Subnoise solid argon detector data trigger acquisition method, system and device based on wavelet transformation reconstruction

Abstract

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

Description

Technical Field

[0001] This invention relates to the field of nuclear radiation detection and data processing technology, specifically to a method, system, and device for triggering and acquiring data from a sub-noise solid argon detector based on wavelet transform reconstruction. Background Technology

[0002] In nuclear radiation detection systems, the front-end electronics module typically employs a leading-edge discriminator to trigger pulse signals from the detector. When a nuclear event signal from ionizing / scintillation light exceeds a set level threshold (trigger threshold), the discriminator outputs a high level for a period to indicate that the current signal needs to be recorded. At this time, the discriminator outputs a trigger signal to initiate the Data Acquisition (DAQ) system for data acquisition. However, radiation detectors and their front-end and back-end electronics systems inherently generate random level fluctuations near the baseline; this phenomenon is commonly referred to as electronic noise. When the level threshold is comparable to the level of electronic noise, noise fluctuations exceeding the threshold can also trigger the leading-edge discriminator, creating a false nuclear event signal. These noise-triggered triggers continuously activate the DAQ system, making it difficult to effectively capture truly useful nuclear event signals. Even worse, noise-generated signals can crowd out the limited buffer space of the DAQ system, preventing the acquisition of genuine nuclear event signals. To avoid false triggering due to noise, traditional methods typically set the trigger threshold above the noise level to prevent noise-triggered acquisition. However, this often leads to an increase in the system energy threshold, making it unable to effectively detect some weak signals.

[0003] A solid argon detector is a particle physics experimental device that uses solid argon as both the target material and the detection medium. Because the nuclear pulse signals generated by solid argon systems are relatively small, many small signals are concentrated near the detector's noise level. To acquire these small signals, the trigger threshold of the data acquisition system must be comparable to the fluctuation level of the electronic noise. This leads to numerous false triggers caused by noise, further resulting in events not being acquired by the solid argon detector. Therefore, online differentiation between noise events and physical events is an essential part of solid argon detection systems.

[0004] Typical noise triggering manifests as only one sampling point suddenly exceeding a threshold (high frequency) among adjacent sampling points, while typical nuclear event signals are relatively smooth (low frequency). This creates a distinguishable space between noise events and nuclear events within the frequency range. However, typical solid argon signals have two components: a fast component and a slow component. The fast component has a decay time of approximately 7 ns, while the slow component has a decay time of approximately 1900 ns. This significant difference in the optical decay times of the two components means that using a traditional bandpass filter will suppress the important fast decay component, ultimately resulting in the loss of some detection signal. Therefore, there is an urgent need for a method that can effectively suppress noise while effectively preserving the characteristics of the nuclear signal to improve detection sensitivity. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a method, system, and device for triggering and acquiring data from a sub-noise solid argon detector based on wavelet transform reconstruction.

[0006] This invention provides a method for triggering and acquiring data from a sub-noise solid argon detector based on wavelet transform reconstruction, comprising the following steps: (1) The original nuclear pulse signal output by the detector is acquired through the front-end analog-to-digital conversion module to obtain discrete sampling signals; (2) Input the discrete sampling signal into the wavelet decomposition and reconstruction module inside the FPGA, and use discrete wavelet transform to perform 6-level Mallat multi-resolution decomposition on the signal to obtain 1 set of approximation coefficients cA6 and 6 sets of detail coefficients cD1~cD6; (3) Perform hierarchical thresholding on the wavelet coefficients; (4) The processed wavelet coefficients are reconstructed by inverse wavelet transform to obtain the denoised kernel pulse signal; (5) Input the noise-reduced signal into the trigger module and compare it with the preset threshold. If the signal amplitude exceeds the threshold, a trigger signal is generated. (6) Start the waveform trigger buffer module based on the trigger signal, sample and store the current nuclear pulse signal, and complete the data acquisition.

[0007] Furthermore, In step (1), the raw nuclear pulse signal output by the acquisition detector is acquired using the AD9695 chip; And / or, in step (2), the discrete wavelet transform adopts the 6th order Daubechies orthogonal wavelet, the wavelet is divided into 6 layers, and wavelet decomposition and reconstruction are performed according to the Mallat algorithm. 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: Decompose the 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] Decompose the 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].

[0008] Further, in step (3), the method for performing hierarchical thresholding on the wavelet coefficients is as follows: a. Retain the sixth-order approximation coefficient cA6; b. Perform mild thresholding on the level 4 and 5 detail coefficients cD4 and cD5. The mild thresholding method is as follows: like ,but = ; like ,but = 0; in, This represents the value of the detail coefficients at level k after processing; k is 4 or 5; sgn ( cD k () represents the detail coefficient value at level k. The absolute value of the detail coefficients at level k; thr k λ is 0.01; λ is 0.4262; c. Apply medium intensity thresholding to the level 3 detail coefficient cD3. The medium intensity thresholding method is as follows: like ,but = ; like ,but = 0; in, This represents the value of the detail coefficients after processing at level 3; sgn ( cD 3) Represents the detail coefficient value for level 3. Thr3 represents the absolute value of the detail coefficients at level 3; thr3 is 0.2λ; λ is 0.4262. d. Set the detail coefficients cD1, cD2, and cD6 for levels 1, 2, and 6 to 0.

[0009] Furthermore, In step (4), the inverse wavelet transform reconstruction of the processed wavelet coefficients is performed based on cA6 and cD1, cD2, cD3, cD4, cD5 and cD6 obtained after the aforementioned layered threshold processing. And / or, in step (5), the threshold is set to a value equivalent to the noise level of the front-end detector; Preferably, the specific coefficients of the filter used for inverse wavelet transform reconstruction are as follows: Reconstructed 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] Reconstructed 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).

[0010] This invention also provides a sub-noise solid argon detector data triggering acquisition system based on wavelet transform reconstruction, comprising: The front-end analog-to-digital converter module is used to acquire the nuclear pulse signal output by the detector and obtain discrete sampled signals; The data receiving and parsing module, located inside the FPGA, is used to receive the discrete sampling signal and fan it out into two data streams; The wavelet decomposition and reconstruction module is used to perform 6-level discrete wavelet transform decomposition, hierarchical thresholding and inverse transform reconstruction on the first data stream, and output the denoised kernel pulse signal. The trigger module is used to compare the denoised signal with a preset threshold and generate a trigger signal. The waveform trigger buffer module is used to sample and store the current nuclear pulse signal after receiving the trigger signal, thus completing data acquisition; The post-processing module is used to perform time-frequency analysis or AI post-processing on the cached kernel signals; The wavelet decomposition and reconstruction module, triggering module, waveform triggering buffer module, and post-processing module are all integrated into the FPGA.

[0011] Furthermore, In the front-end analog-to-digital conversion module, the nuclear pulse signal output by the detector is acquired using the AD9695 chip; And / or, in the data receiving and parsing module, the acquired signal is transmitted to the data receiving and parsing module in the FPGA via JESD204 protocol communication; And / or, in the data receiving and parsing module, of the two data streams, the first stream enters the wavelet decomposition module, and the second stream enters the waveform trigger buffer module; And / or, in the wavelet decomposition and reconstruction module, the discrete wavelet transform adopts the 6th order Daubechies orthogonal wavelet, the wavelet is divided into 6 layers, and wavelet decomposition and reconstruction are performed according to the Mallat algorithm. The wavelet decomposition and reconstruction filter is a 12-tap FIR filter. And / or, in the wavelet decomposition and reconstruction module, discrete wavelet transform is used to perform 6-level Mallat multi-resolution decomposition on the signal to obtain 1 set of approximation coefficients cA6 and 6 sets of detail coefficients cD1~cD6; And / or, in the wavelet decomposition and reconstruction module, the inverse transform reconstruction is to perform inverse wavelet transform 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.

[0012] Furthermore, In the wavelet decomposition and reconstruction module, the specific coefficients of the filter are as follows: Decompose the 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] Decompose the 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]; Reconstructed 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] Reconstructed 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).

[0013] Furthermore, in the wavelet decomposition and reconstruction module, the method for performing hierarchical thresholding on the wavelet coefficients is as follows: a. Retain the sixth-order approximation coefficient cA6; b. Perform mild thresholding on the level 4 and 5 detail coefficients cD4 and cD5. The mild thresholding method is as follows: like ,but = ; like ,but = 0; in, This represents the value of the detail coefficients at level k after processing; k is 4 or 5; sgn ( cD k () represents the detail coefficient value at level k. The absolute value of the detail coefficients at level k; thr k λ is 0.01; λ is 0.4262; c. Apply medium intensity thresholding to the level 3 detail coefficient cD3. The medium intensity thresholding method is as follows: like ,but = ; like ,but = 0; in, This represents the value of the detail coefficients after processing at level 3; sgn ( cD 3) Represents the detail coefficient value for level 3. Thr3 represents the absolute value of the detail coefficients at level 3; thr3 is 0.2λ; λ is 0.4262. d. Set the detail coefficients cD1, cD2, and cD6 for levels 1, 2, and 6 to 0.

[0014] The present invention also provides a sub-noise solid argon detector data triggering acquisition device based on wavelet transform reconstruction, comprising: the aforementioned system.

[0015] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as described above.

[0016] In this invention, sub-noise refers to electronic noise that fluctuates near the baseline, generated by the radiation detector and its front-end and back-end electronic systems.

[0017] Compared with the prior art, the present invention has achieved the following beneficial effects: This invention provides a method, system, and apparatus for triggering and acquiring data from a sub-noise solid argon detector based on wavelet transform reconstruction. This invention effectively reduces the false trigger rate, improves the acquisition efficiency of effective signals, and enhances the detection sensitivity to weak nuclear signals. Furthermore, the system structure is simple, easy to implement, and has promising application prospects.

[0018] Obviously, based on the above description of the present invention, and according to common technical knowledge and conventional methods in the field, various other modifications, substitutions or alterations can be made without departing from the basic technical concept of the present invention.

[0019] The following detailed embodiments further illustrate the above-described content of the present invention. However, this should not be construed as limiting the scope of the present invention to the following embodiments. All technologies implemented based on the above-described content of the present invention fall within the scope of the present invention. Attached Figure Description

[0020] Figure 1 This is a diagram showing the overall framework of the system of the present invention.

[0021] Figure 2 This is a waveform diagram of the decomposed wavelet components.

[0022] Figure 3 The figure shows the comparison results of wavelet transform analysis and FIR low-pass filter. Detailed Implementation

[0023] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] It should be noted that the algorithms for data acquisition, transmission, storage and processing steps not specifically described in the embodiments, as well as the hardware structures and circuit connections not specifically described, can all be implemented using content already disclosed in the prior art.

[0025] Example 1: Data Triggering and Acquisition System for Sub-Noise Solid Argon Detector Based on Wavelet Transform Reconstruction This invention employs wavelet transform to separate detector noise and nuclear pulses, and deploys this method on a programmable logic array (FPGA) for online waveform analysis to remove noise levels. This invention uses an FPGA to design a digital acquisition system, the overall framework of which is as follows: Figure 1 As shown. The system includes: Front-end analog-to-digital conversion module: This module uses the AD9695 chip to acquire signals.

[0026] Data receiving and parsing module: The acquired signal is transmitted to the data receiving and parsing module in the FPGA via the JESD204 protocol. After being converted into a sampled data stream, it is fanned out into two paths. The first path enters the wavelet decomposition module, and the second path enters the waveform trigger buffer module.

[0027] Wavelet decomposition and reconstruction module: The first sampled data stream enters the trigger module after passing through the wavelet decomposition and reconstruction module.

[0028] (1) Wavelet Transform A fast algorithm for wavelet transform (especially DWT) (Mallat algorithm) can be implemented entirely using a pair of FIR filters: given a one-dimensional discrete signal small wavelet ψ (by filter) h , g (definition), and the number of decomposition layers L. After wavelet transform, a series of wavelet coefficients are obtained from the discrete signal. These coefficients are recursively generated using the Mallat multi-resolution decomposition algorithm: in, h , gLow-pass and high-pass filters were constructed using orthogonal wavelet bases, respectively. After separating the signals of different components, the coefficients of the noise components were suppressed.

[0029] In this embodiment, Discrete Wavelet Transform (DWT) is used to divide the wavelet into 6 layers. The first layer, representing slow components, and the fourth and fifth layers, representing fast components, are weakly attenuated. The third layer, between the signal and noise components, is attenuated with moderate intensity, while the remaining layers are strongly attenuated. The processed result is... Finally, the coefficients are subjected to inverse wavelet transform to reconstruct the waveform, resulting in the denoised waveform pulse: in Represents convolution. This indicates inserting zeros between sequences (upsampling). (1.1) Filtering coefficients and wavelet basis In this embodiment, the wavelet basis selected for discrete wavelet transform is the 6th order compactly supported orthogonal Daubechies wavelet (hereinafter referred to as 'db6'), and the filter length is 12.

[0030] The coefficients of the corresponding 12-tap FIR filter are as follows: DB6 low-pass filter coefficients (12 taps): [-0.0010773010853084796, 0.004777257510945511, 0.0005538422011614961, -0.03158203931748603, 0.027522865530305727, 0.09750160558732304, -0.12976686756726194, -0.22626469396543983, 0.31525035170919763, 0.7511339080210954, 0.49462389039845306, 0.11154074335010947] DB6 high-pass filter coefficients (12 taps): [-0.11154074335010947, 0.49462389039845306, -0.7511339080210954, 0.31525035170919763, 0.22626469396543983, -0.12976686756726194, -0.09750160558732304, 0.027522865530305727, 0.03158203931748603, 0.0005538422011614961, -0.004777257510945511, -0.0010773010853084796] (2) FPGA implementation of wavelet denoising (2.1) Wavelet decomposition Based on the principle of the Mallat algorithm, two FIR filters are configured using the FIR Compiler IP to achieve wavelet decomposition in parallel. Specifically, this embodiment employs the Mallat multi-resolution decomposition algorithm, continuously performing six levels of discrete wavelet transform on the input signal. Each level uses the approximation coefficients of the previous level as new input, outputting cA6 and seven sets of sub-band coefficients (cD1~cD6) level by level.

[0031] Signal → Level 1: FIR low-pass + high-pass → downsampling → obtaining cA1 (approximation coefficients), cD1 (detail coefficients) ↘Level 2: FIR low-pass + high-pass → downsampling (for cA1) -> obtaining cA2 (approximation coefficients), cD2 (detail coefficients) Level 3: FIR low-pass + high-pass → downsampling (for cA2) -> obtain cA3 (approximation coefficients), cD3 (detail coefficients) ↘Level 4: FIR low-pass + high-pass → downsampling (for cA3) -> obtain cA4 (approximation coefficients), cD4 (detail coefficients) Level 5: FIR low-pass + high-pass → downsampling (for cA4) -> obtaining cA5 (approximation coefficients), cD5 (detail coefficients) ↘Level 6: FIR low-pass + high-pass → downsampling (for cA5) -> obtaining cA6 (approximation coefficients), cD6 (detail coefficients) Wavelet coefficients at each level were obtained through wavelet decomposition. The next step is to process the wavelet coefficients, suppressing various components to different degrees.

[0032] (2.2) Wavelet hierarchical thresholding The sixth-level approximation coefficient cA6 is completely retained: ; This represents the approximation coefficient for level 6.

[0033] Key retention layers (layers 4 and 5): For The detail coefficients are processed using a light threshold.

[0034] , Where k is 4 or 5; in, sgn (represents the detail coefficients at level k) cD k () represents the detail coefficient value at level k. This represents the absolute value of the detail coefficients at level k.

[0035] Intermediate processing layer (layer 3): For The detail factor is processed using a medium-intensity threshold.

[0036] in, sgn represents the detail coefficients at level 3. cD 3) Represents the detail coefficient value for level 3. This represents the absolute value of the detail coefficients at level 3.

[0037] Strong suppression layers (layers 1, 2, and 6): coefficients set to zero.

[0038] Where k is 1, 2 or 6; The result obtained through processing , Wavelet reconstruction is performed for (k=1, 2, 3, 4, 5, 6).

[0039] (2.3) Wavelet Reconstruction The wavelet reconstruction uses the Daubechies 6 (db6) orthogonal wavelet reconstruction algorithm, and the reconstructed FIR coefficients are as follows: Reconstructed low-pass filter (12 taps): [0.11154074335010947, 0.49462389039845306, 0.7511339080210954, 0.31525035170919763, -0.22626469396543983, -0.12976686756726194, 0.09750160558732304, 0.027522865530305727, -0.03158203931748603, 0.0005538422011614961, 0.004777257510945511, -0.0010773010853084796] Reconstructed high-pass filter (12 taps): [-0.0010773010853084796, -0.004777257510945511, 0.0005538422011614961, 0.03158203931748603,0.027522865530305727, -0.09750160558732304, -0.12976686756726194,0.22626469396543983, 0.31525035170919763, -0.7511339080210954,0.49462389039845306, -0.11154074335010947] cA6, cD6 → Level 6 Reconstruction: FIR Reconstruction Low-pass + High-pass → Upsampling 2:1 → Add → Obtain cA5' cA5', cD5 → Level 5 Reconstruction: FIR Reconstruction Low-pass + High-pass → Upsampling 2:1 → Add → Get cA4' cA4', cD4 → Level 4 Reconstruction: FIR Reconstruction Low-pass + High-pass → Upsampling 2:1 → Add → Obtain cA3' cA3', cD3 → Level 3 Reconstruction: FIR Reconstruction Low-pass + High-pass → Upsampling 2:1 → Add → Obtain cA2' cA2', cD2 → Level 2 Reconstruction: FIR Reconstruction Low-pass + High-pass → Upsampling 2:1 → Add → Obtain cA1' cA1', cD1 → Level 1 Reconstruction: FIR Reconstruction Low-pass + High-pass → Upsampling 2:1 → Add → Get s_denoised s_denoised is the denoised signal, which enters the trigger module.

[0040] Triggering Module: After receiving the denoised signal, the triggering module compares it with a set threshold. When the denoised signal reaches the threshold, the triggering system outputs a high level, entering the waveform trigger buffer module. The threshold is set to a value equivalent to the noise level of the front-end detector.

[0041] Waveform trigger buffer module: When the waveform trigger buffer module receives the high level given by the triggering system, it samples the current waveform and records the core signal, thus completing the data acquisition.

[0042] Post-processing module: The nuclear signal (collected data) is collected in segments according to the time window, and then more complex time-frequency / AI post-processing is performed on the segmented nuclear signal.

[0043] The data receiving and parsing module, wavelet decomposition and reconstruction module, triggering module, waveform triggering buffer module, and post-processing module are integrated on the FPGA.

[0044] This invention focuses primarily on the collection of nuclear signals from detectors and noise processing. Through specific wavelet decomposition and reconstruction, this invention can effectively filter sub-noise and improve the efficiency of acquiring valid signals.

[0045] Example 2: Performance Verification of the Sub-Noise Solid Argon Detector Data Triggering Acquisition System Based on Wavelet Transform Reconstruction of the Present Invention The trigger signal provided to the detector is processed by a wavelet decomposition system specifically designed in Example 1. This system can significantly separate the main body of the nuclear signal from the noise component. The noisy signal x(t) is subjected to multi-level wavelet decomposition to obtain one low-frequency component (coarsest scale) and multiple sets of high-frequency components (at each level of scale). Figure 2 The image shows the wavelet components after wavelet decomposition of a nuclear signal obtained from a nuclear signal detector using the system described in Example 1. The first row shows the overall waveform, and the second to seventh rows show the waveforms at different levels.

[0046] For signals of different frequencies in solid argon signals, this invention extracts a portion of the signal while suppressing noise based on the frequency characteristics under each level of wavelet decomposition. Finally, wavelet reconstruction is performed using the noise-suppressed wavelet coefficients to obtain a denoised signal. The denoised waveform is then used as the trigger for the leading edge discriminator.

[0047] Using classical nuclear signal processing methods, a 4-tap, 50 MHz FIT low-pass filter was applied to process the same nuclear signal. The resulting denoised signal is shown below. Figure 3 As shown. Figure 3 The Low-pass (50 MHz) signal is the denoised signal obtained using the classical kernel signal processing method; the Wavelet signal is the denoised signal obtained after processing by the system described in Example 1. Figure 3It can be seen that, under the condition that the noise level remains consistent, the denoised signal obtained after processing by the system described in Example 1 retains more high-frequency details, that is, it better preserves the shape of the waveform.

[0048] In summary, this invention provides a method, system, and apparatus for triggering and acquiring data from a sub-noise solid argon detector based on wavelet transform reconstruction. This invention effectively reduces the false trigger rate, improves the acquisition efficiency of effective signals, and enhances the detection sensitivity to weak nuclear signals. Furthermore, the system structure is simple, easy to implement, and has promising application prospects.

Claims

1. A method for triggering and acquiring data from a sub-noise solid argon detector based on wavelet transform reconstruction, characterized in that: Includes the following steps: (1) The original nuclear pulse signal output by the detector is acquired through the front-end analog-to-digital conversion module to obtain discrete sampling signals; (2) Input the discrete sampling signal into the wavelet decomposition and reconstruction module inside the FPGA, and use discrete wavelet transform to perform 6-level Mallat multi-resolution decomposition on the signal to obtain 1 set of approximation coefficients cA6 and 6 sets of detail coefficients cD1~cD6; (3) Perform hierarchical thresholding on the wavelet coefficients; (4) The processed wavelet coefficients are reconstructed by inverse wavelet transform to obtain the denoised kernel pulse signal; (5) Input the noise-reduced signal into the trigger module and compare it with the preset threshold. If the signal amplitude exceeds the threshold, a trigger signal is generated. (6) Start the waveform trigger buffer module based on the trigger signal, sample and store the current nuclear pulse signal, and complete the data acquisition.

2. The data acquisition method according to claim 1, characterized in that: In step (1), the raw nuclear pulse signal output by the acquisition detector is acquired using the AD9695 chip; And / or, in step (2), the discrete wavelet transform adopts the 6th order Daubechies orthogonal wavelet, the wavelet is divided into 6 layers, and wavelet decomposition and reconstruction are performed according to the Mallat algorithm. 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: Decompose the 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] Decompose the 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. The data acquisition method according to claim 1, characterized in that: In step (3), the method for performing hierarchical thresholding on the wavelet coefficients is as follows: a. Retain the sixth-order approximation coefficient cA6; b. Perform mild thresholding on the level 4 and 5 detail coefficients cD4 and cD5. The mild thresholding method is as follows: like ,but = ; like ,but = 0; in, This represents the value of the detail coefficients at level k after processing; k is 4 or 5; sgn ( cD k () represents the detail coefficient value at level k. The absolute value of the detail coefficients at level k; thr k λ is 0.01; λ is 0.4262; c. Apply medium intensity thresholding to the level 3 detail coefficient cD3. The medium intensity thresholding method is as follows: like ,but = ; like ,but = 0; in, This represents the value of the detail coefficients after processing at level 3; sgn ( cD 3) Represents the detail coefficient value for level 3. Thr3 represents the absolute value of the detail coefficients at level 3; thr3 is 0.2λ; λ is 0.4262. d. Set the detail coefficients cD1, cD2, and cD6 for levels 1, 2, and 6 to 0.

4. The data acquisition method according to claim 3, characterized in that: In step (4), the inverse wavelet transform reconstruction of the processed wavelet coefficients is performed by reconstructing cA6, cD1, cD2, cD3, cD4, cD5 and cD6 obtained after the layered threshold processing according to claim 3. And / or, in step (5), the threshold is set to a value equivalent to the noise level of the front-end detector; Preferably, the specific coefficients of the filter used for inverse wavelet transform reconstruction are as follows: Reconstructed 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] Reconstructed 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. A data triggering and acquisition system for a sub-noise solid argon detector based on wavelet transform reconstruction, characterized in that: include: The front-end analog-to-digital converter module is used to acquire the nuclear pulse signal output by the detector and obtain discrete sampled signals; The data receiving and parsing module, located inside the FPGA, is used to receive the discrete sampling signal and fan it out into two data streams; The wavelet decomposition and reconstruction module is used to perform 6-level discrete wavelet transform decomposition, hierarchical thresholding and inverse transform reconstruction on the first data stream, and output the denoised kernel pulse signal. The trigger module is used to compare the denoised signal with a preset threshold and generate a trigger signal. The waveform trigger buffer module is used to sample and store the current nuclear pulse signal after receiving the trigger signal, thus completing data acquisition; The post-processing module is used to perform time-frequency analysis or AI post-processing on the cached kernel signals; The wavelet decomposition and reconstruction module, triggering module, waveform triggering buffer module, and post-processing module are all integrated into the FPGA.

6. The data acquisition system according to claim 5, characterized in that: In the front-end analog-to-digital conversion module, the nuclear pulse signal output by the detector is acquired using the AD9695 chip; And / or, in the data receiving and parsing module, the acquired signal is transmitted to the data receiving and parsing module in the FPGA via JESD204 protocol communication; And / or, in the data receiving and parsing module, of the two data streams, the first stream enters the wavelet decomposition module, and the second stream enters the waveform trigger buffer module; And / or, in the wavelet decomposition and reconstruction module, the discrete wavelet transform adopts the 6th order Daubechies orthogonal wavelet, the wavelet is divided into 6 layers, and wavelet decomposition and reconstruction are performed according to the Mallat algorithm. The wavelet decomposition and reconstruction filter is a 12-tap FIR filter. And / or, in the wavelet decomposition and reconstruction module, discrete wavelet transform is used to perform 6-level Mallat multi-resolution decomposition on the signal to obtain 1 set of approximation coefficients cA6 and 6 sets of detail coefficients cD1~cD6; And / or, in the wavelet decomposition and reconstruction module, the inverse transform reconstruction is to perform inverse wavelet transform 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. The data acquisition system according to claim 6, characterized in that: In the wavelet decomposition and reconstruction module, the specific coefficients of the filter are as follows: Decompose the 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] Decompose the 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]; Reconstructed 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] Reconstructed 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. The data acquisition system according to claim 6, characterized in that: In the wavelet decomposition and reconstruction module, the method for performing hierarchical thresholding on the wavelet coefficients is as follows: a. Retain the sixth-order approximation coefficient cA6; b. Perform mild thresholding on the level 4 and 5 detail coefficients cD4 and cD5. The mild thresholding method is as follows: like ,but = ; like ,but = 0; in, This represents the value of the detail coefficients at level k after processing; k is 4 or 5; sgn ( cD k () represents the detail coefficient value at level k. The absolute value of the detail coefficients at level k; thr k λ is 0.01; λ is 0.4262; c. Apply medium intensity thresholding to the level 3 detail coefficient cD3. The medium intensity thresholding method is as follows: like ,but = ; like ,but = 0; in, This represents the value of the detail coefficients after processing at level 3; sgn ( cD 3) Represents the detail coefficient value for level 3. Thr3 represents the absolute value of the detail coefficients at level 3; thr3 is 0.2λ; λ is 0.4262. d. Set the detail coefficients cD1, cD2, and cD6 for levels 1, 2, and 6 to 0.

9. A data triggering and acquisition device for a sub-noise solid argon detector based on wavelet transform reconstruction, characterized in that: include: The system according to any one of claims 5 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 4.

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