Uranium enrichment degree determination method, device, equipment, medium and program product

Abstract

The invention discloses a uranium enrichment degree determination method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring a peak area data set; wherein the peak area data set comprises a plurality of peak areas; the peak area is associated with an energy spectrum curve of radiation rays of the uranium sample; determining a first correlation parameter of the uranium sample based on the peak area data set; wherein the first correlation parameter is correlated to a fixed difference value between a relative efficiency curve of 235U and a relative efficiency curve of 238U in the uranium sample; recursively adjusting the first correlation parameter based on the peak area data set through a gradient descent algorithm to obtain a target correlation parameter; and determining the uranium enrichment degree based on the target correlation parameters.

Description

Technical Field

[0001] This application relates to the technical field of non-destructive assay (NDA) nuclear detection, and particularly to a method, device, equipment, medium, and program product for determining uranium enrichment. Background Art

[0002] International nuclear safeguards, also known as nuclear safeguards, are used to verify whether a country fulfills its international commitment not to divert its nuclear programs and activities for nuclear weapon purposes. The method of analyzing the enrichment of samples by the γ-ray spectrometry relative efficiency method has the advantages of not requiring prior calibration, fast analysis speed, and no special requirements for the geometric shape and chemical form of the sample to be measured. Therefore, this method is widely used in on-site inspections of nuclear safeguards. The accurate fitting of the relative efficiency curve is an important part of the γ-ray spectrometry relative efficiency method.

[0003] When fitting the relative efficiency curve, it is necessary to predict the enrichment of the sample according to the fitting result of the relative efficiency curve and make corresponding adjustments according to the fitting value; and based on 235 U and 238 There is a fixed difference k related to the enrichment between the relative efficiency curves fitted by the data points associated with U, so it is crucial to determine the fixed difference k.

[0004] To solve the above technical problems, related technologies provide a method for iteratively calculating k by least squares fitting. This method usually uses the traversal method to search for the optimal value of k. However, the traversal method takes a long time and has a fixed granularity, resulting in insufficient accuracy of the finally obtained k. Summary of the Invention

[0005] Based on the above technical problems, the embodiments of this application provide a method, device, equipment, medium, and program product for determining uranium enrichment.

[0006] The technical solution provided by the embodiments of this application is as follows:

[0007] The embodiments of this application first provide a method for determining uranium enrichment, and the method includes:

[0008] Obtain a peak area data set; wherein, the peak area data set includes multiple peak areas; the peak area is associated with the energy spectrum curve of the rays emitted by the uranium sample;

[0009] Determine a first correlation parameter of the uranium sample based on the peak area data set; wherein, the first correlation parameter is associated with the fixed difference between the relative efficiency curve of 235 U in the uranium sample and the relative efficiency curve of 238 U;

[0010] Recursively adjust the first correlation parameter based on the peak area data set through the gradient descent algorithm to obtain the target correlation parameter;

[0011] Determine the uranium enrichment degree based on the target correlation parameter.

[0012] In some embodiments, the determining the first correlation parameter of the uranium sample based on the peak area data set includes:

[0013] Determine a first data set based on the peak area data set and the branching ratio set;

[0014] Determine the parameters in the relative efficiency curve of the uranium sample based on the first data set;

[0015] Determine the first correlation parameter through the relative efficiency curve after the parameters are determined.

[0016] In some embodiments, the recursively adjusting the first correlation parameter based on the peak area data set through the gradient descent algorithm to obtain the target correlation parameter includes:

[0017] Determine a prediction function associated with the relative efficiency curve;

[0018] Determine a first data set based on the peak area data set and the branching ratio set;

[0019] Recursively adjust the first correlation parameter based on the first data set and the gradient of the prediction function through the gradient descent algorithm to obtain the target correlation parameter.

[0020] In some embodiments, the recursively adjusting the first correlation parameter based on the first data set and the gradient of the prediction function through the gradient descent algorithm to obtain the target correlation parameter includes:

[0021] Determine the nth-order gradient of the prediction function; where n is an integer greater than or equal to 1;

[0022] Determine the (n + 1)th correlation parameter based on the nth-order gradient and the nth correlation parameter;

[0023] Determine the (n + 1)th data set based on the (n + 1)th correlation parameter and the nth data set;

[0024] Iterate the (n + 1)th correlation parameter based on the (n + 1)th data set to obtain the target correlation parameter.

[0025] In some embodiments, the iterating the (n + 1)th correlation parameter based on the (n + 1)th data set to obtain the target correlation parameter includes:

[0026] Process the (n + 1)-th data set and the (n + 1)-th observation data set through a loss function to obtain the (n + 1)-th degree of difference.

[0027] If the (n + 1)-th degree of difference is less than or equal to the difference threshold, determine the (n + 1)-th correlation parameter as the target correlation parameter.

[0028] In some embodiments, before obtaining the peak area data set, it further includes:

[0029] Determine the initial energy spectrum curve of the uranium sample;

[0030] Adjust the parameters in the initial energy spectrum curve through the least squares method to obtain a target energy spectrum curve corresponding to the initial energy spectrum curve;

[0031] Determine the peak area data set based on the target energy spectrum curve.

[0032] An embodiment of the present application further provides a uranium enrichment degree determination device, and the enrichment degree determination device includes:

[0033] An acquisition module, configured to acquire a peak area data set; wherein, the peak area data set includes a plurality of peak areas; the peak area is associated with the energy spectrum curve of the rays radiated by the uranium sample;

[0034] A determination module, configured to determine a first correlation parameter of the uranium sample based on the peak area data set; wherein, the first correlation parameter is associated with the 235 relative efficiency curve of 238 U and the fixed difference between the relative efficiency curves of

[0035] A processing module, configured to recursively adjust the first correlation parameter based on the peak area data set through a gradient descent algorithm to obtain a target correlation parameter;

[0036] The determination module is further configured to determine the uranium enrichment degree based on the target correlation parameter.

[0037] An embodiment of the present application further provides an electronic device, and the electronic device includes a processor and a memory; a computer program is stored in the memory; when the computer program is executed by the processor, it can implement the uranium enrichment degree determination method as described in any one of the foregoing.

[0038] An embodiment of the present application further provides a computer-readable storage medium, and a computer program is stored in the storage medium; when the computer program is executed by a processor of an electronic device, it can implement the uranium enrichment degree determination method as described in any one of the foregoing.

[0039] An embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor of an electronic device, it can implement the uranium enrichment degree determination method as described in any one of the previous paragraphs.

[0040] In the uranium enrichment degree determination method provided by the embodiment of the present application, after obtaining a plurality of peak area data sets associated with the energy spectrum curve of the rays radiated by the uranium sample, the first correlation parameter of the uranium sample is determined based on the peak area data sets, and the first correlation parameter is associated with the 235 relative efficiency curve of 238 U and the Description of the Drawings

[0041] Figure 1 is a schematic flowchart of the uranium enrichment degree determination method provided by the embodiment of the present application;

[0042] Figure 2 is a schematic structural diagram of the uranium enrichment degree determination device provided by the embodiment of the present application;

[0043] Figure 3 is a schematic structural diagram of the electronic device provided by the embodiment of the present application. Detailed Embodiments

[0044] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application.

[0045] 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.

[0046] International nuclear safeguards, also known as nuclear safeguards, are used to verify whether a country fulfills its international commitment not to divert its nuclear program and nuclear activities to nuclear weapon purposes.

[0047] The γ spectrometry method can utilize 235 U in the uranium sample and 238Analyze and calculate the X-rays and characteristic γ-rays related to U and its decay daughters to achieve rapid and timely non-destructive detection of the enrichment level of uranium samples. This method has a relatively high analysis accuracy and is one of the commonly used means in on-site inspections for nuclear safeguards. At the same time, the method of analyzing the enrichment level of samples by the relative efficiency method of γ-ray spectrometry has the advantages of not requiring prior calibration, fast analysis speed, and no special requirements for the geometric shape and chemical form of the samples to be measured. Therefore, this method is widely used in on-site inspections for nuclear safeguards. The accurate fitting of the relative efficiency curve is an important part of the relative efficiency method of γ-ray spectrometry.

[0048] When fitting the relative efficiency curve, it is necessary to predict the enrichment level of the uranium sample according to the fitting result of the relative efficiency curve and make corresponding adjustments according to the fitting value; and based on 235 U and 238 There will be a fixed difference k related to the enrichment level between the relative efficiency curves fitted by the data points of U. Therefore, it is crucial to determine the fixed difference k.

[0049] To solve the above technical problems, the related technology provides a method for calculating k through least squares fitting iteration. However, in actual calculations, since the enrichment level of the uranium sample is unknown, the value of k is also unknown. Therefore, in the calculation process of least squares fitting, the expression form of the fitting function corresponding to the relative efficiency curve is not fixed, and an iterative method needs to be used for calculation.

[0050] Moreover, in the related technology, when using least squares fitting to iteratively calculate the value of k, the traversal method is usually used to search for its optimal value. This method discretizes the solution space and tries each possible solution one by one; at the same time, in order to systematically check all possible situations in the solution space, this method needs to determine the search range and step size according to the boundary conditions of the problem and the algorithm accuracy to search for the optimal solution that meets the above conditions. However, in most cases, the traversal method takes a long time, and due to its fixed granularity, k may not necessarily be the actual optimal solution.

[0051] Based on the above technical problems, the embodiments of the present application provide a method, device, equipment, medium, and program product for determining uranium enrichment level.

[0052] Figure 1 For the flow chart of the method for determining uranium enrichment level provided by the embodiments of the present application, as Figure 1 shown, this method may include the following processes:

[0053] Step 101, obtain a set of peak area data.

[0054] Among them, the set of peak area data includes multiple peak areas; the peak area is associated with the energy spectrum curve of the rays emitted by the uranium sample.

[0055] In one embodiment, an energy spectrum curve can be used to describe the energy change process of γ rays radiated by a uranium sample; exemplarily, the energy change process of a single γ ray can be described by combining a Gaussian function with a low-energy tail function; exemplarily, the energy spectrum curve can be as shown in Equation (1):

[0056]

[0057] where ε is a unit step function, which can be expressed by Equation (2):

[0058]

[0059] where x is the energy of the ray radiated by the uranium sample, H represents the peak height of the tail, B is used to control the slope of the front tail, and α is a tail parameter used to describe the overall shape of the tail.

[0060] In one embodiment, the peak area can be obtained by integrating at least a partial interval of the energy spectrum curve.

[0061] In one embodiment, the data in the peak area data set can be classified according to the isotope type. For example, the data in the peak area data set can include a first group of data corresponding to 235 U, including 143.76 keV, 163.36 keV, 185.715 keV, and 205.311 keV, and a second group of data corresponding to 238 U, including 258.26 keV, 742.83 keV, 766.4 keV, 880.47 keV, 883.24 keV, 945.95 keV, and 1001.03 keV.

[0062] Step 102: Determine a first correlation parameter of the uranium sample based on the peak area data set.

[0063] where the first correlation parameter is related to the fixed difference between the relative efficiency curves of 235 U and 238 U in the uranium sample.

[0064] In one embodiment, 235 the fixed difference between the relative efficiency curves of 238 U and 238 U can be k; correspondingly, the first correlation parameter can include the initialized k determined based on the peak area data. Exemplarily, the initialized k can be denoted as k0.

[0065] In one embodiment, the first correlation parameter can be determined by the following method:

[0066] Determine a set of relative efficiency curves corresponding to the initial state of the uranium sample, and determine the first correlation parameter based on the differences between the curves in the set of relative efficiency curves; wherein, the set of relative efficiency curves in the initial state may include the relative efficiency curves of 235 U and 238 the relative efficiency curves of U.

[0067] In one implementation, the relative efficiency curves in the set of relative efficiency curves in the initial state can be determined in the following manner:

[0068] Process the set of peak area data to obtain sample points for fitting the relative efficiency curves, and then fit the relative efficiency curves through the above sample points.

[0069] Step 103: Recursively adjust the first correlation parameter based on the set of peak area data through the gradient descent algorithm to obtain the target correlation parameter.

[0070] In one implementation, the target correlation parameter may include the optimal solution of the finally determined k; exemplarily, the target correlation parameter can be denoted as k final .

[0071] In one implementation, the first correlation parameter can be recursively obtained in the following manner:

[0072] Determine the prediction function corresponding to the relative efficiency curve of the uranium sample, determine the set of sample points according to the set of peak area data, and then recursively adjust and optimize the correlation between the sample points in the set of sample points and the first correlation parameter based on the gradient of the prediction function through the gradient descent algorithm, so as to obtain the target correlation parameter.

[0073] Exemplarily, in the process of the above recursive adjustment and optimization, the least squares method can be combined to calculate the error value of the adjustment and optimization, and based on the relationship between the error value and the preset threshold, determine whether to stop the recursive adjustment and optimization operation.

[0074] Specifically, the gradient descent method is a commonly used optimization solution algorithm in machine learning, and the core of its algorithm is to continuously update the parameter values along the gradient direction of the prediction function until the calculation result of the prediction function reaches the minimum.

[0075] In the field of data processing technology, the prediction function is usually a function for fitting training samples, and this function usually needs to contain the variable parameters that change in the gradient descent algorithm, and its expression can be, for example, ff(x1,x2,...,xn) = w1x1 + w2x2 +....... + wnxn + B, where wn is the weight, B is the bias, xn and ff(x1,x2,...,xn) are the input and output variables respectively.

[0076] Step 104: Determine the uranium enrichment based on the target correlation parameter.

[0077] In one embodiment, there may be a correlation between k and the enrichment. That is, the values of the enrichment corresponding to different k values may be different. Therefore, after the target correlation parameter is determined, the uranium enrichment can be calculated through the target correlation parameter. Among them, the above correlation can be represented by Equation (3), and at the same time, the uranium enrichment E can be calculated through Equation (4), which is specifically as follows: final , as shown below:

[0078]

[0079]

[0080] As can be seen from the above, in the uranium enrichment determination method provided by the embodiments of the present application, after obtaining a plurality of peak area data sets associated with the energy spectrum curve of the rays radiated by the uranium sample, the first correlation parameter of the uranium sample is determined based on the peak area data set, and the first correlation parameter is associated with the 235 relative efficiency curve of 238 U and the fixed difference between the relative efficiency curves of

[0081] Based on the foregoing embodiments, in the uranium enrichment determination method provided by the embodiments of the present application, determining the first correlation parameter of the uranium sample based on the peak area data set can be achieved through the following steps:

[0082] Step A1: Determine the first data set based on the peak area data set and the branching ratio set.

[0083] Specifically, there may be a corresponding relationship between the energy corresponding to the peak area data set and the branching ratio. Therefore, based on the energy corresponding to the data in the peak area data set, the corresponding branching ratio can be determined from the branching ratio set. At this time, the ratio of the m-th data in the peak area data set to the m-th branching ratio in the branching ratio set can be determined as the first data set; where m can be each integer greater than or equal to 1 and less than or equal to M, and M can be the number of data in the peak area data set.

[0084] Step A2: Determine the parameters in the relative efficiency curve of the uranium sample based on the first data set.

[0085] Exemplarily, the relative efficiency curve of the uranium sample can be represented by Equation (5):

[0086]

[0087] Where A1 to A3 and a0 to a3 can be the parameters to be determined in the relative efficiency curve, and these parameters can be obtained by fitting with the help of the first data set. z is used to characterize the variable of the corresponding type of the first data set.

[0088] The first half of Equation (5) is composed of a combination and adjustment of several terms in the physical expression of the reaction cross-section of γ-ray interacting with matter; where A2 / z comes from the distal asymptote of the reaction cross-section of the photoelectric effect, and the change trend of this reaction cross-section with energy is to first decrease rapidly according to z -7 2, then slow down, and finally change with z -1 changes.

[0089] It should be noted that Equation (5) combines the empirical formula for the efficiency curve of coaxial high-purity germanium detectors and the relationship between γ-ray and matter. Therefore, it can be a physical analytical formula, that is, a semi-empirical formula.

[0090] Step A3: Determine the first correlation parameter through the relative efficiency curve after the parameters are determined.

[0091] In one implementation, the first correlation parameter can be determined by the relative efficiency curves of 235 U and 238 U respectively.

[0092] As can be seen from the above, in the uranium enrichment degree determination method provided by the embodiments of the present application, determining the first data set based on the peak area data set and the branching ratio set can improve the accuracy of the first data set; and determining the parameters in the relative efficiency curve of the uranium sample based on the first data set realizes the preliminary fitting of the above relative efficiency curve; on this basis, determining the first correlation parameter through the relative efficiency curve after the parameters are determined can improve the accuracy of the first correlation parameter.

[0093] Based on the above embodiments, in the method for determining uranium enrichment provided in the embodiments of the present application, the first correlation parameter is recursively adjusted based on the peak area data set by a gradient descent algorithm to obtain a target correlation parameter, which can be achieved by the following steps:

[0094] Step B1: Determine a prediction function associated with the relative efficiency curve.

[0095] In one embodiment, the prediction function may be associated with the relative efficiency curve, which may be a generalized expression of the relative efficiency curve; illustratively, the prediction function may be as shown in formula (6):

[0096]

[0097] Among them, c0 to c6 can be fitting parameters of the prediction function.

[0098] Step B2: determining a first data set based on the peak area data and the branching ratio set.

[0099] Step B3: recursively adjust the first associated parameter based on the first data set and the gradient of the prediction function through a gradient descent algorithm to obtain a target associated parameter.

[0100] In one embodiment, the gradient of the prediction function may include a first-order gradient, a second-order gradient, ..., an Nth-order gradient of the prediction function. Exemplarily, the above-mentioned gradients can be determined by deriving the prediction function; N is an integer greater than 2.

[0101] In one implementation, the target association parameters may be obtained in the following manner:

[0102] The n-1th enrichment is determined based on the n-1th association parameter, and then the n-1th enrichment is processed based on the n-th order gradient of the prediction function to obtain the nth enrichment, the nth association parameter is determined based on the nth enrichment, and then the n-1th data set is updated based on the nth association parameter to obtain the nth data set, and then the nth data set is processed by the prediction function to obtain the nth relative efficiency, and the difference between the nth relative efficiency and the corresponding observation value is calculated by the least squares method, and the above process is recursively performed to determine the target association parameter; wherein n can be an integer greater than or equal to 2.

[0103] As can be seen from the above, in the uranium enrichment degree determination method provided by the embodiments of the present application, a prediction function associated with the relative efficiency curve is determined. After determining the first data set based on the peak area data set and the branching ratio set, the first associated parameter is recursively adjusted based on the first data set and the gradient of the prediction function through the gradient descent algorithm to obtain the target associated parameter. In this way, through the above processing, targeted adjustment of the first associated parameter can be achieved, which can not only improve the accuracy of the target associated parameter, but also improve the efficiency of determining the target associated parameter.

[0104] Based on the foregoing embodiments, in the uranium enrichment degree determination method provided by the embodiments of the present application, the first associated parameter is recursively adjusted based on the first data set and the gradient of the prediction function through the gradient descent algorithm to obtain the target associated parameter, which can be achieved through the following steps:

[0105] Step C1: Determine the nth-order gradient of the prediction function.

[0106] Where n is an integer greater than or equal to 1.

[0107] Exemplarily, the nth-order gradient of the prediction function can be determined by taking the nth-order derivative of the prediction function.

[0108] Step C2: Determine the (n + 1)th associated parameter based on the nth-order gradient and the nth associated parameter.

[0109] In one implementation, the (n + 1)th associated parameter can be determined in the following manner:

[0110] Determine the nth enrichment degree based on the nth associated parameter, process the nth enrichment degree based on the nth-order gradient and the learning rate of the gradient descent algorithm to obtain the (n + 1)th enrichment degree, and then determine the (n + 1)th associated parameter based on the (n + 1)th enrichment degree.

[0111] Exemplarily, for discrete data, the gradient is the difference of the function along the direction of the independent variable. For example, the gradient descent direction of the prediction function can be determined based on the difference calculation result. Exemplarily, along the direction of the independent variable z of the prediction function, the difference between the ith independent variable and the (i + 1)th independent variable can be calculated, specifically as shown in formula (7):

[0112]

[0113] Exemplarily, if is greater than 0, it can indicate that the gradient descent direction of the prediction function is the negative direction of the coordinate axis corresponding to the independent variable z.

[0114] Exemplarily, the parameter update of the original gradient descent algorithm can be implemented by formula (8):

[0115] Θ 1 = Θ 0 -αJ(Θ)(8)

[0116] Among them, α is the learning rate of the gradient descent algorithm, and its value corresponds to the distance that the parameter variable moves along the negative gradient direction during each execution of the gradient descent algorithm. In the embodiments of the present application, the value of the learning rate can be set to 0.1.

[0117] The principle of the gradient descent algorithm is to continuously approach the optimal solution along the gradient direction of the prediction function with αJ(Θ) as the step size. As the iterative algorithm gets closer to the optimal solution, the value of αJ(Θ) becomes smaller, and the iterative step size also continuously decreases, and finally stops iterating when gradually reaching the optimal solution.

[0118] In the embodiments of the present application, the initial position Θ 0 can be set to the first enrichment degree, i.e., E0, J(Θ) can be set to Y(z), and substituting into Equation (8) and performing the derivative operation can obtain Equations (9) to (14):

[0119]

[0120] Θ 0 = E0(10)

[0121]

[0122]

[0123]

[0124]

[0125] Exemplarily, the (n + 1)-th associated parameter k n+1 can be determined by Θn+1.

[0126] Step C3: Based on the (n + 1)-th associated parameter and the n-th data set, determine the (n + 1)-th data set.

[0127] Exemplarily, the (n + 1)-th data set can be calculated by Equation (15):

[0128]

[0129] Among them, x in Equation (15) is used to represent energy, and it can be determined by any one of the enrichment degrees in the foregoing text.

[0130] Step C4: Iterate the (n + 1)-th associated parameter based on the (n + 1)-th data set to obtain the target associated parameter.

[0131] Exemplarily, through the iterative process in the foregoing embodiments, continuous iteration of the (n + 1)-th associated parameter can be achieved, so as to finally determine the target associated parameter.

[0132] As can be seen from the above, in the uranium enrichment degree determination method provided by the embodiments of the present application, after determining the n-th gradient of the prediction function, based on the n-th gradient and the n-th associated parameter, the (n + 1)-th associated parameter is determined, and based on the (n + 1)-th associated parameter and the n-th data set, the (n + 1)-th data set is determined, and then the (n + 1)-th associated parameter is iterated based on the (n + 1)-th data set to obtain the target associated parameter. Thus, through the above steps, the associated iteration of the n-th associated parameter and the n-th data set is achieved, so that the change processes of the various associated parameters and data sets in the above iterative process can be consistent with the principles of material radiation and detection in the uranium sample, and further the accuracy of the target associated parameter can be improved.

[0133] Based on the foregoing embodiments, in the uranium enrichment degree determination method provided by the embodiments of the present application, iterating the (n + 1)-th associated parameter based on the (n + 1)-th data set to obtain the target associated parameter can be achieved through the following steps:

[0134] Step D1: Process the (n + 1)-th data set and the (n + 1)-th observed data set through a loss function to obtain the (n + 1)-th degree of difference.

[0135] Exemplarily, the loss function can be a sum of squared errors function, which can be specifically expressed by Equation (16):

[0136]

[0137] where can represent any data set, and y i (k 1 ) can represent any observed data set.

[0138] Step D2: If the (n + 1)-th degree of difference is less than or equal to the difference threshold, determine the (n + 1)-th associated parameter as the target associated parameter.

[0139] Correspondingly, if the (n + 1)-th degree of difference is greater than the difference threshold, the (n + 1)-th associated parameter can continue to be recursively adjusted and optimized through the method provided by the foregoing embodiments.

[0140] As can be seen from the above, in the uranium enrichment degree determination method provided by the embodiments of the present application, the loss function is used to process the (n + 1)-th data set and the (n + 1)-th observation data set to obtain the (n + 1)-th degree of difference. If the (n + 1)-th degree of difference is less than or equal to the difference threshold, the (n + 1)-th associated parameter is determined as the target observation parameter. In this way, through the above processing, not only the real-time tracking of the (n + 1)-th degree of difference is realized, but also the precise control of the iterative optimization process of the associated parameter is realized.

[0141] Based on the foregoing embodiments, in the uranium enrichment degree determination method provided by the embodiments of the present application, before obtaining the peak area data set, the following operations may further be performed:

[0142] Determine the initial energy spectrum curve of the uranium sample; adjust the parameters in the initial energy spectrum curve by the least squares method to obtain the target energy spectrum curve corresponding to the initial energy spectrum curve, and determine the peak area data set based on the target energy spectrum curve.

[0143] In one implementation, the initial energy spectrum curve may be Equation (1) in which A, B, and H are all undetermined.

[0144] In one implementation, the functional expression form corresponding to the least squares method may be as shown in Equation (17):

[0145]

[0146] where F(x, xdata i ) and ydata i can be the data calculated by Equation (1) and the corresponding observation data respectively, and i can be an integer greater than or equal to 1.

[0147] Exemplarily, in order to fit the initial energy spectrum curve, first, the energy spectrum of the uranium sample needs to be collected by a high-resolution γ-ray spectrometer, the distance between the sample and the detector is adjusted according to the uranium sample and the measurement conditions, and the dead time during the energy spectrum collection process is controlled within 10%.

[0148] It should be noted that in order to reduce the influence of the peak fitting error on the enrichment degree calculation, the present application will update the calculation method of the k value in the iteration by using the relationship between the characteristic peaks of multiple 235 U and 238 U, their half-lives, and the branching ratios. In actual selection, the calculation result of the peak area with a smaller relative standard deviation (RSD) in the selected energy spectrum curve can be selected to reduce the adverse influence on the enrichment degree calculation result caused by the large calculation error of the single-energy peak area that may occur.

[0149] As can be seen from the above, in the uranium enrichment degree determination method provided by the embodiments of the present application, after determining the initial energy spectrum curve of the uranium sample, the parameters in the initial energy spectrum curve are adjusted by the least squares method to obtain the target energy spectrum curve corresponding to the initial energy spectrum curve, and the peak area data set is determined based on the target energy spectrum curve. In this way, through the above process, the matching between the target energy spectrum curve and the ray radiation state of the uranium sample can be improved, thereby improving the accuracy of the data in the peak area data set.

[0150] Through the solution provided by the embodiments of the present application, the iterative solution of the k value is realized by using the gradient descent algorithm instead of the traversal method, and finally the uranium enrichment degree is calculated through the k value. Compared with the technical solution provided by the related art, this solution can reduce the iteration time, and during the iteration process, the iteration step size can be dynamically changed and adjusted according to the actual situation, so as to quickly and accurately obtain the optimal k value.

[0151] Based on the foregoing embodiments, the embodiments of the present application further provide a uranium enrichment degree determination device. Figure 2 For the structural schematic diagram of the uranium enrichment degree determination device provided by the embodiments of the present application, as Figure 2 shown, the uranium enrichment degree determination device 2 may include:

[0152] An acquisition module 201, configured to acquire a peak area data set; wherein, the peak area data set includes a plurality of peak areas; the peak area is associated with the energy spectrum curve of the rays radiated by the uranium sample;

[0153] A determination module 202, configured to determine a first correlation parameter of the uranium sample based on the peak area data set; wherein, the first correlation parameter is associated with the 235 relative efficiency curve of 238 U and the fixed difference between the relative efficiency curves of

[0154] A processing module 203, configured to recursively adjust the first correlation parameter based on the peak area data set by using the gradient descent algorithm to obtain a target correlation parameter;

[0155] The determination module 202 is further configured to determine the uranium enrichment degree based on the target correlation parameter.

[0156] In some embodiments, the determination module 202 is configured to determine a first data set based on the peak area data set and the branching ratio set; determine the parameters in the relative efficiency curve of the uranium sample based on the first data set; and determine the first correlation parameter through the relative efficiency curve after the parameters are determined.

[0157] In some embodiments, the determination module 202 is configured to determine a prediction function associated with the relative efficiency curve; determine a first data set based on the peak area data set and the branching ratio set;

[0158] A processing module 203, configured to recursively adjust a first correlation parameter based on a first data set and a gradient of a prediction function by using a gradient descent algorithm, so as to obtain a target correlation parameter.

[0159] In some embodiments, a determination module 202 is configured to determine an nth-order gradient of a prediction function, where n is an integer greater than or equal to 1.

[0160] The determination module 202 is further configured to determine an (n + 1)th correlation parameter based on the nth-order gradient and an nth correlation parameter; and determine an (n + 1)th data set based on the (n + 1)th correlation parameter and an nth data set.

[0161] The processing module 203 is configured to iterate on the (n + 1)th correlation parameter based on the (n + 1)th data set, so as to obtain a target correlation parameter.

[0162] In some embodiments, the processing module 203 is configured to process an (n + 1)th data set and an (n + 1)th observed data set by using a loss function, so as to obtain an (n + 1)th degree of difference.

[0163] The determination module 202 is configured to, if the (n + 1)th degree of difference is less than or equal to a difference threshold, determine the (n + 1)th correlation parameter as the target correlation parameter.

[0164] In some embodiments, the determination module 202 is configured to determine an initial energy spectrum curve of a uranium sample.

[0165] The processing module 203 is configured to adjust parameters in the initial energy spectrum curve by using a least squares method, so as to obtain a target energy spectrum curve corresponding to the initial energy spectrum curve.

[0166] The determination module 202 is configured to determine a peak area data set based on the target energy spectrum curve.

[0167] An embodiment of this application further provides an electronic device. Figure 3 As shown in the structural schematic diagram of the electronic device provided in the embodiment of this application, as Figure 3 shown, the electronic device 3 includes a processor 301 and a memory 302; a computer program is stored in the memory 302; when the computer program is executed by the processor 301, the uranium enrichment degree determination method described in any of the foregoing can be implemented.

[0168] An embodiment of this application further provides a computer-readable storage medium, in which a computer program is stored; when the computer program is executed by a processor of an electronic device, the uranium enrichment degree determination method described in any of the foregoing can be implemented.

[0169] The embodiments of the present application also provide a computer program product, which includes a computer program. When the computer program is executed by a processor of an electronic device, it can implement the uranium enrichment degree determination method described in any of the previous ones.

[0170] The descriptions of the above embodiments tend to emphasize the differences between the embodiments. Their similarities or similarities can be referred to each other. For the sake of brevity, they will not be elaborated herein.

[0171] The methods disclosed in the method embodiments provided by the present application can be arbitrarily combined without conflict to obtain new method embodiments.

[0172] The features disclosed in the product embodiments provided by the present application can be arbitrarily combined without conflict to obtain new product embodiments.

[0173] The features disclosed in the method or device embodiments provided by the present application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0174] It should be noted that the above computer-readable storage medium can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it can also be various electronic devices including one or any combination of the above memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc.

[0175] It should be noted that in this document, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising such element.

[0176] The serial numbers of the embodiments of the present application above are for description only and do not represent the superiority or inferiority of the embodiments.

[0177] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus necessary general hardware nodes. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0178] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the specified functions in Figure 1 one or more flows and / or Figure 1 blocks or multiple blocks.

[0179] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the specified functions in Figure 1 one or more flows and / or Figure 1 blocks or multiple blocks.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the process Figure 1 in one process or multiple processes and / or blocks Figure 1 in one block or multiple blocks to specify the functions.

[0181] The foregoing are only preferred embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for determining uranium enrichment, characterized in that: The method comprises: Acquire a peak area data set; wherein the peak area data set includes a plurality of peak areas; the peak areas are associated with an energy spectrum curve of the radiation radiated by the uranium sample; Determine a first correlation parameter for the uranium sample based on the peak area data set; wherein the first correlation parameter is correlated to the peak area in the uranium sample. 235 The relative efficiency curve of U 238 A fixed difference between the relative efficiency curves of U; recursively adjusting the first correlation parameter based on the peak area data set by a gradient descent algorithm to obtain a target correlation parameter; The uranium enrichment is determined based on the target associated parameters.

2. The method according to claim 1, characterized in that Determining a first correlation parameter of the uranium sample based on the peak area data set comprises: determining a first data set based on the peak area data set and the branching ratio data set; determining parameters in a relative efficiency curve for the uranium sample based on the first data set; The first associated parameter is determined through a relative efficiency curve after the parameter is determined.

3. The method according to claim 1, characterized in that The step of recursively adjusting the first correlation parameter based on the peak area data set by a gradient descent algorithm to obtain a target correlation parameter comprises: determining a prediction function associated with the relative efficiency curve; determining a first data set based on the peak area data set and the branching ratio data set; The first associated parameter is recursively adjusted based on the first data set and the gradient of the prediction function by the gradient descent algorithm to obtain the target associated parameter.

4. The method according to claim 3, characterized in that The step of recursively adjusting the first associated parameter based on the first data set and the gradient of the prediction function by the gradient descent algorithm to obtain the target associated parameter includes: Determining an nth order gradient of the prediction function; wherein n is an integer greater than or equal to 1; Determining an (n+1)th association parameter based on the nth order gradient and the nth association parameter; Determining an n+1th data set based on the n+1th association parameter and the nth data set; The n+1th association parameter is iterated based on the n+1th data set to obtain the target association parameter.

5. The method according to claim 4, characterized in that The iterating the n+1th association parameter based on the n+1th data set to obtain the target association parameter includes: Processing the n+1th data set and the n+1th observation data set by using a loss function to obtain an n+1th degree of difference; If the n+1th difference degree is less than or equal to the difference threshold, the n+1th association parameter is determined as the target association parameter.

6. The method according to claim 1, characterized in that Before obtaining the peak area data set, the method further comprises: determining an initial energy spectrum curve of the uranium sample; Adjusting the parameters in the initial energy spectrum curve by the least square method to obtain a target energy spectrum curve corresponding to the initial energy spectrum curve; The peak area data set is determined based on the target energy spectrum curve.

7. A device for determining uranium enrichment, characterized in that: The enrichment determination device comprises: An acquisition module, used for acquiring a peak area data set; wherein the peak area data set includes a plurality of peak areas; and the peak areas are associated with an energy spectrum curve of the radiation radiated by the uranium sample; A determination module is used to determine a first correlation parameter of the uranium sample based on the peak area data set; wherein the first correlation parameter is associated with the peak area of ​​the uranium sample. 235 The relative efficiency curve of U 238 A fixed difference between the relative efficiency curves of U; A processing module, configured to recursively adjust the first correlation parameter based on the peak area data set by a gradient descent algorithm to obtain a target correlation parameter; The determination module is further used to determine the uranium enrichment based on the target associated parameters.

8. An electronic device, characterized in that: The electronic device comprises a processor and a memory; a computer program is stored in the memory; when the computer program is executed by the processor, the method for determining uranium enrichment as claimed in any one of claims 1 to 6 can be implemented.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program; when executed by the processor of the computer electronic device, the method for determining uranium enrichment as claimed in any one of claims 1 to 6 can be implemented.

10. A computer program product, characterized in that The program product comprises a computer program; when the computer program is executed by a processor of an electronic device, the method for determining uranium enrichment as claimed in any one of claims 1 to 6 can be implemented.

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