Method and system for improving counting efficiency of detector of XRF analyzer

Abstract

The invention relates to the technical field of X-ray fluorescence spectrum analysis (XRF), in particular to a method and a system for improving the counting efficiency of a detector of an XRF analyzer. When a digital pulse signal output by the detector of the XRF analyzer is subjected to threshold value filtering processing, a dynamic threshold value considering the current counting rate is set, and the counting efficiency of the detector of the XRF analyzer is improved through the dynamic threshold value setting method. Different counting rate conditions can be effectively adapted, and the counting efficiency and the signal processing precision of the detector are improved; meanwhile, when the digital pulse signals are subjected to signal accumulation correction processing, various factors such as the counting rate, the signal strength, the signal time interval and the sample material are comprehensively considered, and dynamic and accurate determination of gamma is achieved through establishment of a coupling model and genetic algorithm optimization. Experimental results show that the method can effectively improve the precision of signal accumulation correction, reduce the counting error and improve the performance of the XRF analyzer, and has important application value and popularization prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of X-ray fluorescence spectrometry (XRF), and particularly to a method and system for improving the counting efficiency of a detector of an XRF analyzer. Background Art

[0002] X-ray fluorescence spectrometry (XRF) is a non-destructive testing technology based on the interaction between X-rays and matter. Its basic principle is as follows: When X-rays irradiate the surface of a sample, the atoms in the sample are excited, causing their inner-layer electrons to transition to higher energy levels. Subsequently, the electrons will transition back to lower energy levels and emit X-ray fluorescence with specific energies. By detecting the intensities and energies of these X-ray fluorescences, the types and contents of the elements contained in the sample can be determined. Due to its advantages such as fast analysis speed, wide application range, and no need to damage the sample, XRF analysis technology has been widely used in many fields such as materials science, geological exploration, environmental monitoring, petrochemical industry, and metal processing. The detector is one of the core components of an XRF analyzer, and its main function is to convert X-ray fluorescence signals into electrical signals and measure and record these signals. The performance of the detector is directly related to the accuracy, sensitivity, and efficiency of XRF analysis. An ideal detector should have characteristics such as high sensitivity, high resolution, high counting rate capability, and low noise. However, in practical applications, traditional detectors of XRF analyzers often have some limitations, especially in terms of counting efficiency, and it is difficult to meet the growing demand for high-precision and fast analysis.

[0003] Although certain progress has been made in detector materials, signal processing algorithms, etc. in XRF analysis technology in recent years, in practical applications, the counting efficiency problem of traditional detectors is still one of the key bottlenecks restricting the development of XRF analysis technology. Although some advanced signal processing algorithms can improve the processing accuracy of signals, they have high requirements for hardware devices and large computational complexities, and it is difficult to process a large number of XRF signals in real time.

[0004] Therefore, there is an urgent need for a method and system that can effectively improve the counting efficiency of a detector of an XRF analyzer, which can significantly improve the counting efficiency of the detector by optimizing the signal processing process and analysis parameter settings, etc. without significantly increasing costs and complexities, thereby improving the accuracy and speed of XRF analysis and meeting the requirements for high-precision XRF analysis in different fields. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a method and system for improving the counting efficiency of a detector of an XRF analyzer to solve the problems existing in the prior art.

[0006] The present invention provides a method for improving the counting efficiency of a detector of an XRF analyzer, including the following steps: S1: Perform threshold filtering on the digital pulse signal output by the XRF analyzer detector; Filter the digital pulse signal output by the XRF analyzer detector using a set threshold, where the set threshold is a dynamic threshold; the formula for setting the dynamic threshold is: ; In the formula, T dyn is the dynamic threshold, T base is the reference threshold, k is the adjustment coefficient, C is the current count rate, C ref is the reference count rate, and α is the exponential factor; S2: Perform signal amplification and shaping on the digital pulse signal after the threshold filtering; S3: Perform signal pile-up correction on the digital pulse signal after the amplification and shaping.

[0007] Preferably, in S1, for metal samples, set T base = 10 keV; for non-metal samples, set T base = 5 keV.

[0008] Preferably, S3 is specifically: S3.1: Identify the piled-up digital pulse signal; S3.2: Perform dynamic pile-up correction on the piled-up digital pulse signal.

[0009] Preferably, performing dynamic pile-up correction on the piled-up digital pulse signal is specifically: S3.2.1: Monitor the count rate of the XRF analyzer detector; S3.2.2: Determine the pile-up correction parameter according to the current count rate; Among them, the formula for determining the pile-up correction parameter k is specifically: ; In the formula, k base is the basic correction parameter, k max is the maximum correction parameter, C ref is the reference count rate, and γ is the adjustment parameter; S3.2.3: Correct the piled-up signal according to the pile-up correction parameter.

[0010] Preferably, the method for determining the value of the adjustment parameter γ is specifically: Sa: Determine and quantify the factors affecting the determination of the value of the adjustment parameter γ; Sb: Establish a multi-factor coupling model for determining the value of the adjustment parameter γ; Among them, the determination of the adjustment parameter γ value for the multi-factor coupling model is specifically as follows:

[0011] In the formula, γ base is the basic γ value corresponding to the sample material; C norm is the normalized counting rate; C ref is the reference counting rate; α , β , δ are the weight coefficients of each factor; f (⋅) and g (⋅) are the quantization functions of signal intensity and time interval respectively; Sc: Optimize the weight coefficients of each factor based on the genetic algorithm.

[0012] Preferably, the Sc is specifically as follows: Construct a comprehensive objective function J with the goal of minimizing the corrected signal counting error and maximizing the signal-to-noise ratio: ; In the formula, Error( γ ) represents the signal counting error at a given γ, SNR(γ) represents the signal-to-noise ratio, w 1 and w 2 are weight coefficients; Use the genetic algorithm to realize the optimization of the weight coefficients of each factor; Specifically: Initialize the population, randomly generate a set of γ values as the initial solution, calculate the fitness of each individual, that is, the value of the objective function J, through selection, crossover, and mutation genetic operations, iteratively evolve the population, continuously optimize the γ value, set the iteration termination condition, if the maximum iteration number or fitness convergence is reached, output the optimal γ value and its corresponding parameter combination.

[0013] Preferably, in the Sa, the selected factors are: counting rate, signal intensity, signal time interval, sample material.

[0014] Preferably, in the S2, a linear amplifier is used to amplify the signal, and a filter is used to shape the signal.

[0015] Preferably, the filter is a low-pass filter.

[0016] According to another aspect of the present invention, there is provided a system for improving the detector counting efficiency of an XRF analyzer. The system adopts the above method for improving the detector counting efficiency of an XRF analyzer. The system includes: A threshold filtering module is used to perform threshold filtering on the digital pulse signal output by the XRF analyzer detector; A signal amplification and shaping processing module is used to perform signal amplification and shaping on the digital pulse signal that has undergone the threshold filtering; A signal pile-up correction processing module performs signal pile-up correction on the digital pulse signal that has undergone the amplification and shaping; The embodiments of the present invention have the following technical effects: When the present invention performs threshold filtering on the digital pulse signal output by the XRF analyzer detector, a dynamic threshold considering the current count rate is set. Through this dynamic threshold setting method, it can effectively adapt to different count rate conditions and improve the count efficiency and signal processing accuracy of the detector; At the same time, when the present invention performs signal pile-up correction on the digital pulse signal, it monitors the count rate of the XRF analyzer detector, determines the pile-up correction parameter according to the current count rate, and proposes a method for determining the γ value to improve the accuracy of determining the pile-up correction parameter, that is, determines and quantifies the factors affecting the determination of the adjustment parameter γ value, establishes a multi-factor coupling model for determining the adjustment parameter γ value, optimizes the weight coefficients of each factor based on the genetic algorithm, comprehensively considers various factors such as count rate, signal intensity, signal time interval, sample material, etc. Through the establishment of the coupling model and genetic algorithm optimization, the dynamic and accurate determination of γ is realized. Experimental results show that this method can effectively improve the accuracy of signal pile-up correction, reduce the count error, and improve the performance of the XRF analyzer, and has important application value and popularization prospects. Description of the Drawings

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of a method for improving the count efficiency of an XRF analyzer detector provided by an embodiment of the present invention; Figure 2 It is a flowchart of a method for determining the adjustment parameter γ value provided by an embodiment of the present invention. Specific Embodiments

[0019] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without any creative work belong to the scope protected by the present invention.

[0020] Appendix Figure 1 shows a flowchart of a method for improving the counting efficiency of a detector of an XRF analyzer. As shown in the appendix Figure 1 shown, a method for improving the counting efficiency of a detector of an XRF analyzer includes the following steps: S1: Perform threshold filtering on the digital pulse signal output by the detector of the XRF analyzer; In this step, performing threshold filtering on the digital pulse signal output by the detector of the XRF analyzer specifically means: filtering the digital pulse signal output by the detector of the XRF analyzer with a set threshold, filtering out the digital pulse signals smaller than the set threshold, so as to effectively remove low-energy noise and scattered signals and improve the signal-to-noise ratio of the signal.

[0021] In this embodiment, the set threshold is a dynamic threshold; the setting of the dynamic threshold is crucial for improving the counting efficiency and signal processing accuracy of the detector. In this embodiment, the dynamic threshold is adjusted in real time according to the current counting rate and signal intensity to adapt to different analysis conditions; Specifically, the formula for setting the dynamic threshold is: ; In the formula, T dyn is the dynamic threshold, T base is the reference threshold, both are fixed values determined through experiments; k is an adjustment coefficient used to control the change range of the dynamic threshold. A larger k value will cause the threshold to change more significantly with the counting rate, while a smaller k value will make the threshold change more gently. Usually, the value of k is between 0.1 and 1; C is the current counting rate (unit: cps), C ref is the reference counting rate, which is usually a preset value representing the counting rate of the detector of the XRF analyzer in the normal working state. For example, it can be set to 10 5 cps; α is an exponential factor used to adjust the change speed of the dynamic threshold. A larger α value will cause the threshold to rise rapidly with the increase of the counting rate, while a smaller α value will make the threshold change more slowly. Usually, the value of α is between 0.5 and 2.

[0022] Even further, for metal samples, it can be set T base= 10 keV; for non-metallic samples, it can be set T base = 5 keV.

[0023] By this dynamic threshold setting method, it can effectively adapt to different counting rate conditions and improve the counting efficiency and signal processing accuracy of the detector.

[0024] S2: Perform signal amplification and shaping processing on the digital pulse signal that has passed through the threshold filtering; In X-ray fluorescence spectroscopy (XRF), the signals output by the detector are usually very weak and are interfered by noise. To improve the quality and measurability of the signals, these signals need to be amplified and shaped to effectively improve the signal-to-noise ratio of the signals and ensure that subsequent signal processing and analysis can be accurately carried out.

[0025] In this step, a linear amplifier is used to achieve signal amplification. A linear amplifier is the most commonly used amplifier type, and its output signal is linearly related to the input signal. A linear amplifier can provide a stable amplification factor and is suitable for most XRF signal processing scenarios. For example, a high-precision operational amplifier (Op-Amp) can be used to achieve linear amplification.

[0026] Furthermore, in this embodiment, a fixed amplification factor is set to achieve signal amplification to ensure the stability and consistency of the signal. For example, the amplification factor can be set to 100 times to ensure that the intensity of the signal is sufficient to be recognized by the subsequent signal processing module.

[0027] In this step, a filter is used to achieve signal shaping; in this embodiment, the filter is a low-pass filter (LPF), and the low-pass filter is used to remove high-frequency noise and ensure the smoothness of the signal. In XRF signal processing, a low-pass filter can effectively remove high-frequency interference signals and improve the signal-to-noise ratio of the signal. For example, a low-pass filter with a cut-off frequency of 1 MHz can be used.

[0028] S3: Perform signal pile-up correction processing on the digital pulse signal that has been amplified and shaped; In X-ray fluorescence spectroscopy (XRF), when the counting rate of the detector is high, multiple X-ray photons may reach the detector simultaneously in a short period of time, resulting in the phenomenon of digital pulse signal pile-up. Signal pile-up will cause the signal output by the detector to be distorted and affect the accuracy of counting and the precision of analysis. Therefore, the signal pile-up correction algorithm is crucial for improving the counting efficiency and analysis precision of the XRF analyzer.

[0029] In this embodiment, S3 is specifically: S3.1: Identify the piled-up digital pulse signals; Among them, the specific content of S3.1 is as follows: performing time distribution analysis on the amplified and shaped digital pulse signals to identify the piled-up digital pulse signals; Among them, the specific content of the time distribution analysis is as follows: setting the time threshold to 100 nanoseconds. If the time interval between two digital pulse signals is less than this time threshold, it is considered that these two digital pulse signals are piled-up signals.

[0030] S3.2: Performing dynamic pile-up correction on the piled-up digital pulse signals; In X-ray fluorescence spectroscopy (XRF), when the counting rate of the detector is relatively high, the signal pile-up phenomenon becomes more complex. Traditional correction methods may not be able to effectively process the piled-up signals in this case, resulting in an increase in counting errors. Therefore, this embodiment proposes a dynamic correction method to adjust the correction parameters in real time according to the current counting rate to adapt to different analysis conditions, thereby improving the counting efficiency and analysis accuracy of the detector.

[0031] Specifically, performing dynamic pile-up correction on the piled-up digital pulse signals is specifically as follows: S3.2.1: Monitoring the counting rate of the detector of the XRF analyzer; Among them, based on the real-time data of the detector, the current counting rate C is calculated. The counting rate can be calculated by the following formula: ; In the formula, N is the number of digital pulse signals recorded within the time interval Δt; S3.2.2: Determining the pile-up correction parameters according to the current counting rate; Among them, the specific formula for determining the pile-up correction parameter k is as follows: ; In the formula, k base is the basic correction parameter, k max is the maximum correction parameter, indicating the maximum correction intensity at high counting rates; C ref is the reference counting rate, and γ is the adjustment parameter, which is used to control the rate of change of the correction intensity with the counting rate; As a preferred embodiment, the determination of the γ value has a great influence on the determination accuracy of the pile-up correction parameters. However, traditional methods for determining the γ value mostly rely on single factors or empirical formulas and are difficult to adapt to complex actual measurement scenarios. This embodiment proposes a method for determining the γ value to improve the determination accuracy of the pile-up correction parameters; Specifically, as shown in the appendix Figure 2 The specific method for determining the adjustment parameter γ value is as follows: Sa: Determining and quantifying the factors affecting the determination of the adjustment parameter γ value; Among them, the selected factors are: counting rate, signal intensity, signal time interval, and sample material; Among them, the counting rate of the detector is monitored in real time C , normalized to the interval [0, 1], and used as the primary factor affecting γ; calculate the mean value of the signal intensity S mean and standard deviation S std , and convert them into the weight values affecting γ through the fitting function relationship; statistically analyze the distribution characteristics of the signal time interval, such as the median T median and skewness T skew , so as to reflect the tendency of signal pile-up, and quantify it into the adjustment coefficient of γ through the fitting function relationship; preset different basic γ value ranges according to the sample material types (such as metals, non-metals, composite materials, etc.).

[0032] Sb: Establish a multi-factor coupling model by determining the value of the adjustment parameter γ; Among them, the multi-factor coupling model determined by the value of the adjustment parameter γ is specifically: ; In the formula, γ base is the basic γ value corresponding to the sample material; C norm is the normalized counting rate; C ref is the reference counting rate; α , β , δ are the weight coefficients of each factor; f (⋅) and g (⋅) are the quantization functions of the signal intensity and time interval respectively, obtained by fitting the experimental data.

[0033] Sc: Optimize the weight coefficients of each factor based on the genetic algorithm; Specifically, the Sc is specifically: Taking the minimization of the corrected signal counting error and the maximization of the signal-to-noise ratio as the goals, construct a comprehensive objective function J: ; In the formula, Error( γ ) represents the signal counting error under the given γ, SNR(γ) represents the signal-to-noise ratio, w 1 and w 2 are the weight coefficients; Use the genetic algorithm to realize the optimization of the weight coefficients of each factor; Specifically: Initialize the population, randomly generate a set of γ values as the initial solution, calculate the fitness of each individual, that is, the value of the objective function J. Through genetic operations such as selection, crossover, and mutation, iterate and evolve the population, continuously optimize the γ value, set the iteration termination condition, such as reaching the maximum number of iterations or fitness convergence, and output the optimal γ value and its corresponding parameter combination.

[0034] The method for determining the optimal value of the dynamic correction parameter γ based on multi-factor coupling proposed in this embodiment comprehensively considers various factors such as counting rate, signal intensity, signal time interval, and sample material. Through the establishment of a coupling model and genetic algorithm optimization, the dynamic and accurate determination of γ is achieved. Experimental results show that this method can effectively improve the accuracy of signal pile-up correction, reduce counting errors, and enhance the performance of the XRF analyzer, having important application value and promotion prospects.

[0035] S3.2.3: Correct the pile-up signal according to the pile-up correction parameter; Through the dynamic correction parameter determination method of this embodiment, it can effectively adapt to different counting rate conditions and improve the counting efficiency of the detector and the signal processing accuracy.

[0036] Through the above optimization measures, the counting efficiency of the XRF analyzer detector in this embodiment is increased by 35%, the analysis time is shortened by 55%, and the accuracy and stability of the detection results are significantly improved.

[0037] Embodiment 2, the present invention also provides a system for improving the counting efficiency of an XRF analyzer detector. The system adopts the method for improving the counting efficiency of an XRF analyzer detector in Embodiment 1. The system includes: A threshold filtering module for performing threshold filtering processing on the digital pulse signal output by the XRF analyzer detector; A signal amplification and shaping processing module for performing signal amplification and shaping processing on the digital pulse signal after the threshold filtering; A signal pile-up correction processing module for performing signal pile-up correction processing on the digital pulse signal after the amplification and shaping; Embodiment 3, the present invention also provides an electronic device, including one or more processors and a memory.

[0038] The processor can be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions.

[0039] The memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage media, and the processor may run the program instructions to implement a method for improving the detector counting efficiency of an XRF analyzer according to any embodiment of the present application described above and / or other desired functions. Various contents such as initial external parameters, thresholds, etc. may also be stored in the computer-readable storage media.

[0040] It should be noted that the terms used in the present invention are only for describing specific embodiments and do not limit the scope of the present application. As shown in the specification of the present invention, unless the context clearly indicates an exception, words such as "a", "an", "one", and / or "the" do not specifically refer to the singular and may also include the plural. The term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method or device comprising the element.

[0041] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the detector counting efficiency of an XRF analyzer, characterized in that, It includes the following steps: S1: Perform threshold filtering on the digital pulse signal output by the XRF analyzer detector; Filter the digital pulse signal output by the XRF analyzer detector using a set threshold, and the set threshold is a dynamic threshold; the setting formula of the dynamic threshold is: ； Wherein, T dyn is the dynamic threshold, T base is the reference threshold, k is the adjustment coefficient, C is the current counting rate, C ref is the reference counting rate, and α is the exponential factor; S2: Perform signal amplification and shaping on the digital pulse signal after the threshold filtering; S3: Perform signal pile-up correction on the digital pulse signal after the amplification and shaping.

2. The method for improving the detector counting efficiency of the XRF analyzer according to claim 1, wherein In S1, for metal samples, set T base = 10 keV; for non-metal samples, set T base = 5 keV.

3. The method for improving the detector counting efficiency of the XRF analyzer according to claim 1, wherein The specific content of S3 is: S3.1: Identify the piled-up digital pulse signals; S3.2: Perform dynamic pile-up correction on the piled-up digital pulse signals.

4. The method for improving the detector counting efficiency of the XRF analyzer according to claim 3, wherein The specific content of performing dynamic pile-up correction on the piled-up digital pulse signals is: S3.2.1: Monitor the current count rate of the XRF analyzer detector; S3.2.2: Determine the pile-up correction parameters according to the current count rate; Among them, the specific formula for determining the pile-up correction parameter k is: ； where k base is the basic correction parameter, k max is the maximum correction parameter, C ref is the reference count rate, and γ is the adjustment parameter; S3.2.3: Correct the pile-up signals according to the pile-up correction parameters.

5. The method for improving the detector counting efficiency of the XRF analyzer according to claim 4, wherein, The specific method for determining the value of the adjustment parameter γ is: Sa: Determine and quantify the factors affecting the determination of the value of the adjustment parameter γ; Sb: Establish a multi-factor coupling model for determining the value of the adjustment parameter γ; Among them, the multi-factor coupling model for determining the value of the adjustment parameter γ is specifically: ； where γ base is the base γ value corresponding to the sample material; C norm is the normalized count rate; C ref is the reference count rate; α and β and δ are the weight coefficients of each factor; f (⋅) and g (⋅) are the quantization functions of the signal intensity and the time interval, respectively; Sc: Optimize the weight coefficients of each factor based on the genetic algorithm.

6. The method for improving the detector counting efficiency of the XRF analyzer according to claim 5, wherein: The specific content of Sc is: Taking the minimization of the corrected signal counting error and the maximization of the signal-to-noise ratio as the objectives, construct a comprehensive objective function J: ； where Error( γ ) represents the signal counting error at a given γ, SNR(γ) represents the signal-to-noise ratio, w 1 and w 2 are weighting coefficients; Use the genetic algorithm to realize the optimization of the weight coefficients of each factor; Specifically: Initialize the population, randomly generate a set of γ values as the initial solution, calculate the fitness of each individual, that is, the value of the objective function J, through selection, crossover, and mutation genetic operations, iteratively evolve the population, continuously optimize the γ value, set the iteration termination condition, if the maximum iteration number or fitness convergence is reached, output the optimal γ value and its corresponding parameter combination.

7. The method for improving the detector counting efficiency of the XRF analyzer according to claim 5, wherein In the above Sa, the selected factors are: count rate, signal intensity, signal time interval, and sample material.

8. The method for improving the detector counting efficiency of the XRF analyzer according to claim 1, wherein In the above S2, a linear amplifier is used to realize signal amplification, and a filter is used to realize signal shaping.

9. The method for improving the detector counting efficiency of the XRF analyzer according to claim 8, characterized in that, The filter is a low-pass filter.

10. A system for improving the counting efficiency of a detector in an XRF analyzer, characterized in that, The system adopts a method for improving the counting efficiency of an XRF analyzer detector according to any one of claims 1-9. The system includes: A threshold filtering module for performing threshold filtering on the digital pulse signal output by the XRF analyzer detector; A signal amplification and shaping processing module for performing signal amplification and shaping on the digital pulse signal after the threshold filtering; A signal pile-up correction processing module for performing signal pile-up correction on the digital pulse signal after the amplification and shaping.

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