Method and device for determining uncertainty of nuclear material analysis, medium and program product

Abstract

The invention discloses an uncertainty determination method and device for nuclear material analysis, a medium and a program product. The method includes: acquiring a first set and a second set; wherein the first set comprises isotope analysis results obtained by analyzing the nuclear material through a destructive analysis method; the second set comprises isotope analysis results obtained by analyzing the nuclear material through a non-destructive analysis method; determining a data deviation between the first set and the second set; determining a random error of the non-destructive analysis method based on the data deviation; determining a system error of the non-destructive analysis method based on the data deviation; and determining the uncertainty of the non-destructive analysis method for the content of the isotope in the nuclear material based on the system error and the random error.

Description

Technical Field

[0001] The present application relates to the technical field of nuclear material analysis, and in particular to a method, device, medium and program product for determining uncertainty in nuclear material analysis. Background Art

[0002] Non-destructive analysis methods can be used to analyze the isotope content in nuclear materials. However, the analysis results obtained by non-destructive analysis methods usually deviate from the true value of the isotope content in nuclear materials; and the least squares method cannot accurately evaluate the uncertainty of non-destructive analysis methods in the above analysis process. Summary of the invention

[0003] Based on the above technical problems, the embodiments of the present application provide a method, device, medium and program product for determining uncertainty in nuclear material analysis.

[0004] The technical solution provided by the embodiment of the present application is as follows:

[0005] The present application embodiment first provides a method for determining uncertainty in nuclear material analysis, the method comprising:

[0006] Acquire a first set and a second set; wherein the first set includes isotope analysis results obtained by analyzing the nuclear material by a destructive analysis method; and the second set includes isotope analysis results obtained by analyzing the nuclear material by a non-destructive analysis method;

[0007] determining a data deviation between the first set and the second set;

[0008] determining a random error of the non-destructive analysis method based on the data deviation;

[0009] determining a systematic error of the non-destructive analysis method based on the data deviation;

[0010] Based on the systematic error and the random error, the uncertainty of the non-destructive analysis method for the content of the isotope in the nuclear material is determined.

[0011] In some embodiments, determining the random error of the non-destructive analysis method based on the data deviation comprises:

[0012] determining a first sum of squares of deviations of the data;

[0013] Determining the second sum of squares of the data deviations in the item dimension;

[0014] The random error is determined based on the first sum of squares and the second sum of squares.

[0015] In some embodiments, determining the second sum of squares of the data deviations in the item dimension comprises:

[0016] Determining a first measurement parameter for the nuclear material; wherein the first measurement parameter includes at least the number of items of the nuclear material and the total number of measurements for the nuclear material;

[0017] Based on the total number of measurements, statistics are performed on deviation values ​​corresponding to each item of the number of items in the data deviation to obtain a first statistical result;

[0018] The first statistical result is processed based on the number of items to obtain the second sum of squares.

[0019] In some embodiments, determining the random error based on the first sum of squares and the second sum of squares comprises:

[0020] Determining a first measurement parameter for the nuclear material; wherein the first measurement parameter includes at least the number of items of the nuclear material and the total number of measurements for the nuclear material;

[0021] Processing the first sum of squares and the second sum of squares based on the number of items in the first measurement parameter and the total number of measurements to determine a first intermediate result;

[0022] The random error is determined based on the statistical result of the second set and the first intermediate result.

[0023] In some embodiments, determining the system error of the non-destructive analysis method based on the data deviation comprises:

[0024] determining a second intermediate result based on the average deviation of the data deviations; is the mean deviation

[0025] Determining a second measurement parameter; wherein the second measurement parameter includes at least the number of inspection cycles for the nuclear material and the number of measurements in each inspection cycle;

[0026] determining a third intermediate result based on the data deviation and the second measurement parameter;

[0027] The systematic error is determined based on the second intermediate result and the third intermediate result.

[0028] In some embodiments, determining the second intermediate result based on the average deviation of the data deviations comprises:

[0029] Based on the total number of inspections on the nuclear material, a first statistical operation is performed on the average deviation of the data deviation to obtain a second statistical result; wherein the total number of inspections includes the sum of the number of inspections in each inspection cycle;

[0030] Based on the total number of inspections on the nuclear material, performing a second statistical operation on the average deviation of the data deviation to obtain a third statistical result;

[0031] Based on the total number of inspections on the nuclear material, the second statistical result and the third statistical result are processed to obtain the second intermediate result.

[0032] In some embodiments, determining a third intermediate result based on the data deviation and the second measurement parameter includes:

[0033] Determining an average deviation corresponding to the data deviation;

[0034] Based on the total number of measurements on the nuclear material, the data deviation and the average deviation are counted to obtain a fourth statistical result; wherein the total number of measurements includes the sum of the number of measurements in each inspection cycle;

[0035] The fourth statistical result is processed based on the number of measurements in each inspection cycle to obtain the third intermediate result.

[0036] In some embodiments, determining the system error based on the second intermediate result and the third intermediate result includes:

[0037] The second intermediate result and the third intermediate result are processed based on the statistical result of the second set to obtain the system error.

[0038] An embodiment of the present application also provides an uncertainty determination device for nuclear material analysis, the uncertainty determination device for nuclear material analysis comprising a processor and a memory; wherein a computer program is stored in the memory; when the computer program is executed by the processor, it can implement the uncertainty determination method for nuclear material analysis as described in any of the above.

[0039] An embodiment of the present application also 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, it can implement the uncertainty determination method for nuclear material analysis as described in any of the above.

[0040] An embodiment of the present application also provides a computer program product, in which a computer program is stored; when the computer program is executed by a processor of an electronic device, it can implement the uncertainty determination method for nuclear material analysis as described in any of the above.

[0041] The uncertainty determination method for nuclear material analysis provided in the embodiment of the present application determines the data deviation between the first set and the second set after obtaining the first set and the second set, and the first set includes the isotope analysis results obtained by analyzing the nuclear material by the destructive analysis method, and the second set includes the isotope analysis results obtained by analyzing the nuclear material by the non-destructive analysis method. In this way, through the data deviation, the deviation state of the analysis results obtained by the destructive analysis method and the non-destructive analysis method for analyzing the isotopes in the same nuclear material can be fully and accurately reflected; and based on the data deviation, the non-destructive analysis method and the non-destructive analysis method are respectively determined. The random errors and systematic errors of the method can not only improve the accuracy of the random errors and systematic errors, but also, through the random errors and systematic errors, can comprehensively characterize the richness of the errors of the non-destructive analysis method relative to the destructive analysis method; on this basis, based on the systematic errors and random errors, the uncertainty of the non-destructive analysis method for the isotope content in the nuclear material is determined, which can improve the accuracy of the uncertainty; in summary, the above method provided in the embodiment of the present application draws on the top-down uncertainty evaluation method, and realizes the accurate evaluation of the overall uncertainty of the non-destructive analysis method in the process of analyzing isotopes in nuclear materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a flow chart of a method for determining uncertainty in nuclear material analysis provided in an embodiment of the present application;

[0043] Figure 2 Another schematic diagram of a process for determining uncertainty in nuclear material analysis provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of the structure of an uncertainty determination device for nuclear material analysis provided in an embodiment of the present application;

[0045] Figure 4 A schematic diagram of the structure of an uncertainty determination device for nuclear material analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0047] 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.

[0048] In practical applications, non-destructive analysis (NDA) technology has been widely used in nuclear material accounting and control systems. It is specifically used to monitor and verify the feeding and discharging of nuclear materials, the determination of nuclear fuel retention in pipelines and equipment in the process flow, the determination of the content of nuclear fuel such as uranium and plutonium in spent cladding, the classification of barreled α waste, and the measurement of inhomogeneous nuclear materials. At the same time, NDA technology can also be used for rapid verification of items, such as verification and measurement of items, products, fuel rods and assemblies in containers. In short, NDA technology plays an important supporting role in decision-making support in the fields of criticality safety, radiation protection, international safeguards, nuclear material control and waste management.

[0049] In addition, NDA technology can measure and analyze the content of nuclear material isotopes in items without causing obvious physical or chemical changes in the items; therefore, during the execution of NDA, the entire nuclear material item can be measured, thus avoiding the problem of sampling error.

[0050] On the other hand, the effectiveness and accuracy of the nuclear material accounting and control system are closely related to the quality and reliability of NDA technology measurement and analysis. However, when measuring the isotope content of nuclear material items through measurement systems or measuring instruments using NDA technology, the measurement results often deviate from the true value of the isotope content in the nuclear material items. In the field of mathematics, these deviations or errors are usually described as uncertainty values ​​to characterize the range of the measurement results relative to the true value of the nuclear material items at the confidence level. In the nuclear material accounting and control system, the use of uncertainty values ​​can intuitively characterize the difference in transmission and reception during the measurement process and the comparison of inspection data.

[0051] In order to reasonably calculate the uncertainty value, mathematical tools must be used to reasonably describe the various uncertainties or error sources generated during the implementation of NDA technology, so as to evaluate the impact of the transmission of uncertainty or error on the final measurement results.

[0052] However, due to the complexity of the NDA measurement system, the factors that affect the measurement include at least the following categories: the inconsistency of the "infinite" thickness geometric assumption related to the material layer thickness and the detector window coverage, the inhomogeneity of nuclear material items and their outer packaging, the unsuitability of the spectrum decomposition method, the instability of the system temperature and the non-equilibrium state of nuclear materials, etc. Therefore, it is currently impossible to use the least squares method to calculate the uncertainty of the NDA measurement system.

[0053] Based on the above technical problems, the embodiments of the present application provide a method, device, medium and program product for determining uncertainty in nuclear material analysis.

[0054] The uncertainty determination method for nuclear material analysis provided in the embodiment of the present application can be implemented by a processor of an uncertainty determination device for nuclear material analysis; the above-mentioned processor can be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller (MCU), and a microprocessor.

[0055] Figure 1 A flow chart of a method for determining uncertainty in nuclear material analysis provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the process may include the following steps:

[0056] Step 101: Obtain a first set and a second set.

[0057] The first set includes isotope analysis results obtained by analyzing nuclear materials through destructive analysis (DA) method; the second set includes isotope analysis results obtained by analyzing nuclear materials through NDA.

[0058] In one embodiment, during the analysis of nuclear materials, the facility party may use the DA technique, while the inspector party may use the NDA technique; illustratively, the measurement error of the DA technique may be different from the measurement error of the NDA technique.

[0059] In one embodiment, the nuclear material may include multiple items.

[0060] In one embodiment, the type and / or amount of the core element in the core material may be at least one.

[0061] In one embodiment, the type and / or amount of isotopes in the nuclear material may be at least one.

[0062] In one embodiment, the first set may include a set of isotope contents contained in the nuclear material obtained by detecting and analyzing the nuclear material through DA technology.

[0063] In one embodiment, the second set may include a set of isotope contents contained in the nuclear material obtained by detecting and analyzing the nuclear material through NDA technology.

[0064] In one embodiment, the number of measurements of the isotope included in the first set may be the same as the number of measurements of the isotope included in the second set.

[0065] In one embodiment, the measurement values ​​in the first set and the measurement values ​​in the second set may correspond to each other in terms of the item dimension, the inspection cycle dimension, and the number of inspections dimension; that is, the measurement values ​​of the facility party and the measurement values ​​of the inspector party always appear in pairs. Therefore, the measurement values ​​of the facility party and the measurement values ​​of the inspector party within any inspection cycle may be grouped in one-to-one correspondence based on the Material Balance Area (MBA), material layer, and measurement method.

[0066] Step 102: Determine the data deviation between the first set and the second set.

[0067] In one embodiment, data deviation may be determined by:

[0068] The difference between the i-th measurement value in the j-th inspection cycle in the first set and the i-th measurement value in the j-th inspection cycle in the second set is determined as the i-th deviation in the j-th inspection cycle; then the set of deviations in all inspection cycles is determined as the data deviation; wherein j and i are both integers greater than or equal to 1.

[0069] For example, for the MBA and material layer determined in the nuclear material, the deviation d corresponding to item i in inspection cycle j is ij It can be calculated by formula (1):

[0070] d ij =x ij -y ij (1)

[0071] Among them, x ij 为 The measured value obtained by DA analysis of item i in nuclear material during inspection cycle j, y ij It is the measured value obtained by analyzing and testing item i in nuclear material through NDA during inspection cycle j.

[0072] Step 103: Determine the random error of the NDA method based on the data deviation.

[0073] In one embodiment, random errors may include a collection of mutually compensating errors associated with minute random fluctuations in measurement methods, environment, time, and other factors involved in the process of determining the isotope content in nuclear materials using DNA methods.

[0074] In one embodiment, the random error may be determined by:

[0075] A variance analysis is performed on the distribution state of the data deviation to give the variance of the random component within a specific inspection period, and then the random error is determined based on the variance of the random component; illustratively, in an embodiment of the present application, it can be assumed that the random error satisfies the normal distribution law, and it is assumed that the variance of the random error of all inspections is the same, and all measurements are in routine operation.

[0076] For example, the random component variance S 2 (ε) can be calculated by formula (2):

[0077] S 2 (ε)=U 2 (r,O)+U 2 (r,I)-2r(ε)·U(r,O)·U(r,I) (2)

[0078] Where U(r, O) and U(r, I) are the standard uncertainties of the random error components of the facility and inspection measurements, respectively, and r(ε) is the Pearson correlation coefficient of the random errors between the facility and inspection measurements.

[0079] It should be noted that, in a simplified case, since the facility party and the inspection party use different analysis methods, their errors are independent of each other, so the value of r(ε) can be 0; and since the measurement value of the facility party is more precise or accurate than that of the inspection party, U(r, O) and U(r, I) satisfy the relationship shown in formula (3):

[0080] U(r,I)>>U(r,O) (3)

[0081] Therefore, formula (2) can be transformed into formula (4):

[0082] S(ε)≈U(r,I) (4)

[0083] Step 104: Determine the systematic error of the NDA method based on the data deviation.

[0084] In one embodiment, the systematic error may include the non-random error of the NDA method when analyzing isotopes in nuclear materials; illustratively, the systematic error may include bias errors that violate the principle of randomness and / or errors caused by registration records in sampling, etc.; in practical applications, the systematic error will cause the measurement values ​​in the second set to appear larger or smaller as a whole.

[0085] In one embodiment, the system error can be determined by:

[0086] A variance analysis is performed on the distribution state of the data deviation to provide the system component variance within a specific inspection period, and then the system error is determined based on the system component variance; illustratively, in an embodiment of the present application, it can be assumed that the system error satisfies the normal distribution law, and that the variance of the system error of all inspections is the same, and all measurements are in normal operation.

[0087] For example, the system component variance S 2 (△) can be calculated by formula (5):

[0088] S 2 (△)=U 2 (s,O)+U 2 (s,I)-2r(△)·U(s,O)·U(s,I) (5)

[0089] Among them, U(s, O) and U(s, I) can be the standard uncertainties of the measurement values ​​of the facility party and the inspection party respectively, and r(△) is the Pearson correlation coefficient of the systematic error between the facility party and the inspection party.

[0090] It should be noted that, in a simplified case, since the facility party and the inspection party use different analysis methods, their errors are independent of each other, so the value of r(△) can be 0; and since the measurement value of the facility party is more precise or accurate than that of the inspection party, U(s, O) and U(s, I) satisfy the relationship shown in formula (6):

[0091] U(s,I)>>U(s,O) (6)

[0092] Therefore, formula (5) can be transformed into formula (7):

[0093] S(△)≈U(s,I)(7)

[0094] It should be noted that the execution order of steps 103 to 104 can be adjusted sequentially or executed simultaneously, and the embodiment of the present application does not limit this.

[0095] Step 105: Determine the uncertainty of the NDA method for the content of isotopes in the nuclear material based on the systematic error and the random error.

[0096] In one embodiment, the uncertainty of the isotope content in the nuclear material by the NDA method can characterize the error degree or error range of the analysis and measurement process of the isotope content in the nuclear material by the NDA method relative to the analysis and measurement process of the isotope content in the nuclear material by the DA method.

[0097] In one embodiment, the uncertainty of the content of isotopes in nuclear materials by the NDA method can be achieved by:

[0098] The systematic error and random error are statistically averaged, and the result of the statistical average is determined as the above uncertainty.

[0099] The square root of the sum of the squares of the systematic error and the random error is determined as the above uncertainty; for example, the above uncertainty U for item i in the nuclear material is i It can be calculated by formula (8):

[0100]

[0101] Among them, U i (r) and U i (s) can be the random error and systematic error corresponding to item i respectively.

[0102] As can be seen from the above, the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application determines the data deviation between the first set and the second set after obtaining the first set and the second set, and the first set includes the isotope analysis results obtained by analyzing the nuclear material by the DA analysis method, and the second set includes the isotope analysis results obtained by analyzing the nuclear material by the NDA method. In this way, through the data deviation, the deviation state of the analysis results obtained by the DA method and the NDA method for analyzing the isotopes in the same nuclear material can be fully and accurately reflected; and, based on the data deviation, the random error and the systematic error of the NDA method are determined respectively, which can not only improve the accuracy of the random error and the systematic error, but also, through the random error and the systematic error, the richness of the error of the NDA method relative to the DA method can be comprehensively characterized; on this basis, based on the systematic error and the random error, the uncertainty of the NDA method for the isotope content in the nuclear material is determined, which can improve the accuracy of the uncertainty; in summary, the above method provided in the embodiment of the present application draws on the top-down uncertainty evaluation method, and realizes the accurate evaluation of the overall uncertainty of the NDA method in the process of analyzing isotopes in nuclear materials.

[0103] Based on the above embodiments, in the uncertainty determination method for nuclear material analysis provided in the embodiments of the present application, the random error of the NDA method is determined based on the data deviation, which can be achieved by the following steps:

[0104] Step A1: Determine the first sum of squares of data deviations.

[0105] In one implementation, the first square sum may be determined as follows:

[0106] All deviation values ​​in the data deviation are squared respectively, and the squared results are summed to determine the second square sum; illustratively, the first square sum S1 can be calculated by formula (9):

[0107]

[0108] Where n is the total number of measurements on all items in J inspections; both n and J are integers greater than 1.

[0109] Step A2: Determine the second sum of squares of the data deviation in the item dimension.

[0110] In one implementation, the second sum of squares may be determined as follows:

[0111] The data deviations are classified according to the items included in the nuclear materials to obtain the classification results, and then the sum of squares of the deviation values ​​included in the classification results is calculated, and the sum of squares is determined as the second sum of squares.

[0112] Step A3: Determine the random error based on the first sum of squares and the second sum of squares.

[0113] In one embodiment, the random error may be determined by:

[0114] The first sum of squares and the second sum of squares are statistically averaged, and the result of the statistical average is determined as the random error.

[0115] From the above, it can be seen that in the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application, the first sum of squares of data deviations and the second sum of squares of data deviations in the item dimension are determined. In this way, through the first sum of squares and the second sum of squares, the degree of deviation between the first set corresponding to the DA method and the second set corresponding to the NDA method can be reflected from the overall dimension of the data deviation and the item dimension; and, by determining the random error based on the first sum of squares and the second sum of squares, not only the accuracy of the random error can be improved, but also the comprehensiveness of the error contained in the random error can be improved.

[0116] Based on the above embodiments, in the uncertainty determination method for nuclear material analysis provided in the embodiments of the present application, determining the second sum of squares of data deviations in the item dimension can be achieved by the following steps:

[0117] Step B1, determining a first measurement parameter for nuclear material.

[0118] The first measurement parameter includes at least the number of items of nuclear material and the total number of measurements on the nuclear material.

[0119] In one embodiment, the number of items may include the total number of items contained in the nuclear material.

[0120] In one embodiment, the total number of measurements may include the total number of times all items in the nuclear material are measured using the DA or NDA method, respectively.

[0121] In one embodiment, the first measurement parameter may be determined before the nuclear material is analyzed and measured by DA or NDA.

[0122] In one embodiment, the first measurement parameter can be determined by counting the number of items and the number of measurements involved in the analysis and measurement process of nuclear materials through DA or NDA.

[0123] Step B2: Based on the total number of measurements, statistics are collected on the deviation values ​​of each item in the data deviation from the number of items to obtain a first statistical result.

[0124] In one embodiment, the first statistical result may include m results, where m is the number of items involved in NDA analysis and measurement of nuclear materials during J inspection cycles; and m may be an integer greater than 1.

[0125] In one embodiment, the result T corresponding to the i-th item in the first statistical result is ij It can be calculated by formula (10):

[0126]

[0127] where n is the total number of measurements on all items during J inspection cycles for nuclear material.

[0128] Step B3: Process the first statistical result based on the number of items to obtain a second sum of squares.

[0129] In one implementation, the second sum of squares may be obtained as follows:

[0130] The first statistical result is squared and summed based on the number of items, and then the squared sum is statistically averaged in the dimension of the total number of measurements to obtain a second square sum. For example, the second square sum S2 can be obtained by formula (11):

[0131]

[0132] From the above, it can be seen that in the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application, by determining the first measurement parameter for the nuclear material, and the first measurement parameter at least includes the number of items of the nuclear material and the total number of measurements for the nuclear material, thus, through the first measurement parameter, it is possible to comprehensively and accurately characterize the analysis and measurement status of the isotopes in the nuclear material from the dimension of items and the dimension of the number of measurements; and, based on the total number of measurements, the deviation values ​​corresponding to each item in the data deviation and the number of items are counted to obtain a first statistical result, thus, through the first statistical result, it is possible to reflect the deviation state of the data deviation from the dimension of the total number of measurements; at the same time, the first statistical result is processed based on the number of items to obtain a second sum of squares, so that the determination process of the second sum of squares can take into account both the total number of measurements and the number of items, thereby improving the comprehensiveness of the second square.

[0133] Based on the foregoing embodiment, in the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application, the random error is determined based on the first sum of squares and the second sum of squares, which can be achieved by the following steps:

[0134] Step C1, determining a first measurement parameter for the nuclear material.

[0135] The first measurement parameter includes at least the quantity of the nuclear material and the total number of measurements on the nuclear material.

[0136] Step C2: Based on the number of items in the first measurement parameter and the total number of measurements, the first sum of squares and the second sum of squares are processed to obtain a first intermediate result.

[0137] In one embodiment, the first intermediate result may include an integrated result of the first sum of squares and the second sum of squares; illustratively, the first intermediate result may be the standard uncertainty of the random error component of the inspection party's measurement value in the aforementioned embodiment; illustratively, the first intermediate result may be calculated by formula (12):

[0138]

[0139] Step C3: Determine the random error based on the statistical results of the second set and the first intermediate results.

[0140] In one embodiment, the statistical results of the second set may include yij The weighted average of .

[0141] In one embodiment, the random error may be determined by:

[0142] The ratio between the first intermediate result and the statistical result of the second set is determined as the random error; illustratively, the random error U i (r) can be calculated by formula (13):

[0143]

[0144] in, This is the statistical result of the second set.

[0145] From the above, it can be seen that in the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application, by determining the first measurement parameter for the nuclear material, and the first measurement parameter at least includes the number of items of the nuclear material and the total number of measurements for the nuclear material, so that through the first measurement parameter, the analysis and measurement status of the isotopes in the nuclear material can be comprehensively and accurately characterized from the dimension of items and the dimension of the number of measurements; and, based on the number of items and the total number of measurements in the first measurement parameter, the first sum of squares and the second sum of squares are processed to obtain a first intermediate result, and the first sum of squares and the second sum of squares can be integrated from the dimensions of the number of items and the total number of measurements; on this basis, the random error is determined based on the statistical results of the second set and the first intermediate result, so that the accuracy of the random error can be improved.

[0146] Based on the above embodiments, in the uncertainty determination method for nuclear material analysis provided in the embodiments of the present application, the systematic error of the NDA method is determined based on the data deviation, which can be achieved by the following steps:

[0147] Step D1: Determine a second intermediate result based on the average deviation of the data deviations.

[0148] In one implementation, the second intermediate result may include a result of performing statistics on an average deviation of the data deviations.

[0149] In one implementation, the second intermediate result may be a variance of an average deviation of the data deviations.

[0150] In one implementation, the second intermediate result may be determined by:

[0151] The average deviations of the data deviations corresponding to the J inspection cycles are statistically averaged, and the result of the statistical average is determined as the second intermediate result.

[0152] Step D2: Determine a second measurement parameter.

[0153] The second measurement parameter includes at least the number of inspection cycles for nuclear materials and the number of measurements in each inspection cycle.

[0154] In one embodiment, the number of inspection cycles and the number of measurements within each inspection cycle may be predetermined before measuring and analyzing the isotope content in the nuclear material.

[0155] In one embodiment, the number of inspection cycles and the number of measurements within each inspection cycle can be determined by tracking the process of analyzing and measuring the content of isotopes contained in the nuclear material using a DA method or an NDA method.

[0156] Step D3: Determine a third intermediate result based on the data deviation and the second measurement parameter.

[0157] In one embodiment, the third intermediate result may include data obtained by statistically averaging data deviations based on the number of inspection cycles and the number of measurements in the second measurement parameter.

[0158] In one implementation, the third intermediate result may be determined by:

[0159] The mean square error of the data deviation is statistically analyzed based on the number of measurements in each inspection cycle included in the second measurement parameter to obtain first data, and then the first data is statistically averaged according to the number of measurements in each inspection cycle to obtain a third intermediate result.

[0160] Step D4: determining a system error based on the second intermediate result and the third intermediate result.

[0161] In one implementation, the system error can be calculated as follows:

[0162] The difference between the second intermediate result and the third intermediate result is determined as the systematic error.

[0163] From the above, it can be seen that in the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application, the second intermediate result is determined based on the average deviation of the data deviation. In this way, the second intermediate result can comprehensively and accurately reflect the distribution state of the average deviation of the data deviation from the overall dimension of the average deviation of the data deviation; and by determining a second measurement parameter including at least the number of inspection cycles of the nuclear material and the number of measurements in each inspection cycle, the process of isotope detection in the nuclear material can be comprehensively reflected from the dimension of the inspection cycle; on this basis, based on the data deviation and the second measurement parameter, the third intermediate result is determined, which can realize all-round processing of the data deviation from the dimension of the inspection cycle; on the other hand, by determining the systematic error based on the second intermediate result and the third intermediate result, the systematic error can comprehensively and accurately reflect the deviation state of the NDA method for isotope measurement in nuclear materials from the dimension of the inspection cycle.

[0164] Based on the foregoing embodiment, in the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application, determining the second intermediate result based on the average deviation of the data deviation can be achieved by the following steps:

[0165] Step E1: Based on the total number of inspections on nuclear materials, a first statistical operation is performed on the average deviation of the data deviation to obtain a second statistical result.

[0166] The total number of inspections includes the sum of the number of inspections in each inspection cycle.

[0167] In one embodiment, an error model for detecting and analyzing nuclear materials using the NDA method may be established, and the average deviation of the data deviation may be determined according to the error calculation method of the error model; illustratively, the error model may be expressed by formula (14):

[0168]

[0169] in, can be the average deviation of the data deviation, △ j is the systematic error of the data deviation between the facility and the inspection party within the inspection period j, ε ij is the random error of the data deviation between the facility and the inspector for item i within inspection cycle j; and, j is an integer greater than or equal to 1 and less than or equal to J; i is an integer greater than or equal to 1 and less than or equal to the number of items; and J is the total number of inspections.

[0170] In one embodiment, the first statistical operation may include an operation of obtaining the square of the sum of the average deviations of the data deviations; accordingly, the second statistical result S3 may be calculated by formula (15):

[0171]

[0172] Step E2: Based on the total number of inspections on nuclear materials, a second statistical operation is performed on the average deviation of the data deviation to obtain a third statistical result.

[0173] In one implementation, the second statistical operation may include an operation of performing an average calculation on the second statistical result; accordingly, the third statistical result S4 may be calculated by formula (16):

[0174]

[0175] Step E3: Based on the total number of inspections on nuclear materials, the second statistical result and the third statistical result are processed to obtain a second intermediate result.

[0176] In one implementation, the second intermediate result may be obtained by:

[0177] Based on the total number of inspections on nuclear materials, the difference between the second statistical result and the third statistical result is averaged to obtain a second intermediate result; illustratively, the second intermediate result It can be calculated by formula (17):

[0178]

[0179] From the above, it can be seen that in the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application, based on the total number of inspections for nuclear materials, a first statistical operation is performed on the average deviation of the data deviation to obtain a second statistical result, and the total number of inspections includes the sum of the number of inspections in each inspection cycle. In this way, through the second statistical result, the distribution state of the average deviation of the data deviation can be reflected from the dimensions of inspection cycle and number of inspections; and based on the total number of inspections for nuclear materials, a second statistical operation is performed on the average deviation of the data deviation to obtain a third statistical result. In this way, through the second statistical result and the third statistical result, the distribution state of the average deviation of the data deviation can be reflected from different dimensions; on this basis, based on the total number of inspections for nuclear materials, the second statistical result and the third statistical result are processed to obtain a second intermediate result, which can differentially reflect the overall fluctuation state of the average deviation of the data deviation.

[0180] Based on the foregoing embodiment, in the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application, determining the third intermediate result based on the data deviation and the second measurement parameter can be achieved by the following steps:

[0181] Step F1, determining the average deviation corresponding to the data deviation.

[0182] Step F2: Based on the total number of measurements on the nuclear material, statistics are collected on the data deviation and the average deviation to obtain a fourth statistical result.

[0183] The total number of inspections includes the sum of the number of measurements in each inspection cycle.

[0184] In one embodiment, the fourth statistical result may represent the mean square error of the data deviation relative to the average deviation; illustratively, the fourth statistical result S 2 (△) can be calculated by formula (18):

[0185]

[0186] Step F3: Based on the number of measurements in each observation period, the fourth statistical result is processed to obtain a third intermediate result.

[0187] In one implementation, the third intermediate result may be obtained in the following manner:

[0188] Based on the number of measurements in each observation period, the fourth statistical results in the observation period are summed to obtain a sum result, and then the ratio of the sum result to the sum of the number of measurements in each observation period is determined as the third intermediate result; illustratively, the third intermediate result V can be calculated by formula (19):

[0189]

[0190] Among them, n j The number of m verified in inspection cycle j j The total number of measurements performed on an item.

[0191] From the above, it can be seen that in the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application, after determining the average deviation corresponding to the data deviation, the data deviation and the average deviation are statistically analyzed based on the total number of measurements for the nuclear material to obtain a fourth statistical result, and the total number of measurements includes the sum of the number of measurements in each inspection cycle. In this way, through the above operations, a comprehensive statistics of the data deviation in the dimension of the total number of measurements is achieved; and, based on the number of measurements in each inspection cycle, the fourth statistical result is processed to obtain a third intermediate result, thereby achieving refined processing of the fourth statistical result in the dimension of the number of measurements within the inspection cycle.

[0192] Based on the foregoing embodiment, in the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application, the determination of the system error based on the second intermediate result and the third intermediate result can be achieved in the following manner:

[0193] The second intermediate result and the third intermediate result are processed based on the statistical result of the second set to obtain a systematic error.

[0194] In one implementation, the system error can be calculated as follows:

[0195] First, the third intermediate result is weighted based on the number of inspections in each inspection cycle to obtain a weighted result, then the ratio between the weighted result and the total number of inspections is determined, and then the system error is determined based on the second intermediate result, the ratio and the statistical result of the second set; illustratively, the system error U i (s) can be calculated by equations (20) to (21):

[0196]

[0197]

[0198] From the above, it can be seen that the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application obtains a systematic error by processing the second intermediate result and the third intermediate result based on the statistical results of the second set, so that the systematic error can fully and finely reflect the systematic error state of the NDA method when analyzing nuclear materials from the dimensions of the statistical results of the second set, the second intermediate results and the third intermediate results.

[0199] Figure 2 Another schematic flow chart of the uncertainty determination method for nuclear material analysis provided in the embodiment of the present application is as follows: Figure 2 As shown, the process may include the following steps:

[0200] Step 201: Establish an NDA measurement system.

[0201] For example, the NDA measurement system can be used to detect the content of isotopes in nuclear materials using the NDA method.

[0202] Exemplarily, the NDA measurement system may include one or more measuring instruments; wherein the NDA measurement system may include a normally functioning electrically cooled high-purity germanium detector, a gamma energy spectrum acquisition and data analysis software system, a shielding collimator, and an ultrasonic thickness gauge, etc.

[0203] Step 202: Measure the substance to be tested and obtain NDA measurement results.

[0204] Illustratively, the substance to be tested may be the nuclear material in the aforementioned embodiment, and the NDA measurement result may be the second set in the aforementioned embodiment.

[0205] Step 203: Obtain the measurement result of the nominal value DA of the item to be measured.

[0206] Exemplarily, the items to be tested may include items contained in nuclear materials, and the nominal value DA measurement result may be the first set in the aforementioned embodiment.

[0207] It should be noted that the total number of inspections by the inspector within a certain period of time is J, and in each inspection cycle j, a total of m j items, each item i is measured repeatedly n times i times, in the inspection cycle j, j A total of n items were carried out j times of measurement; then, in J inspections, verification measurements were carried out on m items, and the total number of measurements was n. Therefore, a total of n NDA measurement values ​​y of the inspectors were obtained. ij .

[0208] For example, in the present embodiment, the total number of inspections J within a certain period of time may be greater than 6 times, and the repeated measurement n i The number of times can be greater than or equal to 6 times.

[0209] Step 204: Calculate data deviation.

[0210] For example, after the data deviation calculation is completed, the execution order of the first branch corresponding to steps 205 to 207 and the second branch corresponding to steps 208 to 210 can be adjusted successively or executed in parallel, which is not limited in the embodiment of the present application.

[0211] Step 205: Determine the estimated value S of the variance of the random component within a specific inspection period. 2 (ε).

[0212] For example, the data deviation within a specific inspection period can be calculated to determine S 2 (ε).

[0213] Step 206: Simplify the random error model.

[0214] Exemplarily, the random error model can be simplified by equations (2) to (4).

[0215] Step 207: Determine the relative standard uncertainty U of the random component i (r).

[0216] For example, U can be calculated by formula (13): i (r).

[0217] Step 208: Determine the estimated value S of the system component variance within a specific observation period 2 (△).

[0218] For example, S can be calculated by equation (18) in the above embodiment: 2(△).

[0219] Step 209: Simplify the system error model.

[0220] For example, the system error model can be simplified by equation (7).

[0221] Step 210: Determine the relative standard uncertainty U of the system component i (s).

[0222] For example, U can be calculated by formula (21) in the above embodiment. i (s).

[0223] Exemplarily, after the first branch and the second branch are all executed, step 211 may be executed.

[0224] Step 211, synthesize relative standard uncertainty.

[0225] Exemplarily, the relative standard uncertainty may be the uncertainty in the aforementioned embodiment.

[0226] Exemplarily, the relative standard uncertainty can be calculated using formula (8) in the aforementioned embodiment.

[0227] Step 212: Output relative standard uncertainty.

[0228] Through the above process, the method provided in the embodiment of the present application can achieve a comprehensive comparison and evaluation of the measurement errors of the NDA method relative to the DA method from the dimensions of systematic error and random error, thereby improving the accuracy and comprehensiveness of the relative standard uncertainty.

[0229] Based on the foregoing embodiments, the embodiments of the present application further provide a device for determining uncertainty in nuclear material analysis; Figure 3 A schematic diagram of the structure of the uncertainty determination device for nuclear material analysis provided in an embodiment of the present application, such as Figure 3 As shown, the uncertainty determination device 3 for nuclear material analysis may include:

[0230] The acquisition module 301 is used to acquire a first set and a second set; wherein the first set includes isotope analysis results obtained by analyzing the nuclear material through DA; and the second set includes isotope analysis results obtained by analyzing the nuclear material through NDA;

[0231] Determination module 302 is used to determine the data deviation between the first set and the second set; determine the random error of the non-destructive analysis method based on the data deviation; determine the systematic error of the non-destructive analysis method based on the data deviation; determine the uncertainty of the non-destructive analysis method for the content of isotopes in nuclear materials based on the systematic error and the random error.

[0232] In some embodiments, the determination module 302 is used to determine a first sum of squares of data deviations; determine a second sum of squares of data deviations in an item dimension; and determine a random error based on the first sum of squares and the second sum of squares.

[0233] In some embodiments, the determination module 302 is used to determine a first measurement parameter for the nuclear material; wherein the first measurement parameter includes at least the number of items of the nuclear material and the total number of measurements for the nuclear material;

[0234] The determination module 302 is further used to count the deviation values ​​corresponding to each item in the number of items in the data deviation based on the total number of measurements to obtain a first statistical result; and process the first statistical result based on the number of items to obtain a second square sum.

[0235] In some embodiments, the determination module 302 is used to determine a first measurement parameter for the nuclear material; wherein the first measurement parameter includes at least the number of items of the nuclear material and the total number of measurements for the nuclear material;

[0236] The determination module 302 is further used to process the first sum of squares and the second sum of squares based on the number of items in the first measurement parameter and the total number of measurements to determine a first intermediate result; and to determine a random error based on the statistical result of the second set and the first intermediate result.

[0237] In some embodiments, the determination module 302 is configured to determine a second intermediate result based on an average deviation of the data deviations; determine a second measurement parameter; wherein the second measurement parameter includes at least the number of inspection cycles for the nuclear material and the number of measurements in each inspection cycle;

[0238] The determination module 302 is further configured to determine a third intermediate result based on the data deviation and the second measurement parameter; and determine a system error based on the second intermediate result and the third intermediate result.

[0239] In some embodiments, the determination module 302 is configured to perform a first statistical operation on an average deviation of the data deviation based on a total number of inspections of the nuclear material to obtain a second statistical result; wherein the total number of inspections includes the sum of the number of inspections in each inspection cycle;

[0240] The determination module 302 is also used to perform a second statistical operation on the average deviation of the data deviation based on the total number of inspections on nuclear materials to obtain a third statistical result; based on the total number of inspections on nuclear materials, the second statistical result and the third statistical result are processed to obtain a second intermediate result.

[0241] In some embodiments, the determination module 302 is used to determine an average deviation corresponding to the data deviation; based on the total number of measurements of the nuclear material, the data deviation and the average deviation are counted to obtain a fourth statistical result; wherein the total number of measurements includes the sum of the number of measurements in each inspection cycle;

[0242] The determination module 302 is further configured to process the fourth statistical result based on the number of measurements in each inspection cycle to obtain a third intermediate result.

[0243] In some embodiments, the determination module 302 is used to process the second intermediate result and the third intermediate result based on the statistical result of the second set to obtain a system error.

[0244] Based on the above embodiments, the present application also provides an uncertainty determination device for nuclear material analysis. Figure 4 A schematic diagram of the structure of the uncertainty determination device for nuclear material analysis provided in an embodiment of the present application, such as Figure 4 As shown, the uncertainty determination device 4 for nuclear material analysis includes a processor 401 and a memory 402; wherein the memory 402 stores a computer program; when the computer program is executed by the processor 401, it can implement the uncertainty determination method for nuclear material analysis as described in any of the above.

[0245] Based on the aforementioned embodiments, the embodiments of the present application further provide 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, it can implement the uncertainty determination method for nuclear material analysis as described in any of the preceding ones.

[0246] Based on the aforementioned embodiments, the embodiments of the present application further provide a computer program product, in which a computer program is stored; when the computer program is executed by a processor of an electronic device, it can implement the uncertainty determination method for nuclear material analysis as described in any of the aforementioned embodiments.

[0247] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0248] The methods disclosed in the various method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0249] The features disclosed in the various product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0250] The features disclosed in the various method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0251] It should be noted that the above-mentioned 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 magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM) and other memories; it can also be various electronic devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0252] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0253] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0254] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus necessary general hardware nodes, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0255] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that combines the functions of a block or multiple blocks.

[0256] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function in one or more boxes.

[0257] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 steps to execute functions in one or more boxes.

[0258] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for determining uncertainty in nuclear material analysis, characterized in that: The method comprises: Acquire a first set and a second set; wherein the first set includes isotope analysis results obtained by analyzing the nuclear material by a destructive analysis method; and the second set includes isotope analysis results obtained by analyzing the nuclear material by a non-destructive analysis method; determining a data deviation between the first set and the second set; determining a random error of the non-destructive analysis method based on the data deviation; determining a systematic error of the non-destructive analysis method based on the data deviation; Based on the systematic error and the random error, the uncertainty of the non-destructive analysis method for the content of the isotope in the nuclear material is determined.

2. The method according to claim 1, characterized in that Determining the random error of the non-destructive analysis method based on the data deviation comprises: determining a first sum of squares of deviations of the data; Determining the second sum of squares of the data deviations in the item dimension; The random error is determined based on the first sum of squares and the second sum of squares.

3. The method according to claim 2, characterized in that Determining the second sum of squares of the data deviations in the item dimension includes: Determining a first measurement parameter for the nuclear material; wherein the first measurement parameter includes at least the number of items of the nuclear material and the total number of measurements for the nuclear material; Based on the total number of measurements, statistics are performed on deviation values ​​corresponding to each item of the number of items in the data deviation to obtain a first statistical result; The first statistical result is processed based on the number of items to obtain the second sum of squares.

4. The method according to claim 2, characterized in that: The determining the random error based on the first sum of squares and the second sum of squares comprises: Determining a first measurement parameter for the nuclear material; wherein the first measurement parameter includes at least the number of items of the nuclear material and the total number of measurements for the nuclear material; Processing the first sum of squares and the second sum of squares based on the number of items in the first measurement parameter and the total number of measurements to determine a first intermediate result; The random error is determined based on the statistical result of the second set and the first intermediate result.

5. The method according to claim 1, characterized in that Determining the systematic error of the non-destructive analysis method based on the data deviation comprises: determining a second intermediate result based on the average deviation of the data deviations; Determining a second measurement parameter; wherein the second measurement parameter includes at least the number of inspection cycles for the nuclear material and the number of measurements in each inspection cycle; determining a third intermediate result based on the data deviation and the second measurement parameter; The systematic error is determined based on the second intermediate result and the third intermediate result.

6. The method according to claim 5, characterized in that The determining a second intermediate result based on the average deviation of the data deviations comprises: Based on the total number of inspections on the nuclear material, a first statistical operation is performed on the average deviation of the data deviation to obtain a second statistical result; wherein the total number of inspections includes the sum of the number of inspections in each inspection cycle; Based on the total number of inspections on the nuclear material, performing a second statistical operation on the average deviation of the data deviation to obtain a third statistical result; Based on the total number of inspections on the nuclear material, the second statistical result and the third statistical result are processed to obtain the second intermediate result.

7. The method according to claim 5, characterized in that The determining a third intermediate result based on the data deviation and the second measurement parameter includes: Determining an average deviation corresponding to the data deviation; Based on the total number of measurements on the nuclear material, the data deviation and the average deviation are counted to obtain a fourth statistical result; wherein the total number of measurements includes the sum of the number of measurements in each inspection cycle; The fourth statistical result is processed based on the number of measurements in each inspection cycle to obtain the third intermediate result.

8. The method according to claim 5, characterized in that The determining the system error based on the second intermediate result and the third intermediate result comprises: The second intermediate result and the third intermediate result are processed based on the statistical result of the second set to obtain the system error.

9. An uncertainty determination device for nuclear material analysis, characterized in that: The uncertainty determination device for nuclear material analysis includes a processor and a memory; wherein a computer program is stored in the memory; when the computer program is executed by the processor, it can implement the uncertainty determination method for nuclear material analysis as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program; when the computer program is executed by a processor of an electronic device, it can implement the uncertainty determination method for nuclear material analysis as described in any one of claims 1 to 8.

11. A computer program product, characterized in that The program product stores a computer program; when the computer program is executed by a processor of an electronic device, it can implement the uncertainty determination method for nuclear material analysis as described in any one of claims 1 to 8.

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