A method for evaluating the reliability of radiation effect simulation test data

Abstract

The application discloses a kind of radiation effect simulation test data credibility evaluation method, solve due to lack of quantitative data credibility evaluation method, user can only rely on experience to judge the credibility of data, strong subjectivity, lack of systematicness and standard, it can lead to technical problems of decision error;Combining fuzzy comprehensive evaluation method and analytic hierarchy process, the factors influencing the credibility of radiation effect simulation test data are combed from the side of data production process, a judgment matrix is established, some factors with unclear boundaries and not easy to quantify are quantified, and a comprehensive evaluation of the membership grade status of data credibility is carried out, solving the problem of quantitative evaluation of radiation effect simulation test data credibility.The method has strong self-adaptation capability, and the factor set, evaluation set, judgment matrix and membership matrix can be adjusted according to the understanding of data and its use, while the method proposes a constraint condition for the judgment matrix to ensure that the method can be used correctly.

Description

TECHNICAL FIELD

[0001] The present application relates to a radiation effect simulation test method, in particular to a radiation effect simulation test data credibility evaluation method. BACKGROUND

[0002] Data quality is the cornerstone of ensuring the accuracy of research results and the reliability of decision-making, and credibility is an important indicator of data quality. When measuring the quality of radiation effect simulation test data, due to the characteristics of diversity, complexity, timeliness, security, rarity and high cost of radiation effect simulation test data, changes in devices, radiation sources, irradiation conditions and test conditions will all bring differences in data results. Therefore, how to evaluate the credibility of radiation effect simulation test data is the most concerned problem of users when making decisions based on test data.

[0003] In existing radiation effect simulation tests, due to the lack of a quantitative radiation effect simulation test data credibility evaluation method, users can only judge the credibility of the data based on experience, which has strong subjectivity, lacks systematicness and standards, and not only affects the accuracy and reliability of the judgment, but also may lead to decision errors, and qualitative judgment may not meet the subsequent data analysis and decision-making needs. SUMMARY

[0004] The purpose of the present application is to solve the technical problem that due to the lack of a quantitative radiation effect simulation test data credibility evaluation method, users can only judge the credibility of the data based on experience, which has strong subjectivity, lacks systematicness and standards, and may lead to decision errors, and to provide a radiation effect simulation test data credibility evaluation method.

[0005] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0006] A radiation effect simulation test data credibility evaluation method, characterized in that it comprises the following steps:

[0007] Step 1, various factors affecting the credibility of radiation effect simulation test data are taken as elements to form a factor set U, and a proportional scale for the degree of importance is set; the various factors affecting the credibility of radiation effect simulation test data are determined according to the type of the specific radiation effect simulation test performed;

[0008] Step 2, the degree of importance of the elements in the factor set U in step 1 is compared pairwise, and the pairwise comparison results are valued according to the proportional scale to form a judgment matrix A;

[0009] Step 3, the maximum eigenvalue λ max of the judgment matrix A in step 2 is calculated to determine the maximum eigenvalue λ maxThe corresponding eigenvector W;

[0010] Step 4: Using the largest eigenvalue λ max Check the consistency of judgment matrix A. If the consistency of judgment matrix A reaches the preset value, proceed to step 5; otherwise, return to step 2 and modify the importance level of the elements in factor set U.

[0011] Step 5: Construct an evaluation set based on the possible evaluation results of the reliability of the radiation effect simulation test data;

[0012] Step 6: Evaluate the membership degree of elements in factor set U to each evaluation result in evaluation set, and construct a membership evaluation matrix;

[0013] Step 7: Multiply the feature vector W from Step 3 with the membership evaluation matrix to obtain the fuzzy comprehensive evaluation vector M;

[0014] Step 8: Based on the fuzzy comprehensive evaluation vector M in Step 7, determine the evaluation results of the credibility of the radiation effect simulation test data in the evaluation set by adopting the maximum membership principle or the weighted average principle, and complete the credibility evaluation of the radiation effect simulation test data.

[0015] Furthermore, the factor set U mentioned in step 1 is represented as: U = {u1, u2, ..., u} m}, where u1, u2, ..., u m The first, second, ..., m factors that affect the reliability of radiation effect test data, where 3 ≤ m ≤ 11.

[0016] Further, in step 2, the expression for the judgment matrix is:

[0017] A = (u ij ) m×m ;

[0018] in,

[0019] Among them, u ij This indicates that the pairwise comparison results are assigned values ​​according to a certain scale, m represents the total number of factors affecting the reliability of radiation effect test data; i represents the row coordinate of the judgment matrix, and j represents the column coordinate of the judgment matrix.

[0020] Furthermore, the specific process of step 4 is as follows:

[0021] Step 4.1: Define the consistency index CR, with the following expression:

[0022]

[0023] in,

[0024] RI represents the average random consistency index, and the specific value is as follows:

[0025] m 3 4 5 6 7 8 9 10 11 RI 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51

[0026] Step 4.2, judge whether CR≤0.1 is satisfied, if satisfied, judge that the matrix consistency is good; execute step 5; otherwise, judge that the matrix consistency is not good, return to step 2, modify the importance degree of the elements in the factor set U.

[0027] Further, in step 5, the evaluation set is represented as:

[0028] V={v1,v2,…,v n};

[0029] Wherein, v j (j=1,2,…,n) represents the jth possible evaluation result of the credibility of the radiation effect test data, and n represents the total number of evaluation results.

[0030] Further, the specific process of step 6 is:

[0031] Judge the i-th element u i in the factor set U, and the membership degree of the jth possible evaluation result in the evaluation set is r ij , and 0≤r ij ≤1, then the evaluation result of the i-th element is:

[0032] R i =(r i1 ,r i2 ,...,r in );

[0033] Wherein,

[0034] The evaluation matrix of the membership relation of the elements in the factor set U to the evaluation results in the evaluation set is obtained as:

[0035]

[0036] Further, the specific steps of step 8 for determining the evaluation result in the credibility membership evaluation set of the radiation effect simulation test data by using the maximum membership degree principle are:

[0037] The dimension of the fuzzy comprehensive evaluation vector M is n, and the n components are represented as M1, M2,...,M n , and the maximum component in the n components is M k , wherein k represents the serial number of the maximum component in the components;

[0038] M k= max {M1, M2,..., M n};

[0039] The credibility of the radiation effect test data to be evaluated belongs to the kth evaluation result of the evaluation set.

[0040] Further, the specific steps for determining the credibility of the radiation effect simulation test data in step 8 are as follows:

[0041] Taking the grade variable a=(a1, a2,..., a n ) as a variable; wherein a1=1, a2=2,..., a n =n; n represents the total number of evaluation results;

[0042] Taking the n components of the fuzzy comprehensive evaluation vector M as weights, and weighting and summing the values of each grade corresponding to each component in the fuzzy comprehensive evaluation vector M, the relative position D of the credibility of the radiation effect test data to be evaluated in the evaluation set is obtained:

[0043]

[0044] Wherein, M i represents the ith component in the fuzzy comprehensive evaluation vector M.

[0045] The beneficial effects of the present application are as follows:

[0046] 1. The radiation effect simulation test data credibility evaluation method combines the fuzzy comprehensive evaluation method and the analytic hierarchy process, combs the factors affecting the credibility of the radiation effect simulation test data from the data production process side, establishes a judgment matrix, quantifies some factors that are not clear and not easy to quantify, comprehensively evaluates the membership grade condition of the data credibility, establishes a quantitative evaluation method for the credibility of the radiation effect simulation test data, and solves the problem that the accuracy and reliability of the judgment of the data credibility by the user according to experience in the traditional method may be interfered by human factors and may cause decision-making errors.

[0047] 2. The radiation effect simulation test data credibility evaluation method has strong self-adaptability, the factor set, the evaluation set, the judgment matrix, and the membership matrix can be adjusted according to the understanding degree of the data and the use of the data, and the constraint condition for the judgment matrix is provided, so that the method can be correctly used. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 is a flowchart in an embodiment of the radiation effect simulation test data credibility evaluation method. DETAILED DESCRIPTION

[0049] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and embodiments. 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.

[0050] This embodiment presents a method for evaluating the reliability of radiation effect simulation test data. It combines fuzzy comprehensive evaluation and analytic hierarchy process (AHP) to identify factors influencing the reliability of radiation effect simulation test data from the perspective of the data production process. A judgment matrix is ​​established to quantify factors with unclear boundaries and difficulty in quantification. A comprehensive evaluation of the data reliability hierarchy is conducted based on multiple factors. A crucial step in using fuzzy comprehensive evaluation is determining the weight of each factor. This embodiment also combines mathematical and qualitative methods, using a hierarchical ranking process to calculate the weight of each factor and ultimately determine the reliability evaluation result.

[0051] like Figure 1 As shown, it includes the following steps:

[0052] Step 1: Consider the various factors affecting the reliability of radiation effect test data as a set of elements, U; represented as: U = {u1, u2, ..., u...} m}, where u1, u2, ..., u m The first, second, ..., m factors are represented to indicate the reliability of radiation effect simulation test data; 3 ≤ m ≤ 11. A proportional scale for the degree of importance is set. The various factors affecting the reliability of radiation effect simulation test data are selected and determined according to the specific type of radiation effect simulation test conducted.

[0053] Step 2: Perform pairwise comparisons of the importance of elements in the factor set U from Step 1, and assign values ​​to the pairwise comparison results according to a certain scale to form a judgment matrix; the expression is:

[0054] A = (u ij ) m×m ;

[0055] in,

[0056] u ij This indicates that the pairwise comparison results are assigned values ​​according to a certain scale, m represents the total number of factors affecting the reliability of radiation effect test data; i represents the row coordinate of the judgment matrix, and j represents the column coordinate of the judgment matrix.

[0057] Step 3: Calculate the largest eigenvalue λ of the judgment matrix A in Step 2. max Determine the largest eigenvalue λmax corresponding eigenvector W;

[0058] Step 4, determining the maximum eigenvalue λ max checking the consistency of the judgment matrix A, if the consistency of the judgment matrix A reaches a preset value, executing step 5; otherwise, returning to step 2 and modifying the importance degree of the elements in the factor set U; the specific process is as follows:

[0059] Step 4.1, defining a consistency index CR, the expression is as follows:

[0060]

[0061] wherein,

[0062] m represents the total number of factors affecting the reliability of the radiation effect test data; RI represents an average random consistency index, and the specific value is as follows:

[0063] m 3 4 5 6 7 8 9 10 11 RI 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51

[0064] Step 4.2, the smaller the CR is, the better the consistency of the judgment matrix is. When the CR is equal to 0, the judgment matrix is completely consistent. In the embodiment, it is judged whether CR≤0.1 is satisfied, if satisfied, the consistency of the judgment matrix is better; executing step 5; otherwise, the consistency of the judgment matrix is not good, returning to step 2 and modifying the importance degree of the elements in the factor set U.

[0065] Step 5, constructing an evaluation set according to the possible evaluation results of the reliability of the radiation effect simulation test data; the evaluation set is expressed as:

[0066] V={v1,v2,...,v n};

[0067] wherein, v j (j=1,2,...,n) represents the jth possible evaluation result of the reliability of the radiation effect test data, and n represents the total number of evaluation results.

[0068] Step 6, evaluating the membership degree of the elements in the factor set U to each evaluation result in the evaluation set, and constructing a membership relation evaluation matrix; the specific process is as follows:

[0069] evaluating the ith element u i in the factor set U, and the membership degree of the jth possible evaluation result in the evaluation set is r ij , then the evaluation result of the ith element is:

[0070] R i =(r i1 ,r i2 ,...,r in ).

[0071] The evaluation matrix relating the elements in factor set U to the evaluation results in the evaluation set is as follows:

[0072]

[0073] Step 7: Multiply the feature vector W from Step 3 with the membership evaluation matrix to obtain the fuzzy comprehensive evaluation vector M;

[0074] Step 8: Based on the fuzzy evaluation vector in Step 7, use the maximum membership principle or the weighted average principle to determine the evaluation results of the credibility membership evaluation set of the radiation effect simulation test data.

[0075] The fuzzy comprehensive evaluation vector is solved using the maximum membership principle as follows:

[0076] The fuzzy comprehensive evaluation vector M has dimension n, and its n components are represented as M1, M2, ..., M n Let M be the largest component among the n components. k , where k represents the index of the largest component in the components;

[0077] M k =max{M1,M2,...,M n};

[0078] The reliability of the evaluated radiation effect test data belongs to the k-th evaluation result in the evaluation set, i.e., v k .

[0079] The fuzzy comprehensive evaluation vector is solved using the weighted average principle as follows:

[0080] Take the rank variable a = (a1, a2, ..., a n ) as variables; where a1=1, a2=2,…,a n =n; n represents the total number of evaluation results;

[0081] Using the n components of the fuzzy comprehensive evaluation vector M as weights, the values ​​of each level are weighted and summed using the corresponding components in the fuzzy comprehensive evaluation vector M to obtain the relative position D of the reliability of the evaluated radiation effect test data in the evaluation set.

[0082]

[0083] Among them, M i This represents the i-th component in the fuzzy comprehensive evaluation vector M.

[0084] The following steps are performed using a radiation effect simulation test data reliability evaluation method according to this embodiment:

[0085] (1) Based on the understanding of the simulation test data of radiation effects and the investigation results of 15 experts in the field of radiation effects from 15 units engaged in the research of radiation effects nationwide, the factors affecting the reliability of the simulation test data of radiation effects are determined as 4, i.e. ① simulation source, ② irradiation test outline, ③ third-party test report, and ④ sample size. It should be noted that the sample size here refers to the sample number under the condition that the test conditions are exactly the same.

[0086] i.e. U = {simulation source, irradiation test outline, third-party test report, sample size}, m = 4.

[0087] (2) The relative importance of each factor is determined by using the ratio scale value proposed by A.L.Satty as shown in Table 2.

[0088] Table 2

[0089] Scale Meaning 1 Both factors are of equal importance 3 Both factors are of equal importance 5 Both factors are of equal importance 7 Both factors are of equal importance 9 Both factors are of equal importance 2,4,6,8 Between the immediately adjacent values above

[0090] According to the analysis, the following judgment matrix is established:

[0091]

[0092] Generally speaking, the recommended simulation source in the field of radiation effects is equipped with professional hardware conditions and professional test personnel, which ensures that the test is implemented in accordance with the requirements of national standards and specifications, so the simulation source is the most important condition to ensure the reliability of test data, and therefore the simulation source is considered as the most important influencing factor.

[0093] The CR of the judgment matrix A is 0.082, which meets the consistency condition. The maximum eigenvalue λ max of the judgment matrix is 4.222, and the corresponding normalized eigenvector W = [0.620 0.130 0.197 0.053].

[0094] (3) The evaluation set V is established as {credible, basically credible, for reference}.

[0095] (4) The membership matrix is determined. For the following test data "a certain type of electronic device ionizing total dose effect test carried out by technical personnel of a certain unit on a cobalt source approved by experts in a certain unit, with a sample size of 2 and good repeatability, with an irradiation test outline but not audited by experts, without a test report issued by a third party", the following membership matrix is established:

[0096]

[0097] (5) The fuzzy comprehensive evaluation vector M is calculated.

[0098] M = W·R = [0.620 0.1775 0.2025]

[0099] (6) Evaluation conclusion.

[0100] According to the maximum membership principle, k = 1, that is, the data belongs to the first evaluation result "credible".

[0101] According to the weighted average principle, the grade a = {1, 2, 3} is selected, and D = 1.26 is obtained after calculation, that is, the data is between the first evaluation result "credible" and the second evaluation result "basically credible", and is more inclined to the first evaluation result "credible".

[0102] The above is only a specific embodiment of the present application, and the effect of the related comparative example is compared with the specific embodiment, but the protection scope of the present application is not limited to this, any change or replacement within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for evaluating the reliability of radiation effect simulation test data, characterized in that, Includes the following steps: Step 1: Assume the various factors affecting the reliability of radiation effect simulation test data as a factor set U, specifically including the simulation source, irradiation test outline, third-party test report, and sample size. Establish a proportional scale for the degree of importance of these factors; the proportional scale is as follows: The various factors affecting the reliability of radiation effect simulation test data are selected and determined based on the specific type of radiation effect simulation test conducted. Step 2: Compare the importance of each element in the factor set U from Step 1 pairwise, and assign values ​​to the pairwise comparison results according to the proportional scale to form a judgment matrix A; Step 3: Calculate the largest eigenvalue λ of the judgment matrix A in Step 2. max Determine the largest eigenvalue λ max The corresponding eigenvector W; Step 4: Using the largest eigenvalue λ max Check the consistency of judgment matrix A. If the consistency of judgment matrix A reaches the preset value, proceed to step 5; otherwise, return to step 2 and modify the importance level of the elements in factor set U. Step 5: Construct an evaluation set based on the possible evaluation results of the reliability of the radiation effect simulation test data, including {reliable, basically reliable, for reference}; Step 6: Evaluate the membership degree of elements in factor set U to each evaluation result in evaluation set, and construct a membership evaluation matrix; Step 7: Multiply the feature vector W from Step 3 with the membership evaluation matrix to obtain the fuzzy comprehensive evaluation vector M; Step 8: Based on the fuzzy comprehensive evaluation vector M in Step 7, determine the evaluation results of the credibility of the radiation effect simulation test data in the evaluation set by adopting the maximum membership principle or the weighted average principle, and complete the credibility evaluation of the radiation effect simulation test data.

2. The method for evaluating the reliability of radiation effect simulation test data according to claim 1, characterized in that, In step 2, the expression for the judgment matrix A is: A=(u ij ) 3×3 ; in, Among them, u ij This indicates that the pairwise comparison results are assigned values ​​according to a certain scale; i represents the row coordinate of the judgment matrix, and j represents the column coordinate of the judgment matrix.

3. The method for evaluating the reliability of radiation effect simulation test data according to claim 1, characterized in that, Step 4 is as follows: Step 4.1: Define the consistency index CR, with the following expression: in, RI represents the average random consistency index, and its specific values ​​are as follows: m represents the number of factors in the factor set, with a value of 4, and RI has a value of 0.

90. Step 4.2: Determine if CR≤0.1 is satisfied. If it is, the matrix consistency is good; proceed to step 5. Otherwise, the matrix consistency is poor, return to step 2, and modify the importance of elements in factor set U.

4. The method for evaluating the reliability of radiation effect simulation test data according to claim 1, characterized in that, Step 6 is as follows: For the i-th element u in the factor set U i The membership degree of the j-th possible evaluation result in the evaluation set is r. ij The requirement is 0 ≤ r ij If ≤1, then the evaluation result of the i-th element is: R i ＝(r i1 ,r i2 ,...,r in )； in, n represents the number of evaluation results in the evaluation set, n = 3; The evaluation matrix relating the elements in factor set U to the evaluation results in the evaluation set is as follows:

5. The method for evaluating the reliability of radiation effect simulation test data according to claim 1, characterized in that, Step 8, which uses the maximum membership principle to determine the reliability of radiation effect simulation test data and the evaluation results in the membership evaluation set, involves the following specific steps: The fuzzy comprehensive evaluation vector M has dimension n, and its n components are represented as M1, M2, ..., M n Let M be the largest component among the n components. k , where k represents the index of the largest component in the components; M k =max{M1,M2,...,M n }; The reliability of the radiation effect test data being evaluated belongs to the k-th evaluation result in the evaluation set.

6. The method for evaluating the reliability of radiation effect simulation test data according to claim 5, characterized in that, The specific steps for determining the reliability of radiation effect simulation test data belonging to the evaluation set using the weighted average principle in step 8 are as follows: Take the rank variable a = (a1, a2, ..., a n ) as a variable; Where a1=1, a2=2,…,a n =n; n represents the total number of evaluation results; Using the n components of the fuzzy comprehensive evaluation vector M as weights, the values ​​of each level are weighted and summed using the corresponding components in the fuzzy comprehensive evaluation vector M to obtain the relative position D of the reliability of the evaluated radiation effect test data in the evaluation set. Among them, M i This represents the i-th component in the fuzzy comprehensive evaluation vector M.

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