Radionuclide gamma energy spectrum analysis method and device based on sequential Bayesian analysis

Abstract

The invention relates to a radionuclide gamma energy spectrum analysis method and device based on sequential Bayesian analysis. The method comprises the following steps: generating an energy sequence and a time sequence of a radiation event based on a Monte Carlo sampling principle; discriminating the energy sequence and the time sequence of the radiation event based on a preset energy interval and an average arrival time interval; filtering the energy information in the energy sequence of the radiation event passing the discrimination and the arrival time information of the time sequence in the radiation event; and calculating the posterior probability of a radiation event for the filtered energy information and time information, judging whether the radiation event is generated by a radionuclide gamma or not through the posterior probability, and judging whether a target nuclide exists or not in real time according to a Wald sequential probability ratio test rule of the posterior probability.

Description

Technical Field

[0001] The present application relates to the technical field of code segment adaptation, and in particular to a method and device for analyzing radionuclide gamma energy spectra based on sequential Bayesian analysis. Background Art

[0002] As we all know, radionuclides are closely related to nuclear safety. In a broad sense, nuclear safety refers to the necessary and adequate monitoring, protection, prevention and mitigation of nuclear facilities, nuclear activities, nuclear materials and radioactive substances to prevent accidents caused by any technical reasons, human factors or natural disasters, and to minimize the radioactive consequences of accidents, thereby protecting workers, the public and the environment from improper radiation hazards. Whether in military or civilian use, any radionuclide has serious potential threats and should be strictly regulated by the state. Therefore, the detection method of radionuclides has emerged. However, in security inspections (customs, ports, airports and border checkpoints), with the help of a large number of radiation events to obtain statistical results, the traditional radiation detection spectrum analysis method can realize the measurement of radioactivity spectrum, resulting in the inability to identify the type of radionuclides in a short time (seconds), and unable to meet the needs of radiation detection in many application scenarios such as the above-mentioned social development. This requires a new spectrum measurement method that can quickly determine radionuclides. Summary of the invention

[0003] Based on this, it is necessary to provide a method and device for analyzing the gamma energy spectrum of radionuclides based on sequential Bayesian analysis to address the above technical problems.

[0004] A method for analyzing radionuclide gamma energy spectrum based on sequential Bayesian analysis, the method comprising:

[0005] Generate energy and time series of radiation events based on Monte Carlo sampling principle;

[0006] Identify the energy sequence and time sequence of the radiation event based on a preset energy interval and average arrival time interval;

[0007] Filtering the energy information in the energy sequence of the identified radiation events and the arrival time information in the time sequence of the radiation events;

[0008] The posterior probability of the radiation event is calculated for the filtered energy information and time information, and the posterior probability is used to determine whether the radiation event is caused by the radioactive nuclide γ. According to the posterior probability Wald sequential probability ratio test rule, it is determined in real time whether the target nuclide exists.

[0009] In one embodiment, the method further includes: setting a characteristic energy range, arrival time characteristics, and total number of events N of the radionuclide γ;

[0010] Generate event energies using a Gaussian distribution according to the characteristic energy range, randomly generate N event energy values through Monte Carlo sampling, and generate an energy sequence;

[0011] Generate event arrival times using an exponential distribution according to the arrival time characteristic, randomly generate N event arrival times through Monte Carlo sampling, and generate a time sequence;

[0012] Combine the energy sequence and the time sequence to obtain an event sequence.

[0013] In one embodiment, it further includes: obtaining an energy interval according to the characteristic energy and energy resolution of the radionuclide γ, and determining whether the energy sequence in the radiation event is within the energy interval;

[0014] Obtain an average arrival time interval according to the average arrival time interval and time resolution of the radionuclide γ, and determine whether the time sequence in the radiation event is within the energy interval.

[0015] In one embodiment, it further includes: filtering the arrival time information of the time sequence in the radiation event to remove non-linear system noise.

[0016] In one embodiment, it further includes: using a Bayesian model to calculate the posterior probability of the radiation event for the filtered energy information and time information, and determining whether the radiation event is generated by the radionuclide γ through this posterior probability.

[0017] In one embodiment, it further includes: if it is determined through this posterior probability that the radiation event is not generated by the radionuclide γ, then define the radiation event x m (n) probability distribution as f0(x m ), if it is determined through this posterior probability that the radiation event is generated by the radionuclide γ, then define the radiation event x m (n) probability distribution as f1(x m );

[0018] According to the Wald sequential probability ratio test rule, the decision function for the m-th monoenergetic ray source is:

[0019]

[0020] Set the upper and lower thresholds A and B; wherein, when Dm(n) ≥ A, the radiation event is generated by the radionuclide γ, when Dm(n) ≤ B, the radiation event is not generated by the radionuclide γ, and when B < Dm(n) < A, it is determined in real time whether the (n + 1)-th radiation event is generated by the radionuclide γ.

[0021] A radionuclide gamma spectrum analysis device based on sequential Bayesian analysis, the device comprising:

[0022] A sampling module, used to generate energy series and time series of radiation events based on the Monte Carlo sampling principle;

[0023] A discrimination module, used for discriminating the energy sequence and time sequence of the radiation event based on a preset energy interval and an average arrival time interval;

[0024] A filtering module, used for filtering the energy information in the energy sequence of the identified radiation events and the arrival time information in the time sequence of the radiation events;

[0025] The real-time discrimination module is used to calculate the posterior probability of the radiation event based on the filtered energy information and time information, and judge whether the radiation event is caused by the radioactive nuclide γ through the posterior probability, and judge whether the target nuclide exists in real time according to the posterior probability Wald sequential probability ratio test rule.

[0026] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0027] Generate energy and time series of radiation events based on Monte Carlo sampling principle;

[0028] Identify the energy sequence and time sequence of the radiation event based on a preset energy interval and average arrival time interval;

[0029] Filtering the energy information in the energy sequence of the identified radiation events and the arrival time information in the time sequence of the radiation events;

[0030] The posterior probability of the radiation event is calculated for the filtered energy information and time information, and the posterior probability is used to determine whether the radiation event is caused by the radioactive nuclide γ. According to the posterior probability Wald sequential probability ratio test rule, it is determined in real time whether the target nuclide exists.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0032] Generate energy and time series of radiation events based on Monte Carlo sampling principle;

[0033] Identify the energy sequence and time sequence of the radiation event based on a preset energy interval and average arrival time interval;

[0034] Filtering the energy information in the energy sequence of the identified radiation events and the arrival time information in the time sequence of the radiation events;

[0035] The posterior probability of the radiation event is calculated for the filtered energy information and time information, and the posterior probability is used to determine whether the radiation event is caused by the radioactive nuclide γ. According to the posterior probability Wald sequential probability ratio test rule, it is determined in real time whether the target nuclide exists.

[0036] The above-mentioned radionuclide gamma spectrum analysis method and device based on sequential Bayesian analysis, combined with sequential Bayesian analysis, Kalman filtering and particle filtering, realizes real-time, dynamic and high-precision identification of radionuclides. When processing each radiation event, the system uses Monte Carlo sampling to generate data, and optimizes the estimation of energy and arrival time through Kalman filtering and particle filtering respectively, reduces noise interference, and ensures the accuracy of the data. Through sequential Bayesian analysis, the system can continuously update the posterior probability of the target nuclide based on each new input event, gradually accumulate evidence, and improve the accuracy of judgment. The system has excellent real-time processing capabilities, and can still work effectively in complex and uncertain detection environments, and adapt to changes in nonlinear dynamic systems. In addition, the modular structure and flexibility of the system make it highly scalable, and it can be adjusted and customized according to different application scenarios and needs to ensure the reliability and efficiency of nuclide identification, while significantly reducing false alarms and missed detections. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of a process of radionuclide gamma spectrum analysis method based on sequential Bayesian analysis in one embodiment;

[0038] Figure 2 A schematic diagram of the structure of radionuclide gamma spectrum analysis based on sequential Bayesian analysis in one embodiment;

[0039] Figure 3 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] In one embodiment, if Figure 1 As shown, a radionuclide gamma spectrum analysis method based on sequential Bayesian analysis is provided, comprising:

[0042] Step 102: Generate energy sequence and time series of radiation events based on Monte Carlo sampling principle.

[0043] Step 104 : Identify the energy sequence and time sequence of the radiation event based on the preset energy interval and average arrival time interval.

[0044] Step 106: Filter the energy information in the energy sequence of the identified radiation events and the arrival time information in the time sequence of the radiation events.

[0045] Step 108, calculate the posterior probability of the radiation event for the filtered energy information and time information, and use the posterior probability to determine whether the radiation event is caused by the radioactive nuclide γ, and determine in real time whether the target nuclide exists based on the posterior probability Wald sequential probability ratio test rule.

[0046] The above-mentioned radionuclide gamma spectrum analysis method based on sequential Bayesian analysis, combined with sequential Bayesian analysis, Kalman filtering and particle filtering, realizes real-time, dynamic and high-precision identification of radionuclides. When processing each radiation event, the system uses Monte Carlo sampling to generate data, and optimizes the estimation of energy and arrival time through Kalman filtering and particle filtering respectively, reduces noise interference and ensures the accuracy of the data. Through sequential Bayesian analysis, the system can continuously update the posterior probability of the target nuclide based on each new input event, gradually accumulate evidence and improve the accuracy of judgment. The system has excellent real-time processing capabilities and can still work effectively in complex and uncertain detection environments, adapting to changes in nonlinear dynamic systems. In addition, the modular structure and flexibility of the system make it highly scalable, and it can be adjusted and customized according to different application scenarios and needs to ensure the reliability and efficiency of nuclide identification, while significantly reducing false alarms and missed detections.

[0047] In the present invention, any radioactive nuclide can be distinguished from other radioactive nuclides by its half-life and characteristic gamma rays, and all have the "fingerprint" function of half-life and characteristic gamma rays; the corresponding radioactive nuclides are quickly identified by a continuous Bayesian analysis method: under low counting conditions, that is, the total number rate in the 0-2MeV full energy region is ≤102 / s, by implanting the sequential Bayesian analysis processor with the corresponding nuclide fingerprint features (half-life, characteristic ray energy and branching ratio), it is possible to analyze and process the radiation event sequence (including detection output energy and arrival time), and make a judgment on the existence of the corresponding radioactive substance.

[0048] In one of the embodiments, the characteristic energy range, arrival time characteristics and total number of events N of the radionuclide γ are set; event energy is generated using Gaussian distribution according to the characteristic energy range, and N event energy values ​​are randomly generated through Monte Carlo sampling to generate an energy sequence; event arrival time is generated using exponential distribution according to the arrival time characteristics, and N event arrival times are randomly generated through Monte Carlo sampling to generate a time series; the energy sequence and the time series are combined to obtain an event sequence.

[0049] Specifically, the sequential Bayesian method requires that the event sequence to be analyzed and processed be continuously input to the processor, and the event sequence must contain the radiation event energy sequence and arrival time sequence information. The total number of events in which the detector detects the mth single-energy ray source per unit time in the probe radiation field is the total detection rate Gm. Combined with Monte Carlo, the detection efficiency of the identification test is introduced, which provides the possibility of processing for the sampling principle and outputs the event sequence based on numerical simulation. When the maximum energy is ε max In the case of uniformly distributed noise sources and M single energy ray sources, a total of M+1, the test detection rate is G m (m=123...,…,M), the unit is S -1 .

[0050] By calling the database and existing results, the time interval of the radiation source event arrival is exponentially distributed:

[0051]

[0052] In the formula is the mean time between arrivals of radiation events, and

[0053]

[0054] The energy spectrum distribution of a monoenergetic ray source obeys a Gaussian distribution. The detection efficiency G of a monoenergetic ray source test represents the proportion (contribution) of this ray source to the detection rate. Therefore, the types of monoenergetic ray sources roughly obey a discrete uniform distribution. The probability that the current radiation event X originates from the mth monoenergetic ray source is:

[0055]

[0056] In the sampling scheme, the event sequence generation steps are:

[0057] ⑴Data acquisition: Pulse amplitude spectrum Z = (z1, z2, ..., z N ) and the measurement time series t;

[0058] (2) Data initialization: n = 1, τ(0) = 0;

[0059] ⑶ Arrival time sampling of event n: Note that random numbers R1~U(0,1) are drawn,

[0060]

[0061] T(n)=T(n-1)+ Δ T#

[0062] (4) Energy interval sampling of event n: extract random numbers R2~U(0,1) and find a positive integer k that satisfies

[0063] P r (E≤E k-1 )<R2≤P r (E≤E k )#

[0064] therefore

[0065]

[0066] (5) Energy sampling of event n: Let us extract random numbers R3~U(0,1), and calculate the event energy as:

[0067] ε(n)=E k-1 +(E k -E k-1 )*R3#

[0068] (6) Save and record the nth event information X(n) = [ε(n), τ(n)], and repeat steps (3) to (6) until the total number of events meets the requirement.

[0069] In one embodiment, after detection by the detector, the energy and time information of the radiation event is stored in an event sequence X[n], and the event sequence is used for a sequential Bayesian analysis processor to perform calculations to obtain a final nuclide determination result.

[0070] In one of the embodiments, an energy interval is obtained based on the characteristic energy and energy resolution of the radionuclide γ, and it is judged whether the energy sequence in the radiation event is within the energy interval; an average arrival time interval is obtained based on the average arrival time interval and time resolution of the radionuclide γ, and it is judged whether the time series in the radiation event is within the energy interval.

[0071] Specifically, the confidence interval of the energy discriminator is:

[0072]

[0073] In the formula, the confidence coefficient k can be found in the nuclear database or corresponding literature. ε and (radionuclide characteristic beam energy value) Among them, the confidence coefficient was taken as 1.967 in this study; is the energy standard deviation, whose value is related to the detector energy resolution

[0074] Time discrimination confidence interval: The purpose is to screen the average arrival time interval of events. The formula is

[0075]

[0076] Identify the confidence interval for the mean inter-arrival time:

[0077]

[0078] In the formula, is the average time interval detected by the detector, k ΔT The confidence coefficient of this study; is the standard deviation of the arrival time intervals detected by the detector; is the measured average arrival time of the radiation event, and its value satisfies:

[0079]

[0080] When a radiation event X(n) is input, it must satisfy the event discriminator and enter the next module at the same time. Events that do not meet the conditions are rejected. Note: The time discriminator here tests the average time interval under the characteristic energy conditions corresponding to a specific radionuclide, rather than the event interval between two separate events.

[0081] In one embodiment, energy information in the energy sequence of the identified radiation events is filtered to remove measurement noise and system noise.

[0082] Specifically, the algorithm works in a two-step process. In the prediction step, the Kalman filter estimates the current state variables and their uncertainties. Once the results of the next measurement are observed (which are inevitably affected by a certain amount of error (including random noise)), these estimates are updated in real time using a weighted average, with more weights given to higher certainty. Only the current input measurement and the previously calculated state and its uncertainty matrix are used; no additional past information is required to update the feature information of all previous data in real time.

[0083] In one embodiment, the arrival time information of the time series of radiation events is filtered to remove nonlinear system noise.

[0084] Specifically, the particle filter implements a genetic type direct conversion filtering equation mutation selection particle algorithm for prediction and update. Samples obtained from the distribution are represented by a set of particles; each particle has its own weight, representing the probability of sampling this particle from the probability density function. Weight collapse caused by weight differences is a common problem encountered in these filtering algorithms, but including a resampling step can reduce the weight values before the weights become too large. Several adaptive resampling criteria can be adopted, including the variance of the weights and the uniform distribution of relative entropy. During resampling, particles with negligible weights are ignored or replaced by particles with new weight values. The closer the particles are, the greater their weights. With each calculation update of the particle filter, the average arrival time information of the radiation event gets closer to the true value.

[0085] In one embodiment, if it is determined by the posterior probability that the radiation event is not generated by the radionuclide γ, the radiation event x m (n) probability distribution is f0(x m ), and if it is determined by the posterior probability that the radiation event is generated by the radionuclide γ, the radiation event x m (n) probability distribution is f1(x m );

[0086] According to the Wald sequential probability ratio test rule, the decision function for the m-th monoenergetic ray source is:

[0087]

[0088] Set the upper and lower thresholds A and B; where, when Dm(n) ≥ A, the radiation event is generated by the radionuclide γ, when Dm(n) ≤ B, the radiation event is not generated by the radionuclide γ, and when B < Dm(n) < A, it is determined in real time whether the (n + 1)-th radiation event is generated by the radionuclide γ.

[0089] It should be understood that although Figure 1 the steps in the flowchart of Figure 1 are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover,

[0090] In one embodiment, as Figure 2As shown, a radionuclide gamma spectrum analysis device based on sequential Bayesian analysis is provided, comprising: a sampling module 202, a discrimination module 204, a filtering module 206 and a real-time discrimination module 208, wherein:

[0091] A sampling module 202, for generating energy sequences and time sequences of radiation events based on the Monte Carlo sampling principle;

[0092] A discrimination module 204, configured to discriminate the energy sequence and time sequence of the radiation event based on a preset energy interval and an average arrival time interval;

[0093] A filtering module 206, configured to filter the energy information in the energy sequence of the identified radiation events and the arrival time information in the time sequence of the radiation events;

[0094] The real-time discrimination module 208 is used to calculate the posterior probability of the radiation event based on the filtered energy information and time information, and judge whether the radiation event is caused by the radioactive nuclide γ through the posterior probability, and judge whether the target nuclide exists in real time according to the posterior probability Wald sequential probability ratio test rule.

[0095] In one embodiment, the sampling module 202 is also used to set the characteristic energy range, arrival time characteristics and total number of events N of the radionuclide γ; generate event energy using Gaussian distribution according to the characteristic energy range, randomly generate N event energy values ​​through Monte Carlo sampling, and generate an energy sequence; generate event arrival time using exponential distribution according to the arrival time characteristics, randomly generate N event arrival times through Monte Carlo sampling, and generate a time series; combine the energy sequence and the time series to obtain an event sequence.

[0096] In one embodiment, the identification module 204 is further used to obtain an energy interval based on the characteristic energy and energy resolution of the radionuclide γ, and determine whether the energy sequence in the radiation event is within the energy interval; and to obtain an average arrival time interval based on the average arrival time interval and time resolution of the radionuclide γ, and determine whether the time series in the radiation event is within the energy interval.

[0097] In one embodiment, the filtering module 206 is further configured to filter the energy information in the energy sequence of the identified radiation events, so as to remove measurement noise and system noise.

[0098] In one embodiment, the filtering module 206 is further configured to filter the arrival time information of the time series in the radiation event to remove nonlinear system noise.

[0099] In one embodiment, the real-time discrimination module 208 is further configured to use a Bayesian model to calculate the posterior probability of the radiation event based on the filtered energy information and time information, and determine whether the radiation event is generated by a radioactive nuclide γ through this posterior probability.

[0100] In one embodiment, if the real-time discrimination module 208 determines through the posterior probability that the radiation event is not generated by a radioactive nuclide γ, it defines the radiation event x m (n) probability distribution as f0(x m ), and if it determines through the posterior probability that the radiation event is generated by a radioactive nuclide γ, it defines the radiation event x m (n) probability distribution as f1(x m );

[0101] According to the Wald sequential probability ratio test rule, the decision function for the m-th monoenergetic ray source is:

[0102]

[0103] Set the upper and lower thresholds A and B; where, when Dm(n) ≥ A, the radiation event is generated by a radioactive nuclide γ, when Dm(n) ≤ B, the radiation event is not generated by a radioactive nuclide γ, and when B < Dm(n) < A, it is determined in real time that the (n + 1)-th radiation event is generated by a radioactive nuclide γ.

[0104] For the specific limitations of the radioactive nuclide γ energy spectrum analysis device based on sequential Bayesian analysis, reference can be made to the limitations of the radioactive nuclide γ energy spectrum analysis method based on sequential Bayesian analysis in the above text, which will not be elaborated here. Each module in the above radioactive nuclide γ energy spectrum analysis device based on sequential Bayesian analysis can be implemented in whole or in part through software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0105] In one embodiment, a computer device is provided. This computer device can be a server, and its internal structure diagram can be as Figure 3As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store historical clock error and frequency error data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a radionuclide gamma spectrum analysis method based on sequential Bayesian analysis is implemented.

[0106] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0107] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0108] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0109] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for analyzing radionuclide gamma spectrum based on sequential Bayesian analysis, characterized in that: The method includes: Generating an energy sequence and a time sequence of radiation events based on the Monte Carlo sampling principle; Discriminating the energy sequence and the time sequence of the radiation events based on a preset energy interval and an average arrival time interval; Filtering the energy information in the energy sequence of the radiation events that pass the discrimination and the arrival time information in the time sequence of the radiation events; Calculating the posterior probability of the radiation events for the filtered energy information and time information, determining whether the radiation events are generated by radioactive nuclide γ based on the posterior probability, and determining in real time whether the target nuclide exists according to the Wald sequential probability ratio test rule based on the posterior probability.

2. The method according to claim 1, characterized in that Generating an energy sequence and a time sequence of radiation events based on the Monte Carlo sampling principle, including: Setting the characteristic energy range, arrival time characteristics, and total number of events N of radioactive nuclide γ; Generating event energies using a Gaussian distribution according to the characteristic energy range, randomly generating N event energy values through Monte Carlo sampling, and generating an energy sequence; Generating event arrival times using an exponential distribution according to the arrival time characteristics, randomly generating N event arrival times through Monte Carlo sampling, and generating a time sequence; Combining the energy sequence and the time sequence to obtain an event sequence.

3. The method according to claim 1, characterized in that Discriminating the energy sequence and the time sequence of the radiation events based on a preset energy interval and an average arrival time interval, including: Obtaining an energy interval according to the characteristic energy and energy resolution of radioactive nuclide γ, and determining whether the energy sequence in the radiation events is within the energy interval; Obtaining an average arrival time interval according to the average arrival time interval and time resolution of radioactive nuclide γ, and determining whether the time sequence in the radiation events is within the energy interval.

4. The method according to claim 1, characterized in that: Filtering the energy information in the energy sequence of the radiation events that pass the discrimination, including: Filtering the energy information in the energy sequence of the radiation events that pass the discrimination to remove measurement noise and system noise.

5. The method according to claim 1, characterized in that Filtering the arrival time information in the time sequence of the radiation events, including: Filtering the arrival time information in the time sequence of the radiation events to remove non-linear system noise.

6. The method according to claim 1, characterized in that Calculating the posterior probability of the radiation events for the filtered energy information and time information, and determining whether the radiation events are generated by radioactive nuclide γ based on the posterior probability, including: Using a Bayesian model to calculate the posterior probability of the radiation events for the filtered energy information and time information, and determining whether the radiation events are generated by radioactive nuclide γ based on the posterior probability.

7. The method according to claim 1, characterized in that Determining in real time whether the target nuclide exists according to the Wald sequential probability ratio test rule based on the posterior probability, including: If the posterior probability determines that the radiation event is not caused by the radionuclide γ, then the radiation event x is defined as m (n) probability distribution is f0(x m ), if the posterior probability is used to determine that the radiation event is caused by the radionuclide γ, then the radiation event x is defined as m (n) probability distribution is f1(x m ); According to the Wald sequential probability ratio test rule, the decision function for the m-th monoenergetic ray source is: Setting upper and lower thresholds A and B; where, when Dm(n)≥A, the radiation event is generated by radioactive nuclide γ, when Dm(n)≤B, the radiation event is not generated by radioactive nuclide γ, and when B<Dm(n)<A, it is determined in real time that the (n + 1)-th radiation event is generated by radioactive nuclide γ.

8. A radionuclide gamma spectrum analysis device based on sequential Bayesian analysis, characterized in that: The device includes: A sampling module, used to generate energy series and time series of radiation events based on the Monte Carlo sampling principle; A discrimination module, used for discriminating the energy sequence and time sequence of the radiation event based on a preset energy interval and an average arrival time interval; A filtering module, used for filtering the energy information in the energy sequence of the identified radiation events and the arrival time information in the time sequence of the radiation events; The real-time discrimination module is used to calculate the posterior probability of the radiation event based on the filtered energy information and time information, and judge whether the radiation event is caused by the radioactive nuclide γ through the posterior probability, and judge whether the target nuclide exists in real time according to the posterior probability Wald sequential probability ratio test rule.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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