A method and system for improving detector counting efficiency of XRF analyzer

Abstract

The present invention relates to the technical field of X-ray fluorescence spectrometry (XRF), and in particular, to a method and system for improving the counting efficiency of XRF analyzer detectors. This method sets a dynamic threshold that takes the current count rate into account when performing threshold filtering on the digital pulse signal output by the XRF analyzer detector. This dynamic threshold setting method effectively adapts to varying count rate conditions, improving the detector's counting efficiency and signal processing accuracy. Furthermore, the method performs signal pile-up correction on the digital pulse signal, comprehensively considering multiple factors such as count rate, signal strength, signal time interval, and sample material. By establishing a coupling model and optimizing with a genetic algorithm, the method achieves dynamic and precise determination of γ. Experimental results demonstrate that this method can effectively improve the accuracy of signal pile-up correction, reduce counting errors, and enhance the performance of XRF analyzers, demonstrating its significant application value and potential for widespread adoption.

Description

Technical Field

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

[0002] X-ray fluorescence (XRF) is a nondestructive testing technique based on the interaction of X-rays with matter. Its basic principle is that when X-rays strike a sample surface, they excite atoms in the sample, causing their inner electrons to transition to higher-energy orbits. These electrons then transition back to lower-energy orbits, emitting X-ray fluorescence of specific energy. By measuring the intensity and energy of these X-ray fluorescence patterns, the types and concentrations of elements in the sample can be determined. XRF analysis technology, due to its advantages such as rapid analysis speed, wide applicability, and the absence of sample destruction, has gained widespread application in fields such as materials science, geological exploration, environmental monitoring, petrochemical engineering, and metalworking. The detector is a core component of an XRF analyzer. Its primary function is to convert X-ray fluorescence signals into electrical signals, which are then measured and recorded. Detector performance is directly related to the accuracy, sensitivity, and efficiency of XRF analysis. An ideal detector should possess high sensitivity, high resolution, high count rate capability, and low noise. However, in practical applications, conventional XRF analyzer detectors often have limitations, particularly in terms of counting efficiency, making them incapable of meeting the growing demand for high-precision and rapid analysis.

[0003] Although XRF analysis technology has made certain progress in detector materials, signal processing algorithms, etc. in recent years, in practical applications, the counting efficiency 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 signal processing accuracy, they have high requirements for hardware equipment and high computational complexity, making it 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 XRF analyzer detectors. Without significantly increasing the cost and complexity, the counting efficiency of the detector can be significantly improved by optimizing the signal processing process and analysis parameter settings, thereby improving the accuracy and speed of XRF analysis and meeting the needs of different fields for high-precision XRF analysis. 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 an XRF analyzer detector, which are used to solve the problems existing in the prior art.

[0006] The present invention provides a method for improving the counting efficiency of an XRF analyzer detector, comprising the following steps:

[0007] S1: performing threshold filtering on the digital pulse signal output by the XRF analyzer detector;

[0008] The digital pulse signal output by the XRF analyzer detector is filtered using a set threshold value, where the set threshold value is a dynamic threshold value. The setting formula of the dynamic threshold value is:

[0009] ;

[0010] Where, 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, α is the exponential factor;

[0011] S2: performing signal amplification and shaping processing on the digital pulse signal after the threshold filtering;

[0012] S3: performing signal pile-up correction processing on the amplified and shaped digital pulse signal.

[0013] Preferably, in said S1, for the metal sample, set T base =10 keV; for non-metallic samples, set T base =5keV.

[0014] Preferably, the S3 is specifically:

[0015] S3.1: Identify accumulated digital pulse signals;

[0016] S3.2: Perform dynamic pile-up correction on the piled-up digital pulse signal.

[0017] Preferably, the dynamic pile-up correction is performed on the piled-up digital pulse signal as follows:

[0018] S3.2.1: Monitor the count rate of the XRF analyzer detector;

[0019] S3.2.2: Determine a pile-up correction parameter based on the current count rate;

[0020] The pile-up correction parameter k is determined by the formula:

[0021] ;

[0022] Where k base is the basic correction parameter, k max is the maximum correction parameter, C refis the reference count rate, γ is the adjustment parameter;

[0023] S3.2.3: Correct the pile-up signal according to the pile-up correction parameters.

[0024] Preferably, the method for determining the value of the adjustment parameter γ is specifically as follows:

[0025] Sa: Identify and quantify the factors that influence the determination of the value of the adjustment parameter γ;

[0026] Sb: Establish the adjustment parameter γ value to determine the multi-factor coupling model;

[0027] The adjustment parameter γ value is used to determine the multi-factor coupling model:

[0028]

[0029] Where, γ base is the basic γ value corresponding to the sample material; C norm is the normalized count rate; C ref is the reference count rate; α 、 β 、 δ is the weight coefficient of each factor; f (⋅) and g (⋅) are the quantization functions of signal intensity and time interval, respectively;

[0030] Sc: Optimize the weight coefficient of each factor based on genetic algorithm.

[0031] Preferably, the Sc is specifically:

[0032] With the goal of minimizing the corrected signal counting error and maximizing the signal-to-noise ratio, a comprehensive objective function J is constructed:

[0033] ;

[0034] In the formula, Error( γ ) represents the signal counting error under a given γ, SNR(γ) represents the signal-to-noise ratio, w 1 and w 2 is the weight coefficient;

[0035] Genetic algorithm is used to optimize the weight coefficient of each factor;

[0036] Specifically, the population is initialized, a set of γ values ​​are randomly generated as the initial solution, the fitness of each individual, that is, the value of the objective function J, is calculated, the population is iteratively evolved through selection, crossover, and mutation genetic operations, the γ value is continuously optimized, and the iteration termination condition is set. If the maximum number of iterations is reached or the fitness converges, the optimal γ value and its corresponding parameter combination are output.

[0037] Preferably, in the Sa, the selected factors are: counting rate, signal strength, signal time interval, and sample material.

[0038] Preferably, in S2, a linear amplifier is used to achieve signal amplification, and a filter is used to achieve signal shaping.

[0039] Preferably, the filter is a low-pass filter.

[0040] According to another aspect of the present invention, a system for improving the counting efficiency of an XRF analyzer detector is provided. The system adopts the above-mentioned method for improving the counting efficiency of an XRF analyzer detector. The system comprises:

[0041] A threshold filtering module is used to perform threshold filtering on the digital pulse signal output by the XRF analyzer detector;

[0042] A signal amplification and shaping processing module, used for performing signal amplification and shaping processing on the digital pulse signal that has passed the threshold filtering;

[0043] The signal pile-up correction processing module performs signal pile-up correction processing on the amplified and shaped digital pulse signal;

[0044] The embodiments of the present invention have the following technical effects:

[0045] The present invention sets a dynamic threshold that takes the current count rate into account when performing threshold filtering on the digital pulse signal output by the XRF analyzer detector. This dynamic threshold setting method can effectively adapt to different count rate conditions and improve the detector's counting efficiency and signal processing accuracy.

[0046] At the same time, when performing signal pile-up correction on digital pulse signals, the present invention monitors the count rate of the XRF analyzer detector and determines the pile-up correction parameter based on the current count rate. Furthermore, a method for determining the γ value is proposed to improve the accuracy of the pile-up correction parameter. Specifically, the factors influencing the determination of the adjustment parameter γ are identified and quantified, and a multi-factor coupling model for determining the adjustment parameter γ value is established. The weight coefficients of each factor are optimized using a genetic algorithm, comprehensively considering multiple factors such as count rate, signal intensity, signal time interval, and sample material. By establishing a coupling model and optimizing the genetic algorithm, dynamic and accurate determination of γ is achieved. Experimental results demonstrate that this method can effectively improve the accuracy of signal pile-up correction, reduce counting errors, and enhance the performance of XRF analyzers, demonstrating its significant application value and potential for promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific 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.

[0048] Figure 1 This is a flow chart of a method for improving the counting efficiency of an XRF analyzer detector provided by an embodiment of the present invention;

[0049] Figure 2 This is a flow chart of a method for determining a value of an adjustment parameter γ provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0051] Attachment Figure 1 A flow chart of a method for improving the counting efficiency of an XRF analyzer detector is shown in the attached figure. Figure 1 As shown, a method for improving the counting efficiency of an XRF analyzer detector includes the following steps:

[0052] S1: performing threshold filtering on the digital pulse signal output by the XRF analyzer detector;

[0053] In this step, the digital pulse signal output by the XRF analyzer detector is subjected to threshold filtering, specifically: the digital pulse signal output by the XRF analyzer detector is filtered using a set threshold, and digital pulse signals less than the set threshold are filtered out, so as to effectively remove low-energy noise and scattered signals and improve the signal-to-noise ratio of the signal.

[0054] In this embodiment, the set threshold is a dynamic threshold; dynamic threshold setting 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 strength to adapt to different analysis conditions;

[0055] Specifically, the setting formula of the dynamic threshold is:

[0056] ;

[0057] Where, T dyn is the dynamic threshold, T base is the baseline threshold, which is a fixed value determined by experiments; k is the adjustment coefficient, which is used to control the change amplitude of the dynamic threshold. A larger k value will cause the threshold to change more significantly with the count rate, while a smaller k value will make the threshold change more slowly. Usually, the value of k is between 0.1 and 1; C is the current count rate (unit: cps), C ref The reference count rate is usually a preset value, which represents the count rate of the XRF analyzer detector under normal working conditions. For example, it can be set to 10 5 cps; α is an exponential factor used to adjust the speed of change of the dynamic threshold. A larger α value will cause the threshold to rise rapidly as the count rate increases, while a smaller α value will cause the threshold to change more slowly. Typically, the value of α is between 0.5 and 2.

[0058] Furthermore, for metal samples, you can set T base =10keV; for non-metallic samples, you can set T base =5keV.

[0059] This dynamic threshold setting method can effectively adapt to different counting rate conditions and improve the counting efficiency and signal processing accuracy of the detector.

[0060] S2: performing signal amplification and shaping processing on the digital pulse signal after the threshold filtering;

[0061] In X-ray fluorescence spectrometry (XRF), the signals output by the detector are usually very weak and subject to noise interference. To improve the quality and measurability of the signals, these signals need to be amplified and shaped to effectively increase the signal-to-noise ratio and ensure that subsequent signal processing and analysis can be carried out accurately.

[0062] In this step, a linear amplifier is used to amplify the signal. This is the most commonly used amplifier type, as its output signal is linearly proportional to its input signal. This amplifier provides 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.

[0063] Furthermore, in this embodiment, a fixed amplification factor is set to achieve signal amplification to ensure signal stability and consistency. For example, the amplification factor can be set to 100 times to ensure that the signal strength is sufficient to be recognized by the subsequent signal processing module.

[0064] In this step, a filter is used to achieve signal shaping. In this embodiment, the filter is a low-pass filter (LPF), which removes high-frequency noise and ensures signal smoothness. In XRF signal processing, a low-pass filter can effectively remove high-frequency interference signals and improve the signal-to-noise ratio. For example, a low-pass filter with a cutoff frequency of 1 MHz can be used.

[0065] S3: performing signal pile-up correction processing on the amplified and shaped digital pulse signal;

[0066] In X-ray fluorescence (XRF) analysis, when the detector count rate is high, multiple X-ray photons may arrive at the detector simultaneously within a short period of time, leading to a phenomenon called digital pulse signal pile-up. This signal pile-up distorts the detector output signal, affecting both counting accuracy and analysis precision. Therefore, signal pile-up correction algorithms are crucial for improving the counting efficiency and analysis precision of XRF analyzers.

[0067] In this embodiment, S3 is specifically:

[0068] S3.1: Identify accumulated digital pulse signals;

[0069] The step S3.1 specifically includes: performing time distribution analysis on the amplified and shaped digital pulse signal to identify accumulated digital pulse signals;

[0070] The time distribution analysis is specifically as follows: a time threshold is set to 100 nanoseconds, and if the time interval between two digital pulse signals is less than the time threshold, the two digital pulse signals are considered to be accumulation signals.

[0071] S3.2: Performing dynamic pile-up correction on the piled-up digital pulse signal;

[0072] In X-ray fluorescence (XRF) analysis, signal pile-up becomes more complex when the detector count rate is high. Traditional correction methods may not be able to effectively handle pile-up signals in this situation, resulting in increased counting errors. Therefore, this embodiment proposes a dynamic correction method that adjusts correction parameters in real time based on the current count rate to adapt to different analysis conditions, thereby improving the detector's counting efficiency and analysis accuracy.

[0073] Specifically, the dynamic pile-up correction is performed on the piled-up digital pulse signal as follows:

[0074] S3.2.1: Monitor the count rate of the XRF analyzer detector;

[0075] The current counting rate C is calculated through the real-time data of the detector. The counting rate can be calculated by the following formula:

[0076] ;

[0077] Where N is the number of digital pulse signals recorded within the time interval Δt;

[0078] S3.2.2: Determine a pile-up correction parameter based on the current count rate;

[0079] The pile-up correction parameter k is determined by the formula:

[0080] ;

[0081] Where k base is the basic correction parameter, k max is the maximum correction parameter, which indicates the maximum correction intensity at high counting rate; C ref is the reference count rate, and γ is the adjustment parameter used to control the rate at which the correction intensity changes with the count rate;

[0082] As a preferred embodiment, the determination of the γ value has a significant impact on the accuracy of determining the pile-up correction parameters. However, traditional methods for determining the γ value often rely on a single factor or empirical formula, which is difficult to adapt to complex actual measurement scenarios. This embodiment proposes a method for determining the γ value to improve the accuracy of determining the pile-up correction parameters.

[0083] Specifically, as attached Figure 2 As shown, the method for determining the value of the adjustment parameter γ is specifically as follows:

[0084] Sa: Identify and quantify the factors that influence the determination of the value of the adjustment parameter γ;

[0085] Among them, the selected factors are: counting rate, signal intensity, signal time interval and sample material;

[0086] Wherein, the counting rate of the detector is monitored in real time C , normalize it to the [0,1] interval as the primary factor affecting γ; calculate the mean value of the signal intensity S mean and standard deviation S std , by fitting the functional relationship, it is converted into a weight value that affects γ; the distribution characteristics of the statistical signal time interval, such as the median T median and skewness T skew , which reflects the tendency of signal accumulation and is quantified as an adjustment coefficient for γ through a fitting function relationship; different basic γ value ranges are set in advance according to the sample material type (such as metal, non-metal, composite material, etc.).

[0087] Sb: Establish the adjustment parameter γ value to determine the multi-factor coupling model;

[0088] The adjustment parameter γ value is used to determine the multi-factor coupling model:

[0089] ;

[0090] Where, γ base is the basic γ value corresponding to the sample material; C norm is the normalized count rate; C ref is the reference count rate; α 、 β 、 δ is the weight coefficient of each factor; f (⋅) and g (⋅) are the quantization functions of signal intensity and time interval, respectively, which are fitted according to the experimental data.

[0091] Sc: Optimize the weight coefficients of each factor based on genetic algorithm;

[0092] Specifically, the Sc is:

[0093] With the goal of minimizing the corrected signal counting error and maximizing the signal-to-noise ratio, a comprehensive objective function J is constructed:

[0094] ;

[0095] In the formula, Error( γ ) represents the signal counting error under a given γ, SNR(γ) represents the signal-to-noise ratio,w 1 and w 2 is the weight coefficient;

[0096] Genetic algorithm is used to optimize the weight coefficient of each factor;

[0097] Specifically, the method is as follows: 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, iteratively evolve the population through genetic operations such as selection, crossover, and mutation, continuously optimize the γ value, set the iteration termination conditions, such as reaching the maximum number of iterations or fitness convergence, and output the optimal γ value and its corresponding parameter combination.

[0098] This example proposes a method for determining the optimal value of the dynamic correction parameter γ based on multi-factor coupling. This method comprehensively considers factors such as count rate, signal strength, signal time interval, and sample material. By establishing a coupled model and optimizing it with a genetic algorithm, it achieves accurate dynamic determination of γ. Experimental results demonstrate that this method effectively improves the accuracy of signal pile-up correction, reduces counting errors, and enhances the performance of XRF analyzers, demonstrating its significant application value and potential for widespread adoption.

[0099] S3.2.3: Correcting the pile-up signal according to the pile-up correction parameters;

[0100] The dynamic calibration parameter determination method of this embodiment can effectively adapt to different counting rate conditions and improve the counting efficiency and signal processing accuracy of the detector.

[0101] Through the above optimization measures, the counting efficiency of the XRF analyzer detector of 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.

[0102] In Example 2, the present invention further 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 Example 1, and the system includes:

[0103] A threshold filtering module, configured to perform threshold filtering on the digital pulse signal output by the XRF analyzer detector;

[0104] A signal amplification and shaping processing module, used for performing signal amplification and shaping processing on the digital pulse signal that has passed the threshold filtering;

[0105] The signal pile-up correction processing module performs signal pile-up correction processing on the digital pulse signal that has been amplified and shaped;

[0106] Example 3: The present invention also provides an electronic device, including one or more processors and a memory.

[0107] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0108] The memory may include one or more computer program products, which 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. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement a method for improving the counting efficiency of an XRF analyzer detector and / or other desired functions of any embodiment of the present application described above. Various contents such as initial external parameters and threshold values ​​may also be stored in the computer-readable storage medium.

[0109] It should be noted that the terms used in the present invention are only for describing specific embodiments and are not intended to limit the scope of this application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular and may also include the plural. The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, the elements defined by the sentence "comprise a..." do not exclude the presence of other identical elements in the process, method or device comprising the elements.

[0110] 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the counting efficiency of an XRF analyzer detector, characterized in that: The following steps are involved: S1: performing threshold filtering on the digital pulse signal output by the XRF analyzer detector; The digital pulse signal output by the XRF analyzer detector is filtered using a set threshold value, where the set threshold value is a dynamic threshold value. The setting formula of the dynamic threshold value is: ； Where, 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, α is the exponential factor; S2: performing signal amplification and shaping processing on the digital pulse signal after the threshold filtering; in S2, a linear amplifier is used to achieve signal amplification, and a filter is used to achieve signal shaping; S3: performing signal pile-up correction processing on the amplified and shaped digital pulse signal; S3 specifically comprises: S3.1: Identify accumulated digital pulse signals; S3.2: Perform dynamic pile-up correction on the piled-up digital pulse signal; specifically: S3.2.1: Monitor the current count rate of the XRF analyzer detector; S3.2.2: Determine a pile-up correction parameter based on the current count rate; The pile-up correction parameter k is determined by the formula: ； Where k base is the basic correction parameter, k max is the maximum correction parameter, C ref is the reference count rate, γ is the adjustment parameter; the method for determining the value of the adjustment parameter γ is specifically as follows: Sa: Identify and quantify the factors that influence the determination of the value of the adjustment parameter γ; Sb: Establish the adjustment parameter γ value to determine the multi-factor coupling model; The adjustment parameter γ value is used to determine the multi-factor coupling model: ； Where, γ base is the basic γ value corresponding to the sample material; C norm is the normalized count rate; C ref is the reference count rate; α 、 β 、 δ is the weight coefficient 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 genetic algorithm; S3.2.3: Correct the pile-up signal according to the pile-up correction parameters.

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

3. The method for improving the counting efficiency of an XRF analyzer detector according to claim 1, wherein: The Sc is specifically: With the goal of minimizing the corrected signal counting error and maximizing the signal-to-noise ratio, a comprehensive objective function J is constructed: ； In the formula, Error( γ ) represents the signal counting error under a given γ, SNR(γ) represents the signal-to-noise ratio, w 1 and w 2 is the weight coefficient; Genetic algorithm is used to optimize the weight coefficient of each factor; Specifically, the population is initialized, a set of γ values ​​are randomly generated as the initial solution, the fitness of each individual, that is, the value of the objective function J, is calculated, the population is iteratively evolved through selection, crossover, and mutation genetic operations, the γ value is continuously optimized, and the iteration termination condition is set. If the maximum number of iterations is reached or the fitness converges, the optimal γ value and its corresponding parameter combination are output.

4. The method for improving the counting efficiency of an XRF analyzer detector according to claim 1, wherein: In the Sa, the selected factors are: counting rate, signal intensity, signal time interval and sample material.

5. The method for improving the counting efficiency of an XRF analyzer detector according to claim 1, wherein: The filter is a low-pass filter.

6. A system for improving the counting efficiency of an XRF analyzer detector, characterized in that: The system adopts the method for improving the counting efficiency of an XRF analyzer detector according to any one of claims 1 to 5, and 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, used for performing signal amplification and shaping processing on the digital pulse signal that has passed the threshold filtering; The signal pile-up correction processing module performs signal pile-up correction processing on the amplified and shaped digital pulse signal.

Images