution parameters to generate a noise-reduced X-ray fluorescence signal.

[0028] Optionally, reconstructing effective characteristic components of the intermediate processed signal based on the characteristic distribution parameters to generate a noise-reduced X-ray fluorescence signal includes:

[0029] Classify the effective feature components of the intermediate processed signal according to the feature distribution parameters to generate a feature category set;

[0030] Applying the feature category set and the displacement velocity of the preset test point to calculate a dynamic weight coefficient to generate a weight distribution rule;

[0031] Recombining the effective feature components according to the weight distribution rule to generate a reconstructed feature signal;

[0032] The reconstructed characteristic signal and the background component in the original signal sequence are fused to generate a noise-reduced X-ray fluorescence signal.

[0033] Optionally, the noise-reduced X-ray fluorescence signal is input into a preset spectral peak automatic identification algorithm to obtain characteristic peak data, including:

[0034] Decomposing the multi-level spectral lines of the noise-reduced X-ray fluorescence signal to generate a set of candidate spectral peaks and a background residual component;

[0035] Verifying the energy spectrum continuity of each peak in the candidate peak set to generate a continuous peak identifier;

[0036] Analyzing the shape evolution characteristics of the continuous spectrum peak corresponding to the continuous spectrum peak identifier to generate dynamic peak shape parameters;

[0037] Correlating the dynamic peak shape parameter with the temporal relationship of the displacement velocity of the preset test point to generate a peak shape-velocity correlation feature;

[0038] Comparing the peak shape-velocity correlation feature with a preset peak shape benchmark to generate a valid spectrum peak identifier;

[0039] The energy level position parameters of the effective spectrum peak corresponding to the effective spectrum peak identifier are extracted to obtain characteristic peak data.

[0040] Optionally, matching the characteristic peak data with a preset database of precious metal element types to generate element point measurement results includes:

[0041] Analyzing the energy level position parameters of the characteristic peak data to generate an energy level sequence to be matched;

[0042] Extracting element energy level features that meet a preset energy level range threshold from a preset noble metal element type database to generate a candidate element set;

[0043] Calculating the energy level offset between the to-be-matched energy level sequence and the candidate element set to generate an offset parameter set;

[0044] Mapping the offset identifier of the minimum offset parameter in the offset parameter set to the element identifier corresponding to the candidate element set to generate the best matching element identifier;

[0045] The correlation consistency between the optimal matching element identifier and the displacement velocity of the preset test point is verified, and when the verification result meets a preset consistency threshold, the element point measurement result is generated.

[0046] In a second aspect, the present invention provides a precise point measurement system for a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy, comprising:

[0047] An adjustment module is used to apply an X-ray beam to a preset point to be measured, and to adjust the focus position of the X-ray beam using a micro-beam focusing technology to generate a micro-area light spot that is aligned with the point measurement position of the preset point to be measured in real time;

[0048] An acquisition module, used for acquiring X-ray fluorescence signals generated by the micro-area light spot excitation;

[0049] a processing module, configured to perform dynamic noise suppression processing on the X-ray fluorescence signal using a preset adaptive digital filter to generate a noise-reduced X-ray fluorescence signal;

[0050] An input module, configured to input the noise-reduced X-ray fluorescence signal into a preset spectral peak automatic recognition algorithm to obtain characteristic peak data;

[0051] The matching module is used to match the characteristic peak data with a preset database of precious metal element types to generate element point measurement results.

[0052] In a third aspect, the present invention provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any one of the methods for precise point measurement of precious metal analyzers based on micro-area X-ray fluorescence spectroscopy described in the first aspect.

[0053] In a fourth aspect, the present invention provides a computer storage medium having computer program instructions stored thereon, which, when executed by a processor, implements a precise point measurement method of a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy as described in any one of the first aspects.

[0054] This method dynamically adjusts the X-ray beam focus position to compensate for sample displacement deviations in real time, ensuring precise alignment of the micro-area spot with the high-speed moving test point. An adaptive digital filter performs speed-sensitive noise suppression on the collected X-ray fluorescence signal to eliminate motion interference. An automatic spectral peak recognition algorithm extracts noise-reduced characteristic peak data, and combined with a preset database, enables rapid matching of precious metal elements. This method solves the complex problems of displacement drift, signal distortion, and element misidentification in micro-area point measurement in fast scanning scenarios, significantly improving the accuracy and efficiency of precious metal analysis.

[0055] Furthermore, a dynamic noise model is constructed by correlating displacement velocity with time-domain characteristic parameters, adaptively adjusting the filter's frequency response to precisely match the high-speed motion noise spectrum. Analysis of the characteristic distribution of the filtered signal guides the reconstruction of characteristic components, suppressing background noise while fully preserving the weak, characteristic peaks of precious metals. This step overcomes the core drawback of traditional filtering, which often causes feature distortion, in fast-scanning scenarios, providing a high-fidelity signal foundation for subsequent peak identification.

[0056] These and other aspects of the present invention will become more readily apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 A flowchart of a method for precise point measurement of precious metals using a micro-area X-ray fluorescence spectroscopy system according to an embodiment of the present invention;

[0059] Figure 2 A schematic structural diagram of a precise point measurement system for a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy provided by an embodiment of the present invention;

[0060] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0062] In some of the processes described in the specification and claims of the present invention 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 article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. 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 article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0064] Figure 1 The present invention provides a flowchart of a method for precise point measurement of precious metals analyzer based on micro-area X-ray fluorescence spectroscopy, as shown in FIG. Figure 1 As shown, the method includes:

[0065] In a fast scanning scenario, the high-speed movement of the sample causes the existing micro-area X-ray fluorescence analysis technology to face three defects: first, the sample displacement causes the light spot to shift and the excitation position to be misaligned; second, the movement introduces high-frequency noise and signal distortion, and the traditional fixed parameter filter cannot adapt to dynamic interference; third, the spectrum peak drift and deformation lead to element misjudgment. The existing methods lack a displacement compensation mechanism, the noise suppression is disconnected from the motion state, and the peak recognition does not take dynamic distortion into account, resulting in a sharp decline in the accuracy of precious metal point measurement, making it difficult to meet the needs of rapid screening. In response to these problems, the research and development ideas of the present invention are: to track the sample position in real time through dynamic beam focusing technology to ensure that the micro-area light spot accurately locks the test point; to establish a dynamic correlation mechanism between the displacement speed and the noise model, to drive the adaptive filter to adjust the frequency response characteristics as needed, and to suppress motion noise in a targeted manner; to repair signal distortion by combining feature distribution reconstruction technology, and to introduce speed-related spectrum peak recognition and element verification logic to form a three-in-one solution of displacement compensation, noise suppression, and dynamic recognition, fundamentally overcoming the problem of accurate point measurement in high-speed scenarios. Based on this, the present invention provides a method for accurate point measurement of precious metal analyzers based on micro-area X-ray fluorescence spectroscopy, such as Figure 1 ,include:

[0066] Step 101: applying an X-ray beam to a preset point to be measured, adjusting the focus position of the X-ray beam using micro-beam focusing technology, and generating a micro-area light spot that is aligned with the point measurement position of the preset point to be measured in real time.

[0067] In this step, the micro-area spot refers to a high-density X-ray irradiation area with a diameter of less than 100 microns formed by micro-beam focusing technology, which is used to excite the material at the test point to produce characteristic X-ray fluorescence.

[0068] In an embodiment of the present invention, an X-ray beam is first emitted to a preset point to be measured by an X-ray generator. Then, the dynamic optical component in the micro-beam focusing technology is used to adjust the beam focusing position in real time. Then, a position feedback closed-loop control is used to accurately align the micro-area light spot with the preset point to be measured. Finally, a micro-area light spot is generated that stably covers the target area.

[0069] Step 102: collecting X-ray fluorescence signals generated by the micro-area light spot excitation.

[0070] In this step, the X-ray fluorescence signal refers to the secondary X-ray photon flow released after the micro-area light spot excites the sample atoms, which contains the element characteristic energy spectrum and intensity information.

[0071] In the embodiment of the present invention, the original X-ray fluorescence signal generated by micro-area spot excitation is first collected by a multi-channel fluorescence detector, and then a high-speed signal acquisition circuit is used to synchronously capture multi-band photon energy, and finally an X-ray fluorescence signal containing elemental characteristic information is generated.

[0072] Step 103: Using a preset adaptive digital filter to perform dynamic noise suppression processing on the X-ray fluorescence signal to generate a noise-reduced X-ray fluorescence signal.

[0073] In this step, the preset adaptive digital filter refers to a preconfigured adjustable filter based on a motion noise model, which suppresses signal interference caused by high-speed displacement by dynamically adjusting its frequency response characteristics. Dynamic noise suppression processing refers to an adaptive filtering process that combines displacement velocity and time-domain characteristic parameters, including noise modeling, frequency response adjustment, and feature reconstruction operations. The denoised X-ray fluorescence signal refers to a purified signal that retains valid characteristic peaks after dynamic noise suppression processing, with a signal-to-noise ratio improvement of more than 50%.

[0074] In an embodiment of the present invention, the real-time displacement velocity of a preset test point and the time-domain fluctuation characteristics of the X-ray fluorescence signal are first obtained. The displacement velocity and time-domain characteristic parameters are then input into a dynamic noise model generator. A preset adaptive digital filter is then driven to adjust the cutoff frequency and order. Multi-stage filtering is then performed on the signal. Finally, a feature reconstruction unit is used to generate a de-noised X-ray fluorescence signal.

[0075] Step 104: Inputting the noise-reduced X-ray fluorescence signal into a preset spectrum peak automatic recognition algorithm to obtain characteristic peak data.

[0076] In this step, the preset spectral peak automatic identification algorithm refers to the pre-loaded energy spectrum analysis program, which extracts the characteristic energy level position through peak symmetry verification and continuity detection; the characteristic peak data refers to the standardized energy level position parameter set extracted from the noise reduction signal, including the peak energy value and half-peak width information.

[0077] In an embodiment of the present invention, the de-noised X-ray fluorescence signal is first split into discrete spectral lines according to the energy spectrum channel. Next, the peak symmetry and energy level width of each spectral line are analyzed using a preset spectral peak automatic identification algorithm. The spectral peak continuity is then verified and residual noise is filtered. Finally, energy level position parameters that meet the characteristics of precious metals are extracted to generate characteristic peak data.

[0078] Step 105: Match the characteristic peak data with a preset database of precious metal element types to generate element point measurement results.

[0079] In this step, the preset precious metal element type database refers to a structured data set that pre-stores the standard energy levels, peak shape parameters and displacement influence coefficients of precious metal elements; the matching operation refers to the process of calculating the offset between the energy level sequence to be measured and the database element, and combining the displacement speed to verify and screen the optimal element identification; the element point measurement result refers to the final output of the precious metal element type and content determination conclusion, with a confidence level higher than 95%.

[0080] In an embodiment of the present invention, the energy level position sequence in the characteristic peak data is first analyzed, and then the preset precious metal element type database is traversed to match the candidate element with the smallest energy level difference. Then, the influence coefficient of the displacement velocity on the element energy level offset is calculated and the physical logic consistency is verified. Finally, the element point measurement results compatible with the dynamic scene are output.

[0081] For example, an X-ray source first emits a beam toward a preset point on the jewelry to be tested on a high-speed conveyor belt. A piezoelectric ceramic mirror assembly dynamically adjusts the focal position to generate a stable micro-area light spot that covers the jewelry's marked point. A silicon drift detector then acquires the X-ray fluorescence signal excited by the platinum jewelry and converts it into a digital waveform using a high-resolution analog-to-digital converter. A preset adaptive digital filter is then invoked, adjusting the filter order based on the displacement velocity obtained by the laser velocimeter. Dynamic noise suppression is performed on the signal, and a noise-reduced waveform is output. The signal is then input into a preset spectral peak automatic identification algorithm, which decomposes the energy spectrum channels and extracts the characteristic peak data for the platinum element. Finally, a precious metals database is traversed to match the platinum element energy levels. Physical consistency is verified using the displacement velocity, and a point measurement result with a platinum content of 95% is generated.

[0082] The embodiments of the present invention compensate for sample displacement in real time through dynamic beam focusing, eliminating spot offset errors; adaptive noise suppression technology based on displacement velocity overcomes the signal distortion problem caused by motion interference; and combined with velocity-related spectral peak recognition and element verification mechanisms, accurate qualitative and quantitative analysis of micro-area precious metal components is achieved in high-speed scanning scenarios, significantly improving point measurement efficiency.

[0083] To address the problem of the inability to accurately align the micro-area light spot due to the deviation between the actual position of the preset test point and the theoretical coordinates, this step obtains the position deviation and converts it into a beam deflection angle parameter, and then adjusts the X-ray beam focus position in a closed loop until the tolerance is met. The present invention provides a specific embodiment, step 101, applies an X-ray beam to the preset test point, uses micro-beam focusing technology to adjust the focus position of the X-ray beam, and generates a micro-area light spot that is aligned with the point measurement position of the preset test point in real time, specifically comprising the following steps:

[0084] Step 111: Obtain the current actual position of the preset point to be measured to obtain position acquisition data.

[0085] In this step, the position acquisition data refers to the three-dimensional spatial coordinate set of the measured point obtained in real time by the displacement sensor, including the X-axis component value, the Y-axis component value and the Z-axis component value, which is used to represent the current position state.

[0086] In an embodiment of the present invention, the spatial coordinates of the preset measured point are firstly scanned by a laser displacement sensor, and then the electrical signal output by the sensor is captured by a high-speed data acquisition card, and finally the current actual position data containing three-dimensional position information, namely, position acquisition data, is generated.

[0087] Step 112: Calculate the vector difference between the position acquisition data and the coordinates of the preset measured point as the position deviation.

[0088] In this step, the vector difference refers to the scalar distance value obtained by subtracting the components of the current actual position coordinates and the target coordinates in each dimension through square and square root operations, which reflects the degree of spatial position deviation; the position deviation refers to the quantized value of the spatial offset distance calculated based on the vector difference, which is used to drive the beam deflection compensation system.

[0089] In an embodiment of the present invention, the target coordinate storage value of the preset measured point is first read, and then the X-axis component, Y-axis component and Z-axis component of the position acquisition data are subtracted from the corresponding components of the target coordinates respectively, and finally the square root of the sum of the squares of the differences of each component is calculated to generate a vector difference, that is, the position deviation.

[0090] Step 113: Convert the position deviation into a beam deflection angle parameter to generate a focus adjustment instruction.

[0091] In this step, the conversion operation refers to the mathematical processing of inputting the position deviation into the geometric optical model and calculating the beam deflection angle through trigonometric function relationships; the beam deflection angle parameter refers to the set of pitch angle and azimuth angle adjustment values ​​calculated based on the deviation, which is used to generate the focus adjustment instruction; the focus adjustment instruction refers to the digital control signal containing the deflection angle parameter, which is used to drive the optical lens group to change the beam path.

[0092] In an embodiment of the present invention, the position deviation is first input into the geometric optical transformation model, and then the deflection angle is calculated based on the trigonometric function relationship between the beam propagation distance and the position deviation. Finally, the beam deflection angle parameter including the angle adjustment parameter, i.e., the focusing adjustment instruction, is generated.

[0093] Step 114: Apply the focus adjustment instruction to change the focus position of the X-ray beam, and obtain the current actual position of the changed focus position to obtain new position acquisition data.

[0094] In this step, the new position acquisition data refers to the three-dimensional coordinate data set of the test point that is remeasured after the beam position is adjusted, which is used to verify the compensation effect.

[0095] In an embodiment of the present invention, a focusing adjustment instruction is first sent to the piezoelectric ceramic deflection mirror group, and then the deflection mirror is driven to rotate a specified angle, and then the refraction path of the X-ray beam is changed, and finally the current actual position after the change, that is, the new position acquisition data, is obtained through secondary position measurement.

[0096] Step 115: Calculate the vector difference between the new position acquisition data and the coordinates of the preset measured point as the new position deviation.

[0097] In this step, the new position deviation refers to the calculation result of the secondary offset distance between the actual position after adjustment and the target coordinate, which serves as the basis for alignment judgment.

[0098] In an embodiment of the present invention, the target coordinates of the preset measured point are first called, then vector subtraction operation is performed on the coordinates of each dimension of the new position acquisition data and the target coordinates, and finally the Euclidean distance is calculated to generate the new position deviation.

[0099] Step 116: When the new position deviation is less than a preset tolerance threshold, a micro-area light spot is generated that is aligned with the point measurement position of the preset point to be measured in real time.

[0100] In this step, the preset tolerance threshold refers to a preset maximum allowable position deviation value. When the actual deviation is less than this value, the alignment is determined to be successful.

[0101] In an embodiment of the present invention, the new position deviation is first compared with the preset tolerance threshold, and then a position locking signal is triggered when the deviation is less than the threshold. Finally, a micron-level focused light spot is generated that stably covers the preset test point, i.e., a real-time aligned micro-area light spot.

[0102] The embodiment of the present invention dynamically senses displacement deviations through real-time position acquisition and vector difference calculation, and generates precise beam adjustment instructions in combination with optical geometric transformation. A closed-loop control system is formed based on secondary position verification and tolerance threshold determination, which achieves stable alignment of micron-level light spots in high-speed moving scenarios and suppresses displacement errors within five microns.

[0103] To improve the efficiency and accuracy of extracting effective X-ray fluorescence information from physical signals, this step utilizes a multi-channel acquisition device to acquire the original signal, extract key parameters of the characteristic components, and map them to energy spectrum channels to generate a structured X-ray fluorescence signal. The present invention provides a specific embodiment in which step 102, acquiring the X-ray fluorescence signal generated by the micro-area spot excitation, specifically includes the following steps:

[0104] Step 201: using a preset multi-channel acquisition device to receive the physical signal excited by the micro-area light spot to generate an original signal sequence.

[0105] In this step, the physical signal refers to the original X-ray photon flow generated by the micro-area light spot exciting the sample atoms, which contains a continuous energy spectrum distribution and random noise and needs to be digitally processed before analysis; the original signal sequence refers to the discrete voltage waveform data set collected by the multi-channel detector, which records the photon energy and intensity in chronological order, reflecting the prototype of the element's characteristic energy spectrum.

[0106] In an embodiment of the present invention, a preset silicon drift detector array is first used to synchronously capture the X-ray photon flow excited by a micro-area spot. Secondly, a multi-channel ADC converter is used to convert the current pulses output by each detector into a digital waveform. Finally, the original signal sequence containing energy and intensity information is generated by integrating according to the timestamp.

[0107] Step 202: extract key parameters of characteristic components in the original signal sequence to generate a characteristic parameter set.

[0108] In this step, the characteristic component refers to the energy spectrum peak component that characterizes a specific element in the original signal, which is obtained by fitting and separating the Gaussian function, and includes properties such as peak position, intensity, and half-width; the key parameters refer to the core quantitative indicators that describe the characteristic component, including peak energy, integrated intensity, and peak-to-background ratio, which are used for qualitative and quantitative analysis of elements; the characteristic parameter set refers to the characteristic component parameter matrix arranged in ascending order of energy levels, with each row corresponding to a characteristic peak, and the columns are key parameters such as energy values ​​and intensity values.

[0109] In an embodiment of the present invention, Gaussian fitting is first performed on the original signal sequence to extract the characteristic peak positions, then the half-width and peak-to-background ratio parameters of each characteristic peak are calculated, and finally the key peak shape parameters are sorted by energy level to generate a characteristic parameter set.

[0110] Step 203: Map the characteristic parameter set to a preset energy spectrum channel range to generate a structured signal as an X-ray fluorescence signal.

[0111] In this step, the mapping operation refers to the process of classifying and grouping characteristic parameters according to the boundary values ​​of the energy spectrum channel; the preset energy spectrum channel range refers to a set of pre-divided energy intervals, and each channel defines the minimum energy value and maximum energy value boundaries for standardized signal reconstruction; the structured signal refers to the standardized energy spectrum data generated after channel mapping, with the horizontal axis being the channel number and the vertical axis being the integrated value of the characteristic parameters within the channel.

[0112] In an embodiment of the present invention, a preset energy spectrum channel range definition table is first loaded, then the energy level parameters in the characteristic parameter set are classified and mapped according to the channel boundary values, and finally, a structured signal conforming to the standard energy spectrum format is reconstructed and output as an X-ray fluorescence signal.

[0113] The embodiments of the present invention improve signal capture efficiency through multi-channel parallel acquisition, solving the problem of signal omission in high-speed scanning; accurately extract key parameters of characteristic components based on Gaussian fitting to eliminate background interference; and achieve standardized reconstruction of non-standard signals through energy spectrum channel mapping, providing high-quality input for subsequent noise suppression.

[0114] To effectively suppress dynamic noise introduced by the displacement of the measured point and the time-varying characteristics of the signal, this step establishes noise model parameters based on the displacement velocity and the signal's time-domain characteristics, dynamically adjusts the characteristics of the adaptive digital filter, and reconstructs the filtered signal characteristics. The present invention provides a specific embodiment, step 103, in which a preset adaptive digital filter is used to perform dynamic noise suppression processing on the X-ray fluorescence signal to generate a de-noised X-ray fluorescence signal, specifically comprising the following steps:

[0115] Step 301: obtaining the displacement velocity of the preset test point and the time domain characteristic parameters of the X-ray fluorescence signal, correlating the displacement velocity and the time domain characteristic parameters, and generating dynamic noise model parameters.

[0116] In this step, time-domain characteristic parameters refer to the quantitative properties of the X-ray fluorescence signal waveform in the time dimension, including the peak-to-average ratio reflecting the signal fluctuation amplitude and the fluctuation frequency representing the noise oscillation rate. The correlation operation refers to the data fusion process of multiplying the displacement velocity and the fluctuation frequency to generate a coupling coefficient, and then constructing a polynomial equation with the peak-to-average ratio. The dynamic noise model parameters refer to structured data including the frequency weight factor and noise intensity. The frequency weight factor determines the filter stopband position, and the noise intensity controls the attenuation depth.

[0117] In an embodiment of the present invention, displacement velocity data of a preset test point is first acquired in real time using a laser velocity sensor. Peak-to-average ratio (PAR) and fluctuation frequency parameters are simultaneously extracted from the time-domain waveform of the X-ray fluorescence signal as time-domain characteristic parameters. The displacement velocity value and the fluctuation frequency parameter are then multiplied to generate a velocity-frequency coupling coefficient. The PAR parameter and the coupling coefficient are then correlated to construct a polynomial noise model. Finally, dynamic noise model parameters, including a frequency weighting factor and noise intensity, are output.

[0118] Step 302: Using the dynamic noise model parameters, adjust the frequency response characteristics of the preset adaptive digital filter to generate adaptive filtering rules.

[0119] In this step, the frequency response characteristic refers to the gain change curve of the filter for signals of different frequencies, including the three-dimensional properties of passband range, stopband position and attenuation slope; the adjustment operation refers to the parameter optimization process of moving the stopband center point according to the frequency weight factor and increasing the stopband attenuation value according to the noise intensity; the adaptive filtering rule refers to the updated frequency response characteristic set, which specifies the convolution kernel coefficient and window function type of the segmented filtering.

[0120] In an embodiment of the present invention, the initial frequency response curve of a preset adaptive digital filter is first loaded, then the center frequency of the stopband interval is adjusted according to the frequency weight factor in the dynamic noise model parameters, and then the stopband attenuation amplitude is expanded according to the noise intensity parameter; finally, an adaptive filtering rule including the adjusted frequency response characteristics is generated.

[0121] Step 303: Apply the adaptive filtering rule to filter the X-ray fluorescence signal to generate an intermediate processed signal.

[0122] In this step, filtering processing refers to the mathematical operation process of segmenting the signal, performing time-domain convolution calculation with the convolution kernel, and then superimposing and restoring it; the intermediate processed signal refers to the signal that has been initially denoised after segmented filtering, retaining the fundamental component but leaving some low-frequency distortion.

[0123] In an embodiment of the present invention, the X-ray fluorescence signal is first segmented according to time windows. Then, an adaptive filtering rule is applied to each segment of the signal and a convolution operation is performed. The filtering results of each segment are then superimposed. Finally, an intermediate processed signal is generated that retains the fundamental component but suppresses high-frequency noise.

[0124] Step 304: Analyze the characteristic distribution state of the intermediate processed signal to generate characteristic distribution parameters.

[0125] In this step, the characteristic distribution state refers to the degree of position offset of the characteristic peak on the energy spectrum and the discreteness of the intensity distribution, which reflects the level of signal distortion; the analysis operation refers to the quantitative process of calculating the total intensity of the characteristic peak area by integrating the energy spectrum, and statistically analyzing the discreteness coefficient and the offset; the characteristic distribution parameter refers to a data set containing the discreteness coefficient and the peak position offset, the discreteness coefficient represents the degree of peak broadening, and the offset reflects the energy level drift value.

[0126] In an embodiment of the present invention, the energy spectrum of the intermediate processed signal is first integrated, then the intensity distribution discreteness of the characteristic peak area is statistically calculated, and then the peak position offset is calculated; finally, characteristic distribution parameters including the discreteness coefficient and the offset value are generated.

[0127] Step 305: reconstructing the effective characteristic components of the intermediate processed signal based on the characteristic distribution parameters to generate a noise-reduced X-ray fluorescence signal.

[0128] In this step, the effective characteristic component refers to the energy spectrum peak component that characterizes the precious metal elements in the intermediate processing signal, and its characteristic intensity needs to be enhanced through reconstruction; the reconstruction operation refers to the signal repair process of shrinking the peak width according to the discrete coefficient to restore the symmetry, correcting the peak position according to the offset, and increasing the intensity weight.

[0129] In an embodiment of the present invention, the characteristic peak symmetry is first reconstructed based on the dispersion coefficient in the characteristic distribution parameters. Then, the peak position coordinates are corrected according to the offset value. Then, the intensity weights of the effective characteristic components are enhanced. Finally, the reconstructed components are fused with the background to generate a noise-reduced X-ray fluorescence signal.

[0130] The embodiment of the present invention constructs a precise noise model through the dynamic correlation between displacement velocity and time domain characteristics, guiding the filter to optimize the frequency response characteristics in real time. The adaptive reconstruction mechanism based on characteristic distribution parameters deeply suppresses motion noise while repairing signal distortion, outputting high-fidelity X-ray fluorescence signals and doubling the detection rate of weak-intensity precious metal characteristic peaks.

[0131] To more accurately preserve the effective feature components related to displacement velocity during the noise reduction process, this step classifies the components based on the feature distribution and calculates dynamic weight coefficients based on the displacement velocity. The effective feature components are then reconstructed and fused with the background. The present invention provides a specific embodiment, step 305, which reconstructs the effective feature components of the intermediate processed signal based on the feature distribution parameters to generate a noise-reduced X-ray fluorescence signal, specifically comprising the following steps:

[0132] Step 351: Classify the effective feature components of the intermediate processed signal according to the feature distribution parameters to generate a feature category set.

[0133] In this step, the classification operation refers to the data processing process of setting the threshold interval according to the discrete coefficient of the feature distribution parameter, classifying the effective feature components into different interval groups according to the discrete value, and then dividing the subclasses according to the offset direction; the feature category set refers to a structured data set containing classification labels, each label corresponds to a feature component group with a specific discrete interval and offset direction, which is used to guide weight allocation.

[0134] In an embodiment of the present invention, first, the effective feature components are divided into high discreteness category, medium discreteness category and low discreteness category according to the discreteness coefficient in the feature distribution parameter. Secondly, they are further subdivided into positive offset subclasses and negative offset subclasses according to the direction of the peak offset. Finally, all classification results are integrated to generate a feature category set containing category labels.

[0135] Step 352: Calculate a dynamic weight coefficient by applying the feature category set and the displacement velocity of the preset test point to generate a weight allocation rule.

[0136] In this step, the dynamic weight coefficient refers to the intensity adjustment factor calculated based on the displacement velocity, and its value is equal to the displacement velocity value multiplied by the historical velocity correlation factor of the category, which is used to control the reorganization intensity of the characteristic components; the weight allocation rule refers to the set of instructions that stipulate the intensity adjustment amplitude of the characteristic components of each category, including the three-dimensional parameters of attenuation coefficient, enhancement coefficient and peak correction value.

[0137] In an embodiment of the present invention, the real-time displacement velocity value of the preset test point is first obtained, and then the historical velocity correlation factor is extracted for each category in the feature category set. Then, the current displacement velocity value is multiplied by the correlation factor to generate a dynamic weight base. Finally, the weight coefficient is allocated according to the size of the base and a weight allocation rule is generated.

[0138] Step 353: recombining the effective feature components according to the weight distribution rule to generate a reconstructed feature signal.

[0139] In this step, the reorganization operation refers to the signal reconstruction process of performing intensity adjustment and peak position correction on the characteristic components of each category according to the weight distribution rules, and then reintegrating them in the order of energy levels; the reconstructed characteristic signal refers to the integration of characteristic components after intensity optimization and peak position correction, and its peak shape symmetry is significantly improved and the energy level position is standardized.

[0140] In an embodiment of the present invention, first, an intensity attenuation coefficient is applied to the feature components of the high discreteness category according to the weight distribution rule, and then an intensity enhancement coefficient is applied to the feature components of the low discreteness category. Then, the peak coordinates of the positive and negative offset subclasses are adjusted to the reference position, and finally, all the adjusted feature components are recombined to generate a reconstructed feature signal.

[0141] Step 354: Fusing the reconstructed characteristic signal with the background component in the original signal sequence to generate a noise-reduced X-ray fluorescence signal.

[0142] In this step, the background component refers to the continuous spectrum background signal separated by Gaussian fitting in the original signal sequence, which contains non-characteristic radiation components such as Compton scattering; the fusion operation refers to the mathematical operation of aligning the energy spectrum of the reconstructed characteristic signal and the background component, and then superimposing them according to the channel energy values ​​to generate a complete energy spectrum.

[0143] In the embodiment of the present invention, the background component is first separated from the original signal sequence, and then the time domain waveform of the reconstructed characteristic signal and the continuous spectrum of the background component are energy-normalized and aligned. Finally, the two signals are superimposed to generate a noise-reduced X-ray fluorescence signal.

[0144] The embodiments of the present invention achieve precise management of characteristic components through dual classification of discreteness and offset direction; a dynamic weight allocation mechanism based on displacement velocity specifically enhances the intensity of weak characteristic components; and a high-fidelity energy spectrum signal is reconstructed through background fusion, which significantly improves the signal-to-noise ratio of the characteristic peaks of platinum group elements and significantly reduces the peak position drift error.

[0145] To improve the accuracy of automatically identifying true characteristic spectral peaks under dynamic scanning conditions, this step screens valid spectral peaks and extracts energy level positions by decomposing spectral lines, verifying continuity, analyzing peak shape evolution and its correlation with displacement velocity, and comparing with a benchmark. The present invention provides a specific embodiment, step 104, inputting the de-noised X-ray fluorescence signal into a preset spectral peak automatic identification algorithm to obtain characteristic peak data, specifically comprising the following steps:

[0146] Step 401: Decompose the multi-level spectral lines of the noise-reduced X-ray fluorescence signal to generate a set of candidate spectral peaks and a background residual component.

[0147] In this step, multi-level spectral lines refer to the hierarchical intensity distribution curve formed in the energy dimension of the X-ray fluorescence signal after noise reduction; decomposition operation refers to the process of decomposing the original spectral lines into characteristic components and non-characteristic components using signal separation technology; candidate spectral peak set refers to the spectral peak object group that is preliminarily determined to contain elemental characteristic peaks after decomposition; background residual component refers to the stable background noise signal component remaining after decomposition.

[0148] In this embodiment of the present invention, the denoised X-ray fluorescence signal is first decomposed into independent components according to the energy level hierarchy using a wavelet packet decomposition algorithm. Secondly, components containing characteristic peak signals are classified into a candidate spectral peak set based on the peak morphology characteristics, while non-oscillatory stationary components are classified as background residual components. Finally, two independent data sets, the separated candidate spectral peak set and the background residual component, are output.

[0149] Step 402: Verify the continuity of the energy spectrum of each peak in the candidate peak set to generate a continuous peak identifier.

[0150] In this step, energy spectrum continuity refers to the property that the intensity attenuation of characteristic peaks in adjacent energy level channels must conform to physical laws; verification operation refers to the process of determining the continuity of spectrum peaks by gradient calculation and threshold comparison; continuous spectrum peak identification refers to the digital label that marks the position of the spectrum peak that has passed continuity verification in the energy spectrum.

[0151] In an embodiment of the present invention, first, the intensity gradient change rate of adjacent energy level channels is calculated for each spectral peak in the candidate spectral peak set; secondly, based on the continuity judgment rule of the gradient change rate, it is verified whether the spectral peak meets the energy spectrum continuity condition; finally, a continuous spectral peak identifier is generated for the spectral peak that passes the verification to mark its spatial position.

[0152] Step 403: Analyze the shape evolution characteristics of the continuous spectrum peak corresponding to the continuous spectrum peak identifier to generate dynamic peak shape parameters.

[0153] In this step, the continuous spectrum peak refers to the complete characteristic peak object that meets the energy spectrum continuity verification conditions; the shape evolution characteristics refer to the dynamic characteristics of the spectrum peak geometric parameters changing with time; the analysis operation refers to the process of quantifying the dynamic characteristics of the peak shape using a mathematical fitting algorithm; the dynamic peak shape parameters refer to the quantitative data set that characterizes the deformation law of the spectrum peak under the motion state.

[0154] In an embodiment of the present invention, the corresponding continuous spectrum peak is first extracted based on the continuous spectrum peak identifier; secondly, the Gaussian fitting algorithm is used to analyze the shape evolution characteristics of the spectrum peak in the scanning time sequence, such as the half-height width and peak symmetry; finally, a quantized dynamic peak shape parameter set is generated, which includes the peak shape change data at each time node.

[0155] Step 404: Correlate the dynamic peak shape parameters with the temporal relationship of the displacement velocity of the preset test point to generate a peak shape-velocity correlation feature.

[0156] In this step, the temporal relationship refers to the corresponding correlation between the displacement velocity and the peak shape parameters changing over time; the association operation refers to establishing a mathematical mapping relationship between the velocity vector and the peak shape parameter vector; the peak shape-velocity correlation feature refers to the characteristic matrix reflecting the correlation between the motion state and the spectral peak distortion.

[0157] In an embodiment of the present invention, first, the displacement velocity time series data of the preset test point collected in real time by the displacement sensor is obtained; secondly, the correlation coefficient matrix between the dynamic peak shape parameter change rate and the displacement velocity change rate is calculated; finally, a peak shape-velocity correlation characteristic matrix representing the motion-peak shape coupling relationship is generated.

[0158] Step 405: Compare the peak shape-velocity correlation feature with a preset peak shape reference to generate a valid spectrum peak identifier.

[0159] In this step, the preset peak shape reference refers to the standard peak shape parameter database constructed under static calibration conditions; the comparison operation refers to the process of matching dynamic features with static references through a similarity algorithm; and the effective spectrum peak identification refers to the unique position code that is finally confirmed to be the true characteristic peak.

[0160] In an embodiment of the present invention, first, the static standard peak shape parameters in the preset peak shape reference library are called; second, the peak shape-velocity correlation feature matrix is ​​matched with the static reference for similarity calculation; finally, the qualified spectral peaks are screened according to the matching results to generate valid spectral peak identifiers.

[0161] Step 406: extracting the energy level position parameters of the effective spectrum peak corresponding to the effective spectrum peak identifier to obtain characteristic peak data.

[0162] In this step, the effective spectrum peak refers to the element characteristic signal spectrum peak that has passed dynamic verification; the energy level position parameter refers to the precise coordinate value of the energy level channel at the center of the characteristic peak.

[0163] In an embodiment of the present invention, first, the corresponding effective spectrum peak is located according to the effective spectrum peak identifier; secondly, the median coordinates of the energy level channel at the center of the spectrum peak are calculated by the center of gravity method; finally, the coordinate value is extracted as the energy level position parameter in the characteristic peak data.

[0164] The embodiment of the present invention significantly improves the detection accuracy in a moving environment through a triple closed-loop verification mechanism: first, background interference is eliminated through spectral line decomposition, second, pseudo peaks are filtered out through continuity verification, and finally, the peak shape-velocity correlation model is used to dynamically compensate for displacement distortion, so that the energy level positioning error of the effective spectral peak is significantly reduced, providing anti-interference characteristic peak data for the identification of precious metal elements.

[0165] To accurately match precious metal element types in the presence of energy level shifts and motion at the test point, this step analyzes the energy level sequence to be matched, screens candidate elements, calculates the offset to determine the optimal match, and verifies its consistency with the displacement velocity. The present invention provides a specific embodiment, step 105, matching the characteristic peak data with a preset precious metal element type database to generate element point measurement results, specifically including the following steps:

[0166] Step 501: Analyze the energy level position parameters of the characteristic peak data to generate an energy level sequence to be matched.

[0167] In this step, the analytical operation refers to the process of structurally reorganizing the energy level position parameters in the characteristic peak data; the energy level sequence to be matched refers to the characteristic peak energy level coordinate data set arranged in order according to energy values.

[0168] In an embodiment of the present invention, first, a set of energy level position parameters in the characteristic peak data is called; secondly, an energy level sorting algorithm is used to rearrange the energy level position parameters in ascending order of energy value; finally, a structured energy level sequence to be matched is generated, which contains the characteristic peak energy level coordinates arranged in order according to the energy hierarchy.

[0169] Step 502: extracting the element energy level characteristics that meet the preset energy level range threshold from the preset noble metal element type database to generate a candidate element set.

[0170] In this step, the preset energy level range threshold refers to the pre-set characteristic energy level matching allowed energy floating range; the element energy level characteristic refers to the standard characteristic X-ray energy level value of the precious metal element stored in the database; the candidate element set refers to the group of candidate precious metal element types that meet the energy level range threshold conditions.

[0171] In an embodiment of the present invention, the characteristic energy level data of all elements in a preset precious metal element type database are first read; secondly, the energy level characteristics of elements in the database that overlap with the energy interval of the energy level sequence to be matched are screened according to a preset energy level range threshold; finally, the energy level characteristics of elements that meet the conditions are summarized to generate a set of candidate elements.

[0172] Step 503: Calculate the energy level offset between the energy level sequence to be matched and the candidate element set, and generate an offset parameter set.

[0173] In this step, the energy level offset refers to the energy difference between the measured energy level position and the energy level characteristic of the standard element; the offset parameter set refers to the data set containing the energy level offsets of all candidate elements.

[0174] In an embodiment of the present invention, each energy level position parameter in the energy level sequence to be matched is first traversed; secondly, the absolute energy difference between the parameter and the corresponding characteristic energy levels of all elements in the candidate element set is calculated; finally, all the differences are summarized to generate an offset parameter set containing the offset data of each element.

[0175] Step 504: Map the offset identifier of the minimum offset parameter in the offset parameter set to the element identifier corresponding to the candidate element set to generate the best matching element identifier.

[0176] In this step, the minimum offset parameter refers to the energy level offset with the smallest value in the offset parameter set; the offset identifier refers to the unique number label used to distinguish different energy level offsets; the mapping operation refers to the matching process of establishing the correspondence between the offset identifier and the element identifier; the element identifier refers to the unique code that characterizes the category of precious metal elements; the best matching element identifier refers to the element type code determined by the minimum offset mapping.

[0177] In an embodiment of the present invention, first, the minimum offset parameter and its unique offset identifier are identified in the offset parameter set; second, a mapping relationship is established between the offset identifier and the element identifiers in the candidate element set; finally, the mapping result is output to generate the optimal matching element identifier.

[0178] Step 505: verifying the consistency of the association between the optimal matching element identifier and the displacement velocity of the preset point to be measured, and generating an element point measurement result when the verification result meets a preset consistency threshold.

[0179] In this step, association consistency refers to the degree of matching between the physical properties of the element and the displacement velocity; verification operation refers to the process of verifying the correlation between the element identification and the displacement velocity through an algorithm; verification result refers to the quantitative matching value output by the association consistency verification; and the preset consistency threshold refers to the minimum correlation standard required to determine whether the element matching is valid.

[0180] In an embodiment of the present invention, first, the displacement velocity time series data of the preset test point recorded by the displacement sensor is obtained; secondly, the correlation consistency between the element physical properties corresponding to the optimal matching element identifier and the displacement velocity is verified; finally, when the correlation degree reaches a preset consistency threshold, the element point measurement result containing the element type and position information is output.

[0181] The embodiment of the present invention improves dynamic matching accuracy through a triple verification mechanism: first, the candidate range is narrowed by using the energy level range threshold, second, the optimal element is locked by the minimum offset, and finally, the displacement speed verification is combined to eliminate false matches due to inconsistent physical properties, thereby significantly improving the accuracy of precious metal element identification in a moving state and reducing the misjudgment rate caused by environmental interference.

[0182] Figure 2The present invention provides a structural diagram of a precise point measurement system for a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy. Figure 2 As shown, the system includes:

[0183] An adjustment module 21 is configured to apply an X-ray beam to a preset point to be measured, and to adjust the focus position of the X-ray beam using a micro-beam focusing technique to generate a micro-area light spot aligned with a point measurement position of the preset point to be measured in real time;

[0184] An acquisition module 22 is used to acquire X-ray fluorescence signals generated by the micro-area light spot excitation;

[0185] The processing module 23 is configured to perform dynamic noise suppression processing on the X-ray fluorescence signal using a preset adaptive digital filter to generate a noise-reduced X-ray fluorescence signal;

[0186] An input module 24 is used to input the noise-reduced X-ray fluorescence signal into a preset spectrum peak automatic recognition algorithm to obtain characteristic peak data;

[0187] The matching module 25 is used to match the characteristic peak data with a preset database of precious metal element types to generate element point measurement results.

[0188] Figure 2 The precise point measurement system of precious metal analyzer based on micro-area X-ray fluorescence spectroscopy can be performed Figure 1 The implementation principles and technical effects of the precise point measurement method for a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy described in the illustrated embodiment are not further elaborated. The specific manner in which the various modules and units in the precise point measurement system for a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy in the aforementioned embodiment perform their operations has been described in detail in the related embodiments of the method and will not be further elaborated here.

[0189] In one possible design, Figure 2 The embodiment shown is a precise point measurement system for a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy, which 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;

[0190] 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 .

[0191] The processing component 32 is configured to: apply an X-ray beam to a preset point to be measured, adjust the focus position of the X-ray beam using micro-beam focusing technology, and generate a micro-area light spot that is aligned in real time with the point measurement position of the preset point to be measured; collect an X-ray fluorescence signal generated by the micro-area light spot; perform dynamic noise suppression processing on the X-ray fluorescence signal using a preset adaptive digital filter to generate a de-noised X-ray fluorescence signal; input the de-noised X-ray fluorescence signal into a preset spectral peak automatic recognition algorithm to obtain characteristic peak data; and match the characteristic peak data with a preset database of precious metal element types to generate an element point measurement result.

[0192] 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.

[0193] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as 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.

[0194] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0195] 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.

[0196] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0197] 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.

[0198] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1The embodiment shown is a precise point measurement method of a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention 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 various embodiments of the present invention.

Claims

1. A precise point measurement method for precious metal analyzers based on micro-area X-ray fluorescence spectroscopy, characterized in that: include: Applying an X-ray beam to a preset point to be measured, adjusting the focus position of the X-ray beam using micro-beam focusing technology, and generating a micro-area light spot that is aligned with the point measurement position of the preset point to be measured in real time; collecting X-ray fluorescence signals generated by the excitation of the micro-area light spot; Acquiring a displacement velocity of the preset test point and a time-domain characteristic parameter of the X-ray fluorescence signal, correlating the displacement velocity and the time-domain characteristic parameter to generate dynamic noise model parameters; adjusting a frequency response characteristic of a preset adaptive digital filter using the dynamic noise model parameter to generate an adaptive filtering rule; applying the adaptive filtering rule to filter the X-ray fluorescence signal to generate an intermediate processed signal; analyzing a characteristic distribution state of the intermediate processed signal to generate characteristic distribution parameters; classifying effective characteristic components of the intermediate processed signal according to the characteristic distribution parameters to generate a characteristic category set; calculating a dynamic weight coefficient using the characteristic category set and the displacement velocity of the preset test point to generate a weight distribution rule; and recombining the effective characteristic components according to the weight distribution rule to generate a reconstructed characteristic signal; fusing the reconstructed characteristic signal with the background component in the original signal sequence to generate a noise-reduced X-ray fluorescence signal; Inputting the de-noised X-ray fluorescence signal into a preset spectral peak automatic recognition algorithm to obtain characteristic peak data; The characteristic peak data is matched with a preset database of precious metal element types to generate element point measurement results.

2. The method according to claim 1, characterized in that Applying an X-ray beam to a preset point to be measured, adjusting the focus position of the X-ray beam using micro-beam focusing technology, and generating a micro-area light spot aligned with the point measurement position of the preset point to be measured in real time, including: Obtain the current actual position of the preset test point to obtain position acquisition data; Calculating a vector difference between the position acquisition data and the coordinates of the preset measured point as a position deviation; Converting the position deviation into a beam deflection angle parameter to generate a focus adjustment instruction; Applying the focus adjustment instruction to change the focus position of the X-ray beam, and obtaining the current actual position of the changed focus position to obtain new position acquisition data; Calculating the vector difference between the new position acquisition data and the coordinates of the preset measured point as the new position deviation; When the new position deviation is smaller than a preset tolerance threshold, a micro-area light spot is generated that is aligned with the point measurement position of the preset point to be measured in real time.

3. The method according to claim 1, characterized in that Collecting X-ray fluorescence signals generated by the micro-area light spot excitation includes: Using a preset multi-channel acquisition device to receive the physical signal excited by the micro-area light spot to generate an original signal sequence; Extracting key parameters of characteristic components in the original signal sequence to generate a characteristic parameter set; The characteristic parameter set is mapped to a preset energy spectrum channel range to generate a structured signal as an X-ray fluorescence signal.

4. The method according to claim 1, wherein The noise-reduced X-ray fluorescence signal is input into a preset spectral peak automatic identification algorithm to obtain characteristic peak data, including: Decomposing the multi-level spectral lines of the noise-reduced X-ray fluorescence signal to generate a set of candidate spectral peaks and a background residual component; Verifying the energy spectrum continuity of each peak in the candidate peak set to generate a continuous peak identifier; Analyzing the shape evolution characteristics of the continuous spectrum peak corresponding to the continuous spectrum peak identifier to generate dynamic peak shape parameters; Correlating the dynamic peak shape parameter with the temporal relationship of the displacement velocity of the preset test point to generate a peak shape-velocity correlation feature; Comparing the peak shape-velocity correlation feature with a preset peak shape benchmark to generate a valid spectrum peak identifier; The energy level position parameters of the effective spectrum peak corresponding to the effective spectrum peak identifier are extracted to obtain characteristic peak data.

5. The method according to claim 1, characterized in that The characteristic peak data is matched with a preset database of precious metal element types to generate element point measurement results, including: Analyzing the energy level position parameters of the characteristic peak data to generate an energy level sequence to be matched; Extracting element energy level features that meet a preset energy level range threshold from a preset noble metal element type database to generate a candidate element set; Calculating the energy level offset between the to-be-matched energy level sequence and the candidate element set to generate an offset parameter set; Mapping the offset identifier of the minimum offset parameter in the offset parameter set to the element identifier corresponding to the candidate element set to generate the best matching element identifier; The correlation consistency between the optimal matching element identifier and the displacement velocity of the preset test point is verified, and when the verification result meets a preset consistency threshold, the element point measurement result is generated.

6. A precise point measurement system for precious metal analyzers based on micro-area X-ray fluorescence spectroscopy, characterized in that: include: An adjustment module is used to apply an X-ray beam to a preset point to be measured, and to adjust the focus position of the X-ray beam using a micro-beam focusing technology to generate a micro-area light spot that is aligned with the point measurement position of the preset point to be measured in real time; An acquisition module, used for acquiring X-ray fluorescence signals generated by the micro-area light spot excitation; a processing module configured to obtain a displacement velocity of the preset test point and a time-domain characteristic parameter of the X-ray fluorescence signal, associate the displacement velocity and the time-domain characteristic parameter to generate dynamic noise model parameters; use the dynamic noise model parameters to adjust a frequency response characteristic of a preset adaptive digital filter to generate an adaptive filtering rule; apply the adaptive filtering rule to filter the X-ray fluorescence signal to generate an intermediate processed signal; analyze a characteristic distribution state of the intermediate processed signal to generate characteristic distribution parameters; classify effective characteristic components of the intermediate processed signal according to the characteristic distribution parameters to generate a characteristic category set; calculate a dynamic weight coefficient using the characteristic category set and the displacement velocity of the preset test point to generate a weight distribution rule; and reorganize the effective characteristic components according to the weight distribution rule to generate a reconstructed characteristic signal; fusing the reconstructed characteristic signal with the background component in the original signal sequence to generate a noise-reduced X-ray fluorescence signal; An input module, configured to input the noise-reduced X-ray fluorescence signal into a preset spectral peak automatic recognition algorithm to obtain characteristic peak data; The matching module is used to match the characteristic peak data with a preset database of precious metal element types to generate element point measurement results.

7. A computing device, characterized in that It includes 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 precise point measurement method of a precious metal analyzer based on micro-area X-ray fluorescence spectroscopy as described in any one of claims 1 to 5.

8. 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 precise point measurement of precious metals based on micro-area X-ray fluorescence spectroscopy according to any one of claims 1 to 5 is implemented.

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