A method and apparatus for evaluating a scaling factor of a radioactive solid waste nuclide

Abstract

The application relates to a method and device for evaluating a proportion factor of a radioactive solid waste nuclide, which comprises the following steps: obtaining an original data group; processing the original data group to obtain a logarithmic data group; respectively judging whether the original data group and the logarithmic data group satisfy normal distribution; if the original data group and the logarithmic data group satisfy normal distribution, respectively evaluating the proportion factor of the radioactive solid waste nuclide of the original data group and the logarithmic data group by using a first correlation analysis method; and if the original data group and the logarithmic data group do not satisfy normal distribution, respectively evaluating the proportion factor of the radioactive solid waste nuclide of the original data group and the logarithmic data group by using a second correlation analysis method. The proportion factor evaluation method adopted by the application can be realized based on small sample data, does not need to use a large amount of data to evaluate and determine the proportion factor relationship, and can avoid the problem that the activity of the radioactive solid waste nuclide cannot be evaluated due to the lack of a large amount of data support.

Description

Technical Field

[0001] This invention relates to the technical field of radioactive waste management, and more specifically, to a method and apparatus for assessing the proportion factor of radioactive solid waste nuclides. Background Technology

[0002] According to the regulations on the classification of radioactive waste in nuclear power plants, it is required that the corresponding radionuclides and their activity data in each type of waste be known.

[0003] For sealed solid waste packages, nuclear power plants mostly use non-destructive analysis methods (external gamma nuclide measurement technology) to measure the activity concentration of gamma nuclides (easily detectable nuclides) in the waste. However, some long-lived beta and alpha nuclides (difficult-to-detect nuclides) cannot be directly measured using external measurement technology due to poor penetration. These difficult-to-detect nuclides are also a key focus of disposal site safety assessments. Currently, the proportionality factor method is generally used. After sampling and performing a certain number of radiochemical analyses in the laboratory, the proportional relationship between difficult-to-detect nuclides and easily detectable nuclides is established, and the proportionality factor between them is obtained. Thus, by measuring the activity concentration and proportionality factor of easily detectable gamma nuclides, the activity concentration of difficult-to-detect nuclides in the solid waste package can be estimated.

[0004] A key step in the proportionality factor method is analyzing the obtained radiochemical measurement data to assess whether a proportionality factor relationship exists between difficult-to-detect and easily-detectable nuclides. If so, the proportionality factor relationship is determined. Current methods involve acquiring a large amount of data (typically tens to hundreds of data sets), analyzing and evaluating the correlation between pairs of difficult-to-detect and easily-detectable nuclides, and then fitting the relationship. However, on the one hand, there is very little practical experience in proportionality factor measurement and analysis in domestic nuclear power plants, resulting in a severe lack of data accumulation; on the other hand, due to limitations imposed by nuclear power plant operation and radiation protection factors, the amount of data that can be sampled and analyzed in a short period is very limited. Therefore, the aforementioned proportionality factor method is not applicable to the assessment of radionuclide activity in domestic nuclear power plants. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and apparatus for evaluating the proportion factor of radioactive solid waste nuclides, in order to address the deficiencies of the prior art.

[0006] The technical solution adopted by this invention to solve its technical problem is: to construct a method for evaluating the proportion factor of radioactive solid waste nuclides, comprising the following steps:

[0007] Obtain the original data set;

[0008] The original data set is processed to obtain a logarithmic data set;

[0009] Determine whether the original data set and the logarithmic data set satisfy a normal distribution;

[0010] If the original data set and the logarithmic data set satisfy a normal distribution, then the first correlation analysis method is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set respectively;

[0011] If the original data set and the logarithmic data set do not satisfy a normal distribution, then the second correlation analysis method is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set, respectively.

[0012] In the method for evaluating the proportion factor of radioactive solid waste nuclides according to the present invention, the step of determining whether the original data set and the logarithmic data set satisfy a normal distribution includes:

[0013] The Kolmogorov-Sminov test was used to determine the normality of the distributions of the original data set and the logarithmic data set, respectively, to obtain the two-sided significance of the original data set and the two-sided significance of the logarithmic data set.

[0014] Determine whether the original data set satisfies a normal distribution based on the two-sided significance of the original data set.

[0015] Determine whether the logarithmic data set satisfies a normal distribution based on the two-sided significance of the logarithmic data set.

[0016] In the method for evaluating the proportion factor of radioactive solid waste nuclides described in this invention, the step of determining whether the original data set satisfies a normal distribution based on the two-sided significance of the original data set includes:

[0017] If the two-sided significance of the original data set is less than 0.05, then the original data set is determined not to satisfy a normal distribution; if the two-sided significance of the original data set is greater than or equal to 0.05, then the original data set is determined to satisfy a normal distribution.

[0018] The step of determining whether the logarithmic data set satisfies a normal distribution based on the two-sided significance of the logarithmic data set includes:

[0019] If the two-sided significance of the logarithmic data set is less than 0.05, then the logarithmic data set is determined not to satisfy a normal distribution; if the two-sided significance of the logarithmic data set is greater than or equal to 0.05, then the logarithmic data set is determined to satisfy a normal distribution.

[0020] In the method for evaluating the proportion factor of radioactive solid waste nuclides according to the present invention, the step of evaluating the proportion factor of radioactive solid waste nuclides by using a first correlation analysis method on the original data set and the logarithmic data set respectively if the original data set and the logarithmic data set satisfy a normal distribution includes:

[0021] The Pearson correlation coefficient method was used to perform correlation analysis on the original data set and the logarithmic data set, respectively.

[0022] Based on the correlation analysis results of the original data set, the radionuclide ratio factor of the original data set was evaluated.

[0023] The radionuclide ratio factor of the logarithmic data set was evaluated based on the correlation analysis results of the logarithmic data set.

[0024] In the method for evaluating the proportion factor of radioactive solid waste nuclides according to the present invention, the step of evaluating the proportion factor of radioactive solid waste nuclides by using a second correlation analysis method on the original data set and the logarithmic data set respectively if the original data set and the logarithmic data set do not satisfy a normal distribution includes:

[0025] The Spearman rank correlation coefficient method was used to perform correlation analysis on the original data set and the logarithmic data set, respectively.

[0026] Based on the correlation analysis results of the original data set, the radionuclide ratio factor of the original data set was evaluated.

[0027] The radionuclide ratio factor of the logarithmic data set was evaluated based on the correlation analysis results of the logarithmic data set.

[0028] In the method for evaluating the proportion factor of radioactive solid waste nuclides according to the present invention, the step of evaluating the proportion factor of radioactive solid waste nuclides based on the correlation analysis results of the original data set includes:

[0029] Based on the correlation analysis results of the original data set, the Spearman rank correlation coefficient of the original data set is obtained;

[0030] Based on the Spearman rank correlation coefficient of the original data set, determine the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set;

[0031] Based on the correlation levels between difficult-to-detect and easily-detectable nuclides in the original dataset, a radioactive solid waste nuclide ratio factor assessment was performed on the original dataset.

[0032] In the method for evaluating the proportion factor of radioactive solid waste nuclides according to the present invention, the step of evaluating the proportion factor of radioactive solid waste nuclides in the original data set based on the correlation level between difficult-to-detect and easily-detectable nuclides in the original data set includes:

[0033] If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set is strong, then a linear regression analysis with bootstrap sampling is performed on the original data set, and the ratio factor relationship between the difficult-to-detectable nuclides and the easily-detectable nuclides in the original data set is obtained based on the results of the linear regression analysis.

[0034] If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set is moderate, then a linear regression analysis with bootstrap sampling is performed on the original data set, and the ratio factor relationship between the difficult-to-detectable nuclides and the easily-detectable nuclides in the original data set is obtained based on the results of the linear regression analysis.

[0035] If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set is weak, then it is determined that the proportional factor method will not be used to evaluate the radioactive solid waste nuclides.

[0036] In the method for evaluating the proportion factor of radioactive solid waste nuclides according to the present invention, the step of evaluating the proportion factor of radioactive solid waste nuclides based on the correlation analysis results of the logarithmic data set includes:

[0037] Based on the correlation analysis results of the logarithmic data set, the Spearman rank correlation coefficient of the logarithmic data set was obtained;

[0038] Based on the Spearman rank correlation coefficient of the logarithmic data set, determine the correlation level between the difficult-to-detect nuclides and the easy-to-detect nuclides in the logarithmic data set;

[0039] Based on the correlation levels between difficult-to-detect and easily-detectable nuclides in the logarithmic data set, the radioactive solid waste nuclide ratio factor is evaluated in the logarithmic data set.

[0040] In the method for evaluating the proportion factor of radioactive solid waste nuclides according to the present invention, the step of evaluating the proportion factor of radioactive solid waste nuclides based on the correlation level between difficult-to-detect and easily-detectable nuclides in the logarithmic data set includes:

[0041] If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the logarithmic data set is strong, then a linear regression analysis with bootstrap sampling is performed on the logarithmic data set, and the ratio factor relationship between the difficult-to-detectable nuclides and the easily-detectable nuclides in the logarithmic data set is obtained based on the results of the linear regression analysis.

[0042] If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the logarithmic data set is moderate, then a linear regression analysis with bootstrap sampling is performed on the logarithmic data set, and the ratio factor relationship between the difficult-to-detectable nuclides and the easily-detectable nuclides in the logarithmic data set is obtained based on the results of the linear regression analysis.

[0043] If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the logarithmic data set is weak, then it is determined that the proportional factor method will not be used to evaluate the radioactive solid waste nuclides.

[0044] In the method for evaluating the proportion factor of radioactive solid waste nuclides according to the present invention, the method further includes:

[0045] If both the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set and the logarithmic data set are correlated, then the adjusted coefficient of determination for the original data set and the adjusted coefficient of determination for the logarithmic data set are obtained respectively.

[0046] Determine the larger of the adjusted coefficient of determination for the original data set and the adjusted coefficient of determination for the logarithmic data set;

[0047] The ratio factor relationship between difficult-to-detect nuclides and easy-to-detect nuclides is determined based on the data set corresponding to the larger value.

[0048] The present invention also provides a device for evaluating the proportion factor of radioactive solid waste nuclides, comprising:

[0049] The acquisition unit is used to acquire the raw data set;

[0050] A data processing unit is used to process the original data set to obtain a logarithmic data set;

[0051] The judgment unit is used to determine whether the original data set and the logarithmic data set satisfy a normal distribution, respectively.

[0052] The first evaluation unit is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set respectively using the first correlation analysis method if the original data set and the logarithmic data set satisfy a normal distribution.

[0053] The second evaluation unit is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set respectively using the second correlation analysis method if the original data set and the logarithmic data set do not meet the normal distribution.

[0054] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the radioactive solid waste nuclide ratio factor assessment method as described above by calling the computer program stored in the memory.

[0055] The method and apparatus for evaluating the proportion factor of radioactive solid waste nuclides according to the present invention have the following beneficial effects: It includes: acquiring a raw data set; processing the raw data set to obtain a logarithmic data set; determining whether the raw data set and the logarithmic data set satisfy a normal distribution; if the raw data set and the logarithmic data set satisfy a normal distribution, then using a first correlation analysis method to evaluate the proportion factor of radioactive solid waste nuclides in the raw data set and the logarithmic data set respectively; if the raw data set and the logarithmic data set do not satisfy a normal distribution, then using a second correlation analysis method to evaluate the proportion factor of radioactive solid waste nuclides in the raw data set and the logarithmic data set respectively. The proportion factor evaluation method used in this invention can be implemented based on small sample data, without requiring a large amount of data to evaluate and determine the proportion factor relationship, thus avoiding the problem of being unable to evaluate the activity of radioactive solid waste nuclides due to a lack of sufficient data support. Attached Figure Description

[0056] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0057] Figure 1 This is a schematic flowchart of the method for evaluating the proportion factor of radioactive solid waste nuclides provided in an embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the overall process of Example 1 of the proportional factor evaluation using small sample data provided by the present invention;

[0059] Figure 3 This is a schematic diagram of the overall process of Example 2 of the proportional factor evaluation using small sample data provided by the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] refer to Figure 1This invention provides a method for evaluating the proportion factor of radioactive solid waste nuclides. This method can assess the proportion factor relationship between difficult-to-detect nuclides (β-nuclides and α-nuclides) and easily-detectable nuclides (γ-nuclides) in radioactive solid waste from nuclear power plants based on small sample data. The small sample data generally refers to data with no more than 10 sets.

[0062] Specifically, such as Figure 1 As shown, the method for assessing the proportion factor of radioactive solid waste nuclides includes the following steps:

[0063] Step S10: Obtain the original data set.

[0064] The original data set consists of original, difficult-to-detect nuclides and original, easily-detectable nuclides. The acquisition methods for the original, difficult-to-detectable nuclides and original, easily-detectable nuclides are conventional and not specifically limited in this invention. The original data set can be represented as:

[0065] Data set O: {(y1,x1), (y2,x2), ..., (y i ,x i )}, where y i Indicates undetectable nuclides, x i Indicates easily detectable nuclides.

[0066] Step S20: Process the original data set to obtain the logarithmic data set.

[0067] Specifically, processing the original data set involves taking the logarithm of the original data, resulting in a new data set, known as the logarithmic data set. The logarithmic data set can be represented as:

[0068] Data set N: {(ln(y1),ln(x1)), (ln(y2),ln(x2)), ..., (ln(y1),ln(x1))} i ),ln(x i ))}.

[0069] Step S30: Determine whether the original data set and the logarithmic data set satisfy a normal distribution.

[0070] In some embodiments, determining whether the original data set and the logarithmic data set satisfy a normal distribution includes: using the Kolmogorov-Sminov test to determine the normality of the distributions of the original data set and the logarithmic data set, respectively, to obtain the two-sided significance of the original data set and the two-sided significance of the logarithmic data set; determining whether the original data set satisfies a normal distribution based on the two-sided significance of the original data set; and determining whether the logarithmic data set satisfies a normal distribution based on the two-sided significance of the logarithmic data set.

[0071] Both the original and logarithmic data sets need to be tested for the normality of their distribution using the Kolmogorov-Sminov test (KS test).

[0072] Specifically, the Kolmogorov-Sminov test is used to determine the normality of the original data set distribution to obtain the two-sided significance of the original data set. Then, based on the two-sided significance of the original data set, it is determined whether the original data set satisfies a normal distribution. If the two-sided significance of the original data set is less than 0.05, it is determined that the original data set does not satisfy a normal distribution; if the two-sided significance of the original data set is greater than or equal to 0.05, it is determined that the original data set satisfies a normal distribution.

[0073] Similarly, the Kolmogorov-Sminov test is used to determine the normality of the logarithmic data set distribution to obtain the two-sided significance of the logarithmic data set. Then, based on the two-sided significance, it is determined whether the logarithmic data set satisfies a normal distribution. Specifically, if the two-sided significance of the logarithmic data set is less than 0.05, it is determined that the logarithmic data set does not satisfy a normal distribution; if the two-sided significance of the logarithmic data set is greater than or equal to 0.05, it is determined that the logarithmic data set satisfies a normal distribution.

[0074] Step S40: If the original data set and the logarithmic data set satisfy a normal distribution, then the first correlation analysis method is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set respectively.

[0075] Specifically, existing proportionality factor assessment schemes generally require that the data distribution conforms to a normal distribution. This can usually only be achieved when the amount of accumulated data is large (sufficiently large). For small sample data, it is generally difficult to meet the normal distribution. However, when small sample data does meet the normal distribution, the first correlation analysis method can be used to assess the proportionality factor of radioactive solid waste nuclides for the original data set and the logarithmic data set, respectively.

[0076] Specifically, such as Figure 2 As shown, when evaluating the proportion factor based on small sample data, if the small sample data satisfies a normal distribution, that is, both the original data set and the logarithmic data set satisfy a normal distribution, then the Pearson correlation coefficient method is used to perform correlation analysis on the original data set and the logarithmic data set respectively. Then, the radioactive solid waste nuclide proportion factor is evaluated on the original data set and the logarithmic data set respectively based on the correlation analysis results of the original data set and the correlation analysis results of the logarithmic data set.

[0077] The assessment of the radionuclide proportion factor of the original data set based on the correlation analysis results includes: obtaining the Pearson correlation coefficient of the original data set based on the correlation analysis results; determining the correlation level between difficult-to-detect nuclides and easily-detectable nuclides in the original data set based on the Pearson correlation coefficient; and then assessing the radionuclide proportion factor of the original data set based on the correlation level between difficult-to-detect nuclides and easily-detectable nuclides.

[0078] Specifically, let the Pearson correlation coefficient between the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set be ρ1.

[0079] If ρ1 > 0.7, it indicates that the difficult-to-detect nuclide y is... i With key nuclide X i The correlation level is: strong correlation. In this case, the regression method is used to fit the linear equation to obtain the ratio factor relationship between the difficult-to-detect nuclides and the easy-to-detect nuclides.

[0080] If 0.5 ≤ ρ1 ≤ 0.7, it indicates that the difficult-to-detect nuclide y... i With key nuclide X i The correlation level is: moderate correlation, and the ratio factor relationship between difficult-to-detect nuclides and easy-to-detect nuclides can be obtained by fitting linear equations using regression method.

[0081] If ρ1 < 0.5, it indicates that the difficult-to-detect nuclide y is... i With key nuclide X i The correlation level is: weak correlation, indicating that it is not suitable to use the proportional factor for evaluation.

[0082] like Figure 2 As shown, the radionuclide proportion factor assessment of the original data set based on the correlation analysis results includes: obtaining the Pearson correlation coefficient of the original data set based on the correlation analysis results; determining the correlation level between difficult-to-detect nuclides and easily-detectable nuclides in the original data set based on the Pearson correlation coefficient; and then assessing the radionuclide proportion factor of the original data set based on the correlation level between difficult-to-detect nuclides and easily-detectable nuclides.

[0083] Specifically, let the Pearson correlation coefficient between the difficult-to-detect nuclides and the easily-detectable nuclides in the logarithmic data set be ρ1.

[0084] If ρ1 > 0.7, it indicates that the difficult-to-detect nuclide y is... i With key nuclide X i The correlation level is: strong correlation. In this case, the regression method is used to fit the linear equation to obtain the ratio factor relationship between the difficult-to-detect nuclides and the easy-to-detect nuclides.

[0085] If 0.5 ≤ ρ1 ≤ 0.7, it indicates that the difficult-to-detect nuclide y... i With key nuclide X i The correlation level is: moderate correlation, and the ratio factor relationship between difficult-to-detect nuclides and easy-to-detect nuclides can be obtained by fitting linear equations using regression method.

[0086] If ρ1 < 0.5, it indicates that the difficult-to-detect nuclide y is... i With key nuclide X i The correlation level is: weak correlation, indicating that it is not suitable to use the proportional factor for evaluation.

[0087] Furthermore, such as Figure 2 As shown in this embodiment of the invention, after evaluating the proportion factor that satisfies a normal distribution using the above method, if there is a correlation (strong or moderate correlation) between the difficult-to-detect nuclides and the easily-detectable nuclides in both the original and logarithmic data sets, then it is necessary to select any one of the original or logarithmic data sets to determine the correlation between the difficult-to-detectable nuclides and the easily-detectable nuclides in the radioactive solid waste. Optionally, in this embodiment of the invention, an adjusted coefficient of determination (adjusted R0) can be used. 2 The selection is made using the (Adj.R-Squar) method.

[0088] Specifically, the adjusted coefficients of determination for the original data set and the logarithmic data set are obtained respectively; the larger value between the adjusted coefficients of determination for the original data set and the logarithmic data set is determined; and the ratio factor relationship between the difficult-to-detect nuclides and the easy-to-detect nuclides is determined based on the data set corresponding to the larger value.

[0089] Let the coefficient of determination after adjusting the original dataset be R1. 2 The coefficient of determination for the logarithmic dataset after adjustment is R². 2 Compare R1 2 and R2 2 The size of R1 2 Greater than R2 2 Then, the scaling factor relationship obtained from the original data set is selected as the scaling factor relationship between difficult-to-detect nuclides and easily-detectable nuclides in radioactive solid waste. If R1 2 Less than R2 2 If the proportionality factor relationship obtained from the logarithmic data set is selected, then the proportionality factor relationship between the difficult-to-detect nuclides and the easy-to-detect nuclides in radioactive solid waste is selected.

[0090] It should be noted that the adjusted coefficient of determination is used to characterize the goodness of linear fit. This adjusted coefficient of determination is a value less than 1; the closer it is to 1, the stronger the linear correlation. Generally, an adjusted coefficient of determination of 0.8 or higher is considered to indicate a good linear correlation.

[0091] Step S50: If the original data set and the logarithmic data set do not meet the normal distribution, the second correlation analysis method is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set respectively.

[0092] Specifically, it is difficult for small sample data to meet the normal distribution, making it difficult to evaluate the scaling factor. To solve this problem, in this embodiment of the invention, when the small sample data does not meet the normal distribution, that is, neither the original data group nor the logarithmic data group meets the normal distribution, the Spearman rank correlation coefficient method is used for correlation analysis.

[0093] Specifically, such as Figure 3 As shown, if the original data set and the logarithmic data set do not satisfy a normal distribution, the second correlation analysis method is used to evaluate the radionuclide proportion factor of the original data set and the logarithmic data set respectively. This includes: using the Spearman rank correlation coefficient method to perform correlation analysis on the original data set and the logarithmic data set respectively; evaluating the radionuclide proportion factor of the original data set based on the correlation analysis results of the original data set; and evaluating the radionuclide proportion factor of the logarithmic data set based on the correlation analysis results of the logarithmic data set.

[0094] The assessment of the radionuclide proportion factor of the original data set based on the correlation analysis results includes: obtaining the Spearman rank correlation coefficient of the original data set based on the correlation analysis results; determining the correlation level between difficult-to-detect and easily-detectable nuclides in the original data set based on the Spearman rank correlation coefficient; and assessing the radionuclide proportion factor of the original data set based on the correlation level between difficult-to-detect and easily-detectable nuclides.

[0095] Based on the correlation level between difficult-to-detect and easily-detectable nuclides in the original dataset, the radioactive solid waste nuclide proportioning factor assessment of the original dataset includes: if the correlation level between difficult-to-detect and easily-detectable nuclides in the original dataset is strong, then a linear regression analysis with bootstrap sampling is performed on the original dataset, and the proportioning factor relationship between difficult-to-detect and easily-detectable nuclides in the original dataset is obtained based on the results of the linear regression analysis; if the correlation level between difficult-to-detect and easily-detectable nuclides in the original dataset is moderate, then a linear regression analysis with bootstrap sampling is performed on the original dataset, and the proportioning factor relationship between difficult-to-detect and easily-detectable nuclides in the original dataset is obtained based on the results of the linear regression analysis; if the correlation level between difficult-to-detect and easily-detectable nuclides in the original dataset is weak, then it is determined that the proportioning factor method will not be used to assess the radioactive solid waste nuclides.

[0096] Specifically, let the Pearson correlation coefficient between the difficult-to-detect nuclides and the easy-to-detect nuclides in the original data set be ρ2.

[0097] If ρ² > 0.7, it indicates that the difficult-to-detect nuclide y... i With key nuclide X i It exhibits strong correlation and performs linear regression analysis with bootstrap sampling on the original data set (where the number of resampling times is ≥1000) to obtain the ratio factor relationship between difficult-to-detect nuclides and easy-to-detect nuclides.

[0098] If 0.5 ≤ ρ² ≤ 0.7, it indicates that the difficult-to-detect nuclide y... i With key nuclide X i With moderate correlation, linear regression analysis with bootstrap sampling was performed on the original data set (where the number of resampling times is ≥1000) to obtain the ratio factor relationship between difficult-to-detect nuclides and easy-to-detect nuclides.

[0099] If ρ² < 0.5, it is difficult to determine the nuclide y. i With key nuclide X i The correlation is weak, so it is not suitable to use the proportional factor for evaluation.

[0100] like Figure 3 As shown, the assessment of the radionuclide proportion factor of the logarithmic data set based on the correlation analysis results includes: obtaining the Spearman rank correlation coefficient of the logarithmic data set based on the correlation analysis results; determining the correlation level between difficult-to-detect and easily-detectable nuclides in the logarithmic data set based on the Spearman rank correlation coefficient; and assessing the radionuclide proportion factor of the logarithmic data set based on the correlation level between difficult-to-detect and easily-detectable nuclides.

[0101] Based on the correlation level between difficult-to-detect and easily-detectable nuclides in the logarithmic dataset, the radioactive solid waste nuclide proportioning factor assessment includes: if the correlation level between difficult-to-detect and easily-detectable nuclides in the logarithmic dataset is strong, then a linear regression analysis with bootstrap sampling is performed on the logarithmic dataset, and the proportioning factor relationship between difficult-to-detect and easily-detectable nuclides in the logarithmic dataset is obtained based on the results of the linear regression analysis; if the correlation level between difficult-to-detect and easily-detectable nuclides in the logarithmic dataset is moderate, then a linear regression analysis with bootstrap sampling is performed on the logarithmic dataset, and the proportioning factor relationship between difficult-to-detect and easily-detectable nuclides in the logarithmic dataset is obtained based on the results of the linear regression analysis; if the correlation level between difficult-to-detect and easily-detectable nuclides in the logarithmic dataset is weak, then it is determined that the proportioning factor method is not used to assess radioactive solid waste nuclides.

[0102] Specifically, let the Pearson correlation coefficient between the difficult-to-detect nuclides and the easy-to-detect nuclides in the logarithmic data set be ρ2.

[0103] If ρ² > 0.7, it indicates that the difficult-to-detect nuclide y... i With key nuclide X i It exhibits strong correlation and performs linear regression analysis with bootstrap sampling on logarithmic data sets (where the number of resampling times is ≥1000) to obtain the ratio factor relationship between difficult-to-detect nuclides and easy-to-detect nuclides.

[0104] If 0.5 ≤ ρ² ≤ 0.7, it indicates that the difficult-to-detect nuclide y... i With key nuclide X i With moderate correlation, linear regression analysis with bootstrap sampling was performed on the logarithmic data set (where the number of resampling times is ≥1000) to obtain the ratio factor relationship between difficult-to-detect nuclides and easy-to-detect nuclides.

[0105] If ρ² < 0.5, it is difficult to determine the nuclide y. i With key nuclide X i The correlation is weak, so it is not suitable to use the proportional factor for evaluation.

[0106] Furthermore, such as Figure 3 As shown in this embodiment of the invention, after evaluating the proportion factor that does not satisfy a normal distribution using the above method, if there is a correlation (strong or moderate correlation) between the difficult-to-detect nuclides and the easily-detectable nuclides in both the original and logarithmic data sets, then it is necessary to select any one of the original or logarithmic data sets to determine the correlation between the difficult-to-detectable nuclides and the easily-detectable nuclides in the radioactive solid waste. Optionally, in this embodiment of the invention, an adjusted coefficient of determination (adjusted R0) can be used. 2 The selection is made using the (Adj.R-Squar) method.

[0107] Specifically, if both the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set and the logarithmic data set are correlated, then the adjusted coefficients of determination for the original data set and the logarithmic data set are obtained respectively; the larger value between the adjusted coefficients of determination for the original data set and the logarithmic data set is determined; and the ratio factor relationship between the difficult-to-detectable nuclides and the easily-detectable nuclides is determined based on the data set corresponding to the larger value.

[0108] Let the coefficient of determination after adjusting the original dataset be R1. 2 The coefficient of determination for the logarithmic dataset after adjustment is R². 2 Compare R1 2 and R2 2 The size of R1 2 Greater than R2 2Then, the scaling factor relationship obtained from the original data set is selected as the scaling factor relationship between difficult-to-detect nuclides and easily-detectable nuclides in radioactive solid waste. If R1 2 Less than R2 2 If the proportionality factor relationship obtained from the logarithmic data set is selected, then the proportionality factor relationship between the difficult-to-detect nuclides and the easy-to-detect nuclides in radioactive solid waste is selected.

[0109] It should be noted that the adjusted coefficient of determination is used to characterize the goodness of linear fit. This adjusted coefficient of determination is a value less than 1; the closer it is to 1, the stronger the linear correlation. Generally, an adjusted coefficient of determination of 0.8 or higher is considered to indicate a good linear correlation.

[0110] Furthermore, in this embodiment of the invention, if the amount of data is small (i.e., the amount of data does not exceed 10 groups), the strength of the correlation between the data can also be intuitively judged by using a scatter plot.

[0111] The proportionality factor assessment method for radioactive solid waste nuclides of the present invention can be based on small sample data (no more than 10 sets), which expands the applicability of traditional proportionality factor assessment methods; it reduces the sampling and measurement work of radioactive samples, which can reduce the workload and radiation dose of nuclear power plant workers; when the amount of data is small, the strength of data correlation can be intuitively judged through scatter plots; when linear (original data set) and nonlinear (logarithmic data set) correlation relationships may coexist, the adjusted coefficient of determination can be used to represent the goodness of linear fit, and the optimal correlation relationship can be selected.

[0112] The present invention also provides a device for evaluating the proportion factor of radioactive solid waste nuclides, comprising:

[0113] The acquisition unit is used to acquire the raw data set.

[0114] The data processing unit is used to process the original data set to obtain the logarithmic data set.

[0115] The judgment unit is used to determine whether the original data set and the logarithmic data set satisfy a normal distribution, respectively.

[0116] The first assessment unit is used to assess the radionuclide ratio factor of the original data set and the logarithmic data set respectively using the first correlation analysis method if the original data set and the logarithmic data set satisfy a normal distribution.

[0117] The second assessment unit is used to assess the radionuclide ratio factor of the original data set and the logarithmic data set respectively using the second correlation analysis method if the original data set and the logarithmic data set do not meet the normal distribution.

[0118] Specifically, the specific operational procedures between the various units in the radioactive solid waste nuclide ratio factor assessment device can be referred to the above-mentioned radioactive solid waste nuclide ratio factor assessment method, and will not be repeated here.

[0119] Furthermore, an electronic device of the present invention includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the radioactive solid waste nuclide ratio assessment method as described above. Specifically, according to embodiments of the present invention, the processes described above with reference to the flowchart can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, when the computer program is downloaded, installed, and executed by an electronic device, it performs the functions defined above in the methods of the embodiments of the present invention. The electronic device of the present invention can be a terminal such as a laptop, desktop computer, tablet computer, or smartphone, or it can be a server.

[0120] Furthermore, this invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for evaluating the proportioning factor of radioactive solid waste nuclides described above. Specifically, it should be noted that the computer-readable storage medium described above can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0121] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0122] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0123] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0124] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0125] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They do not limit the scope of protection of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A method for evaluating the proportion factor of radioactive solid waste nuclides, characterized in that, Includes the following steps: Obtain the original data set; The original data set is processed to obtain a logarithmic data set; Determine whether the original data set and the logarithmic data set satisfy a normal distribution; The step of determining whether the original data set and the logarithmic data set satisfy a normal distribution includes: using the Kolmogorov-Smirnov test to determine the normality of the distributions of the original data set and the logarithmic data set, respectively, to obtain the two-sided significance of the original data set and the two-sided significance of the logarithmic data set; determining whether the original data set satisfies a normal distribution based on the two-sided significance of the original data set; and determining whether the logarithmic data set satisfies a normal distribution based on the two-sided significance of the logarithmic data set. The step of determining whether the original data set satisfies a normal distribution based on the two-sided significance of the original data set includes: if the two-sided significance of the original data set is less than 0.05, then the original data set is determined not to satisfy a normal distribution; if the two-sided significance of the original data set is greater than or equal to 0.05, then the original data set is determined to satisfy a normal distribution. The step of determining whether the logarithmic data set satisfies a normal distribution based on the two-sided significance of the logarithmic data set includes: if the two-sided significance of the logarithmic data set is less than 0.05, then the logarithmic data set is determined not to satisfy a normal distribution; if the two-sided significance of the logarithmic data set is greater than or equal to 0.05, then the logarithmic data set is determined to satisfy a normal distribution. If the original data set and the logarithmic data set satisfy a normal distribution, then the first correlation analysis method is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set respectively; If the original data set and the logarithmic data set do not satisfy a normal distribution, then the second correlation analysis method is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set, respectively.

2. The method for evaluating the proportion factor of radioactive solid waste nuclides according to claim 1, characterized in that, If the original data set and the logarithmic data set satisfy a normal distribution, then the first correlation analysis method is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set, respectively, including: The Pearson correlation coefficient method was used to perform correlation analysis on the original data set and the logarithmic data set, respectively. Based on the correlation analysis results of the original data set, the radionuclide ratio factor of the original data set was evaluated. The radionuclide ratio factor of the logarithmic data set was evaluated based on the correlation analysis results of the logarithmic data set.

3. The method for evaluating the proportion factor of radioactive solid waste nuclides according to claim 1, characterized in that, If the original data set and the logarithmic data set do not satisfy a normal distribution, then the second correlation analysis method is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set, respectively, including: The Spearman rank correlation coefficient method was used to perform correlation analysis on the original data set and the logarithmic data set, respectively. Based on the correlation analysis results of the original data set, the radionuclide ratio factor of the original data set was evaluated. The radionuclide ratio factor of the logarithmic data set was evaluated based on the correlation analysis results of the logarithmic data set.

4. The method for evaluating the proportion factor of radioactive solid waste nuclides according to claim 3, characterized in that, The assessment of the radionuclide proportion factor of the original data set based on the correlation analysis results of the original data set includes: Based on the correlation analysis results of the original data set, the Spearman rank correlation coefficient of the original data set is obtained; Based on the Spearman rank correlation coefficient of the original data set, determine the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set; Based on the correlation levels between difficult-to-detect and easily-detectable nuclides in the original dataset, a radioactive solid waste nuclide ratio factor assessment was performed on the original dataset.

5. The method for evaluating the proportion factor of radioactive solid waste nuclides according to claim 4, characterized in that, The step of evaluating the radioactive solid waste nuclide proportion factor of the original data set based on the correlation level between difficult-to-detect and easily-detectable nuclides in the original data set includes: If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set is strong, then a linear regression analysis with bootstrap sampling is performed on the original data set, and the ratio factor relationship between the difficult-to-detectable nuclides and the easily-detectable nuclides in the original data set is obtained based on the results of the linear regression analysis. If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set is moderate, then a linear regression analysis with bootstrap sampling is performed on the original data set, and the ratio factor relationship between the difficult-to-detectable nuclides and the easily-detectable nuclides in the original data set is obtained based on the results of the linear regression analysis. If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set is weak, then it is determined that the proportional factor method will not be used to evaluate the radioactive solid waste nuclides.

6. The method for evaluating the proportion factor of radioactive solid waste nuclides according to claim 3, characterized in that, The assessment of the radionuclide proportion factor of the logarithmic data set based on the correlation analysis results of the logarithmic data set includes: Based on the correlation analysis results of the logarithmic data set, the Spearman rank correlation coefficient of the logarithmic data set was obtained; Based on the Spearman rank correlation coefficient of the logarithmic data set, determine the correlation level between the difficult-to-detect nuclides and the easy-to-detect nuclides in the logarithmic data set; Based on the correlation levels between difficult-to-detect and easily-detectable nuclides in the logarithmic data set, the radioactive solid waste nuclide ratio factor is evaluated in the logarithmic data set.

7. The method for evaluating the proportion factor of radioactive solid waste nuclides according to claim 6, characterized in that, The assessment of the radioactive solid waste nuclide proportion factor based on the correlation level between difficult-to-detect and easily-detectable nuclides in the logarithmic data set includes: If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the logarithmic data set is strong, then a linear regression analysis with bootstrap sampling is performed on the logarithmic data set, and the ratio factor relationship between the difficult-to-detectable nuclides and the easily-detectable nuclides in the logarithmic data set is obtained based on the results of the linear regression analysis. If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the logarithmic data set is moderate, then a linear regression analysis with bootstrap sampling is performed on the logarithmic data set, and the ratio factor relationship between the difficult-to-detectable nuclides and the easily-detectable nuclides in the logarithmic data set is obtained based on the results of the linear regression analysis. If the correlation level between the difficult-to-detect nuclides and the easily-detectable nuclides in the logarithmic data set is weak, then it is determined that the proportional factor method will not be used to evaluate the radioactive solid waste nuclides.

8. The method for evaluating the proportion factor of radioactive solid waste nuclides according to claim 3, characterized in that, The method further includes: If both the difficult-to-detect nuclides and the easily-detectable nuclides in the original data set and the logarithmic data set are correlated, then the adjusted coefficient of determination for the original data set and the adjusted coefficient of determination for the logarithmic data set are obtained respectively. Determine the larger of the adjusted coefficient of determination for the original data set and the adjusted coefficient of determination for the logarithmic data set; The ratio factor relationship between difficult-to-detect nuclides and easy-to-detect nuclides is determined based on the data set corresponding to the larger value.

9. A device for evaluating the proportion factor of radioactive solid waste nuclides, characterized in that, include: The acquisition unit is used to acquire the raw data set; A data processing unit is used to process the original data set to obtain a logarithmic data set; The judgment unit is used to determine whether the original data set and the logarithmic data set satisfy a normal distribution, respectively. The step of determining whether the original data set and the logarithmic data set satisfy a normal distribution includes: using the Kolmogorov-Smirnov test to determine the normality of the distributions of the original data set and the logarithmic data set, respectively, to obtain the two-sided significance of the original data set and the two-sided significance of the logarithmic data set; determining whether the original data set satisfies a normal distribution based on the two-sided significance of the original data set; and determining whether the logarithmic data set satisfies a normal distribution based on the two-sided significance of the logarithmic data set. The step of determining whether the original data set satisfies a normal distribution based on the two-sided significance of the original data set includes: if the two-sided significance of the original data set is less than 0.05, then the original data set is determined not to satisfy a normal distribution; if the two-sided significance of the original data set is greater than or equal to 0.05, then the original data set is determined to satisfy a normal distribution. The step of determining whether the logarithmic data set satisfies a normal distribution based on the two-sided significance of the logarithmic data set includes: if the two-sided significance of the logarithmic data set is less than 0.05, then the logarithmic data set is determined not to satisfy a normal distribution; if the two-sided significance of the logarithmic data set is greater than or equal to 0.05, then the logarithmic data set is determined to satisfy a normal distribution. The first evaluation unit is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set respectively using the first correlation analysis method if the original data set and the logarithmic data set satisfy a normal distribution. The second evaluation unit is used to evaluate the radioactive solid waste nuclide ratio factor of the original data set and the logarithmic data set respectively using the second correlation analysis method if the original data set and the logarithmic data set do not meet the normal distribution.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the method for assessing the proportion factor of radioactive solid waste nuclides as described in any one of claims 1 to 8 by calling the computer program stored in the memory.

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