CN117452471A

CN117452471A - Energy spectrum decomposition method and device, electronic equipment and storage medium

Info

Publication number: CN117452471A
Application number: CN202311278992.XA
Authority: CN
Inventors: 周伟; 袁婷萱; 惠涵宇; 敖子逸; 柯嘉旭; 杨雨森
Original assignee: Chengdu Univeristy of Technology
Current assignee: Chengdu Univeristy of Technology
Priority date: 2023-09-28
Filing date: 2023-09-28
Publication date: 2024-01-26

Abstract

The disclosure provides an energy spectrum decomposition method, an energy spectrum decomposition device, electronic equipment and a storage medium, and relates to the technical field of signal processing. The specific implementation scheme is as follows: determining a fitting function based on the sum of the first Sigmoid function and the second Sigmoid function, wherein the first Sigmoid function and the second Sigmoid function have the same function structure but different parameters; fitting the detection energy spectrum of the first decay nuclide and the detection energy spectrum of the mixed decay nuclide by using the fitting function to obtain a fitting energy spectrum of the mixed decay nuclide, wherein the mixed decay nuclide comprises the first decay nuclide and the second decay nuclide; and carrying out spectrum decomposition on the fitting energy spectrum of the mixed decay nuclide to obtain the fitting energy spectrum of the first decay nuclide and the fitting energy spectrum of the second decay nuclide. By adopting the technical scheme disclosed by the invention, the accuracy of energy spectrum decomposition of the mixed nuclide can be improved, and the relative error of detection activity of each nuclide in the mixed nuclide is further reduced.

Description

Energy spectrum decomposition method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of signal processing technologies, and in particular, to the field of energy spectrum data processing. The disclosure relates to a spectrum decomposition method, a device, an electronic device and a storage medium.

Background

With the rapid development of nuclear power industry in the global area, the emission of radioactive effluents generated by the nuclear power industry and the radiation effect on the environment thereof have become an important problem for radioactive waste management, radiation protection and environmental impact evaluation. During power generation in a nuclear power plant, radioactive fission products, and neutron activation products are generated in the fuel, structural materials and cladding materials, and the fission products may be released from a small amount of fuel damaged by the cladding into the reactor coolant, causing radioactive contamination of the coolant and causing the coolant to flow out of the coolant to contain ⁹⁰ Sr、 ¹³⁷ Cs and ⁶⁰ co, and the like. Wherein the method comprises the steps of ⁹⁰ Sr、 ¹³⁷ Cs、 ⁶⁰ Co will undergo beta decay to produce beta rays, particularly ⁹⁰ Sr and its daughter ⁹⁰ Y is a pure beta decay nuclide, one of the most dangerous radionuclides, and is also a carcinogen, ⁹⁰ sr is absorbed by bone after entering human body, once absorbed, the Sr cannot be discharged out of the body, and meanwhile, the Sr is positioned and quantified in view of the radiation toxicity ⁹⁰ The Sr activity is critical to evaluate the radiation impact of nuclear power plant workers. It is therefore important to measure the beta radionuclides in the nuclear fuel and radioactive effluent waste to understand the activity of the nuclear fuel and the radioactive content of the radioactive waste for the management and treatment of the radioactive waste.

In recent years, excessive nuclear power plant radioactive material leakage events have occurred. Radioactive materials can accumulate in the ocean food chain for long periods of time, possibly having passed through the food chain back to the human dining table, with potential health risks. In nuclear waste water ³ H、 ¹⁴ C is a pure beta-decay nuclide, and in case of emergency such as radioactive substance leakage or nuclear accident, the beta-radionuclide in the air, water and soil needs to be measured rapidly and accurately, and the surrounding ring is monitored in real timeThe concentration and energy spectrum characteristics of beta radionuclides in the environment, thereby timely evaluating radiation risks and taking corresponding protective measures.

In conclusion, the step of measuring the beta radionuclide in nuclear power radioactive effluent and polluted ecological environment samples is important. The system is suitable for on-site measurement of beta nuclides, has little environmental pollution, is convenient to analyze by measuring the beta spectrum line, and has certain economic, social and scientific values.

Disclosure of Invention

The present disclosure provides a method, apparatus, electronic device, and storage medium for energy spectrum decomposition, which can solve the above-mentioned problems.

According to an aspect of the present disclosure, there is provided an energy spectrum decomposition method, including:

Determining a fitting function based on the sum of the first Sigmoid function and the second Sigmoid function, wherein the first Sigmoid function and the second Sigmoid function have the same function structure but different parameters;

fitting the detection energy spectrum of the first decay nuclide and the detection energy spectrum of the mixed decay nuclide by using the fitting function to obtain a fitting energy spectrum of the mixed decay nuclide, wherein the mixed decay nuclide comprises the first decay nuclide and the second decay nuclide;

and carrying out spectrum decomposition on the fitting energy spectrum of the mixed decay nuclide to obtain the fitting energy spectrum of the first decay nuclide and the fitting energy spectrum of the second decay nuclide.

According to another aspect of the present disclosure, there is provided an energy spectrum splitting apparatus, including:

a fitting function determining module, configured to determine a fitting function based on a sum of the first Sigmoid function and the second Sigmoid function, where the function structures of the first Sigmoid function and the second Sigmoid function are the same but the parameters are different;

the energy spectrum fitting module is used for fitting the detection energy spectrum of the first decay nuclide and the detection energy spectrum of the mixed decay nuclide by utilizing the fitting function to obtain the fitting energy spectrum of the mixed decay nuclide, wherein the mixed decay nuclide comprises the first decay nuclide and the second decay nuclide;

And the energy spectrum decomposition module is used for decomposing the fitting energy spectrum of the mixed decay nuclide to obtain the fitting energy spectrum of the first decay nuclide and the fitting energy spectrum of the second decay nuclide.

According to another aspect of the present disclosure, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any one of the energy spectrum decomposition methods of the embodiments of the present disclosure.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform any of the energy spectrum decomposition methods of the embodiments of the present disclosure.

According to the technology disclosed by the disclosure, an asymmetric double Sigmoid function is constructed by using the Sigmoid function as a fitting function, and the fitting function is utilized to fit the detection energy spectrum of the first decay nuclide and the detection energy spectrum of the mixed decay nuclide formed by mixing the first decay nuclide and the second decay nuclide, so that the fitting energy spectrum of the mixed decay nuclide can be accurately obtained by fitting. Furthermore, when the fitting energy spectrum is subjected to spectrum decomposition, the accuracy of spectrum decomposition of the mixed nuclide can be improved, so that the relative error of the detection activity of each nuclide in the mixed nuclide is improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a flow chart of a method of energy spectrum resolution according to an embodiment of the present disclosure;

FIG. 2 is a schematic illustration of a functional image of an embodiment of the present disclosure;

FIG. 3 is a schematic representation of a fit spectrum of an embodiment of the present disclosure;

FIG. 4 is a schematic illustration of a fit spectrum of another embodiment of the present disclosure;

FIG. 5 is a schematic illustration of a fit spectrum of another embodiment of the present disclosure;

FIG. 6 is a block diagram of a dual channel signal processing and digital multi-channel system according to an embodiment of the present disclosure;

FIG. 7 is a block diagram of a beta spectrum analysis system according to an embodiment of the present disclosure;

FIG. 8 is a graph of measured pulse signals according to an embodiment of the present disclosure;

FIG. 9 is a flow chart of a bipolar tip forming algorithm according to an embodiment of the present disclosure;

FIG. 10 is a schematic diagram of a bipolar tip forming algorithm according to an embodiment of the present disclosure;

FIG. 11 is a signal diagram after a bipolar tip forming process according to an embodiment of the present disclosure;

FIG. 12 is a block diagram of the structure of an FPGA logic circuit of an embodiment of the present disclosure;

FIG. 13 is a flow diagram of data processing at the PS end of an embodiment of the disclosure;

FIG. 14 is a flow chart of a beta log spectroscopy script of an embodiment of the present disclosure;

fig. 15 is a schematic diagram of a spectroscopic analysis of an embodiment of the present disclosure.

FIG. 16 is a block diagram of a spectral analysis apparatus according to an embodiment of the present disclosure;

fig. 17 is a block diagram of an electronic device for implementing the energy spectrum resolution method of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a flow chart of a method of energy spectrum resolution according to an embodiment of the present disclosure.

As shown in fig. 1, the energy spectrum decomposition method may include:

s110, determining a fitting function based on the sum of a first Sigmoid function and a second Sigmoid function, wherein the first Sigmoid function and the second Sigmoid function have the same function structure but different parameters;

S120, fitting the detection energy spectrum of the first decay nuclide and the detection energy spectrum of the mixed decay nuclide by using a fitting function to obtain a fitting energy spectrum of the mixed decay nuclide, wherein the mixed decay nuclide comprises the first decay nuclide and the second decay nuclide;

s130, performing spectrum decomposition on the fitting energy spectrum of the mixed decay nuclide to obtain the fitting energy spectrum of the first decay nuclide and the fitting energy spectrum of the second decay nuclide.

For example, both the first Sigmoid function and the second Sigmoid function may employ asymmetric double Sigmoid functions.

The formula of the asymmetric double Sigmoid function is as follows:

as shown in fig. 2, which is a graph of an asymmetric double Sigmoid function with a change in parameters. The corresponding spectrum, y (x), is understood to be the count of the addresses E, A being the maximum that y (x) can reach, which is often significantly greater than the ordinate of the highest point of the spectrum. xc is the channel address value corresponding to the energy spectrum peak value center; w1 is the full width at half maximum; w2 is the low energy side variance; w3 is the high-energy side variance. As shown in fig. 2, the function curve 1 serves as a comparative basis for the remaining groups. Curve 2: as w1 becomes smaller, symmetry does not change, and overall amplitude is reduced; curve 3: as w2 becomes smaller, symmetry changes, the low energy side of the curve becomes steeper and the highest vertex of the curve moves upward; curve 4: as w3 becomes smaller, symmetry changes, the curve high energy side becomes steeper and the highest peak of the curve moves upward; curve 5: as xc becomes larger, symmetry does not change and the symmetry axis moves toward the high-energy end.

Based on this, the fitting function may be:

wherein A, xc, w1, w2 and w3 are respectively a first weight parameter, a first intercept parameter, a first ratio parameter, a second ratio parameter and a third ratio parameter of the first Sigmoid function; b, xb, wb1, wb2 and wb3 are respectively the second weight parameter, the second intercept parameter, the second ratio parameter, the fourth ratio parameter, the fifth ratio parameter and the sixth ratio parameter of the second Sigmoid function.

In one embodiment, the fitting the detected spectrum of the first decay nuclide and the detected spectrum of the mixed decay nuclide by using the fitting function to obtain a fitted spectrum of the mixed decay nuclide may include: fitting the detection energy spectrum of the first decay nuclide by using a first Sigmoid function in the fitting function to obtain an initial value of a first part of parameters in the first Sigmoid function; determining an initial value of a second partial parameter in the first Sigmoid function and an initial value of a third partial parameter in the second Sigmoid function based on a count peak in a detected energy spectrum of the mixed decay species; updating the fitting function based on the initial value of the first partial parameter, the initial value of the second partial parameter, and the initial value of the third partial parameter; and fitting the detection energy spectrum of the mixed decay nuclide by using the updated fitting function to obtain the fitting energy spectrum of the mixed decay nuclide.

In an example, the first partial parameters may include the first ratio parameter, the second ratio parameter, and the third ratio parameter described above; the second partial parameters may include first intercept parameters and the third partial parameters may include second intercept parameters.

In an example, the determining the initial value of the second partial parameter in the first Sigmoid function and the initial value of the third partial parameter in the second Sigmoid function based on the count peaks in the detected spectrum of the mixed decay species includes: determining an initial value of a first intercept parameter based on a trace address value corresponding to a count peak of a first decay species in a detection energy spectrum of the mixed decay species; an initial value of the second intercept parameter is determined based on a trace address value corresponding to a count peak of the second decay species in the detected spectrum of the mixed decay species.

Taking the above fitting function as an example, the first decay species is ⁹⁰ Y, the second decay nuclide being ⁹⁰ Sr, introducing a fitting process of a fitting energy spectrum of the mixed nuclide, and specifically, the following steps:

1. in the fitting function, 10 parameters (a, xc, w1, w2, w 3) and (B, xb, wb1, wb2, wb 3) are counted, and initial values of the fitting parameters (xc, xb, w1, w2, w 3) need to be set in advance. Wherein, the first Sigmoid function in the fitting function is used for actually measuring the current liquid flash spectrometer ⁹⁰ And performing curve fitting on the beta logarithm energy spectrum of the Y nuclide to obtain initial values of parameters (w 1, w2 and w 3). And according to actual measurement ⁹⁰ Sr/ ⁹⁰ Beta log energy spectrum of Y mixed nuclide ⁹⁰ Y count peak corresponds to the address value ⁹⁰ The counted peak value of Sr corresponds to the address value, and initial values of the parameter xc and the parameter xb are determined, respectively.

As shown in FIG. 3, the liquid flash spectrometer is measured in advance ⁹⁰ The Y beta logarithmic spectrum data is fitted, and initial values of the parameters (w 1, w2, w 3) are determined to be 186.9, 106.8 and 17.45 respectively.

According to ⁹⁰ Sr/ ⁹⁰ Beta log energy spectrum of Y mixed nuclide ⁹⁰ Y count peak corresponds to the address value ⁹⁰ The counted peaks of Sr correspond to the address values, and the fitting parameter initial values of the shape parameters (xc, xb) are determined to be 800 and 580, respectively.

2. Of the 10 parameters of the fitting function, the initial values of the parameters (xc, xb, w1, w2, w 3) have been set. Based on the above, the LM algorithm optimized robust nonlinear least square method is used for actually measuring the parameters based on the fitting function of the initial values of the set partial parameters ⁹⁰ Sr/ ⁹⁰ Y mixingThe beta log energy spectrum of the nuclide is fitted to determine fitting values for 10 parameters (a, xc, w1, w2, w3, B, xb, wb1, wb2, wb 3). Filling fitting values of 10 parameters into the fitting function to obtain ⁹⁰ Sr/ ⁹⁰ Fitting energy spectrum of Y mixed nuclide.

As shown in FIG. 4, which shows ⁹⁰ Sr/ ⁹⁰ Fitting energy spectrum of Y mixed nuclide.

3. Bringing the two finally determined sets of shape parameters (A, xc, w1, w2, w 3) and (B, xb, wb1, wb2, wb 3) into a first Sigmoid function and a second Sigmoid function, respectively, in a fitting function, thereby obtaining ⁹⁰ Fitting energy spectrum sum of Sr nuclides ⁹⁰ Fitting energy spectrum of Y nuclide.

As shown in FIG. 5, which shows ⁹⁰ Sr/ ⁹⁰ Fitting energy spectrum of Y mixed nuclide, ⁹⁰ Fitting energy spectrum sum of Sr nuclides ⁹⁰ Fitting energy spectrum of Y nuclide.

Based on ⁹⁰ Fitting energy spectrum sum of Sr nuclides ⁹⁰ Fitting energy spectrum of Y nuclide can be calculated respectively ⁹⁰ Activity and of Sr nuclides ⁹⁰ Activity of Y species.

It should be noted that, the detection spectrum and the fitting spectrum may be logarithmic spectrums.

The detection process may be identical for the detection spectrum of the first decay species and the detection spectrum of the mixed species. This detection process will be described below, specifically as follows:

in one embodiment, the method may further include: detecting a radiation signal of the first decay nuclide to obtain a first pulse detection signal; performing linear amplification processing and logarithmic conversion processing on the first pulse detection signal respectively to obtain a first linear signal and a first logarithmic signal; performing bipolar peak forming on each peak in the first linear signal to obtain a first effective pulse signal mark; sliding smoothing filtering is carried out on the first logarithmic signal to obtain a first filtering signal; and extracting the amplitude of the first filtering signal by using the first effective pulse signal mark to obtain the detection energy spectrum of the first decay nuclide.

In one embodiment, the method may further include: detecting the radiation signals of the mixed decay nuclides to obtain second pulse detection signals; performing linear amplification processing and logarithmic conversion processing on the second pulse detection signal respectively to obtain a second linear signal and a second logarithmic signal; performing bipolar peak forming on each peak in the second linear signal to obtain a second effective pulse signal mark; sliding smoothing filtering is carried out on the second logarithmic signal to obtain a second filtering signal; and extracting the amplitude of the second filtering signal by using the second effective pulse signal mark to obtain the detection energy spectrum of the mixed decay nuclide.

In one example, a detector may be used to detect the radiation signal of the nuclear species, resulting in a corresponding pulse detection signal.

In one example, an analog amplifier may be used to linearly amplify the pulse detection signal to obtain a corresponding linear signal.

In one example, an analog logarithmic amplifier may be used to logarithmically convert the pulse detection signal to a corresponding logarithmic signal.

In one example, a sliding smoothing filter circuit may be employed to filter the logarithmic signal.

In one example, a bipolar tip shaping algorithm may be used to process the linear signal to obtain an effective pulse signal signature, so that the signature is used to extract the amplitude of the filtered logarithmic signal to obtain the detection spectrum of the nuclear species.

The digital beta spectrum processing system provided by the embodiment of the disclosure is described below.

Beta log energy spectrum systems offer advantages over linear energy spectrum systems in terms of both performance and analysis of the resulting spectral lines. And through investigation, the solid flash spectrometer has the beta logarithmic spectrum measuring function in China at present, so the design is intended for Eu/CaF ₂ The output signal of the detector is correspondingly processed to realize the function of measuring the beta logarithmic energy spectrum, and the beta logarithmic energy spectrum is rapidly analyzed. The corresponding design is explained below.

1. Dual-channel signal processing system

The nuclear pulse signals output by the detector system are subjected to logarithmic conversion, so that effective low-nuclear pulse signals corresponding to low-beta energy particles can be ensured, larger amplification factor is obtained through logarithmic conversion, the amplitude of the beta low-effective pulse signals which can be submerged by noise floor is measured, and the energy resolution of a beta energy spectrum low-energy part is improved.

The system uses LADC (Logarithmic ADC) scheme to carry out beta-nuclear pulse signal logarithmic conversion, LADC is a special information converter structure, has logarithmic or quasi-logarithmic input-output law, and can realize more effective quantization of sampled data relative to the same input dynamic range of linear ADC.

The analog logarithmic amplifier and the traditional ADC cascade scheme are selected, so that logarithmic conversion of the input beta-nuclear pulse signal in the design can be met. The input and output of the analog logarithmic amplifier are in one-to-one corresponding logarithmic relationship within a certain precision range, namely the transfer function of the following formula. V (V) _IN Is the input voltage, V _OUT Is the output voltage, V _X Is the "cut-off voltage" of the logarithmic amplifier, when V _IN ＝V _X V at the time of _OUT ＝0，V _Y Is a slope voltage.

Analog logarithmic amplifiers can be divided into two types: a multistage logarithmic amplifier and a direct current logarithmic amplifier. The working principle of the multistage logarithmic amplifier is as follows: and the amplifiers in the same series are sequentially connected in series to finish amplitude limiting output, so that the logarithmic relation between input and output is realized. Another multi-stage logarithmic amplifier topology is a continuous detection logarithmic amplifier, i.e. peak detectors are added after each amplifier stage of the amplifier and the outputs of these peak detectors are summed. The multistage logarithmic amplifier is widely used in the fields of radar and communication application, can process high-frequency signals above a plurality of GHz, and the AD8310 of AD company is a commercial multistage logarithmic amplifier IC.

Direct current logarithmic amplifier principle: a series diode, or series diode-connected transistor (triode), in the feedback loop of the op-amp is derived from the characteristics of the transistor when in operation: inter-electrode Pressure V _BE And current I _C And reverse saturation current I _ES The ratio of the output voltage V is proportional to the logarithm of the ratio _OUTPUT And input current I _INPUT And reverse saturation current I _ES The logarithm of the ratio is proportional. The specific formula is as follows:

wherein T is absolute temperature, K is Boltzmann constant, q is electron charge, soIs a constant term. I _ES Closely related to temperature, two DC logarithmic amplifiers can be connected in a differential manner to reduce I _ES Bringing about the problem of temperature drift. The LOG114 logarithmic amplification chip of TI company adopts a differential structure, thereby reducing the DC logarithmic amplification circuit I _ES The temperature drift effect (of the reverse saturation current) has excellent gain stability in a large temperature variation range.

Meanwhile, the logarithmic converted nuclear pulse signal (output signal of the preamplifier) is not a fast rising and slow falling negative exponential pulse signal in the traditional sense, and the conventional nuclear signal digital forming algorithm: algorithms such as trapezoidal shaping, bipolar pinnacle shaping and the like cannot meet the requirement of digital processing of logarithmic nuclear pulse signals in the system.

In order to solve the above technical difficulties, as shown in fig. 6, a dual-channel analog signal processing circuit structure is designed in a dual-channel signal processing system, and a dual-channel digital filter forming circuit structure is designed in a digital multi-channel system. And the double-channel signal processing system is used for respectively carrying out logarithmic amplification and linear amplification on the pre-amplification output pulse signals.

As shown in fig. 6, the logarithmic and linear two-channel signals output by the two-channel signal processing system are collected by the ADC and then enter the digital multi-channel system, and the digital multi-channel system is constructed by selecting a ZYNQ chip.

In a digital multichannel system, a PL end of ZYNQ carries out sliding smoothing filtering treatment on a logarithmic channel signal, improves the signal to noise ratio of the logarithmic signal acquired by an ADC, is beneficial to subsequent amplitude extraction, and defines the PL end channel as: amplitude extraction slow channel; meanwhile, the PL end of ZYNQ is used for processing signals of a linear channel by using a bipolar tip forming algorithm, the width of a pulse after bipolar tip forming is far smaller than the original pulse width, and the PL end channel is defined as: the beacon forms a channel quickly. Under the actual measurement scene, the amplitude extraction slow channel is easy to generate nuclear pulse accumulation, the amplitude extraction of the later stage is influenced, a beacon rapid forming channel is required to be used as an effective pulse signal sign of the amplitude extraction slow channel, and after the accumulated pulses in the slow channel are judged and abandoned, the base line estimation, the amplitude extraction, the energy spectrum statistics and the like are carried out, so that the real-time acquisition of beta logarithmic energy spectrum data is realized.

The PL end of ZYNQ realizes the real-time acquisition of beta-logarithmic spectrum data and simultaneously transmits the spectrum data to the PS end through an AXI bus. The PS end of ZYNQ realizes data interaction with the upper computer through a serial port protocol, on one hand, energy spectrum data is transmitted to the upper computer in real time to perform further spectral line analysis, on the other hand, the upper computer downloads adjustment parameters to the PS end, and the PS end realizes real-time control of a baseline adjustment and gain adjustment circuit in the two-channel signal processing module through an SPI protocol.

2. Beta energy spectrum analysis system

As shown in fig. 7, the beta-spectroscopy system consists of two parts. The first part is matched upper computer software, namely a beta energy spectrum measuring system. The device is used for communicating with a lower computer, acquiring beta logarithmic spectrum data measured by the lower computer, displaying and storing the beta logarithmic spectrum data in an upper computer interface, and simultaneously downloading parameters to the lower computer to realize real-time control of a baseline adjusting and gain adjusting circuit in the two-channel signal processing module.

The second part is a beta logarithmic spectrum analysis script software used for analyzing the beta logarithmic spectrum of the liquid flash and solid flash mixture. Because the activity analysis of the mixed beta nuclides at home and abroad mainly adopts chemical modes (such as a nitrate precipitation method, a tributyl phosphate extraction method, an ion exchange method, an HDEHP chromatographic column adsorption method, an extraction chromatographic method (a placement method) and the like), the beta nuclides in the mixed beta nuclide sources are separated by using chemical reagents such as resin or crown ether, and then the radioactivity of the beta nuclides after chemical separation is measured by using a radioactivity measuring device such as a liquid flash spectrometer, the steps are complicated, the analysis period is long, the influence factors in the analysis flow are numerous, and the environment is polluted for many times by using reagents.

As in environmental samples ⁹⁰ Methods for determining the radioactivity of Sr can be classified into direct measurement and indirect measurement, because of the environment ⁹⁰ Sr and its daughter ⁹⁰ Y will not substantially exist alone and they are all pure beta decay species. The method of direct measurement is that, ⁹⁰ Sr/ ⁹⁰ chemical separation is carried out on the Y environmental sample, and the pure sample is extracted ⁹⁰ Sr source component and is measured immediately using a radioactivity measuring device. Indirect measurement due to purity ⁹⁰ The Sr source will be combined with its daughter after waiting for about 14 days ⁹⁰ Y reaches a long-term equilibrium state (at this time) ⁹⁰ Sr、 ⁹⁰ The Y activity is the same), so it can be measured and measured ⁹⁰ After Sr reaches balance ⁹⁰ Y radioactivity to indirectly measure ⁹⁰ Sr activity. The direct measurement method or the indirect measurement method has the problems of complex operation, long experimental period, large environmental pollution and the like.

In order to avoid expensive and complicated chemical separation flow and time cost, it is important to develop a mixed beta energy spectrum analysis method for calculating beta nuclide activity. Therefore, the second part of beta logarithmic spectrum analysis script software designs an Aysm2Sig method on the basis of analyzing a large amount of beta logarithmic spectrum data of the liquid flash spectrometer and the beta logarithmic spectrum data simulated by GEANT4, and tries to quickly analyze the mixed beta logarithmic spectrum data.

3. Bipolar tip forming algorithm

As shown in fig. 8, the negative-index nuclear pulse signal is prone to pile-up problem in the actual measurement scene, if the pile-up pulse signal is extracted in amplitude, not only the accurate pulse amplitude cannot be extracted, but also the energy resolution is reduced, and at this time, the pile-up pulse amplitude should be discarded. In analog spectrometers, a stack rejection circuit is typically used to discard the stack pulse signals, while in digital spectrometers, a rapid-shaping algorithm is typically used to shape the pulse signals, and the pulse width is narrowed after shaping, thereby effectively identifying the stack of pulse signals.

Because the prior nuclear signal digital shaping algorithm can not meet the requirement of logarithmic amplification nuclear pulse signal digital processing in the system, the synchronous acquisition of linear amplification nuclear pulse signals is subjected to rapid shaping processing, namely a beacon rapid shaping channel is used as a pulse signal trigger mark of an amplitude extraction slow channel, and accumulated pulses in the slow channel are discarded, so that the accuracy of amplitude extraction is improved. Therefore, the design adopts a bipolar tip forming algorithm to be applied to beacon rapid forming, the pulse width after bipolar tip forming is far smaller than the original pulse width, and the stacking pulse can be effectively identified to carry out stacking judgment and rejection. A schematic diagram of the bipolar tip forming algorithm is shown in fig. 9.

As shown in fig. 10, a principle diagram of bipolar tip forming is visually observed, wherein the zero crossing position of the bipolar tip forming is that

In the Simulink simulation platform, bipolar pinnacle forming is carried out on the negative-index pulse signals amplified linearly in the beacon rapid forming channel. The simulation result is shown in fig. 11, and it can be seen that the pulse amplitude is remarkable after the bipolar tip is formed, the width is obviously narrowed, and the accumulation of pulse signals can be effectively screened.

In addition, in order to improve the signal-to-noise ratio of the logarithmic amplification pulse signal acquired by the ADC, the subsequent amplitude extraction is facilitated, the logarithmic channel signal is subjected to sliding smoothing filtering treatment, and the sliding smoothing filtering has no base line inhibition effect, so that a base line is required to be subtracted by using a digital base line estimation method in the subsequent step.

4. Beta energy spectrum decomposition algorithm

The spectrum resolution process described in the foregoing embodiments may be referred to specifically, and will not be described in detail herein.

5. ZYNQ PL end logic circuit

The logic design of the ZYNQ PL terminal FPGA is crucial to the beta energy spectrum measurement system. As shown in fig. 12, the present logic design module includes: the device comprises an ADC dual-channel acquisition synchronization module, a clock module, a bipolar pinnacle forming module, a sliding smoothing filter module, a pulse triggering module, a baseline estimation module, a stacking judgment module, an amplitude extraction module, a statistical pulse module and a statistical spectrum forming module.

The PL end controls two high-speed ADCs to respectively carry out analog-to-digital conversion of the logarithmic and linear two-channel pulse signals. After synchronous processing, the log and linear two-channel data are respectively input into an amplitude extraction slow channel and a beacon fast forming channel.

As described above, the beacon rapid-forming channel performs the bipolar spike forming process, i.e., the rapid-forming process, on the pulse signal of the linear channel; triggering the pulse signal to obtain the time information of the pulse signal; inputting the time information into a stacking judging module, and marking pulse signals which are stacked; and inputting the stacking marks into an amplitude extraction module as keys for extracting and accepting the amplitude.

Firstly, carrying out sliding smoothing filtering processing on logarithmic channel signal data by an amplitude extraction slow channel, and improving signal-to-noise ratio of the logarithmic channel signal data; then, after being processed by modules such as baseline estimation, amplitude extraction, accumulation judgment and the like, amplitude data of non-accumulation logarithmic pulse signals are obtained; finally, particle energy data corresponding to the pulse amplitude of the logarithmic channel is written into the AXI-BRAM through a statistics spectrum forming module, and the counted energy distribution data, namely spectrum data, is written into the AXI-BRAM; and writing the energy spectrum data and the statistical pulse count into a lower computer at the PS end through an AXI-4 bus.

6. ZYNQ PS end programming

In the traditional FPGA+MCU digital multichannel system structure, MCU is generally used as a communication bridge between the FPGA and the PC upper computer. On one hand, the MCU is communicated with the FPGA through external buses such as SPI, UART and the like to read energy spectrum data and control the signal processing module; on the other hand, the MCU communicates data and commands with the PC upper computer through communication interfaces such as USB, UART, ethernet and the like. The design of the digital multichannel system benefits from the structural advantage of the ZYNQ chip, the ZYNQ PS end is used as an MCU in the digital multichannel system structure, and the software development of the PS end is completed by using a Xilinx Vivado SDK development environment.

As shown in fig. 13, the PS end communicates with the PC host computer through the UATR peripheral device at a baud rate of 115200bps, so as to upload the energy spectrum data, and receive the baseline adjustment parameter, the gain adjustment parameter, the trigger parameter and the related control command. When the ZYNQ PS receives the start command, the channel address count of each channel in the energy spectrum data and the total pulse count are uploaded with a period of 3 milliseconds. The communication protocol between the ZYNQ PS end and the upper computer consists of a 2Byte bit frame header, a 1Byte command and 4Byte data.

7. Upper computer software design

The matched upper computer software, namely the beta energy spectrum measuring system of the embodiment of the disclosure, is developed by adopting C# language, the development environment is Microsoft Visual Studio, and a WinForm framework is used. The functions of the device are as follows: UART communication between the upper computer software and the ZYNQ PS end in the double-channel DMCA is realized, configuration parameters and a down command are sent to the PS end lower computer, and the real-time receiving, visualization and information storage of energy spectrum data are realized.

And the upper computer runs a beta logarithmic spectrum analysis script.

The beta logarithmic spectrum analysis script can realize the analysis of beta logarithmic spectrum data of liquid flash actual measurement, solid flash actual measurement and GEANT4 simulation by a Fourier fitting interpolation method and an asymmetric double-Sigmoid function method. The script is developed based on MATLAB App Designer visual development tools and adopts m language, and a beta logarithmic spectrum analysis tool which can be operated by MATLAB is designed.

MATLAB App Designer is a visual application development tool introduced in MATLAB R2016a version that provides a Graphical User Interface (GUI) designer that allows users to create and design interactive application interfaces. The interface comprises buttons, text boxes, drop-down lists, graphics, tables and other components, and when the application program interface is designed, a user can also add MATLAB codes to realize the functions of the application program, and in addition, the App Designer provides the integration functions with other tool boxes of the MATLAB and the application program. After the application program is developed, the user can pack the application program into an independent application program and share the application program for other people to use, so that the operation of the MATLAB is separated.

The beta logarithmic spectrum analysis script interface can be divided into 5 panel modules, a spectrogram display module, a fitting effect inspection module, a preprocessing module, a spectrum decomposition module and an output module. Each module is provided with an operating light state, red for the inactive state, yellow for the to-do state, and green for the done state.

As shown in fig. 14, a flowchart of a β log spectrum analysis script provided by an embodiment of the present disclosure is shown. The flow is specifically described with reference to fig. 14, and is not described in detail herein.

8. Energy spectrum analysis test

For a pair of ⁹⁰ Sr/ ⁹⁰ Y、 ⁹⁰ Measuring a Y sample source, setting the interval between the source and a scintillator incident window to be 1mm, measuring the length of time to be 7200s, and matching the Y sample source with an Asym2Sig method by using a Fourier fitting interpolation method in a beta logarithmic analysis script of the system ⁹⁰ Sr/ ⁹⁰ And (3) analyzing the energy spectrum of the Y sample source. System and method for controlling a system ⁹⁰ Sr/ ⁹⁰ The measured detection efficiency of the Y sample source is 25.721%, and the simulated full spectrum detection efficiency of Geant4 is 27.24%. ⁹⁰ Sr/ ⁹⁰ Measurement of Y sample Source Activity 371.44Bq ⁹⁰ Sr/ ⁹⁰ Sample preparation time of Y sample source time interval exceeds 14 days, and determining the sample according to related nuclear physical theory ⁹⁰ Sr and Sr ⁹⁰ Y is already in a long-term equilibrium state, ⁹⁰ sr and Sr ⁹⁰ The activity of Y is the same, i.e ⁹⁰ Y actual activity 185.72Bq.

According to the procedure of the Asym2Sig method described hereinabove, an asymmetric double Sigmoid function is first used for purifying ⁹⁰ Curve fitting is carried out on the measured logarithmic spectrum data of the Y sample source, initial values of parameters (w 1, w2, w 3) in the first asymmetric double Sigmoid function are determined to be 109.9, 74.75 and 12.92, and the initial values are determined according to the parameters ⁹⁰ Sr/ ⁹⁰ Beta log energy spectrum of Y sample source ⁹⁰ Y is as follows ⁹⁰ Setting initial values of parameters (xc, xb) to be 350 and 180 at peak value counting address positions corresponding to Sr;

on the basis, the sum pair of the first and second asymmetric double Sigmoid functions is passed ⁹⁰ Sr/ ⁹⁰ Curve fitting the beta logarithmic spectrum of Y to determine final fit values of 10 parameters (a, xc, w1, w2, w 3) and (B, xb, wb1, wb2, wb 3) as 3054, 347.8, 110.9, 74.35, 12.92 and 2503, 182.1, 143.8, 56.98, 14.5; finally, the fitting values of the determined parameters (A, xc, w1, w2, w 3) and the fitting values of the parameters (B, xb, wb1, wb2, wb 3) are respectively brought into a first term asymmetric double-Sigmoid function and a second term asymmetric double-Sigmoid function to obtain ⁹⁰ Sr and Sr ⁹⁰ Log energy spectrum of Y. Asym2Sig method pair ⁹⁰ Sr/ ⁹⁰ The results of the spectrum analysis performed by the Y sample source are shown in fig. 15.

The analysis result by the Asym2Sig method can be obtained: ⁹⁰ y average count rate per minute 3154.871CPM, i.e ⁹⁰ Y corresponds to 25.721 percent, the actual detection efficiency activity is 204.434Bq, and the relative error is 10.077 percent; ⁹⁰ y corresponds to 27.24%, the simulated full spectrum detection efficiency activity is 193.029Bq, the relative error is 3.936%, and the test expectation is met.

In conclusion, the system is actually measured by an Asym2Sig method and a Fourier fitting interpolation method ⁹⁰ Sr/ ⁹⁰ The slightly analyzed results of the Y sample source log energy spectrum show that the two spectrum resolving algorithms are analyzed by using the actual detection efficiency ⁹⁰ The Y activity errors are about 10%, and the errors are within 5% when the simulation full spectrum detection efficiency is used, so that the experimental expectation is met. And the Asym2Sig method is compared with the Fourier fitting interpolation method ⁹⁰ The relative error of Y activity is smaller, i.e. the analysis effect is better, but the Asym2Sig method needs to be used for purity in advance ⁹⁰ And the Y sample source is detected, so that the analysis process is more complicated compared with a Fourier fitting interpolation method. In the actual analysis process, the two energy spectrum analysis methods can be selected according to the field experimental conditions.

Fig. 16 is a block diagram of a spectrum decomposition device according to an embodiment of the present disclosure.

As shown in fig. 16, the spectrum decomposition device may include:

a fitting function determining module 401, configured to determine a fitting function based on a sum of the first Sigmoid function and the second Sigmoid function, where the function structures of the first Sigmoid function and the second Sigmoid function are the same but the parameters are different;

a spectrum fitting module 402, configured to fit, using the fitting function, a detected spectrum of a first decay nuclide and a detected spectrum of a mixed decay nuclide to obtain a fitted spectrum of the mixed decay nuclide, where the mixed decay nuclide includes the first decay nuclide and a second decay nuclide;

The spectrum decomposition module 403 is configured to decompose the fitted spectrum of the mixed decay nuclide to obtain a fitted spectrum of the first decay nuclide and a fitted spectrum of the second decay nuclide.

For descriptions of specific functions and examples of each module and sub-module of the apparatus in the embodiments of the present disclosure, reference may be made to the related descriptions of corresponding steps in the foregoing method embodiments, which are not repeated herein.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Fig. 17 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile apparatuses, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 17, the apparatus 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a spectral analysis method. For example, in some embodiments, a method of spectrum resolution may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of one of the energy spectrum resolution methods described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform a spectral analysis method in any other suitable way (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. that are within the principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A method of spectral analysis comprising:

2. The method as recited in claim 1, further comprising:

detecting the radiation signal of the first decay nuclide to obtain a first pulse detection signal;

performing linear amplification processing and logarithmic conversion processing on the first pulse detection signal respectively to obtain a first linear signal and a first logarithmic signal;

performing bipolar tip forming on each peak in the first linear signal to obtain a first effective pulse signal mark;

sliding smoothing filtering is carried out on the first logarithmic signal to obtain a first filtering signal;

and extracting the amplitude of the first filtering signal by using the first effective pulse signal mark to obtain the detection energy spectrum of the first decay nuclide.

3. The method as recited in claim 1, further comprising:

detecting the radiation signals of the mixed decay nuclide to obtain a second pulse detection signal;

performing linear amplification processing and logarithmic conversion processing on the second pulse detection signal respectively to obtain a second linear signal and a second logarithmic signal;

Performing bipolar peak forming on each peak in the second linear signal to obtain a second effective pulse signal mark;

sliding smoothing filtering is carried out on the second logarithmic signal to obtain a second filtering signal;

and extracting the amplitude of the second filtering signal by using the second effective pulse signal mark to obtain the detection energy spectrum of the mixed decay nuclide.

4. The method of claim 1, wherein the fitting function is:

5. The method of claim 4, wherein fitting the detected spectrum of the first decay species and the detected spectrum of the mixed decay species using the fitting function results in a fitted spectrum of the mixed decay species, comprising:

fitting the detection energy spectrum of the first decay nuclide by using a first Sigmoid function in the fitting function to obtain an initial value of a first part of parameters in the first Sigmoid function;

Determining an initial value of a second partial parameter in the first Sigmoid function and an initial value of a third partial parameter in the second Sigmoid function based on count peaks in a detected spectrum of the mixed decay species;

updating the fitting function based on the initial value of the first partial parameter, the initial value of the second partial parameter, and the initial value of the third partial parameter;

and fitting the detection energy spectrum of the mixed decay nuclide by using the updated fitting function to obtain a fitting energy spectrum of the mixed decay nuclide.

6. The method of claim 5, wherein the first portion parameter comprises the first ratio parameter, the second ratio parameter, and the third ratio parameter; the second partial parameters include the first intercept parameters and the third partial parameters include the second intercept parameters.

7. The method of claim 6, wherein determining an initial value of a second partial parameter in the first Sigmoid function and an initial value of a third partial parameter in the second Sigmoid function based on count peaks in a detected spectrum of the mixed decay species, comprises:

Determining an initial value of the first intercept parameter based on a trace address value corresponding to a count peak of the first decay species in a detection energy spectrum of the mixed decay species;

and determining an initial value of the second intercept parameter based on a trace address value corresponding to a count peak of the second decay nuclide in the detection energy spectrum of the mixed decay nuclide.

8. An energy spectrum splitting device, comprising:

9. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor;

wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-6.