CN111538069A - Energy spectrum analysis system and method and energy spectrum data analysis equipment - Google Patents
Energy spectrum analysis system and method and energy spectrum data analysis equipment Download PDFInfo
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- G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
- G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
- G01T1/36—Measuring spectral distribution of X-rays or of nuclear radiation spectrometry
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Abstract
A power spectrum analysis system comprising: a nuclide library for storing data information of nuclides; the calibration module is used for carrying out energy calibration and peak calibration on the detector and outputting an energy calibration coefficient and a peak calibration coefficient; the peak searching module is used for calling the output result of the calibration module, analyzing the energy spectrum of the nuclide to be detected acquired by the detector and determining the peak position; and a nuclide identification module for calling the output result of the peak searching module, setting a nuclide threshold value, calling the nuclide library to match the output result of the peak searching module, calculating a weight factor of the matched target nuclide, and determining the corresponding target nuclide of the nuclide to be detected based on the weight factor.
Description
Technical Field
The embodiment of the invention relates to the technical field of gamma energy spectrum analysis, in particular to an energy spectrum analysis system and method and energy spectrum data analysis equipment.
Background
The gamma energy spectrum analysis technology is an important component in the technical field of nuclide identification, and the energy spectrum research of gamma rays generally adopts a multichannel analyzer, namely, after gamma rays enter a detector, photons are converted into photoelectrons, the photoelectrons are multiplied by a photomultiplier tube and converted into electric signals, the electric signals are converted into signals meeting the requirements of the multichannel analyzer through electronic devices such as an amplifying circuit and the like, and the multichannel analyzer outputs channel addresses corresponding to the signals according to the size of the input signals.
For different energy spectrum measuring instruments, the inherent characteristics of the energy spectrum measuring instruments determine the difference corresponding to different electronic noises, and the acquired energy spectrum data needs to be subjected to denoising processing in order to acquire an accurate detection result and further determine the species of nuclides to be measured; in order to improve the detection efficiency, adapt to the processing capability of the portable spectrometer and reduce the calculation cost in the spectrum resolving process as much as possible, it is also very necessary.
Disclosure of Invention
According to an embodiment of the present invention, there is provided an energy spectrum analysis system to solve at least one aspect of the problems in the prior art described above.
The invention provides an energy spectrum analysis system, comprising: a nuclide library for storing data information of nuclides; the calibration module is used for carrying out energy calibration and peak calibration on the detector and outputting an energy calibration coefficient and a peak calibration coefficient; the peak searching module is used for calling the output result of the calibration module, analyzing the energy spectrum of the nuclide to be detected acquired by the detector and determining the peak position; and a nuclide identification module for calling the output result of the peak searching module, setting a nuclide threshold value, calling the nuclide library to match the output result of the peak searching module, calculating a weight factor of the matched target nuclide, and determining the target nuclide corresponding to the nuclide to be detected based on the weight factor.
In some embodiments, the nuclide library includes an isotope name, half-life, energy branch ratio, and isotope abundance of the nuclide.
In some embodiments, the nuclide threshold corresponds to the nuclide atomic number, and when the atomic number matches the nuclide threshold set, the corresponding nuclide is called.
In some embodiments, the energy scaling step comprises: selecting a sample nuclide; acquiring an energy spectrum of the sample nuclide measured by the detector; calling corresponding energies of the sample nuclides in the nuclide library; and performing polynomial fitting on the corresponding relation between the sample nuclide energy spectrum and the energy to determine an energy scale coefficient.
In some embodiments, the peak shape scaling step comprises a denoising unit for removing electronic noise, the denoising unit determining a corresponding peak region of the peak position based on a noise model.
In some embodiments, the peak scale comprises the steps of: selecting a sample nuclide; acquiring an energy spectrum of the sample nuclide; smoothing the energy spectrum of the sample nuclide through the noise model; and determining the energy-full width at half maximum data combination of the sample nuclide by selecting a Gaussian function model, and determining a peak shape scale coefficient by a polynomial fitting method.
In some embodiments, the denoising unit determines a peak region corresponding to the peak position based on a noise model setting boundary.
In some embodiments, the denoising unit analyzes a noise model of the energy spectrum through a machine learning method, and updates the noise model to converge with time based on the judgment of the determined peak region boundary energy spectrum.
In some embodiments, the peak searching module includes a peak position determining unit, and the peak position determining unit calls the output result of the scaling module, performs cycle division on the energy spectrum data, performs saliency calculation on the addresses in a cycle, and determines the peak position based on a set tolerance threshold of the saliency.
In some embodiments, the peak searching module further includes an optimizing unit, where the optimizing unit is configured to perform the significance statistics on the addresses corresponding to the peak positions, and remove an interference peak based on a statistical result of the significance.
In some embodiments, the nuclide identification module includes a matching unit that calls a target nuclide of the nuclide library, and determines a nuclide corresponding to an output result of the peak finding module by setting a nuclide branch ratio threshold.
In some embodiments, the nuclide identification module includes a matching unit, where the matching unit calls an output result of the peak finding module, calls a target nuclide in the nuclide library that matches the nuclide threshold, calculates a weight factor of the target nuclide, and performs iterative calculation on the weight factor, so that nuclide information corresponding to the nuclide to be detected is determined when a difference between the weight factors of a plurality of target nuclides reaches a preset value.
In another aspect of the present invention, there is provided a method for energy spectrum analysis, including: selecting nuclides and establishing a nuclide library; selecting a sample nuclide from the nuclide library, acquiring energy spectrum data of the sample nuclide measured by a detector, determining an energy scale coefficient, determining a noise model based on continuous energy spectrum data of the sample nuclide, dividing a peak area boundary based on the noise model and determining a peak shape scale coefficient; acquiring an energy spectrum of a nuclide to be detected, and determining the peak position of the nuclide to be detected based on the energy scale coefficient and the peak shape scale coefficient; setting a nuclide threshold value based on the nuclide to be detected, calling a target nuclide which is larger than the nuclide threshold value in a nuclide library, matching the peak position of the nuclide to be detected with the target nuclide, calculating a weight factor of the target nuclide, and determining a nuclide name, a characteristic energy peak and a branch ratio corresponding to the nuclide to be detected based on the weight factor.
In another aspect of the present invention, there is provided a spectral data analysis apparatus comprising: the data receiving module is used for receiving the energy spectrum data from the multi-channel analyzer; the energy spectrum analysis system is in communication connection with the data receiving module and adopts any one of the energy spectrum analysis systems; and the output module is in communication connection with the energy spectrum analysis system and outputs the result of the energy spectrum analysis system to an upper computer.
The embodiment according to the invention has the following beneficial effects: the nuclide library is called by setting the nuclide threshold part, so that the calculation amount of a nuclide identification stage is reduced, the nuclide identification efficiency is improved, and meanwhile; the denoising unit of the scale module updates the noise model, so that the accuracy of the peak scale is improved; the peak searching module is provided with the optimization unit to remove interference peaks based on statistical data, so that the accuracy of the peak searching module is improved; the nuclide identification module is provided with a matching unit, so that the nuclide identification module can correspond to the actual requirements of different application scenes, and the detection and identification efficiency is improved.
Drawings
Embodiments of the invention will now be described in more detail, by way of non-limiting examples only, with reference to the accompanying drawings, in which like reference numerals refer to like parts throughout, and in which:
FIG. 1 is a schematic block diagram of a power spectrum analysis system according to one embodiment of the present invention;
FIG. 2 is a schematic block diagram of a power spectrum analysis system according to another embodiment of the present invention;
fig. 3 is a schematic block diagram of an energy spectrum data analyzing apparatus according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details.
A schematic diagram of a power spectrum analysis system according to an embodiment of the invention is schematically shown in fig. 1. The energy spectrum analysis system 100 can be used to receive the output data of the multi-channel analyzer, and analyze the output data to finally determine the species and activity of the radionuclide. The invention is suitable for a fixed embedded hardware system, and is used for environmental monitoring, security inspection and application in an extremely unattended environment; embedded desktop or server computer systems for use in laboratory environments or other localized scenarios; the method is applied to portable hardware (intelligent handheld phones, tablet computers and the like) based on Android or operating systems such as IOS, WINCE and the like, and is matched with a portable detector and a multi-channel analyzer to be used in mobile application scenes such as inspection and the like.
The energy spectrum analysis system 100 according to one embodiment of the present invention includes a nuclide library 10, a calibration module 20, a peak finding module 30, and a nuclide identification module 40.
The nuclide library 10 is used for storing nuclide data information, and the stored nuclide-related data information can be called by the calibration module 20 and the nuclide identification module 40. The data source of the nuclide library 10 complies with the ANSI standard nuclide library recommended by IAEA, and contains nuclide species that may be a subset of the nuclide composition of interest, or a set of a plurality of subsets organized according to a certain rule, such as atomic number, isotope number, and the like. The data information of the nuclides is pre-recorded into the nuclide library 10 of the system and can be called up by the system as required.
The calibration module 20 is used for performing energy calibration and peak calibration on the detector and outputting an energy calibration coefficient and a peak calibration coefficient.
It will be appreciated by those skilled in the art that the energy scale is a plot of the relationship between measured energy and track address, and the track address can be converted to energy according to this relationship. A polynomial fitting method may be applied to the energy scale of the detector to obtain energy scale coefficients. The peak shape scale is to perform mathematical fitting on the full width at half maximum of the characteristic energy peak generated by the known radioactive isotope to the full width at half maximum of the characteristic energy peak generated by the crystal detector or the semiconductor detector to different energy rays to obtain the peak shape scale coefficient.
The peak searching module 30 calls the output result of the calibration module 20 to analyze the energy spectrum of the nuclide to be detected acquired by the detector and determine the peak position.
The peak searching module 30 is a process of calculating the energy spectrum data of the nuclide to be detected, which is acquired by the detector, based on the known energy scale coefficient and peak shape scale coefficient by calling the scale result of the detector by the scale module 20. The output result of the peak finding module 30 includes the track address, the full width at half maximum, and the like corresponding to the characteristic energy peak of the nuclide to be detected. In other embodiments, the peak finding module 30 may be invoked alone to analyze spectral data acquired by a detector of a known scale model.
The nuclide identification module 40 calls the output result of the peak searching module 30, sets a nuclide threshold, calls the nuclide library 10 to match the output result of the peak searching module 30, calculates a weight factor of the matched target nuclide, and determines the target nuclide corresponding to the nuclide to be detected based on the weight factor.
In the process of identifying the nuclide to be detected by the system using the nuclide identification module 40, the nuclide identification module 40 may set a threshold value based on a specific application scenario of the nuclide to be detected, and perform screening through the set threshold value, so that the nuclide meeting the threshold value in the nuclide library 10 is called to match the output result of the peak finding module 30. For example, a subset of commonly used radionuclides is established for a known radioactive measurement site, a corresponding response signal is set, and the subset is called by inputting a threshold corresponding to the response signal during use, wherein the threshold may be an assignment performed to determine whether the nuclide belongs to the subset, or the like. Of course, those skilled in the art will appreciate that in other embodiments, the threshold may correspond to the atomic number of a species or the assignment of values to other subsets of species.
The nuclide identification module 40 calls the output result of the peak searching module 30, that is, obtains the energy corresponding to the peak position of the energy spectrum of the nuclide to be detected, and the significance and uncertainty corresponding to the peak position, matches the nuclide called in the nuclide library 10 according to the output result, and determines the weight factor of the nuclide according to the matching process, wherein the weight factor is the result of performing real-time iterative computation on the uncertainty of the peak position and the branch ratio.
The energy spectrum analysis system 100 of the embodiment reduces the calculation cost in the matching process and improves the nuclide identification efficiency by setting the threshold value to realize partial calling of the nuclide library 10. In other embodiments, the calibration module 20 may be invoked individually to evaluate physical characteristics such as energy response linearity, signal-to-noise ratio, etc. of a system-connected detector or multi-channel analyzer. In other embodiments, the peak finding module 30 and the nuclide identification module 40 may be invoked simultaneously to directly identify the radionuclide species, given that the calibration model of the detector is known.
The nuclide library 10 includes the isotope name, half-life, energy branch ratio, and isotope abundance of the nuclide. As will be appreciated by those skilled in the art, the information of the nuclides described above is pre-recorded into the nuclide library 10, and different data information of the corresponding nuclides may set corresponding response conditions. For example, nuclide thresholds that meet the detector workplace or application scenario are invoked by assigning values that make it possible to respond to the input nuclide thresholds during the invocation. The logging and calling rules of the nuclide library 10 are not limited by this embodiment.
The nuclide identification module 40 calls a nuclide threshold for the nuclide library 10 that may also correspond to the atomic number of the nuclide, and when the atomic number matches the set nuclide threshold, the corresponding nuclide is called. In this embodiment, the invocation of partial nuclides is implemented by the atomic number of the corresponding nuclide, for example, a plurality of nuclide library subsets are set based on the response ranges of different detectors, the atomic coefficients corresponding to the nuclides of different subsets are divided, and the nuclide is matched by inputting the nuclide threshold according to the response range corresponding to the detector.
Fig. 2 shows a schematic diagram of an energy analysis system according to another embodiment of the present invention, and as shown in fig. 2, the calibration module 20 includes an energy calibration submodule 21 and a peak calibration submodule 22, wherein the calibration result of the energy calibration submodule 21 can be called by the peak calibration submodule 22.
The steps of the calibration module 20 for energy calibration of the detector include: selecting a sample nuclide; acquiring an energy spectrum of a sample nuclide measured by a detector; calling corresponding energy of sample nuclides in the nuclide library; and performing polynomial fitting on the corresponding relation between the sample nuclide energy spectrum and the energy to determine an energy scale coefficient.
Specifically, the energy calibration sub-module 21 performs energy calibration on the detector, and the energy calibration sub-module includes:
s01, selecting a number of known nuclear species radioactive sources as sample nuclear species.
And S02, sequentially measuring the radioactive sources of the known sample nuclides by using a detector, and acquiring corresponding energy spectrums.
S03, counting the energy spectrum of the sample nuclide obtained in the previous step, calling the characteristic energy of the sample nuclide from the nuclide library 10 through inputting a corresponding threshold value, determining the corresponding channel address in the energy spectrum, and repeating the steps for a plurality of sample nuclides to obtain a plurality of energy-channel address data combinations.
And S04, performing linear fitting on the energy-track address data combination by using a polynomial fitting method, and determining an energy scale coefficient.
The peak shape scale submodule 22 of the scale module 20 further includes a denoising unit 221 for removing electronic noise, and the denoising unit 221 determines a peak region corresponding to the peak position based on the noise model. In this embodiment, the energy spectrum is approximated by a gaussian peak model, the left and right boundaries of the peak region need to be defined by determining the fading trends at two sides of the gaussian peak, usually the influence of the electronic noise on the energy spectrum needs to be considered, and the denoising unit 221 performs smooth denoising on the energy spectrum by using a set noise model to reduce the influence of the electronic noise on the boundary of the peak region, that is, to avoid the situation that the judgment result of the boundary of the peak region is too narrow due to the superposition of the electronic noise, which causes false scaling.
In some embodiments, the denoising unit 221 determines a peak region corresponding to the peak position based on the noise model setting boundary. The denoising unit 221 determines a boundary corresponding to the peak region by receiving the set boundary value, thereby shielding the influence of noise oscillation on the peak shape. The data may be empirical data statistically acquired based on measurements of the target multi-channel analyzer. In further embodiments, a noise model may be obtained by machine learning and the spectrum may be denoised based on the noise model.
The denoising unit 221 identifies a noise model superimposed in the energy spectrum by a machine learning method, judges a determined peak region boundary using the identified noise model, and updates the noise model based on the judgment result so as to converge with time. The automatic identification of the noise model improves the identification efficiency of the system to the noise superposition spectrum and simultaneously improves the interference shielding capability of the system to the noise.
The peak scale of the scale module 20 comprises the steps of: selecting a sample nuclide; acquiring an energy spectrum of a sample nuclide; smoothing the energy spectrum of the sample nuclide through a noise model; and selecting a Gaussian function model, determining the energy-full width at half maximum data combination of the sample nuclide, and determining a peak shape scale coefficient by a polynomial fitting method.
Specifically, the step of scaling the peak shape of the detector comprises:
u01, selecting a known sample nuclide as a radiation source.
U02, using a detector to measure the radiation source of a sample species, acquires spectral data of the sample species.
U03, calling a denoising unit 221 to smooth the energy spectrum of the sample nuclide to obtain energy spectrum data after interference noise is removed.
U04, calling the characteristic energy of the sample nuclide in the nuclide library 10, calling the energy scale coefficient output by the energy scale unit 21 to determine the channel address corresponding to the peak position, adopting the Gaussian function peak shape, calculating the full width at half maximum corresponding to the peak position, performing polynomial fitting on the energy-full width at half maximum data group corresponding to the peak position channel address, and determining the peak shape scale coefficient.
As shown in fig. 2, the peak searching module 30 includes a determining unit 31, the peak position determining unit 31 calls the output result of the calibration module 20, the energy spectrum data is divided into periods, the track addresses in the periods are calculated according to the significance, and the peak position is determined based on the set tolerance threshold of the significance.
Specifically, the peak position determining unit 31 performs peak searching by using a generalized second-order difference method, periodically divides the entire spectrum data according to a certain window width, for example, iterates by using 50 channels as a period, calculates a ratio of a second-order difference and a standard deviation of the array channel-by-channel data in the period, defines the ratio as a significance, and determines that a corresponding track address is a peak position when the significance is greater than a preset tolerance threshold by presetting a significance tolerance threshold. Then, the energy scale coefficient and the peak shape scale coefficient of the scale module 20 are called to calculate the energy and the full width at half maximum corresponding to the peak position, and meanwhile, the significance and the uncertainty corresponding to the peak position are output. Where uncertainty is the reciprocal of the mean squared difference of the sum of the significances of all peaks identified.
The peak searching module 30 further includes an optimizing unit 32, and the optimizing unit 32 is configured to perform significance statistics on the peak position, and remove an interference peak based on a statistical result of the significance.
When the interference intensity of the environmental noise on the energy spectrum is large enough, that is, the noise reaches a tolerance threshold corresponding to the significance, the peak position judgment unit 31 may identify the noise as a peak position, and further affect the identification of the nuclide. In order to remove the interference peak generated by the noise, the optimization unit 32 counts the energy spectrum data of the superimposed noise based on machine learning, compares the change of the significance indexes of the real peak and the interference peak with time and the horizontal oscillation amplitude of the peak position, and removes the interference peak.
As shown in fig. 2, the nuclide identification module 40 further includes a matching unit 41, where the matching unit 41 calls the target nuclide in the nuclide library 10, and determines the nuclide corresponding to the output result of the peak finding module 30 by setting a nuclide branch ratio threshold. The nuclide identification module 40 receives the output result of the peak finding module 30, including the energy branch and branch specific gravity corresponding to the peak position in the energy spectrum, and completes the identification of the nuclide to be detected by calling the nuclide library 10 to match the nuclides with the same energy branch and the same branch ratio.
In another embodiment, the matching unit 41 calls the output result of the peak finding module 30, calls the target nuclide in the nuclide library 10 matching the nuclide threshold, calculates the weight factor of the target nuclide, and performs iterative calculation on the weight factor to determine the nuclide information corresponding to the nuclide to be detected when the difference between the weight factors of the plurality of target nuclides reaches a preset value.
Specifically, the matching unit 41 calls the nuclides in the nuclide library 10 by acquiring the energy corresponding to the peak position determined by the peak finding module 30 and the branch ratio and setting the corresponding energy branch ratio threshold value, so that only the nuclides with the energy branch ratio greater than the threshold value are called to match the output result of the peak finding module 30. Setting a weight factor for the selected nuclide, performing weighted calculation on the nuclide matched with the energy peak branch ratio and the peak significance, performing continuous sampling period iteration on the weight factor of the target nuclide group to gradually increase the difference of the weight factors corresponding to each nuclide in the target nuclide group, and determining the nuclide with the maximum weight factor as the nuclide to be detected when the difference reaches a set target value.
Based on the energy spectrum analysis system of any embodiment, an energy spectrum analysis method is provided, which includes: selecting nuclides and establishing a nuclide library; selecting a sample nuclide from a nuclide library, acquiring energy spectrum data of the sample nuclide measured by a detector, determining an energy scale coefficient, determining a noise model based on continuous energy spectrum data of the sample nuclide, dividing a peak region boundary based on the noise model and determining a peak shape scale coefficient; acquiring an energy spectrum of the nuclide to be detected, and determining the peak position of the nuclide to be detected based on the energy scale coefficient and the peak shape scale coefficient; setting a nuclide threshold value based on a nuclide to be detected, calling a target nuclide which is larger than the nuclide threshold value in a nuclide library, matching the peak position of the nuclide to be detected with the target nuclide, calculating a weight factor of the target nuclide, and determining a nuclide name, a characteristic energy peak and a branch ratio corresponding to the nuclide to be detected based on the weight factor.
And T01, selecting nuclides recorded in the nuclide library according to the performance of a common detector or a multi-channel analyzer and a common operation scene, wherein nuclide information can comprise the name, half-life period, characteristic energy peak and branch ratio of the nuclide.
And T02, selecting a sample nuclide from the nuclide library to perform energy calibration on the detector, performing denoising processing on the acquired energy spectrum of the sample nuclide by setting a boundary, determining a peak region boundary, performing peak shape calibration on the detector based on the energy calibration coefficient, and outputting an energy calibration coefficient and a peak shape calibration coefficient.
And T03, measuring the nuclide to be detected by using a detector, setting a peak searching threshold (namely a significance tolerance threshold), periodically dividing the acquired nuclide energy spectrum to be detected, performing iterative processing on each channel of data in the period, calculating the ratio of second-order difference and mean square error corresponding to each channel, namely the significance, determining the peak position when the ratio is greater than the peak searching threshold, removing an interference peak based on machine learning, and determining the energy corresponding to the channel of the peak position and the energy peak branch ratio by combining an energy scale coefficient and a peak shape scale coefficient.
And T04, matching the energy spectrum measurement result of the nuclide to be measured with the nuclides in the nuclide library, setting a weight factor, weighting the nuclides by combining peak position uncertainty and energy peak branch ratio, iterating the weight factor along with the measurement period, gradually increasing and stabilizing the weight factor difference corresponding to each nuclide, and determining that the nuclide to be measured is the nuclide with the largest weight factor.
As shown in fig. 3, a spectral data analysis apparatus 300 includes: the data receiving module 310 receives the energy spectrum data from the multi-channel analyzer, the energy spectrum analysis system 100 is in communication connection with the data receiving module 310, and the output module 320 is in communication connection with the energy spectrum analysis system 100 and outputs the result of the energy spectrum analysis system 100 to the upper computer.
The energy spectrum data analysis device 300 may be a mobile memory including the energy spectrum analysis system 100 of the embodiment of the present invention, and may be adapted to receive the energy spectrum data of the detector and perform operations by accessing an upper computer. The analytical equipment can be connected into a multi-channel analyzer, a computer terminal and the like to analyze the received nuclide energy spectrum to be detected and finally output a calculation result.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (14)
1. An energy spectrum analysis system, comprising:
a nuclide library for storing data information of nuclides;
the calibration module is used for carrying out energy calibration and peak calibration on the detector and outputting an energy calibration coefficient and a peak calibration coefficient;
the peak searching module is used for calling the output result of the calibration module, analyzing the energy spectrum of the nuclide to be detected acquired by the detector and determining the peak position; and
and the nuclide identification module calls the output result of the peak searching module, sets a nuclide threshold value, calls the nuclide library to match the output result of the peak searching module, calculates a weight factor of the matched target nuclide, and determines the target nuclide corresponding to the nuclide to be detected based on the weight factor.
2. The system of claim 1, wherein the nuclide library comprises an isotope name, a half-life, an energy branch ratio, and an isotope abundance of the nuclide.
3. The system of claim 2, wherein the nuclide threshold corresponds to the nuclide atomic number, and wherein the corresponding nuclide is invoked when the atomic number matches the set nuclide threshold.
4. The system of claim 1, wherein the energy calibration step comprises:
selecting a sample nuclide;
acquiring an energy spectrum of the sample nuclide measured by the detector;
calling corresponding energies of the sample nuclides in the nuclide library;
and performing polynomial fitting on the corresponding relation between the sample nuclide energy spectrum and the energy to determine an energy scale coefficient.
5. The system of claim 1, wherein the peak shape scaling step comprises a denoising unit for removing electronic noise, the denoising unit determining a corresponding peak region of the peak position based on a noise model.
6. The system of claim 5, wherein the peak shape scaling step comprises the steps of:
selecting a sample nuclide;
acquiring an energy spectrum of the sample nuclide;
smoothing the energy spectrum of the sample nuclide through the noise model;
and determining the energy-full width at half maximum data combination of the sample nuclide by selecting a Gaussian function model, and determining a peak shape scale coefficient by a polynomial fitting method.
7. The system of claim 5, wherein the denoising unit determines a peak region corresponding to the peak position based on a noise model setting boundary.
8. The system of claim 5, wherein the denoising unit analyzes a noise model of the energy spectrum by a machine learning method, and updates the noise model to converge with time based on the determination of the determined peak region boundary energy spectrum.
9. The system according to claim 1, wherein the peak searching module comprises a peak position judging unit, the peak position judging unit calls the output result of the calibration module, the energy spectrum data is divided into periods, the track addresses in the periods are calculated according to the significance, and the peak position is determined based on the set tolerance threshold value of the significance.
10. The system of claim 9, wherein the peak searching module further comprises an optimizing unit, and the optimizing unit is configured to perform the significance statistics on the addresses corresponding to the peak positions, and remove an interference peak based on the statistics of the significance.
11. The system as claimed in claim 2 or 3, wherein the nuclide identification module comprises a matching unit, the matching unit calls a target nuclide of the nuclide library, and a nuclide corresponding to the output result of the peak finding module is determined by setting a nuclide branching ratio threshold.
12. The system as claimed in claim 1, wherein the nuclide identification module includes a matching unit, the matching unit calls the output result of the peak finding module, calls a target nuclide in the nuclide library that matches the nuclide threshold, calculates a weight factor of the target nuclide, and performs iterative calculation on the weight factor, so that when a difference value between the weight factors of a plurality of target nuclides reaches a preset value, nuclide information corresponding to the nuclide to be detected is determined.
13. A method of energy spectrum analysis, comprising:
selecting nuclides and establishing a nuclide library;
selecting a sample nuclide from the nuclide library, acquiring energy spectrum data of the sample nuclide measured by a detector, determining an energy scale coefficient, determining a noise model based on continuous energy spectrum data of the sample nuclide, dividing a peak area boundary based on the noise model and determining a peak shape scale coefficient;
acquiring an energy spectrum of a nuclide to be detected, and determining the peak position of the nuclide to be detected based on the energy scale coefficient and the peak shape scale coefficient;
setting a nuclide threshold value based on the nuclide to be detected, calling a target nuclide which is larger than the nuclide threshold value in a nuclide library, matching the peak position of the nuclide to be detected with the target nuclide, calculating a weight factor of the target nuclide, and determining a nuclide name, a characteristic energy peak and a branch ratio corresponding to the nuclide to be detected based on the weight factor.
14. An apparatus for analyzing spectral data, comprising:
the data receiving module is used for receiving the energy spectrum data from the multi-channel analyzer;
a power spectrum analysis system, communicatively connected to said data receiving module, using the power spectrum analysis system of any one of claims 1-12; and
and the output module is in communication connection with the energy spectrum analysis system and outputs the result of the energy spectrum analysis system to an upper computer.
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