High-resolution gamma-ray energy spectrum analysis method and device based on double-index trapezoid forming

Abstract

The invention provides a high-resolution gamma-ray energy spectrum analysis method and device based on double-index trapezoidal shaping, and the method comprises the steps: enabling a gamma ray of soil to be detected to enter a scintillator of a gamma-ray energy spectrometer, generating photons, and enabling the photons to be gathered to a photomultiplier of the gamma-ray energy spectrometer, thereby obtaining a voltage pulse; the method comprises the following steps: sampling a pulse attenuation stage of a voltage pulse, acquiring data of two adjacent points, multiplying the data to obtain a pulse flat top width, and substituting the pulse flat top width and a preset trapezoidal pulse rising length and a preset trapezoidal pulse falling length into a double-index pulse trapezoidal forming time domain expression to obtain a forming result. And sending the forming result into an ADC (Analog to Digital Converter) of the gamma-ray spectrometer for sampling and multi-channel pulse amplitude analysis so as to obtain spectral line data of the to-be-detected soil. According to the double-index trapezoidal forming algorithm provided by the invention, the spectrum resolution can be improved, and the energy spectrum analysis is more accurate.

Description

Technical Field

[0001] This technology involves the use of gamma ray spectrometers for soil testing in smart agriculture, especially the detection of radioactive nuclides. Specifically, it involves a method for pulse signal shaping using a gamma ray spectrometer. Background Art

[0002] The soil nutrient digital gamma spectrum detector usually uses a detector of NaI, CsI or other scintillation crystals (NaI is used as an example below) + photomultiplier tube + preamplifier circuit + single-ended differential circuit + ADC. The output pulse signal is generally a single exponential pulse signal, but the RC circuit of the subsequent amplifier circuit makes the pulse signal closer to a double exponential pulse. The existing technology uses a single exponential trapezoidal algorithm to restore the waveform that is actually a double exponential. The model does not match, so the accuracy is reduced.

[0003] Since the pulse signal output by the detector has the characteristics of short rise time, slow fall, exponential decay and very sharp pulse peak, and due to the limitation of ADC sampling frequency, it is difficult to accurately obtain the pulse amplitude simply by directly collecting the pulse signal. Even if the pulse amplitude can be accurately obtained by collecting the pulse amplitude, the collected pulse amplitude has ballistic loss due to the charge collection time of the detector, which is not conducive to the subsequent accurate extraction of the amplitude, and thus affects the energy resolution of the energy spectrometer. Therefore, the pulse signal collected by the ADC must be filtered and shaped to make the energy spectrum analysis more accurate.

[0004] The filter shaping methods currently used in digital gamma spectrometers mainly include Gaussian shaping and single exponential trapezoidal shaping (when the flat top width is 0, it is called triangular shaping).

[0005] Digital research and application of Gaussian pulse shaping algorithm. Although this method has good energy resolution characteristics and is relatively simple to implement, it is weak in peak hold and ballistic loss suppression capabilities. The rays are captured in the front scintillating crystal and then converted into electrons to move in a straight line to the path in front of the receiver, which is equivalent to a bullet running in a barrel. If it hits the barrel wall, it will lose some energy due to friction, which is ballistic loss. The trajectory of each movement is different, and the energy lost is different.

[0006] The single exponential pulse shaping algorithm is used in most energy spectrometers due to its simple implementation. However, in practical applications, the introduction of the amplifier circuit makes the detector output closer to a double exponential pulse signal. Summary of the invention

[0007] Aiming at the problems of energy leakage (the sum of ballistic loss and energy attenuation caused by RC circuit) encountered in the operation of gamma energy spectrum detector, the present invention proposes an adaptive double exponential trapezoidal shaping algorithm, which can improve the spectrum resolution and make the energy spectrum analysis more accurate.

[0008] In summary, in view of the shortcomings of the existing technology, such as Figure 6 As shown, the present invention proposes a high-resolution gamma spectrum analysis method based on double exponential trapezoidal formation, which includes:

[0009] Initial step S1, after the gamma rays of the soil to be tested are incident on the scintillator of the gamma spectrometer, photons are generated, and the photons are focused on the photomultiplier tube of the gamma spectrometer to obtain voltage pulses;

[0010] Analysis step S2, by sampling the pulse attenuation stage of the voltage pulse, collecting data from two adjacent points and taking the quotient, obtaining the pulse flat-top width, bringing the pulse flat-top width and the preset trapezoidal pulse rise length and fall length into the double-exponential pulse trapezoidal shaping time domain expression, obtaining the shaping result, and sending the shaping result to the ADC of the gamma spectrometer for sampling and multi-channel pulse amplitude analysis, thereby obtaining the spectral line data of the soil to be tested.

[0011] The high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping, wherein the analysis step comprises:

[0012] The data are sampled and compared at the flat-top time position of the forming result according to the timing. If the difference between the two data obtained is within the preset error range, the forming result is sent to the ADC of the gamma spectrometer for sampling and multi-channel pulse amplitude analysis; otherwise, the pulse flat-top width is fine-tuned to update the forming result until the difference between the two data obtained is within the preset error range.

[0013] The high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping adopts an IIR filter to realize the double exponential pulse trapezoidal shaping of the shaping result, and each time the IIR filter outputs the shaping result, the IIR filter is reset.

[0014] The high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping, wherein the analysis step comprises:

[0015] The time domain expression of the double exponential pulse signal is:

[0016] Formula (1):

[0017] Where A is the double exponential pulse amplitude, τ f With τ u are the falling time constant and the rising time constant respectively, and vi represents the real-time amplitude of the signal;

[0018] The time domain expression of the isosceles trapezoidal pulse is:

[0019] Formula (2):

[0020] Where v tra (t) is an isosceles trapezoidal pulse; u(t) is a unit step function; t r is the rising end point of the trapezoidal pulse; t t is the flat-top end point of the trapezoidal pulse; t d It is the falling end point of the trapezoidal pulse;

[0021] Discretize equations (1) and (2) with Ts as the sampling period and perform Z transformation to obtain:

[0022] Formula (3): v i(z) =n r ·(α-β)·(1-z -1 ) 2

[0023] Formula (4): v tra (z) = (1-z -nr -z -nt +z -nd )·(1-α·z -1 )·(1-β·z -1 )

[0024] Where the trapezoidal pulse rise length n r =t r / Ts; pulse flat top width n t =t t / Ts determines the pulse flat-top width; the trapezoidal pulse falling length n d =td / Ts;

[0025]

[0026] Divide equation (4) by equation (3) and then perform Z inverse transformation to obtain the time domain expression of the double exponential pulse trapezoidal shaping:

[0027] v o (n) = 2·v0(n-1)-v0(n-2)+[(v i (n)-v i (nn r )-v i (nn t )+v i (nn d ))-(α+β)·(v i (n-1)-v i(nn r -1)-v i (nn t -1)+v i (nn d -1))+α·β·(v i (n-2)-v i (nn r -2)-v i (nn t -2)+v i (nn d -2))] / [n r ·(α-β)] (5)

[0028] Where v i (n) is the input double exponential pulse sequence; n is the sampling sequence number; v o (n) is the output sequence after trapezoidal formation.

[0029] like Figure 7 As shown, the present invention also proposes a high-resolution gamma spectrum analysis device based on double exponential trapezoidal formation, which includes:

[0030] In the initial module, the gamma rays of the soil to be tested are incident on the scintillator of the gamma spectrometer, generating photons, which are focused on the photomultiplier tube of the gamma spectrometer to obtain voltage pulses;

[0031] The analysis module samples the pulse attenuation stage of the voltage pulse, collects data from two adjacent points and makes a quotient to obtain the pulse flat-top width, brings the pulse flat-top width and the preset trapezoidal pulse rise length and fall length into the double-exponential pulse trapezoidal shaping time domain expression to obtain a shaping result, and sends the shaping result to the ADC of the gamma spectrometer for sampling and multi-channel pulse amplitude analysis, thereby obtaining the spectral line data of the soil to be tested.

[0032] The high-resolution gamma spectrum analysis device based on double exponential trapezoidal shaping, wherein the analysis module comprises:

[0033] Compare the sampled data at the flat-top time position of the shaping result according to the time sequence. If the difference between the two data is within the preset error range, send the shaping result to the ADC of the gamma spectrometer for sampling and multi-channel pulse amplitude analysis; otherwise, fine-tune the pulse flat-top width to update the shaping result until the difference between the two data is within the preset error range.

[0034] An IIR filter is used to implement the double exponential pulse trapezoidal shaping of the shaping result, and each time the IIR filter outputs the shaping result, the IIR filter is reset.

[0035] A high-resolution gamma spectrum analysis device based on double exponential trapezoidal formation, wherein the analysis module comprises:

[0036] The time domain expression of the double exponential pulse signal is:

[0037] Formula (1):

[0038] Where A is the double exponential pulse amplitude, τ f With τ u are the falling time constant and the rising time constant respectively, and vi represents the real-time amplitude of the signal;

[0039] The time domain expression of the isosceles trapezoidal pulse is:

[0040] Formula (2):

[0041] Where v tra (t) is an isosceles trapezoidal pulse; u(t) is a unit step function; t r is the rising end point of the trapezoidal pulse; t t is the flat-top end point of the trapezoidal pulse; t d It is the falling end point of the trapezoidal pulse;

[0042] Discretize equations (1) and (2) with Ts as the sampling period and perform Z transformation to obtain:

[0043] Formula (3): v i(z) =n r ·(α-β)·(1-z -1 ) 2

[0044] Formula (4): v tra (z) = (1-z -nr -z -nt +z -nd )·(1-α·z -1 )·(1-β·z -1 )

[0045] Where the trapezoidal pulse rise length n r =t r / Ts; pulse flat top width n t =t t / Ts determines the pulse flat-top width; the trapezoidal pulse falling length n d =td / Ts;

[0046]

[0047] Divide equation (4) by equation (3) and then perform Z inverse transformation to obtain the time domain expression of the double exponential pulse trapezoidal shaping:

[0048] v o (n) = 2·v0(n-1)-v0(n-2)+[(v i (n)-v i (nn r )-v i (nn t )+v i (nn d ))-(α+β)·(v i (n-1)-v i (nn r -1)-v i (nn t -1)+v i (nn d -1))+α·β·(v i (n-2)-v i (nn r -2)-v i (nn t -2)+v i (nn d -2))] / [n r ·(α-β)] (5)

[0049] Where v i (n) is the input double exponential pulse sequence; n is the sampling sequence number; v o (n) is the output sequence after trapezoidal formation.

[0050] The present invention also proposes an electronic device, which includes the high-resolution gamma spectrum analysis device based on double exponential trapezoidal formation. The electronic device may be connected to an information display device, which is used to display the evaluation results according to display parameters and attributes set by the user or through an artificial intelligence model.

[0051] The present invention also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the high-resolution gamma spectrum analysis method based on double-exponential trapezoidal shaping are implemented.

[0052] The present invention also proposes a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping are implemented.

[0053] It can be seen from the above scheme that the advantages of the present invention are:

[0054] This algorithm has good trapezoidal shaping filtering characteristics for ideal double exponential pulse signals.

[0055] For the actual input signal, the algorithm optimizes the shaping accuracy under different input amplitudes by adjusting the double exponential coefficients.

[0056] After actual verification, this algorithm can obtain very good energy resolution. Look at the flatness of the top of the trapezoid. The flatter the better. Figure 2 is good, Figure 3 and Figure 4 To be worse. Figure 2 is a valid detection waveform, Figure 3 and Figure 4 is an invalid detection waveform. Figure 2 , 3 , 4 together indicate that the present invention is perfectly applicable to valid waveforms, and will not cause algorithm failure and errors for invalid waveforms. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a logic flow chart of adaptive learning of the present invention;

[0058] Figure 2 This is the trapezoidal shaping effect diagram of the medium amplitude input pulse;

[0059] Figure 3 This is the trapezoidal shaping effect diagram when a small signal input pulse is applied;

[0060] Figure 4 This is the trapezoidal shaping effect diagram when the saturated input pulse occurs;

[0061] Figure 5 It is a schematic diagram of a trapezoidal pulse;

[0062] Figure 6 is a flow chart of the method of the present invention;

[0063] Figure 7 It is a module diagram of the device of the present invention;

[0064] Figure 8 This is a schematic diagram of the structure of a first electronic device of the present invention;

[0065] Fig. 9 This is a schematic diagram of the application environment structure of the first electronic device of the present invention;

[0066] Fig.10 It is a schematic diagram of the structure of a second electronic device of the present invention.

[0067] Reference numerals:

[0068] A-First electronic device;

[0069] B-High-resolution gamma spectrum analysis device based on double exponential trapezoidal formation;

[0070] C-data acquisition equipment;

[0071] D-information display device;

[0072] 1000 - second electronic device;

[0073] Ⅰ-computational unit;

[0074] II-ROM;

[0075] III-RAM;

[0076] IV-bus;

[0077] V-interface;

[0078] VI - input unit;

[0079] VII-output unit;

[0080] VIII- Storage medium;

[0081] Ⅸ-Communication unit. DETAILED DESCRIPTION

[0082] It should be noted that, in this application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0083] Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0084] The processor described in the present invention is the control center of the electronic device, which can be a processor or a general term for multiple processing elements. For example, it can be one or more central processing units (CPUs), or application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0085] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0086] In a specific implementation, as an embodiment, the processor may include one or more CPUs. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions). Electronic devices may include: servers, desktop computers, laptops, smart phones, tablet computers, embedded computers, etc., wherein the embedded computers include vehicles and robots, etc.

[0087] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0088] It should be noted that the structure of the electronic device shown in the drawings of the present invention does not constitute a limitation thereto, and the actual knowledge structure recognition device may include more or fewer components than shown in the drawings, or a combination of certain components, or a different arrangement of components.

[0089] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0090] It should also be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0091] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0092] It should also be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0093] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0096] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0097] The present invention performs trapezoidal shaping on the pulse so that the original pulse can be converted into a trapezoidal pulse of the same amplitude to increase the peak width. At the same time, the filtering characteristics of the algorithm can filter out a portion of the high-frequency noise while preserving the low-frequency effective components of the pulse. By adjusting the flat-top width of the trapezoidal pulse, the influence of ballistic loss is effectively eliminated, ensuring the extraction of accurate pulse amplitude. The trapezoidal shaping model of the present invention adopts a double exponential model, while the prior art adopts a single exponential model. The trapezoidal shaping algorithm model of the double exponential model can improve the accuracy of the amplitude measurement of the energy spectrum measurement system. At the same time, the adaptive method is used to eliminate the differences between different devices and adapt to different circuit parameters in a self-learning manner.

[0098] In view of the problems in practical application of double exponential pulse shaping, the present invention focuses on the following three key technical points:

[0099] Key point 1: Since the essence of double exponential pulse shaping is to obtain the system function by Z transform, the infinite impulse response digital filter IIR is used to implement the algorithm. There is a feedback path, so there is a possibility of instability. In order to solve the potential instability problem, the filter is reset after the algorithm loop searches for the double exponential input pulse to complete a trapezoidal shaping to solve the potential instability problem;

[0100] Key point 2: In order to improve accuracy and facilitate FPGA implementation, the original pulse signal is appropriately amplified or reduced to improve accuracy;

[0101] Key point 3: In order to adapt to different circuit parameters, a self-learning method is designed to use different individual units.

[0102] In order to make the above features and effects of the present invention more clearly and understandably described, embodiments are given below and described in detail with reference to the accompanying drawings. This specification discloses one or more embodiments that include the features of the present invention. The disclosed embodiments are only for illustration. The scope of protection of the present invention is not limited to the disclosed embodiments, and the present invention is defined by the attached claims.

[0103] This algorithm uses a double exponential pulse signal as the input function model, and its expression in the time domain is:

[0104] Formula (1):

[0105] Where A is the double exponential pulse amplitude, τ f With τ u are the fall time constant and rise time constant respectively, and vi represents the real-time amplitude of the signal.

[0106] The time domain expression of the isosceles trapezoidal pulse is:

[0107] Formula (2):

[0108] Where v tra (t) is an isosceles trapezoidal pulse; u(t) is a unit step function; t r for Figure 5 Trapezoidal pulse rising end point; t t is the flat-top end point of the trapezoidal pulse; t d It is the falling end point of the trapezoidal pulse.

[0109] Discretize equations (1) and (2) with Ts as the sampling period and perform Z transformation to obtain:

[0110] Formula (3): v i(z) =n r ·(α-β)·(1-z -1 ) 2

[0111] Formula (4): v tra (z) = (1-z -nr -z -nt +z -nd )·(1-α·z -1 )·(1-β·z -1 )

[0112] Where: n r =t r / Ts, determines the length of the rising edge of the trapezoidal pulse; n t =t t / Ts determines the pulse flat-top width; n d=td / Ts determines the falling length of the trapezoidal pulse;

[0113]

[0114] Dividing equation (4) by equation (3) and then performing a Z inverse transform, the time domain expression of the double exponential pulse trapezoidal shaping algorithm can be obtained:

[0115] v o (n) = 2·v0(n-1)-v0(n-2)+[(v i (n)-v i (nn r )-v i (nn t )+v i (nn d ))-(α+β)·(v i (n-1)-v i (nn r -1)-v i (nn t -1)+v i (nn d -1))+α·β·(v i (n-2)-v i (nn r -2)-v i (nn t -2)+v i (nn d -2))] / [n r ·(α-β)] (5)

[0116] Where: v i (n) is the input double exponential pulse sequence; n is the sampling sequence number; v o (n) is the output sequence after trapezoidal formation.

[0117] The implementation of the adaptive method is as follows:

[0118] From the double exponential trapezoidal algorithm, we know that the parameter is only n r 、n t 、n d Three, generally speaking, for different inputs, we need to control the rise time and flat top time of the output trapezoid to be consistent, that is, the adaptive parameter is only n t .

[0119] Due to the different τ values ​​of the input signal, the mathematical relationship between them is directly related to n t Therefore, an algorithm is needed to adjust the shaping algorithm parameter n in time according to the real-time characteristics of the waveform. tIn the design, the pulse decay phase is sampled, and the data of two adjacent points are collected to make a quotient, and the result is n t , by finding n t The value is brought into the double exponential trapezoidal forming algorithm to obtain the first forming result, and then the sampled data at the flat top time position are compared according to the time sequence. If the difference between the two obtained data is within the allowable error range, the forming is considered to be completed, n t The value is the final forming parameter. If the two data are very different, n is increased or decreased according to the size relationship. t The value is then sampled and the output is iterated multiple times to get the final result. The double exponential trapezoidal adaptive flow chart is as follows: Figure 1 shown.

[0120] Where τ is the time parameter of the gamma spectrometer, which is formed by the sodium iodide crystal + photomultiplier tube PMT + resistors and capacitors in the circuit. For the gamma spectrometer, the value of τ is a fixed value.

[0121] The following is a system embodiment corresponding to the above method embodiment. This embodiment can be implemented in conjunction with the above embodiment. The relevant technical details mentioned in the above embodiment are still valid in this embodiment. In order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above embodiment.

[0122] like Figure 7 As shown, the present invention also proposes a high-resolution gamma spectrum analysis device based on double exponential trapezoidal formation, which includes:

[0123] In the initial module, the gamma rays of the soil to be tested are incident on the scintillator of the gamma spectrometer, generating photons, which are focused on the photomultiplier tube of the gamma spectrometer to obtain voltage pulses;

[0124] The analysis module samples the pulse attenuation stage of the voltage pulse, collects data from two adjacent points and makes a quotient to obtain the pulse flat-top width, brings the pulse flat-top width and the preset trapezoidal pulse rise length and fall length into the double-exponential pulse trapezoidal shaping time domain expression to obtain a shaping result, and sends the shaping result to the ADC of the gamma spectrometer for sampling and multi-channel pulse amplitude analysis, thereby obtaining the spectral line data of the soil to be tested.

[0125] The high-resolution gamma spectrum analysis device based on double exponential trapezoidal shaping, wherein the analysis module comprises:

[0126] Compare the sampled data at the flat-top time position of the shaping result according to the time sequence. If the difference between the two data is within the preset error range, send the shaping result to the ADC of the gamma spectrometer for sampling and multi-channel pulse amplitude analysis; otherwise, fine-tune the pulse flat-top width to update the shaping result until the difference between the two data is within the preset error range.

[0127] An IIR filter is used to implement the double exponential pulse trapezoidal shaping of the shaping result, and each time the IIR filter outputs the shaping result, the IIR filter is reset.

[0128] A high-resolution gamma spectrum analysis device based on double exponential trapezoidal formation, wherein the analysis module comprises:

[0129] The time domain expression of the double exponential pulse signal is:

[0130] Formula (1):

[0131] Where A is the double exponential pulse amplitude, τ f With τ u are the falling time constant and the rising time constant respectively, and vi represents the real-time amplitude of the signal;

[0132] The time domain expression of the isosceles trapezoidal pulse is:

[0133] Formula (2):

[0134] Where v tra (t) is an isosceles trapezoidal pulse; u(t) is a unit step function; t r is the rising end point of the trapezoidal pulse; t t is the flat-top end point of the trapezoidal pulse; t d It is the falling end point of the trapezoidal pulse;

[0135] Discretize equations (1) and (2) with Ts as the sampling period and perform Z transformation to obtain:

[0136] Formula (3): v i(z) =n r ·(α-β)·(1-z -1 ) 2

[0137] Formula (4): v tra (z) = (1-z -nr -z -nt +z -nd )·(1-α·z -1 )·(1-β·z -1 )

[0138] Where the trapezoidal pulse rise length n r =t r / Ts; pulse flat top width n t =t t / Ts determines the pulse flat-top width; the trapezoidal pulse falling length n d =td / Ts;

[0139]

[0140] Divide equation (4) by equation (3) and then perform Z inverse transformation to obtain the time domain expression of the double exponential pulse trapezoidal shaping:

[0141] v o (n) = 2·v0(n-1)-v0(n-2)+[(v i (n)-v i (nn r )-v i (nn t )+v i (nn d ))-(α+β)·(v i (n-1)-v i (nn r -1)-v i (nn t -1)+v i (nn d -1))+α·β·(v i (n-2)-v i (nn r -2)-v i (nn t -2)+v i (nn d -2))] / [n r ·(α-β)] (5)

[0142] Where v i (n) is the input double exponential pulse sequence; n is the sampling sequence number; v o (n) is the output sequence after trapezoidal formation.

[0143] like Figure 8 As shown, the present invention further proposes a first electronic device A in another embodiment, which includes the high-resolution gamma spectrum analysis device based on double exponential trapezoidal shaping.

[0144] like Fig. 9As shown, the first electronic device A can also be connected to the data acquisition device C and the information display device D through a wired or wireless information transmission scheme, the data acquisition device C is used to collect gamma rays, and the information display device D is used to display the forming results and / or spectral line data obtained by the analysis of the present invention.

[0145] The information display device D can process the data output by the first electronic device A based on the information display mechanism to improve the readability of the data output by the first electronic device A. The information display mechanism can be manually preset, for example, the data output by the first electronic device A is visually displayed, which can be based on the display parameters and / or attributes set by the user. The display parameters can be, for example, the display data range, and the display attributes can be, for example, the display font, color, whether to scroll and play, etc. The user is presented with the key information specified by the user, for example, the analysis data based on the spectral line data, etc. The user can understand this information more timely without having to access the secondary page or scroll the page, saving the user's operation. Or the information display mechanism can be an artificial intelligence AI display model, which can learn the user's key information based on the user's previous usage habits, such as viewing time, number of clicks, number of edits, etc., and then automatically present rich and necessary key information to the user.

[0146] The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a readable storage medium. When the computer program is executed by a processor, the computer can execute the high-resolution gamma energy spectrum analysis method based on double exponential trapezoidal shaping provided by the above methods.

[0147] The present invention also proposes a storage medium VIII in another embodiment for storing a computer program for executing the high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping. It should be understood that the storage medium in the embodiment of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus random access memory (DR RAM).

[0148] Fig.10 A schematic block diagram of a second electronic device 1000 that can be used to implement an embodiment of the present invention is shown. The second electronic device 1000 electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The second electronic device 1000 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the present invention described and / or required herein. The second electronic device 1000 may be the same or different from the first electronic device A.

[0149] The second electronic device 1000 includes a computing unit I, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory II (ROM) or a computer program loaded from a storage medium VIII into a random access memory (RAM) III. In RAM III, various programs and data required for the operation of the device 1000 can also be stored. The computing unit I, ROM II, and RAM III are connected to each other via a bus IV. An input / output (I / O) interface V is also connected to the bus IV.

[0150] A plurality of components in the second electronic device 1000 are connected to the I / O interface V, including: an input unit VI, such as a keyboard, a mouse, etc.; an output unit VII, such as various types of displays, speakers, etc.; a storage medium VIII, such as a disk, an optical disk, etc.; and a communication unit IX, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit IX allows the second electronic device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0151] The computing unit I may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of computing unit I include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit I performs the various methods and processes described above, such as method steps S1-S2. For example, in some embodiments, the method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage medium VIII. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 1000 via ROM II and / or a communication unit IX. When the computer program is loaded into RAM III and executed by the computing unit I, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit I may be configured to execute the method in any other appropriate manner (e.g., by means of firmware).

[0152] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping, characterized in that: include: In the initial step, gamma rays of the soil to be tested are incident on the scintillator of the gamma spectrometer, generating photons, which are focused on the photomultiplier tube of the gamma spectrometer to obtain voltage pulses; The analysis step is to sample the pulse attenuation stage of the voltage pulse, collect data from two adjacent points and make a quotient to obtain the pulse flat-top width, bring the pulse flat-top width and the preset trapezoidal pulse rise length and fall length into the double-exponential pulse trapezoidal shaping time domain expression to obtain a shaping result, and send the shaping result to the ADC of the gamma spectrometer for sampling and multi-channel pulse amplitude analysis, so as to obtain the spectral line data of the soil to be tested.

2. The high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping according to claim 1, characterized in that: The analysis steps include: The data are sampled and compared at the flat-top time position of the forming result according to the timing. If the difference between the two data obtained is within the preset error range, the forming result is sent to the ADC of the gamma spectrometer for sampling and multi-channel pulse amplitude analysis; otherwise, the pulse flat-top width is fine-tuned to update the forming result until the difference between the two data obtained is within the preset error range.

3. The high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping according to claim 1, characterized in that: An IIR filter is used to implement the double exponential pulse trapezoidal shaping of the shaping result, and each time the IIR filter outputs the shaping result, the IIR filter is reset.

4. The high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping according to claim 1, characterized in that: The analysis steps include: The time domain expression of the double exponential pulse signal is: Formula (1): Where A is the double exponential pulse amplitude, τ f With τ u are the falling time constant and the rising time constant respectively, and vi represents the real-time amplitude of the signal; The time domain expression of the isosceles trapezoidal pulse is: Formula (2): Where v tra (t) is an isosceles trapezoidal pulse; u(t) is a unit step function; t r is the rising end point of the trapezoidal pulse; t t is the flat-top end point of the trapezoidal pulse; t d It is the falling end point of the trapezoidal pulse; Discretize equations (1) and (2) with Ts as the sampling period and perform Z transformation to obtain: Equation (3): V i(z) = n r ·(α - β)·(1 - Z -1 ) 2 Equation (4): V tra (z) = (1 - z -nr - z -nt + z -nd ) · (1 - α · z -1 ) · (1 - β · z -1 ) Where the trapezoidal pulse rise length n r =t r / Ts; pulse flat top width n t =t t / Ts determines the pulse flat-top width; the trapezoidal pulse falling length n d =td / Ts; Divide equation (4) by equation (3) and then perform Z inverse transformation to obtain the time domain expression of the double exponential pulse trapezoidal shaping: v o (n)=2·v0(n-1)-v0(n-2)+[(v i (n)-v i (nn r )-v i (nn t )+v i (nn d ))-(α+β)·(v i (n-1)-v i (nn r -1)-v i (nn i -1)+v i (nn d -1))+α·β·(v i (n-2)-v i (nn r -2)-v i (nn t -2)+v i (nn d -2))] / [n r ·(α-β)] (5) Where v i (n) is the input double exponential pulse sequence; n is the sampling sequence number; v o (n) is the output sequence after trapezoidal formation.

5. A high-resolution gamma spectrum analysis device based on double exponential trapezoidal shaping, characterized in that: include: In the initial module, the gamma rays of the soil to be tested are incident on the scintillator of the gamma spectrometer, generating photons, which are focused on the photomultiplier tube of the gamma spectrometer to obtain voltage pulses; The analysis module samples the pulse attenuation stage of the voltage pulse, collects data from two adjacent points and makes a quotient to obtain the pulse flat-top width, brings the pulse flat-top width and the preset trapezoidal pulse rise length and fall length into the double-exponential pulse trapezoidal shaping time domain expression to obtain a shaping result, and sends the shaping result to the ADC of the gamma spectrometer for sampling and multi-channel pulse amplitude analysis, thereby obtaining the spectral line data of the soil to be tested.

6. The high-resolution gamma spectrum analysis device based on double exponential trapezoidal shaping according to claim 5, characterized in that: The analysis module includes: Compare the sampled data at the flat-top time position of the shaping result according to the time sequence. If the difference between the two data is within the preset error range, send the shaping result to the ADC of the gamma spectrometer for sampling and multi-channel pulse amplitude analysis; otherwise, fine-tune the pulse flat-top width to update the shaping result until the difference between the two data is within the preset error range. An IIR filter is used to implement the double exponential pulse trapezoidal shaping of the shaping result, and each time the IIR filter outputs the shaping result, the IIR filter is reset.

7. The high-resolution gamma spectrum analysis device based on double exponential trapezoidal shaping according to claim 5, characterized in that: The analysis module includes: The time domain expression of the double exponential pulse signal is: Formula (1): Where A is the double exponential pulse amplitude, τ f With τ u are the falling time constant and the rising time constant respectively, and vi represents the real-time amplitude of the signal; The time domain expression of the isosceles trapezoidal pulse is: Formula (2): Where v tra (t) is an isosceles trapezoidal pulse; u(t) is a unit step function; t r is the rising end point of the trapezoidal pulse; t t is the flat-top end point of the trapezoidal pulse; t d It is the falling end point of the trapezoidal pulse; Discretize equations (1) and (2) with Ts as the sampling period and perform Z transformation to obtain: Equation (3): v i(z) = n r ·(α - β)·(1 - z -1 ) 2 Equation (4): v tra (z) = (1 - z -nr - z -nt + z -nd ) · (1 - α · z -1 ) · (1 - β · z -1 ) Where the trapezoidal pulse rise length n r =t r / Ts; pulse flat top width n t =t t / Ts determines the pulse flat-top width; the trapezoidal pulse falling length n d =td / Ts; Divide equation (4) by equation (3) and then perform Z inverse transformation to obtain the time domain expression of the double exponential pulse trapezoidal shaping: v o (n)=2·v0(n-1)-v0(n-2)+[(v i (n)-v i (nn r )-v i (nn i )+v i (nn d ))-(α+β)·(v i (n-1)-v i (nn r -1)-v i (nn t -1)+v i (nn d -1))+α·β·(v i (n-2)-v i (nn r -2)-v i (nn t -2)+v i (nn d -2))] / [n r ·(α-β)] (5) Where v i (n) is the input double exponential pulse sequence; n is the sampling sequence number; v o (n) is the output sequence after trapezoidal formation.

8. An electronic device, characterized in that: It includes a high-resolution gamma spectrum analysis device based on double exponential trapezoidal shaping as described in claims 5-7, and the electronic device is connected to an information display device, which is used to display the evaluation result with display parameters and attributes set by the user or through an artificial intelligence model.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping according to any one of claims 1 to 4 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the high-resolution gamma spectrum analysis method based on double exponential trapezoidal shaping described in any one of claims 1 to 4 are implemented.

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