Neutron gamma signal identification method, storage medium and system

Abstract

The present invention relates to a neutron gamma signal identification method, comprising the steps of: obtaining raw data of neutron gamma pulses; processing the raw data using a variety of neutron gamma identification algorithms to obtain a plurality of identification parameters; compounding the plurality of identification parameters to form a distribution diagram of all signals; analyzing and classifying the data on the distribution diagram to solve the optimal identification threshold; and using the optimal identification threshold to complete the identification of neutron gamma signals. The present invention also provides a storage medium and a neutron gamma signal identification system. The neutron gamma signal identification method, storage medium and system described in the present invention can improve the identification capability of neutron and gamma signals.

Description

Technical Field

[0001] The present invention belongs to the field of nondestructive testing of nuclear materials, and in particular relates to a neutron gamma signal identification method, a storage medium and a system. Background Art

[0002] With the rapid development of nuclear technology, nuclear materials and nuclear technology have been widely used in the fields of energy, medicine, customs and security. Nuclear technology has successfully transformed from military to civilian use and is developing rapidly. Due to the widespread use of nuclear materials in the civilian field, the probability of nuclear proliferation is also increasing significantly. In order to strengthen the effective tracking and supervision of civilian nuclear materials, and to strengthen the verification and accounting capabilities of military nuclear materials, non-destructive testing technology for nuclear materials has also emerged.

[0003] The nondestructive testing technology of nuclear materials is mainly to detect neutron signals. Liquid scintillator detectors have the characteristics of high hydrogen density, and neutrons can easily interact with H elements to produce easily detected secondary particles. Therefore, liquid scintillator detectors have higher neutron detection efficiency and faster time response.

[0004] However, in the environment of measuring neutrons, there are generally a large number of gamma rays. Liquid scintillator detectors are not only sensitive to neutron signals, but can also detect gamma signals. Therefore, the neutron gamma discrimination capability of liquid scintillator detectors is a key parameter affecting their neutron detection efficiency. Summary of the invention

[0005] In view of the defects existing in the prior art, the object of the present invention is to provide a neutron gamma signal discrimination method, a storage medium and a system to enhance the discrimination capability of a liquid scintillator detector for neutron signals and gamma signals.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is: a neutron gamma signal identification method, comprising the steps of: acquiring raw data of neutron gamma pulses; processing the raw data using a variety of neutron gamma identification algorithms to obtain a plurality of identification parameters; compounding the multiple identification parameters to form a distribution diagram of all signals; analyzing and classifying the data on the distribution diagram to solve the optimal identification threshold; and using the optimal identification threshold to complete the identification of neutron gamma signals.

[0007] Furthermore, the raw data of the neutron gamma pulse is acquired through an oscilloscope or a digital acquisition card.

[0008] Furthermore, a BC-501A liquid scintillator detector was used. 252 The Cf neutron source can measure neutron and gamma signals at the same time, and the waveforms are recorded by a digital acquisition card with a sampling rate of 500Ms / s.

[0009] Furthermore, the identification algorithm includes a charge comparison method, a pulse width identification method and a zero-crossing time method.

[0010] Furthermore, the method of compounding a plurality of discrimination parameters to form a distribution diagram of all signals also includes the steps of: marking the discrimination parameters obtained by each discrimination algorithm respectively; and considering each marked point as a coordinate parameter to form a spatial distribution diagram.

[0011] Furthermore, the analysis and classification of the data on the distribution graph to solve the optimal discrimination threshold also includes the steps of: obtaining an initial discrimination threshold corresponding to the discrimination parameters obtained by each discrimination algorithm; forming a straight line at the intersection of multiple initial discrimination thresholds; translating the straight line up and down by equal distances, and changing the slope of the straight line, and counting the number of pulse signals within the up and down translation interval of the straight line under different slopes; the straight line with the slope corresponding to the minimum number of pulses in the interval is the optimal discrimination threshold.

[0012] Furthermore, the method of using the optimal discrimination threshold to complete the discrimination of neutron and gamma signals is as follows: the coordinate value of any signal is substituted into the optimal threshold, so that the neutron signal and the gamma signal in the signal can be discriminated.

[0013] Furthermore, after the neutron gamma signal is identified by using the optimal identification threshold, the identification parameter FOM is used to prove the effectiveness of the neutron gamma signal identification method.

[0014] The present invention also provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the neutron gamma signal discrimination method when running.

[0015] The present invention also provides a neutron gamma signal identification system, comprising: a memory and a processor, wherein the memory stores executable instructions of the processor; wherein the processor is configured to execute the neutron gamma signal identification method by executing the executable instructions.

[0016] The effect of the present invention is that: through the composite identification of multiple methods, compared with a single identification method, the identification ability of the liquid scintillator detector for neutrons and gamma signals is improved, thereby improving the detection efficiency of the liquid scintillator detector for neutrons. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the steps of a neutron gamma signal identification method of the present invention;

[0018] Figure 2 It is a schematic diagram of the principle flow of a neutron gamma signal discrimination method of the present invention;

[0019] Figure 3 is the energy distribution diagram of the collected signal in the present invention;

[0020] Figure 4 It is the two-dimensional spatial distribution diagram of the signal obtained by using two discrimination parameters

[0021] Figure 5 It is a schematic diagram of the screening results obtained by using a single screening method;

[0022] Figure 6 It is a schematic diagram of the identification results obtained by combining two identification parameters. DETAILED DESCRIPTION

[0023] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0024] like Figure 1-6 As shown, the present invention provides a neutron gamma signal identification method, which includes the steps of:

[0025] S1, obtain the raw data of neutron gamma pulse;

[0026] Specifically, the raw data of the neutron gamma pulse is obtained by an oscilloscope or a digital acquisition card. In this embodiment, the experimental data is obtained by using a BC-501A liquid scintillator detector. 252 The Cf neutron source can measure neutron and gamma signals at the same time, and the waveforms are recorded by a digital acquisition card with a sampling rate of 500Ms / s. The energy distribution of the collected signals is as follows: Figure 3 As shown, it can be seen from the figure that the lowest equivalent energy of the signal collected this time is about 40keVee, indicating that the energy of the data processed in this embodiment has reached a very low energy threshold, and at the low energy threshold, neutron and gamma signals are difficult to distinguish.

[0027] It can be understood that in other embodiments, any type of digital acquisition card and oscilloscope can be used to acquire the pulse signal.

[0028] S2, using a variety of neutron gamma discrimination algorithms to process the original data and obtain a number of discrimination parameters;

[0029] Specifically, by performing identification processing on the original data through a variety of identification algorithms, a plurality of identification parameters can be obtained, such as charge comparison method, pulse width identification and zero-crossing time method.

[0030] S3, compounding multiple discrimination parameters to form a distribution map of all signals;

[0031] Specifically, after performing the identification algorithm processing and obtaining multiple identification parameters, the parameters obtained by each identification algorithm are marked, such as marking the identification parameters obtained by the charge comparison method as X, and marking the identification parameters identified by the pulse width identification method as Y. Then, multiple identification parameters are compounded to obtain a data distribution in a multidimensional space. If only two identification methods are used, the identification parameters of the two identification algorithms can be marked separately, that is, they can be marked as coordinate parameters to form a two-dimensional identification parameter distribution diagram. Similarly, when N identification methods are used, an N-dimensional identification parameter distribution diagram can be formed.

[0032] Take a specific example as an example. When the charge comparison method and pulse width discrimination method are used to process the raw data, the discrimination parameter obtained by the charge comparison method is recorded as PSD. The discrimination parameter obtained by the pulse width discrimination method is recorded as TIME. Using the composite parameter method, the coordinates of each pulse signal can be recorded as (psd, time). At this time, we can get a two-dimensional spatial distribution of the collected signal as follows: Figure 3 shown.

[0033] S4, analyzing and classifying the data on the distribution map to find the optimal discrimination threshold;

[0034] Specifically, after forming the distribution graph of all signals, the data in the distribution graph is analyzed and classified to obtain the initial discrimination threshold corresponding to each discrimination parameter. Then, the intersection of each discrimination parameter corresponding to the initial discrimination threshold is found to form a straight line passing through the intersection, and the straight line is translated up and down by equal distances, and then the slope of the straight line is changed, and the number of pulse signals in the interval formed by the upward and downward translation of the straight line is counted. When the number of pulses in the interval is the smallest, the straight line corresponding to the slope is the optimal discrimination threshold.

[0035] It can be understood that the discrimination parameters obtained by each discrimination algorithm and the corresponding initial discrimination threshold are reflected as a straight line in the distribution diagram.

[0036] Taking a specific example for illustration, when only two discrimination algorithms are used, the first discrimination method can obtain a discrimination threshold L1, and the second discrimination method can obtain a discrimination threshold L2. Denote the intersection point of the two discrimination thresholds as A(x1, y1), and specify a straight line L3 passing through A. The expression of this straight line L3 is: (Y - y1) = k(X - x1), where k is the slope of the straight line L3. To obtain the optimal discrimination threshold, denote the straight line obtained by translating L3 downward by a distance D as L4, and its expression is (Y - y1 + D) = k(X - x1). Denote the straight line obtained by translating L3 upward by a distance D as L5, and its expression is (Y - y1 - D) = k(X - x1). Change the value of k, and count the number of pulse signals falling between L4 and L5. Denote the coordinates of any pulse signal as (x, y). Substitute x into the equations of L4 and L5 respectively to obtain Y4 = k(x - x1) + y1 - D and Y5 = k(x - x1) + y1 + D. When Y4 < y < Y5, it indicates that the signal falls between L4 and L5. Denote the number of pulse signals falling between L4 and L5 as N. When N takes the minimum value, denote the k value at this time as k1. k1 is the optimal slope, and the determined discrimination threshold is the optimal discrimination threshold, and its expression is (Y - y1) = k1(X - x1).

[0037] In this embodiment, the discrimination threshold L1 obtained by the pulse width discrimination method is TIME = 34.0, and the discrimination threshold L2 obtained by the charge comparison method is PSD = 0.23. The intersection point of the two discrimination thresholds is A(34, 0.23). At this time, the expression of L3 passing through point A is

[0038] (PSD - 0.23) = k(TIME - 34).

[0039] Translate L3 upward and downward by 0.1 respectively to obtain:

[0040] The expression of L4 is (PSD - 0.13) = k(TIME - 34)

[0041] The expression of L5 is (PSD - 0.33) = k(TIME - 34)

[0042] Substitute the time of each signal (psd, time) into the expressions of L4 and L5 to determine whether it falls between L4 and L5, and count the number of signals falling between L4 and L5 as N. When k = 0.00793, the value of N obtained is the smallest. At this time, the expression of the optimal discrimination threshold can be obtained as PSD = -0.00793TIME + 0.4996.

[0043] S5. Use the optimal discrimination threshold to complete the discrimination of neutron gamma signals;

[0044] Specifically, after obtaining the optimal threshold, substituting the coordinate values of any signal into the optimal threshold can be used to distinguish neutron signals and gamma signals in the signal.

[0045] Taking a specific example for illustration, denote the coordinate values of any signal as (x, y). Substituting x into the discrimination threshold expression, we can get Y = k1(x - x1) + y1.

[0046] When y > Y, it indicates that this point falls above the discrimination threshold, and at this time the discrimination result is a neutron signal;

[0047] When y < Y, it indicates that this point falls below the discrimination threshold, and at this time the discrimination result is a gamma signal.

[0048] It can be understood that if only one discrimination method is used for neutron-gamma discrimination, such as Figure 3 As shown in the figure, discrimination is carried out using the straight lines L1 and L2 perpendicular to the coordinate axes. If a method of combining two discrimination parameters is used for discrimination, that is, using L3 for discrimination, subjectively, it can be judged that the discrimination result of the discrimination threshold L3 obtained by using the combined parameters is significantly better than using any one discrimination method alone. The discrimination results of using only the pulse width discrimination method and the charge comparison method are as shown in Figure 4 As shown in the figure, the discrimination results obtained by using the multi-parameter combination method are as shown in Figure 5 As shown in the figure. By comparison, it can be seen that when using any one discrimination method alone, it is difficult to distinguish at the intersection of the neutron peak and the gamma peak, that is, the discrimination effect is poor, while the discrimination result obtained by the multi-parameter combination discrimination method has been significantly improved.

[0049] Using the internationally common discrimination parameter FOM to prove the effectiveness of this method, the larger the value of FOM, the better the discrimination result. The FOM obtained by using the charge comparison method is 0.896, the FOM obtained by using the pulse width discrimination method is 0.960, and the FOM obtained by using the multi-parameter combination method is 1.160. It can be seen that the FOM value obtained by using the multi-parameter combination method is the largest, so the discrimination result is the best.

[0050] Furthermore, step S3 also includes sub-steps:

[0051] S31, respectively mark the discrimination parameters obtained by each discrimination algorithm;

[0052] S32, regard each marked point as a coordinate parameter to form a spatial distribution map;

[0053] Furthermore, step S4 also includes sub-steps:

[0054] S41, obtain the initial discrimination thresholds corresponding to the discrimination parameters obtained by each discrimination algorithm;

[0055] S42, forming a straight line at the intersection of multiple initial discrimination thresholds;

[0056] S43, translating the straight line up and down by equal distances, changing the slope of the straight line, and counting the number of pulse signals within the up and down translation interval of the straight line under various slopes;

[0057] S44, taking the straight line with the slope corresponding to the minimum number of pulses in the interval as the optimal discrimination threshold;

[0058] The present invention also provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the above method steps when it is run. The storage medium may include, for example, a floppy disk, an optical disk, a DVD, a hard disk, a flash memory, a USB flash disk, a CF card, an SD card, an MMC card, a SM card, a memory stick, an XD card, etc.

[0059] The computer software product is stored in a storage medium, and includes several instructions for enabling one or more computer devices (which may be personal computer devices, servers or other network devices, etc.) to execute all or part of the steps of the method of the present invention.

[0060] The present invention also provides a neutron gamma signal identification system, which includes a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, a neutron gamma signal identification method is implemented.

[0061] It can be seen from the above embodiments that the present invention improves the discrimination capability of neutron and gamma signals through composite discrimination by multiple methods compared with a single discrimination method, thereby improving the detection efficiency of the liquid scintillator detector for neutrons.

[0062] The method and system described in the present invention are not limited to the embodiments described in the specific implementation manner. Those skilled in the art may derive other implementation manners based on the technical solution of the present invention, which also fall within the technical innovation scope of the present invention.

Claims

1. A neutron gamma signal identification method, characterized in that: include: Get the raw data of neutron gamma pulses; The raw data is processed using a variety of neutron gamma discrimination algorithms to obtain multiple discrimination parameters; Multiple discrimination parameters are combined to form a distribution map of all signals; Analyze and classify the data on the distribution map to find the optimal discrimination threshold; The optimal discrimination threshold is used to complete the discrimination of neutron gamma signals; The step of compounding the multiple discrimination parameters to form a distribution diagram of all signals also includes the following steps: Mark the discrimination parameters obtained by each discrimination algorithm separately; regard each marked point as a coordinate parameter to form a spatial distribution map; The analyzing and classifying of the data on the distribution graph and solving the optimal discrimination threshold also includes the steps of: Obtain the initial discrimination threshold corresponding to the discrimination parameters obtained by each discrimination algorithm; form a straight line at the intersection of multiple initial discrimination thresholds; translate the straight line up and down by equal distances; change the slope of the straight line, and count the number of pulse signals in the up and down translation interval of the straight line under different slopes; when the number of pulses in the interval is the smallest, the straight line with the corresponding slope is the optimal discrimination threshold.

2. A neutron gamma signal identification method as claimed in claim 1, characterized in that: The raw data of neutron gamma pulse is obtained through an oscilloscope or a digital acquisition card.

3. A neutron gamma signal identification method as claimed in claim 2, characterized in that: Adopt BC-501A liquid scintillator detector, use 252 The Cf neutron source can measure neutron and gamma signals at the same time, and the waveforms are recorded by a digital acquisition card with a sampling rate of 500Ms / s.

4. A neutron gamma signal identification method as claimed in claim 1, characterized in that The identification algorithm includes a charge comparison method, a pulse width identification method and a zero-crossing time method.

5. A neutron gamma signal identification method as claimed in claim 1, characterized in that : The method of using the best discrimination threshold to complete the discrimination of neutron and gamma signals is as follows: the coordinate value of any signal is substituted into the best discrimination threshold, so that the neutron signal and the gamma signal in the signal can be discriminated.

6. A neutron gamma signal identification method as claimed in claim 1, characterized in that : After the neutron gamma signal is identified by using the optimal identification threshold, the identification parameter FOM is used to prove the effectiveness of the neutron gamma signal identification method.

7. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the neutron gamma signal discrimination method described in any one of claims 1 to 6 when running.

8. A neutron gamma signal identification system, characterized in that: include: A memory and a processor, wherein the memory stores executable instructions of the processor; wherein the processor is configured to execute the neutron gamma signal discrimination method according to any one of claims 1 to 6 by executing the executable instructions.

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