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CN-122017918-A - Sub-noise argon fixation detector data trigger acquisition method, system and device based on wavelet transformation reconstruction

CN122017918ACN 122017918 ACN122017918 ACN 122017918ACN-122017918-A

Abstract

The invention provides a method, a system and a device for triggering and collecting data of a sub-noise argon-fixing detector based on wavelet transformation reconstruction, and relates to the technical field of nuclear radiation detection and data processing. The invention effectively reduces false triggering rate, improves the acquisition efficiency of effective signals, improves the detection sensitivity of weak nuclear signals, and has simple system structure, easy realization and good application prospect.

Inventors

  • XING HAOYANG
  • Fang Changhao

Assignees

  • 四川大学

Dates

Publication Date
20260512
Application Date
20251215

Claims (10)

  1. 1. The sub-noise argon fixation detector data triggering and collecting method based on wavelet transformation reconstruction is characterized by comprising the following steps: (1) Acquiring an original nuclear pulse signal output by a detector through a front-end analog-to-digital conversion module to obtain a discrete sampling signal; (2) Inputting the discrete sampling signals into a wavelet decomposition reconstruction module in an FPGA, and performing 6-level Mallat multi-resolution decomposition on the signals by adopting discrete wavelet transformation to obtain 1 group of approximation coefficients cA6 and 6 groups of detail coefficients cD 1-cD 6; (3) Performing hierarchical threshold processing on the wavelet coefficients; (4) Performing inverse wavelet transformation reconstruction on the processed wavelet coefficients to obtain a noise-reduced nuclear pulse signal; (5) Inputting the noise-reduced signal into a trigger module, comparing the noise-reduced signal with a preset threshold value, and generating a trigger signal if the signal amplitude exceeds the threshold value; (6) And starting a waveform trigger buffer module based on the trigger signal, sampling and storing the current nuclear pulse signal, and completing data acquisition.
  2. 2. The acquisition method according to claim 1, characterized in that: in the step (1), the original nuclear pulse signal output by the acquisition detector is acquired by adopting an AD9695 chip; And/or, in the step (2), the discrete wavelet transform adopts 6-order Daubechies orthogonal wavelets, the wavelets are divided into 6 layers, wavelet decomposition and reconstruction are carried out according to a Mallat algorithm, and the wavelet decomposition and reconstruction filter is a 12-tap FIR filter; Preferably, in step (2), the specific coefficients of the filter are as follows: decomposing low pass filter coefficients ：[-0.0010773010853084796, 0.004777257510945511, 0.0005538422011614961, -0.03158203931748603, 0.027522865530305727, 0.09750160558732304, -0.12976686756726194, -0.22626469396543983, 0.31525035170919763, 0.7511339080210954, 0.49462389039845306, 0.11154074335010947] Decomposing high pass filter coefficients ：[-0.11154074335010947, 0.49462389039845306, -0.7511339080210954, 0.31525035170919763, 0.22626469396543983, -0.12976686756726194, -0.09750160558732304, 0.027522865530305727, 0.03158203931748603, 0.0005538422011614961, -0.004777257510945511, -0.0010773010853084796].
  3. 3. The method according to claim 1, wherein in the step (3), the method for hierarchical thresholding of the wavelet coefficients is as follows: a. Preserving the 6 th-level approximation coefficient cA6; b. And carrying out light thresholding on the level 4 and level 5 detail coefficients cD4 and cD5, wherein the light thresholding method comprises the following steps: If it is Then = ; If it is Then = 0; Wherein, the Represents the processed value of the detail coefficient of the kth level, k is 4 or 5, sgn (cD k ) represents the detail coefficient value of the kth level, The absolute value of the detail coefficient representing the kth level, thr k is 0.01λ, λ is 0.4262; c. And carrying out medium-intensity threshold processing on the level 3 detail coefficient cD3, wherein the medium-intensity threshold processing method comprises the following steps: If it is Then = ; If it is Then = 0; Wherein, the Representing the level 3 detail coefficient processed value, sgn (cD 3 ) representing the level 3 detail coefficient value, Represents the absolute value of the detail coefficient of level 3, thr 3 is 0.2λ, λ is 0.4262; d. The level 1,2, 6 detail coefficients cD1, cD2, cD6 are set to 0.
  4. 4. A method of acquisition according to claim 3, characterized in that: in step (4), inverse wavelet transform reconstruction is performed on the processed wavelet coefficients, and according to cA6 and cD1, cD2, cD3, cD4, cD5, and cD6 obtained by hierarchical thresholding in claim 3; and/or, in step (5), the threshold is set to a value commensurate with the front end detector noise level; Preferably, specific coefficients of the filter performing inverse wavelet transform reconstruction are as follows: reconstructing low pass filter coefficients ：[0.11154074335010947, 0.49462389039845306, 0.7511339080210954, 0.31525035170919763, -0.22626469396543983, -0.12976686756726194, 0.09750160558732304, 0.027522865530305727, -0.03158203931748603, 0.0005538422011614961, 0.004777257510945511, -0.0010773010853084796] Reconstructing high pass filter coefficients ：[-0.0010773010853084796, -0.004777257510945511, 0.0005538422011614961, 0.03158203931748603, 0.027522865530305727, -0.09750160558732304, -0.12976686756726194, 0.22626469396543983, 0.31525035170919763, -0.7511339080210954, 0.49462389039845306, -0.11154074335010947].
  5. 5. The sub-noise argon fixation detector data trigger acquisition system based on wavelet transformation reconstruction is characterized by comprising the following components: The front-stage analog-to-digital conversion module is used for acquiring a nuclear pulse signal output by the detector to obtain a discrete sampling signal; The data receiving and analyzing module is arranged in the FPGA and is used for receiving the discrete sampling signals and fanning out into two paths of data streams; The wavelet decomposition reconstruction module is used for carrying out 6-level discrete wavelet transform decomposition, hierarchical threshold processing and inverse transformation reconstruction on the first path of data stream and outputting a nuclear pulse signal after noise reduction; The triggering module is used for comparing the noise-reduced signal with a preset threshold value to generate a triggering signal; The waveform triggering buffer module is used for sampling and storing the current nuclear pulse signal after receiving the triggering signal, and completing data acquisition; The post-processing module is used for carrying out time-frequency analysis or AI post-processing on the cached core signals; the wavelet decomposition reconstruction module, the triggering module, the waveform triggering buffer module and the post-processing module are integrated in the FPGA.
  6. 6. The acquirer system according to claim 5, wherein: In the front-stage analog-to-digital conversion module, a nuclear pulse signal output by the acquisition detector is acquired by adopting an AD9695 chip; and/or in the data receiving and analyzing module, the acquired signals are transmitted into the data receiving and analyzing module in the FPGA through JESD204 protocol communication; And/or in the data receiving and analyzing module, the first path of data flow enters the wavelet decomposition module and the second path of data flow enters the waveform triggering buffer module; And/or, in the wavelet decomposition reconstruction module, the discrete wavelet transformation adopts 6-order Daubechies orthogonal wavelets which are divided into 6 layers, wavelet decomposition and reconstruction are carried out according to a Mallat algorithm, and the wavelet decomposition and reconstruction filter is a 12-tap FIR filter; And/or in the wavelet decomposition reconstruction module, performing 6-level Mallat multi-resolution decomposition on the signal by adopting discrete wavelet transformation to obtain 1 group of approximation coefficients cA6 and 6 groups of detail coefficients cD 1-cD 6; And/or, in the wavelet decomposition reconstruction module, the inverse transformation reconstruction is to perform inverse wavelet transformation reconstruction on cA6 and cD1, cD2, cD3, cD4, cD5 and cD6 obtained after hierarchical threshold processing; and/or, in the triggering module, the threshold is set to a value equivalent to the noise level of the front-end detector.
  7. 7. The acquirer system according to claim 6, wherein: In the wavelet decomposition reconstruction module, specific coefficients of the filter are as follows: decomposing low pass filter coefficients ：[-0.0010773010853084796, 0.004777257510945511, 0.0005538422011614961, -0.03158203931748603, 0.027522865530305727, 0.09750160558732304, -0.12976686756726194, -0.22626469396543983, 0.31525035170919763, 0.7511339080210954, 0.49462389039845306, 0.11154074335010947] Decomposing high pass filter coefficients ：[-0.11154074335010947, 0.49462389039845306, -0.7511339080210954, 0.31525035170919763, 0.22626469396543983, -0.12976686756726194, -0.09750160558732304, 0.027522865530305727, 0.03158203931748603, 0.0005538422011614961, -0.004777257510945511, -0.0010773010853084796]; Reconstructing low pass filter coefficients ：[0.11154074335010947, 0.49462389039845306, 0.7511339080210954, 0.31525035170919763, -0.22626469396543983, -0.12976686756726194, 0.09750160558732304, 0.027522865530305727, -0.03158203931748603, 0.0005538422011614961, 0.004777257510945511, -0.0010773010853084796] Reconstructing high pass filter coefficients ：[-0.0010773010853084796, -0.004777257510945511, 0.0005538422011614961, 0.03158203931748603, 0.027522865530305727, -0.09750160558732304, -0.12976686756726194, 0.22626469396543983, 0.31525035170919763, -0.7511339080210954, 0.49462389039845306, -0.11154074335010947].
  8. 8. The system according to claim 6, wherein in the wavelet decomposition reconstruction module, the method for performing hierarchical thresholding on the wavelet coefficients is as follows: a. Preserving the 6 th-level approximation coefficient cA6; b. And carrying out light thresholding on the level 4 and level 5 detail coefficients cD4 and cD5, wherein the light thresholding method comprises the following steps: If it is Then = ; If it is Then = 0; Wherein, the Represents the processed value of the detail coefficient of the kth level, k is 4 or 5, sgn (cD k ) represents the detail coefficient value of the kth level, The absolute value of the detail coefficient representing the kth level, thr k is 0.01λ, λ is 0.4262; c. And carrying out medium-intensity threshold processing on the level 3 detail coefficient cD3, wherein the medium-intensity threshold processing method comprises the following steps: If it is Then = ; If it is Then = 0; Wherein, the Representing the level 3 detail coefficient processed value, sgn (cD 3 ) representing the level 3 detail coefficient value, Represents the absolute value of the detail coefficient of level 3, thr 3 is 0.2λ, λ is 0.4262; d. The level 1,2, 6 detail coefficients cD1, cD2, cD6 are set to 0.
  9. 9. The data trigger acquisition device of the sub-noise argon fixation detector based on wavelet transformation reconstruction is characterized by comprising the system of any one of claims 5-8.
  10. 10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 4.

Description

Sub-noise argon fixation detector data trigger acquisition method, system and device based on wavelet transformation reconstruction Technical Field The invention relates to the technical field of nuclear radiation detection and data processing, in particular to a method, a system and a device for triggering and collecting data of a sub-noise argon fixation detector based on wavelet transformation reconstruction. Background In a nuclear radiation detection system, front-end electronics modules typically employ a front-end discriminator (LEADING EDGE Discriminator) to trigger a pulse signal from the detector. When the nuclear instance signal from the ionizing/blinking light exceeds a set level threshold (trigger threshold), the discriminator will give a high level for a period of time to mark that the current signal is needed to be recorded, at which point the discriminator outputs a trigger signal for initiating the Data acquisition system (Data AcQuisition, DAQ) for Data acquisition. However, the radiation detector and its front-end and back-end electronics systems inherently produce random level fluctuations that fluctuate near the baseline, a phenomenon commonly referred to as electronic noise. When the level threshold is comparable to the level of electronic noise, noise fluctuations exceeding the threshold also cause the leading edge discriminator to be triggered, thereby creating the illusion of a nuclear case signal. These triggers by noise generation continue to activate the DAQ system, making it difficult to effectively capture the truly useful nuclear case signals. Furthermore, the noise-generated signal may occupy a limited buffer area of the DAQ system, resulting in that the actual core instance signal is not collected. To avoid false triggers of noise, conventional approaches typically set the trigger threshold above the noise level to avoid noise-triggered acquisitions. But this tends to result in an increase in the system energy threshold, which is not effective in detecting some weak signals. The solid argon detector is a particle physical experimental device which uses solid argon as a target substance and a detection medium. Because the amplitude of the nuclear pulse signal generated by the argon fixation system is small, many small signals are concentrated near the noise level of the detector. If these small signals are to be acquired, the trigger threshold of the data acquisition system must be comparable to the fluctuating level of electronic noise. This results in a very large number of false triggers from noise, further causing instances of the solid argon detector to not be acquired. Thus, on-line distinguishing between noise and physical instances in a data system is an essential part of a solid argon detection system. Typical noise triggers appear as having only one sample point suddenly exceeding a threshold (high frequency) among the adjacent sample points, while typical kernel instance signals are relatively flat (low frequency). This allows noise and kernel instances to have distinguishable space in the frequency interval. However, a typical argon fixation signal has two components, a fast component and a slow component. Wherein the decay time of the fast component is about 7ns and the decay time of the slow component is about 1900 ns. The large difference in the optical decay times of the two components results in significant fast decay components being suppressed by the filter if a conventional bandpass filter is used, eventually leading to the loss of part of the detection signal. Therefore, a method for effectively suppressing noise and effectively preserving nuclear signal characteristics is needed to improve detection sensitivity. Disclosure of Invention Aiming at the problems, the invention provides a method, a system and a device for triggering and collecting data of a sub-noise argon-fixation detector based on wavelet transformation reconstruction. The invention provides a sub-noise argon fixation detector data trigger acquisition method based on wavelet transformation reconstruction, which comprises the following steps: (1) Acquiring an original nuclear pulse signal output by a detector through a front-end analog-to-digital conversion module to obtain a discrete sampling signal; (2) Inputting the discrete sampling signals into a wavelet decomposition reconstruction module in an FPGA, and performing 6-level Mallat multi-resolution decomposition on the signals by adopting discrete wavelet transformation to obtain 1 group of approximation coefficients cA6 and 6 groups of detail coefficients cD 1-cD 6; (3) Performing hierarchical threshold processing on the wavelet coefficients; (4) Performing inverse wavelet transformation reconstruction on the processed wavelet coefficients to obtain a noise-reduced nuclear pulse signal; (5) Inputting the noise-reduced signal into a trigger module, comparing the noise-reduced signal with a preset threshold value, and generating a trigger